Cannabis Grow

Although it also conflicts with prior evidence of disparities in nicotine and cannabis product use observed among lesbian and gay  adolescents  and young adults , it does align with two recent studies in which bisexual-identified, but not lesbian- or gay-identified adults, reported higher current use of some tobacco products relative to heterosexual-identified adults . The findings of the current study should be considered in the context of its limitations. One limitation is the relatively low representation of sexual minority  young adults in our sample. While the percentage of sexual minority participants in the current report is comparable to other studies of sexual orientation and young adult substance use , a judicious interpretation of our findings is therefore encouraged, particularly for prevalence estimates that had wide confidence intervals. The small sample size also limited our ability to examine sexual identity differences in poly-substance use of nicotine and cannabis products, which we would expect to be high among sexual minority young adults . An additional study limitation relates to our inability to assess all three dimensions of sexual orientation , a practice that is increasingly normative in research with young adults, who may still be establishing their sexual identity. Although the data that correspond with the current analyses did not include items assessing sexual behavior or sexual attraction, these items will be administered to participants at the next wave of data collection for the Southern California Children’s Health Study. Our assessment of sexual identity is further limited by our inability to assess sexual minority identity labels that are non-traditional but increasingly common  among sexual minority individuals, which will also be assessed in subsequent waves of the study.

There are also several limitations to the generalizability of our findings to sexual minority young adults outside of the current study. First, school-based studies necessarily exclude high school dropouts,home schooled students, and adolescents not in attendance at the time of survey administration. Given that sexual minority  adolescents are more likely to drop out of school, face housing instability, and have poor school attendance , the school-based design of initial enrollment into the Southern California Children’s Health Study may be a source of sampling bias. Additionally, our findings may not generalize to sexual minority young adults outside of the Southern California region. Many of the prevalence estimates for nicotine and pot for growing marijuana product use for bisexual  young adults reported here are larger than estimates observed for sexual minority  young adults in similar cross-sectional studies , particularly with respect to cannabis use. This may be attributable to the unique legal landscape in California on issues that likely impact nicotine and cannabis product use among sexual minority young adults relative to other areas of the country . Future replication in U.S. settings that differ from California on these dimensions is warranted to better understand the role that contextual factors specific to California may have played in the current study. While the appeal of e-cigarettes and cannabis is on the rise among young adults in general, our data suggest that bisexual young adults may be especially at risk. Although gender-stratified results of the interaction analysis suggest that this risk may be compounded among female– relative to male—bisexual young adults, these findings warrant cautious interpretation given that the interactive effect of sexual identity and gender on lifetime product use only reached statistical significance for cigarette use. Nevertheless, future research examining substance use trends among young adult sexual minority populations should further explore the potentially moderating role of gender within this relationship. The prevalence of bisexual self-identification appears to be increasing rapidly relative to other sexual minority identities in the U. S.—especially among younger populations . Thus, the already disproportionate public health burden that bisexual individuals face will likely grow wider yet in the coming years, particularly if concerning trends around bisexual young adults’ substance use persist unchecked. The results reported here underscore the urgent need to prioritize this population as among the highest risk subgroup in need of enhanced substance use prevention efforts across the domains of research, policy, and clinical practice.

It is also imperative that future research elucidate why existing substance use screening, prevention, and intervention services continue to fall short for bisexual adolescents and young adults and how such programming can be tailored to address factors that play a unique role in motivating their substance use. To this end, identifying risk or protective factors that may influence the disproportionate nicotine and cannabis use observed in this vulnerable population is warranted. In the cannabis industry, be it medicinal or recreational, there is an abundance of control measures in place to ensure product safety and efficacy. Contamination of cannabis plants with toxic heavy metals such as arsenic, cadmium, lead etc. can result from numerous origins. Sources of contamination include environmental pollution such as emissions from factories and automobiles, contaminated water, some pesticides, and naturally occurring metals in soil and fertilisers. The contamination of the herbal material ultimately leads to contamination of the products during various stages of the manufacturing process. During growth, metals accumulate in the biomass of specific plants. Studies conducted on industrial hemp show that the cannabis plant bio-accumulates heavy metals from the soil, and thus is readily employed for phytoremediation of contaminated soils. There have been reported cases of post processing adulteration of cannabis buds, adding heavy metals to increase the weight of the product to purposely increase the street value. Pesticides that contain arsenic and mercury as part of their structures were commonly utilised until a few years ago, and are still employed to date. These toxic substances are likely to be present in many foods due to their abundance in nature, and it is important to note that associated ingestion or inhalation of these cannabis products would add to the accumulation of heavy metals consumed by people, even if best practice guidelines are followed. As a result of the new regulation imposed by the USP in collaboration with the ICH, the detection limits for certain metals have been lowered. Heavy metal residues in pharmaceutical end products, active pharmaceutical ingredients and excipients need to be controlled and should be at a certain limit for safe human consumption. Furthermore it can be noted in the somewhat unique case of cannabis based products, that an alternative route of administration of these products does occur, namely inhalation. The pharmacopeial guideline stipulates three routes of administration namely: Parenteral, Oral and Inhalation.ICP-MS  is employed to detect heavy metal contamination. As a result low residue limits can be imposed by USP 232. Heavy metals are classified into different classes according to their toxic potential, class 1  being the most dangerous, class 2 less toxic and class 3 having the highest limits and being the least toxic. This study will focus on Class 1 and 2 metal residues given they present the greatest health risk to consumers. It is the aim of this study to analyse a segment of the South African cannabis-based products in circulation and provide a detailed overview of the elemental impurities/heavy metal residues contained in these products. Furthermore, the adherence of these samples to the imposed inhalation as well as oral limits by the ICH and USP will also be evaluated.

To date no data of this kind exist in South Africa. With these data, regulators, medical doctors and the public can gain a better sense of the dangers currently being faced regarding cannabis based products in South Africa. A total of 310 samples were submitted to a South African contract laboratory for analysis. Manufacturers are defined as any type of user, retailer, reseller, producer, or importer of cannabis-based products. Whether these manufacturers maintain the full value chain or only a portion thereof they are defined as manufacturers for the purpose of this study. Manufacturers may include cultivators of plants, producers of products, importers, resellers, and pharmaceutical manufacturers. Sample data will be presented anonymously. It should be noted that samples were analysed as received by the laboratory irrespective of whether plant material was dry or wet. Dry plant material will have a larger portion of elemental impurities as a result of the moisture mass loss during the drying process. The moisture content of the sample may influence results significantly since they are reported in a mass per mass unit. All samples were analysed in duplicate. Class 1 and 2 heavy metal test panels were analysed . It should be noted that the majority of samples were cannabis-based products, with very few samples submitted for the analysis of soil and water. Consent was provided to employ the data for research purposes. The samples were categorised into seven different types and are shown in Table 2, the same as reported by a potency study conducted by the same laboratory. Approximately 200 mg of each sample was weighed, digested in 10% HNO3 for 1 h at 100◦C and diluted 35 times to a total volume of 7 mL. Internal Standard Y  and In  was used for matrix interference correction. Since large isolate as well as extract sample quantities are scarce, a method needed to be developed to be able to employ as small as possible sample quantity while still being able to reach the USP232/ICHQ3D limits. Analysis started with a blank run, then a 5 point calibration curve, followed by a control standard every 10 duplicates, to avoid instrumental drift. Sample sets were ended by analysing a control standard to ensure all samples within a sample set adhered to bias and variation limits as per USP232/ICHQ3D. The data were grouped into two major groups containing categories applicable to either the inhalation limits and/or categories applicable to the oral specification limits as per USP232/ICHQ3D. All categories were included in the oral specification limit, since all samples needed to be compared to a specification. As for the inhalation specification,container for growing weed the following categories were grouped together for comparison; Extract, Liquid and Plant Material. Since it is not known by the laboratory what the final intended use of the products were, these three categories posed the highest likelihood being dosed in inhalation form. The two major dosage form groups were also subdivided into two different categories. Tabulated results of the data are shown in Appendix A, Tables A1–A4. Individual elemental data are grouped within categories according to specification as mentioned above. It should be noted that the applicable categories together with metal residues that failed will be displayed. If a metal was present in the test method but had no failures it will not be displayed on the figures.

The presence of each residue is also displayed for the analysed 310 samples, together with the limit of detection for each residue in Table A3. Additionally, Appendix A, Table A4, shows the number of samples that failed when they are evaluated against the different specification limits. Furthermore, the table also shows the number of samples for which heavy metal residues could be detected, irrespective of the concentration. A visual representation of the data is given in Figs. 1, 2 and 3. Represented in this data set is a small portion of samples obtained from the South African market. It is by no means a representative sample of the entire South African market, but inferences can be made nonetheless. The dataset will be discussed in two sections, relating to individual heavy metal residues as well as relating to samples. When comparing the individual heavy metal residues that are presented in Fig. 1 against the oral limit, it is evident that the following 3 metals are responsible for most failures: lead , arsenic and nickel . Detection of Class 1 residues above the USP/ICH oral specification limits were responsible for 91%  of all residue failures identified in this study. It is further interesting to note, that only four of the seven categories contained heavy metals at concentrations high enough to cause a failure. The categories that did not contain residues at concentrations high enough to fail could be as result of the concentration dilution being performed or alternatively the process removes metal residues to some degree. For example, when Infusions are considered a dilution of concentration cannabinoids is prepared and consequently other residues like heavy metals and solvents are also diluted.

There is substantial need for improved knowledge in this domain, as the majority of active cannabis users should be encouraged to transition from smoking to alternative modes. Clear and consistent public health messaging from primary stakeholders is required to facilitate this. The literature unequivocally recognizes intensive or frequent cannabis use patterns as a primary predictor of acute/chronic adverse outcomes . Based on available data, between one and two in five cannabis users in Canada engage in frequent/intensive cannabis use, and form a distinct ‘high-risk’ group for potentially severe cannabis-related harm. While some of these ‘intensive’ users may be reached by simple prevention messaging emphasizing decreased frequency of use, a sizeable proportion are likely to feature criteria for CUD. These users are likely less receptive or able to follow simple behavior-change advice and instead may require professional help or treatment . Driving immediately following cannabis use involves  impairment, and is a behavior that about doubles risk for traffic crash involvement, and related injury and/or fatalities . Cannabis-impaired driving is also a main contributor to cannabis-related disease burden, as it provides a  cause of direct cannabis-related mortality, and thus represents a primary target for prevention . The Canadian surveys indicate that substantial minorities of users engage in cannabis-impaired driving, with a further subset of these  engaging in driving co-impaired by alcohol, which further amplifies risk for injury . Moreover, as the surveys relied on short and varying time periods for impairment risk , these rates likely represent under-estimates of the risk total.

Irrespectively, these risk behavior rates are high and disconcerting overall. They are likely facilitated by multiple factors, including common beliefs about nonexistent, or only very limited ‘impairment’ effects of cannabis grow tent, as well as a low likelihood of apprehension under current enforcement for cannabis-impaired driving . These circumstances urgently require intensified targeted education and enforcement efforts. These efforts should draw on crucial lessons from alcohol/drunk driving intervention strategies, which have achieved substantial decreases in alcohol-impaired driving and related crashes . While some evidence exists about cannabis use-related adverse reproductive/infant health outcomes during pregnancy and/or breastfeeding, rather small minorities of women reported ongoing use during these periods. While cannabis compounds may be passed on to the foetus via intrauterine transmission or through breast milk, some women use cannabis ‘therapeutically’ to combat pregnancy-related nausea . Overall, adverse outcomes for newborns are uncertain and likely limited . However, avoiding cannabis use during pregnancy and breastfeeding represents a relatively simple prevention effort of possible harm to others . Moreover, this recommendation aligns with other precautionary health behavior adjustments among pregnant women or new mothers . Overall, current indicator data from major surveys indicate that respective majorities of cannabis users in Canada – with the exception of ‘smoking’ as the primary mode of use – are generally mostly compliant with the main LRCUG’ recommendations for which such data exist. At the same time, the proportion of users non-compliant with other LRCUG recommendations represent sizeable sub-populations of the currently 4–5 million cannabis users, many of which likely engage in more than just one risk behavior, and thus face considerable risk for acute and/or chronic adverse health outcomes . While population-level harms for cannabis are more limited than those for alcohol or tobacco, the ensuing disease burden is substantial, also given that cannabis use disproportionately occurs among youth/young adults where key LRCUG-defined risk behaviors are commonly concentrated . Thus, in order to achieve legalization’s objective of improved public health outcomes, key cannabis-related risk behaviors need to be more effectively addressed. Active and widespread dissemination and promotion of the LRCUG recommendations may lead to increased awareness and adjustment of relevant risk-behaviors among users . The behavioral uptake potential of interventions such as the LRCUG is uncertain, for example among intensive, chronic users.

However, it should be emphasized that the LRCUG represent a targeted prevention measure, rather than a  treatment tool for individuals possibly characterized by CUD . Nevertheless, other complementary, targeted intervention measures combined with appropriate regulatory provisions focusing on specific risk behaviors  are required in order for a prevention tool like the LRCUG to be effective . In addition, the impacts of such targeted measures on cannabis related risk behaviors require consistent assessment and improved understanding . The data used in the present review feature some limitations. Specifically, the survey sources for the indicator data relied on different sampling frames, essential methods details and item design , limiting the surveys’ reference populations full comparability. Only some of the surveys are considered population representative; the CAMH Monitor is an Ontario-based survey, not generalizable to populations elsewhere in Canada. All the surveys rely on  self-report data, which also may be burdened by recall or other biases. In addition, survey items for certain indicators were based on differential operational definitions , or included subjective estimates with unknown reliability in select instances . These may entail limitations for possible intrinsic and extrinsic indicator data validity or comparability. Overall, while the scientific evidence behind the LRCUG is evolving, consistent population-level measurement of risk-behavioral indicators for cannabis use-related health outcomes is essential for effective monitoring of public health-related cannabis risks and harm outcomes, especially in the era of legalization as an ongoing ‘policy experiment’. Substance use disorders are currently a major public health crisis in the US . Cannabis is the most commonly used illicit substance in the world . With more than 200 million users of cannabis worldwide, its harmful health effects have become a serious global problem . During the past two decades, the laws and policies related to cannabis use have also changed drastically throughout the world. For example, countries such as Canada, Spain, and Germany have legalized cannabis for medical use while some have even legalized its non-medical use, e.g., Uruguay in 2015 and Canada in 2018 . Not surprisingly, the legalization trend continues in the US, with 33 states and the District of Columbia legalizing medical marijuana use, and 11 states and the District of Columbia legalizing adult non-medical marijuana use . Regardless of the developing accord about the usefulness of medical marijuana for several serious illnesses, there is a widespread concern that this may cause adverse effects . According to a study on the effects of medical marijuana laws, the likelihood of current as well as regular use of cannabis among people aged 21 or older has increased after the laws came into effect .

This also appears to have contributed to an increased prevalence of illicit cannabis use and cannabis use disorder . In particular, among adult males, arrests due to illegal marijuana possession in major cities have increased by 15–20% and the treatment provided in rehabilitation facilities for such arrests have increased by 10–20% . This article focuses on cannabis use disorder . Earlier, there was a consensus that CUD is rare, which is no longer true. It is estimated that about 34% of cannabis users develop CUD during their lifetime based on the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders  . Furthermore, a recent study based on DSM-V criteria found that about 27% of cannabis users develop CUD during their lifetime . Another research shows that after legalizing marijuana for recreational use, the prevalence of CUD among past year cannabis users between the ages of 12 and 17 rose from 22.8% to 27.2% . Thus, given that the prevalence of CUD is expected to increase further, it is imperative to predict the risk of developing CUD for cannabis users, especially for adolescents and emerging adults, based on their personal risk factors. Identifying individuals at high risk of CUD will allow the possibility of applying early intervention, which may potentially help stem the increasing prevalence of the disorder. Several risk factors have been reported for substance use disorders in general and specifically for CUD. These include male sex, early exposure to traumatic events, early use initiation, family history of substance use, childhood depression, and conduct disorder symptoms . High impulsivity and certain personality traits are also associated with the disorders . In particular, work by coauthor Filbey’s lab showed that openness distinguishes cannabis-only users from nicotine-only users, co-morbid marijuana and nicotine users, and non-users . The results from this study also indicate that conscientiousness is lower among grow lights for cannabis users. Some brief screening tools such as BSTAD  and S2BI  have been developed for adolescents . For example, the cutoff for CUD based on BSTAD is at least two days of marijuana use in the past one year. A relatively lengthy tool, Transmissible Liability Index, assesses the inherited risk for disorders based on a 45-item questionnaire . Also, a recent study has developed a simple cumulative risk index for substance dependence in adulthood using risk factors in childhood and adolescence .

It can be used to screen adolescents who are likely to develop persistent disorder in adulthood. A similar study has developed a risk score by counting the number of early life risk factors present in an individual and associating it with cannabis use and CUD in early adulthood . However, a key limitation of the existing tools is that none of them provides a quantitative risk of developing the disorder based on personal risk factors, which restricts their practical utility. Models for predicting such risks have been developed for several diseases, including breast cancer , contralateral breast cancer , heart disease , depression , and psychiatric disorders , and they are in wide clinical use. However, currently there is no such quantitative risk prediction tool for CUD. In this study, we build upon the findings of Ketcherside et al. in a cannabis-using adult population and perform a secondary analysis of the data. More specifically, we build a preliminary quantitative risk prediction model to estimate the chance that a cannabis user will develop CUD based on various demographic, behavioral, psychiatric, and cognitive risk factors. The initial data set obtained after applying the inclusion criterion consisted of 118 cannabis users. We used CUD as the outcome variable, which was derived based on the DSM-IV criteria for dependence.The variable selection process to identify potential risk factors was the following. First, the variables with more than 50% missing values were discarded. Then, among the remaining variables, only those that remain relatively stable over time were chosen. Given the cross-sectional nature of the data, focusing attention on such type of variables protects against using risk factors that may actually be an effect of CUD. This resulted in 30 variables. These included measures of impulsivity and personality traits. The former were obtained using two questionnaires, namely, Impulsive SensationSeeking Scale , a 19-item self-reported questionnaire from the Zuckerman-Kuhlman Personality Questionnaire and Barratt Impulsivity Scale , a 30-item self-reported questionnaire where the items can be grouped into six first-order factors that measure different aspects of impulsivity . Both ImpSS and BIS were considered because there are some characteristics of impulsivity that are captured by ImpSS but not by BIS and vice versa, and the two have been used together in several studies . The personality traits were obtained using Neuroticism, Extraversion, and Openness inventory , a five-factor inventory for measuring five different dimensions of personality . The actual measures derived from these questionnaires were total score on the ImpSS questionnaire, scores on the six factors from the BIS questionnaire, and scores on the five factors from the NEO questionnaire. Only 46 of the 118 subjects had complete data on all 30 variables. To guard against loss of subjects due to missing data on potentially unimportant variables, univariate logistic regression models were fitted with each of these variables as a predictor. Thereafter, the predictors with univariate model p-value less than or equal to 0.3 were selected into the final set of potential predictors for a multivariate model . The resulting data set had 12 potential risk factors and 94 subjects with complete observations on them. This final data set was used for the rest of the model building exercise. The data analysis was performed using five common statistical and machine learning models for classification , namely, logistic regression with LASSO penalty, K-Nearest Neighbor , support vector machine  with radial kernel, random forest, and gradient boosting.

All interviews were coded by J.E., and six interviews were independently coded by D.D. Throughout the analysis process, J.E. and D.D. discussed codes and coding structures. Findings were viewed through an HTA lens, and attention was paid to the impact of the technology  on the participants  and their interactions with end users . In the following section, some quantifying language is used to provide a sense of consistency within themes. For example, the term “most” indicates that a theme was present in at least 10  participant accounts, while the term “many” indicates that at least 6 accounts included the theme. The terms “some” or “several” indicate that less than half of participant accounts included a theme; however, the absence of a theme in a participant account does not indicate that a belief was not held, only that it was not raised during the interview.The medical cannabis field was perceived by participants to be rapidly evolving, and participants expressed a desire to learn more about cannabis, including about cannabis-based products and cannabinoids, noting that increased knowledge would allow them to better counsel patients . No participants had received formal training about medical cannabis, and several expressed the need for its addition to medical school curricula. Participants reported learning about medical cannabis by attending rounds, reading journal articles, viewing web seminars, and through discussions with colleagues . Several participants described a desire for guidelines from Canadian professional associations, stating that it would facilitate their practice by allowing them to better counsel patients.Most participants reported discussing costs with families, and some described working with parents to calculate costs based on the projected dose. Several participants noted that the cost of treatment sometimes limited their ability to reach a therapeutic dose because parents are typically paying out-of-pocket for the cannabis products. Neurologist 10 summarized the issue as follows: “Families sometimes don’t advance the treatment to a therapeutic dose because the cost is prohibitive and in that case, I think that’s all a kind of a waste.

It’s like going through the trouble of spending your money to take an aspirin a day when really you need eight aspirins a day. Wasting your money, one aspirin a day won’t do anything but you’ll never get up to eight because you can’t afford it.” Because of this issue, mobile grow system some participants described choosing between licensed producers based on the availability of compassionate discounts for pediatric patients. One neurologist reported that parents were sometimes surprised to learn that cannabisbased products are not typically covered by insurance, especially since the legalization of recreational cannabis in Canada in October 2018 . This was echoed by some neurologists who stated a belief that cannabis-based products should be covered by insurance programs, citing benefits to the health care system .Participants who had not previously authorized medical cannabis described a variety of reasons for not authorizing its use, including personal and institutional factors. Insufficient evidence or guidance: Some participants reported not authorizing medical cannabis because of a lack of evidence and/or guidance . Out of scope: Out of scope: Others described feeling that authorizing medical cannabis was outside their scope of practice, that they considered it a “regulatory hassle” , or that they could not do a “better job” than physicians at cannabis clinics. Restrictive policies: Some participants described policies at their hospital or within their department that prohibit authorization, while others reported not wanting to be seen as going against people in leadership positions . Several participants noted that consensus had been reached within their department or group to not authorize cannabis and that a unified position was felt to prevent any one neurologist from being given the most complex cases and/or “the most demanding families” .Participants who do not authorize medical cannabis described referring patients to other health care providers  or to cannabis clinics, often in the patient’s community , or in one case, to in an adjacent province, stating that there was no closer alternative. Others described hospital or departmental policies that prevented them from referring patients, leaving it up to parents to find a cannabis clinic or care provider to provide authorization .

Of note, some neurologists were strongly against the practice of referring patients to non-neurologists for the purpose of obtaining cannabis authorization , citing patient safety as a key concern.Most participants reported that caring for children using medical cannabis does not affect their workflow, in terms of the number of patient visits or tests ordered. In one neurologist’s experience, the number of visits may be fewer, at least during the dose-optimization phase, for children who received authorization from another physician . In terms of testing, participants described treating cannabis “like another drug,” with no additional testing performed, while some order additional tests at baseline and while titrating the dose or if the child presented in a “sleepy state,” or being “a lot more cautious with the other medications” prescribed concurrently. Several neurologists described providing counselling and education to families about medical cannabis, which may add to the length of the clinical visit, and one neurologist described offering parents the opportunity to ask follow-up questions via telemedicine after an initial discussion about cannabis. One neurologist described difficulties entering nonformulary medicines into hospital electronic medical records and, for parents who receive authorization at a cannabis clinic, having to rely on parents to supply information about the treatment plan because of a lack of communication from some cannabis clinics .In this qualitative interview study, we explored the experiences and perceptions of neurologists in Canada about the use of medical cannabis for treatment of pediatric drug-resistant epilepsy. Most of the neurologists interviewed for this study viewed medical cannabis as a viable option, particularly after other options had been explored; however, important gaps in the evidence-base were identified, including limited knowledge about the medical properties of cannabinoids beyond CBD, the inability to predict which patients are most likely to benefit from cannabis treatment, and a lack of long-term safety data. Most neurologists reported having overall positive experiences with medical cannabis, although several commented that it’s not a magic pill and that the benefits are likely oversold by the media. In 2013, CNN aired a documentary about Charlotte Figi, a 5-yearold girl with Dravet syndrome. In an attempt to control her drug resistant seizures, Charlotte’s parents initiated a regimen of medical cannabis, reducing her seizures by more than 90 %. Charlotte’s case received considerable media attention and has led, at least in part, to increased interest among parents in the use of cannabis as an alternative or complementary treatment for epilepsy and to increased requests to physicians for cannabis authorization.

Participants in our study also identified the legalization of recreational cannabis in Canada in 2018 as a potential driver of additional interest in medical cannabis. Compared with the public, the medical community has been more slow to adopt cannabis as a treatment for pediatric epilepsy, owing largely to a lack of published clinical studies. Although our study was not intended to quantify support for medical cannabis, we observed that most participants were supportive of a trial of medical cannabis for children whose epilepsy had not responded to other treatments. This is consistent with the findings of a 2019 systematic review, which reported that medical practitioners were largely supportive of the use of medical cannabis across multiple indications, with higher levels of support when other options had been “exhausted.”The Canadian League Against Epilepsy  recently published recommendations regarding the use of medical cannabis in the treatment of epilepsy. First, CLAE recommends that patients should make the decision to use cannabis “in consultation with their health care provider to ensure their safety.” Neurologists echoed this, voicing concerns that some parents may be administering cannabis to their children without oversight of a health care professional, which could lead to unsafe situations for the child. The use of complementary and alternative medicine is common among children with epilepsy, and parents may not disclose its use . This further supports the need to establish open communication between parents and health care providers. Second, CLAE “encourages clinicians and researchers to continue to seek further knowledge and education” about medical cannabis.Several neurologists in our study also expressed a desire for additional and ongoing education, in order to better counsel their patients. In a 2015 educational needs assessment of Canadian physicians, 64 % of respondents perceived a strong need for cannabis education, and 70 % felt that receiving cannabis education would better allow them to care for their patients using cannabis. Similarly, a recent systematic review highlighted a lack of self-perceived knowledge among clinicians about medical cannabis, and several groups have called for increased cannabis education. However, there are also several notable differences between our findings and CLAE’s recommendations. First, CLAE acknowledges the differences between purified CBD oil  and the CBD:THC cannabis oils available in Canada, stating that “evidence is lacking” for products containing both CBD and THC. In contrast, some participants in this study described inferring the safety and effectiveness of CBD:THC cannabis oils based on studies of Epidiolex, which further highlights the need for additional education about the differences between cannabis-based products. Notably, Epidiolex is not available in Canada at this time, mobile vertical rack and there are important differences between Epidiolex and the products available in Canada. The Canadian CBD:THC oils are whole plant isolates, containing CBD as well as THC in various ratios, as well as other phytocannabinoids , flavonoids, and terpenes. There is also considerable variation in terms of the CBD-THC oils available within Canada.

Most licensed producers in Canada offer multiple products with various ratios and concentrations of CBD and THC, including those high in THC , balanced oils , and those high in CBD . The concentration of cannabinoids is variable across preparations, such that a cannabinoid oil with a 20:1 ratio of CBD to THC from one producer does not necessarily contain the same concentration of CBD as a 20:1 oil from a different producer. There is also potential for variability in cannabinoid concentration between batches of the same product, leading to uncertainty that may affect dosing, cost, adverse effects, and therapeutic efficacy. These complex issues related to the use CBD:THC oils further reinforce the CLAE’s recommendation that clinicians continue to seek additional education about medical cannabis. Second, some participants described referring patients and their families to cannabis clinics or to health care providers who may not be experienced in caring for patients with epilepsy. However, CLAE recommends that “treatment with CBD:THC cannabis oil be managed by a physician knowledgeable and experienced with epilepsy care and anti-seizure medications, preferably with experience in CBD:THC cannabis oil.” Some participants noted departmental policies that prevented them from authorizing cannabis or that neurologists in their group had reached consensus that none would authorize cannabis, instead referring patients to cannabis clinics or leaving families to find other health care providers to authorize use. One participant was particularly critical of the practice of referring complex neurology patients to non-neurology health care professionals, stating that “they owe it to the patient” to find an appropriate neurology referral if the family wishes to pursue medical cannabis.Cannabis sativa L., Cannabaceae, has a long history of exploitation as a medicine, in pain relief and epilepsy, and also in food, textile, and paper industries. The scientific research of Cannabis sativa demonstrates an exponential increase in the past 30 years, as most of its ingredients were isolated and characterized, but the breakthrough was achieved in the discovery of the endogenous cannabinoids and the endocannabinoid system. Also, many study results, personal and anecdotal testimonies changed the public perception, asserting pressure for legislation changes in the definition of Cannabis as a Schedule 1 substance resulting in passing laws that legalize its use for medical purposes. Even though each country/state that passed such law brought specific provisions that vary considerably , the common feature of these laws is that they permit the legal use of cannabis for medical treatment if the patient has obtained appropriate medical authorization.

Reviews of controlled trials suggest that short-term, low dose administration of medical cannabis are effective to treat neuropathic pain related to cancer and other chronic conditions.However, a 4-year cohort study in Australia with over 1200 pain patients using prescription opioids reported that for most participants, cannabis use had no effect on their opioid use and actually led to greater pain severity.Some patients who reported feeling symptoms return after tapering off use of medical cannabis might have also been experiencing acute withdrawal symptoms, but this was not reported by participants. Therefore, given the complex evidence, primary care providers need to better understand the pharmacology of the cannabis plant and dosing options.Doing so will enable them to monitor for positive health outcomes and toxicity associated with its use and make informed recommendations. To meet the first objective, the research identified that patients were referred to medical cannabis by healthcare providers and encountered challenges related to cost, quality, and availability of the product in dispensaries. To meet the second objective, the survey research findings indicated that the small number of Black/African American respondents reported higher satisfaction with the effects of medical cannabis than white respondents for the following indicators: increased appetite, decreased seizures, and increased energy. The research did not identify any difference based on race and ethnicity of the patients in terms of perceived benefits or preference for administration. Most patients preferred vaping for administration. Age was negatively correlated with patient benefits for most indicators, possibly suggesting that as patients age, the perceived benefits of medical cannabis may be diminished because of more severe symptoms or possibly comorbidities reducing the efficacy of the drug. By contrast, age was positively correlated with decrease in spasms or tremors in this study. Preliminary studies have shown that modulating the cannabinoid system may be useful to treat some motor symptoms in Parkinson’s disease, mobile grow system which is more frequent in older people, but clinical studies are inconclusive regarding effectiveness of cannabinoid-based medicines.

The results for gender indicated women experienced lower benefits for decreased seizures compared to men. In the study by Crowell et al.,women experienced higher benefits for decreased inflammation and increased mood but lower benefits for increased energy. These findings suggest that there could be differential benefits depending on gender that require further study. This mixed methods research study provided valuable information about patients’ explanations for their patterns of use and rationale for using medical cannabis to address their medical conditions. The data provided preliminary findings which could be used to further examine the benefits that patients perceive from their use of the drug. Areas of future study include focusing on specific medical conditions, such as chronic pain-related conditions, and comparing pain management effectiveness between patients using prescription opioids and medical cannabis. A qualitative study in Illinois reported patients used medical cannabis as an alternative to prescription medications, as a method to wean themselves off of these medications, and as a complementary drug for their medications.Like the present study, most patients in the Illinois study used medical cannabis as an alternative to prescription medicines, citing negative side effects such as damage to the liver, and a vast majority reported daily use.Surprisingly, patients were using medical cannabis to treat several medical conditions at once, which contrasts with consumers’ typical use of a pharmaceutical drug to treat one specific medical condition . There was some underlying stigma toward the perception of the patient’s use of medical cannabis by others, relating to the fact that some patients only shared their usage with trusted family and friends and did experience some discrimination from health care workers at the office of their primary health care provider. It may be the case as was identified in another study, that patients are reluctant to share their use of medical cannabis with their primary health care provider and people outside of their immediate family.In terms of research challenges, it was difficult to recruit Black/African American and Hispanic participants to better reflect the diversity of Florida’s population, so future studies would need to design more innovative recruitment strategies to reach this population of medical marijuana patients. This study identified some areas of potential benefit in terms of symptom relief that might vary by race and ethnicity, but due to the small sample size of minority patients in the survey sample, these are tentative findings and a study limitation. For example, the significant finding that Black/African Americans experienced more benefits for increased appetite, decreased seizures, and energy than white respondents might be the result of a Type I error that could be addressed by increasing the sample size of minority participants in future studies.

From a research participation perspective, a positive study outcome was identifying the willingness of respondents to complete the survey after seeing posts on social media in Florida medical marijuana patient Facebook groups. Some members of this patient population are very focused on advocacy for medical cannabis and took a very positive view toward research participation. However, the majority of respondents were women, middle-aged, and white so there was an element of selection bias in the sample receptive to recruitment messages posted on social media group sites, and possibly fewer minority respondents who were members of these online groups. Medical marijuana patients were very willing to participate in both the surveys and interviews and took an active interest in wanting to learn about the outcomes from this research study. This finding bodes well for future research on understanding patient perspectives of the use of cannabis-based medicines.There is a growing interest in this field and the depth of research being conducted on medical cannabis is broad, but without many concrete conclusions due to current policies, limited drug supply, and methodological limitations.The limited number of double-blind clinical trials demonstrating the potential medicinal effects of cannabinoids calls for additional research questions to be explored.Moreover, most studies are equivocal due to lack of standardization and quality control of the cannabis products examined.Future research can be expected on the subject of medical cannabis, but how are physicians and healthcare trainees staying informed? Previous studies have demonstrated a large gap between the public interest, current use of medical cannabis, and medical providers’ ability to educate and counsel patients.In other words, the use of cannabis was introduced to the field of medicine by patient inquiry rather than through extensive research.Indeed, the majority of medical cannabis regulations in the United States and around the world have been implemented as a result of patient advocacy.Even in some medical schools, such as the University of Vermont Larner College of Medicine, student interest drove the university to offer an elective, and furthermore integrate medical cannabis into the curriculum due to overwhelming interest.A systematic review of healthcare professionals’ attitudes and knowledge on medical cannabis recently reported on a lack of selfperceived knowledge on medical cannabis across the fields of medicine, nursing, and pharmacy.It further demonstrated a common desire for additional education and resources to access information about medical cannabis. In general, while several studies have shown that healthcare professionals support the use of medical cannabis in clinical practice, in particular for cancer and hospice patients, mobile vertical rack others have reported on more conservative positions.Such a gap in attitudes and knowledge among healthcare professionals on this topic illustrates the need for a standardized medical cannabis education during training.

The use of medical cannabis is supported by scientific evidence for only a few conditions, such as chronic pain and chemotherapy-induced nausea and vomiting.In addition, two cannabis-based pharmaceuticals have regulatory approval in many countries, namely Nabiximols for spasticity of multiple sclerosis patients,and Epidiolex for refractory seizures.Nevertheless, the lack of quality research due to regulatory restrictions makes it difficult for healthcare professionals to address growing questions by the public.For example, it is important for clinicians to have sufficient knowledge of the interaction between cannabis and the novel coronavirus, SARS-CoV-2 . It has been stated that smoking results in airway inflammation, putting COVID-19 patients at increased risk for severe complications, including cerebrovascular dysfunction.While inhaled cannabis was included in the previous statement, other studies currently in progress are evaluating the potential use of cannabinoids  as an adjunct to antiviral treatments for patients with COVID-19.The COVID-19 pandemic emphasizes the lack of in-depth understanding of the effects of cannabis on the human body and its therapeutic effects. It is integral to bridge this gap in knowledge, which may be possible only with proper training and education of healthcare professionals. Some countries and states that have legalized medical cannabis require general education for recommending providers; however, the standards vary significantly.This often results in superficial counseling for patients.Only a handful of states have established a requirement for licensed professionals to give medical advice. For example, Connecticut requires every dispensary to have a pharmacist on staff.Part of the current gap between public demand and education provided by healthcare providers is in large due to major lack of education at all levels of healthcare.For example, the 2017 National Academies of Sciences, Engineering, and Medicine  Report concluded that medical cannabis is effective for the management of chronic pain in adults.However, the recommendation of medical cannabis to patients has not been widely adopted by physicians.In order to create specific educational recommendations for schools, this gap of education and mixed beliefs within healthcare education needs to be bridged. Therefore, the aim of this study was to analyze the existing literature surrounding the education of medical cannabis in allied health professional training programs worldwide. Additionally, we report the beliefs and views of trainees and faculty surrounding medical cannabis as treatment, as well as the depth of understanding of the therapeutic benefits and potential risks of medical cannabis.

This scoping review on medical cannabis education among medical and allied healthcare trainees was based on online database searches for peer-reviewed publications in English. The research team generated a list of search terms relevant to this topic and modeled after previous studies’ search syntax and keywords.This list was then expanded to include other common terminology and synonyms so that they could be included in the search syntax [see supplementary materials. These terms were used to generate a syntax for searching through PubMed, ERIC, CINAHL, and Web of Science. To expand the scope of this review, relevant “grey literature” was also searched to account for non-published academic material. Finally, the search included all references of the papers selected, as well as any other articles that referenced our selection. This consisted of searching Google Scholar, MedEd, Medline, and Dissertations and Theses section of Proquest using the same search terms. The literature search took place between June 10th, 2020 and July 1st, 2020. The search resulted in a total of 412 articles, which were imported into Covidence for duplicate removals. Two hundred and fourteen duplicate articles were removed, resulting in 198 articles for title and abstract screening. These articles were reviewed independently by two members of the team, who discussed any conflicts that arose concerning their inclusion or exclusion. Of the 198 articles, 167 were deemed out ofscope of this study and were excluded. The remaining 31 articles were then assessed with a full text review, and 8 publications were subsequently excluded. The exclusion criteria consisted of findings that were not based on an empirical study, examined populations outside the scope of this review, or studied substance use or misuse among students. The full text screening was done by multiple members of the research team independently, so that each study was examined by a minimum of three members. The data of the included studies were analyzed, extracted, and categorized by healthcare field, study design, study location, and important outcomes. The process of data search and extraction is presented in a PRISMA diagram in Fig. 1. A summary of the 23 studies that were included for the current analysis is presented in Table 1. The vast majority of studies assessed attitudes, beliefs, and knowledge about medical cannabis among trainees or faculty using a survey that included Likert scales to rate levels of agreement with various statements. In contrast, the 2020 study by Moeller et al. administered a quiz to pharmacy students to determine student knowledge about medical cannabis.

It was also legal in Australia, but narrow qualifying medical conditions meant that very few people were able to access it. In mid- 2018, recreational cannabis was nationally illegal in all four countries. However, in the US, 8 states and DC had legalized cannabis for recreational use; Canada was in the process of implementing the Cannabis Act to legalize recreational cannabis ; and some Australian states had decriminalized possession in small quantities . The main aim of this descriptive study was to examine cannabis use by cigarette smokers in countries with relatively more permissive cannabis policies  versus  less permissive policies  based on laws that were in place at the time of the survey. For example, laws were more permissive in North America with regard to wide medical access in Canada and the US, as well as recreational cannabis legalization in some US states. With regard to tobacco smoking, all four countries had similar cigarette smoking rates . National tobacco control policies  were stronger in Canada, Australia, and England compared to the US, where tobacco control laws varied widely between states. This study also examined cross-country differences among co-users: frequency of cannabis use and of cigarette smoking, relative harm perceptions of smoked cannabis compared to cigarettes, and frequency of smoking cannabis mixed with tobacco among those who reported smoking cannabis.Weighting survey data is one of the major components in survey sampling, and involves attaching a weight to each unit of the selected sample in order to obtain estimates of population parameters of interest. This process essentially incorporates a method of re-balancing the data, in order to more accurately reflect the population. This is especially important for complex survey designs . In the current study, cross-sectional weights were computed for all respondents. A raking algorithm was used to calibrate the weights on smoking status, geographic region, and demographic measures . Finally, the weights were rescaled to sum to the sample size for each country to allow for cross-country comparisons.

This study found significant cross-country differences in patterns of cannabis co-use among cigarette smokers, where smokers from Canada and the US  had higher rates of co-use, daily cannabis use, dual-daily co-use of cannabis and cigarettes, were more likely to smoke cannabis without tobacco,vertical grow systems for sale and believe that smoked cannabis is less harmful than cigarettes than co-users in England and Australia. These findings, obtained during a period of liberalization in many countries, introduce a number of important issues for future research on the impact of cannabis liberalization in general, and on tobacco-cannabis co-use. Currently there is mixed evidence about the effects of cannabis legislation on actual changes in cannabis use, and the majority of the available studies originate from the US . Reviews have shown that cannabis use may increase among adults in locations that have legalized medical  or recreational  cannabis, and a recent large cross-sectional study that examined the population-level impact of recreational cannabis legalization in Canada and across US states in 2018, found that both the prevalence and frequency of cannabis use were higher in US states that have legalized recreational cannabis compared to Canada  and US ‘illegal’ states . Recent national data from Canada  and the US  have shown that adult cannabis use has been increasing where liberalization of cannabis laws has occurred. Some research has also shown that the prevalence of co-use is rising in the US , with higher co-use rates in US states where medical cannabis has been legalized . It is currently unclear however if higher rates of cannabis use, co-use, and/or increases in use are attributable to policy changes, or if studies are detecting pre-existing trends that were in motion prior to liberalization, partly owing to the sophisticated illicit markets in Canada and the US. Moreover, while some studies have examined how cannabis use and co-use patterns may change during the period immediately following a policy change , very little is known about how cannabis liberalization may impact longer-term patterns of tobacco and cannabis co-use. One public health implication to cannabis liberalization is the possibility that increased access to cannabis may weaken, or even reverse, longstanding downward trends in tobacco use. Ongoing, long-term research utilizing longitudinal study designs is critical to further explore the relationship between co-use and liberalization of medical and recreational cannabis. With growing public support and social acceptibility of cannabis in many countries , coupled with cannabis policy liberalization, harm perceptions of cannabis may be impacted. For example, some studies have shown that perceptions of absolute cannabis risks are lower, or have decreased, in jurisdictions that have legalized cannabis , and lower harm perceptions are associated with use and appeal of drugs , including cannabis .

Not much is known about how product regulations may shape or change absolute perceptions about cannabis, and to our knowledge, there are no studies that have compared perceptions of relative risk between smoked cannabis and tobacco, particularily among co-users residing in different cannabis policy environments. A study by Popova et al. found that yong adults in Colorado  perceive combustion-smoking  as more harmful than non-combustible products , but there was nocomparison between cigarettes and smoked cannabis . A qualitative study of young adults has suggested that co-users in Maryland  relate to their use of both substances in different ways, and may underestimate the harms of tobacco use in relation to their cannabis use, as well as underestimate the harms of cannabis use . There is no evidence however if the underestimation of these risks varies between legal and illegal cannabis jusisdictions, as well as between single product users and co-users. While our study cannot determine this, our data do show that co-user’s perceptions of lower relative risk of cannabis compared to cigarettes was substantially more common in Canada and the US. This is worrisome because tobacco smoke and cannabis smoke have been found to contain many of the same carcinogenic chemicals , and some of these harmful constituents  have been found in marijuana smoke at greater concentrations than in tobacco smoke . However, regardless of the harmful constituents within each product, tobacco smoking is more deadly and addictive than cannabis. Tobacco smoking  is attributed to 8 million global deaths each year, and the total annual global economic cost of smoking is estimated to be 1.4 trillion USD . Currently, there is much less evidence about the health effects of cannabis due to its status as a prohibited substance in most jurisdictions. Studies have shown that regular cannabis use is related to important adverse health outcomes including impaired decision making and memory deficits, increased risks of acute injuries, including impaired driving, dose-dependent risk of developing psychotic disorders, and high health care costs . However the scope and magnitude of these risks are substantially less than tobacco, which is a primary risk factor for a wide range of diseases including several non-communicable diseases, and more than a dozen forms of cancer . This study has demonstrated that the majority of co-users, regardless of the cannabis regulatory environment, are smoking cannabis alongside smoking cigarettes. One main difference was that fewer cousers from Canada and the US mixed tobacco with their cannabis compared to co-users in England and Australia.

Research has consistently shown that co-use practices differ by country and region , which our findings also support. Simultaneous use  is more common in European countries  and Australia, while sequential use  is more common in North America . However, in the US specifically, smoking ‘blunts’  is a common and increasing method of cannabis use . Because tobacco is not directly mixed with cannabis, users may not consider this to be simultaneous use , therefore this use pattern could have been underestimated by US co-users in this study. Research suggests that simultaneous use  is associated with greater risk of problematic cannabis dependence, negative cannabisrelated outcomes, lower motivation to reduce tobacco consumption, and lower rates of smoking cessation . On the other hand, it has been found that sequential users use cannabis on more days per month, more cannabis per day, and found that not mixing tobacco with their cannabis to be more pleasurable in comparison to those who mix tobacco with their cannabis . Monitoring unique patterns of both simultaneous and sequential co-use occurring in different regions warrants significant public health attention. Notably however, regardless of couse patterns, nearly one-third of the sample in this study smoked cigarettes and used cannabis daily. While this study is not representative of cannabis-dependent people, there are several smokers who are at much higher risk of the additive effects of co-use. Physicians and other healthcare professionals should be vigilant in identifying co-users and offer tailored treatment, especially for co-users with cannabis dependence, as these users are significantly less likely to quit smoking and problematic cannabis use than those without cannabis dependency . Although this is a large study with representative smokers from four countries, there are some limitations to consider. First, comparing different policy environments is challenging, owing to the diversification of cannabis supply, possession, and use laws, both across and within countries, poor comparisons between national surveys, illicit cannabis markets, and because changing laws are in very early stages . Future research is needed that tackles the difficult challenge of incorporating information about illicit cannabis into analyses of the legal market. Second, the countries included herein were treated as single jurisdictions , which has the potential to mask important sub-national differences. Third, this is a cross-sectional study, therefore temporality issues exist, and causality cannot be determined. Fourth, the sample was limited to adult smokers, so observations may not apply to other populations of interest. Fifth, Canada had not yet officially legalized recreational cannabis at the time of data collection; therefore users would have purchased cannabis illegally or from a legal medical source . Sixth, cannabis use may be underestimated  due to respondents’ reluctance to admit to cannabis use. Finally, four high-income Western countries were included in the analyses presented in this paper; therefore, these results may not apply to other countries.Severe acute respiratory syndrome coronavirus 2  has caused >9.4 million infections and >232,000 deaths in the US as of November 4th, 2020. State governments have enforced social distancing, quarantine, and shelter-in-place policies, thereby sequestering communities and limiting access to medical care.

Although critical for protecting public health, such policies have psychological consequences, including fear, anxiety, depression, and post-traumatic stress symptoms. Further, inability to access healthcare may exacerbate chronic disease symptoms. People using cannabis medically represent one potentially vulnerable population, as many use cannabis for chronic conditions such as chronic pain, cancer, and multiple sclerosis. Although cannabis remains Schedule I under the Controlled Substances Act, use of cannabis medically has increased nationwide – even in states without legal cannabis. Of thirty-three states allowing medical cannabis, mobile grow systems only twenty-three have designated cannabis businesses as essential, allowing them to stay open during shelter-in-place orders. This disruption in cannabis access may potentially cause negative health effects for people using cannabis medically through anxiety about cannabis access, symptom flares, and changes in substance use to compensate for the lack of cannabis. For example, some people intentionally substitute cannabis for other medications due to better symptom management and a favorable side effect profile, so some may re-initiate or increase use of other medications due to decreased cannabis access. Alternately, studies show that individuals using medical cannabis report greater use/misuse of prescription drugs and alcohol than those who do not. This population may thus be at particular risk of increasing substance use to cope with COVID-19-related stressors and lockdown/quarantine policies. We sought to understand how people using cannabis medically are affected by COVID-19, with specific focus on cannabis access and use of other medications and substances . We hypothesized that concern about/decreased access to cannabis would be associated with increased use of medications and substances.Participants  were from 44 states and Washington, DC, with the highest proportions from California , Pennsylvania , Florida , and New York . Ninety-two percent reported current shelter-in-place policies. Consistent with a nationally representative sample of people using medical cannabis, approximately one-third of participants rated their emotional and physical health as poor/fair, one-third good, and one-third very good/excellent . Most  participants used cannabis both medically and recreationally, a proportion similar to that reported among people using cannabis medically vs. medically and recreationally in nationally representative data.

In addition, to validate the pupillography as a medicolegal proof, studies with a larger sample are needed as well as pupillographic analysis in subjects who have taken poli-drugs or drugs and alcohol together. Road traffic injuries are the leading cause of death among people aged between 15 and 29 years and it will rise to become the fifth leading cause of death by 2030. These subjects represent the main prevention target of this method.Neuropathic pain is initiated by a damage to the nervous system which might be attributed to infectious agents such as human immuno deficiency virus , metabolic disease, neurodegenerative disease, multiple sclerosis  and physical trauma . Regardless of the cause, damage to the nervous system and subsequent neuropathic pain can be accompanied by dysesthesia or allodynia. As the pathophysiology of neuropathic pain is complex , the current therapeutic modalities are still limited. Hence, it is imperative to find a new therapeutic agent that helps treat or minimize the symptoms associated with neuropathic pain disorder. Cannabis is a promising plant-based medicine that has garnered much attention of late for the treatment of various conditions associated with pain and inflammation. The potential health implications of cannabis are accredited to Δ-9-tetrahydrocannabinol  and cannabidiol. In the majority of studies todate, THC and CBD alone or in combination have been examined for the treatment of various disorders, such as pain and inflammation. However, few studies have investigated the biological benefits of full spectrum cannabis plant extract. Given that cannabis is known to produce a large number of cannabinoids along with numerous other biologically relevant products including terpenes and others, it stands to reason that studies involving purified THC and/or CBD may not accurately reflect the potential biological benefits of the full-spectrum cannabis extract especially with regard to their crucial role in the treatment of neuropathic pain and inflammation.

Therefore, the goal of this review is to discuss the current knowledge about the potential beneficial effects of full-spectrum cannabis extract in pre-clinical studies involving rodents with neuropathic pain and inflammation.In 1964, Dr. Raphael Mechoulam discovered THC, which was the first identified cannabinoid. This groundbreaking work paved the way for the discovery of the endogenous cannabinoid system of which anandamide and 2-arachidonoylglycerol are considered the main endogenous cannabinoids in higher order mammals,vertical grow system including humans. Both anadamide and 2-arachidonoylglycerol regulate the sensitivity of serotonin, dopamine, gamma-aminobutyric acid  and glutamate in the central nervous system, thus demonstrating how these endogenous cannabinoids regulate many physiological and pathological processes such as pain, immune response, appetite, thermoregulation, energy metabolism, depression, memory and fertility.Anandamide was the first endocannabinoid isolated and it is chemically characterized as N-arachidonoylethanolamine. The name of anandamide originates from Sanskrit term ananda, which refers to “bliss”. Bliss is defined as euphoria that involves physiologic and psychologic harmony. Anandamide is synthesized from the precursor N-arachidonoyl phosphatidylethanolamine by phosphodiesterase phospholipase D enzyme. Once anadamide is synthesized, it is released from the neuronal terminal in a calcium ion-dependent manner and binds to presynaptic cannabinoid receptors. Anandamide is then rapidly up taken by neurons and astrocytes where it is degraded by fatty acid amide hydrolase  into ethanolamine and arachidonic acid. The other endogenous cannabinoid is 2- arachidonoylglycerol, which is synthesized by the hydrolysis of an inositol-1,2-diacylglycerol by phospholipase C. Similar to anadamide, 2-arachidonoylglycerol binds to CB receptors and undergoes rapid biological degradation and catalytic hydrolysis, which is mediated by monaoacylglycerol lipase. Of importance, MGL along with FAAH are considered potential therapeutic targets that can regulate endocannabinoid levels.The most well characterized phytocannabinoids are THC, CBD, cannabinol , cannabigerol  and cannabichromene . These botanical cannabinoids exist as inactive monocarboxylic acids containing precursors referred to as tetrahydrocannabinoic acid , cannabidiolic acid , cannabigerolic acid , and cannabichromenic acid , respectively. The presence of a carboxylic acid moiety on these chemicals precludes cannabinoids, particularly THC, from being bioavailable and binding to either CB receptors or other biological targets. Thus, the conversion of THCA, CBDA, CBGA, and CBCA to THC, CBD, CBN, CBG, and CBG, respectively, through decarboxylation is necessarily before any biological effect can be observed.

Decarboxylation of these carboxylic acids can be promoted by heating the plant above 105 °C, which can be achieved during the smoking or baking process .THC is the primary psychoactive component of Cannabis sativa and chemically analogous to N-arachidonoylethanolamine. THC is a euphoric agent that has anti-nociceptive, anti-inflflammatory, sedative and muscle relaxant effects. Additionally, THC increases appetite, dilates bronchial muscle and it has anti-emetic, anti-spasmodic, neuroprotective and anti-oxidant properties. Mechanistically, the physiological effect of THC is mediated primarily through the activation of CB1 and CB2 receptors with preferential binding to CB1 receptors.This effect might be attributed to the ability of THC to inhibit N-methylD-aspartate  receptor activity in addition to the decrease in the hippocampal acetylcholine release . The decrease in acetylcholine release may be due to the activation of the CB1 receptor on parasympathetic neurons. Intriguingly, it has recently been shown that a low dose of THC reversed the age-related decline in cognitive performance in aged but not young mice. This effect was associated with increased expression of synaptic marker proteins and enhanced hippocampal spine density through glutamatergic CB1 receptors-dependent mechanism . Thus, this study raises the possibility that THC or full-spectrum cannabis extracts may have the potential to reverse cognitive decline in the elderly and suggests an agedependent effect of THC.CBD is the primary non-psychoactive component of Cannabis sativa and possesses sedative, anti-inflammatory, anti-convulsive and anti-psychotic actions, but does not have the typical THC side effects. Of importance, the powerful anti-convulsant effect of CBD appears to be mediated through a CB receptor-independent mechanism. Indeed, CBD mediates neuronal inhibition and anti-epileptic effects through gamma-aminobutyric acid A  and adenosine A1 receptors dependent mechanisms. In addition, CBD has anti-psychotic and neuroprotective effects that are mediated via increasing the effect of dopamine and norepinephrine, activating the 5-hydroxytryptamine 1A  receptor, inhibiting adenosine transporter, blocking T-type voltage-gated calcium channels and reducing glutamate induced-neurotoxicity. Numerous additional effects of CBD have also been reported. For instance, in the heart, CBD inhibits THC-induced tachycardia through the activation of adenosine A1 receptor.

Moreover, it has been reported that CBD protects against cardiac dysfunction, fibrosis, oxidative stress, and cell death signaling pathways in diabetic cardiomyopathy and doxorubicin-induced cardiotoxicity. In addition to the cardiac effects, CBD has recently been shown to be cytotoxic in estrogen receptor-positive and triple negative breast cancer cells through the induction of apoptosis aswell as it increases the uptake of the chemotherapeutic agent, doxorubicin, to induce apoptosis in these cells through transient receptor potential vanilloid type-2 -dependent mechanism. Thus, the potential benefits of CBD are extensive, even independent from the classical endocannabinoid system involving CB receptors.CBN is an oxidized by-product of THC produced in trace amounts by aged cannabis upon long exposure to air. Studies have shown that while CBN is inactive when administered alone to healthy volunteers, it still can potentiate the sedative effect of THC. Given that CBN is closely related to CBD in terms of the chemical structure, it shares the anti-convulsant and anti-inflammatory effects with CBD. The physiological effect of CBN is attributed to the modulation of the CB2 receptor with lower affinity for the CB1 receptor in comparison to THC .CBC is one of the main phytocannabinoids and appears to have no affinity to CB1 and CB2 receptors. Similar to CBD and THC, CBC possesses anti-inflammatory and anti-nociceptive effects through the inhibition of the cyclooxygenase enzyme and its associated prostaglandins. In contrast to CBD, CBC neither has an anti-convulsant effect nor inhibits the activity of cytochrome P450.CBG is the precursor phytocannabinoid compound of THC, CBD and CBC and is only produced in trace amounts in cannabis. Although CBG has low affinity to CB receptors, it is still capable of reducing pain, erythema and inflammation through the inhibition of peripheral lipooxygenase enzyme and the activation of central α2-adrenergic receptor. Furthermore, CBG has an anti-depressant effect because it is a potent anadamide uptake inhibitor as well as a moderate 5- HT1a antagonist.diate their pharmacological actions by binding to CB1 and CB2 receptors and through the regulation of the production and the degradation of endogenous endocannabinoids. Both CB1 and CB2 receptors are 7-domain Gi/o-protein coupled receptors decreasing the level of cyclic-AMP by suppressing adenylate cyclase. CB1 receptors are abundant and widely expressed throughout the CNS  and they are responsible for the psychopharmacological and analgesic effects of THC. Of particular interest, CB1 receptors have high expression level in areas of the brain that are implicated in nociceptive perception, such as the thalamus and amygdala,cannabis grow equipment the midbrain periaqueductal grey matter cells, and the substantia gelatinosa of the spinal cord.

The presynaptic localization of CB1 receptors enables cannabinoids to modulate neurotransmitter release such as dopamine, noradrenaline, glutamate, GABA, serotonin and acetylcholine. The activation of the CB1 receptors in the aforementioned brain areas modulates nociceptive thresholds and produces multiple biological effects by regulating the balance between excitatory and inhibitory neurotransmitters. While the CB2 receptor has limited expression in sensory and CNS cells, it is mainly expressed in peripheral tissues, including keratinocytes and tissues of the immune system such as the lymphatic system. The CB2 receptor was shown to contribute to analgesia through suppressing the release of inflammatory mediators by cells located adjacent to nociceptive nerve terminals. In addition, activation of peripheral CB2 receptors blocks the transduction of pain signals into the CNS. Given that CB2 receptors are expressed in several types of inflammatory cells and immunocompetent cells, it is reasonable to assume that the activation of peripheral CB2 receptors may contribute to analgesic effect in conditions of inflammatory hyperalgesia and neuropathic pain such as MS. Consistent with this notion, increased numbers of microglia/macrophage cells expressing CB2 receptor have been reported in spinal cords derived from MS patients relative to controls, suggesting the involvement of CB2 receptor in the regulation of pain and inflammation in MS patients. Based on these findings, it was proposed that cannabinoid-based pharmacotherapies might be effective therapies for the reduction of pain due to MS.The anti-nociceptive effect of cannabinoids might not necessarily be due entirely to the activation of CB1 and CB2 receptors. Indeed, the analgesic effects may be due to the modulation of the transient receptor potential vanilloid 1 . The evidence supporting this is based on the observation that the anti-nociceptive effect of CBD in neuropathic rats was completely reversed by capsazepine, a known TRPV1 activator. Other receptor sites implicated in the action of CBD include the suppression of putative novel cannabinoid G protein coupled receptor GPR55, NMDAR and α1-adrenoreceptors and the activation of 5HT1A, adenosine A2, and the peroxisome proliferator-activated gamma  receptors. In addition, THC and CBD are positive allosteric modulators of the μ- and δ-opioid receptors, suggesting the involvement of these receptors in the anti-nociceptive effect of THC and CBD. Moreover, CBD has been shown to block low-voltage-activated  Ca+2 channels, stimulate the glycine-receptor, and modulate the activity of FAAH.

The action of CBD via these pathways may be responsible for the suppression of neuronal excitability and pain perception. In addition, there is evidence that CBD inhibits synaptosomal uptake of dopamine, noradrenaline, GABA, serotonin in addition to cellular uptake of anandamide. The modulation of these neurotransmitters might explain the neuroprotective and the anti-nociceptive effects of CBD. Moreover, CBD and THC have been shown to inhibit the cycloxygenase-2 enzyme and the production of arachidonic acid metabolites, prostaglandins, suggesting anti-inflammatory effects. Of note, the inhibition of cycloxygenase-2 was associated with an increase in the level of endocannabinoids, anandamide and 2-AG. This observation suggests that the suppression of cycloxygenase-2 enzyme by CBD and THC may not only decrease nociceptive and inflammatory prostaglandins but it may produce an indirect increase in the level of endocannabinoids, anandamide and 2-AG .Another important biological system that is affected by cannabinoids, at least when consumed orally, is the gastrointestinal microbiota. The gut microbiota is known to produce various metabolites resulting from the fermentation of molecules of either exogenous source  or from endogenous origin. These metabolites can act as signals that can contribute to the maintenance of host immunity and physiology . For example, the gut bacteria Lactobacillus acidophilus, metabolizes tryptophan from dietary sources such as eggs, milk, red meat, and vegetables into diverse metabolites, including indole propionic acid, which can signal through the aryl hydrocarbon receptor.

Illicit drugs are substances that either stimulate or inhibit the central nervous system or cause hallucinogenic effects  to the effect that their nonmedical use has been prohibited globally . For some substances, like cannabis, the prohibition or legalization status varies widely over time and over different countries and states . In the present paper we focus on illicit drugs in a broad sense, including cannabis, ecstasy, stimulants, opioids. We do not consider substances that are legal in the Netherlands, such as nicotine and alcohol. Cannabis is one of the most widely consumed drugs worldwide, with 192.2 million past-year users in 2016, corresponding to 3.9 per cent of the global population aged 15–64 years . Despite the increasing use of cannabis for medicinal purpose and an ongoing debate about medicalization and decriminalization, associations with adverse health effects have been reported. These adverse health effects include development of dependence, cardiovascular disease, impaired respiratory function and mental health problems . Another increasingly popular drug is ecstasy, a psychoactive drug that consists of MDMA. The prevalence in the global population aged 15–64 years is estimated to be 0.4 % . In Europe, approximately 1.7 % of young adults  have used ecstasy, with estimates ranging from 0.3%–5.5% between countries . Other relatively popular illicit drugs include amphetamine and cocaine , with worldwide past year estimated prevalences of 0.77 %, and 0.35 % respectively . The past year prevalence of opioids  was 0.37 % worldwide in 2017 . For all illicit drug use together, the overall disease burden was estimated to be 27.8 million attributable disability-adjusted life-years  in 2017. DALYs reflect the number of years lost due to ill-health, disability or early death. The mortality rate due to illicit drugs was 6.9 deaths per 100,000 people in 2017 . Substance use, including cannabis use, is moderate to highly heritable ; Verweij et al., 2010, 2017. The largest genomewide association  study for cannabis use to date has successfully identified 35 genes  associated with lifetime cannabis use .

Two other genome-wide association studies identified genes for cannabis dependence and cannabis disorder . In the current study we have information on use , and will therefore use the GWA for cannabis use  as discovery sample. Epidemiological studies have consistently shown correlations between use of different substances, such that individuals that use one substance are more likely to also use another . The phenotypic correlations between substances are partly explained by common genetic influences . Many genetic variants, each with a small effect size, contribute to complex behaviors, such as substance use. With methodological advances in molecular genetics and increased sample sizes in GWA studies it has become viable to use many measured genetic variations in individuals to estimate their genetic vulnerability for a certain trait. To do this, polygenic scores  in individuals from a target dataset can be calculated based on their genome-wide genetic data and the genetic effect sizes estimated in large GWA studies . If the PGS in the target set, for example reflecting the genetic vulnerability for cannabis use, is associated with drug use, for example ecstasy, this would suggest that there is overlap in the genes underlying grow cannabis in containers and ecstasy use. In the present study, we used summary-level data from the largest GWA study for lifetime cannabis use to date  to generate PGSs in an independent sample of 8348 individuals registered at the Netherlands Twin Register . We tested the association of the PGS for lifetime cannabis use with ecstasy, stimulants  and a broad category of drug use, including stimulants, opioids and hallucinogens.A significant association  may indicate that there are common underlying genetic predispositions to the use of these substances, or can be the result of a causal association  between the use of the different substances. In that last case, use of cannabis may lead to use of ecstasy or other drugs, and therefore genes associated with cannabis use will also –indirectly- be associated with use of other drugs. The different explanations are not mutually exclusive and are difficult to distinguish.

If a significant association is found between the cannabis PGS and use of other drugs, we will explore the nature of this relationship by repeating the same analyses separately in cannabis users and non-users. If the association between the polygenic risk for cannabis and drug use is only significant in cannabis users and not in never users, this might indicate that causal effects play a role , although other explanations  are still possible. To further explore the causal role of cannabis in other drug use, we also explored drug use in monozygotic twins discordant for cannabis use.Prediction analyses were carried out using generalized estimation equations with a logit link function. To account for familial relatedness, this method uses an exchangeable covariance matrix, allowing for correlated residuals between family members. Analyses were run using robust standard errors for the parameter estimates. Sex, age, and 10 genetic principal components were included as covariates in all analyses. Principal components were included to correct for effects of population stratification. Age was negatively correlated with the outcome measures  and males had a higher prevalence of drug use than females. To explore possible sex differences we tested the interaction between the cannabis PGS and sex for ecstasy, stimulants and any illicit drug use . Estimates of the explained variance  were obtained from logistic regressions by subtracting the pseudo-R2 estimates of the model with only covariates from the model including both the PRSs and covariates. Odds ratios were also obtained through the regression analyses.To inspect how drug use varied with increasing cannabis PRS we used quintile plots. The cannabis PRS was divided in quintiles, and we calculated the odds ratio for respectively ecstasy use, stimulants and any illicit drug use within each quintile. For the twin analyses, we compared the prevalence of drug use in the cannabis using twins to that of their non-using co-twins with a McNemar test . In this design, genetic and common environmental influences are controlled for because MZ twins share all their genetic material and their  home environment. If the association between cannabis use and other drug use is solely explained by genes and/or shared environmental factors, then the twins who have used cannabis and their co-twins who have not should be equal in their use of other drugs. In contrast, if the association is to some extent causal or explained by environmental factors for which twin pairs are discordant, we would expect to find significantly higher prevalences in the cannabis users compared to their unaffected MZ co-twins.We showed that the genetic liability underlying cannabis use significantly explained variability in ecstasy, stimulant, and any illicit drug use.

When the sample was stratified for lifetime cannabis use, this association seemed to be stronger in cannabis users compared to nonusers for ecstasy and stimulants, but not for any drug use. However, this trend was not significant after correction for multiple testing. The observation that the PGSs for cannabis use were significantly associated with the examined drug use variables , suggests genetic overlap between the traits. The explained variance ranged between 0.5 and 1.2 %, which is quite low but consistent with other PGS studies of addictive phenotypes . As far as we know this is the first study exploring the genetic overlap of the genetic vulnerability for cannabis with other illicit drug use. Only a few studies explored genetic overlap across substances using a PGS method. A previous study showed genetic overlap between PGS for cigarettes per day with glasses of alcohol per week and cannabis initiation as well as between PGS for age at onset of smoking and age at regular drinking. However the PGSs for smoking initiation and smoking cessation did not significantly predict alcohol or cannabis use, possibly due to limited power . Demontis et al. showed that a PGS-for lifetime smoking was associated with cannabis use disorder . Recently, Chang et al. tested the association between 5 PGSs for licit substances  with 22 target phenotypes for illicit substance use and substance use disorders. Only 9 of the 110 tested associations were significant. Interestingly, the stimulants  showed some significant results, while associations with sedatives or pain killers were not significant. In particular, the PGS for smoking initiation significantly explained variation in the risk of cocaine, amphetamine, hallucinogens, ecstasy and pot for cannabis initiation, as well as DSM-5 alcohol use disorder . The PGS for drinks per week significantly explained variation in cocaine, amphetamine and ecstasy initiation . Taken together, these results indicate genetic overlap between the use of different substances, although in previous studies not all tested associations were significant. As explained in the introduction, genetic overlap may indicate that there are common underlying genetic predispositions to the use of these substances . In case of drug use, this could be genes involved in the vulnerability for reward , but could also reflect genetic vulnerability for more general personality traits, such as impulsivity, risk-taking behavior or sensation seeking which are also often associated with drug use  or educational attainment . On the other hand, genetic overlap can also be the result of a causal association . To explore whether cannabis use itself caused the use of ecstasy, stimulants or any drugs we tested the association between the PGS for cannabis and the outcome variables in cannabis users and never users separately. The association of the cannabis PGS with ecstasy and stimulant use seemed stronger in cannabis users compared to never users which could point to a causal relationship . This effect was only observed in people born after 1968, but given the fact that the prevalence is higher in this younger group there is probably more power to detect an association than in the older group. Since the association was not significant after correction for multiple testing we must be cautious with drawing conclusions. In addition, we explored the differences in drug use prevalence in MZ twin pairs discordant for cannabis use.

The twins who used cannabis had more often used drugs, compared to their MZ co-twins who never used cannabis. This is in accordance with previous research using the co-twin control methodology . This finding suggest that the differences in illicitit drug use between twins who used cannabis and their unaffected co-twins cannot solely be explained by genetic influences  but that individualspecific environmental factors such as cannabis use play a role. Together, this suggested that cannabis use could be a causal factor for other drug use. Future studies should explore causality with more advanced methods such as Mendelian Randomization , but larger samples sizes are needed than available in the current study to obtain enough power. In previous studies using two-sample bi-directional Mendelian Randomization analyses, no evidence was found for causal relationships between smoking, alcohol, caffeine, and cannabis  but these studies did not includeother illicit drugs. There might not be a sequential order of use for initiation of smoking, alcohol use or caffeine consumption since these substances are widely available and some people start with smoking while others start with drinking first. A gateway from licit substance use to illicit drug use or from one drug  to other drugs  might be more plausible. Ideally, causality should be tested in two directions, because some studies have also found evidence supporting a reverse-gateway hypothesis . For example, cannabis could influence ethanol  levels, although existing findings are inconclusive , and a recent MR study did not find evidence for a causal relationship ). A limitation of the PGS approach is that currently only large GWA studies are available for lifetime cannabis   and opiod use disorder , but not for other illicit drugs such as ecstasy. Large genome wide association studies for illicit drugs are needed as input to calculate reliable PGSs. A strength of the current study is the large discovery sample for cannabis . It is known that a larger discovery sample leads to a more reliable  PGS in the target sample. In the present study we showed as a proof of concept that the PGS for lifetime cannabis use was significantly associated with cannabis use in the target sample.

Two herb spice tobacco grinders were purchased from commercial retailers. The Cannabis research program at the National Institute of Standards and Technology  provided 20 cannabis samples, all of which had the % total THC and % total CBD previously determined through Liquid Chromatography-Photodiode Array .First, 10 µL of the plant extract or reference standards were pipetted onto the PSPME substrate. Next, 10 uL of 0.1% FBBB solution was then pipetted onto the substrate followed by 10 uL of 0.1 N NaOH.The solvents evaporate within 1–2 min as the color develops. A red color is indicative of THC and an orange color is indicative of CBD. The FBBB test was performed in 5 replicates per extract. Each substrate was photographed with a Dino-Lite AM4115ZT digital microscope . A Dino-Lite AM4115T-GRFBY Digital Microscope was used to capture fluorescence images of the substrates. The Dino-Lite AM4115T-GRFBY uses a 480 nm excitation light source and contains emission filters for 510 nm and 610 nm. These images were taken in the absence of ambient light to remove interference from outside sources of light. The visible and fluorescence images are analyzed using the ImageJ software using the RGB measure plugin to obtain the average RGB numerical code across each substrate. All five blue ridge hemp samples and 20 cannabis samples of known cannabinoid concentrations  were evaluated using the FBBB reagent with 5 replicates. For each replicate, a color image, a fluorescence image, and fluorescence spectra were obtained. The color results for all samples are summarized in Table 5. All of the hemp samples formed an orange color when reacted with FBBB, except for Sample 11 and Sample 12, which did not have any reaction. Of the 13 samples that are marijuana , 6 of them produced an orange color instead of the red color indicative of THC.

These six were Sample 6, Sample 9, Sample 10, Sample 18, Sample 19, and Sample 20. For samples 6, 9, 10, and 20 the total CBD was at a higher concentration than total THC, all containing a THC:CBD ratio below 1. Samples 18 and 19 had THC:CBD ratios of 1.0 and 1.4 respectively. The other marijuana type samples had a THC:CBD ratio much higher than 2 and formed a red color. Samples that had a THC:CBD ratio below 2  did not fluoresce brightly under the Dino-Lite microscope at 480 nm excitation. Importantly, the marijuana-type samples that either had no CBD or a high THC:CBD ratio did fluoresce brightly under the Dino-Lite at the same excitation. These results suggest that when there is more CBD than THC in the cannabis grow set up plant, or if the concentrations are similar, the FBBB will produce an orange color indicative of hemp rather than a red color indicative in marijuana. In addition, when the THC:CBD ratio is low, the fluorescence of the chromophore will also be low. The fluorescence spectra from the VSC2000 for hemp-type samples showed a low % intensity at 655 nm, typically between 10% and 20%, and a higher intensity at 695 nm, between 15% and 40%. The exception to this were samples 11 and 12 whose extracts did not react with FBBB and had similar spectra to the blank. The marijuana-type samples with a low THC:CBD  showed similar spectra to the hemp samples, with fluorescence intensities at or below 20% at 655 nm for those with THC:CBD significantly lower than 1. Samples 18, 19, and 20, which have THC:CBD from 0.48 to 1.4, all showed slightly higher intensities at 655 nm than the hemp samples . For marijuana-type samples with a THC:CBD above 2, the intensity of fluorescence increases between 40 and 70% at 655 nm and 695 nm. Low fluorescence intensity for hemp samples at 655 nm is expected since there is very little THC in these samples. For samples 6, 9, and 10 there was much more CBD than THC in the cannabis plant leading FBBB + CBD to form over FBBB + THC. Samples 18, 19, and 20 showed a slightly more intense band at 655 nm. This increase could be attributed to the fact that there is a similar concentration of CBD and THC in these samples and allowed for FBBB to react with both THC and CBD.

In addition, all cannabis extracts contain a band at 695 nm. This interference is likely due to chlorophyll and other pigments from the plant material, however, even with this interference, the difference in fluorescence intensity between hemp and marijuana-type cannabis with a high THC:CBD is noticeable. When the THC:CBD ratio is below 2, the fluorescence intensity decreases. This is consistent with the results obtained from the color images and fluorescence images using the Dino-Lite microscopes. A comparison of a marijuana-type sample and a hemp-type sample through color images, fluorescence images, and the fluorescence spectra is shown in Fig. 7. Linear Discriminant Analysis  was used as a supervised technique to determine whether FBBB can be used to correctly classify hemptype cannabis and marijuana-type cannabis. Each sample described in Table 5 was evaluated in 5 replicates. For each replicate RGB of the color image, RGB of the fluorescence image, and the % intensity at 655 nm and 695 nm in the fluorescence spectra were recorded. The LDA analysis was performed using the JMP software. The first LDA model was constructed using % intensity at 655 nm and % intensity at 695 nm values as the variables. The resulting model had an R2 of 0.61 and misclassified samples 6, 9, 10, 14, 15, 18, 19, 20. Samples 6, 9, 10, and 18–20 are marijuana-type samples with THC:CBD below 2, showing similar fluorescence spectra to hemp samples leading to their misclassification. Samples 6, 9, 10, and 18–20 were removed from the data set and LDA was performed again using the data from the 7 remaining marijuana-type samples  and the 12 hemp-type samples. This analysis resulted in an R2 of 0.999 and no misclassifications. LDA was also performed using the R, G, and B codes for each color image and fluorescence image. LDA of all the samples using RGB for the color images produced an R2 of 0.51 and misclassified samples 3,4, 10, 18–20, and one replicate of 16 and 17 each. To improve the model, all marijuana type samples with THC:CBD below 2 were removed from the data set. Samples 11 and 12 were removed as well since they did not produce a color as they were likely the cause of the misclassification of samples 3 and 4, which produced a light red color.

This did improve the model with the R2 value of 0.95 and only misclassifying one replicate of sample 3. This indicates when using only RGB of the visible image, one should exclude samples that do not form a color as it may cause misclassification. An LDA model of all samples using RGB of the fluorescence images taken for each replicate was also made. This LDA model misclassified multiple hemp-type and marijuana-type samples resulting in an R2 of 0.46. When the marijuana type samples with THC:CBD below 2 were removed from the data set, there were no misclassifications and R2 was 0.995. Finally, an LDA model was made to classify the marijuana-type samples with a high THC:CBD and all the hemp-type samples using the R,G and B  from the color images and R-F,G-F and B-F  from the fluorescence images for a total of 6 variables. This model resulted in a clear separation between hemp-type and marijuanatype cannabis resulting in an R2 of 1.0  with G  providing the highest correlation to hemp  and R-F  providing the highest correlation to THCrich cannabis . A Receiver Operating Characteristic  of the model showed that the area under the curve for both hemp and marijuana are 1, displaying excellent selectivity and sensitivity when combining color and fluorescence to discriminate from hemp-type cannabis  and marijuana type cannabis. The FBBB test was used to evaluate 6 different cannabinoids, 5 commercial hemp strains, 20 cannabis samples, and various herbs and spices. It was determined that when FBBB reacts with THC, it forms a red chromophore that fluoresces under 480 nm light. Conversely, when reacted with CBD or CBD-rich products, such as outdoor cannabis grow, an orange chromophore is formed, and this chromophore does not fluoresce. This is the first time, to the author’s knowledge, that the fluorescence of the FBBB + THC chromophore/fluorophore is reported for a colorimetric test. This fluorescence is easily visualized using a portable Dino-Lite microscope and its spectra obtained with a VSC2000 spectrometer. The intensity and wavelength of the fluorescence for the chromophore combined with the distinct red color it displays makes for a more selective and sensitive test to differentiate between marijuana and hemp. The structure for FBBB + THC has been previously determined by the Almirall lab, as shown in Fig. 1. The chromophore results from an extended conjugation of π-bonds decreasing the distance between energy transitions between the ground state and excited state.

This extended conjugation causes a “red shift” of the FBBB chromophore, which is responsible for the red color and the fluorescence that is observed when THC reacts with FBBB. One theory for CBD + FBBB lacking fluorescence intensity is that CBD has a less rigid structure than THC. It is known that structure rigidity and a fused ring structure increases the quantum efficiency, and therefore fluorescence of a molecule. Since CBD is less rigid than THC and does not have a fused ring structure, it is prone to relaxation through internal conversion rather than through radiative means. Therefore, FBBB + CBD likely relaxes through nonradiative mechanisms, which decreases overall fluorescence. The difference in both color and fluorescence that is observed for FBBB + THC and FBBB + CBD is an advantage that the FBBB test has compared to other tests for presumptive analysis of cannabis, which only use color. The selectivity of the FBBB test was evaluated by analyzing 5 other cannabinoids, herbs, spices, essential oils, tobacco, and hops. None of these substances produced color like that of FBBB + THC nor fluorescence observed. For the colorimetric calibration experiments, it was shown that when the ratio of THC:CBD is above 1, a red color forms indicating that there is marijuana present. These experiments also found that the absolute LODs for THC on the PSPME substrates was as low as 500 ng, which is significantly lower than the LOD for the D-L test . The THC LOD for the 4-AP test is not currently known but expected to be >500 ng. This study demonstrates that the FBBB test is very selective and sensitive for THC, forming a red color and an intense fluorescence that can be distinguished from other chromophores. In addition, this chromophore is long lasting, allowing the color and fluorescence to be observed long after the test is performed. This longlasting color is attributed to the nature of the FBBB being a diazonium salt, which are known to be stable and even used to form dyes in textiles. One limitation that was discovered for the FBBB test is that the reagent is not stable at room temperatures over more than a few days, losing its color and producing no reaction with THC or CBD. The FBBB reagent and the preloaded FBBB substrate were stable in the refrigerator/cooler for at least 45 days. The temperature instability is not ideal for field work since a kit using the Fast Blue BB test would likely be exposed to temperatures above 4 ◦C. For this reason, future work will focus on determining a method to maintain the FBBB stable at ambient temperatures. The analysis of the Blue Ridge Hemp and NIST samples demonstrate that FBBB is very effective at discriminating between hemp-type samples with THC content <0.3%  and marijuana-type samples with a high THC content or THC:CBD ratios. Marijuana-type cannabis containing >0.3% THC and high CBD could be misclassified as hemp but these types of samples are uncommon in seized drugs. The results of these LDA models using RGB inputs support the observed findings of the visual evaluation of the Blue Ridge and NIST samples with FBBB.

We further sought to determine whether gender, dose, cannabinoid content of cannabis used and/or cannabis use sessions across time would predict changes in symptom severity. Results revealed that, on average, respondents self-identifying as having PTSD reported a 62% reduction in the severity of intrusive thoughts, a 51% reduction in flashbacks, a 67% reduction in irritability, and a 57% reduction in the severity of anxiety, from before to after inhaling cannabis. Moreover, these symptom reductions were reported in the majority of cannabis use sessions for intrusive thoughts , flashbacks , irritability , and anxiety . While inhaled cannabis resulted in significant and substantial reductions in ratings of all four of the PTSD symptoms that we assessed, it is important to note that we detected significant heterogeneity in these effects across individuals, indicating that cannabis may not uniformly reduce PTSD symptoms for everyone. Concretely, while the four baseline LCS models confirmed that the reported symptom reductions were statistically significant, the variance estimates for all four models revealed significant individual differences in the rates of change among participants for each symptom. Taken together, these results provide strong evidence that cannabis can provide temporary relief from symptoms of PTSD, but that the magnitude of these effects varies across individuals. One source of this heterogeneity may have stemmed from differences in baseline ratings of the symptoms. The LCS models revealed significant covariance estimates between symptom severity before cannabis use and the latent change factor for each symptom, which indicates that those with more severe symptoms reported greater reductions in their symptoms after cannabis use. This may indicate that cannabis is more effective for more severe symptoms. Alternatively, this finding could also simply reflect the fact that there is more room for improvement of more severe symptoms. While the LCS models indicated that gender did not predict changes in symptom severity from before to after cannabis use ,cannabis drying racks comparisons of men and women’s mean severity ratings before and after cannabis use revealed small but statistically significant gender differences.

Specifically, women reported significantly greater symptom severity before cannabis use for all four PTSD symptoms we assessed. Women also reported significantly greater post-cannabis use severity for intrusions, flashbacks, and anxiety. This finding that women reported more severe symptoms of PTSD than did men is consistent with previous research indicating women are more likely to meet criteria for PTSD and to demonstrate worse symptom severity . The results further revealed that women reported significantly more cannabis use sessions during which flashback and anxiety severity were reduced than did men. In contrast, men reported significantly more sessions during which irritability was reduced than women. Nevertheless, while these differences were statistically significant, they were small in size and rather trivial . Both genders reported that their symptoms were reduced in the vast majority of cannabis use sessions. Concentrations of THC, CBD, and interactions between THC and CBD appeared to have no influence on changes in any of the four symptoms assessed. Cannabis can contain up to 120 cannabinoids, over 250 terpenes, around 50 flavonoids, as well as a number of other molecules that may exert biological action  and therefore it may be one of these other constituents or an entourage effect that is responsible for the therapeutic effects of cannabis on these PTSD symptoms. Unfortunately, information on these other constituents was too sparse in the obtained data to permit for meaningful analyses. Clinical trials are needed where THC, CBD, minor phytocannabinoids and/or terpenes are directly manipulated by investigators to determine the concentrations of these constituents that provide the greatest relief from PTSD symptoms. Results pertaining to the time/cannabis use session predictor in the LCS models revealed no changes in the efficacy of cannabis in reducing anxiety or flashback severity across cannabis use sessions over time. In contrast, time was a significant predictor of reductions in intrusions and irritability, with later cannabis use sessions predicting greater symptom relief than earlier cannabis use sessions. These findings may indicate that cannabis becomes a more effective treatment for intrusions and irritability as it continues to be used to manage these symptoms over time.

Alternatively, this finding may represent a statistical artifact, such that individuals who obtain the greatest relief in intrusions and irritability from cannabis may simply be the most likely to use cannabis for longer periods of time. Further longitudinal studies are required to better establish the direction of this effect. Moreover, results of multilevel models further revealed that the dose of cannabis used increased significantly across time/cannabis use sessions for anxiety, which may be an indicator of tolerance. Collectively these two sets of results indicate that people are using consistent doses to achieve larger reductions in intrusions over time and higher doses to achieve larger reductions in anxiety over time. The escalations in dose for anxiety adds credence to concerns of individuals with PTSD developing cannabis dependence , especially given that excess cannabis use has been associated with more negative long-term outcomes in individuals with PTSD . Interestingly, the severity of baseline symptom ratings did not change significantly across time/cannabis use sessions. This may suggest that while acute use of cannabis leads to perceived reductions in acute symptom severity, these effects may not extend beyond the period of intoxication and regular use of cannabis may simply maintain the disorder over time. In other words, while cannabis intoxication can provide transient relief from PTSD symptoms, long-term cannabis use may not ultimately improve the severity of this disorder. These findings, however, contradict longitudinal data demonstrating long-term benefit of THC on PTSD symptoms and diagnosis over the course of one year  as well as previous research demonstrating that cannabis/cannabinoids impair retrieval of emotionally aversive memories and promote the extinction of fear memories . Alternatively, it is possible that the present finding of consistent baseline symptoms over time simply reflects a tendency for people to self-medicate with cannabis once their symptoms reach a specific threshold.

More controlled longitudinal research is clearly needed to disentangle these complex bi-direction temporal associations.The present study has a number of limitations that should be noted. First, respondents self-identified as having PTSD and it was not possible to verify these diagnoses. As such, some of the individuals in the present sample may have been experiencing sub-clinical PTSD. Further, not all clinically recognized symptoms of PTSD were assessed.The evidence for individual differences in the efficacy of cannabis in reducing symptoms further supports this idea that not all individuals will find cannabis equally effective at reducing their symptoms. Finally, it was not possible for us to include a placebo control group. In the absence of this group, it is likely that some of the reported effects were driven by expectations about the therapeutic potential of cannabis for reducing symptoms of PTSD. Finally, because the app was created for industry, rather than research, purposes only a single item was used to assess each symptom and standard definitions of these symptoms were not provided for users. While single item indicators of constructs such as stress have been demonstrated to possess content, criterion, and construct validity , it is unclear whether this would generalize to indicators of intrusions, flashbacks, irritability, and anxiety. Further, users may have varied in what they considered an intrusion vs. a flashback. Thus, future research should attempt to replicate these findings with a larger sample of patients with clinician-verified diagnoses of PTSD, using a double-blind placebo controlled clinical trial, and standardized measures of the symptoms being assessed. These limitations are offset by numerous strengths of the study. First, this study utilized a large sample of over 400 medical cannabis users who tracked over 11,000 cannabis use sessions over a 31-month period of time. These medical users were able to use a large variety of cannabis products in their own natural environment, affording our study very high ecological validity. We also limited analyses to sessions during which lab-verified THC and CBD data were obtained in order to increase confidence in the THC and CBD concentrations. Thus, the present study has excellent ecological validity, and threats to internal validity are more likely to be implicit, in the form of expectancy effects.Cannabis is currently legal for adult use  in 11 US states, the District of Columbia, Canada, and several other countries, and retailer licensing laws vary widely . California legalized cannabis for medicinal use in 1996 and for adult use in 2016 . The 2016 Control, Regulate, and Tax Adult Use of Marijuana Act allows the state, counties, and cities to regulate commercial medicinal and adult-use retail cannabis grow tray sales. effective January 1, 2018, cannabis retailers must obtain a state license from the California Bureau of Cannabis Control  as well as local authorization .

State law grants cities and counties the right to allow, prohibit, or choose not to regulate cannabis businesses in their jurisdictions . Incorporated cities may have their own local ordinances for regulating commercial cannabis activities that are separate from county regulations. The BCC began accepting applications for retail licenses in December 2017. To obtain a license, retailers must document acceptable procedures for transportation, inventory, quality control, and security, provide the business formation and ownership documents, demonstrate compliance with environmental and labor laws, and prove that they own or lease a location that is not near schools or on Tribal land. Licensed retailers were allowed to open on January 1, 2018. Washington State established a similar retailer licensing process in 2012. Individual counties and cities implemented various temporary and permanent restrictions on retail cannabis sales, resulting in a patchwork of local ordinances throughout the state.  Furthermore, numerous unlicensed retailers appeared during the two years following legalization of adult-use cannabis, but prior to the issuance of cannabis retail licenses . This sequence of events appears to be repeating in California. Numerous unlicensed cannabis retailers opened throughout the state following the law’s passage in November 2016 but before the licensing application process began in December 2017 . Even after licensing began, the number of applications quickly outpaced the BCC’s ability to review them, creating a backlog of pending applications. Enforcement efforts to close unlicensed retailers also lagged; local regulators stated whenever they closed an unlicensed retailer, several more appeared . Therefore, in 2018–2019, a combination of licensed and unlicensed retailers operated throughout California . This illustrates some of the challenges faced by state and local governments in regulating adult-use retail cannabis. The high prevalence of unlicensed cannabis retailers might thwart municipalities’ efforts to prevent youth access to cannabis and cannabis-related health emergencies such as acute psychosis . A comparison of 37 licensed and 92 unlicensed cannabis retailers in Los Angeles County  found that unlicensed dispensaries were more likely to sell high potency cannabis products, allow onsite consumption, sell products designed to be attractive to children, and sell products without child-resistant packaging. As of 2019, only 108  of California’s 485 municipalities allow any type of cannabis business to operate in their jurisdictions, and 18 of the 58 counties permit cannabis businesses in their unincorporated areas  ; these numbers have fluctuated throughout 2018 and 2019 as municipalities without regulations began to pass new ordinances . The licensing process has been slower than expected  because of the high cost of establishing a cannabis business, as well as public safety concerns associated with cannabis operations in a community. Meanwhile, unlicensed retailers have proliferated . Studies in several states have found that both licensed and unlicensed cannabis retailers tend to locate in areas with more racial and ethnic minority residents, more poverty, and more alcohol outlets . This is similar to alcohol and tobacco retailers, which are more concentrated in areas with more racial and ethnic minorities, more low-income households, and lower social capital . A high concentration of unlicensed retailers in disadvantaged communities could exacerbate health disparities in chronic respiratory diseases, acute respiratory distress from contaminated THC, motor vehicle accidents, and unintentional overdoses of mislabeled products . Research is needed to understand the disparities created by locations of unlicensed vs. licensed cannabis retailers.

NEET categorization identifies youth who are disconnected from employment and education structures, i.e., not engaged in any form of employment, education, or training structures . Precarious/institutional housing status included any participants who indicated living in a rooming or boarding house, group home, foster care, supportive/transitional housing, treatment facility, or shelter, or who were couch surfing or living on the street . The items in the GAIN-SS are endorsed based on recency of symptoms, i.e. 0 , 1 , 2  and 3 . For the purposes of the current analyses, past month and 2–12 months were combined to indicate past year symptom endorsement. Each scale score is based on the number of symptoms endorsed in the past year, with scores ranging from 0 to 5. Based on scale standards, a youth is considered to have a high probability for a diagnosis if three or more items in a subscale are endorsed in the past year. In the current study, the GAIN-SS domains were analyzed as continuous scores  rather than cutoff scores for the likelihood of a diagnosis due to a ceiling effect. With permission from Chestnut Health Systems to the project leads at [BLIND], the GAIN-SS was modified by adding seven items to create a 27 item version that was used in this project. The seven additional items screen for traumatic stress , distorted thinking , excessive internet or videogame use , gambling issues  and eating concerns .The Trauma History Screen  was used . It asks respondents to endorse whether they have ever experienced any of 13 specific forms of traumatic events, including accidents, natural disasters, sexual trauma, bullying, etc., plus one item referring to any “other” type of traumatic event. The test-retest reliability of the exposure to the assessed stressors has been found to be .93 for the total scale score . To tailor the tool to an adolescent sample, our team removed an item referring to military trauma and added two items referring to experiencing bullying ; other minor adjustments were made to adapt to a youth population . The resulting scale had 14 specific items and one ‘other’ item. For the purposes of this study, the trauma variable is defined as the sum of the number of types of trauma to which the participant has been exposed ; analyses were rerun excluding the bullying items given that they are newly added, unvalidated items.

The exposure variable of interest in the current analyses is the age of first use of cannabis, dichotomized as < 14 years of age versus 14+, followed by age of onset as a continuous variable in the final analyses. Using descriptive statistics, we characterized the sample on demographic characteristics using chi-square analyses. We conducted the subsequent analyses controlling for sex and duration of use, given that there were duration of use differences between the two groups in association with age and that sex differences are consistently found in cannabis use behaviours . We then conducted multiple logistic regression analyses, vertical grow system controlling for sex and duration of use, independently for individual exposure variables, which included each substance use variable . Crosstabulations described proportions by age group. We conducted ANCOVAs to analyze the association between cannabis age of onset groups and GAIN-SS domains, controlling for duration of use and sex, with logistic regressions for the GAIN-SS extension items. We then identified the substances that participants reported most often as the substance first used based on the AADIS-age of onset variable; since almost all participants reported their youngest age of onset for cannabis, tobacco, and/or alcohol, these three substances were further explored. Venn diagrams were drawn using EulerAPE software  to characterize the age of the first substance of use  in the 14+ and < 14 groups, examining age-of-first use percentages for each substance and then age-of-first use percentages for co-occurring substances when there was overlap, with chi-square tests for significance. Based on the exploratory findings and to expand on the findings for the two dichotomous age groupings, we conducted multiple regression analyses to identify factors uniquely associated with age of first cannabis use as a continuous variable; entered into the model were sex and duration of use in Block 1 as control variables, then in Block 2, each of the primary variables identified as significant in the between-group comparisons . Collinearity diagnoses demonstrated that none of the variables were highly correlated. Pairwise deletion was used. For multiple comparisons, the False Discovery Rate  correction was used . Statistical analyses were conducted with using SPSS 24.0 . This study characterized clinical risk profiles for those initiating cannabis use in early adolescence , i.e., prior to the transition to secondary school and in an age range rarely considered in research, in comparison to those initiating cannabis use in mid-to-late adolescence through to early adulthood , through direct comparison of patterns of substance use behaviours and co-occurring concerns. Nearly 30% of service-seeking youth reported initiating cannabis use before the age of 14. Results support distinct and clinically meaningful differences between these age groups, with earlier cannabis use initiation serving as an important marker for more problematic concurrent mental health and substance use concerns.

The under 14 and 14+ groups had similar sociodemographic profiles, with some important differences: the under 14 group was more likely to be NEET, precariously housed, and involved in the legal system. The under 14 group also reported more frequent  polysubstance use, with an earlier age of onset for all substances. Youth initiating cannabis use at under age 14 endorsed more externalizing disorder symptoms, more crime/violence-related behaviors, and more co-occurring concerns. This finding did not hold up in the current study; however, internalizing disorder symptoms were high across both early and later onset cannabis users in the current study, suggesting a ceiling effect. The association between early cannabis use and externalizing disorders found in the current study has been previously demonstrated . Co-occurring challenges were highly endorsed: those in the under 14 group in the current sample were more likely to endorse symptoms from all four domains of the GAIN-SS . Higher trauma exposure is an additional notable finding, given the demonstrated association between trauma, mental health challenges, and self-medication via substance use . Further research is required to better understand the role of a diversity of risk factors — including mental health, concurrent disorders, trauma, environmental and social risk factors — and how they may influence each other leading to varying levels of risk for early age of cannabis use onset. In terms of polysubstance use profiles, participants who began using cannabis under the age of 14 were more likely to begin their substance use trajectories with cannabis rather than alcohol, which differed from the profiles of those who initiated cannabis at a later age; early cannabis initiators also initiated other substances at a younger age. Behrendt et al.  found that alcohol use preceded cannabis use for a vast majority of young people and that only 4.4% reported initiating cannabis and alcohol use in the same year; given changes in the social acceptability of cannabis, the increased rate of cannabis as a first substance of use and of concurrent onset of cannabis and alcohol may be a cohort effect that requires further attention in research. Previous literature has supported an association between early cannabis initiation and the development of CUD . However, little guiding research is available to shed light on the trajectories of the earliest cannabis initiators. Previous research has pointed to the role of polysubstance use in ongoing substance use trajectories ), a finding that was supported in the current study. There are a number of possible risk and protective factors that may potentially mediate and moderate progression to CUDs, such as resilience, substance use among peers, polysubstance use, legal system involvement, and mental health service. An important future research direction will be to investigate the role of diverse risk factors and protective factors in the progression towards and away from CUD. A secondary and more exploratory goal of the current study was to identify which of the sociodemographic, substance use and mental health variables would hold as unique associations with earlier onset cannabis use. Legal system involvement and crime/ violence behaviors were most strongly associated with early cannabis initiation. The relation between early cannabis use onset and factors such as crime/violence and externalizing disorders have previously been demonstrated . Youth with legal system involvement are often found to have social networks that consist of peers with behavioral issues and substance use , and are often characterized as having risk associated with particular personality profiles. 

Legal system involvement may reflect social and/or personality risk factors that may be more predictive of early initiation of cannabis grow equipment. Combined with the higher externalizing disorder symptoms, criminal justice involvement may point to behavioral concerns that may lead to both early cannabis use and crime and violence challenges, although the directionality is unclear. However, it should be noted that most of the data was collected while cannabis use was illegal in Canada for people of all ages locally; further work should explore how these findings might change with changes in legislation. Nevertheless, previous research has suggested that youth with cannabis use who are referred by the criminal justice system may stay in treatment longer than those who were not referred , and that they can benefit substantially from substance use treatment . Additional supports for navigating the criminal justice system may be warranted for some youth in this group. These findings have important implications for cannabis-use prevention, early intervention, and treatment initiatives. Findings are mixed on whether prevention initiatives are effective in younger or older adolescents. A meta-analysis of cannabis prevention interventions for adolescents  found interventions are more effective among high school students  than among younger students, a finding that was attributed to developmental factors . However, another systematic review found programs designed to prevent cannabis use among adolescents and young adults  to be more effective when targeted towards a younger age group , since the program would potentially precede the onset of cannabis use . Based on the findings of the current study, interventions aiming to prevent or delay the first use of cannabis should start early, particularly for more vulnerable children and youth. Targeting older students may be too late for some youth. Optimal prevention and early intervention efforts should be developed with the knowledge that some youth will have already tried cannabis, even during childhood; as these may be the most vulnerable youth, it should be kept in mind that they may also have considerably more concurrent issues. Preventionists and early interventionists are encouraged to continue working to optimize cannabis prevention programs in age-appropriate ways for children and youth at different ages and with different levels of cannabis experience or non-experience. Overall, these findings have meaningful clinical implications for treatment among youth seeking services across sectors. Notably, the results highlight the importance of taking early cannabis initiation into account to understand the vulnerabilities and concurrent mental health, behavioral, substance use, and other concerns of youth. However, as about half of youth among both early cannabis initiators and later initiators began using cannabis, alcohol, and tobacco at about the same time, the use of any of these substances should be taken into account as markers that may suggest the need for further assessment of substance use. Given the comorbidity of mental health and substance use problems in youth, especially youth with higher levels of vulnerability, like those who initiate cannabis use early, youth-focused service providers are encouraged to consider youth substance use as part of routine youth mental health and wellness services. For youth presenting with substance-related problems, service providers should consider current and previous substance use, particularly age of cannabis use onset, as part of the assessment and service planning process. For treatment services, asking the age of onset of cannabis use may provide insight into historical and current vulnerabilities, as the duration of use is strongly associated with multiple outcomes.