Cannabis Grow

If the dispensary had any online activity within the past month , it would be considered active1 . After removing inactive businesses, businesses not selling marijuana, and businesses without storefronts during the verification procedure, the 2,121 unique records were reduced to 826 businesses . These 826 dispensaries constituted the call-verified, combined database of active brick-and-mortar dispensaries in California. Validity statistics, including sensitivity, specificity, positive predictive value , and negative predictive value were computed for each of the four secondary data sources when applicable. Definitions and calculations were described in Technical Note S1. To compute validity statistics, a gold standard must be defined that can identify the “true positive” and the “true negative”. Field census is typically considered the gold standard in retail outlet research. However, it is infeasible in this study due to budget and time constraints for a statewide census. Two gold standards were adopted alternatively to answer the two research questions. To answer the first question regarding the validity of online crowd sourcing platforms in enumerating licensed brick-and-mortar marijuana dispensaries, the first gold standard was whether a record was listed in the BCC state licensing directory . To answer the second question regarding the validity of state licensing directory and online crowd sourcing platforms in enumerating active brick-and-mortar marijuana dispensaries, the second gold standard was whether a record was included in the call-verified, combined database of active dispensaries . We must also define a test that can identify the “positive test” and the “negative test” in validity statistics calculations. Two tests were conducted. The first test was whether a record was present in a given data source after online data cleaning . We used this test to examine the validity of using a single data source with simple online data cleaning for dispensary identification,vertical farming supplies an approach requiring moderate resources.

The second test was whether a record passed call verification; in other words, whether the record was verified to be an active brick-and-mortar dispensary . We used this test to examine the validity of using a single data source with simple online data cleaning plus call verification for dispensary identification, an approach requiring much more resources. To illustrate these validity statistics in the context of this study, we provide an example below . In this example, the data source of interest is Weed maps, the gold standard is whether a record on Weed maps was present in the BCC state licensing directory, and the test is whether a record was present on Weed maps after online data cleaning. Sensitivity measures the probability of a record present on Weed maps conditional on the record being included in the BCC directory, calculated as the number of records that were present on both Weed maps and the BCC directory divided by the number of records present on the BCC directory. Specificity measures the probability of a record absent on Weed maps conditional on the record being excluded from the BCC directory, calculated as the number of records that were neither present on Weed maps nor present on the BCC directory divided by the number of records excluded from the BCC directory. PPV measures the probability of a record included in the BCC directory conditional on the record being present on Weed maps, calculated as the number of records that were present on both Weed maps and the BCC directory divided by the number of records present on Weed maps. NPV measures the probability of a record excluded from the BCC directory conditional on the record being absent on Weed maps, calculated as the number of records that were neither present on Weed maps nor present on the BCC directory divided by the number of records being absent on Weed maps. You will notice that specificity and NPV cannot be calculated in this example, because we were not able to identify a “true negative”, a record that was excluded from Weed maps and also absent in the BCC directory. In fact, not all validity statistics were applicable to a combination of a gold standard and a test with the current study design . Following tobacco outlet research , we considered validity statistics 0-0.2 to be poor, 0.21-0.4 to be fair, 0.41-0.6 to be moderate, 0.61-0.8 to be good, and 0.81-1.0 to be very good. R Version 3.5.3 was used to calculate 95% confidence intervals for all the validity statistics. We computed overall statistics as well as the statistics by dispensary category and county population size . Locations of call-verified active brick-and-mortar dispensaries in California were mapped with ArcGIS Version 10.5.

A total of 2,121 business records were combined from BCC and the three online crowd sourcing platforms after online data cleaning. BCC, Weed maps, Leafly, and Yelp had 630, 811, 535, and 1,468 records included in the combined database, respectively. The overlaps across the data sources were presented in Figure S1. Only 240 records were present in all four data sources. Following call verification, the 2,121 records were reduced to 826, which were confirmed to be active brick-and-mortar dispensaries. Among the 1,295 records removed during call verification, 56.0% were closed, 4.2% were not open yet, 38.0% were not selling marijuana, and 1.8% had no storefronts . BCC, Weed maps, Leafly, and Yelp had 486, 659, 459, and 471 records included in these 826 verified dispensaries, respectively. The overlaps across the data sources were presented in Figure S2. The 826 records included 77 recreational-only, 65 medical-only, and 684 recreational & medical dispensaries.Table 1 reports validity statistics using the BCC licensing directory as the gold standard. When the test was whether being present on each online crowd sourcing platform after online data cleaning, Leafly had good sensitivity and Weed maps and Yelp had moderate sensitivity . It indicated that 70% of the BCC licensing directory could be found on Leafly. Leafly also had very good PPV , yet Yelp’s PPV was only fair . It indicated that 83% of Leafly records were included in the BCC licensing directory. When the test was whether passing call verification, Leafly still had the highest sensitivity and PPV , and Yelp had the highest specificity and NPV . It indicated that, call-verified Leafly records performed the best for identifying truly licensed dispensaries and call-verified Yelp records performed the best for identifying truly unlicensed dispensaries in this scenario. Table 2 reports validity statistics using the call-verified, combined database as the gold standard. When the test was whether being present in each data source after online data cleaning, Weed maps had the highest sensitivity and BCC, Leafly, and Yelp all had moderate level of sensitivity ranging from .56 to .59. It indicated that 80% of the call-verified, combined database of active dispensaries could be found on Weed maps. Leafly and Weed maps had very good PPV , and Yelp’s PPV was only fair . It indicated that 86% of Leafly records were included in the call-verified, combined database of active dispensaries. When the test was whether passing call verification, sensitivity statistics remained the same as when the test was whether being present in each data source. This was because call-verified businesses in each data source were a subset of the businesses included in each data source before call verification, such that the numerators and denominators for sensitivity calculation remained the same. Yelp had the highest NPV and Leafly had the lowest NPV . It indicated that call-verified Yelp records performed the best for identifying truly not active brick-and-mortar dispensaries.Table 3 reports the agreement between BCC, online crowd sourcing platforms, and call verification in terms of the category of the 630 licensed dispensaries.

Approximately 25% of the licensed dispensaries on Weed maps and 29% of the licensed dispensaries on Leafly posted their category that disagreed with what was approved in the BCC license. Approximately 12% of the call-verified, licensed dispensaries stated their category in call verification that disagreed with what was approved in the BCC license. Most of the businesses that stated an unapproved category on online crowd sourcing platforms and/or in call verification claimed themselves to be recreational & medical when they were only licensed for recreational-only or medical-only. Table S3 quantifies category-specific validity statistics when the gold standard was whether being present in the BCC licensing directory. Leafly had the highest sensitivity in recreational-only and recreational & medical categories and Weed maps had the highest sensitivity in medical-only category,cannabis indoor greenhouse regardless of the definition of a test. Table S4 quantifies category-specific validity statistics when the gold standard was whether being present in the call verified, combined database. When the test was whether being present in each data source after online data cleaning, Weed maps had the highest sensitivity in identifying recreational-only and medical-only dispensaries, yet BCC had the highest sensitivity in identifying recreational & medical dispensaries. When the test was whether passing call verification, Weed maps overall had the highest sensitivity in all three categories. In 2019, California had 16 counties with a population size above one million and 42 counties with a population size below one million. Table S5 reports validity statistics by county population size when the gold standard was whether being present in the BCC licensing directory. Leafly had the highest sensitivity regardless of test definition and county population size. Table S6 reports validity statistics by county population size when the gold standard was whether being present in the call-verified, combined database. Regardless of test definition, Weed maps had the highest sensitivity in more populated counties and BCC had the highest sensitivity in less populated counties. This study is the first to assess the validity of secondary data sources in identifying brick and-mortar marijuana dispensaries across a large state. We reported the validity of online crowd sourcing platforms in enumerating licensed dispensaries and the validity of state licensing directory and online crowd sourcing platforms in enumerating active dispensaries. Regarding the validity of using online crowd sourcing platforms in identifying the BCC licensing directory, all three online crowd sourcing platforms were able to include over 50% records in the BCC directory, with Leafly containing the largest number of licensed dispensaries . These findings suggested that the online crowd sourcing platforms could serve as a reasonable proxy for the licensing directory. It evidences the validity for many existing and future studies to utilize online crowd sourcing platforms for dispensary identification, especially if a licensing system is not open to the public or is updated infrequently.

It should be noted, however, that the dispensary category registered in the BCC directory may be mismatched with the “de facto” category in which dispensaries operated. Over 25% licensed dispensaries on online crowd sourcing platforms posted their category that disagreed with the BCC license and over 10% call-verified, licensed dispensaries stated their category in call verification that disagreed with the BCC license. Particularly, most of such dispensaries claimed themselves to be recreational & medical while they were only licensed for recreational only or medical only. Such disagreement might be intentionally used as a means of attracting customers or be reflective of how dispensaries operate in practice. Regarding the validity of using the state licensing directory in identifying active brick and-mortar dispensaries, over 20% licensed dispensaries did not pass call verification. This indicated that business licenses may not accurately represent businesses’ operation status in reality. For instance, a business may have been closed before its license is expired and a business may not be open yet even though its license has been approved. In the final 826 call-verified dispensaries, 58.8% were included in the BCC licensing directory. This indicated that the BCC directory failed to capture unlicensed dispensaries, which accounted for over 40% of the total active dispensaries in California. Solely relying on a state licensing directory would overestimate active, licensed dispensaries whereby overlook active, unlicensed dispensaries. Regarding the validity of using online crowd sourcing platforms in identifying active brick-and-mortar dispensaries, Weed maps had a nearly very good sensitivity; it contributed 80% of the records in the final call-verified, combined database. It had the highest sensitivity in identifying recreational-only and medical-only dispensaries. It was also the most sensitive database in identifying dispensaries in more populated counties, which were mostly urban areas. The high concentration of dispensaries and intense competition in urban areas may motivate more businesses to promote themselves on this highly visible and popular platform . Leafly had the lowest sensitivity in identifying active dispensaries. It also had the lowest sensitivity in identifying all three dispensary categories. It is likely because the costs of advertising on Leafly were substantially higher than other online crowd sourcing platforms specialized in marijuana .

Two rats were excluded during the acquisition phase because of the failure of catheter patiency. In the course of the escalation phase, three rats were excluded from the study because of the failure of catheter patiency at the end of the study, and one rat in the vehicle group died unexpectedly during the day of from cocaine self-administration before the study was completed, thus leaving n=6 rats/group for the final analysis. Te exclusion of those data did not affect the results of the statistical analysis.Te present study found that adolescent WIN exposure increased irritability-like behavior in adolescence, which persisted into adulthood, induced cross-sensitization to the locomotor-stimulating effect of cocaine in adolescence, which did not persist into adulthood, decreased the speed of acquisition but not the rate of cocaine self-administration in adulthood, and had no effect on the escalation of cocaine self-administration in adulthood. Overall, these results demonstrate that although cannabinoid exposure in adolescence induces irritability-like behavior and cross-sensitization to the psychostimulant effect of cocaine during adolescence, it does not promote cocaine self-administration once the animals reach adulthood. However, the effect of adolescent WIN exposure on cocaine self-administration in adolescence was not investigated in the present study because the animals reached adulthood by the time they had recovered from the surgeries that were required for self-administration. Reductions of both body weight and food intake were observed during WIN treatment. Although the activation of cannabinoid receptors typically produces an increase in food intake in adulthood,hydroponic drain table accumulating evidence suggests that adolescent exposure to THC or WIN in rats decreases food intake and body weight.

Te increase in water intake during WIN exposure in the present study confirms the role of cannabinoid receptors in homeostatic responses that regulate not only energy homeostasis but also fluid balance. Irritability, anxiety, and dysphoria are key negative emotional states that characterize the withdrawal syndrome in humans, which arises when access to the drug is prevented and contributes to drug relapse. Irritability has also been reported to be greater in adolescents at higher risk for substance use. Irritability-like behavior has also been shown to increase during withdrawal from alcohol and nicotine in rodents. However, to our knowledge, whether early exposure to cannabinoids affects irritability-like behavior has not been studied in animal models. In the present study, we found that WIN exposure induced irritability-like behavior in adolescence and adulthood, suggesting that cannabinoid exposure in adolescence induces long-lasting neurobehavioral adaptations that can persist months after WIN exposure. However, further studies are needed to investigate whether this finding has translational relevance. An alternative explanation is that, despite blind randomization of the subjects to the two groups, the increase in irritability-like behavior that was observed in WIN-treated rats may be attributable to preexisting differences in irritability-like behavior. Further studies are needed to investigate whether this finding has translational relevance. Numerous human studies demonstrate that early cannabis use is associated with greater vulnerability to the later development of drug addiction and psychiatric illness. A recent study reported a pivotal role for cannabinoid receptors as molecular mediators of adolescent behavior and suggested that cannabinoid receptors may be important in adolescent-onset mental health disorders. Chronic adolescent exposure to WIN has also been shown to induce anxiety-like behavior in rats. However, contradictory findings have also been published, with either no change or even a decrease in anxiety-like behavior after cannabinoid exposure in adolescence.Interestingly, a previous study also demonstrated that long-term cognitive and behavioral dysfunction that was induced by adolescent THC exposure could be prevented by concurrent cannabidiol treatment.

Importantly, WIN acts as a full cannabinoid receptor agonist, in contrast to THC, which only acts as a partial agonist. Moreover, cannabis is known to consist of dozens of additional phytocannabinoids apart from THC. Furthermore, different strains of cannabis differ in their THC content, and THC levels in cannabis have increased year after year because of consumer demand, thus making direct comparisons of human data across time and across studies difficult. Nevertheless, we chose this model of early cannabinoid exposure and followed it precisely because it has been shown to induce cocaine cross-sensitization, thus supporting the gateway hypothesis. Further studies are needed to investigate whether the long-term irritability-like behavior that was observed in the present study can be prevented by concurrent cannabidiol treatment or whether adolescent exposure to cannabis smoke induces long-lasting irritability-like behavior in rats. Epidemiological data consistently document that cannabis exposure precedes the use of other illicit drugs. However, epidemiological data cannot provide causal evidence of this sequence. Animal models are particularly useful for studying effects that are related to cross-sensitization because they allow sequential administrations of the studied drugs while controlling for confounding variables. Several studies have reported behavioral cross-sensitization between cannabinoids and stimulants in rodents. WIN treatment during adolescence in rats induces long-lasting cross-tolerance to morphine, cocaine, and amphetamine, potentiates amphetamine-induced psychomotor sensitization, and induces cocaine-induced psychomotor sensitization in adolescence. WIN exposure also leads to increases in methylenedioxymethamphetamine-induced and cocaine-induced conditioned place preference.

In the present study, WIN exposure in adolescence induced cross-sensitization to the stimulatory effect of cocaine in adolescence. However, this effect was no longer present in adulthood when the rats had self-administered cocaine for several weeks, suggesting that cannabinoid exposure in adolescence may increase the psychomotor effects of cocaine during the first exposure to cocaine, but this effect is not necessarily long-lasting. Cannabinoid exposure increased irritability-like behavior and the psychomotor effects of cocaine, but it did not promote the acquisition or escalation of cocaine self-administration. Indeed, we observed the slower acquisition of cocaine self-administration with 1-h short-access to cocaine in male rats with prior exposure to WIN compared with controls. In contrast, a previous study reported a trend toward an increase in cocaine self-administration during the short acquisition phase in female rats with prior exposure to the cannabinoid receptor agonist CP55,940 but not in male rats. However, this study did not discriminate between inactive and active levers, and no difference in cocaine self-administration was observed during the 14-day maintenance phase in either sex. A recent study showed that adolescent WIN exposure caused impairments in an attentional set-shifting task, a measure of cognitive fexibility, in adulthood. An alternative hypothesis is that the slower acquisition of cocaine self-administration in adulthood that was observed in the present study may be attributable to cognitive impairment that slows the acquisition of operant responding. In humans, several studies have indicated that the adolescent use of cannabis can lead to long-term cognitive deficits, including problems with attention and memory. During escalation, no differences were observed between the rats that were exposed to vehicle in adolescence and the rats that were exposed to WIN in adolescence. This suggests that if cognitive impairments affected the initial acquisition of self-administration, then they did not produce long-term deficits. Te model of long-access to cocaine self-administration is one of the most validated animal models of cocaine use disorder and drug addiction in general. This model has been shown to result in all seven of the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition , and seven of the 11 DSM-5 criteria, including most of the criteria that are required for severe use disorder: tolerance, withdrawal, substance taken in larger amount than intended, unsuccessful efforts to quit, considerable time spent to obtain the drug, important social, work,rolling benches hydroponics or recreational activities given up because of use, and continued use despite adverse consequences.

Te present study found no effect of adolescent cannabinoid exposure in the escalation model, suggesting that adolescent WIN exposure may not facilitate the acquisition, maintenance, or escalation of cocaine use in adulthood. An alternative hypothesis is that the effect of cannabinoid use may not be observed on cocaine intake per se; instead, cannabinoid exposure may produce an increase in the motivation for cocaine, leading to an increase in compulsive cocaine seeking. Indeed, prior exposure to another potential gateway drug, alcohol, was found to have no effect on subsequent cocaine self-administration per se but produced greater motivation and compulsive-like cocaine seeking under a PR schedule of reinforcement. However, we observed no differences between the WIN-exposed and control groups in adulthood when we used a PR schedule of reinforcement to examine whether rats with prior exposure to WIN express alterations of the motivation to self-administer cocaine.One limitation of long-term behavioral studies in adolescent rats, including the present study, is that puberty in rats is relatively short. Compared with adults, rats that are allowed to self-administer cocaine during adolescence have been shown to be more vulnerable to cocaine addiction. Unfortunately, in the model of cannabinoid exposure during adolescence , cocaine self-administration can only be studied starting in late adolescence and continuing into adulthood because rats exit puberty by PND60. Because of this limitation, one possibility is that cannabinoid exposure during adolescence may affect cocaine intake in adolescence. Te present results demonstrate that chronic exposure to cannabinoids does not facilitate the acquisition of cocaine self-administration or compulsive-like cocaine intake in adulthood, measured by the escalation of cocaine self-administration and PR responding in a relevant model of cocaine use disorder. These results suggest that cannabinoid exposure per se is unlikely to be causally responsible for the association between prior cannabis use and future cocaine use in adulthood as purported by the gateway hypothesis. However, we found that cannabinoid exposure produced long-lasting increases in irritability-like behavior, which may indirectly facilitate the emergence of social conflicts and other mental disorders that may contribute to the abuse of drugs other than cocaine. Additionally, the cross-sensitization between WIN and cocaine in adolescence—which was not observed in adulthood—may highlight a short-term increase in the vulnerability to cocaine-induced behaviors. In summary, the present results showed that cannabinoid exposure during adolescence in rats produced cross-sensitization to cocaine in adolescence and a long-lasting increase in irritability-like behavior in adulthood.

However, it did not facilitate the acquisition or escalation of cocaine self-administration or compulsive-like responding for cocaine in adulthood.SUD is a chronic, relapsing disease. CM is a behavioral treatment based on operant conditioning principles that involves providing incentives for meeting specified goals or engaging in target behaviors. CM related to SUD treatment generally involves giving patients tangible rewards such as prizes, cash, or vouchers to reinforce goal behaviors, such as abstinence, medication adherence, or greater/continued engagement with treatment. SUD services such as counseling are already a Medi-Cal covered benefit. CM is often intended as a way to improve the outcomes of these services. CM is not a benefit that directly covers a health care screening, treatment, service, or item. Rather it is an incentive, analogous to, for example, incentive payments for members participating in wellness programs to encourage healthy behaviors. The total cash value a patient could receive through CM ranges widely, with a mean of $914.46 and a median of $466 earned. CHBRP has assumed that CM for SUD treatment programs would be allowed for Medi-Cal beneficiaries .Treatments for SUD include residential, inpatient, and outpatient care using behavioral therapy, counseling, and/or prescription medication. Mutual help groups also support those with SUD to achieve and maintain sobriety. CM can be used as an adjunct to psychosocial treatments for SUD or as a standalone behavioral treatment. Descriptions of treatments for stimulant and cannabis use disorder follow. Stimulants are a class of drugs that includes prescription medications to treat ADHD as well as illicit drugs such as cocaine and methamphetamine. Repeated misuse of stimulants can lead to psychological consequences, such as hostility, paranoia, psychosis, as well as physical consequences of high body temperatures, irregular heartbeats, and the potential for cardiovascular failure or seizures. In California, it is estimated that 33% of all admissions to state- and county-contracted SUD programs are for stimulant use disorders – representing nearly 50,000 admissions annually. It is estimated that there are approximately 3,035 deaths from stimulant use disorder in California each year. Cannabis, also known as marijuana, is the most commonly used psychoactive drug in the United States, after alcohol. Acute effects of cannabis use include nausea, vomiting, and abdominal pain, while chronic impacts include cognitive impairment, pulmonary disease, and sleep disturbance.

Gonzalez et al. found no differences on the BART in a sample of young adult marijuana users versus non-using controls; however, Gonzales et al. allowed for recent marijuana use , with a median of three days since past use. Because previous studies of young marijuana users allowed for recent use, the effects of residual marijuana levels may have affected task performance. In the current study, we examined risk-taking via the BART in late adolescent marijuana users with at least two weeks of abstinence from marijuana, in comparison to non-using controls. This approach considers how marijuana users function relative to their non-using peers and reduces possible residual effects from recent substance use. We hypothesized that participants reporting greater substance use would demonstrate riskier BART performance. Further, previous studies have not yet examined the relationship of risk-taking to executive functioning in adolescent marijuana users. Executive function is a complex collection of abilities primarily modulated by the prefrontal cortex.Completing the BART has also been linked to increased prefrontal cortex activation in healthy controls , and a recent meta-analysis of neuro imaging studies suggested that individuals with substance use disorders may have altered risk processing compared to healthy controls, primarily in ventromedial prefrontal cortex, orbitofrontal cortex, striatum, and other areas involved in risk and decision-making . Given the involvement of the prefrontal cortex in both risk-taking and executive functioning, we examined whether elevated risk-taking, as measured by the BART, was associated with poorer executive functioning,cannabis grow facility layout as measured by traditional neuropsychological tests. We hypothesized that a riskier approach to the BART would be associated with poorer performance on executive function tests.Participants were part of a longitudinal study of marijuana’s effects on neurocognition during adolescence and young adulthood, with assessments at intake and at 18- and 36-month follow-ups . Adolescents were recruited from local high schools.

Teens and their parents/guardians were screened for demographics, psychosocial functioning, and family history of Diagnostic and Statistical Manual for Mental Disorders, 4th Ed. , 2000 substance use and other Axis I disorders. Confidentiality was ensured within legal limits to encourage full disclosure. Prior to participation, written informed assent and consent were obtained in accordance with the University of California, San Diego Human Research Protections Program. At study intake, exclusionary criteria included history of psychiatric disorder other than substance use disorder, serious medical problem or head trauma, premature birth, prenatal drug or alcohol exposure, and substance use during monitored abstinence. Intake classification criteria for the marijuana-user group included >60 lifetime marijuana experiences; past month marijuana use; <100 lifetime uses of drugs other than marijuana, alcohol, or nicotine; and not meeting Cahalan criteria for heavy drinking status . To produce an adequate sample size, controls were included if they had <5 lifetime experiences with marijuana , no previous use of any other drug except nicotine or alcohol, and did not meet criteria for heavy drinking status. The current data were collected at the 18-month follow-up, when participants were aged 17–20 years. A total of 48 marijuana users and 52 controls completed the BART task at the 18-month follow-up; however, 24 marijuana users and 18 controls were excluded from analyses based on the following abstinence requirements: at least two weeks since last use of marijuana, other drugs, or alcohol binge ; and at least three days since last use of any alcohol or psychiatric medications . Beyond the abstinence requirements, follow-up controls were further excluded for meeting abuse or dependence criteria for alcohol or any other substance . One participant in the baseline marijuana group had no marijuana uses in the previous 18 months and was also excluded, and one additional control was excluded due to meeting DSM-IV criteria for current post-traumatic stress disorder. Following these exclusions, the resulting sample of 58 demographically matched adolescents and young adults included 24 marijuana users and 34 non-using controls. At the 18-month follow-up, marijuana users were about seven months older , and as expected, reported higher levels of marijuana, alcohol, and other drug use than controls. marijuana users had 200+ lifetime marijuana use episodes and <130 lifetime experiences with other drugs.

In addition, 10 marijuana users met DSM-IV criteria for marijuana abuse and seven for marijuana dependence , 10 met criteria for alcohol abuse, and two met criteria for other drug abuse. At the 18-month follow-up, the 34 controls had ≤15 lifetime experiences with marijuana, minimal to no previous other drug use except nicotine or alcohol .Participants were administered the Customary Drinking and Drug Use Record to evaluate their lifetime, past three-month, and past 18-month use of nicotine, alcohol, marijuana, stimulants , hallucinogens, inhalants, opiates , dissociatives , sedatives , and abuse of over-the-counter or prescription medications. Teens were also assessed for alcohol and drug withdrawal symptoms, related life problems, and DSM-IV abuse and dependence criteria . The Timeline Follow back facilitated recall of substance use over the past 28 days through a calendar layout.The BART is a computer-based risk-taking assessment . Participants used the space bar to pump 30 simulated balloons one at a time to achieve the highest possible score. Balloons pop at an unpredictable rate , and a noise follows each response . The points earned for a balloon are lost if it pops, but temporary points can be saved by choosing “Save Points.” Participants weigh the increasing risk of popping each balloon against the potential gain of continuing to pump the balloon . The primary outcome measures were the mean number of pumps for balloons that did not pop and the total number of popped balloons during the session. High values on either variable suggest greater risk taking. The number of points earned on any balloon and the total points saved are not revealed to the participant – only whether they had earned a small, medium, big, or bonus prize depending on the amount of points saved. They were shown the possible candy rewards prior to starting the task and received the reward immediately upon completion of the task. Participants had no practice trials to assess risk, and each participant completed the same task . This measure has good test-retest reliability .Participants were abstinent from marijuana, other drugs, and alcohol binge for at least two weeks prior to the assessment, verified with biweekly breathalyzer tests and urine screens including at the neuropsychological testing session.

The urine screen tested for major substances including amphetamines, barbiturates, benzodiazepines, cocaine metabolites, marijuana metabolites, and opiates. Exclusions for recent substance use are described above in the section on participants. All participants completed questionnaires and the neuropsychological battery. Teens and their parents/guardians received monetary compensation upon study completion.We used Fisher’s Exact Tests to compare categorical variables between groups and analysis of variance to examine group differences on continuous variables. Some alcohol and drug use variables did not meet requirements for parametric analysis; therefore we used the Mann-Whitney procedure to compare these characteristics between groups. Because marijuana users were slightly older than controls, age was controlled in analyses of test performance using univariate analysis of covariance . Effect sizes are presented as partial eta-squared , and interpretations of statistical significance were made if p<0.05. We used Pearson correlations to examine associations between risk-taking, demographic, and neuropsychological variables. As an exploratory analysis, we performed hierarchical multiple regressions to examine whether BART performance predicted past 18-month substance use, as described below. Distributions of substance use variables were examined and appropriately log10 transformed to meet the assumptions of parametric analysis.This study examined risk taking via the BART in late adolescents with or without a history of marijuana use. As hypothesized, participants reporting greater substance use evidenced riskier BART performance. Specifically, marijuana users with at least two weeks of abstinence from marijuana, other drugs,indoor grow shelves or alcohol binge popped more balloons than non-using controls throughout the task, especially in the first 20 balloons. Although speculative, it appears that the marijuana users started the task with a higher level of risk taking. After receiving feedback about their performance , they attempted to modify their approach to avoid popping balloons. The controls may have taken a similar approach, as illustrated in Figure 1; however, the marijuana users remained slightly more “risky” in their approach throughout the test. Notably, the groups did not significantly differ in average adjusted pumps, which is the most commonly used variable for this task. Importantly, Pleskac et al. have suggested that the average adjusted pumps score may be biased and an underestimate of risky responses because it excludes the trials in which the balloon popped, as explained further below. For this reason, the number of popped balloons may be a more sensitive measure of risk-taking. Importantly, the groups were matched on self-reported levels of depressive, anxiety, and internalizing symptoms; marijuana users scored higher on externalizing behavior, as expected.

BART performance was not associated with these self-reported mood and personality characteristics or demographic variables including age. This suggests that group differences in risk taking may be due to marijuana or other substance use, rather than other personal characteristics. Previous studies have found mixed results. Consistent with the current study, some found that alcohol and other substance use was related to riskier BART performance ; however, others did not find group differences between non-using controls and at-risk/ addicted individuals or recently abstinent marijuana users using the BART average adjusted pumps variable . Further, BART performance did not relate to cannabis use disorder symptoms in Gonzalez et al., 2012. Our study is consistent with Meda et al. and Gonzalez et al. with regard to finding no group difference on average adjusted pumps; however, the previous studies did not examine group differences in the number of popped balloons. We also found that having a riskier BART performance significantly predicted a higher number of other drug use episodes in the past 18 months, above and beyond the effects of age. The equation using popped balloons to predict past 18-month marijuana use was also significant, but higher age was a stronger predictor than popped balloons. Having a riskier BART performance did not predict recent alcohol use. In other words, it appears that BART performance was associated with other drug use but not alcohol or marijuana use when also considering age. However, that result did not remain significant when controls were removed from the analysis. The BART may therefore have had relatively low sensitivity for measuring additional risk among regular marijuana users in this sample. Future studies could explore whether BART performance is a useful predictor of additional risk above and beyond alcohol and marijuana use. In addition to elevated BART risk-taking, abstinent marijuana users performed worse than controls on one aspect of executive functioning measured, consistent with previous studies reporting deficient executive skills or abnormal brain activation among marijuana users in this and other samples . Specifically, marijuana users exhibited poorer visuomotor set-shifting relative to non-using controls. This suggests that young, abstinent marijuana users may have a mild weakness in cognitive flexibility in the context of changing task demands. Nevertheless, it is not clear if the average group difference on this task is clinically meaningful, and marijuana users did not differ from controls on other aspects of executive skills including working memory, verbal fluency, and planning. Although not correlated with putative measures of executive function, riskier BART performance was associated with faster psychomotor sequencing speed. It is possible that a faster rate of responding may produce more popped balloons, or as speculation, risky behavior without adequate forethought may result in losses. This may concur with Solowij et al. who reported that marijuana using adolescents demonstrated “reflection impulsivity,” having faster response times even when uncertain and making more errors. Vigil-Colet also found that BART performance was most strongly related to “functional impulsivity,” a style in which decisions are made quickly and impulsively under certain beneficial circumstances. On the other hand, Meda et al. used principal components analysis to show that risk-taking may be distinct from other measures of the multidimensional construct of impulsivity . Therefore the relationship between a faster processing speed, impulsivity, and risk-taking is not entirely clear and warrants additional study. Overall, the BART appears to measure distinct aspects of risk-taking that have been associated with real-world behavior , suggesting it is a useful tool for assessing risk-taking in adolescents and young adults. Since the BART was not correlated with established tests of executive functioning, this suggests that it is measuring a behavior distinct from executive function, or at least distinct from the present tests of executive functions.

To make these cost calculations we accounted for inventory that first fails testing, but then is remediated.In addition, to understand the opportunity cost of cannabis used in the tests or lost in the process, we use data from wholesale prices and a survey of retail cannabis prices conducted by the University of California Agricultural Issues Center.Based on this information, we developed a cost per unit of cannabis tested for representative labs of three different sizes to approximate the distribution of costs in the industry.For simplicity, we assumed that testing labs of different sizes use the same inputs, but in different proportions, to provide testing services.We assume economies of scale with higher share of capital costs per unit of output for the smaller labs.We used information reported by the Bureau of Cannabis Control in the first half of 2018 to compile a list of cannabis licensed testing laboratories and distributors in California.We used information on the geographic location of testing labs relative to cannabis production and consumption to assess the cost of transporting samples from distributors to testing labs.In March 2019, there were 49 active testing licensees and 1,213 licensed distributors.Both testing licensees and distributors are located in many areas across the state, but they are concentrated in traditional cannabis production areas in the North Coast region of California and in large population centers.Table 5 shows capacities, annualized capital costs,indoor growers and other annual expenses for three size categories of testing labs: small, medium and large.The size categories are based on the number of samples analyzed annually and were chosen to represent typical firms, based on our discussions with the industry.We assume about 25% of labs are small, 25% are large and the remaining half are in the medium category.By regulation, these labs test only cannabis.

The annualized cost of specific testing equipment and other general laboratory equipment is a significant share of total annual costs.The cost of equipment and installation is about $1.5 million fora small lab, about $2.4 million for a medium lab and about $3.8 for a large lab.These costs are expressed as annual flows in table 5.Our survey and discussions with laboratories provide the rest of the estimated costs.Equipment maintenance costs, rent, utilities and labor also are large cost categories.Each of these costs is less than proportional to the number of samples tested and thus contributes to economies of scale.This cost of consumable supplies is calculated on a per sample basis and thus is proportional to the number of samples tested.Finally, the return to risk and profit is estimated as 15% of the sum of the foregoing expenditures.Our estimated total annual costs are about $1.6 million for small labs, $3.3 million for medium labs and $7.0 million for large labs.The scale advantage of larger testing labs is reflected in the testing cost per sample: $324 for large labs, compared with $562 for medium labs and $750 for small labs.These cost differences arise from economies in scale in the use of laboratory space, equipment and labor.Each large testing lab processes about 10 times the number of samples as a small lab but has annualized operating costs only about five times those of a typical small testing lab.That means that small-scale labs tend to specialize in servicing more remote cultivators or manufacturers that have products handled by smaller and more remote distributors located at a cost-prohibitive distance from large labs.We used data on the annual testing capacities of small, medium and large labs and our assumption about the number labs of each size to calculate the share of testing done by labs of each size category.We expect that small labs will test about 6% of all legal cannabis in the state by volume, medium-sized labs will test about 33% of legal cannabis, and large labs will test 61% of legal cannabis.Using these shares, the weighted average cost per sample tested is about $428.Let us now turn from the cost per batch tested to the cost per pound of cannabis marketed.

The per pound costs of laboratory testing depends on the number of pounds tested in each test.Therefore, we must consider batch size.We expect that the batch size will differ within this constraint depending on the product type and origin and size of the cultivator and manufacturer and explore implications of batch size differences.Using the weighted average cost per sample of $428, the testing cost for a small batch of 5 pounds is $85.60, while for the largest-allowed batch size of 50 pounds, the cost is just $8.56 per pound.Next, we turn to several costs not included in the cost of testing a sample in the lab.First and most straight forward is the cost of compliance with security measured including video surveillance and archival, disposal and quarantine, and other compliance costs that we estimated were equivalent to $4.88 per pound for small labs, $4.06 per pound for large labs and $3.25 per pound for large labs for a weighted average of $3.62 per pound.The cost of testing requirements on a retail cost basis is best expressed as the full cost per unit of cannabis that reaches the market.Expressing the full cost in this way raises two additional costs.The first is simple: the value of the cannabis used up in the testing procedure.Based on MAUCRSA, the sample size must be at least 0.35% of the total batch of cannabis tested.We use an average wholesale value of $1,360 per pound of cannabis flower equivalent at the testing stage, which represents a recent weighted average price across outdoor grown, greenhouse grown and indoor grown cannabis and products.Thus, for each pound of cannabis tested, flower worth $4.76 is used up.The second issue, costs associated with a failure to pass the test, is more complex.These costs include the cost of the testing process as well as the cost of the cannabis that must be destroyed when it is considered unacceptable to be marketed by virtue of a failed test.Stringent maximums for pesticides, microbials and other contamination mean that there will be a significant chance that a sample is rejected.In some cases, the owner will attempt to remediate or process that batch, intending to eliminate the cause of the non-passing the test.A batch can be remediated up to two times.If a batch fails its testing after its second remediation, regulations mandate that that batch must be destroyed in a verifiable way.This is a major cost of the testing regime required by California legislation and regulation.To estimate the cost of such rejections, we used a range of potential rejection rates, drawing from information that was available on contamination of cannabis in other states.However, the experience of other states is of limited value and must be adjusted based on information from industry sources.Washington state mandates tests on potency, moisture, foreign matter, microbiological and mycotoxin screening, residual solvent and heavy metals, but, unlike in California, testing on pesticide residues is not mandatory.Washington state enforcement is based on spot checks.Based on Washington state data, we found that in 2017, the second year after the testing began, 8% of the total samples submitted failed one or more tests.

Colorado state mandates tests on residual solvents, microbial, mycotoxins, heavy metals, pesticides and potency.The Colorado Marijuana Enforcement Division reported that during the first six months of 2018, 8.9% of total samples of adult-use cannabis failed testing.Testing on pesticide residues only became mandatory in August of 2018 in Colorado, so systematic data on test results were not available.However, the Department of Agriculture in Colorado informed us that 60% of spot-checks based on complaints or concerns between 2015 and 2017 found pesticide residues.Given the cost of cannabis that must be destroyed in case of failed tests, cultivators and manufacturers may pre-test to decrease the chances of failing official tests.For our cost analysis, we assume that 25% of cannabis is pre-tested before being submitted for the formal and binding tests.To express costs in terms of the pounds of cannabis legally marketed, and account for pretesting and pounds lost because of testing, we need to express the ratio of pounds tested to pounds that pass testing.The costs of establishing and operating a cannabis testing facility that meets California’s mandates are largely accounted for by investment in precise equipment, the cost of highly skilled labor and costs of materials.Testing is expensive, but the lost value of cannabis that fails tests to enter the legal retail market is an even bigger issue.It is difficult to predict rejection rates with great confidence; the data we present, however,vertical hydroponic system is consistent with reports of pesticide detection in California food crops and information available from other states.Evidence suggests that major drivers of both direct laboratory costs and lost cannabis costs are low or zero tolerance levels set for pesticides and the difficulty of dealing with microbial contamination.We have shown that if these low tolerance levels were applied to other California food crops, a significant proportion would have failed tests in recent years.Thus California’s safety standards for cannabis are tight compared to other states’ standards and to standards for other products within California.We note that there may be safety reasons that cannabis is subject to such tight tolerance levels, but they are not in the literature and are beyond the scope of this article.California’s system for testing cannabis has been under pressure since the implementation of the state’s testing regime in July 2018 because of difficulties in supplying the market with product that has passed the tests and has been labeled correctly.Some producers, after receiving inconsistent test results for contaminant residues from different laboratories, have voluntarily recalled product.However, California has not yet reported detailed data on official test rejection rates.Costs of testing will be reflected in the price of marketed legal cannabis.Thus it is crucial to understand the value that testing creates for consumers compared to the costs.Competition between legal cannabis and untested illegal cannabis is a major issue in cannabis policy.Rules that help ensure safe and high-quality products for consumers of legal cannabis can encourage some consumers to shift from the illegal supply chain to the legal, licensed supply chain.Before the passage of AUMA in 2016, the low prevalence of testing in California’s essentially unregulated market for medicinal cannabis indicated that many consumers entertained a limited willingness to pay for higher safety standards.

This suggests that at least some consumers may remain today in the illegal, low-priced market, even though certified, tested products are available in the licensed supply chain.Taxes and regulations will make legal cannabis more expensive than illegal cannabis.However, safety testing is the basis of product differentiation for legal cannabis sold through licensed retailers.In some agricultural product industries, growers have urged product safety and consistency standards, as well as more stringent testing standards, to increase demand.As the regulated cannabis market develops, we expect that increased access to data will help clarify the impact on demand of mandatory testing rules.PM2.5 concentrations were measured continuously, using two, co-located laser photometers , placed 80-100 cm above the floor, for five weeks in 2019.Room occupancy was not monitored.In week 1, instruments were located 30-122 cm from the sources.During week 2 and weeks 3-5, they were 6-9 and 2- 4 meters from the nearest sources, respectively.Photometers were operated with impactors to exclude particles over 2.5 µm in diameter.The photometers were zeroed once a day and calibrated gravimetrically using a controlled cigarette smoke generation system before and after each experiment.Gravimetric data from 20 cigarette smoke experiments, when plotted against the matching photometric data and forced through zero, yielded a calibration factor of 0.31 , which was was applied to the dispensary photometric data.Cannabis PM2.5 samples were also collected in the dispensary on filters for one week , and a preliminary photometer calibration factor was calculated as above.PM2.5 concentrations in outdoor air were estimated using data from an US EPA monitoring station located 2.5 km from the dispensary in an area with similar ambient pollution sources.The retail and consumption space was a single room of approximately 400 m3.Cannabis consumption occurred at three tables in one corner of the room, with sales counters located in the opposite corner.The room was served by building HVAC and by four window air conditioners that did not admit fresh air.The air conditioners had dust filters and we were unable to examine filtration in the building HVAC system.

The soil that remained adhered to the roots after removal from the ground was used to produce the rhizosphere soil samples.The rhizosphere soil was removed from the roots by shaking the root into a whirlpak bag.All samples were immediately transferred to storage at 4uC for shipping back to the laboratory for processing.All root samples were rinsed with alcohol and sterile water before the extraction.DNA was isolated from 0.25 g of soil or root per extraction using standard protocol for PowerSoil DNA Isolation Kit , with the modification of heating the extraction at 65uC for 10 minutes prior to the initial vortex step.The soil physicochemical data was generated by Fruit Growers Laboratory , including total carbon and nitrogen concentrations, pH, salinity, and water content for all samples.Endorhiza, rhizosphere, and bulk soil samples for the second experiment were taken from 6 organically-grown Cannabis plants of two different strains from two locations in August, 2012: Vista and Orange County, California.Triplicate samples were taken from each of the six plants and surrounding rhizosphere , as well as from each of the two bulk soils used in the different locations , totaling 42 samples.In contrast to the first experiment, all samples were taken two weeks prior to harvest.Additionally, triplicate samples from the second experiment were taken from different roots on the same plant.Cannabinoid data was taken from the buds of three White Widow plants and one Mauie Wowie plant.All cannabinoid data was processed at Delta-9-Technologies, LLC.Otherwise, sampling procedure matched the first experiment.Recent literature has suggested a two-step selection model for the endorhiza, where bulk-soil microbial communities are filtered by increased concentration of rhizodeposits, followed by convergent host genotype-dependent selection on endophytic communities.Results from both experiments support many of the expectations produced by this model.Most importantly,dry rack cannabis the principal coordinate analysis plots for the second experiment demonstrate highly significant clustering patterns.

First, soil type is the main determinant of PC1 for the unweighted analysis of the second experiment, revealing that soil is undoubtedly the most important factor in all samples for determining what microbes are present.Second, communities within both soil types demonstrate a similar community shift from bulk soil to endorhiza samples along PC2 , which is dominated by differentiation between sample types.Specifically, endorhiza samples have high, positive values along PC2, rhizosphere samples have intermediate values, and bulk soil samples have more negative values.Third, Cannabis strain is the main determinant of PC1 for the weighted analysis of all samples in the second experiment, suggesting that convergent host genotype-dependent selection acts through controlling community structure more than composition.PCoA results exhibit how all sample types form significantly differentiated clusters in weighted analyses but that only rhizosphere and endorhiza samples form significantly differentiated clusters in unweighted analyses, suggesting niche-filtering of microbes in rhizosphere and endorhiza samples from bulk soil.Furthermore, there were no significant segregating OTUs based on unweighted analysis between cultivars in endorhiza and rhizosphere samples in the second experiment, however there were 71 when abundance was accounted for.This differs greatly from the 657 OTUs that significantly differ between soil types in the same dataset.Testing of the two-step selection model with pairwise comparisons of shared OTUs between endorhiza and bulk soil samples also validated the hypothesis that a portion of the endophytic microbes are inherited and selected from the surrounding soil, showing significantly more OTU overlap between endorhiza and their own bulk soil compared to endorhiza and foreign bulk soil.Given the results from the second experiment strongly suggesting that Cannabis cultivars have important structuring effects on both rhizosphere and endorhiza samples, it may seem troubling that results from the first experiment do not suggest this for the rhizosphere samples.However, differences in Cellvibrio abundance between experiments show that root decay could have diminished the rhizosphere effect, thus diminishing this potential signal.Sampling for the first experiment was done post-harvest, when plant tissues were undergoing senescence and decay, while samples for the second experiment were taken from actively growing plants.Considering the extensive work demonstrating the importance of plant growth stage on the microbiota, as well as the plant-soil feed backs identified in structuring below ground microbial communities, the differences between the first and second experiments are unsurprising.

The similarities, however, are surprising.In particular, that cultivar-specificity could be identified in the microbiota within the endorhiza samples in the first experiment without any input of cultivar-specific metabolites from the living plant for weeks.Although we have presented several highly significant findings supporting expectations of the two-step selection model, some expectations remain to be validated.Specifically, although the mean beta-diversity distances indicate that rhizosphere and endorhiza samples are closer than bulk soil and endorhiza samples, this difference was not significant and thus provides little evidence for the first differentiation step of the two-step selection model.Future work with the Cannabis micro-biome should focus on elucidating the role of cultivar on rhizosphere, as well as what aspects of host genotype are producing the structure observed across Cannabis strains.Increased testing of cannabinoids and decoupling this variation from edaphic factors will improve our understanding of the importance of cannabinoid production in structuring endorhiza communities.Sampling a time series of endorhiza communities across several plants may help us to understand natural variation in the endorhiza during the reproductive cycles of Cannabis.Understanding this natural variation will help direct future mechanistic studies aimed at using microbial communities to increase plant fitness, suppress disease, or augment desired metabolite production.Legalization of cannabis use is increasingly widespread across the United States, but the ramifications are unknown.In 2016 California approved Proposition 64 legalizing recreational cannabis.Unintentional pediatric ingestion is one possible ramification, as occurred in Colorado after legalization in 2009.Regional poison center cases involving marijuana increased by an average 34% per year from 2009 to 2015 in Colorado.During that time, 34% of cases involved self-reported cases of poor product storage.Children who unintentionally ingest cannabis can present with lethargy, ataxia, tachycardia, mydriasis, and hypotonia, which can lead to preventable emergency department visits, invasive workups, and hospital admissions.Despite the institution of safe storage guidelines in Colorado, a recent study found continuing sub-optimal storage practices in that state.5 This trend was mirrored in the use of medical marijuana, as oncology patients and their caregivers reported sub-optimal storage practices and had received little storage education from healthcare providers.

The 2016 California legislation did not include regulations on the safe storage and disposal of cannabis products, creating a potential for similarly unsafe storage practices.The purpose of this study, which was based on a community presenting to a pediatric ED, was to assess the prevalence of cannabis and how it is stored in the home and, secondly, to assess attitudes regarding use of cannabis and storage education among Californians who live in households with children.We conducted a cross-sectional survey with a goal enrollment of 400 adult visitors in an academic pediatric ED in California from June 8–August 16, 2018.During this time, a convenience sample between the hours of 8 am -10 pm was conducted daily in which all adult visitors were screened for eligibility and subsequently approached.The survey was generated and finalized by the investigators and research assistants based on similar studies found during literature review.The survey contained 42 yes-no or Likert-scale questions regarding cannabis use and storage, and education on cannabis storage.Eligible participants were >18 years old and lived in a household with children <18 years old.Participants were excluded if they did not speak English or Spanish, or if the patient was critically ill.Only one survey was administered per household.All participants were notified that their responses were not shared with law enforcement or their care team, and they completed the survey in the absence of a RA.English-speaking participants filled out the survey electronically and submitted their responses directly into Research Electronic Data Capture.Spanish-speaking participants filled out a Spanish-language survey on paper, which was subsequently placed in a lockbox after which these de-identified surveys were uploaded to REDCap weekly.We used descriptive statistics to analyze data.Subjects who were screened but excluded were not tracked during this study.The UC Davis Institutional Review Board approved this study.A 2017 national survey indicated that as many as 11.5% of California adults reported regular cannabis use.Of adults surveyed, 14.5% reported cannabis use in a home with children.Since the legalization of cannabis in California, there has been a steady rise in prevalence of use,7 likely due to increased availability.In our sample, users perceived cannabis to be significantly safer for both adult use and possession inside a home with children, as compared with non-users.Further study is warranted to investigate how the public perceives the risk of cannabis as more time passes since legalization.Currently, little research exists on cannabis storage in homes with children,roll bench and there is no research that describes sources of storage information.Both users and non-users strongly felt that safe storage was important despite poor compliance with safe storage practices.Our results suggest a lack of educational sources regarding safe storage despite 23 years of medical cannabis use in California.The Public Health Department of Colorado set guidelines including locking, hiding, and using child-resistant packaging, yet California does not currently define safe storage.Providing guidelines at a local or state level may provide a reference for cannabis users as well as healthcare providers.

Based on participant responses, cannabis dispensaries may also serve as another point for the distribution of safe storage information.With the increasing prevalence of cannabis use in California,downstream effects on the pediatric population should be further investigated.Healthcare providers in primary care, pediatrics, and the ED should be prepared to screen and educate families on cannabis use and the importance of safe storage in homes with children.Nonpharmaceutical interventions were developed in response to the 2009 H1N1 pandemic and included a protocol to slow the spread of future novel respiratory influenza A virus pandemics.NPIs are strategies for disease control when no pharmaceutical alternative exists and include actions at the personal, environmental, and community level.Specifically, NPIs put in place during the pandemic included travel restriction, restriction of mass gatherings and recommendations for transition to virtual events, social distancing measures and stay-athome orders, closure of non-essential work spaces and schools, and cloth face covering guidance.3,8 However, it is unclear what effect, both intended and unintended, these policy implications have on populations.It is also unclear what effect the pandemic itself has had and will have on populations.A study from the Centers for Disease Control and Prevention examined differences among stay-at-home orders across US states from March 1st to May 31st, 2020, on population movement.Stay-at-home orders were associated with decreased population movement; however, movement increased significantly as states began lifting restrictions.Kaufman et al.reported the initial effect of state variation in social distancing policies and non-essential business closures on COVID-19 rates.Social distancing and closure of non-essential businesses and public schools were shown to reduce daily COVID-19 cases by 15.4% with effects varying across states.10 Finally, Pan et al.showed that there was heterogeneity in NPI domains across the US census region and concluded that states with the most aggressive policies had the highest mitigation of COVID-19 infection.11 While heterogeneity in intensity and duration of state policies on COVID-19 mitigation were demonstrated, all such studies have been restricted to the initial wave of the pandemic and have only assessed the associations of policies on COVID-19 infection spread at the population level.The COVID-19 pandemic and policy interventions such as social distancing, closure of spaces for gathering, and stay-at-home orders may have had varying economic, health, and social effects across different populations.Of particular concern, are negative implications on lesbian, gay, bisexual, transgender, and queer and other sexual minority populations, an already vulnerable, marginalized, and stigmatized group with even larger disparities among racially marginalized communites.The COVID-19 pandemic has highlighted and exacerbated challenges for the LGBTQ community which include rising food and shelter insecurity, economic fallout, job loss, disruption in health care, elevated risk of domestic and family violence, social isolation, increased anxiety, scapegoating/discrimination/stigma, abuse of state power, and concerns about organizational survival.There is an estimated 16 million LGBTQ adults and youth in the United States of which, 5 million work in jobs that are more likely impacted due to the COVID- 19 pandemic.For instance, 15% work in restaurants, 7.5% in hospitals, 7% in K-12 education, 7% in colleges/universities, and 4% in retail, all industries that have been impacted by the pandemic.Moreover, LGBTQ communities before and during the pandemic were more likely to be unemployed, at increased risk of poverty, have issues affording health care, and experience greater workplace discrimination compared to cisgender and straight people.

Responses by managers was a common theme discussed by workers as managers have the direct ability to ban customers based on inappropriate behavior and were often the first point-of-contact for workers to report an experience of harassment.The risk of continued harassment is exasperated by apathetic management teams who were described as prioritizing customer satisfaction over workers’ safety.Workers also illustrated the unique history of cannabis as a heavily sexualized industry whose legacy continues to permeate the industry today and negatively impact worker’s experience with sexual harassment.According to interviews, workers believed that customers either could not or refused to distinguish legally operating dispensaries from trap shops where women were explicitly used as props to sell product.The lack of distinction encourages the entitlement customers feel towards exploiting cannabis workers without repercussions.Interview results presented a paradox within the cannabis industry in which legalization introduced new protections to workers while simultaneously ushering in the corporate model of the “customer is always right,” which previous studies highlight as a detriment to the ability of workers to protect themselves from threatening customers.Furthermore, regarding research question five, data indicated workers were most interested in sexual harassment-based training for all workers, managers and supervisors as well establishing and publicizing clear policies on harassment.As previously mentioned, insufficient handling of sexual harassment cases by mangers was a common theme among all interviewees, highlighting the need for their additional training.Through interviews,drying cannabis workers were able to explain, in more detail, what they would like to see covered in a sexual harassment training.Likewise, interviews provided the opportunity for workers to explain exactly what policies or guidelines they would like established and publicized in their workplace.

Examples of policies included being able to ask a security guard to walk you home, an anonymous hotline number and permission to ask security guards for assistance in closing a store for the night.Given the unique study population of this project, there are several limitations to consider when interpreting the results.As previously mentioned, the sample of survey respondents was a convenience sample of cannabis employees represented by UFCW Local 770; respondents were not randomly sampled from all cannabis retail employees in Los Angeles County.Likewise, interview participants were also recruited through snowball sampling and were not randomly selected.All workers in the study sample are represented by a labor union and therefore the generalizability of the findings to all workers in the cannabis industry is limited as rank-and-file cannabis members of UFCW represent only a small subset of the cannabis industry.And as labor unions continue to grow their membership among cannabis workers, additional studies should compare the experiences with sexual harassment among unionized and non-unionized workers.The small sample size reduced the power of the study and created challenges for isolating effect sizes between the outcomes of interest and the independent variables, particularly in the multivariate models.With the understanding that sexual harassment is an under reported phenomenon, the analyses were likely impacted by respondents who reported never experiencing sexual harassment as it is possible workers simply did not feel comfortable disclosing such information.A larger sample size of workers across the state or ideally the nation as more states legalize the consumption and commerce of cannabis may also reveal regional differences in the effects of laws and policies that protect workers’ safety.There were also several issues regarding the survey tool.Although the survey was able to gauge frequency of harassment experienced by respondents using a Likert scale, it was not conducive to creating a discrete variable of harassment and consequently incidence rate could not be calculated.For example, the response option “once a month or less” could imply harassment was experienced anywhere from two to 12 times in the last 12 months as it was the second option after reporting experiencing harassment “once” in the past 12 months.

Providing more definitive response options may aid in developing more epidemiologically accurate variables.Questions from the SEQ-DoD were also originally developed to measure sexual harassment as it is experienced by women and studies show it is not as effective for capturing the experiences of men.In order to capture more precisely the phenomena of sexual harassment by all participants, future studies should utilize a more comprehensive survey for sexual harassment.Recall bias was also present in data collection as respondents were asked to remember specific examples of sexual harassment and the frequency at which those experiences occurred in the last 12 months.Finally, without a direct comparison to another workplace or industry applying the same methodology, it is difficult to make definitive statements about prevalence of sexual harassment in cannabis relative to other industries.In 2016, when voters approved Proposition 64, they set the stage for radical change across California’s cannabis landscape.Licensed, regulated cannabis stores would soon throw open their doors.The state’s vast cannabis industry would begin to emerge from illegality, though unlicensed operations would surely persist.UC researchers immediately understood that cannabis legalization would present California with pressing new questions, along numerous dimensions, that could only be answered through rigorous, broad ranging research.How would legalized cannabis cultivation affect the state’s water, wildlife and forests? How might impaired driving, or interconnections between cannabis and tobacco, influence public health? How would tax and regulatory policy affect the rate at which cannabis cultivators abandoned the illegal market? These questions and many more are now the subject of research around the UC system, and multiple campuses are establishing centers dedicated to cannabis research.This article surveys UC’s emerging architecture for cannabis research in the legalization era — and presents a sampling of notable research projects, both completed and ongoing.

The Cannabis Research Center at UC Berkeley is an interdisciplinary program that, bringing together social, physical and natural scientists, evaluates the environmental impacts of cannabis cultivation; investigates the policy-related and regulatory dimensions of cultivation; and directly engages cannabis farmers and cannabis-growing communities.The center, according to Ted Grantham — one of three CRC co-directors and a UC Cooperative Extension assistant specialist affiliated with UC Berkeley’s Department of Environmental Science, Policy, and Management — is “focused on cannabis as an agricultural crop, grown in particular places by particular communities with unique characteristics.” For Grantham and the center’s co-founders, establishing the program was “a chance to develop policy-relevant research at the time of legalization and a time of rapidly shifting cultivation practices.” The center’s co-directors, in addition to Grantham, are Van Butsic — a UCCE assistant specialist affiliated with UC Berkeley’s Department of Environmental Science, Policy, and Management — and Eric Biber, a UC Berkeley professor of law.Other CRC researchers are associated with entities such as the UC Berkeley Department of Integrative Biology, the UC Berkeley Geography Department, the UC Merced Environmental Engineering program and The Nature Conservancy.The center itself is affiliated with the UC Berkeley Social Science Matrix.The CRC formally launched with a public event in January.The center’s ongoing research includes a multifaceted project to assess specific aspects of Northern California’s cannabis farms, including the number and size of non-compliant cultivation sites; the environmental effects of non-compliant sites ; and the challenges to regulatory compliance that cannabis cultivators encounter.According to a grant proposal associated with the research, the project is motivated by an urgent need to understand the environmental threats posed by non-compliant farms and the reasons that some farms successfully navigate state regulations while others fail.The researchers are combining high-resolution satellite images with local and state permitting data to identify permitted and non-permitted cultivation sites.In parallel, the researchers are combining permit specifications with water use models to estimate the effects on stream flows of non-permitted versus permitted cultivation.Additionally, they are determining which factors associated with cannabis cultivation are most closely linked to compliance — whether parcels are large or small, old or new — and, through written grower surveys and in-person interviews, they are seeking to understand what stands in the way of cultivator compliance.Ultimately, the work will yield a policy report outlining ways in which state and local governments can decrease the harm of non-compliant cannabis cultivation while increasing rates of compliance.The research is supported by a grant from the Campbell Foundation, provided through the Resource Legacy Fund.In another example of CRC research focused on cannabis and the environment, last year Butsic, Jennifer Carah and additional co-authors published the results of their work on “agricultural frontiers”.These are places where,ebb flow due to increased profit potential for agricultural activity, land is newly cultivated — frequently resulting in environmental impacts such as forest fragmentation and threats to sensitive species.Such transformations, the authors write, occur when economic circumstances are altered by some new mechanism — such as, in the case of cannabis, a new legal status.The researchers, documenting the emergence of such a frontier, studied cannabis cultivation sites in Humboldt and Mendocino counties from 2012 to 2016.

Using satellite imagery to develop a database of cultivation sites, the researchers correlated site characteristics such as remoteness and erosion potential with the spread of agricultural frontiers.They report that, over the study period, cannabis cultivation sites in the study area nearly doubled in number, with total acreage under cultivation likewise nearly doubling, and that a significant portion of the new cultivation occurred in areas such as sensitive watersheds.They found, for example, that nearly 90% of the areas newly developed for cannabis cultivation had been covered in natural vegetation as late as 2006.The researchers argue that agricultural frontiers can develop “almost anywhere institutions fail to prevent” them — and note that, for 18 years after medicinal cannabis use became legal in California with the 1996 Compassionate Use Act, the state devoted no funds to regulating cannabis cultivation and production.In this issue of California Agriculture, Grantham and four co-authors from the North Coast Regional Water Quality Control Board present the results of their research into cannabis cultivators’ patterns of water use in several Northern California counties.For the research that resulted in “Watering the Emerald Triangle: Irrigation sources used by cannabis cultivators in Northern California” , Grantham and his colleagues analyzed reports submitted to the board by cannabis cultivators.The researchers determined how many cultivators sourced their water from wells, surface water diversions, spring diversions and other sources; how water sourcing behavior changed over the course of a year; and how water use patterns varied according to whether growers operated within the state’s legal cannabis market.The researchers determined that cannabis growers rely on well water to a greater degree than is generally supposed — and that their reliance on well water may increase as more growers join the legal market because of well water’s less restrictive permitting requirements.In separate research, Michael Polson — a postdoctoral researcher in UC Berkeley’s Department of Environmental Science, Policy, and Management — has investigated the environmental dimensions of cannabis from an anthropological perspective.In a paper published earlier this year, Polson shows how cannabis has been identified as an environmental problem that requires public intervention.On the basis of participant observation and more than 70 interviews with subjects across the cannabis spectrum — from park rangers to environmentalists to “criminalized people” — Polson demonstrates how cannabis production has been defined as pollution — “dovetail[ing] with [cannabis] prohibition’s history of marking people and substances as socially polluting.” Polson argues, as he highlights the legacy of cannabis prohibition in environmental debates, that policy making is at its most innovative when it includes a broad range of cultivators and when stigmas are explicitly addressed.Research into the environmental aspects of cannabis is also underway at UC Davis, where Mourad Gabriel is a research associate member in UCD’s School of Veterinary Medicine.In 2018, Gabriel and co-authors, including Robert Poppenga — a professor of molecular bio-sciences at the California Animal Health and Food Safety Lab at UC Davis — published the results of their research on the effects of rodenticides on owls in northwestern California forests.The researchers, working on privately owned timberland in Humboldt and Del Norte counties, investigated the prevalence of anticoagulant rodenticides in areas characterized by illegal cannabis cultivation.Anticoagulant rodenticides, used by some cannabis cultivators to control pests, are known to affect non-target species in urban areas and recently have been shown to affect carnivores in California’s remote forest areas as well.Gabriel and his coauthors undertook to determine whether the northern spotted owl, a threatened species, is exposed to anticoagulant rodenticides in the study area — and also to determine if barred owls, a common species, can be used as a surrogate to determine exposure levels in northern spotted owls.