First, different trends in availability by country may explain differences in the contribution that perceived availability makes to marijuana use. In Chile, perceived availability generally declined from 2001 to the late 2000’s and then increased until 2016, though at lower rates than use. Local context may affect such trends in perceptions of availability. For example, marijuana availability may depend on where individuals buy, grow, or use marijuana, neighborhood police presence and enforcement of laws, or norms about diversion of marijuana to youth . Second, alternative contributing causes of marijuana use may have arisen in Chile to a greater extent than in Uruguay and Argentina, displacing the contribution that perceived availability make to marijuana use. Such factors may include changes in peer or family substance use , changes in the illegal drug market , or social and cultural changes toward marijuana, influenced by strong lobbying for drug policy reform–particularly for cannabis– in a context of massive social movements among students . While examination of such exposures was outside the scope of the current study, future research should examine whether the contribution of perceived availability and risk to marijuana use is moderated by other potential contributing causes in the local environment. In Uruguay, the increase in perceived availability is not surprising, as the 2013 law created a concrete path to marijuana access for adults. Therefore, greater perceived availability in Uruguay likely corresponds to greater actual availability. For example, it is possible that a surplus of self-cultivated marijuana has tipped over into the streets. Second, even though minors cannot buy it, marijuana may seem more accessible because it sold to adults in pharmacies. Third,rolling benches for growing there may be increased exposure to marijuana use and contact with peers or family members who use marijuana, which could predictably result in a growing sense of availability.

Our findings provide several insights about the availability/use association. First, in this region, where marijuana regulation is becoming progressively liberal and where adolescent marijuana use is increasing, perceived availability may be an increasingly important driver of marijuana use trends . Second, given the strengthening of the availability/use relationship in Argentina and Uruguay, and the high prevalence of perceived easy availability in all three countries, public health professionals in the Southern Cone may consider devoting additional resources towards regulating and intervening on the pathways by which adolescents gain access to marijuana. Relatedly, our findings raise questions about how perceptions of availability relate to real access to marijuana, including how adolescents most often obtain marijuana and whether modes of acquisition have changed over time. Lastly, future studies examining the effect of marijuana legalization on adolescent marijuana use should assess mediation by marijuana availability as a potentially important locus of intervention, particularly as more governments are likely to adopt liberalized marijuana policies. Researchers should seek to better understand this association and its environmental determinants, including in South America with additional years of data, in other legalization contexts, and where perceived availability and marijuana use have not increased, such as the US. The present study has several strengths. We used 1) a large, representative sample, 2) novel methods to analyze time-varying associations, and 3) comparable surveys among secondary school students across multiple years and countries. This is the first study to our knowledge to examine time-varying associations between adolescent risk factors for marijuana use and use of marijuana amid changing marijuana policies in the Southern Cone. This study should also be considered in light of its limitations. First, cross-sectional data preclude assuming temporal relationships between risk factors and marijuana use. Although this was not a study of causal effects, potentially important confounders were not considered. Because of finite sample limitations, our ability to examine covariates was restricted, particularly in Uruguay where the sample size was smallest.

Relatedly, we were not able to model perceived risk and perceived availability together in Argentina, or include all countries in a single model because this would have restricted the analysis to only four years , resulting in insufficient coverage of the time axis for TVEM models . In light of these limitations, we modeled the relationships separately per country, utilizing all years of available data, and note that the strength of associations across countries and within Argentina and Uruguay should be compared with caution. Additionally, these data are self-report, which may contribute to mis-classification, for example by social desirability bias. However, methods of data collection and validation used aimed to minimize these biases . While these data are designed to be representative of secondary school students in urban areas, they are not generalizable to other populations. Lastly, although a strength of our study is that it compares secondary school-attending adolescents across countries, slight differences in student populations between Argentina , Uruguay , and Chile may have limited comparability.The U.S. Cannabis Administration and Opportunity Act introduced in 2021 would decriminalize marijuana federally. California’s policy changes related to tobacco and cannabis use would inform these discussions on the short-term and long-term impact of such policies, especially among young people. The state of California has legalized recreational marijuana use for adults who are aged 21 years or older from January 1, 2018, but the state also fortified a policy environment reducing cigarette and vaping use. Individuals under 21 years old were prohibited to purchase tobacco products and e-cigarettes in California since June 9, 2016; and because e-cigarettes have been counted in California’s smoke-free laws, both tobacco products and e-cigarettes are prohibited in workplaces and many public areas. The cigarette tax was raised by $2 per pack to discourage cigarette smoking in California on April 1, 2017. Thus, all these state-level policies adopted between June 2016 and January 2018 made the state to be a natural experimental ground to investigate young adults’ tobacco and marijuana use behaviors and related risk factors in the context of the adoption of the policies . Young adults are particularly at risk for harm and addiction, as the use of tobacco products in any form and long-term recreational marijuana use can be harmful to their health and well-being .

The use of electronic cigarettes and marijuana among young adults can harm the developing brain, which continues to develop until about age 25 . Use in early adulthood also increases the risk of future addiction to other drugs .The mental health of young adults is especially concerning as smoking and psychological distress are known to co-occur . While there are numerous potential explanations for the co-occurrence, very few studies have looked at the effects of psychological distress and the use of cigarettes, e-cigarettes,vertical cannabis grow and/or marijuana simultaneously. In this study, we focus on the current use of cigarettes, e-cigarettes, and marijuana among Californians ages 18 to 25. We compared the rates in current use between 2017 and 2018 , then examined detailed data from 2018 on patterns of use by sociodemographic characteristics. We further investigated the relationship between the use of cigarettes, e-cigarettes, or marijuana with each of the two products/substance separately, and the use of these products/substance in relationship with psychological distress. This population-based study on young adults gives us an important insight into cigarette, e-cigarette, and marijuana use behaviors in the context of major policy changes. The findings will enhance the understanding of the use and inform the design of programs aimed at curbing the use of these products/substances simultaneously by young adults. All procedures described here were approved by the Institutional Review Boards of the Universities of California, Los Angeles. All participants provided informed consent. The study population was drawn from the 2018 California Health Interview Survey in conjunction with data from the 2017 CHIS annual data file. Starting in 2011, CHIS became a continuous survey, generating an annual household sample of approximately 20,000, enabling the provision of timely population-representative health information for Californians in response to rapidly changing social, economic, public health, and health care environments. CHIS covers dozens of health topics including health and well being, health behaviors, and health insurance coverage. CHIS households were selected through random-digit-dial , and within each household, an adult respondent was randomly selected and interviewed via telephone. To capture the diversity of California populations, CHIS is administered in English, Spanish, Cantonese, Mandarin, Korean, Tagalog, and Vietnamese throughout the state of California. Adjustment factors for the selection mechanisms have been incorporated into the data’s sample weights. Please refer to the CHIS methodology report for details of the design, sampling, and data processing . Our analytical sample was limited to young adults ages 18–25. From the CHIS 2018 adult data file, a total of 3,929 young adults were identified and retained for the main analyses. We also used 2018 CHIS data to compare the rates of current cigarette/e-cigarette/marijuana use with those in 2017.

We used the responses to several questions to define the outcome variables: current users . Specifically, to define current cigarette smoking, respondents who answered yes to the CHIS question, “Altogether, have you smoked at least 100 or more cigarettes in your entire lifetime?” were asked, “Do you now smoke cigarettes every day, some days, or not at all?” If the respondents said they were now smoking every day or some days, they were also asked “In the past 30 days, when you smoked, how many cigarettes did you smoke per day?” For the respondents who had positive responses to these questions or had more than one cigarette in the past 30 days, they were defined as current users. For e-cigarette smoking, CHIS asked adult respondents: “Have you ever used any type of e-cigarette, vape pen, or e-hookah, such as Blu, NJOY, or Vuse, or any larger devices for vaping, sometimes called vapes, tanks, or mods?” Among those who responded positively, a follow-up question was asked: “During the past 30 days, on how many days did you use electronic cigarettes?” For marijuana use, the question was: “Have you ever, even once, tried marijuana or hashish in any form?” Then, to determine marijuana’s current use, CHIS asked the question: “How long has it been since you last used marijuana or hashish in any form?” For the respondents who reported any use within the past month, they were defined as current users.The Kessler 6 scale was administrated to adult respondents to collect self-reports on non-specific psychological distress. It contains six questions on a 5-point scale about the frequency of anxiety and depression symptoms in the past 30 days . The total score of K6 ranges from 0 to 24, K6 scores of 0–4 were usually defined as having no or mild distress, and K6 scores of 5–12 were defined as having moderate psychological distress, and 13 and above were severe psychological distress . Thus, we categorized psychological distress into 3 categories : no/mild psychological distress, moderate psychological distress, and severe psychological distress for this study, as has been done in other studies . Covariates that CHIS 2017represent potential confounding were included if they were known to be related to cigarette or marijuana use behaviors . The fully adjusted model contained individual/household level socio-demographic characteristics collected during CHIS interviews , including age , race/ethnicity , sex , household income standardized by federal poverty level , psychological distress, residence in urban/rural area which is assigned using the Claritas urbanicity model , and seven regions by grouping 58 counties in California according to their geographic locations, such as Greater Bay Area, Sacramento Area, San Joaquin Valley, Los Angeles, other Southern California, and North/Sierra Counties. The Asian, African American, and White race categories were tabulated as non-Latino ethnicity. The “Others” category aggregates non-Latino Native Hawaiians/Pacific Islanders, American Indians/Alaska Natives, and multi-racial individuals. The bivariate analysis chi-squared tests were used to determine if there were significant changes in the rates of using cigarettes, e-cigarettes, and marijuana, separately or any use of the three between 2017 and 2018. We also did the same analyses to examine the differences in the use across the subgroups by socio-demographic and other characteristics using CHIS 2018 data. Logistic models regressing the odds of using cigarettes, e-cigarettes, and marijuana, separately or any use of the three while accounting for sampling weights were conducted. These models have adjusted for covariates, which include age, race/ethnicity, sex, FPL, urban/rural status, region of residence, and psychological distress using CHIS 2018 data.

BP was measured during the resting state, in triplicate with 1 min intermissions, using a random-zero sphygmomanometer at Y0–15 with the first- and fifth-phase Korotk off sounds corresponding to systolic and diastolic BP, respectively, with the average of the last two measurements used. BP at Y20 and Y25 was measured with an Omron HEM907XL oscillometer and calibrated to the random-zero readings. Body weight was measured using a calibrated balance-beam scale to the nearest 0.2 kg, with participants in light clothing. Height was measured to the nearest 0.5 cm using a vertical ruler, with BMI calculated as the weight in kilograms divided by the squared height in metres. WC was measured midway between the iliac crest and the lowest lateral portion of the rib cage. Diet was assessed using an interviewer-administered CARDIA diet questionnaire at examination Y0, Y7 and Y20 and a diet score was created, as previously described. We used two distinct methods to evaluate the metabolic effects of self-reported marijuana use in CARDIA participants. First, to assess the cross-sectional association between self-reported marijuana use and prediabetes and diabetes, data from examination Y25 were analysed. For this cross-sectional evaluation, of the 3,496 individuals present at examination Y25, we excluded those who had fasted for less than 8 h prior to the visit and those with an undeterminable diabetes status or missing relevant covariate information , resulting in a sample of 3,034 participants. Individuals who had diabetes at Y25 were excluded from prediabetes analyses , and prediabetes status was undetermined for one individual. Therefore, 2,676 individuals were included in prediabetes analyses. The second analytical approach was to prospectively evaluate the association between self-reported marijuana drying rack use and incident prediabetes and diabetes. Fasting glucose was not measured at CARDIA Y2 and Y5, and diabetes status was determined by medication use. In order to include the ADA criteria in determining incident diabetes at each examination, data from examination Y7 were used as the analysis baseline.

Individuals were excluded from analysis if they did not participate in the Y7 examination , presented with a fasting time of less than 8 h prior to the Y7 examination , did not return for follow-up in all of Y10–25 or were missing covariate information at the Y7 examination . When assessing marijuana use and incident diabetes, individuals with prevalent diabetes at Y7 or those whose diabetes status was undetermined on follow-up were excluded , resulting in an analysis sample of 3,174 participants. For the association between marijuana use and incident prediabetes, 468 people were excluded based on baseline prediabetes, diabetes and underdetermined prediabetes status on follow-up, giving a final analysis sample of 2,758 participants. Those excluded were on average older and were more likely to be male, African-American and less educated, with a longer history of smoking, higher levels of fasting glucose and CRP, and greater lifetime frequency of marijuana use compared with the included participants. Categories of all unique forms of self-reported drug use were determined by status and total use . Former use was defined as an affirmative response to the question ‘Ever use?’, but with no reported use in the previous 30 days. Current use was determined by a report of use on one or more of the last 30 days. Along with other illicit drug use, we considered several additional covariates as potential confounders. Cigarette smoking status was based entirely on current use. Regular alcohol consumption was classified as none, up to one drink daily and more than one drink daily. Educational attainment was characterised into three groups: ≤12 , 13–16 or >16 years of education . Systolic BP, BMI, WC, LDL- and HDL-cholesterol, and CRP variables were modelled continuously, as were physical activity and diet scores. Antihypertensive and lipid-lowering medication use was taken into account in models that included adjustment for BP and cholesterol levels. Given the strength of association between BMI and diabetes and to reduce potential residual confounding, all adjusted models containing BMI also included a BMI2 term to account for a possible nonlinear relationship. Participant characteristics were calculated across categories of self-reported marijuana use. Univariate models were used to assess the crude direction and magnitude of each association, with sequential models adjusting for the potential confounders noted above. The association between marijuana use and the presence of prediabetes and diabetes at CARDIA examination Y25 was estimated with logistic regression, obtaining crude and adjusted ORs and 95% CIs. For longitudinal analyses, crude and adjusted HRs and 95% CIs were estimated using Cox proportional hazards models.

Contributed person-time to the study was calculated as the duration from date of examination Y7 to either: the examination at which the event of interest was ascertained; or administrative censoring of the participant’s last examination visit. The proportional hazards assumption was assessed by including a product term between marijuana use category and natural log of contributed person-time. To investigate whether the risk of prediabetes and diabetes according to marijuana use differed by sex or race, separate multiplicative interactions were tested by adding product terms to the proportional hazards model. Sensitivity analyses were also performed, repeating the main analyses with data from different CARDIA examination years to confirm whether associations were similar regardless of the examination from which participant data were used. For example, for cross-sectional analyses, marijuana use and prevalence of prediabetes and diabetes were assessed using data from each CARDIA examination Y0–20. For prospective analyses, we assessed marijuana use at each CARDIA examination Y0–20 and incidence of prediabetes and diabetes through to Y25. Statistical analyses were performed using SAS statistical software version 9.3 . The self-reported marijuana use status of the individuals present at each examination is displayed in Fig. 1. The per cent of individuals reporting ‘never’ or ‘current’ use of marijuana declined over time, while the per cent who reported ‘former’ use of marijuana increased, particularly in the early years. Baseline participant characteristics for the prospective analysis are presented in Table 1 by category of lifetime frequency of marijuana use. In both the cross-sectional and longitudinal analyses, older age, male sex, white race, current smoking, greater daily alcohol consumption,vertical grow rack system current use of marijuana, other illicit drug use and greater participation in physical activity were all associated with a greater lifetime frequency of marijuana use, while longer time in education and greater BMI were associated with lower frequency of marijuana use. At CARDIA examination Y25, 45% of the analysis population had prediabetes . Unadjusted analysis found marijuana use was associated with higher odds of prediabetes, regardless of status or frequency of use . Specifically, individuals who reported current use and those who reported a lifetime use of ≥100 times had significantly higher odds of prediabetes compared with those who reported never using marijuana. The greatest attenuation of estimates was observed with adjustment for age, sex, and race, while the greatest strengthening of estimates was observed when use of other illicit drugs was included. There were 357 cases of prevalent diabetes identified at Y25 for the cross-sectional analysis. Without adjustment for covariates, individuals who reported a history of marijuana use when marijuana use was modelled by status or lifetime frequency had marginally lower odds of diabetes compared with never-users . Adjustment for demographic and lifestyle characteristics reversed the apparent direction of the association from <1 to >1, although the 95% CIs continued to span 1 .

Estimates were most sensitive to adjustment for alcohol use, field centre, BP and use of other illicit drugs. The results for the prediabetes and diabetes analyses did not materially change when CRP level was excluded from the models or when BMI, BMI2 and WC were inserted into the models. More than half of the participants without prediabetes or diabetes at the start of follow-up developed prediabetes over an average of 13.8 years of follow-up . Table 3 presents the crude and fully adjusted HRs with 95% CIs and crude incidence rates for prediabetes and diabetes according to self-reported marijuana use category. Unadjusted models for the association between marijuana use and incident prediabetes found a suggestive increase in the hazard for prediabetes for individuals with the greatest frequency of use at baseline . Adjustment for covariates strengthened the observed association in this group, with the 95% CIs no longer spanning 1 after adjustment for demographics, tobacco use, alcohol intake and dietary pattern. Compared with those who reported never using marijuana, individuals who reported use of ≥100 times had a significantly increased risk for prediabetes , after adjustment for demographic, lifestyle and clinical characteristics. There were 351 incident cases of diabetes identified during 50,569 years of follow-up in the prospective analysis, giving an overall crude incidence of 694 cases per 100,000 personyears. In unadjusted analysis, a decreased risk of diabetes was found for those who reported marijuana use compared with never-users, but this did not attain statistical significance. The associations were attenuated after adjustment for basic demographic and lifestyle characteristics; further adjustment for dietary pattern and BP resulted in the greatest attenuation of estimates. Irrespective of the outcome , the results did not differ when fasting glucose, BMI and pack-years of cigarette smoking were included in the final model. For all prospective analyses, inclusion of age and illicit drug use at baseline in the model resulted in considerable strengthening of estimates. Otherwise, any strengthening of the associations with the incremental inclusion of individual variables was far less in magnitude and balanced by the covariates that attenuated the associations. Formal tests of interaction were not significant for any of the potential effect modifiers for any of the analyses in this study. No violations to the proportional hazards assumption were detected. Results from sensitivity analyses confirmed the primary analyses ; patterns of the associations were similar and did not depend on the year from which participant data were used. Electronic supplementary material Table 1 shows the fasting glucose levels at the time of censoring: either the examination at which prediabetes and diabetes was ascertained or administrative censoring of the last examination visit. There was no observable linear trend in glucose levels at the time of censoring across marijuana use categories for diabetes. However, a statistically significant positive linear trend was observed for prediabetes, although this was no longer apparent after adjustment for illicit drug use. In this cohort of healthy men and women, marijuana use was associated with a higher prevalence of prediabetes during middle adulthood after controlling for potential confounding variables, but was not associated with the presence of diabetes at this age. Similarly, marijuana use in young adulthood was associated with the incidence of prediabetes in middle age. The greatest lifetime frequency of use at baseline was associated with the highest incidence of prediabetes over the study’s follow-up, compared with participants who reported never using marijuana. Marijuana use was not associated with the incidence of diabetes. Marijuana use was modelled categorically in two different ways , contributing to the interpretation and robustness of these findings. The findings of this study are important, given the previously reported associations of marijuana use with various metabolic outcomes. The impact of BMI on the association between marijuana use and incident diabetes and prediabetes is unclear . In this study, the results were unchanged with the addition of BMI, BMI2 and WC to the statistical model, consistent with the minimal estimate shift observed in a recent meta-analysis, and we found no cross-sectional association between marijuana use and BMI , in contrast to previous findings on marijuana use and metabolic health. A previous study assessed marijuana use in relation to obesity status in two population-based, nationally representative samples of US adults. Using the National Epidemiologic Survey on Alcohol and Related Conditions, researchers found that individuals who reported cannabis use on ≥3 days per week had 39% lower odds of obesity compared with individuals who reported no use in the past 12 months, after adjustment for demographics, education, marital status, religion and tobacco smoking status. This association was attenuated when researchers studied individuals from the National Comorbidity Survey—Replication; adjusted estimates no longer attained statistical significance. The prevalence of current marijuana use was <8% in this study, and the prevalence of use among young adults was below the national average and that found in our cross-sectional analysis. 

Research Question #5 was “Is the effectiveness of dispensary bans dependent on them being associated with less dispensaries located near high schools?” The hypotheses associated with RQ5 propose that, H5.1) dispensary bans are negatively associated with the number of dispensaries being located near high schools; H5.2) the number of dispensaries located near schools is positively associated with students’ likelihood of using marijuana; and H5.3) the relationship between city dispensary bans and high school students’ marijuana use is mediated by the number of dispensaries operating near schools. Table 7.20 presents the results of the mediation analysis assessing the association between the count of unlicensed dispensaries located within 2,000 feet of a school on students’ lifetime marijuana use. The relationship between dispensary bans and lifetime marijuana use was negative and non-significant , as reported in Chapter 5. Whether a city had a dispensary ban was then regressed on the number of unlicensed dispensaries located within 2,000 feet of a participant’s school . The association between dispensary bans and dispensaries being located within 2,000 feet of a student’s high school was negative and statistically significant , which supports H5.1 for lifetime marijuana use, i.e., that dispensary bans would be associated with less unlicensed dispensaries being located near schools than policies that allow dispensaries. The number of unlicensed dispensaries within 2,000 feet of the school was then regressed on the likelihood of lifetime marijuana use, which revealed a statistically significant positive association between unlicensed dispensaries being located near a high school and the proportion of students there reporting lifetime marijuana use , confirming H5.2., that the number of unlicensed dispensaries located near a school is positively correlated with lifetime marijuana use. Finally,rolling grow tables the regression analysis of dispensary bans and lifetime marijuana use was repeated including the number of unlicensed dispensaries within 2,000 feet of the school in the model.

Accounting for the number of unlicensed dispensaries within a 2000-foot radius of the school slightly increased the association between dispensary bans and lifetime marijuana use among participating high school students but did not make it statistically significant , therefore refuting H5.3, that the effectiveness of dispensary bans is dependent on how well they prevent unlicensed dispensaries from being located near schools compared to city policies that allow dispensaries.These steps were repeated for the recent marijuana use outcome. As path c and a were the same for recent marijuana I began by regressing the number of unlicensed dispensaries within 2,000 feet of the school on the likelihood of recent marijuana use, which revealed a non-statistically significant positive association with the proportion of students there reporting recent marijuana use , which in contrast to the relationship observed for the number of dispensaries located near a school and lifetime marijuana use, did not support the hypothesis that the number of unlicensed dispensaries located near schools is positively correlated with recent marijuana use among the students attending that school . Finally, the regression analysis of dispensary bans and lifetime marijuana use was repeated including the number of dispensaries within 2,000 feet in the model. Accounting for the number of dispensaries within a 2000-foot radius of the school slightly increased the effect but did not make the association between dispensary bans and lifetime marijuana use among participating high school students statistically significant , and therefore did not support , my theory that there was an indirect relationship masking an association between dispensary bans and lower rates of recent marijuana use among students, i.e., that the effectiveness of dispensary bans was dependent on them allowing less dispensaries to be located within 2,000 feet of schools compared to city policies that allow dispensaries. Experimentation with marijuana typically begins in adolescence but the later in life and less frequently a young person uses marijuana, the less likely they will be to experience mental, physical, or social problems related to marijuana use .

Preventing youth use is a frequently stated goal of dispensary bans but to date there have been no known studies focusing on the effectiveness of dispensary bans in preventing adolescent marijuana use. Despite the frustration that the California tradition of local control has caused for medical marijuana consumers and law enforcement, it has also provided a valuable opportunity to evaluate whether city policies that ban marijuana outlets have a localized impact on youth marijuana use. This is important because there are few tools available at a city level to prevent underage youth marijuana use. The question that logically follows whether dispensary bans are effective in preventing youth marijuana use is how they influence adolescent marijuana use behavior. For example, does the effectiveness of dispensary bans depend on how successful they are in keeping dispensaries out of city? Or, is the effectiveness of dispensary bans dependent on them being more effective at preventing dispensaries from being located near high schools? To answer these questions, I examined trends in high school students’ marijuana use behavior before and after a restrictive dispensary policy was enacted in the City of Los Angeles, tested for cross-sectional associations between dispensary bans and student marijuana use and explored explanatory theories for why dispensary bans and more restrictive policies might have an effect. Overall, I found limited support for the efficacy of dispensary bans but strong support for enforcement-related factors. The focal relationship for this dissertation, the effect of MMD bans on adolescent marijuana use was not statistically significant among 57 cities in LA County. There was nevertheless a great deal of information learned from the difference-in-difference analysis. I found that in the City of Los Angeles, Proposition D represented a clear voter mandate for better enforcement and tighter regulations on dispensaries that when carried out was powerful enough to reverse an increasing trend in lifetime marijuana use among city students despite the continued presence of dispensaries in the city. I found that only the continous distance to the nearest dispensary within LA County had a statistically significant mediating effect on the relationship between dispensary bans and student marijuana use , although it fell short of making the relationship between dispensary bans and student marijuana use statistically significant.

This result indicates that the prevenative influence of dispensary bans is partially dependent on the degree to which they are associated with longer distances between schools and the nearest unlicensed MMD. The results from the mediation analyses also supported the importance of enforcement, particularly when it concerns closing down or preventing unlicensed outlets and the need for further study of localized effects. It is possible there was data missing for too many cities to detect an effect for city MMD bans or the rate of MMDs at a city level but that results were found for local measures and not for comparisons conducted by city could indicate that variation in adolescent marijuana use occurs within cities. In this study, students’ marijuana use was more strongly associated with the proximity of the nearest unlicensed dispensary to their school and the density of dispensaries within a several blocks from their school. These localized effects highlight the importance of enforcing city regulations that restrict dispensaries from operating near schools,growing rack whether those regulations are minimum distance requirements or policies that ban dispensaries altogether. Furthermore, that localized effects were noted only for unlicensed outlets and not for licensed dispensaries indicates that enforcing existing ordinances by closing unlicensed outlets near schools could be an excellent first step for cities looking to prevent marijuana use among their students. My first research question concerned whether city regulations that ban or restrict dispensaries influenced trends in high school students’ marijuana use . I hypothesized that over time, cities that allow dispensaries would experience greater increases in marijuana use among high school students relative to cities that allowed dispensaries throughout the study period . I was unable to test the impact of dispensary bans over time among the all the cities in LA County due data limitations, but when I tested the impact of polict that significantly tightened regulatiuons on dispensaries in the City of Los Angeles I found that enacting tighter dispensary regulations can have an impact on rates of high school students’ marijuana use, even as a smaller number of dispensaries remain in active in a city. This hints at a threshold effect, suggesting that the impacts of dispensaries in at a city level can be minimized if their number is kept below a certain point. However, this would not prevent localized effects within cities in the neighborhoods where the dispensaries are located. When comparing all of the LA County cities represented in the data, dispensary bans had little effect on outcomes in high school students’ marijuana use. Although the observed effects for city dispensary bans in the multivariate regression models were in the expected direction, the influence of dispensary bans on students’ marijuana behaviors was qualitatively small and was not statistically significant when accounting for individual characteristics and school type.

It is possible that because people under the age of 18 are not able to access marijuana directly from dispensaries, attending school in a city that allows dispensaries has little effect on their ability to obtain marijuana, which state and national-level studies have documented as already being quite easy . It is possible that city dispensary policies are too distal to what is important to high school students to have a qualitatively important impact on their attitudes and behaviors pertaining to marijuana use. Alternatively, there may be other, more influential distal factors at work, like societal attitudes toward marijuana and social norms surrounding substance use that been changing statewide and even nationally. One possible explanation for the lack of impact of city dispensary bans on high school students’ marijuana use behavior and attitudes is that storefront medical marijuana dispensaries are not the only type of medical marijuana business in operation in LA County. Interestingly, however, analyses conducted for earlier survey years that were subject to similar data limitations . did identify a statistically significant negative association between a city having a dispensary ban and lifetime marijuana use . It appears that by the next year, the 2015/2016 school year, dispensary bans were no longer associated with lower rates of even lifetime use among high school students. What could explain this change? One difference between the 2014/2015 school year and the 2015/2016 school year was that only four LA County cities allowed medical marijuana dispensaries in 2014, compared to six in 2016 and of the two new cities included in this group one did not have any active dispensaries before the end of the study period, while the other, Huntington Park, allowed four dispensaries but only in industrial zones. As this dissertation has demonstrated, the distance to the nearest dispensary was among the most powerful influences on student marijuana use behavior. By allowing dispensaries to locate only in industrial zones, the City of Huntington Park may have prevented their city ordinance allowing MMDs from having an impact on marijuana use behaviors among the young people attending their schools. Another possible explanation for the association between dispensary bans and lifetime marijuana use noted during the 2014/2015 school year disappearing for the 2015/2016 and 2016/2017 school years may be an almost exponential growth in medical marijuana delivery services in LA County. For example, even after the number of storefront MMDs has been curbed in some areas of LA County, growth in medical marijuana delivery services seems to have continued unabated, regardless of the city policy where they are located. Only 25 percent of the cannabis consumed in the state is purchased from government-approved brick-andmortar retailers, according to a report released in February 2019 by the Cannabis Growers Association. Much of the rest is sold door-to-door by hundreds of unlicensed, small and independent couriers . I was not able to test or control for this factor in my analysis. Empirical data measuring the growth in marijuana delivery services in LA County is available only via the same commercial services by which I obtained addresses for LA County storefront dispensaries, but I do not have access to any data archives from these sources for the years between 2015 and 2017 and did not track the numbers of delivery services other than when I recorded the number and location of unduplicated dispensaries listed by Weedmaps and other dispensary listing websites in September 2016.

Among the small number of cities in LA County that allowed dispensaries, this variation in the number of students from the City of Los Angeles had such a significant influence as to effect estimates of marijuana use among the cities that allowed dispensaries from year to year. Using two combined school years solved this problem and resulted in a greater number of schools being included in the sample, which increased the validity of the geographic analyses in the chapters that follow. Table 5.2 displays results for the cross-tabulations between city dispensary bans and student and school characteristics that I hypothesized would be associated with high school students’ self-report of lifetime marijuana use. Just under a quarter of the students in the cross sectional sample reported having ever used marijuana. Similar numbers of males and female students reported having ever used marijuana, so this marijuana use was not found to differ significantly by gender. Reports of lifetime marijuana use did vary significantly by race/ethnicity and was higher among Hispanic students and African American students than among White students, while use among Asian students was lower than among White students. Attending an after-school program at least 1 day a week was significantly associated with lower rates of lifetime use, while receiving free or reduced-price school meals was significantly associated with higher rates of marijuana use . The proportion of students attending non-traditional schools who reported lifetime marijuana was more than double the proportion reported by students attending traditional schools. Finally, students attending school in a city that banned dispensaries were significantly less likely to have reported lifetime marijuana use than students who attended schools in cities that allowed dispensaries . Table 5.3 displays results for the cross-tabulations between city dispensary bans and student and school characteristics that I hypothesized would be associated with high school students’ self-report of recent marijuana use.

The number of students who reported having used marijuana within the previous 30 days was a little over half the number who reported ever having used it . Similar numbers of males and female students had reported having ever used marijuana,vertical farming equipment but significantly more males reported using marijuana within the last 30 days compared to females. Reports of recent marijuana use also varied significantly by race/ethnicity and was highest among Hispanic students while recent use among Asian students was less than half the proportion reported among the all the other racial/ethnic categories. Attending an after-school program at least 1 day a week was significantly associated with lower rates of recent us . Interestingly, while receiving free or reduced-price school meals was significantly associated with higher rates of lifetime marijuana use , it was associated with significantly lower rates of recent use, indicating that low family income may present a barrier to more frequent use. The proportion of students attending non-traditional schools who reported lifetime marijuana was almost triple the proportion reported by students attending traditional schools . Finally, the proportion of students attending school in a city that banned dispensaries who reported recent marijuana use was greater than the proportion who reported recent use among students who attended school in a city that allowed dispensaries , but the difference fell just short of statistical significance . After assessing bivariate associations, I ran separate multilevel logistic regression models for lifetime and recent marijuana use to assess the impact of city bans on each measure of student marijuana use, while controlling for the student and school characteristics that the bivariate analyses had revealed were significantly associated with each measure of marijuana use. Two level random intercept HGLM models were conducted using PROC GLIMMIX in SAS 9.4 with city as the level-2 variable to account for the multilevel structure of individuals being clustered in cities. School-level weights for the LA Unified School District schools were included in each model, per CHKS documentation .

The multivariate models presented here show that most of the student and school characteristics that I hypothesized would be important influences on students’ marijuana use based on the body of literature were indeed strongly associated with these outcomes. It was therefore important to control for these characteristics when attempting to quantity the effect of dispensary bans in student marijuana use. By controlling for these characteristics, I was able to demonstrate that city dispensary bans do not have a direct association with lower rates of marijuana use among high school students in LA County and conclude that hypotheses H1.1 was not supported. It is possible that city dispensary policies are too many links above students in the marijuana supply chain to directly have an impact on how much marijuana they can access or how reliably. It also possible, however, that the effectiveness of marijuana policies is dependent on factors that I did not include in the models above, such as enforcement practices.This population represents an excellent target for school-based secondary prevention interventions and screening for clinical levels of substance use disorder. The finding that receiving free or reduced-price school meals was significantly associated with higher rates of lifetime marijuana use but was associated with significantly lower rates of recent use is consistent with literature demonstrating that adolescent substance use is responsive to pricing. It’s possible that the adolescents from low income families may have had less money to spend on marijuana than their peers and thus were able to use marijuana less frequently, although the higher rates of lifetime use may indicate that they are just as likely or more likely to have access to it for experimentation or occasional use. That finding that participation in after school programs was protective against both lifetime and recent marijuana is consistent with research showing that youth who participate in after school programs are less likely to report substance use .

This finding also supports an important function of the Adult Use Marijuana Act that mandates that a portion of the tax revenue from recreational marijuana be used to support after school programs . The cross-sectional analysis presented here measured the association between city dispensaries and student marijuana while controlling for students being clustered in cities and for potentially confounding student and school characteristics. City dispensary policies, however, are not the exclusive determinant of the actual availability of marijuana in a community. As will be demonstrated in the following chapters, factors such as enforcement and local context also determine access to marijuana in a city. To expand on the relationship between city dispensary policies and adolescent marijuana use,4×4 grow tray the following chapter will investigate the long-term effects of implementing a more restrictive dispensary policy in the City of Los Angeles. Chapter 7 will continue to elaborate on the relationship by testing indirect mechanisms through which 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 risk to their health, and/or by preventing dispensaries from operating near their high schools. Prevention research supports the idea that more convenient access to legal substances for adults often has the end result of creating easier access for youth , which may mean that youth living in or attending school in a city that allows dispensaries can obtain cannabis more easily or more often from adults in their social network. If this is the case, a dispensary policy making access less convenient for adults could have the additional effect of making it less conveniently obtained by teens. Considering that adolescents report older relatives and the illicit market as their primary sources of cannabis tightening dispensary regulations could have a dampening effect on youth marijuana use in Los Angeles even though adolescents are not allowed to access dispensaries directly. To date, little is known about the effectiveness of dispensary bans or other dispensary regulations at preventing youth access to marijuana. By comparing how students’ marijuana use changed over time in a city that allowed dispensaries compared to a group of cities that did not, I hoped to provide some insight into how city policies regulating dispensaries influence marijuana use among youth.

I undertook this task by comparing marijuana use rates among City of Los Angeles high school students before and after the City enacted Proposition D, a voter approved ballot measure that capped the number of outlets allowed to operate in the City at a fraction of their existing number, ordered hundreds of remaining dispensaries to close down, and prohibited all new outlets . I hypothesized that this radical policy change would have an important influence on the availability of marijuana in the City and that rates of adolescent marijuana use in the city would decline after the policy was enacted . To test this hypothesis, I used a difference-in-difference approach to compare change in student marijuana use in the city of Los Angeles, to change in a group of cities that had banned dispensaries throughout the study period. By using the control group to account for any trends in high school students’ marijuana use unrelated to the implementation of Proposition D in the City of Los Angeles I could determine whether the stricter regulations enacted in the City of Los Angeles had a discrete impact on rates of marijuana use among the City’s students . This hypothesis was also a good fit for analysis using a difference-in-difference design given that Proposition D represented a discrete policy change that could be used to clearly distinguish pre- and post-intervention periods in Los Angeles. Difference-in-difference analyses can be performed using different regression techniques. The difference-in-difference coefficient is an interaction term included in a regression equation that compares the difference in change between the two groups over time. In the case of these analyses, it quantifies the impact of Proposition D in Los Angeles relative to the cities where it did not apply. The difference-in-difference coefficients for the covariates presented in in this chapter are presented as risk ratios and can be interpreted as they would be in any Poisson regression model. In this case, they represent the relative risk of a Los Angeles student reporting marijuana use relative to the reference group reporting marijuana use and holding constant all the other covariates in the model. I used robust Poisson regression analyses to test the impact of Proposition D in Los Angeles on the dependent variables, student self-reports of lifetime and recent marijuana use. Although my dependent variables were binary and not count variables, I chose to use a Poisson regression because I was reporting prevalence ratios of behaviors that were not rare, i.e., the prevalence of marijuana use for both measures was over 10%. Under these circumstances using logistic regression and reporting odds ratios can overestimate the prevalence ratio , potentially leading to false conclusions about the volume and statistical significance of intervention effects. I used clustered standard errors in the Poisson regression to account for the grouping of participants in the cities where they attended high school. In such circumstances there may be independence across clusters but correlation within clusters. When this is the case, statistical inference based on the usual assumption of independent observations is no longer appropriate . A common approach to control for clustering is by computing cluster-robust standard errors that control for clustering at the level of the primary theoretical grouping, which in this case was city . I will use this approach to account for the fact that students are nested in cities and there may be unmeasured city effects that influence marijuana use behavior . This approach was used to account for students being clustered within cities rather than a multilevel analysis for simplicity and because the city-level analytical variables used to answer Research Questions 2-5 were not available for the survey years used in the trend analysis. Study weights were included in the descriptive and regression analyses presented below but were available only for LA Unified Students and for the 2016/2017 school year in the datasets I obtained from WestEd. I compared results including and excluding the weights and there were no differences, but I included the school weight where applicable in the descriptive statistics and regression analyses to control for the survey design to the extent that I could.

Among the small number of cities in LA County that allowed dispensaries, the City of Los Angeles had a significant and potentially confounding influence that varied between odd and even years to such a degree as to effect outcomes in students’ marijuana use from year to year. This concern was addressed by using a combined 2 school year period as the unit of analysis for time, which halved the amount of data points available for the trend analysis but provided a much more reliable estimate of trends in students’ marijuana use behaviors over the twelve-year study period. CHKS data is not without limitations. The CHKS survey data set that I obtained represents only the public high schools in LA County and the marijuana use behavior of public high school students may differ from students at private high schools who are not surveyed. Furthermore, the CHKS survey and sampling strategy was designed to measure student health and school climate over time by school district, rather than by city. In many cities these units are interchangeable because there is one district high school district per city, but not this is not the case in every city. Although administering CHKS is a requirement for public schools receiving Tobacco-Use Prevention Education from the State of California , participation by school districts, schools, and students is voluntary. Participation for some schools is relatively low , although offering school districts incentives for participation after 2011 was effective in improving participation . Even though CHKS data has been sampled proportionally to generate population-based reports of student behavior at the state level, at the County level, where I have included every school that participated apart from special education schools, the protocol of voluntary participation makes the CHKS study sample more of a convenience sample. Ideally,vertical growing systems the results of this study should not be generalized outside of LA County or to students at private high schools and should be followed by more extensive data collection efforts using a large enough population-based sample to study the impacts of city policies on adolescent health behaviors. CHKS has been administered at a large enough majority of schools in LA County to provide estimates of student marijuana use that schools from 76 of the 88 incorporated cities in the County were represented in the data at some point during the study period.

In the cross sectional analysis using the 2015/2016 and 2016/2017 school years the number of cities that had schools that participated in the survey was less representative. Only 53 cities out of the 88 cities in LA County had schools that participated in the CHKS survey in the 2015/2016 and 2016/2017 school years. Although the cities that participated represent 87% of the LA County population, in an analysis of the impacts of city-level policies this is a serious limitation. Despite these limitations, the very large sample size, the opportunity to make comparisons to state-level data, and the consistency of data collected over multiple years make CHKS a valuable tool to measure substance use among California students. The Los Angeles County Department of Public Health has also used the CHKS in a recent impact assessment of the potential impacts of allowing retail and medical marijuana outlets in the unincorporated areas of LA County on the health and safety of LA County residents . The addresses of all the public high schools in LA County were obtained online from a secondary source, the California Department of Education website . School directory data is available for download as an Excel file and contains the address and geographic coordinates of each public school within California, as well as administrative details such as school type .The school directory file was downloaded, filtered to obtain all the public high schools that served LA County students during the 2015/2016 and 2016/2017 school years . The school addresses were then geocoded using ArcMap 10.4 to generate latitude and longitude coordinates for each school and thus identify where they were located within LA County. I then performed a spatial join to the city boundary shapefiles available from the LA County GIS portal to identify which city each high school was located in, as this information is not available in the CHKS dataset. The geocoded high schools were matched to schools in the CHKS dataset by their CDS code, a unique ID provided by the California Department of Education to all California public schools. Once matched to the CHKS data it was possible to link the geographic location of the school to the students’ behavioral data, which included rates of lifetime and recent marijuana use and perceptions of the risk of marijuana use, along with other behavioral and demographic data. Municipal codes and zoning laws are public information and are generally published by cities at their own expense for the benefit of city residents. The cities in LA County used online municipal database services like Municode.com and American Legal Publishing Corporation to publish searchable directories and archives of their city ordinances and zoning codes.

Using these services, I was able to determine whether and when the cities in LA County passed ordinances banning or allowing dispensaries between 2005 and 2017. By September 2016, 79 out of the 88 cities in LA County had either specifically banned dispensaries or had zoning laws that prohibited any kind of land use not expressly listed in the municipal or zoning code. Among the remaining cities, six explicitly allowed dispensaries, and three had no business districts and therefore no commercial zoning codes. A simple tally of changes to medical marijuana ordinances within just two years documents that this was a dynamic era for city-level marijuana policy in LA County . Data characterizing city dispensary ordinances were obtained via primary data collection. I reviewed municipal codes for the 88 incorporated cities within Los Angeles County semi-annually from August 2014 and through August 2016. For cities where municipal codes were available online, search terms such as “marijuana,” “cannabis,” and “dispensary” were used to find the sections of municipal and zoning codes that regulated medical marijuana dispensaries. For the cities without municipal codes accessible online, City Clerks were contacted via email and phone to obtain the full text of the city ordinance, but this was only necessary for the cities of Avalon and Maywood. As I compiled the city policy data, I created a database listing whether the dispensary policies of the 88 incorporated cities within Los Angeles County banned or allowed dispensaries. The database included links to the full text of each ordinance and detailed notes about how it was obtained for each city. Three main categories of marijuana policy emerged as data collection proceeded; policies that addressed: storefront dispensaries, cultivation, and delivery services. In the case of delivery and cultivation policies, many communities did not explicitly state in their municipal code if these activities are allowed, but unless a local ordinance bans these activities, the local law defaults to the State law,curing marijuana which allows personal use cultivation and medical marijuana delivery to qualified medical marijuana patients. Several cities presented special cases in this analysis. In the absence of a policy banning or specifically allowing dispensaries, the cities of Long Beach, Los Angeles, and West Hollywood initially allowed dispensaries to operate according to California law before passing ordinances that restricted their number and enacted additional regulations . In the case of Long Beach, these restrictions were eventually followed by a dispensary ban , which has since been reversed again by a local ballot measure, Measure MM . As these three cities were known to have allowed dispensaries to operate openly within city borders after they became legal under state law , they were coded as allowing dispensaries starting from the 2005/2006 school year forward for this analysis. Although dispensaries likely cropped up in other cities and in the unincorporated areas of the County in advance of an official policy allowing them, my research of news reports and the background provided in city ordinance texts has not identified any other cities where dispensaries were sanctioned the way they were in Long Beach, Los Angeles, and West Hollywood prior to these cities enacting local ordinances that restricted their operation beyond California law. All the other cities and unincorporated LA County were therefore coded as not allowing dispensaries until an ordinance was passed that specifically stated that they were allowed. I recorded the location of dispensaries to measure their presence in communities directly, rather than assuming a city ban meant that there were no dispensaries operating in a city.

As soon as I started collecting data on the number and locations of dispensaries in LA County it became clear that city dispensary bans were not a reliable determinant of whether dispensaries were actively operating in a city. This backed my theory that it would be important to adjust for discrepancies between expectations based on city policy and the practical availability of marijuana from dispensaries in a particular city based on how dispensaries were operating there. 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 dispensary location data were not obtained from an official source and were intended to link medical marijuana customers to marijuana businesses rather than for research purposes. However, a greater limitation than the source of the marijuana location data is how quickly it can change. The marijuana market and policy environment in LA County is an environment where dispensaries are frequently shut down and found to crop up in other locations . Using the verified counts of the dispensaries helped address this limitation and assure that the influence of dispensaries was more contemporaneous with when marijuana use was measured among students . Once obtained and de-duplicated, the addresses of the dispensaries and LA County Public High Schools were geocoded using ArcMap 10.4. Geocoding is a process where a Geographic Information Systems software program matches an address to a database that contains latitude and longitude coordinates for all of the known addresses in an area, and then places the address locations as points on a map. For this analysis I used the “LA County Locator”, which is publicly available for download from the County of Los Angeles GIS Portal , a website that is maintained by the County of Los Angeles GIS Steering Committee to serve as a central location for GIS data created, maintained, licensed, and stored by LA County government agencies. After placing the geocoded addresses of the dispensaries and high schools within a map of LA County as points, I associated the shapefiles that placed the location of the dispensaries as points within LA County with shapefiles that defined the borders of the cities and unincorporated areas of LA County using a spatial join. When a point layer is joined to a polygon layer, a count field is created that tallies the number of points that fall within the boundaries of each polygon . I used this process to create dispensary counts per city in ArcMap. I then imported the .dbf file that ArcMap creates as part of each shapefile into SAS to be linked with the other data sources by city name. The city boundary shapefiles I used contain information about the population of the cities and the unincorporated area within LA County, which I used to account for the different sizes of the cities in LA County by calculating rates of dispensaries by the city and unincorporated area population. To do this, the counts of MMDS per city were divided by the population of the city and multiplied by 10,000 to obtain a rate of dispensaries per 10,000 residents.

Teasing out the impact of adult attitudes on adolescent marijuana use is difficult in the context of rapidly changing marijuana policy and is further complicated by the fact that there is seldom data available to account for whether a law that granted easier access to marijuana for adults increased the supply of marijuana available to adolescents. Marijuana laws may also have an influence on youth attitudes toward marijuana and marijuana use behavior that is independent of any increase in availability. For example, Miech and colleagues found that passage of a 2010 California law decriminalizing possession of personal use quantities of marijuana for adults was followed by a 25% increase in 12th graders’ likelihood of using marijuana, a 20% decrease in their likelihood to perceive regular marijuana use as dangerous, and a 20% decrease in the likelihood of strong disapproval of marijuana use . That these trends were in evidence when the law had been approved but not yet enacted suggests that the policy change may have enhanced impressions among adolescents that marijuana use is socially acceptable and/or decreased perceptions of harm without having made any change in the accessibly of marijuana use . When California voters approved the Compassionate Use Act of 1996, California became the first state to decriminalize possession of small quantities of marijuana for medical use. Under the Compassionate Use Act medical marijuana patients and their primary caregivers were permitted to possess and cultivate marijuana for personal use with a recommendation from a licensed physician. The purpose of the Compassionate Use Act was to ensure that seriously ill Californians could use marijuana to treat serious medical conditions such as cancer, AIDS, and seizure disorders without being vulnerable to criminal prosecution. The phrase “any other illness for which marijuana provides relief” is a broad definition of illness and symptoms compared to the medical marijuana laws that have followed in other states,indoor cannabis grow system and in practice it allows physicians in California to recommend marijuana for any condition they think it might it help with.

The language of the Compassionate Use Act directly addressed the rights of the marijuana patient and their primary caregiver but did not provide guidelines for how a person could obtain medical marijuana without growing it themselves or buying it on the illicit market. Nor did it address details of enforcement such as how law enforcement officers could distinguish qualified patients from recreational users. A regulatory structure was intended to be decided with subsequent legislation and the Act explicitly encouraged the State government to implement “a plan for the safe and affordable distribution of marijuana to all patients in medical need of marijuana”. Unfortunately, although multiple state and assembly bills were proposed over the years following the enactment of the Companionate Use Act in 1996, legislation to develop a truly comprehensive regulatory structure for medical marijuana in California was not successfully passed until twenty years later, with the Medical Marijuana Regulation and Safety Act of 2016. State-level estimates for marijuana use in California are not reliably available prior to 2002, but in response to the passage of the Compassionate Care Act in 1996, the National Household Survey of Drug Abuse , the precursor to the NSDUH, supplemented the survey sample in California during the 1997 and 1998 survey years to measure the impact of legalizing medical marijuana. In addition, the NHSDA sample in California was large enough in 1995 and 1996 to allow examination of longer-term trends. A 1999 supplemental report issued by the Substance Abuse and Mental Health Services Administration indicates that there was no significant change in marijuana use between 1997 and 1998 in California among adults or among adolescents between 12-17 years old. Instead, the NHSDA survey results indicated that rates of both adolescent and adult marijuana use had been stable since 1995 and that perceptions of the health risk associated with using marijuana also remained fairly constant . The Medical Marijuana Protection Act became effective on January 1, 2004. It created a voluntary identification card system for purchasing medical marijuana, the California Department of Public Health Medical Marijuana Program , that was administered by county health departments.

The MMP created a state-licensed medical marijuana identification card program and a registry database for verification of qualified patients and their primary caregivers . The identification cards were issued to people with physician recommendations to use marijuana to treat medical conditions, as well as to their designated primary caregiver. The action of the law that was most relevant to this dissertation, however, was that SB 420 allowed for the establishment of storefront dispensaries. Whereas previously caregivers could only supply medical marijuana privately to 5 patients, with the enactment of SB 420 a collective of caregivers could provide for hundreds of patients and sell marijuana at retail storefronts as long as they operated as a non-profit business . State-level estimates of NSDUH data for California show a small and gradual increase in rates of current use among adolescents aged 12-17 not long after SB 420 was enacted in 2004 and storefront medical marijuana dispensaries became a reality . The trend was minimal, however, until there an expansion in the medical marijuana industry after 2009 that is explained in further detail in the next section. The Federal policy environment may likewise be an important influence on marijuana use behaviors and perceptions of risk among LA County youth. Changes in state laws may have played an influential role in the rapid changes in teenagers’ attitudes toward marijuana that have been noted at a national level. Following California’s precedent of legalizing medical marijuana use in 1996, an increasing number of U.S. states have enacted policies legalizing some degree of access to marijuana. By 2009, over half of the population of the United States lived in a state sanctioning some level of marijuana use , although marijuana remains an illegal substance under Federal law today. A total of 34 states, the District of Columbia, Guam, Puerto Rico and the US Virgin Islands have now approved comprehensive, publicly available medical marijuana programs and an additional 12 states allow use of “low THC, high cannabidiol ” products for medical reasons in limited situations or as a legal defense . The increasingly liberal state marijuana laws being passed in the U.S. have coincided with declining perceptions of the risk of marijuana among American teenagers, but not with increased use . Thus, it seems that to date the primary impact of state medical marijuana laws has been increasingly liberal social norms and attitudes toward marijuana use rather than a change in marijuana use behaviors. Alternatively, cannabis grow equipment the increasingly liberal state laws governing marijuana may instead reflect a secular change in social norms and attitudes toward marijuana that precedes the passage of laws allowing greater access to marijuana.

Indeed, both processes may occur simultaneously in a self-reinforcing cycle of increasingly liberal marijuana laws and attitudes. In contrast with the change in secular attitudes toward marijuana use that has been noted on a national level, there are several Federal policies and enforcement efforts that have had a direct effect on the availability of marijuana in LA County. Marijuana is regulated under the Controlled Substances Act of 1970 and with the exception of two FDA-approved medications derived from cannabinoids that are available by prescription it remains classified as a Schedule I drug. Schedule I drugs are defined as having a high potential for abuse, as having no currently accepted medical use for treatment in the U.S., and as unsafe for use without medical supervision . Possession and sale of Schedule 1 drugs are subject to severe criminal penalties, which has important implications for law enforcement. The inconsistency between Federal and state laws governing marijuana raises legal questions that are in active debate by the nation’s highest courts. In the meantime, the Department of Justice has been obliged to issue a series of memoranda defining the Federal position on medical marijuana and the extent of their judicial and law enforcement authority. These memoranda, while directed to state legislatures and law enforcement agencies, also had a significant impact on the medical marijuana market in California and by extension the availability and visibility of marijuana outlets in LA County. The first, the “Ogden Memorandum” of 2009, stated that Federal resources should not be directed toward prosecuting “individuals whose actions are in clear and unambiguous compliance with existing state laws providing for the medicinal use of marijuana” . The development of a commercial marijuana industry in California occurred largely after this memo signaled that the Federal government would not prosecute dispensaries that were operating in compliance with state laws. Increases in the number of dispensaries were noted throughout the state , but the exact number of dispensaries that were established soon after the Ogden Memo was released is unknown . Data from the City of Los Angeles, however, documents that in 2007 the City reported 186 dispensaries, whereas by 2010 they reported 545, an increase of nearly 200% . In 2011, a new Department of Justice memorandum had an opposite, dampening effect on the marijuana marketplace. The “Cole Memorandum” narrowed the policies set forth in Ogden and drew a clear distinction between individual patients and commercial dispensaries, extending protection from Federal prosecution to registered patients but not to dispensaries . This memorandum was followed by a series raids by Federal prosecutors conducted throughout California in October of 2011. Hundreds of dispensaries across the state had their inventory confiscated and more than 200 dispensaries were shut down in Los Angeles County and surrounding counties . Although the Cole Memorandum and the subsequent raids targeted businesses and were not widely publicized, they had a significant impact on the number of dispensaries operating in LA County that reduced the accessibility of marijuana for adults and is likely to have resulted in reduced exposure to marijuana outlets among youth. According to Drug Use Social Norm theory, this reduced exposure to active dispensaries would be expected to result in LA County adolescents having less favorable perceptions of the acceptability of marijuana use. To date, however, this effect has not been investigated. The growth of the number of dispensaries in communities throughout California after the Ogden Memo of 2009 was correlated with a temporary increase in youth and adult use that reversed after the Cole Memo signaled a stricter federal stance toward marijuana businesses in 2011. Although the changes in youth use were not drastic any point over these years, the degree to which they correspond with these policy decisions is striking. That an increase in youth use was not noted following enactment of the Compassionate Use Act and there was a minimal trend of increase after SB 420 was enacted is important. It suggests that the impact of making marijuana available as a legal product and in retail settings has much to do with the number of marijuana outlets in our communities and how tightly they are regulated . The Adult Use Marijuana Act legalized marijuana possession, cultivation and use for adults over the age of 21 without requiring a recommendation from a California-licensed physician. As stated in the ballot measure text, the purpose of the AUMA is to “establish a comprehensive system to legalize, control and regulate the cultivation, processing, manufacture, distribution, testing, and sale of non-medical marijuana, including marijuana products, for use by adults 21 years and older, and to tax the commercial growth and retail sale of marijuana.” The act employed a dual license structure similar to the MCRSA that allows local jurisdictions to define polices that are more restrictive than state law. A notable exception to the dual license law is that cities and counties cannot ban personal use cultivation of less than 6 plants if cultivated indoors.The Bureau of Marijuana Control, housed in the Department of Consumer Affairs, oversees the marijuana legal market, and began issuing licenses to marijuana retailers and distributors in January of 2018. The Department of Food and Agriculture licenses and oversees marijuana cultivation and enforces environmental regulations on cultivation and food safety regulations on edibles.

The travel distance was also increasing over time. An interesting exploratory observation indicated that, compared to RMDs located further away from schools, a larger proportion of RMDs in reachable distance to schools had interior child-appealing marketing. It is possible that RMDs intentionally targeted children if they were in closer proximity of schools. Unfortunately, our study was not able to test this hypothesis directly. Almost all the audited RMDs followed California rules on age verification. If continuous monitoring and enforcements are not in place, however, children might get access to abundant child-appealing marketing practices inside of the dispensaries, the consequences of which could be grave. Furthermore, exterior signs of age limit were absent in over 80% RMDs and security personnel were only observed in 40% RMDs. These might increase the risks of accidental or even intentional attempts of children to enter RMD premises, who would be then exposed to interior marketing in waiting area. Compared to laws in other states, California regulations on child-appealing marketing seem to be vague and less comprehensive during the study period. Because content restrictions are inherently subjective, it might be challenging for California RMDs to comply and for regulators to enforce without objective, operationalizable measures of “child-appealing”. Fortunately, after this study was completed, California released new regulations in January 2019 on child-relevant products and marketing. Specifically, marijuana products and packages “shall not use any depictions or images of minors” and “shall not contain the use of objects, such as toys, inflatables, movie characters, cartoon characters, or include any other display, depiction, or image designed in any manner likely to be appealing to minors”. These texts are expected to provide clearer guidance to law compliance and enforcements. In addition to prohibitions in laws, California could also consider screening content materials such as packages before they are available in RMDs. For instance, Massachusetts allows manufacturers to submit artwork to a regulatory board for review to ensure non-child-appealing packaging.

Standardized packaging might be another alternative,cannabis drying trays which has shown effectiveness in tobacco control outside of the US. This study has limitations. First, this study used a cross-sectional design to capture a snapshot in summer 2018, approximately half a year after California’s commercialization of marijuana. This unique transition period was characterized with a lack of law enforcement, delay of dispensary licensing, and inadequate understanding of laws. As the legal market matures and government makes endeavors on law interpretation and enforcement, we might expect a stronger compliance with laws and possibly a reduction in marketing practices. The findings may not be generalizable to other time points in California. Second, our observations were largely constrained within the regulatory regime in California and may not be generalizable to other states where different regulatory measures are in place. Third, frequency or quantity measures in each marketing category would be more informative than simple binary indicators for availability. Unfortunately, a dispensary often displays hundreds or even thousands of products, packages, paraphernalia, and advertisements. Obtaining frequency or quantity information requires the field workers to spend a considerably longer time evaluating the RMD environment, which is infeasible in practice. Fourth, California laws lacked specific details related to children during the study period. The classification of child-appealing was informed by laws in other states and constructed with authors’ own understanding, which may not reflect California lawmakers’ intention or completely align with recently released new regulations. Further, there might be inevitable measurement errors even after two field workers discussed and resolved discrepancies between them. Lastly, this study only gathered data on RMDs in closest proximity to public schools. To improve representativeness, future research is encouraged to audit a random sample of RMDs. The increasing spread of marijuana use, especially among adolescents and young adults , has heightened societal awareness of the risks associated with this drug and has highlighted the need to fully understand its mechanism of action. Basic research has shown that D9 -tetrahydrocannabinol , the main active constituent of marijuana, produces its effects by combining with selective receptors present on the membrane of cells in the brain, the vasculature and the immune system .

Research has also revealed that a group of lipid-derived substances produced by the body engages these receptors and participates in biological processes as diverse as pain perception, memory formation and blood pressure regulation. This knowledge has allowed researchers to interpret the pharmacological properties of marijuana, but remains inadequate to the task of developing strategies for the medicinal management of marijuana dependence. No such strategies exist at present , despite the fact that pharmacotherapy—alone or in combination with behavioral therapy—is considered a primary treatment option for drug dependence when abuse prevention fails . Several basic questions, which are relevant to the pharmacotherapy of marijuana dependence, remain unanswered. For example, while it is clear that D9 -THC acts by hijacking the brain endocannabinoid system, its impact on the various components of this system—synthetic and catabolic enzymes, transporters, and receptors—is still largely undefined. Does D9 -THC produce rapid adaptive changes in neuronal endocannabinoid signaling, as recent evidence indicates ? And, if so, do such changes contribute to the pharmacological actions of the drug? Does prolonged exposure to D9 -THC cause stable alterations in endocannabinoid signaling? And, if so, do such alterations contribute to marijuana dependence and, most importantly, can they be safely reversed to restore normality? Answering these questions may not only help develop effective therapeutic strategies for marijuana dependence, but in light of the broad roles played by the endocannabinoid system in the control of brain reward processes , might also shed new light on fundamental mechanisms of drug addiction. To accomplish this task, it seems important to move forward in two convergent directions: the molecular characterization of endocannabinoid signaling, much of which is still uncharted; and the development of pharmacological agents that interfere with specific components of this system. In the present review, I outline recent progress made in these directions, specifically focusing on endocannabinoid deactivation, and discuss some of the challenges lying ahead.Anandamide was the first endocannabinoid substance to be isolated and structurally characterized . Its formation inneural cells is thought to require two enzymatic steps, which are illustrated in Fig. 1. The first is the activity-dependent cleavage of the phospholipid precursor N-arachidonoyl-PE . This reaction, which is mediated by a unique D-type phospholipase , produces anandamide and phosphatidic acid, which is recycled to produce other glycerol-containing phospholipids.

The cellular stores of NAPE are small, but can be refilled by an N-acyltransferase activity, which catalyzes the intermolecular passage of anarachidonic acid group from the sn-1 positionof phosphatidylcholine to the head group of phosphatidylethanolamine . Incultures of rat cortical neurons, NAT activity is controlled by two intracellular second messengers: Ca2+, which is required to activate the enzyme, and cyclic 30 , 50 -adenosine monophosphate , which stimulates protein kinase A-dependent protein phosphorylation and, via an unknown mechanism, enhances NAT activity . Although separate enzymes catalyze the syntheses of anandamide and NAPE,heavy duty propagation trays the two events are likely to occur simultaneously because Ca2+- stimulated anandamide production is often accompanied by de novo formation of NAPE . Anandamide synthesis can be elicited in vitro by a variety of agents that elevate intracellular Ca2+ levels. For example, the Ca2+ ionophore ionomycin stimulates [ 3 H]anandamide formation in cultures of rat striatal and cortical neurons labeled by incubation with [ 3 H]ethanolamine . In the same neurons, Ca2+-dependent [3 H]anandamide production may be elicited by the glutamate receptor agonist, kainate, by the K+ channel blocker 4- aminopyridine, and by membrane-depolarizing concentrations of K+ ions . Depolarizationof neural cells was also shownto evoke Ca2+-dependent anandamide release in vivo . Along with Ca2+ entry, activation of certain G protein-coupled receptors can also initiate anandamide generation. Administration of the dopamine D2-receptor agonist quinpirole causes a profound stimulation of anandamide synthesis in the rat basal ganglia, which is prevented by the D2 antagonist raclopride . Importantly, cocaine elicits a similar response , suggesting a role for anandamide in the actions of these psychostimulant drugs. The ability of the anandamide transport inhibitor AM404 to reduce D2 agonist induced hyperactivity, discussed below, further supports this possibility .The prototype of this class of drugs, the arachidonate derivative AM404 , has provided important information on the properties of anandamide transport, not only aiding the in vitro characterization of this process, but also helping to reveal its possible functions in animals. Importantly, the partial cannabimetic profile exhibited by this agent in vivo suggests that anandamide transport might provide a useful target in disease conditions in which the endocannabinoid system is hypofunctional . Evidence indicates that one such condition could be opiate withdrawal, which is markedly reduced in rodents by administering AM404 . These theories have been hindered by the fact that the putative transport system responsible for anandamide internalization is still uncharacterized at the molecular level. In fact, the presence of such a system has been recently questioned, based onthe observation that [ 3 H]anandamide uptake in certain cell lines is saturable at longer , but not at shorter incubationtimes . This finding has been interpreted to suggest that fatty-acid amide hydrolase —a key enzyme of intracellular anandamide degradation, described in a subsequent section—may be responsible for the saturation of uptake noted at longer incubation times . However, the result may also be explained on purely technical grounds, as the high concentration of serum albumin used inthe experiments of Glaser and collaborators was previously shownto prevent [3 H]anandamide internalization . Consistent with this interpretation, recent studies have provided additional evidence for the existence of an anandamide transport system independent of FAAH . In particular, one of these studies has shown that cultures of cortical neurons isolated from the brain of FAAHnull mice internalize anandamide as efficiently as do neurons that express normal levels of the enzyme. The same study also demonstrated that the transport inhibitor AM404 is equally effective at reducing anandamide internalization in neurons of FAAH-null and wild-type mice. These results indicate that FAAH does not provide the driving force for anandamide uptake or serve as a target for AM404. Invivo experiments further support this conclusion, showing that AM404 not only enhances the actions of exogenous anandamide in FAAH-null mice, but acts more effectively in this mutant strain than it does in control animals. This implies that AM404 is not in fact a FAAH inhibitor, as it has been proposed , but a FAAH substrate. In support of this idea, it was found that membranes prepared from the brains of normal mice rapidly hydrolyze AM404, whereas those prepared from mice that lack FAAH are unable to carry out this reaction.The fact that FAAH is not directly involved in anandamide internalization raises the question of what mechanism provides the driving force for this process. One possibility is that an intracellular protein may sequester anandamide at the membrane, driving its internalization and facilitating its movement to the mitochondria and the endoplasmic reticulum, where FAAH is primarily localized . If selective for anandamide, such a protein might participate in the transport process as well as serve as a target for transport inhibitors. This hypothetical model is consistent with fattyacid transport into cells, which is also thought to require the cooperation of membrane transporters and intracellular fatty-acid binding proteins .AM404 increases endogenous anandamide levels in brain tissue and peripheral blood of rats and mice . This effect is accompanied by a series of behavioral responses that, though blocked by the CB1 antagonist rimonabant , are clearly distinguishable from those of direct cannabinoid agonists. For example, administration of AM404 into the cerebral ventricles of rats decreases exploratory activity without producing catalepsy and analgesia, two hallmarks of direct CB1 receptor activation. Inaddition, AM404 reduces two characteristic effects caused by activation of D2 family receptors: the yawning response elicited in mice by low doses of the D1/D2-receptor agonist apomorphine; and the stimulationof locomotor activity evoked in rats by the D2- receptor agonist quinpirole . These effects are observed at doses of AM404 that selectively target anandamide transport and produce only mild hypokinesia when the drug is administered alone .

After doing a thorough review of the social movement literature, I was able to build a theoretical vocabulary to explain this transition as a shifting of fields, from the political field to the commercial field. By working in the hybrid field of medical cannabis, I experienced the quotidian shifts in discourse and practice that facilitate the transition between these two fields of practice. The unique perspective I gained as an employee in a medical cannabis dispensary also gave me a front row seat to the framing strategies that people use at an active site, or modality, of drug policy reform. I was able to learn and practice the shift in diction that my fellow employees and I used to accomplish the discursive shift of changing a previously illicit substance into a legitimate or licit substance . On a practical level, by working at a dispensary I was able to meet other activists, medical cannabis patients, and attend numerous drug policy events as a volunteer. My status as an employee gave me entrée into the world of drug policy reform and also made my research feasible with minimal outside funding. I used participant observation to explore the sites where the drug policy movement constitutes itself. This element of the study looked at two locations where participants in this movement most often interact with one another face-to-face, festivals and conferences. As noted by social movement scholars, face-to-face interactions are necessary to supplement the technologically based networking of participants through the Internet and other communication technologies. In addition to providing demographic data about attendees, the public speakers, panel discussions and presentations at these events offered rich qualitative data about the movement. I used this data to analyze how drug policy reformers frame their actions and to discover the key concerns of movement actors. I also used these events as convenient places to gather literature from various organizations. In addition to attending hemp fests, and conferences hosted by organizations, I attended several types of meetings during the course of my research project. I attended monthly and annual meetings of organizations, city council meetings, and city medical cannabis indoor grow system task force or commission meetings. These various meetings proved to be excellent sites for gathering qualitative data on how organizations and city governments work to regulate the emergent phenomenon of medical cannabis.

To illuminate how organizations change drug policy, how various organizations work together, and the biographical dimensions of drug policy activism, I conducted in-depth qualitative interviews with the members of several different drug policy reform organizations. I employed a snowball sampling technique to reach the leaders and members of drug policy organizations. I sought out key figures in the medical cannabis movement to gain access to their unique knowledge of the movement’s history, policy outcomes , collaborating with other organizations and elite benefactors, and interactions with government officials. My interviews with key figures helped me to answer my research questions about the political opportunity structures that allow for novel drug policies. I also asked my interview subjects about their biographies, how they became involved in activism and what led to changes in their political consciousness. Occasionally, participants in the drug policy reform movement engage in public protest and acts of civil disobedience to decry existing drug policy and institute new policy arrangements. I attended and participated in a medical cannabis protest in November 2011. The events that precipitated the protest, the number and types of people in attendance and the slogans, speeches, and chants that the protesters used provided rich data for examining how medical marijuana is both a social movement and an industry. Under what circumstances do activists engage in civil disobedience? What metaphors, slogans and symbols do protestors deploy? By using interview data, however, I open myself up to issues that threaten internal validity. In addition to relying on the veracity of my informants, I also face the pitfalls of memory recall. According to Banks , the recollection of past stances is “notoriously subject to modifications over time.” I plan to increase the internal validity of my study by using corroborating sources, including newspapers and official documents . The success of the project hinges on my ability as an interviewer to gain the trust of informants, which in turn can lead to issues of sympathetic portrayals of subjects’ behavior.

Fenno argues that sympathy with subjects can detract from a researcher’s ability to be critical about data collection and research findings. Regarding reliability, because this research design relies heavily on my ability to gain entrée to a specific population, it would be impossible for a researcher without my connections to replicate the interviews that I conducted. Because I am using a non-probability snowball sampling technique, I do not contend that the findings of this research will be generalizable to other populations or to other medical cannabis reform movements. However, the findings of this project could contribute to general theories of activism and to specific analyses of drug policy innovation. I seek to contribute to our knowledge of how drug policy activists forge change under the repressive reality of US drug prohibition. It is my hope that this study will contribute to the sociology of social movement tactics that will be useful to other social movement scholars and activists. By using a hybrid approach to theory testing and theory building I seek to contribute new theoretical insights to the sociological field of social movements and drug policy.What unites the diverse organizations, funders and participants of the drug policy reform movement is a belief that prohibition as an overarching approach to dealing with illicit drug use creates many problems for individuals and society. Although not all organizations and individuals in the movement agree that prohibition should be rolled back in its entirety, all the organizations in the movement find at least some aspects of prohibition to create more problems than it solves. In the 1970s, organizations sought to decriminalize the adult use of cannabis because they viewed its prohibition as an affront to individual liberties, and because it relegated a whole class of otherwise law-abiding individuals to criminal status . In the 1980s, the harm reduction movement began as a public health based response to the spread of HIV and Hepatitis C among injection drug users. Eventually harm reduction blossomed into a philosophy under girding an alternative approach to drug problems . It was not until the mid 1980s that a wholly anti-prohibitionist branch of the movement coalesced around the issues of racial injustice and the prison boom, human rights and instability in drug producing countries , and a reintegration of earlier branches of the movement . All three branches of the movement actively challenge the discourse of drug prohibition,cannabis equipment in addition to specific policies sustained by the “drug control industrial complex” . At an abstract level, the various organizations and participants of the drug policy reform movement are engaging in a collective argument with supporters of drug prohibition. Billig uses a discursive approach to the conduct of social movements. In the tradition of social psychology, he emphasizes the importance of language for movements. “Social movements can be seen as a conducting arguments against prevailing common sense” . This makes the rhetorical tasks of social movements challenging because most attempts at persuasive discourse appeal to common sense. Essentially the movement argues “prohibition creates more problems than it solves.” As seen with the Occupy movement that began in New York City’s Wall Street district in September 2011, one of the most powerful effects a movement can have is on changing the national discussion or debate.

While sociologists and economists have decried income stratification, income inequality and the ever shrinking middle class in the U.S. for decades, the Occupy movement was able to shatter the commonly held and widely disseminated myth that the U.S. is overwhelmingly a middle class society typified by a high degree of mobility. Although politicians and journalists have decried the central tactic of the Occupy movement, by physically occupying public space the movement was able to change the public debate much more quickly than movements that rely primarily on social movement organizations to make things happen. What makes the argument particularly difficult for the movement to win is an imbalance in access to what I have termed the means of representation. Until the 1990s, supporters of prohibition have had privileged access to the means of representation. As I show in chapter two, the drug policy reform movement is using the Internet to address this disparity with increasing success. In addition to challenging the discourse of prohibition on the Internet and increasingly in the mainstream news media, the drug policy reform movement converges at conferences and hemp rallies to vocalize, experience, and broadcast its challenge to the discourse of drug prohibition. The movement challenges both the policies enforced in the name of prohibition and on a more abstract level, representations of drug users and drug use that prohibitionist discourses seek to portray. By challenging policies and representations that are part and parcel of those policies, the movement collapses a conceptual division that New Social Movements theorists including Alberto Melucci and Manuel Castells seek to draw, the idea that movements are about cultural stakes and not legal or political stakes. I consider the question of whether the drug policy reform movement seeks political or cultural change during my research, and will revisit this dichotomy in later chapters. At the outset, I wish to make it known that I am not only an academic observer of drug policy reform, but I am also an active participant. My position as both an advocate for and observer of drug policy reform presents a difficult balancing act. While I strive to objectively represent and analyze the drug policy reform movement, I wholeheartedly support the basic argument of drug policy reform; prohibition is an ineffective way to deal with drug use and it creates more harmful consequences than it addresses. By having a stake in the struggle I am writing about, I am following in a long line of social analysts who present an engaged view of the social problems they study. While taking a normative position on drug policy precludes me from any pretense of “values free” sociology, I do not recuse myself from the goal of presenting as objective a picture as possible of drug policy reform and medical marijuana. I first became conscious that people and organizations were seeking to reform cannabis laws in 1994. At that time, I had no idea that a wider drug policy reform movement existed. I attended a “hemp rally” in Lafayette Park in Washington, D.C. on the fourth of July, and was introduced to a loosely organized group of activists and speakers who had set up tables at the event. Activists were distributing literature, compiling mailing lists and talking to attendees. I was shocked that attendees were openly smoking cannabis within view of the White house. I was also shocked that somewhat formal looking organizations were in attendance. This small act of civil disobedience was remarkable to me for several reasons, it was collective, it was fun and I felt like I was part of something bigger than myself . The police did not arrest anyone, despite the rampant law breaking that was going on. During the event, attendees transformed cannabis smoking from a private act of criminality to a public statement of defiance. This experience opened my eyes to the political dimensions of drug use and to the existence of a collective challenge to drug policy. While attending college at the University of Virginia, my consciousness of the political ramifications of drug use and drug policy expanded greatly. I went to National Organization for the Reform of Marijuana Laws meetings and learned about the consequences of drug use and policy from the experiences of several friends. During the year before I arrived at the school, the DEA had conducted a joint operation with local, state and university police that targeted LSD users on campus. In a series of sting operations, undercover police purchased LSD from several college students.

About a third of participants reported that they sometimes or regularly reduced their opioid medication when using cannabis. However, the prevalence of opioid discontinuation was not significantly different between daily or neardaily marijuana users and non-users. To date, data on self-reported improvement of symptoms has not been substantiated by studies that have monitored opioid and marijuana use. More research on this topic is clearly needed. Nonetheless, our findings suggest that even if objective measures do not support that marijuana is sub-stitutive for opioid use, patients perceive that marijuana use has reduced their opioid use. Perhaps the commercialization of marijuana and the favorable media coverage surrounding the health effects of marijuana are fostering such a perception. More research including clinical trials on the efficacy of cannabis in pain management with the inclusion of patient-centered outcomes is needed to shed light on the role of marijuana on pain management. Our study has several limitations that deserve comment. Our study was a cross-sectional survey of a relatively small number of respondents with a history of marijuana use and opioid use within the past year. Our survey question provided a limited number of examples of opioid medications , so it is possible that we did not identify all opioid users. Although our survey specifically asked about the use of opioids for pain, it is possible that we captured individuals who were using opioids for other reasons such as opioid use disorder. Likewise, it is possible that respondents may be using marijuana for reasons other than pain. Thus, we cannot conclude with certainty that patients are using marijuana as an alternative to opioids for pain per se. We also relied on respondents’ retrospective judgment regarding reduction or cessation of opioid use attributable to marijuana use. Thus, our findings could also be limited by recall bias, although this was minimized by restricting the sample to those who used opioids within the past year. Under reporting of substitution could have occurred,microgreen grow rack particularly in states in which marijuana has not been legalized.There is widespread belief that, unlike tobacco smoke, marijuana smoke is benign.

While the psychoactive substance in marijuana is tetrahydrocannabinol rather than nicotine, marijuana smoke is still the result of biomass combustion and contains many of the same toxins as tobacco smoke,including fine particles that cause cardiovascular morbidity and mortality.Tobacco secondhand smoke alone is responsible for 50 000 deaths in the United States each year, with 46 000 from cardiovascular disease,and implementation of laws prohibiting smoking in public places and workplaces is followed by drops in hospital admissions for acute myocardial infarction, other cardiac events, stroke, and pulmonary diseases.However, due to the illegality of marijuana, it has been difficult to prospectively study the effects of marijuana smoke, and the rare secondhand marijuana smoke studies have focused on whether exposed people test positive on drug tests.The increasing number of states legalizing medicinal and recreational marijuana, and increasing potential for corporate expansion within the cannabis industry,make it important to understand the health consequences of secondhand exposure to marijuana smoke. Vascular health can be evaluated by measuring arterial flow mediated dilation , the extent to which arteries vasodilate in response to increased blood flow.FMD ensures sufficient blood flow to peripheral tissues and the heart. FMD is quantified in humans by ultrasound as the percent vasodilation of the brachial artery in response to restoration of blood flow after transient occlusion.Brachial artery FMD is a well established clinical prognostic indicator of endothelial function that correlates with endothelium-dependent vasodilation of the coronary arteries and other measures of cardiovascular health.Decreased brachial FMD correlates with adverse cardiovascular outcomes that are increased by cigarette smoke, including myocardial infarction and atherosclerosis.FMD is impaired in tobacco smokers relative to nonsmokers.People who report frequent SHS exposure exhibit poor FMD even when smoke is not present during the testing, and a 30-minute exposure to SHS at real-world levels impairs FMD in humans.The nicotine in tobacco is not responsible for the entire adverse effect of tobacco smoke on FMD,and vasodilatory function is also impaired by diesel exhaust and by smoke from incense and candles.These observations, along with the similar chemical composition of tobacco and marijuana smoke,led us to hypothesize that marijuana smoke would also impair FMD.

We developed an animal model that uses micro-ultrasound and a simple reversible surgical occlusion of blood flow to the leg to measure FMD in the femoral arteries of living rats,analogous to the measurement of brachial artery FMD in humans. We extensively validated this technique physiologically and used it to demonstrate age-related changes in the mechanisms underlying FMD,and the beneficial vascular effects of pharmacological preservation of bio-available intracellular nitric oxide.Subsequently, we showed that 30 minutes of tobacco SHS exposure at real-world levels impairs FMD in rats, and that even 1 minute of SHS impairs FMD.We observed similar impairment from exposure to tobacco SHS from little cigars.We, therefore, used this rat model to determine whether marijuana SHS also has adverse effects on the vasculature.Acquisition and possession of marijuana was approved by the Drug Enforcement Agency, the Food and Drug Administration, the Research Advisory Panel of California, and the University of California, San Francisco Office of Environmental Health and Safety. Marijuana cigarettes were supplied by RTI International , contracted through the National Institute on Drug Abuse. Marijuana was from sinsemilla plants with stems removed, and consisted of leaf fragments, small leaves, bracts, and buds, and was grown in the absence of pesticides. The cigarettes were machine rolled with the same dimensions as standard tobacco cigarettes and fit in our cigarette smoking machine without further modification. Upon arrival, marijuana cigarettes were individually wrapped in plastic wrap and numbered, and were stored in airtight containers at 20°C. In accordance with requirements from the Drug Enforcement Agency, the cigarettes were stored in a padlocked freezer with high-security lock and a code deactivated open-door alarm that communicated with the University of California Police Department, physically attached to a heavy Steel case desk, in a controlled-access room outfitted with a solid door with high-security lock, and hinge pins that were non-removable from the outside. Prior to each experiment, marijuana cigarettes were humidified overnight at room temperature by placing them in an airtight container over 50 mL of saturated sodium chloride solution as per instructions on use from RTI, in a locked desk drawer. Logs documenting removal of cigarettes from the freezer and desk were kept as required by the Drug Enforcement Agency. Cigarettes were used within 5 minutes after humidification.We used a modified cigarette smoking system described previously for tobacco SHS experiments.Briefly, the system collects side stream smoke from the burning tip of the cigarette in a 21-L Plexiglas exposure chamber as a ventilator pump simulates human puffing. A Sidepak AM510 personal aerosol monitor , calibrated for cigarette smoke particles and excluding those >2.5 lm, monitors the concentration of respirable suspended particles in the exposure chamber and exhausts back into the chamber. Smoke is collected in the chamber and the cigarette is extinguished, and excess smoke is then vented from the chamber to obtain the desired starting concentration. Air in the chamber is mixed with a small fan. The wall of the chamber contains a gasket through which the nose of an anesthetized rat is inserted to breathe the smoky air. Because the system requires the cigarette to be extinguished before exposure of the rat,ebb and flow flood table adsorption of smoke particles to surfaces in the exposure chamber causes a continued progressive decrease in the levels over time; thus, most of the exposure occurs over the first several minutes. Tobacco cigarettes were smoked according to standard conditions.

Marijuana cigarettes were smoked using the same protocol, with the exception that the puff duration was 1 s for our initial 30-minute exposure experiment due to an instrument calibration error discovered afterward. The difference between a 1-s puff and a 2-s puff is not expected to have a substantial effect on the side stream smoke generated. For each experiment, a single cigarette was lit, smoked for 3 minutes, and extinguished, and particle concentration in the exposure chamber was adjusted until the desired RSP starting concentration was reached . At that time, an individual anesthetized rat, after baseline FMD measurement, was exposed for the specified duration and was then returned to the ultrasound system for post smoke FMD measurement. Negative controls consisted of the same duration of exposure to non-smoky air in the exposure chamber. As in our tobacco study,it took roughly 10 minutes after the end of the exposure period to prepare the rat for an initial post exposure FMD measurement. For some experiments, we measured FMD again 30 minutes later to evaluate recovery.Our hypothesis that FMD is impaired by combustion products common to smoke from burned plant material raises the question of whether FMD is impaired by smoke from burned rolling paper, rather than the tobacco or marijuana. The question is relevant because some people smoke marijuana in pipes. We tested the hypothesis that impairment is dependent on paper smoke by assembling marijuana cigarettes in which the paper was replaced by a fine stainless steel mesh to mimic the properties of rolling paper, which is ventilated. In a confirmatory group of 4 rats, FMD was significantly impaired to a similar extent as that by regular marijuana cigarettes . Since these mesh cigarettes were prepared using the THC-free marijuana, the results confirm that FMD is impaired by SHS from marijuana lacking both THC and rolling paper.Because most of the exposure during our 30-minute period occurred during the first 10 minutes, the question remained of whether a very brief exposure impairs FMD. We previously reported that tobacco SHS exposure for 1 minute at the high restaurant level impairs FMD,34 so we repeated that experiment with marijuana SHS at comparable particle concentration. FMD was substantially decreased by 1 minute of marijuana SHS with and without THC . Measurement of pre-occlusion baseline diameter revealed that the marijuana SHS directly induced vasodilation, even with the THC-free marijuana. This result was in contrast to our previous experiment in which rats were exposed to 30 minutes of declining RSP levels that fell to roughly zero before post exposure FMD was measured, with no observed smoke-induced vasodilation. To reconcile this apparent contradiction, we exposed another group to marijuana SHS and waited a total of 25 minutes after the 1-minute exposure before measuring FMD. This allowed the baseline vasodilation to subside, but FMD still decreased , confirming that 1 minute of marijuana SHS exposure causes endothelial dysfunction that persists beyond any transient vasodilatory effects of the marijuana. All cannabinoids are missing from the THC-free marijuana, and we did not observe significant baseline vasodilation after 1 minute of tobacco SHS in our previous report34 . The identity of the noncannabinoid vasodilator in marijuana is unknown.To determine whether the substantial impairment of FMD involved functional or physical inhibition of vascular smooth muscle function, we performed a separate experiment in which FMD was impaired by 1 minute of marijuana SHS as before, and then after impaired FMD was confirmed, an intravenous bolus of nitroglycerin was injected to induce endothelium-independent vasodilation. This injection caused vasodilation even while FMD was still impaired, as confirmed by a subsequent FMD measurement after the nitroglycerin effect had subsided . Therefore, the impairment of FMD by marijuana SHS was mediated by an endothelium-dependent mechanism, not a direct effect on the smooth muscle of the vessel wall.There is growing awareness that marijuana use in general may lead to cardiovascular complications, an effect normally ascribed to THC, but little attention has been paid specifically to the effects of the generic biomass combustion components. Our inclusion of control groups exposed to SHS from marijuana lacking cannabinoids confirms that THC was not required for the impairment of FMD. Similarly and notably, the finding that FMD is impaired by exposure to marijuana SHS, which is chemically similar to tobacco SHS but does not contain nicotine, confirms that the decrease in FMD caused by tobacco SHS is not dependent on nicotine. Together, our results demonstrate that in rats, FMD is impaired by 1 or more constituents of smoke not specific to marijuana or tobacco, either the products of combustion or other generic plant chemicals that persist after combustion.

The study was limited by recall bias related to the marijuana use assessment; otherwise, it was well-designed. Although some cross-sectional studies in this review suggested that marijuana has metabolic benefits , those with more robust analytic designs found no evidence of benefit , and other prospective studies found potentially harmful effects . These findings are of particular interest. Many articles in the lay press have suggested to the public that marijuana use has cardiovascular benefits, reduces blood pressure, stabilizes blood sugar levels, or improves cholesterol profiles . Our review found insufficient evidence to support these claims. Given public opinion that marijuana is safe or even beneficial, the insufficiency of the literature is concerning . Finally, despite the popular belief that marijuana use causes “the munchies” , we found no evidence that it is associated with weight gain or obesity. An important consideration in our understanding of marijuana effects relates to the standards of evidence necessary to identify harms. Using experimental trials to study marijuana harms is unethical; only observational studies are feasible, despite their inherent biases. Further, the greatest clinical uncertainty concerns older patients at higher risk for cardiovascular disease who use marijuana regularly over long periods. Therefore, the best possible study to assess the effect of marijuana use on cardiovascular outcomes would be a prospective cohort study among higher-risk participants, with several exposure assessments during follow-up and a robust evaluation of baseline characteristics and outcomes. The best evidence currently available, in contrast, is from the MIOS and CARDIA cohorts, although both have serious flaws . Whereas MIOS assessed marijuana exposure only once and was limited by recall bias, CARDIA made several assessments of marijuana exposure, but the overall exposure in the cohort was minimal and the cohort was young and likely under powered to assess the outcomes of stroke and cardiovascular mortality. Our systematic review also highlights other important evidence gaps. First,hydroponic table most studies failed to capture current and lifetime marijuana use adequately.

More robust exposure assessment tools are necessary to allow evaluation of the acute and long-term health effects of marijuana . Second, almost a quarter of the studies failed to report the specific route of cannabis use and the chemical constitution of the cannabis examined. The number of marijuana users, as well as the variety of routes , is increasing; therefore, collection of data regarding use must be more standardized, because the various forms may differ in toxic effects. In particular, high quality safety data on the effects of edible marijuana on the cardiovascular system are lacking. The effects of THC persist in the body longer after oral administration than inhalation. Prospective studies examining the effects of edible marijuana on other cardiovascular events, such as acute myocardial infarction and stroke, are necessary, especially because use of edible forms is increasing among older adults, who are at higher risk for cardiovascular disease . Our study has several limitations that deserve comment. We excluded articles not published in English; thus, we may have overlooked relevant studies. The diverse representation of outcomes across studies, variation in study design, and frequent lack of effect size reporting precluded a meta-analysis. In addition, most studies inadequately assessed marijuana exposure. Finally, most studies in this review were rated as high ROB, so their results should be interpreted with caution. In summary, although several studies suggested a metabolic benefit from marijuana use, they were based on cross-sectional designs and not supported by prospective studies. Evidence examining the effect of marijuana on diabetes, hyperlipidemia, acute myocardial infarction, stroke, and cardiovascular mortality was insufficient. Adequately powered prospective studies are needed to determine the effect of chronic marijuana use on cardiovascular health. Head and neck squamous cell carcinomas, which include cancers of the oral cavity, oropharynx, and larynx, are the sixth most common cancers worldwide with an estimated annual burden of 355,000 deaths and 633,000 incident cases . In addition to traditional risk factors, such as tobacco and alcohol use, human papillomavirus infection has recently been established as a major etiologic factor for a subset of Head and Neck Squamous Cell Carcinomas—cancers arising from the oropharynx, including the base of tongue, tonsil, and other parts of the pharynx . The incidence of a majority of head and neck cancer subsets has declined significantly during the past 2 decades in the U.S. and other developed countries, largely due to declines in cigarette smoking .

In contrast to this overall pattern, the incidence of oropharyngeal and oral tongue cancers has significantly increased during the same period, especially among individuals <45 years of age . While increases in oropharyngeal cancer incidence are attributed to increased acquisition of oral HPV through changes in sexual behaviors among recent birth cohorts , the reasons underlying increasing oral tongue cancer incidence are largely unknown. Notably, HPV infection is not currently believed to play a major role in the etiology of oral tongue cancers . Marijuana use has significantly increased among individuals born after 1950 , raising the hypothesis of a role of marijuana use as a risk factor for oropharyngeal and oral tongue cancer development . A recent case-control study reported that marijuana use was strongly associated with increased risk of HPV-positive oropharyngeal cancer . Conversely, a case-control study of HNSCC demonstrated an inverse association of marijuana use on cancers of the oral cavity . However, epidemiologic studies that have examined the association of marijuana use with Head and Neck Squamous Cell Carcinomas have been inconsistent . We therefore investigated the association of marijuana use with risk of oropharyngeal and oral tongue cancers in a large pooled analysis consisting of 9 case-control studies that were part of the International Head and Neck Cancer Epidemiology consortium.All studies included in this analysis collected data on lifetime marijuana use from cases and controls, including duration of use and frequency of use. Four of the studies ) asked each subject to report the average frequency of marijuana use over their lifetime, while the remaining fivestudies [Schwartz], Latin America, Boston, Los Angeles, and North Carolina obtained information about marijuana use during different periods of the subject’s lifetime. For these later five studies the lifetime average frequency of marijuana use was calculated by weighting the frequency of each specific period by the duration of that period relative to the total years of marijuana use. For analysis, marijuana use was defined as ever/never,grow rack frequency of use per week and duration of use . Lastly, a “joint-year” variable was created as a measure of cumulative marijuana exposure, and defined as the number of joints per day multiplied by the duration of marijuana use in years and was categorized into a-priori categories . Four out of the nine studies defined marijuana use specifically as smoking marijuana whereas the remaining five studies defined marijuana use in any form.

All studies collected information on tobacco use including ever vs. never use of cigarettes and cigars/pipes. In six out of nine studies [Schwartz], Seattle-LEO [Vaughan], North Carolina , Los Angeles, Houston, and Boston) ever smoking cigarettes was defined as anyone smoking at least 100 cigarettes in their lifetime. Three studies defined ever smoking cigarettes as smoking one or more cigarettes per day for greater than or equal to one year. Lastly, “pack-years” of cigarette smoking was created as a cumulative measure of cigarette smoking duration and intensity and treated as a continuous variable in the analysis. For each study, pack-years was directly calculated by multiplying the number of cigarettes smoked by the age of initiation and cessation of smoking . Cigar and Pipe use was defined as ever vs. never. Four studies [Schwartz], North Carolina , Los Angeles, and Seattle-LEO [Vaughan] defined ever cigar/pipe use as use for six months or greater at anytime in the past. Two studies defined ever cigar/pipe use as smoking once per day for at least one year or more. One study defined ever pipe use as ever smoking 12 ounces of tobacco and cigar use as smoking one cigar per week for at least one year. Lastly, two studies collected “ever vs. never” information from questionnaire data without defining a frequency or duration of use cut-off. Alcohol consumption was defined as ever vs. never for all studies. Ever use of alcohol was defined as either greater than four or more drinks in a year [Schwartz] and Baltimore [HOTSPOT], greater than or equal to one drink per week for greater than or equal to one year , greater than either one or four drinks per month, or ever consumed in a lifetime . Total alcohol consumption was calculated as the total volume of pure ethanol consumed from beer, wine, and liquor multiplied by the age of initiation and cessation . Total alcohol consumption was treated as a continuous variable in all analyses. Odds ratio and 95% confidence intervals were estimated using logistic regression to assess the association between marijuana use and oropharyngeal and oral tongue cancer diagnosis. Given that all the case-control studies included in this analysis utilize incident cases derived from open and dynamic populations, the odds ratio estimated in this study approximates the relative risk. To control for heterogeneity in effects across study, study indicator was included as a random effects intercept term in all regression models. We tested for heterogeneity across study using a log likelihood ratio test for the goodnesss of fit of the model with and without a product term for marijuana use and study. Furthermore, we quantified the among-study variability of the association of ever marijuana use with both cancer outcomes by estimating the population effects interval which is derived from the point estimate of the association and the τ 2 estimated from meta-regression analysis . Regression models were adjusted for age , sex, education , race/ethnicity , pack-years of cigarette smoking , ever pipe/cigar smoking , and intensity of alcohol drinking . The Tampa study was excluded from analyses on duration and frequency of marijuana use because there were insufficient cases and controls in each category of marijuana use. For subjects with missing data on education level , multiple imputation analysis was performed. Logistic regression was used to predict education level using age, sex, race/ethnicity, study, and case-control status. Five imputations were created and a summary estimate for the association of marijuana use and cancer outcomes was calculated using logistic regression using the MI ESTIMATE command in STATA. Analysis excluding individuals with missing educational status demonstrated similar associations of marijuana use with cancer . Tobacco and alcohol use is a recognized risk factor for both oropharyngeal and oral tongue cancers and is strongly correlated with marijuana use . Therefore, sub-group analyses were performed to further assess the presence of residual confounding by smoking status by restricting the study sample to never tobacco users/never drinkers. Given the relatively small number of oral tongue cancer cases who were NSND, light smokers and light drinkers were categorized as never tobacco users/never drinkers for this analysis. The potential multiplicative interaction of tobacco and alcohol use on the association of marijuana use and cancer outcomes were compared by the inclusion of a product term of marijuana use and tobacco/alcohol use in the logistic regression model to estimate the ratio of odds ratios . In addition, the additive interaction of tobacco and alcohol use on the association of marijuana use with cancer outcomes was also tested through estimation of the Relative Excess Risk due to Interaction using a generalized linear model . Because sexual behaviors and marijuana use could be highly correlated, we conducted two separate analyses to evaluate the potential confounding effects of HPV on the observed associations of marijuana use with risk of oropharyngeal cancer. First, analyses were stratified by HPV 16 L1 serologic status. Data on HPV L1 antibodies were available in four studies: Boston, Latin America, Houston, and Seattle [Schwartz]. Second, given the absence of either detailed information on oral sexual behaviors or oral HPV status in a majority of studies, we utilized external information to indirectly adjust the marijuana-oropharyngeal cancer association for confounding by HPV using the methods described by Steenland and Greenland . These analyses utilized external information on the association of marijuana use with oral HPV prevalence , the association of current marijuana use and oral HPV infection , and the association of oral HPV infection with oropharyngeal cancer risk to calculate a bias factor .