Cannabis Grow

In a few instances the difference between these two variables is zero, which appears to be a reporting error as they reported all their usage in the last 30 days and yet that they started at a young age. We code them as a zero at t1 under the presumption that this earlier usage was very limited, and perhaps experimental. However, if the difference is non-zero, since the In-School Survey was conducted at least six months before the wave-1 In-Home Survey, we divide this difference by 5 to average over five months [i.e., /5]. Those with values less than 1 were categorized as non-users at t1 , those with values between 1 and 10 were categorized as light users and those with values above 10 were categorized as heavy users . Light users comprised about 16% of adolescents in Sunshine High and 17% of adolescents in Jefferson High. Likewise, heavy users comprised about 5% of the adolescents in Sunshine High and 8% of the adolescents in Jefferson High. Overall, this reconstruction strategy enabled us to estimate a three-wave SAB model for each of the two samples without discarding any data. The last step of the reconstruction procedure for the heavy marijuana users is not perfectly accurate and might mistakenly categorize a few light users as heavy users, since they could have used marijuana outside of the last five months. The proportion of cases that might have been misclassified is less than 10%. Furthermore, sensitivity tests in which the level of marijuana use for these uncertain cases was randomly assigned to “light” or “heavy” use exhibited similar results over a large number of samples .Our estimated SAB models include gender , grade , race , and parental education level . Depressive symptoms are included as a factor score based on 19 ordinal items modified from the Center for Epidemiologic Studies Depression Scale . Parental support and parental monitoring are constructed as standardized factor scores through confirmatory factor analysis,horticulture trays with Root Mean Squared Error of Approximation about .05 and Comparative Fit Index greater than .95, which both suggest a good fit.

Parental support is based on how adolescents rated their parents in 6 aspects: whether they communicated well, were “warm and loving”, had a “good relationship” , and whether the adolescents felt cared about, felt close , and discussed personal problems with their parents . Parental monitoring is based on 9 items: whether parents were home before school , after school , at bedtime , present during dinnertime , and whether adolescents were allowed to decide their weeknight bedtime, weekend curfew, people they hung around with, and how much television and which television program they watched .Regarding missing data, for students in Sunshine High the response rates were 76% at t1 , 82% at t2 , and 75% at t3 . In Jefferson High the response rates were 79% , 81% , and 74% across the three waves. We imputed missing network data using the technique described in Wang et al. given the evidence that failing to do so can result in in biased estimates. Other actor attributes at t1 were imputed using the multiple imputation system of chained equations implemented in Stata. For the later waves, missing data is handled within the Stochastic Actor-Based models in RSiena software as suggested by Huisman and Steglich and Ripley et al. . The 501 and 166 students who graduated at t3 and were no longer in the network are treated as structural zeros in the Stochastic Actor-Based models at the last wave.Network statistics are measured at three waves. As shown in Table 1, in both school samples the number of out-going ties decreased over time due to limited nomination restrictions, graduation, moving, dropping out, and sample attrition/non-response/missing network data. The reciprocity index is the proportion of ties that were reciprocal. The proportion of reciprocal ties over all out-going ties was 4% to 10% higher in Jefferson High than in Sunshine High ateach wave. The transitivity index is the proportion of 2-paths that were transitive , which is similar in the two schools. The Jaccard index measures the network stability between consecutive waves.

There were substantial changes in friendship ties across waves, with the Jaccard index staying at .16 in Sunshine High and ranging from .21 to .22 in Jefferson High. Due to a survey implementation error in Add Health, some adolescents could only nominate one female and one male friend at t2 and t3 . Most limited nomination restrictions happened at wave 2, and involved less than 5% in the two schools. With respect to smoking behavior, there were between 69% and 78% non-smokers in Sunshine High over the three waves, and between 7% and 10% heavy-smokers . In Jefferson High, there were between 42% and 53% non-smokers and between 26% and 32% heavy smokers. Sunshine High also had more non-drinkers than Jefferson High , and more non-users of marijuana . The descriptive statistics of covariates are reported in the lower part of Table 1.As shown in Table 2, our estimated SAB model includes a smoking behavior equation, a drinking behavior equation, a marijuana use equation, and a network equation. Based on the smoking behavior equation, those who were one point higher on the marijuana scale are 25% [exp = 1.25] and 15% [exp = 1.15] more likely to increase their own smoking behavior at the next time point in Sunshine High and Jefferson High, respectively. Those who drank alcohol did not smoke more over time. There is no evidence of cross substance influence, as having more friends who drank or used marijuana did not impact a respondent’s own smoking over time. In ancillary models, we measured average level of drinking or marijuana use for friends and these effects were also statistically insignificant. These results are shown in S1 Table. Regarding the other measures in the smoking behavior equation, we detect a negative smoking behavior linear shape parameter in both school samples along with a positive smoking behavior quadratic shape parameter. This suggests that adolescents were inclined to adopt lower levels of smoking behavior over time, but they also tended to stay as or become non-smokers or escalate to heavy-drinkers due to a pull towards extreme values of this scale. Turning to the peer influence effect, we find that adolescents’ own smoking levels were affected by that of their best friends in both schools.

There is no evidence that parental support or monitoring reduced levels of smoking over time in either sample. African Americans and Latinos smoked less than Whites in Sunshine High. Depressive symptoms were found to increase smoking behavior in Jefferson High. In the drinking behavior equation, we find that an adolescent who was one point higher on the marijuana use measure was 22% and 16% more likely to increase their own alcohol use at the next time point in Sunshine High and Jefferson High, respectively. However,sliding grow tables respondents’ drinking was not related to their greater cigarette use. There is no evidence that friends’ smoking behavior or marijuana use affected respondents’ drinking behavior. This was the case whether measured as the number of friends who smoked or used marijuana, or as the average level of such behaviors. A negative linear shape effect and a positive quadratic shape effect are also confirmed regarding drinking behavior. An adolescents’ drinking level was positively predicted by that of one’s best friends. Whereas there is no evidence in these two networks that high levels of parental support impacted drinking levels of adolescents, we do see that higher levels of parental monitoring were associated with lower levels of drinking behavior over time in Jefferson High. In Sunshine High, African Americans were found to drink less than Whites, and depressive symptoms were found to increase drinking levels. The marijuana use equation suggests no evidence that increasing usage of the other two substances leads to increasing marijuana use. We once again see no evidence of cross-substance influence, as the number of friends who smoked or drank or the average smoking or drinking level of friends is not related to ego’s marijuana use levels over time. A negative linear shape effect and a positive quadratic shape effect are also detected on marijuana use behavior. Across both samples there is very strong evidence of a peer influence effect from anadolescent’s best friends’ marijuana use to an individual’s own marijuana use. Higher levels of parental support or monitoring were not found to reduce levels of marijuana use over time. For all three substance use behaviors, there was no evidence that adolescents who are more “popular” were any more likely to increase their substance use over time. In the network equation the expected patterns are detected regarding the endogenous network structural effects across samples.

At the dyadic level, adolescents did not randomly nominate peers as friends, since friendship ties inherently require the investment of time and energy, as indicated by the negative out-degree parameters; instead, adolescents tended to nominate peers who had already nominated them as friends previously, as indicated by the positive reciprocity parameters. At the triadic level, adolescents tended to nominate a friend’s friend as a friend but avoided ending in 3-person cyclic relationships. The negative out-degree/in-degree popularity parameters and the out-out degree assortativity parameters suggest that adolescents were less likely to befriend peers who have already made/received many friendship nominations or have similar out-degrees. Instead, they were more likely to befriend peers with similar in-degrees, as indicated by the positive in-in degree assortativity parameters. We also find that adolescents were more likely to nominate peers as friends if they were of the same gender, race , and grade. Grade is a particularly strong effect, as adolescents were 86% and 77% more likely to nominate a friend if they were in the same grade than if they were in a different grade in Sunshine High and Jefferson High, respectively. Lastly, the limited nomination parameter shows that for adolescents who encountered the administrative error of being limited to nominate only one male or one female friend, their odds of nominating friends is re-adjusted by the SAB models to be 132% larger in Sunshine High and 297% larger in Jefferson High than those with no such problem.Whereas our initial models tested the relationship between interdependent substance use behavior, they assumed that these effects are symmetric: that is, usage of one substance equally increases or decreases usage of another substance. In our next set of models, we relax this assumption and test whether usage of one substance increases behavior of another substance or decreases behavior , or both. These models were estimated separately as the combined model exhibited extreme collinearity. As shown in Table 3, there is a significantly positive creation function from marijuana use to drinking in both samples, implying that respondents’ marijuana use increased their odds of drinking initiation. Thus, one unit higher marijuana use made a nondrinker 62% and 60% more likely to start drinking rather than stay as a non-drinker at the next time point in Sunshine High and Jefferson High, respectively. On the other hand, the endowment function from marijuana use to drinking is not statistically significant at either school, implying that marijuana use does not affect the likelihood of stopping drinking behavior. We detect a statistically significant creation function in Sunshine High: a one unit increase in marijuana use increases the odds 62% that adolescent non-smoker will initiate smoking rather than stay as a non-smoker. There was no evidence of a statistically significant endowment function in Sunshine High. On the other hand, the pattern is reversed in Jefferson High with a statistically significant endowment function but a statistically insignificant creation function. Thus, in Jefferson High although marijuana use does not impact respondent’s likelihood of smoking initiation, one unit higher marijuana use made smokers 27% more likely to stay as smokers rather than quit smoking at the next time point. To understand the magnitude of these effects , we engaged in a small simulation study in which we omitted some of the effects from the SAB model shown in Table 2 and assessed the consequences for the level of substance use behavior in the schools. That is, we changed a particular parameter value from the one estimated in the model to zero, and then simulated the networks and behaviors forward 1000 times. We then assessed the average level of smoking, drinking, and marijuana use in the network at the end of the simulation runs.

Few group differences in cortical thickness were observed; the majority of areas were no longer significant after controlling for lifetime alcohol use. Thicker medial temporal lobe estimates were found in the user group at baseline and follow-up when ICV and lifetime alcohol use were controlled for. We observed subtle decreasing thickness estimates from baseline to follow-up in seven regions, but no interactions were identified. We found quite divergent relationships between cumulative lifetime alcohol and marijuana use and cortical thickness in the user group. More reported marijuana use was related to thinner cortices in temporal and frontal regions, and more lifetime alcohol use was related to thicker cortices in all four lobes of the cortex bilaterally. Improvement in cognitive functioning over time in both the users and controls was observed, given the short retest interval and anticipated gains in performance. We did not see greater improvement in cognition in the user group following abstinence or, surprisingly, consistent group differences in cognition across the five domains as suggested in the literature and previous studies in our laboratory . MJ + ALC did not improve in their performance on the Complex Figure Copy sub-test; however, this pattern of performance was not found across the other tests. In general, thinner cortices were related to better global cognitive performance in the larger sample with the exception of the right entorhinal cortex and cognitive functioning in the user group at baseline, in which thicker cortices were associated with better attentional processing. Lopez-Larson and colleagues cross-sectionally examined cortical thickness in teens, ages 16–19 years, with heavy cannabis vertical farming use histories. They found decreased thickness in frontal regions and the insula, along with increased thickness in lingual, temporal, and parietal regions .

Our findings are similar given that we found increased thickness in temporal and posterior regions, such as the entorhinal cortex , and relationships showing thinner cortices associated with increased severity of use in frontal regions. The authors discuss multiple pathways for tissue disruption, including altered neurodevelopmental trajectories and/or tissue loss or remodeling. Similarly, Mata and colleagues found flattening and thinning of the sulciin frontal regions in adolescent marijuana users, suggesting chronic cannabinoid exposure may link to atypical trajectories of the gyral folding process. Marijuana may interfere with the cannabinoid system by altering patterning, plasticity, and connectivity during neurodevelopment, and trigger neurochemical and protein activity in response to neural injury . Macrostructural findings typically focus on structures with a high density of cannabinoid type 1 receptors , and findings show larger structural volume in areas such as the anterior cerebellum and amygdala , whereas others show decreased gray matter volume and density . Cousijn and colleagues found that amygdala and hippo campal volume negatively correlated with weekly cannabis use, as more use was related to smaller limbic structures. In our study, cortical thickness differences persisted after controlling for alcohol in the entorhinal cortex. Given the high concentration of CB1 receptors in temporal lobe structures , marijuana use may be particularly influencing developmental events in this anatomical region. Positive associations between thickness in the entorhinal cortex and cognitive functioning is in contrast to the finding that thinner cortices are related to better global cognitive performance in the larger sample.

However, greater reported marijuana use and earlier age of initiation were also associated with thinner cortices in frontal and temporal brain regions. We suggest that endocannabinoid system alterations or marijuana-related toxicity may trigger developmental consequences such as premature cortical thinning and subsequent declines in cognitive functioning. It is unclear why associations between age of initiation and thickness estimates were observed at follow-up only. Subtle neural architectural changes may be occurring over the abstinence period, and the acute impact on neural development may not be fully captured at baseline for this predictor. Neural recovery is likely to extend past 28 days and well into the year following cessation of use, as residual effects of marijuana use have been reported in cognitive and neuroimaging markers from days to months following cessation of use . Pre-existing structural differences are also likely to contribute, as smaller orbitofrontal cortex volume predicted initiation of cannabis use by age 16 . The positive dose-dependent relationships with lifetime alcohol use are particularly notable given the sub-clinical heavy episodic drinking patterns reported by the sample. Thickness estimates in these regions were related to number of heavy drinking episodes reported, which is particularly concerning given the consistently high rates of heavy episodic drinking reported by adolescents in the United States . The cerebral cortex is highly vulnerable to the effects of alcohol . Squeglia and colleagues found that female heavy episodic drinkers had thicker cortices in frontal brain regions compared to female controls, and thicker cortices were associated with worse cognitive functioning for both males and females. However, thinner cortices were identified for male heavy episodic drinkers compared to controls , similar to recent prospective findings showing decreased thickness estimates in adolescents who transitioned into sub-clinical heavy episodic drinking .

Our findings suggest widespread increases in cortical thickness with increased lifetime alcohol use and heaving drinking episodes. The present study, combined with Squeglia et al. , suggests that more often thicker cortices are associated with worse neurobehavioral performance. Despite the quantity of reported alcohol use in our users being more modest compared to that of treatment seeking individuals, differences in these studies of adolescents reporting similar alcohol use patterns may be attributed to methodological design or an interaction between co-occurring alcohol and marijuana use. Neuroprotective properties of marijuana may modulate neurotransmission and mitigate ethanol-induced neural injury; however, marijuana may trigger neurotoxic chemical cascades leading to changes in endocannabinoid signaling, altered developmental trajectories, increased alcohol administration, and worse psychosocial outcomes in the developing brain . Although initial cross-sectional studies in our laboratory suggested evidence for white matter neuroprotection in those using cannabis drying rack and alcohol , poorer outcomes for co-occurring use from adolescence to young adulthood were found after a 3-year follow-up . The mechanism of alcohol-related toxicity on the cerebral cortex remains unclear. Alcohol may interfere with temporal sequences of neurodevelopment , myelination, and/or generation and survival of cortical cells ; overall, the unanticipated associations with alcohol found in this study underscore the deleterious impact adolescent alcohol use likely has on neurodevelopment when used independently or concomitantly with marijuana. Our sample was predominantly male . Therefore, it is unlikely that excluding our female participants would have changed the observed relationships. However, gender may moderate these findings . Studies have found gender to play a significant role in gray and white matter neural architecture and neurocognition in both healthy adolescents/ young adults and those engaging in substance use . We suspect that findings represent a “longer-term” impact of marijuana and alcohol use, given the monitored abstinence period. The majority of findings present at baseline were present at follow-up, but this was not the case for all regions . The prospect of neural recovery after cessation of use is understudied in the adolescent literature, although there is some suggestion that brain structural changes can occur within the initial 24 months of abstinence from alcohol in an adult sample of former heavy alcohol users reporting 1 month to 26 years of abstinence . Our preliminary findings need to be replicated and expanded upon. Longer follow-up periods are necessary to understand changes in marijuana use trajectories over time and differences in residual versus acute effects. Given the preliminary and exploratory nature of this work, large number of analyses conducted, and modest effect sizes, replication of findings is crucial. Subtle alterations in neurodevelopmental trajectories may have long-term consequences for cognition and daily functioning. To identify how alcohol and marijuana use leads to brain changes, disentangling pre-existing, substance-related, and acute versus residual effects is important. The present findings raise concern for adolescent alcohol and marijuana users and provide more evidence for a longer-term effect on neural tissue development. Our future work will integrate longer follow-up periods with pre- and post-initiation data to understand how such commonly used substances affect the developing brain.Young adulthood is a critical developmental period that commonly includes multiple important life changes . This period is also marked by increased access and susceptibility to risky behaviors, including tobacco and other drug use.

Recent national data on 18-24 year old young adults indicate past-month prevalence of 22-25% for marijuana and 29.1% for cigarettes . In both cases rates were higher than those of older adults. While increasing use of marijuana and tobacco each raise public health concerns, recent escalation of concurrent use of both is evident in the literature. This trend has ignited interest in exploring whether marijuana use may potentiate exposure to tobacco-related harms. Recent analyses of national data suggest rates of co-use of tobacco and marijuana increased by 18.2% from 2003 to 2012, and 40.6 % of adults aged 18-25 reported past-month use of both products in 2012 . Our own analyses of Population Assessment of Tobacco and Health data suggest users of combustible tobacco products, e-cigarettes, and multiple tobacco products were 4-8 times more likely to report current marijuana use, and concurrent users of tobacco and marijuana were less likely to attempt tobacco cessation . National data indicate that co-use is particularly common among daily marijuana users and non-daily tobacco smokers . Experimentation with marijuana among tobacco smokers and experimentation with tobacco among marijuana users may in part be facilitated by product modifications that allow for consumption of both products simultaneously . Relatedly, advertisements for tobacco products may be designed to indicate that the products can be used to consume marijuana . Some initial findings suggest using tobacco products to deliver marijuana may both increase and normalize young adults’ use of tobacco products . Use of both products may potentiate smoking-related disease by not only increasing exposures to two sources of harmful constituents but by potentiating persistent use. Frequency of marijuana use has been linked consistently to greater nicotine dependence and more persistent tobacco use . Users of both products perceive marijuana as safer , report low interest in quitting both marijuana and tobacco , and are less likely to successfully quit using tobacco . Thus, young adults who use both products may be disproportionately vulnerable to doing so chronically. Evidence for overlapping negative health consequences of tobacco and marijuana smoking suggest high priority for understanding and preventing use in young adulthood. The context of both tobacco and marijuana use have changed dramatically in the past decade as a result of increasing availability, perceived safety and acceptability of non-cigarette tobacco products, and growing legalized access to marijuana . While a number of studies have demonstrated cross-sectional links between use of both products, less is known about the interplay between use of both over time among young adults. This potential bidirectional relationship may be especially important in the context of non-daily tobacco smoking. Pooled data from multiple national surveys show that adults aged 18-24 are more likely to be non-daily smokers than older adults . Further, preliminary evidence suggests a link between non-daily cigarette smoking and recent increases in daily cannabis use from 2.8% to 8.0% between 2002-2014 . However, the extent to which trajectories of marijuana and tobacco use may interact is unknown, and examination of young adult non-daily cigarette smokers provides an opportunity to identify risk factors for tobacco progression. Thus, the first goal of this study was to test the hypothesis that, among 18-24 year old non-daily cigarette smokers, greater frequency of marijuana use over two years would be positively associated with cigarette quantity and frequency, frequency of non-cigarette tobacco product use, and likelihood of poly tobacco use over time. Second, we tested for the existence of a bidirectional relationship, hypothesizing that more frequent use of tobacco would predict heavier marijuana use. Participants were recruited primarily via paid Facebook posts that were targeted by age and location. Clicking on these posts led to the study website, where eligibility was determined. Interested and eligible individuals provided informed consent and completed the baseline assessment on the website. They completed additional quarterly electronic assessments 3, 6, 9, 12, 15, 18, 21 and 24 months later via SurveyMonkey . At the baseline, 12, and 24 month time points, assessments consisted of a single survey that was typically completed in 15-20 minutes and for which participants received $25 compensation. At the 3, 6, 9, 15, 18, and 21 month time points, participants completed brief daily assessments for 9 consecutive days, and were compensated with $4 per day completed plus an additional $4 if all 9 days were completed . Because evidence suggests young adults smoke more cigarettes on weekend days , each 9-day period began on a Friday to standardize the number of weekend days included.

In other words, workforce participation at age 23 is associated with lower marijuana-use rate over subsequent years for both males and females. This finding supports key concepts from the life-course theory which emphasizes salient life events such as employment or marriage to explain both continuity in childhood deviant behavior and changes during the life-course, and highlights the need for integrated drug programs that provide substance abuse treatment in conjunction with occupational trainings. Third, marijuana-use during the initial observation period was negatively correlated with slope of employment trajectory for males, indicating that marijuana-use is associated with decreased levels of workforce participation and has an adverse consequence on subsequent career growth. Clearly, the implication is that the harm of marijuana-use on users’ socioeconomic aspects of life is long term and chronic. One possible explanation for why this is uniquely found for males may lie in the dissimilarity of level of workforce participation and level of marijuana consumption. As illustrated in Figure 1, on average, females appear to be employed less time than males and tend to engage in marijuana-use to a lesser degree. Kaestner calls to attention the importance of including demographic contexts such as educational achievement, marital status and number of dependent children in examining the association of marijuana-use and employment. These demographic factors have been reported as important determinants of work participation and also show influences on level of marijuana consumption . Therefore, indoor grow rack further studies that simultaneously examine the association of employment with drug use as well as the demographic contexts are recommended.

Finally, slope of employment trajectory is not significantly correlated with slope of marijuana-use trajectory for either gender, indicating that the association between changes in marijuana-use and work participation over age are not systematic. The lack of consistency in the association between the two longitudinal trajectories suggests that the magnitude and direction of relationship between employment and drug use are not consistent over age, and that the direction of causality is complicated and uncertain. Again, this is consistent with other longitudinal studies , and it highlights the need for sophisticated causal inference approaches in future studies, especially given the limitations of empirical analyses on providing credible evidence for causal relationships. Despite significant findings, the present study has several limitations. The sample used for the analysis was a subset of the National Longitudinal Survey of Youth 1979 cohort who completed the 2004 follow-up survey. While utmost care was taken in examining participants’ demographic characteristics for any possible systematic missing patterns of subjects, the generalizability of the findings from this study sample to the entire NLSY79 cohort may be limited. Furthermore, the measures used in NLSY to record substance use are relatively coarse. A dichotomous measure of marijuana-use are relative crude and may differ drastically from person to person when identifying patterns of marijuana-use that affect employment. The current approach to estimating a BRISM is also limited in its ability to incorporate time varying covariates , as these would need to be treated as additional longitudinal trajectories within a multivariate random intercept and slope model. In addition, inclusion of quadratic, or higher order terms, within the bivariate longitudinal model resulted in a lack of model convergence. Work is currently ongoing to develop methods that allows for the incorporation of time-varying covariates.

One possible solution to be explored in future studies include taking a Bayesian approach to fitting the model with informative prior distributions that are derived from empirical studies . In sum, our results highlight the cross-correlational longitudinal effects of substance use and employment outcomes for young adults, while properly accounting for dynamic interdependencies between two concurrent repeated-measures outcomes. Additional research is encouraged to determine whether the findings endure with other data sets, different types of drugs and different employment variables. In particular, future research should closely examine how these two concurrent longitudinal outcomes may differ by race/ ethnicity groups through assessment of their interaction effect with the inter-dependent trajectories. Marijuana continues to be legalized in many states, generally with limited public health input. Although valid medicinal applications exist, the National Academies of Science, Engineering, and Medicine concluded that substantial evidence suggests that marijuana use is also associated with significant harms, including psychosis, schizophrenia, problem marijuana use, motor vehicle collisions, low birth weight, and respiratory symptoms.Evidence is emerging regarding the association of marijuana use with youths’ cognition and cardiovascular disease,as well as other areas, and the 2019 vaping epidemic demonstrated the hazards of rapid product innovation without due evaluation of safety.With widespread lifetime and adolescent use of marijuana, reaching 43.6% of 12th-grade students nationally, and 51.5% of 18- to 25-year-olds in 2018, even modest increases in risk may have a significant effect on population health.

Vaping of marijuana in the past 30 days, which typically involves high-potency concentrates, increased from 5% of 12th-grade students in 2017 to 14% in 2019, with 3.5% vaping near daily in 2019.10 The potential magnitude of mental health effects associated with the growing market of high-potency marijuana products is evidenced by estimates of the population-attributable fraction of first-episode psychosis due to use of high potency marijuana at 12% in 11 primarily European cities studied, and by elevated risk for first-episode psychosis found in individuals using these products daily. Treatment data also suggest reason for concern. In 2014, marijuana was the leading drug used by clients entering drug treatment in a study of 22 European countries, representing 46% of all new clients, up from 29% in 2003.Both marijuana-related new clients and daily users in treatment more than doubled between 2003 and 2014. Prior to legalization of adult use of marijuana in California, as legalization advanced nationally, identification of key policy concerns and calls for caution emerged. Barry and Glantz recommended that “to protect public health, marijuana should be treated like tobacco, legal but subject to a robust demand reduction program modeled on evidence-based tobacco control programs before a large industry develops and takes control of the market and regulatory environment.”Authors noted that the transition from small-scale marijuana growers and retailers to large-scale industrial consolidation and marketing would bring risks, including aggressive lobbying, campaign contributions, and efforts to create favorable regulation. Richter and Levy noted the parallels between modern trends in marijuana product diversification and past transformations of tobacco to a deadly industrialized product designed to boost nicotine delivery and enhance addictive potential and palatability. Volkow et al at the National Institutes of Health raised concerns over the potential effects of rising product potency and of use on the developing brain.Subsequently, in 2019, Ayers et al called attention to emerging patterns of marijuana branding, marketing health claims,indoor farming equipment lack of health warnings, and appeals to youths and called for federal regulation. California’s tobacco control oversight experts called for broad application of lessons learned from tobacco control to commercial marijuana.Others called for legalization processes to intentionally advance social equity through criminal justice policy, offering economic opportunity to communities hard hit by the war on drugs, and reinvesting revenues in those communities.In November 2016, a California ballot initiative, Proposition 64, successfully legalized production and sale of marijuana for adult use, 20 years after legalization of medicinal use of marijuana in the state. An important part of that initiative was the assurance that local control would be preserved and cities and counties would have broad discretion to allow legal marijuana commerce, or not, and to regulate its practice.

In 2017 and 2018, the state created a regulatory framework for legal cultivation, manufacturing, and retailing of marijuana, which generally prioritized facilitating the shift from the illegal market to the legal market rather than demand reduction strategies. The first legal marijuana dispensaries for adult use in California opened January 2018. Three marijuana industry behaviors—extensive increases in potency , manufacturing of products to attract youths, and aggressive marketing—that were directly adopted from tobacco industry practices became immediately evident across the state. Despite the threat to public health, state regulations failed to constrain these practices, even though California has led tobacco control efforts in the United States and pioneered tobacco control policies such as public smoking bans, flavored product bans, and electronic cigarette bans. Cities and counties are often “laboratories” of innovation in public policy, and, notably, in tobacco control. Because California law allowed significant local control, we therefore asked: To what extent have recommendations from the public health community and potential lessons from tobacco control and other legal, but harmful, products been adopted in the marijuana legalization process?In a cross-sectional study with data collection and analysis from February 1 to November 30, 2019, we studied laws and regulations in California to understand the extent to which public health recommendations and tobacco control best practices, and in some cases, alcohol control best practices with evidence of effectiveness and potential relevance, had been incorporated into marijuana legislation by January 31, 2019. We followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline. The jurisdiction law review reported here was determined not to be human participant research by the Public Health Institute Institutional Review Board.Selected practices were identified during an earlier literature review and national consultation with 62 stakeholders during key informant interviews conducted from 2017 to 2019 with experts in marijuana, tobacco, and alcohol regulation, the First Amendment, tobacco and alcohol law, local government, community organizing, criminal justice, and substance abuse and marijuana research, as well as marijuana industry participants. Collectively, the results of these interviews informed production of 2017 model ordinances for marijuana retailing, marketing, and taxation for California local government.Potential best practices identified included restrictions on retail outlets, buffer zones, certain product types, delivery, marketing, and conflicts of interest, as well as requiring preservation of smoke-free air, health warnings, pricing and taxation measures, and equity policies in licensing, hiring, and revenue capture.Although only practices considered legally defensible were recommended, descriptions of local measures were collected regardless of whether they went beyond recommendations .Of 147 jurisdictions allowing medical or adult use storefront commerce, 93 limited the number of dispensaries, with a mean of 1 store for every 19 058 residents . The state imposed no limits on the number of dispensaries or delivery businesses that could be licensed. Forty-two jurisdictions imposed a buffer between retailers and schools greater than the stater equired 600 feet, yet 6 jurisdictions allowed retailers to locate closer to schools than the state’s requirement, at a mean of 258 feet. More than 100 jurisdictions added establishments to the state’s list of “sensitive use” sites from which storefront dispensaries must be distanced, which consisted of kindergarten through grade 12 schools, day care centers, or youth centers. Locally adopted examples included colleges, public beaches, libraries, tutoring centers, and recreation centers. More than one-third of jurisdictions imposed buffers between retail locations, with a median of 600 feet.Provisions to promote economic equity and diversity in marijuana licensing were limited to 5 of the largest cities. Oakland, Long Beach, and the city of Los Angeles gave a defined class of “equity” applicants priority in licensing and a reduction in certain costs, and required that certain percentages of employees be low-income, local, or transitional workers. Sacramento also had equity licensing priority and reduced costs, and San Francisco had equity licensing priority and employee requirements. The state did not establish an equity licensing system. Proposition 64 established the right to expunge certain past marijuana convictions, and state legislation subsequently approved a process for automatic expungement, reducing barriers for eligible individuals to benefit.Among jurisdictions allowing retail sale, only 8 imposed restrictions on types of marijuana products for sale, beyond state regulations. One jurisdiction, Contra Costa County, pioneered the prohibition of sale of flavored products for combustion or inhalation, 3 jurisdictions prohibited the sale of marijuana-infused beverages resembling “alcopops” , 5 jurisdictions restricted products appealing to youths, and 5jurisdictions imposed restrictions on edible marijuana products beyond state regulations. No jurisdictions limited the potency of products sold, although 1 jurisdiction established a potency linked tax. The state did not limit or tax potency, except for establishing a maximum 10-mg THC dose for edible marijuana products , nor did they limit manufacturing or sale of flavored products, such as flavored vaping liquids or prerolled cigarettes, although state regulations did create restrictions on products resembling existing foods or with characteristics that were particularly attractive to children.

Education efforts about the adverse impact marijuana use can have on depression are needed, with a focus on subgroups at risk for poor outcome. Together, our results warrant replication and indicate a need for providers to ask depressed patients about their marijuana use, to inform those using marijuana of its potential risks and determine treatment needs. Patients were participants in a randomized controlled trial of alcohol and drug use among psychiatry outpatients and had PHQ-9 depression of ≥ 5, limiting generalizability. Patients were using substances other than marijuana, limiting our ability to draw firm conclusions, although all models adjusted for non-marijuana use. Models were unadjusted for patient’s premorbid functioning/marijuana use, which could have impacted the results. Substance use variables were dichotomized due to low frequency, reducing statistical power, our ability to determine quantity/frequency, and our understanding of patterns over time. We do not know how patients used marijuana , whether problems were associated with use, or the primary reason/condition for medical use. Longitudinal analyses are limited to a 6-month follow-up, suggesting further research will be needed over longer periods of time. Marijuana is the most widely used controlled substance in the world . In 2016, 192.2 million people used marijuana . Regular marijuana use, particularly initiated in adolescence, is associated with a range of adverse consequences,grow benches including poor cognitive and educational outcomes, low self-reported life satisfaction , downward socioeconomic mobility , psychiatric illness , marijuana-involved injury , and substance use disorders .

Perceived risk and perceived availability of marijuana have historically been important drivers of adolescent marijuana use, and often targets of interventions to prevent or reduce adolescents’ use . However, these relationships may be changing. Most extant research on the changing associations between adolescent perceived risk, availability, and use of marijuana has been conducted in the United States , where 28 states and the District of Columbia have legalized medical marijuana since 2000, and 10 states and DC have legalized recreational marijuana since 2012 . In this context, more adolescents now perceive no/low risk of marijuana use, but the prevalence of marijuana use has not increased simultaneously . Research on changes in the individual-level association between no/low perceived risk and use has been mixed. Some have found that the association weakened in recent years , while others have reported that it strengthened or remained stable . Additionally, perceived easy availability of marijuana has largely declined among US adolescents . Evidence suggests that the association between perceived availability and use of marijuana has remained strong and stable over time . Understanding these relationships is particularly important in light of recent liberalization of marijuana access, as perceived risk and availability are two key mechanisms through which legalization could impact use. In this study, we focus on the Southern Cone context for two reasons. First the Southern Cone has recently experienced changes in marijuana regulation, which could impact perceived risk and availability. Second, trends in adolescent marijuana use and perceived availability are different from those in the US, which could suggest distinct relationships between perceived risk and availability and use of marijuana. In 2013, Uruguay enacted a law providing the government full regulation over the largescale production and sale of recreational marijuana.

Adults in Uruguay can purchase marijuana at pharmacies, grow marijuana at home, or acquire it through a cannabis club . In Argentina, possession of marijuana for personal use continues to be illegal ; however, a 2009 court judgment marked the beginning of a paradigm shift in the criminalization of marijuana since it raised a contradiction between Law 23,737 and Article 19 of the Constitution, which protects individuals’ freedom from state regulation . In 2017, Argentina approved access to medical marijuana under specific circumstances . In Chile, marijuana is decriminalized, a limited set of cannabis-based pharmaceutical products are available for medical use, and a new bill allowing other sources of access and formulations is under debate . Since the early 2000’s, past-month adolescent marijuana use has increased in Uruguay , Chile , and Argentina . These trends are distinct from the US where marijuana use has remained stable , and from other South American countries where past-year use is less than 5% . Although perceived risk of marijuana use has decreased in both the Southern Cone and the US, perceived availability has increased in the Southern Cone, but decreased in the US . We know of no study that has assessed the individual-level relationships between adolescent perceived risk, availability, and use of marijuana in the context of the Southern Cone. Such research may inform the priority and scope of context-specific public health interventions to prevent adolescent marijuana use and help identify the drivers of use during these historical shifts. As more regions debate or enact policies to decriminalize or legalize marijuana use, the impetus for cross-country comparisons increases. In this study, we used survey data from adolescents in Argentina, Chile, and Uruguay to 1) estimate associations between perceived availability and perceived risk of marijuana use and past-month marijuana use, and 2) describe how these associations changed over time.

Individual-level data from adolescents enrolled in secondary education in Uruguay, Chile, and Argentina were obtained from the National Surveys on Drug Use Among Secondary School Students . These cross sectional surveys, carried out every 2–3 years, collect information on substance use and related risk factors. The sampling design and survey instruments are similar to the Monitoring the Future Surveys and were implemented comparably across countries. Surveys were self-report and administered confidentially in students’ classrooms. The sample included 8th, 10th, and 12th graders in schools classified as public, private, and other in mostly urban areas. Net secondary school enrollment was 80–90% in Chile and Argentina over the last decade and increased from 67.6–82.8% from 2007 to 2016 in Uruguay . The sample was selected via clustered, multistage random sampling from areas with 10,000+ and 30,000+ inhabitants in Uruguay and Chile, respectively, and schools with at least 20 students in the grades under study in Argentina. In Uruguay and Argentina, strata were types of school within urban areas of geographical regions in each country; primary sampling units were schools followed by classrooms. In Chile,grow racks with lights strata were school type by grade within mostly urban areas and primary sampling units were classrooms. Individual-level survey weights were used. Recent school cooperation rates ranged from 76–86%. This study was determined not human subjects research by the University of California, Davis Institutional Review Board. We restricted the sample in two ways. First, we removed adolescents with poor quality data [i.e. those who responded “yes” to any past-month use of five or more illicit substances , lifetime users of a fictional drug, and those with more than four inconsistencies in reporting age of initiation of use, past month, past year, and lifetime use of marijuana] based on criteria used in Uruguay before data entry. To maintain comparability, we removed observations in Chile and Argentina based on the same criteria. Second, we restricted the sample to the years our variables of interest were collected. Analyses were done in Stata 15.1 and SAS 9.4 and conducted separately per country. We used the svy suite in Stata for descriptive analyses and weighted time-varying effect modeling in the SAS macro %Weighted TVEM to estimate odds ratios for past-month marijuana that vary smoothly over time, accounting for the complex survey design . This method, made available in 2017 with the %Weighted TVEM macro, uses unpenalized B-splines to estimate coefficients and point-wise 95% confidence intervals as continuous functions over time, thus relaxing parametric assumptions about how the relationships between perceived risk, availability and marijuana use vary over time, and allowing for non-linearity . TVEM has been used to assess historical trends in the US in associations between adolescent marijuana use and related attitudes .

TVEM models were run on each imputed data set, and then estimates and standard errors were combined with the PROC MIANALYZE procedure in SAS. For each country and analytic sample, we present bivariate TVEM results. Graphs are shown with consistent x-axes , but different y-axes per country to aid interpretation. Because of the small sample sizes given the relatively low prevalence of marijuana use, our ability to adjust for covariates was limited. When possible, we adjusted for grade and gender, alcohol use, and tobacco use. Additionally, in Chile, the only country with enough observations and overlapping years of data to run TVEM models, we also included risk and availability simultaneously. For this analysis, we used a separate imputation model that included both variables in the same process. All covariates were time-varying. Finally, to better understand trends in the relationships between perceptions and use, we examined how the prevalence of marijuana use changed over time within each level of perceived risk and availability.Across countries and years, approximately 5–10% of adolescents reported past-month marijuana use, with greater prevalence in Chile followed by Uruguay . Marijuana use generally increased over time , and there was a notable increase in use among Chilean adolescents from 2009–2013 . On average, approximately 7–25% of adolescents perceived no/low risk of regular marijuana use, with greater prevalence in Chile followed by Uruguay . The proportion of adolescents who perceived marijuana to pose no/low risk increased over the study period despite year-to-year fluctuations . In Chile, there was a large increase in the proportion of adolescents who perceived no/low risk from 2011–2013, in addition to greater overall change and yearly variation, compared to Argentina and Uruguay, where the prevalence increased to a lesser degree with less variation . On average, one-third to one-half of adolescents perceived marijuana to be easily available, with greater prevalence in Uruguay followed by Chile . Trends varied over time . In Argentina, perceived availability increased from 2005 to 2009–2011 and then decreased or plateaued until 2014 . In Chile, perceived availability of marijuana generally declined from 2001 to 2009–2011 and increased thereafter . In Uruguay, perceived availability increased from 2005 to 2007–2009, plateaued or declined slightly until 2011, then increased until 2016 . Consistent with prior studies from South America and the US, our results indicate that the less risk an individual attributes to marijuana use, the more likely he/she is to use marijuana . However, in the Southern Cone countries, the overall magnitude of this association weakened, although it strengthened again most recently in Argentina. This suggests that risk perceptions became a weaker correlate of adolescent marijuana use over time. There are several implications of these results. First, given the overall increase in the proportion of adolescents who perceive marijuana use to pose no/low risk of harm, marijuana use would have likely increased to a greater degree in the Southern Cone had the risk/use relationship not weakened. Second, factors other than risk perceptions, such as marijuana availability, may have played a greater role in the increase in adolescent marijuana use observed during our study period. This highlights the need to consider changes in multiple individual and environmental determinants of marijuana use. Third, there may be a cross-national weakening of the risk/use relationship. We found this trend in all Southern Cone countries, and some have identified the weakening of this relationship in the US as well . This would suggest that risk perceptions may be, at least in part, shaped by broader societal norms that extend beyond local or national context. Increases in global information sharing via internet use, social media, and international news coverage may contribute to this trend . Consistent with extant research in Europe and the US , we found that adolescents who perceive marijuana to be easily available are more likely to use marijuana. However, the stability of this association varied over time and between countries. In Chile, the availability/use association weakened, and became increasingly similar to the risk/use association, both in magnitude and trajectory, when risk and availability were modeled together. In contrast, the relationship between availability and use strengthened in Argentina and Uruguay, becoming stronger at times than the relationship between perceived risk and use in both countries. However, because we were not able to model both variables together in Argentina and Uruguay due to finite sample limitations, it is unclear how the associations relate to one another. Variation in the relationship between perceived availability and use of marijuana over time and between countries may be explained by several factors.

Furthermore, the number of dispensaries the cities in LA County have so far allowed may not have reached the threshold where their number has had an effect on the marijuana use behaviors of the adolescents who live/attend school there, but as licensing for recreational outlets proceeds the number of dispensaries in many LA County cities is increasing or is planned to increase. For example, the City of Los Angeles Department of Cannabis Regulation estimates that an additional 200 licenses for retail storefronts will be able to be given out under current regulations. When added to the 170 existing medical marijuana dispensaries currently permitted by the City there will be close to 400 licensed dispensaries operating in the City of Los Angeles. Continued research on the impact to youth and other vulnerable populations as increasing numbers of recreational marijuana use outlets are licensed in LA County cities is crucial to determine their effects compared to medical marijuana outlets. It will also be important to monitor cities’ progress in reducing the number of unlicensed dispensaries and how this may impact adolescent marijuana use and other health and safety outcomes. Although it may have raised as many questions as it has answered, it has also hopefully resulted in some useful findings about the impact of local policy implementation on adolescent marijuana use. The most important implication for policy and practice identified here is the importance of local enforcement. The example of the City of Los Angeles was first to tolerate dispensaries under state law,bud drying rack but the lack of enforcement from the state combined with what was a very underdeveloped regulatory structure instead resulted in the presence of dispensaries having unwanted impacts on youth and public health. In Los Angeles it took many attempts and a voter mandate from a ballot measure to develop and implement an adequate regulatory approach with Proposition D.

Then it took time and the dedication of staff and financial resources in enforcement to make it successful. The decline in rates of marijuana use after Proposition D was enacted show that it is possible to take an out of control marijuana market in hand and that doing so can have a preventative impact on youth use. However, the experience of the City of Los Angeles also indicates that a complete ban on dispensaries is not necessary to curb undesirable outcomes. Instead it suggests that a robust local regulatory structure may allow for adult access to marijuana while reversing trends of marijuana use among adolescents and, by extension, health harms. Cities that allow dispensaries are forced to balance the tax and potentially other economic gains that can come from hosting marijuana businesses with the potential costs of enforcing marijuana regulations and the potential harms that dispensaries could cause. Many cities may have not been willing to take on the challenge, which could be one reason why most of the cities in LA County have enacted bans on dispensaries. However, preventing youth use is also a frequently mentioned reason for banning dispensaries, if not always the primary reason . The results of this study suggest that allowing dispensaries to operate in a city may not bring about significant harm to youth as long as dispensaries are located far enough away from schools. This information should guide the cities of LA County in choosing an approach to dispensary regulation. If a city feels it has the resources to vigorously enforce a dispensary ordinance and the capacity to host dispensaries far from sensitive areas, it may make economic sense to allow a small number of marijuana outlets serve their adult residents. Furthermore, continuing to ban dispensaries does not obviate the need for enforcement, which means that cities with dispensary bans must spend resources on marijuana control without the benefit of marijuana taxes. Finally, an important finding from a forthcoming impact evaluation on medical and recreational marijuana outlets in LA County found than the numbers of unlicensed outlets has been decreasing in cities where dispensaries are allowed, while they have held steady in cities and unincorporated areas where they are banned .

Licensing a small number of dispensaries may offer cities more local control over marijuana if allowing a few marijuana outlets cuts down on the number of unlicensed outlets a city must identify and close down. It my hope that by using evidence-based policy we can create an environment where the young people of LA County will use marijuana less and later. With this research my aim was to determine the effectiveness of a common approach to dispensary regulation, dispensary bans, on preventing adolescent marijuana use. It also aimed to build on our understanding of how city policies like dispensary bans can be effective, whether it is by their association with a lower number of dispensaries in a city, with increasing perceptions of the risk of marijuana use among young people, with a greater distance between schools and dispensaries, or with a lower number of dispensaries located near schools. Although this analysis shows that enacting and enforcing strict controls on marijuana outlets can have a preventative impact on a city’s students, dispensary bans were not found to have an independent association with lower rates of marijuana use in cross sectional analyses. Given that minors ostensibly cannot access marijuana directly from storefront dispensaries, it may not be surprising that city ordinances that allow storefront dispensaries should have little substantive effect on adolescents’ marijuana use. I hypothesized that dispensary bans would make access to marijuana less convenient for adults on a city level, which in turn could impact availability for youth, but there are many alternate sources for marijuana available to adults other than a dispensary in their city. There was also substantial variation in enforcement among the cities in LA County that have enacted dispensary bans, where some do not seem to have the resources or political will to enforce them. In the absence of rigorous enforcement to prevent unlicensed outlets, city bans on storefront marijuana outlets are evidently more symbolic than effective. Although the number of dispensaries in a city was not significantly associated with student marijuana use, future research should monitor adolescent marijuana use rates as the number of dispensaries in cities increases with additional adult use/non-medical outlets. There may be a threshold effect for the ratio of dispensaries per resident that a city can host without a concomitant increase in adolescent marijuana use, but this threshold is unknown to date.

Furthermore, increased density of outlets can have other undesirable effects such as marijuana abuse and dependence among adults . The multivariate analyses presented here also found little evidence for an effect of dispensary bans on young people’s attitudes toward the risk of marijuana use. Instead these attitudes seem to be driven by other factors that were not measured in this study. Therefore,grow solutions greenhouse it appears that cities may be better served worrying about their capacity to enact tight regulations on dispensaries and to enforce them than being concerned whether enacting an ordinance allowing dispensaries will send a message to young people that marijuana use is safe and acceptable and thus encourage use. The most potent effects on student marijuana use were related to the proximity of unlicensed outlets. The negative association between dispensary bans and student marijuana use, was significantly dependent on dispensary bans being associated with a greater distance to the nearest dispensary in the County compared to city dispensary policies that allowed dispensaries. The continuous distance to the nearest dispensary had a powerful association with students’ marijuana use within LA County, at one mile, and at short distances such as 2,000 feet. These local effects prove the primary importance of keeping unlicensed outlets much farther away from schools than current regulations in any city in LA County require. Future policy efforts should place greater importance on preventing the localized effects of unlicensed outlets and explore different approaches, such as clustering dispensaries in industrial zones or other areas far from the business and residential neighborhoods where schools are found. Together these findings support a rigorous but nuanced approach to regulating marijuana outlets. It is evident that enforcement was key in preventing marijuana use among the youth in this study. Whether a city allowed dispensaries or not, the presence of unlicensed dispensaries seemed to drive associations with youth marijuana use, indicating that the quality of enforcement is more important than the kind a policy a city chooses. Future research should focus on minimizing the localized effects of unlicensed dispensaries and undertake to better understand why unlicensed outlets have such a disparate impact on youth marijuana compared to licensed outlets. Marijuana is the most frequently used illicit drug in America, with an estimated 18.9 million people aged 12 years or older identifying as current users in 2012. The prevalence of marijuana use has increased since 2002, and this trend can be expected to continue as states enact policies to permit medicinal or recreational use. Despite the growing movement to legalise marijuana, however, little is known about its effect on metabolic health.

Research on the association between marijuana use and various metabolic indices suggests a paradox in which greater marijuana use is associated with increased caloric consumption, but with decreases in the levels of various metabolic risk indices, including BMI, waist circumference , fasting insulin and HOMA-IR. A recent meta-analysis of eight studies suggested that current cannabis smoking is associated with 30% lower odds of diabetes. However, previous studies have been limited to cross-sectional analyses and might have lacked proper adjustment for confounding. A prospective assessment of health outcomes in relation to prior marijuana use would limit the potential bias that might result from individuals’ decisions to alter marijuana exposure based on their own health status. The purpose of this study was multifaceted. First, we aimed to assess the association between self-reported marijuana use and prediabetes and diabetes mellitus using cross-sectional and prospective analyses, considering both status and quantity used. Second, we aimed to examine the role of BMI and WC as potential confounding or mediating factors of these associations. Finally, given the varying diabetes risk profiles by race and sex, we sought to evaluate the heterogeneity of effects in the associations by race and sex.The Coronary Artery Risk Development in Young Adults study is a longitudinal observational study intended to investigate the development of coronary artery disease risk factors in four healthy metropolitan populations of black and white adults aged 18–30 years at recruitment. Participants were contacted by telephone annually and invited to participate in follow-up examinations at 2, 5, 7, 10, 15, 20 and 25 years after enrolment. Demographic information was obtained, BP and chemistries were measured, and anthropometry and structured questionnaires on manifold health characteristics were conducted, following standardised protocols that were harmonised over time. The institutional review board at each study site granted approval, and informed consent was obtained from the 5,115 participants at enrolment in 1985–1986 and at each follow-up examination. Details of the study design have been published previously. Individuals were asked to present in a fasting state on the morning of their clinical examination and to forgo tobacco use and strenuous physical activity . Appointments were generally scheduled to begin between 08:00 hours and 12:00 hours. At each clinical examination, individuals were asked to update their sociodemographic information and were questioned about their medical and family history and individual lifestyle characteristics, including educational attainment, tobacco use , regular alcohol consumption, and moderate and strenuous physical activity. A valid and reliable metric for leisure-time physical activity was developed by CARDIA researchers, as previously described. Venous blood was drawn and serum separation was performed, following which aliquots were stored at −70°C and shipped on dry ice to a central laboratory. Serum glucose was measured using the hexokinase method, and per cent HbA1c was assessed using Tosoh G7 HPLC and standardised across examinations. The 2 h serum glucose levels were measured from a standard 2 h OGTT at Y10, Y20 and Y25. Procedures for collection, storage and determination of plasma lipids and C-reactive protein have been previously described.

Proximity to dispensaries in neighborhoods has been shown to have a positive association with more frequent marijuana use among adults , but the effect of proximity to marijuana outlets to schools on adolescent marijuana use is unknown. Research Question #4 was: “Is the effect of dispensary bans on student marijuana use dependent on the distance of the nearest dispensary from a student’s high school? The hypotheses associated with RQ4 propose that H4.1) dispensary bans are associated with longer distances between dispensaries and schools; H4.2) the proximity of dispensaries is positively associated with students’ likelihood of using marijuana; and H4.3) the relationship between city dispensary bans and high school students’ marijuana use is mediated by dispensary bans effectiveness at keeping dispensaries a greater minimum distance from schools than city policies that allow dispensaries. Table 7.12 presents the results of the mediation analysis assessing the mediating effect of the distance to the nearest dispensary within LA County on lifetime marijuana use. As reported earlier, the relationship between dispensary bans and lifetime marijuana use was negative and non-significant . Whether a city had a dispensary ban was regressed on the distance in miles from the participant’s school to the nearest unlicensed dispensary using an HLM model controlling for the covariates and with a random intercept for city. The association between dispensary bans and the distance between the students’ high school and the nearest unlicensed dispensary was positive and statistically significant ,plant racks which supports H4.1 for lifetime marijuana use, i.e., my theory that dispensary bans would be associated with a greater average distance from schools than in cities that allow dispensaries.

The distance in miles from the school to the nearest unlicensed dispensary was then regressed on the lifetime marijuana use, which revealed a statistically significant negative association between distance and reports of lifetime use, meaning that students in schools located further from unlicensed dispensaries were less like likely to report lifetime marijuana use , confirming H4.2., that shorter distances between the participants’ schools and the nearest dispensary were associated with higher prevalence of lifetime marijuana use. Finally, the regression analysis of dispensary bans and lifetime marijuana use was repeated including the distance in miles from the school to the nearest unlicensed dispensary. Adding the distance measure increased the effect of dispensary bans and in the expected direction, i.e., it was hypothesized that dispensary bans would be negatively associated with marijuana use and adding the variable quantifying the distance to the nearest dispensary into the regression strengthened the negative association between dispensary bans and lifetime marijuana use, but the overall relationship still fell just short of statistical significance . This result indicates that indirect mediation was occurring, where the effectiveness of dispensary bans was partially dependent on how far away they keep unlicensed dispensaries from high schools compared to city policies that allow dispensaries. The above steps were repeated for the recent marijuana use outcome, with similar results. The focal relationship between dispensary bans was negative and not statistically significant, as reported earlier. Path a, the association between dispensary bans and the distance to the nearest dispensary was the same as described above for recent use. Path b, the effect of the distance between the nearest unlicensed dispensary and recent marijuana use was negative and statistically significant , indicating the further away unlicensed dispensaries were located from the participants’ schools, the less likely they were to report recent marijuana use .

Path c’, the relationship between dispensary bans and recent marijuana use was strengthened substantially and in the expected direction but fell short of statistical significance, which indicated partial mediation. Hypothesis 4.3 was therefore partially supported. In Table 7.14 I present the results of a sensitivity analysis conducted a series of multilevel logistic regression models on increments of one mile, continuing to increments of a quarter mile for recent marijuana use, where the distance to the nearest MMD was not statistically significant at one mile. For each measure of marijuana use I started with a distance of over five miles and worked inward in increments of one mile, until I reached the point at which the distance to the nearest MMD had a statistically significant association with student marijuana use. For lifetime marijuana use, this occurred at a distance of one mile. For recent marijuana use, the distance from the school to the nearest MMD was not statistically significant at one mile, so I continued using smaller distances in quarter mile increments until I reached the point where the distance between the school and the nearest unauthorized MMD became statistically significant, which occurred at a distance of between ½ mile and ¾ mile of the nearest unauthorized MMD and the participant’s school. This analysis also revealed that the distance from the school to the nearest MMD became significantly negatively associated with student marijuana use at longer distances. The association of distance becomes protective against lifetime marijuana use at a distance of over 5 miles from the participants’ school and for recent marijuana use this occurs between 3 and 4 miles. As the distance used was the distance from school to only the nearest MMD from the participant’s school, these distances were not cumulative.

The ranges of distances were treated as bands, where if the nearest dispensary was located within a certain range of distance from the participants’ school distance from their school the value for that range of distance was coded as ‘1’ and all other rangers of distance were coded as ‘0’. For the distances within one mile I chose increments of a quarter mile because it is hard to visualize distance by feet at longer distances. I created variables using the distance to the nearest MMD from each student’s school to indicate whether the nearest MMD to the student was located within 1 mile , between three quarters of a mile and one mile, and between three quarters of mile and a half mile . There were too few MMDs located within a half mile from schools to produce statistically significant and reliable results without including all of them instead of only the nearest , so I stopped at a distance of a half mile from the school. Students that did not have MMDs located within any of these distances had a zero value for each of these variables. As these were binary variables I present odds ratios in Table 7.14. The sensitivity analysis indicates that there was no “safe distance” within a mile that unauthorized MMDs could be located near schools without having an association with significantly higher rates of lifetime marijuana use among students. The distance between the school and the nearest unauthorized MMD was significantly associated with greater prevalence of recent marijuana use at a distance of a ½ mile to a ¾ mile. The association between the distance between participants’ schools and the nearest unlicensed dispensaries was remarkably consistent for lifetime marijuana use with the exception of a non-statistically-significant result for distance between ½ and ¾ mile and lifetime use. Overall, there is a clear relationship with the distance between participants’ schools and the nearest unlicensed MMD,plant growing trays where shorter distances were associated with significantly greater odds of marijuana use. The association with distance to the nearest unlicensed dispensary then decreased in size and lost statistical significance at longer distances until eventually becoming associated with lower odds of marijuana use as distances increased. The distances at which this research showed statistically significant associations with student marijuana use are much further away from schools than the state requirement of 600 feet, but it is important to note the same associations did not apply for licensed dispensaries. In contrast to the associations with distance from schools found for unlicensed dispensaries, there was not a consistent association with the distance that licensed dispensaries were located from participants’ schools and their odds of reporting lifetime or recent marijuana use . It is therefore possible that smaller distances such as 600 feet are sufficient to prevent licensed outlets from being associated with greater prevalence of marijuana use among students, as licensed marijuana outlets seem to have less of an effect on student use than unlicensed outlets. As presented in Table 7.15, the associations between the distance from the participants’ schools to the nearest unlicensed MMDs were largely inconsistent or were not statistically significant.

For lifetime marijuana use the associations were consistently negative for distances below a mile, meaning that shorter distances to licensed MMDs were associated with lower odds of lifetime marijuana use among students, but these associations were not statistically significant below a mile and were inconsistent at distances greater than a mile. The inconsistent results for distances greater than a mile may indicate confounding effects from city borders, indicating that measurements of distance to authorized outlets greater than mile should be interpreted with caution and may be of limited utility in studies of the effects of distances between schools and dispensaries on high school students’ marijuana use behavior. Similarly, the association between the distance from participants’ schools to the nearest licensed MMDs and recent marijuana use was significant and protective at one mile, but was not statistically significant below one mile and inconsistent with any discernable pattern at distances greater than one mile. Rather than concluding that the presence of licensed MMDs is protective against marijuana use among high school students at distances of one mile, I think it is safer to conclude that there is not a clear relationship between the distance to licensed MMDs and student marijuana use and that any future studies of the effect of this distance should be bound by city borders to avoid any confounding effects of authorized dispensaries being found only within cities that allow them, while unlicensed dispensaries were found both in cities that allowed MMDs and cities with bans. That there was not a clear pattern of association with the distance from schools to the nearest licensed MMD when the association was clear for unlicensed dispensaries is surprising. This finding has important implications for marijuana regulation at a city level, as it suggests that any associations between the proximity of MMDs to areas young people frequent and their marijuana use behavior may be driven by unlicensed outlets. It also reflects a need for further study of why the effects of unlicensed and licensed MMDs should be so noticeably different. It is evident from the results presented above that the continuous distance between schools and unlicensed dispensaries is a mediator of effectiveness for dispensary bans, while there appears to be little association between student marijuana use and the distance to the nearest licensed dispensary. Although I found in Chapter 5 that dispensary bans were not effective on their own, they were associated with a longer distance between participants’ schools and the nearest unlicensed dispensary, which was in turn strongly correlated with a lower prevalence of student marijuana use. It is therefore important to consider what is actually driving adolescent marijuana use and how best to prevent it using city policy. This research suggests that it may need not necessarily be a ban. Although dispensary bans were associated with longer distances to the nearest unlicensed marijuana outlet, strict enforcement of distance requirements and closing down unauthorized outlets while allowing some dispensaries to operate far from sensitive areas could possibly achieve the same aim. The cities in LA County that allow dispensaries have use local ordinances that specify the conditions under which dispensaries can operate in the city. All six of the cities that allowed dispensaries in September of 2016 required them to be at least 500 feet from schools. Current State regulations require a minimum distance of 600 feet, but at the time of data collection, the distances dispensaries were required to be kept from schools in these six cities ranged between 500 – 1,000 feet . Distances specified in dispensary ordinances may be based on somewhat arbitrary criteria, as no empirical research has established what a “safe” distance is. I suspected the presence and number of dispensaries within a larger radius could still be influential and therefore tested the mediating role of the number of dispensaries located 2,000 feet of the study participants’ schools.

The independent variable was whether or not a city had a dispensary ban and was determined by the city policy that was in effect when the count of dispensaries per city was obtained in September 2016. The research question was broken up into three testable hypotheses: H2.1) there is a direct relationship between city dispensary bans and the number of dispensaries in a city, where city dispensary bans are associated with lower numbers of dispensaries; H2.2) fewer dispensaries in a city is associated with less availability of marijuana to high school students ; H2.3) the effect of city dispensary bans on adolescent marijuana use is dependent on them having a suppressing effect on the number of dispensaries operating in a city. To test the hypotheses associated with Research Question 3, I used a variable that indicated whether students perceived great risk from frequent marijuana use as the mediating variable and controlled for factors known to influence marijuana use among adolescents, such as gender, race/ethnicity, and social/economic status. As with Research Question 2, the independent variable was whether the city had a dispensary ban and was determined by the city policy that was in effect when the count of dispensaries per city was obtained in September 2016. The outcome variable was self-reported student marijuana use . Research Question 3 was broken up into three testable hypotheses: H3.1) there is a direct relationship between city dispensary bans and students’ perceptions of the risk of frequent marijuana use, so that dispensary bans are positively associated with perceived risk; H3.2) there is a direct inverse relationship between students’ perception of the risk of frequent marijuana use and their likelihood of using marijuana,plant benches so that students who perceive great risk from frequent marijuana use are less likely to use marijuana; and H3.3) the relationship between city dispensary bans and student marijuana use is dependent on dispensary bans being associated with greater perceptions of the risk of frequent marijuana use among students and an inverse relationship between perceptions of risk and student marijuana use.

To assess the mediating effect of perceived risk on student marijuana use and test hypothesis H3.1 I calculated the association between city dispensary bans and whether students perceived great risk from frequent marijuana use. If the coefficient was significant and positive, then H3.2 was supported. The second step was to test H3.3 by establishing whether there was a significant positive association between the moderating variable and the outcome variable . This model contained both the focal independent variable and the moderator . Finally, using the same regression model, I assessed the net direct effect of the focal independent variable on the outcome variable while accounting for the indirect effect of the moderator . I hypothesized that students’ perceptions of risk mediated the effect of city policy to some degree, and that the direct effect of the focal relationship coefficient would therefore decrease in magnitude when I controlled for students’ perceptions of risk. To test whether the mediation effect of students’ perceptions of risk was statistically significant from zero, I used a Sobel Test. Because perceived risk was measured at the individual level, I used the single level of the model to test the mediated relationship. To test the hypotheses associated with Research Question 4, I used a continuous measure of the distance in miles between the school and the closest dispensary in LA County as the mediating variable and controlled for factors known to influence marijuana use among adolescents, such as gender, race/ethnicity, and social/economic status. As with Research Questions 2 – 3, the independent variable was whether or not a city had a dispensary ban and was determined by the city policy that was in effect when the count of dispensaries per city was obtained in September 2016. The outcome variable was self-reported student marijuana use .

Research Question 4 was similarly broken up into three testable hypotheses: H4.1) there is a direct relationship between city dispensary bans and the proximity of dispensaries to the students’ high school, so that dispensary bans are associated with longer distances from dispensaries; H4.2) there is a direct relationship between the proximity of dispensaries and students’ likelihood of using marijuana, so that the lesser the distance between the school and the closest dispensary, the greater a student’s likelihood to use marijuana; and H4.3) the relationship between city dispensary bans and high school students’ marijuana use is mediated to some degree by dispensary bans being associated with a longer distance between dispensaries and schools. To assess the mediating effect of the distance to the closest dispensary located within a mile of their high school on students’ marijuana use, I first tested hypothesis H4.1 by determining whether there was a relationship between the independent variable and the moderating variable by calculating the association between city dispensary bans and the continuous distance to the nearest dispensary within LA County. If the coefficient was significant and positive, then H4.2 was supported. The second step was to test H4.3 by establishing whether there was a significant positive association between the moderating variable and the outcome variable . This model contained both the focal independent variable and the moderator . Finally, using the same regression model, I assessed the net direct effect of the focal independent variable on the outcome variable while accounting for the indirect effect of the moderator . I hypothesized that city dispensary bans would be associated with longer distances between participants’ schools and the nearest dispensary in the County and that the association between dispensary bans and student marijuana use would be statistically significant when accounting for this factor.

I used a Sobel Test to determine whether the mediation effect of the distance to the nearest dispensary from the school was statistically significant. To test the hypotheses associated with Research Question 5, I used the number of verified dispensaries located within 2,000 feet of the students’ high schools as the mediating variable and controlled for factors known to influence marijuana use among adolescents, such as gender, race/ethnicity,gardening rack and social/economic status. I also explored whether there were any different effects for the number of unlicensed dispensaries compared to licensed dispensaries. As with Research Questions 2 – 4, the independent variable was whether or not a city had a dispensary ban and was determined by the city policy that was in effect when the count of dispensaries per city was obtained in September 2016. The outcome variable was self-reported student marijuana use . Research Question 5 was similarly broken up into three testable hypotheses: H5.1) there is a direct relationship between city dispensary bans and the number of dispensaries located within 2,000 feet of the students’ high school, such that dispensary bans are associated with less dispensaries being located within a quarter mile of the school; H5.2) there is a direct relationship between the number of dispensaries located within 2,000 feet of the school and students’ likelihood of using marijuana, so that the number of dispensaries located within 2,000 feet is positively associated with students’ likelihood to use marijuana; and H5.3) the relationship between city dispensary bans and high school students’ marijuana use is mediated to some degree by dispensary bans being more effective at preventing dispensaries from locating near schools than city policies that allow dispensaries. To assess the mediating effect of the number of dispensaries located within 2,000 feet of their high school on students’ marijuana use, I first tested hypothesis H5.1 by determining whether there was a relationship between the independent variable and the moderating variable by calculating the association between city dispensary bans and how many dispensaries were located within 2,000 feet of each school. If the coefficient was significant and positive, then H5.2 was supported. The second step was to test H5.3 by establishing whether there was a significant positive association between the moderating variable and the outcome variable . Finally, using the same regression model, I assessed the net direct effect of the focal independent variable on the outcome variable while accounting for the indirect effect of the moderator . I hypothesized that the number of dispensaries located within 2000 of their school would mediate the effect of city policy to some degree, and that the direct effect of the focal relationship coefficient would therefore decrease in magnitude when I controlled for the number of dispensaries located within 2,000 feet the school. To test whether the mediation effect of having dispensaries located near the students’ high schools was statistically significant from zero, I used Sobel Test. The analyses presented above had several important limitations.

The trend analysis relied on a series of cross-sectional surveys, so I was not able to follow the same students over time. The students surveyed were also limited to students who participated in a school-based survey, so the findings from these analyses cannot be generalized to students who are out of school. The students surveyed were also exclusively public high school students, and may have differed in substantial ways from students attending private high schools. The results of this analysis should therefore not be generalized to students attending private high schools. The results may also not be generalizable to LA County as a whole, as only 57 cities out the 88 cities in the County had schools that participated in the CHKS survey during the 2105/2016 and 2016/2017 school years. Instead, this research should be taken as evidence of the need for representative data that can be used for further study of the effects of city dispensary policies on the neighborhood-level conditions that were shown to have a significant influence on students’ marijuana use behaviors; the distance to the nearest dispensary and the number of dispensaries located within several blocks of schools. Finally, I was unable to measure compliance with city or state laws regulating business practices among dispensary owners. I could not measure efforts to enforce dispensary bans or restrictions and did not undertake tomeasure how strict city dispensary ordinances were relative to each other. Future studies of city and county dispensary ordinances should assess these factors to determine how they may mediate or moderate the effect of dispensary ordinances on local conditions that facilitate adolescent marijuana use. Although there are compelling reasons to believe the presence of dispensaries would be correlated with adolescent marijuana use, there is equally credible evidence to suggest that dispensary bans may have little effect on students’ marijuana use. For example, a notable result from the preliminary analyses presented in Chapter 4 was that rates of marijuana use increased from baseline through the 2011/2013 time point even among cities that banned dispensaries. The proportion of students in LA County attending school in a city that allowed dispensaries more than quadrupled during the study period from 3.48% in 2005/2006 to 14.45% in 2016/2017. During the same period, the proportion of students in the County reporting lifetime marijuana use declined from 30% to 25% and recent marijuana use declined from 16% to 14% . These findings suggest that whether their city allows dispensaries may not the primary determinant of rates of marijuana use among LA County high school students. For this analysis, I used a 2-level Hierarchical Generalized Linear Model with students as the level 1 variable and city as the level 2 variable to compare the proportion of students who reported lifetime and recent marijuana use among all the LA County cities that had students participating in the CHKS survey during the 2015/2016 and 2016/2017 school years. The independent variable is whether the city the students lived in/attended school in a city that allowed or banned dispensaries as of September 2016. Covariates included gender, grade, race/ethnicity, participation in after school programs, whether the student received free or low cost meals , whether one or more of the students’ parents had a college degree, and whether they attended a non-traditional school. This analysis uses two pooled years of CHKS survey data, for a combined total of 101,521 students. Combining two school years of CHKS data was necessary because most schools administer the survey every other year and therefore, in any given year half of them are off cycle. More importantly, preliminary analyses indicated that the average number of participants from Los Angeles schools on odd years was approximately double the average from even years.

Visual cues to marijuana use such as billboard and magazine advertising for cannabis are strongly associated with adolescents’ intentions to use marijuana and eventual use . The presence of dispensaries may be analogous to advertising because many dispensaries in LA County use their exterior walls as advertising space like any other store . It is therefore possible that repeatedly seeing dispensaries located near their school will have an impact on high school students’ likelihood to use marijuana, even if they are not able to obtain it directly from these outlets. Furthermore, among people who have already used a psychoactive substance, visual reminders of that substance activate a chemical response that triggers a craving for the substance, increasing their propensity to use substances to which they are frequently exposed to reminders of . This means that among high school students who have already tried marijuana, the sight of dispensaries may trigger cravings for marijuana and thus increase their propensity to use it. Although measuring individual enforcement efforts by city or county police or code enforcement officers was beyond the scope of my analysis, enforcement is nevertheless an important construct in the conceptual model for this dissertation and the analyses that follow. In the conceptual model below, the effects of city policies banning or enacting stricter regulations on storefront dispensaries are hypothesized to be dependent on effective enforcement. For example, the impact of a city policy allowing dispensaries on adolescent substance, such as dispensary density in a city,cannabis curing is determined not just by how many dispensaries the city ordinance allows, but also on how many dispensaries are actually in operation.

Similarly, city policies that allow dispensaries often require them to be located a specific distance away from schools, but dispensaries have often been found located near schools in violation of these policies. Keeping dispensaries away from sensitive areas is therefore also dependent on effective enforcement.Key informant interviews conducted with city officials as part of the LA County Department of Public Health Cannabis Health Impact Evaluation indicate that preventing unlicensed outlets is a central goal for city dispensary ordinances. Therefore, the number of unlicensed outlets per 10,000 city residents was included as a proxy measure of the effectiveness of the city dispensary ordinance, with a higher proportion of unlicensed dispensaries per residents indicating less effective enforcement. Figure 3.2 presents the conceptual model of this dissertation. At the individual level, the focal relationship is between city dispensary bans and students’ self-reported marijuana use . The additional variables that explain and influence the focal relationship are described in the research questions and hypotheses that follow. The conceptual model also presents the backdrop of potentially confounding external influences, which include a general trend toward greater acceptance of marijuana use in American society, changes in state laws that have seen the majority of U.S. states enact laws that allow some level of access to marijuana, and changes in the Federal government’s stance on enforcement priorities concerning marijuana. Although these changes occur outside the scope of this dissertation, they are relevant from the standpoint of the Social Ecological Model and the Drug Normalization Framework and are accounted for in the study design wherever possible.

This study draws upon diverse data sources and uses several different methodological approaches to arrive at a greater understanding of the impact that the dispensary bans enacted throughout Los Angeles County over the past decade have had on high school students’ marijuana use. In the five descriptive and explanatory data analyses for this dissertation that follow, I first used primary and secondary data sources to construct an administrative data set that documented which cities in LA County have enacted medical marijuana dispensary bans. Second, I used school-based CHKS survey data to measure marijuana use among 9th and 11th grade students in each city. Third, I geocoded school addresses from the California Department of Education school directory and mapped their locations within city and county boundaries. Fourth, I linked the CHKS data set to the geographic location of the schools using the unique ID assigned to each school by the California Department of Education. Lastly, I used street addresses from commercial listings of marijuana businesses to establish the location of dispensaries in cities and near schools. These data sources and methods were required to compare long term trends in student marijuana use by whether a city bans or allows dispensaries and to test different ways that city bans may influence high school students’ marijuana use . The population of interest for this dissertation was adolescents living within LA County. The study population was 9th and 11th grade students at public high schools that participated in the CHKS survey between the 2005/2006 and 2016/2017 school years. Students’ demographic and socio-economic characteristics and their marijuana use behavior were recorded using restricted-use secondary data from a school-based survey of student health and school climate, the California Health Kids Survey. Dispensary policies for each of the 88 cities in Los Angeles were obtained from online municipal code databases and categorized by whether they allowed or banned dispensaries. City dispensary policies were linked to student behavior by the city where their high school was located, which according to California public school residency requirements is most often the city where they live .

Addresses of dispensaries were then downloaded from commercial listings of marijuana businesses and mapped to determine their location and density within cities and near schools. Each of these data sources were required to address the central question of this dissertation; whether dispensaries bans prevent adolescent marijuana use. The dependent variables,drying weed lifetime and recent marijuana use, are self-reported data from the CHKS survey. The independent variable is whether each city had a dispensary ban. I also conducted a mediation analysis using measures of marijuana density within cities and relative to schools as well as student perceptions of the health risks of marijuana use to test whether the effectiveness of MMD bans was dependent on any of these variables. The survey sample used for this dissertation was comprised of students who completed the California Healthy Kids Survey at LA County public high schools between the 2005/2006 school year and the 2016/2017 school year. The CHKS is a statewide survey that covers a range of health perceptions and behaviors and is administered annually in school districts throughout the state. The initial population was 532,200 LA County high school students who participated in the CHKS survey between 2005/2006 and 2016/2017. In Los Angeles County during the school years studied, most high schools administered the CHKS every other year to 9th and 11th grade students. However, about 10% of the surveys each year were administered to 10th and 12th grade students or students who chose categories of “don’t know” or “ungraded/other” for grade. These students were excluded to draw more precise conclusions about the behavior of students in 9th and 11th grade and for comparability with the other research published using CHKS survey data, which focuses on these grades. After excluding a handful of remaining students who attended special education schools or who were missing important data, the population available for analysis over the 12 years of the study period numbered 487,354. Criteria for exclusion from the study sample are presented below in Table 4.2. The study period spanning the 2005/2006 school year and the 2016/2017 school year was chosen for several reasons. An original motivation for this study was to learn whether rates of marijuana use among LA County high school students increased overall as the number of cities in the County that allowed dispensaries increased after medical marijuana entered the formal marketplace after SB 420 in 2004 allowed medical marijuana collectives to operate as businesses.

The endpoint for the study period, the 2016/2017 school year, preceded the licensure of non-medical marijuana storefronts throughout the state of California and LA County that began in January of 2018. Ending data collection in 2017 allowed this analysis to focus on the impacts of medical marijuana dispensaries and to serve as a comparison point for non-medical marijuana sales after 2018. As noted above, the data presented in this dissertation were drawn from multiple sources. These sources are reviewed in more detail below. The data source used to measure high school students’ perceptions of the health risks of marijuana use and marijuana use behaviors is a restricted-use secondary data set obtained from a state-level survey of California middle and high school students; the California Healthy Kids Survey . I documented whether the 88 incorporated cities within Los Angeles County had ordinances that banned or allowed dispensaries by reviewing municipal code texts using online municipal code databases such as Municode.com and categorizing city dispensary policies according to whether or not they allowed dispensaries and several other criteria . The number and location of dispensaries within each city were obtained from online dispensary listing and rating services such as Weedmaps.com, which I then used to map dispensary locations using ArcMap 10.4 geographic information system mapping software . The addresses of the high schools came from the California Schools Directory, which was downloaded from the California Department of Education . The CHKS survey is the largest statewide survey of resiliency, protective factors, and risk behaviors in the United States . It administered annually and anonymously at most public schools in California to measure middle and high school students’ attitudes and behaviors related to substance use and other health behaviors. Collecting data on student substance use has been an important goal of the survey dating from its inception. The precursor to the CHKS survey was the California Student Survey of Substance Use , which began collecting data from a representative state sample of secondary students in 1985. Over time, the focus of the CSS was expanded to include questions on other health-risk behaviors, resiliency, school climate, and school safety, which then formed the bulk of the CHKS Core Module when it was developed in 1998. In 2003, the California Department of Education mandated that CHKS serve as the primary data collection tool to document change in alcohol, tobacco, and drug use among California schools , which means that all school districts that receive funding under the federal Safe and Drug Free Schools and Communities Act or state Tobacco Use Prevention Education program must administer the CHKS survey at least once every two years and report the results publicly . As a consequence, the CHKS survey is administered by the majority of California secondary schools every other year on a staggered basis that means data is available for every year at state level but may need to be aggregated into two-year ranges to capture data for all the schools in a region. In terms of psychometric properties, a 2007 evaluation of the CHKS survey’s psychometric properties indicated that the survey exhibited good internal consistency, adequate reliability, and demonstrated measurement equivalence across racial/ethnic groups, males and females, and grades . The current iteration of the CHKS survey is built around a general Core Module and five optional supplements. The analyses presented in this dissertation relied on the Core Module, which assesses demographic information, substance use, exposure to school violence, and other behaviors that contribute to physical and mental health. Most of the items used in the CHKS Core Module were derived from the biennial California Student Survey of Substance Use and the Centers for Disease Control and Prevention Youth Risk Behavior Surveillance System . The analyses presented throughout this dissertation were conducted using two combined school years. This was necessary because school districts generally administer the CHKS survey every two years, at 9th and 11th grade. Therefore, on any given single school year, half of the schools in LA county may not have administered the survey, which could have introduced bias into the analysis. Preliminary analyses indicated that across all schools in the County, the number of participants for odd to even years varied in tandem with whether a majority of LA Unified School District schools had administered the survey that year. For example, the average number of City of Los Angeles schools on odd years was double the average number on even years.

Each business included in the analyses was verified as being in active operation via phone calls, checking WeedMaps message boards for current ratings and comments, and by whether a dispensary was photographed at that location using Google Street View. Once verified as being active, the address of each storefront was geocoded using geographic information systems software to pinpoint their location within city boundaries and determine their location relative to LA County public high schools. The primary practical use of this research is to establish whether dispensary bans are effective on a city level, or if spillover effects and the many other ways young people can access marijuana render them symbolic. The theoretical relevance is gained by establishing why city dispensary ordinances may or may not be effective. The results of this dissertation will determine whether dispensary bans work by making access less convenient at a city level, by changing teens’ perceptions of risk, or by limiting the number of outlets near areas young people frequent. Research on city dispensary policies and the local impacts of dispensaries on youth use is so scarce that it is unknown if all of these mechanisms apply…or none of them. In this review of the literature I will first assess the physiological, psychological, developmental and social consequences of cannabis grow supplier among adolescents. I will then review the current prevalence of marijuana use as well as trends in attitudes and norms toward marijuana use in California over the past two decades following the legalization of medical marijuana use in 1996. I will then cover current state and local policy approaches to regulating marijuana use and conclude by reviewing gaps in the literature.

Given that the goal of this research is to assess the role of city dispensary policies in limiting exposure to marijuana among adolescents, community and societal-level approaches to marijuana use prevention are the focus of this literature review, rather than family and peer influences.Marijuana and cannabis are used as general terms to refer to the many extracts and preparations that can be made from this plant. Marijuana products vary in effects and potency by genetic strain, cultivation technique, and by how it is processed . Traditionally used by smoking the dried flowers, marijuana can also be used by heating flowers, oils, or other concentrated forms in electronic vaporizing devices, by baking extracted oils into foods, and in pills, tinctures, sprays, creams, ointments, eye drops, and suppositories . The chemical contents of marijuana include over 100 cannabinoids; chemical compounds with physiological and/or psychoactive effects. The distinct effects of most cannabinoids have yet to be studied in laboratory settings and are poorly understood . The two best-known cannabinoids are delta-9 tetrahydrocannabinol and cannabidiol . THC is the primary psychoactive ingredient in marijuana that causes intoxication and euphoria. CBD is thought to be responsible for the anti-convulsive and pain-relieving properties of marijuana but is not intoxicating. Recent research on therapeutic uses of cannabinoids has shown that they have considerable promise to treat appetite loss, nausea, chronic pain, insomnia, inflammation, and glaucoma , but marijuana products containing THC also produce potent psychoactive side effects. Favorable psychoactive effects from THC include calming, relaxing, stimulating, or uplifting feelings, but unpleasant effects like anxiety, panic attacks, and paranoia can also occur . THC products act on the central and peripheral nervous systems by binding to receptors for endogenous cannabinoids called “endocannabinoids”.

Endocannabinoids are neurotransmitters naturally produced in the human brain that bind to and activate cannabinoid receptors found in the prefrontal cortex, hippocampus, basal ganglia, thalamus, hypothalamus, and cerebellum . The psychoactive effects of marijuana are produced by displacing endocannabinoids with exogenous cannabinoids such as THC, thus altering cognitive function. Documented effects of marijuana use on cognitive function include chronic short-term memory problems, loss of balance and coordination, difficulty concentrating, changes in sensory perceptions, impaired ability to perform complex tasks, decreased alertness, and decreased reaction time . Long-term cognitive effects from marijuana use are mild compared to those described above but can last for weeks after acute effects wear off . The most enduring cognitive effects are seen in decision-making, concept formation, and planning . Cognitive effects also differ in severity depending on the quantity of regular use, how recently a person used, how old they were when they started using marijuana, and how long they have been using it . Marijuana use that occurs during adolescence has been associated with use of other drugs , poor school performance, and a higher likelihood of substance abuse or dependence in adulthood . Adolescents are more vulnerable to harmful effects from marijuana use because of the active processes of brain development that occur during this stage . For example, developing dependence on marijuana is more likely among adolescents than among adults and the effects of frequent use at this age appear to be much longer lasting. The overall likelihood of developing dependency among people who use marijuana is estimated to be 9% , but among people who begin using marijuana before age 18, the likelihood to develop dependence is almost doubled and developing symptoms of problem use is estimated to be 4 to 7 times more likely .

Among adults the cognitive effects of marijuana use generally disappear within a month, when the last traces of the fat-soluble THC molecule dissipate . In contrast, neuroimaging studies have shown that regular marijuana use in adolescence is associated with changes to areas of the brain involved in executive functions like memory, attention, learning, retention, and impulse control . Advances in brain imaging technology have made it possible to directly observe the impact of substance use on the brain ,cannabis drainage system which has resulted in a greater understanding of the mechanisms by which marijuana use interferes with normal brain development. Studies using prospective case-control and other longitudinal designs have found that cognitive effects from repeated marijuana use during adolescence persist into adulthood . Windle and colleagues recently reported results from a prospective study that followed U.S. children into adulthood where they found that substance use between the ages of 13 and 15 years old was associated with a smaller amygdala, a brain region that develops earlier in adolescence and is crucial to emotional regulation. The authors also found that substance use between the ages of 16 and 18 years old was associated with a lower volume of gray matter in the pars opercularis, a region of the brain that develops later in adolescence and is responsible for cognitive control. These very recent research findings suggest that not only does substance use during adolescence result in changes to the brain that persist into adulthood, but that initiating regular use at different times may affect different regions of the brain according to which regions are in active development at the age when the substance use is occurring. Recent research has documented that endocannabinoids are instrumental to the final processes of brain development that occur during adolescence . Receptors for endocannabinoids begin to increase in the subcortical and frontal cortical regions of the brain during childhood and peak in adulthood, which has led scientists to conclude that the endocannabinoid system is a mechanism through which greater degrees of cognitive control are achieved between childhood and adulthood . During normal neural development endocannabinoid receptors are pruned as part of the consolidation of neuronal pathways that increases efficiency in signals to and from the prefrontal cortex, which in turn increases the capacities for cognitive control and self-directed behavior . If THC molecules replace the endocannabinoids that drive these processes, it alters the way the brain consolidates neuronal pathways throughout adolescence, which may in turn result in less capacity for cognitive control and self-directed behavior in adulthood . The Dunedin Longitudinal Study, a prospective cohort study conducted in Dunedin, New Zealand, has provided the best evidence of the long-term effects of marijuana use on intelligence and life prospects. In this study, 1,037 people were followed from birth into adulthood. Their intelligence was assessed at the ages of 7, 9, 11, and 13 years of age using the Intelligence Quotient test .

The Dunedin Longitudinal Study investigators found that repeated marijuana use before the age of 15 years old was associated with declines across multiple domains of cognitive functioning, even after controlling for years of education . Their research was the first to document a decline in cognitive functioning from adolescence to adulthood among adolescent-onset marijuana users compared to non-users and that cessation of marijuana use after adolescence did not fully restore neuropsychological functioning in adulthood. More recently, Cerda and colleagues used the Dunedin Longitudinal Study data to demonstrate that chronic marijuana use during adolescence and adulthood was associated with downward socioeconomic mobility and more financial difficulties, and workplace problems in early midlife, even when controlling for socioeconomic adversity, childhood psychopathology, achievement orientation, family structure, marijuana-related criminal convictions, early onset of marijuana dependence, and comorbid substance dependence. The threshold of adolescent marijuana use where loss of cognitive potential occurs or where dependence becomes a risk is unknown and likely differs by individual. Nevertheless, it is clear from the literature that earlier and more frequent marijuana use during childhood and adolescence is associated with a greater potential to disrupt normal brain development and to develop problem substance use . More randomized controlled trials and prospective cohort studies are needed to definitively characterize the impacts of marijuana use on adolescent brain development, but the current body of literature persuasively documents the importance of minimizing exposure to THC during adolescence . The 2017 National Survey on Drug Use and Health Annual Report indicates that 25% of 9th graders and 37% of 11th graders in the U.S. report lifetime marijuana use, while 13% of 9th graders and 18% of 11th graders in the U.S. report having used within the past 30 days . The Monitoring the Future study found that as of 2017, 6% of 12th graders in the U.S. report daily use of marijuana, which corresponds to about one in 16 high school seniors . Rates of recent marijuana use among adolescents in California are over three times higher than the national average; 22% of California adolescents aged 12-17 reported using marijuana in the past 30 days in 2017 , compared to 6.5% nationally . NSDUH trend data for California between 2002 and 2014 indicates that among youth aged 12-17 there has been an overall increase of 16%, but also that there was not a consistent trend of increase. Instead, marijuana decreased from the 2002-2003 study years through the 2005-2006 study years and held relatively steady before increasing again during the 2010-2011 study years . A Community Needs Assessment conducted by the LA County Department of Public Health in 2017 indicates that nearly half of LA County residents aged 12 or older have used marijuana at least once in their lifetime and that 14% had used marijuana in the past 30 days. It also found that residents were an average age of 17 years old when they first used marijuana, with a majority using marijuana for the first time before age 18. Similar to national and state reports of the perceived accessibility of marijuana, the LA County assessment found that most County residents over the age of 12 perceived it easy to access marijuana in their neighborhood. The marijuana users in the study most commonly obtained their marijuana from a friend , followed by a dispensary , family/relative , or the illicit market . More specific data is available from the YRBSS survey for the Los Angeles Unified School District and indicates a local pattern different from what has been observed at a state level . Changing attitudes to perceive marijuana use as more socially acceptable and less of a health risk have been noted among youth populations, but whether changes in these attitudes are the result of policy changes or of a general secular change in attitudes toward marijuana is often difficult to determine. Some research supports the idea that the increasingly liberal state laws governing marijuana in the U.S. stem from more positive adult attitudes toward marijuana rather than the reverse. For example, rates of marijuana use among adults is higher in states that have approved medical and recreational marijuana laws, but the higher rates of marijuana use in these states preceded enactment of the laws, suggesting that the more liberal attitudes toward marijuana use were a motivation for liberalizing marijuana laws .

Two hundred adolescents were consented into the study and completed the baseline visit. Of those, 28 denied smoking cigarettes in the past 30 days and 7 declined to answer the question about marijuana use and were thus excluded from the analysis. The resulting sample had a mean age of 16.1 years and was racially diverse, with 28% participants identifying as White, 19% African American, 19% Hispanic and 34% other. Participants averaged 3.01 CPD for a duration of 1.98 years . Fifty-one participants reported daily cigarette smoking and 111 reported non-daily smoking . Mean scores were 2.56 on the mFTQ , 4.52 on the HONC , -1.75 on the NDSS , and 10.13 on the ICD-10 . Most participants reported marijuana use in the past 30 days with 43 using weekly, and 62 reporting daily use. Frequency of marijuana use was correlated with CPD , but not with the frequency of alcohol use .In general linear models controlling for age, years of smoking, and daily versus non-daily smoking, frequency of marijuana use was significantly and positively associated with nicotine addiction . The findings were consistent across all four measures of dependence and remained significant for the mFTQ after removing the question on CPD. When examining the NDSS sub-scales, only the drive and priority sub-scales were significantly associated with marijuana frequency. Older age, more years smoking, and daily smoking were associated with greater nicotine dependence in all models. The total percent of variance predicted ranged from 25% for the HONC to 44% for the mFTQ and NDSS. Illicit drug use may co-occur across substances,cannabis growing equipment and follow-up analyses sought to examine whether the finding of an association with nicotine dependence was specific to marijuana.

Therefore, we also assessed co-use with other illicit substances. In the past 3 months, 40 participants reported ecstasy use. A small number of participants reported use of cocaine/crack , methamphetamine , mushrooms/ mescaline , heroin , Percocet/Vicodin , or LSD , preventing inclusion in analyses. Ecstasy, included as a covariate in the fully adjusted general linear models, was not a significant contributor with p-values ranging from .24-.99 and the effects for marijuana remained largely unchanged. Marijuana smoking was prevalent in this adolescent sample of tobacco smokers: 80% reported past month marijuana use and more than a third smoked marijuana daily. Notably, among adolescent tobacco smokers who also smoked marijuana, the frequency of marijuana use was associated with greater levels of nicotine addiction on all three major scales used in studies with adolescents plus the ICD-10. Moreover, models incorporating age, frequency and years of tobacco smoking with marijuana accounted for 25-44% of variance in adolescent nicotine dependence. Interestingly, CPD was only minimally associated with the frequency of marijuana use and made minimal contribution to the model since associations with the mFTQ were similar after removing the question about CPD.The finding that with the exception of drive and priority, the other sub-scales of the NDSS were not significantly associated with marijuana frequency was not surprising since most of these adolescent smokers were light and intermittent tobacco users and dimensions of dependence such as stereotypy and tolerance become more prominent as teens develop more regular and established patterns of smoking . However, despite relatively light tobacco use, the drive sub-scale, which measures the compulsion to smoke, and the priority sub-scale, which measures the preference of smoking over other reinforcers, were associated with marijuana use. It is possible that since both marijuana and tobacco share common pathways of use, smoking cues for one substance may trigger craving for the other, and thus reinforce patterns of use. As such, tobacco and marijuana may serve as reciprocal reinforcers.

Some limitations of this brief include the relatively small sample size and the lack of detailed information on the timing of the initiation of marijuana use with regard to cigarette smoking. Future studies will need to examine how the proximity of marijuana use to cigarette smoking affects the degree of nicotine addiction. For example, examining whether concomitant use impacts the level of nicotine addiction more than smoking marijuana separately from tobacco. The sample largely consisted of light smokers, which reflects adolescent smoking in the US. That we found such a strong association between marijuana use and nicotine addiction in this group of relatively light tobacco smokers is notable, and reinforces the relevance of the association. Recreational marijuana commercialization is gaining momentum in the US. Among the 11 states and Washington DC that have legalized recreational marijuana since 2012, retail markets have been opened or anticipated in 10 states, where over a quarter of the US population live. The presence of recreational marijuana dispensaries increased rapidly following the commercialization. Children are at a high risk of initiating marijuana use and developing adverse consequences related to marijuana. The rapidly evolving environment poses considerable concerns about children’s exposure to marijuana and related marketing and creates significant challenges for pediatricians preventing, treating, and educating about marijuana related harms among children. As stated in its most recent policy statement about marijuana commercialization, the American Academy of Pediatrics “strongly recommends strict enforcement of rules and regulations that limit access and marketing and advertising to youth”. The presence of RMDs in neighborhoods and point-of-sale marketing such as advertising and promotional activities in RMDs might increase the visibility and awareness of marijuana products among children, whose perceptions and behaviors may be influenced. A study in Oregon found that dispensary storefront was the most common source of advertising seen after commercialization. 

Self-reported exposure to medical marijuana advertising was found to be related to higher levels of use and intentions of future use among children in California schools. Products, packages, and advertisements that are designed to be appealing to children are particularly concerning. Tobacco and alcohol literature repeatedly suggested that children are common targets of marketing. Despite the fact that all the states with marijuana commercialization have some form of prohibitions on child-appealing products and marketing, it remains undocumented as to what extent the marijuana industry is complying. This study is the first to comprehensively assess point-of-sale marketing practices in RMDs with a focus on those relevant to children. Unlike previous marijuana research relying on individual self-reported exposure measures, we adopted the direct and objective observation approach that has been commonly used in tobacco and alcohol studies on retail outlets. We audited RMDs near a representative and large sample of schools in California, the largest legal retail market in the US where over 10 million children can be potentially influenced. We identified product and packaging characteristics, advertising and promotional activities,cannabis grow table and access restrictions in these dispensaries. Six trained field workers audited retail environments in RMDs in closest proximity to the 333 schools . We first identified dispensaries using crowd sourced online websites, including Weedmaps, Wheresweed, Leafly, and Yelp. State licensing records were not used because they could not provide a complete list of dispensaries at the time of data collection. Specifically, 1) Marijuana commercialization in California took effect in January 2018. During the study period, California was in a transition stage when annual licenses were just issued, and most were not approved. 2) The licensing policy in California was not enforced, with a large portion of dispensaries operating without licenses. 3) For licensed dispensaries, the registered and actual business name and address often mismatched. Alternatively, we utilized crowd sourced databases, which were considered as reliable, up-to date, and comprehensive sources of dispensary directories. To identify the dispensary closest to a school, field workers entered school zip code in the online searchable databases. The street addresses of all the dispensaries with the school zip code were geocoded and mapped in ArcGIS to compute their distances to the school. Field workers then called the dispensary with the shortest distance to verify its address and operational status. These procedures were repeated if a dispensary was permanently closed or not verifiable via multiple calls until an active dispensary was identified. The primary focus was RMDs. Yet, medical marijuana dispensaries that require a doctors’ recommendation or state patient ID cards coexisted in California in 2018. During call verifications, if dispensary staff indicated that a doctors’ recommendation or a patient ID was required to enter the dispensary and make purchase, the dispensary was categorized as a MMD.i Fieldworkers also verified dispensary classification during the subsequent auditing. For those verified as MMDs, we repeated the aforementioned procedures until an active RMD was identified.

The six trained workers in teams of two audited verified RMDs.ii On average, each RMD visit took 10-15 minutes. The 103 RMDs had unique RMD-school pairs and the 60 RMDs were the closest ones to two or more schools out of the remaining 230 schools. In the main analysis, we reported observations in the unique RMDs . In the secondary analysis, we reported observations on RMDs using school as the unit of analysis . The 60 RMDs shared by two or more schools were counted multiple times or over-weighted in the secondary analysis, reflecting their potential to influence children in multiple schools. The Human Research Protections Program at the University of California San Diego deemed this research non-human-subject and required no review. We validated SMDA-CF through a pilot test on 18 RMDs in California. To calculate inter-rater reliability, two workers in a team independently audited the same dispensaries. Reliability analysis indicated moderate to high reliability for SMDA-CF as a whole . Because of the concerns about some low-reliability items, in the formal field work of auditing 163 RMDs, the two workers in a team audited dispensaries together and discussed to resolve discrepancies before submitting observations. This study demonstrated that, in the early stage of marijuana commercialization in California, point-of-sale marketing practices that are appealing to children were minimal on the exterior of the RMDs around schools. However, such practices were abundant on the interior. Marketing practices not specifically appealing to children were common on both the interior and exterior of the RMDs. Given the age limit, RMDs’ exterior marketing might be the most concerning source of exposure for children. It is reassuring that child-appealing marketing was rarely observed on the exterior of the RMDs around schools. Yet, three quarters of the RMDs had some form of child appealing marketing on the interior, which violated the California laws. Although children should have little direct access to the interior, child-appealing items may be available to children through indirect pathways and should not be overlooked. For instance, children’s social networks such as older relatives, peers, or caregivers are their important sources of drugs. A study reported that almost three quarters of underage users obtained marijuana from friends, relatives, or family members. Child-appealing products, paraphernalia, or promotional materials could then be made available to children through these adults who are eligible for marijuana purchase. Particularly, about 30% RMDs violated the California law to offer free samples, which could be taken out of the dispensaries and given away to children. These child-appealing items in RMDs could be also resold to children in illicit markets by street dealers. Research on tobacco and alcohol have suggested that children are exposed to and influenced by tobacco and alcohol products and point-of-sale marketing despite the age limit for purchase . Whether and how the marketing activities inside of RMDs impact children’s perceptions and behaviors should be examined in future research. Meanwhile, exterior retail environments not specifically relevant to children still warrant further attention. For instance, 63% RMDs had image or wording indicative of marijuana on the exterior. One third of the RMDs had generic advertisements, and some advertisements were of a relatively big size. Marijuana could be smelled outside of 25% RMDs. All of these might potentially increase perceived presence of RMDs in the neighborhoods and shape children’s social norms. Approximately half of schools had RMDs located within a 3-mile distance that is reachable to children by walking, cycling, or driving. Some RMDs were located further away, especially in suburban or rural areas. Nonetheless, children are not free from exposure to RMDs even if RMDs are located more than 3 miles away from schools. In 2009, the average travel distance from home to school among all school children was 4.4 miles; among high school students, the average distance was even longer .