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Journal of Cannabis Research

Aims and scope.

The Journal of Cannabis Research  is an international, fully open access, peer-reviewed journal covering all topics pertaining to cannabis , including original research, perspectives, commentaries and protocols. Our goal is to provide an accessible outlet for expert interdisciplinary discourse on cannabis research.

Changes in health-related quality of life over the first three months of medical marijuana use

Authors: Michelle R. Lent, Thomas R. McCalmont, Megan M. Short and Karen L. Dugosh

Genome-wide identification of cannabinoid biosynthesis genes in non-drug type Cannabis ( Cannabis sativa L.) cultivar

Authors: Benny Jian Rong Sng, Yu Jeong Jeong, Sing Hui Leong, Jae Cheol Jeong, Jiyoung Lee, Sarojam Rajani, Cha Young Kim and In-Cheol Jang

Assessment of frontal lobe functions in a sample of male cannabis users currently in abstinence: correlations with duration of use and their functional outcomes

Authors: El-Shimaa Tag-Eldeen, Magda Fahmy, Khaled Anwar and Omneya Ibrahim

Thermo-chemical conversion kinetics of cannabinoid acids in hemp ( Cannabis sativa L .) using pressurized liquid extraction

Authors: Urvashi, Joon-Hee Han, Min Hong, Tae-Hyung Kwon, Melvin Druelinger, Sang-Hyuck Park, Chad A. Kinney and Kenneth J. Olejar

The attitudes, knowledge and confidence of healthcare professionals about cannabis-based products

Authors: Emilio Russo, Paula Martinez Agredano, Peter Flachenecker, Charlotte Lawthom, Duncan Munro, Chandni Hindocha, Makarand Bagul and Eugen Trinka

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Processing and extraction methods of medicinal cannabis: a narrative review

Authors: Masoumeh Pourseyed Lazarjani, Owen Young, Lidya Kebede and Ali Seyfoddin

Delta-8-THC: Delta-9-THC’s nicer younger sibling?

Authors: Jessica S. Kruger and Daniel J. Kruger

Cannabis consumption is associated with lower COVID-19 severity among hospitalized patients: a retrospective cohort analysis

Authors: Carolyn M. Shover, Peter Yan, Nicholas J. Jackson, Russell G. Buhr, Jennifer A. Fulcher, Donald P. Tashkin and Igor Barjaktarevic

Δ9-Tetrahydrocannabivarin (THCV): a commentary on potential therapeutic benefit for the management of obesity and diabetes

Authors: Amos Abioye, Oladapo Ayodele, Aleksandra Marinkovic, Risha Patidar, Adeola Akinwekomi and Adekunle Sanyaolu

Total and differential white blood cell count in cannabis users: results from the cross-sectional National Health and Nutrition Examination Survey, 2005–2016

Authors: Omayma Alshaarawy

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Editor-in-Chief

We are recruiting!

Journal of Cannabis Research is recruiting Associate Editors. As the growth of the journal continues, we are looking to expand our editorial team. If you have experience in any form of cannabis research, we would like to hear from you. Please follow the link below to find out more about the role and apply.

Webinar Series: Institute of Cannabis Research

Register for the Webinar Series hosted by the Institute of Cannabis Research and watch a comprehensive archive of cutting edge science presented by experts across the spectrum of cannabis research. The Cannabis Research Series hosted in collaboration with the Lambert Center at Thomas Jefferson University Cannabis Plant Science and Cultivation , hosted in collaboration with the Volcani Institute in Israel.

The Two Sides of Hemp

About the Institute

The Journal of Cannabis Research is the official publication of the Institute of Cannabis Research (ICR), which was established in June 2016 through an innovative partnership between Colorado State University Pueblo, the state of Colorado, and Pueblo County.

The ICR is the first US multi-disciplinary cannabis research center at a regional, comprehensive institution. The primary goal of the ICR is to conduct or fund research related to cannabis and publicly disseminate the results of the research, which it does so in partnership with the Journal. It also advises other Colorado-based higher education institutions on the development of a cannabis-related curriculum and supports the translation of discoveries into innovative applications that improve lives. 

Find out more about the the Institute via the link below, as well as the Colorado state University–Pueblo website and Institute's research funding outcomes .

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New momentum in exploring marijuana's medical benefits

Marijuana has been lauded and lambasted for its potential effects on the body. in part 1 of a 2-part series, we investigate the accumulating evidence for the effectiveness of cannabis in treating some medical conditions.

Bryn Nelson PhD

David B. Kaminsky MD, FIAC

Researchers have long asserted that the science has badly trailed the claims and counterclaims of supporters and detractors. Over the past 5 years, however, high-quality trials and reviews have begun to close the gap. Of the cannabis plant's roughly 140 unique chemical constituents, or phytocannabinoids, 2 of the best studied are Δ-9-tetrahydrocannabinol (THC), the plant's main psychoactive ingredient, and cannabidiol (CBD).

In an April 2021 telebriefing on cannabis and health sponsored by the nonprofit journalism service SciLine, experts noted that CBD is gaining prominence mainly because of its significant differences with THC. “What is unique about CBD is that although it is psychoactive—so we think that it is helpful for some indications for the central nervous system and in the brain, like anxiety, and perhaps pain—it is not intoxicating,” says Ziva Cooper, PhD, director of the University of California, Los Angeles Cannabis Research Initiative. Some scientists believe that other phytocannabinoids and hundreds of separate chemicals called terpenes might likewise have therapeutic effects, although Dr. Cooper cautions that most research on these chemicals has so far been limited to animal studies.

Conclusive or Substantial Evidence for Some Effects

Despite the unknown effects, evidence is steadily mounting that at least some cannabis health claims are backed by science. In 2017, the National Academies of Sciences, Engineering, and Medicine released The Health Effects of Cannabis and Cannabinoids: The Current State of Evidence and Recommendations for Research , a report by an expert committee that reviewed the available evidence for 20 indications. 1 As expected, the committee found conclusive evidence that oral cannabinoids were effective in preventing and treating chemotherapy-induced nausea and vomiting. The committee also found substantial evidence that cannabis or cannabinoid compounds were likely to significantly reduce the symptoms of patients with chronic pain and that the short-term use of some oral cannabinoid medications improved the self-reported symptoms of adults with multiple sclerosis–related muscle spasms.

The committee found moderate evidence that cannabis or cannabinoids could improve the symptoms of fibromyalgia and, at least in the short term, improve sleep disturbances associated with obstructive sleep apnea syndrome. The report cited limited evidence of effectiveness for increasing appetite and decreasing weight loss associated with HIV and AIDS, for improving the symptoms of Tourette syndrome and posttraumatic stress disorder, and for aiding the public speaking ability of patients with social anxiety disorders. Dr. Cooper, a member of the review committee, emphasizes that most of the assessed studies did not use the cannabis plant itself, and none used medical dispensary products. “So, we have very little knowledge about the whole cannabis plant and what people are using,” she says.

Since the report's release, other studies have offered compelling evidence that a purified extract of CBD called Epidiolex is effective in alleviating epileptic seizures associated with 2 rare but severe conditions: Lennox-Gastaut syndrome and Dravet syndrome. 2 On the basis of that evidence, in fact, the Food and Drug Administration (FDA) approved Epidiolex in 2018, and this made it “the first FDA-approved drug that contains a purified drug substance derived from marijuana” according to the agency.

Igor Grant, MD, director of the Center for Medicinal Cannabis Research at the University of California, San Diego, says that the National Academies report agreed with his group's longstanding conclusions about the usefulness of cannabis for managing pain, particularly neuropathic pain. “I think what is not known is how enduring the therapeutic effect is,” he says. At least so far, Dr. Grant says, studies have suggested that low-dose cannabis is a relatively safe pain treatment and provides relief comparable to the relief provided by some commonly used drugs such as the anticonvulsant lamotrigine, although not as much relief as some antidepressants.

The effectiveness of cannabis against low- to mid-grade chronic pain has, in fact, widened discussions about its potential as a safer alternative to prescription opioids. A 2014 analysis found that US state laws permitting medical cannabis “are associated with significantly lower state-level opioid overdose mortality rates.” 3 In a 2019 commentary, Thomas Clark, PhD, a professor of biology at Indiana University South Bend, cited evidence that moderate use of marijuana can help to reduce reliance on prescriptions for multiple drug classes. The result of fewer overdose deaths from more dangerous drugs, he argued, should be factored into the potential health benefits of cannabis. 4

A Drug of Many Contradictions

What is becoming clear, Dr. Grant says, is that cannabis seems to have a biphasic effect for many indications. It is ineffective at low doses, effective at modest doses, and then again ineffective, even harmful, at high doses. This effect can be readily observed with anxiety: Although cannabis has calming and antianxiety effects when taken in low to moderate doses, it can produce significant anxiety and even paranoia and hallucinations at higher doses.

“[CBD] may be a useful treatment for some forms of psychosis or schizophrenia.” –Igor Grant, MD

Another study in rats is examining the potential of CBD to treat alcohol addiction by targeting what is known as the “craving effect.” Craving, Dr. Grant says, can lead to relapse by creating a neural memory of the high and an urge to avoid the discomfort of withdrawal. “There is a little bit of evidence in the animal literature that cannabidiol may actually lessen this craving effect,” he says. The potential effect, if confirmed in humans, would suggest that although some cannabis constituents such as THC can produce addiction, CBD may have the opposite effect.

Hymie Anisman, PhD, professor of neuroscience at Carleton University in Ottawa, Ontario, Canada, cowrote a 2019 commentary pointing out that most studies examining the potential of cannabinoids to treat posttraumatic stress disorder have been small and of low quality. 5 Nonetheless, he and his coauthors concluded that the few reported studies support further detailed investigation into the potential therapy. The number of untested claims has only proliferated since his commentary.

“I'm more confused now than I was then,” Dr. Anisman says. Even so, he senses “a lot of excitement in Canada about cannabis research and the potential for various therapeutics.” The rub is that “much more needs to be done,” he says. “We also take for granted that what happens in rodents will naturally translate to humans, and it doesn't always work like that.”

More research tools may be on the way. After decades with only 1 federally approved cannabis growing facility at the University of Mississippi, the US Drug Enforcement Administration announced in May 2021 that it had issued memoranda of agreement to several additional growers. “I think it's definitely a positive step,” Dr. Grant says. More growing facilities may help to supply plants with a broader diversity of THC:CBD ratios, enable more methods of administration such as edibles and vaping, and achieve higher phytocannabinoid concentrations that reflect what real-world users are consuming.

An even more helpful step from a public health standpoint, Dr. Grant says, would be legislation that moves cannabis to a more permissive category of controlled substances to ease the ability of research centers such as his to study the effects of products that are legal at the state level. “What are Californians using and what are Coloradoans using, and what are the good, bad, and ugly effects of that? We just don't know,” he says. As more people turn to cannabis for recreation or medicine, finding out will become increasingly urgent.

• 1 National Academies of Sciences, Engineering, and Medicine . The Health Effects of Cannabis and Cannabinoids: The Current State of Evidence and Recommendations for Research . National Academies Press; 2017 . doi: 10.17226/24625 Google Scholar
• 2 Devinsky O , Cross JH , Laux L , et al. Trial of cannabidiol for drug-resistant seizures in the Dravet syndrome . N Engl J Med . 2017 ; 376 : 2011 - 2020 . doi: 10.1056/NEJMoa1611618 10.1056/NEJMoa1611618 CAS PubMed Web of Science® Google Scholar
• 3 Bachhuber MA , Saloner B , Cunningham CO , Barry CL . Medical cannabis laws and opioid analgesic overdose mortality in the United States, 1999-2010 . JAMA Intern Med . 2014 ; 174 : 1668 - 1673 . doi: 10.1001/jamainternmed.2014.4005 10.1001/jamainternmed.2014.4005 PubMed Web of Science® Google Scholar
• 4 Clark TM . Analyses of the public health impact of cannabis must include the health benefits of moderate use . J Drug Abuse . 2019 ; 5 : 3 . Google Scholar
• 5 Abizaid A , Merali Z , Anisman H . Cannabis: a potential efficacious intervention for PTSD or simply snake oil? J Psychiatry Neurosci . 2019 ; 44 : 75 - 78 . doi: 10.1503/jpn.190021 10.1503/jpn.190021 PubMed Web of Science® Google Scholar

Volume 129 , Issue 8

August 2021

Pages 575-576

• CytoSource: Current issues for Cytopathology

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• Balancing risks and...

Balancing risks and benefits of cannabis use: umbrella review of meta-analyses of randomised controlled trials and observational studies

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Benefits and risks of cannabinoids

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• Peer review
• Marco De Toffol , psychiatrist 8 ,
• Jong Yeob Kim , public health doctor 9 ,
• Min Je Choi , psychiatrist 9 ,
• Brendon Stubbs , advanced fellow 10 11 ,
• Trevor Thompson , associate professor of clinical research 12 ,
• Joseph Firth , research fellow 13 14 ,
• Alessandro Miola , psychiatrist 15 ,
• Giovanni Croatto , psychiatrist 16 ,
• Francesca Baggio , psychiatrist 16 ,
• Silvia Michelon , psychiatrist 17 ,
• Luca Ballan , psychiatrist 17 ,
• Björn Gerdle , professor 18 ,
• Francesco Monaco , psychiatrist 19 20 ,
• Pierluigi Simonato , psychiatrist 21 ,
• Paolo Scocco , psychiatrist 22 ,
• Valdo Ricca , Professor 23 ,
• Giovanni Castellini , associate professor 23 ,
• Michele Fornaro , assistant professor 24 ,
• Andrea Murru , researcher 25 ,
• Eduard Vieta , professor 25 ,
• Paolo Fusar-Poli , professor 5 26 ,
• Corrado Barbui , professor 27 ,
• John P A Ioannidis , professor 28 29 30 ,
• Andrè F Carvalho , senior researcher 31 ,
• Joaquim Radua , associate professor 32 ,
• Christoph U Correll , professor 7 33 34 ,
• Samuele Cortese , professor 6 35 36 37 38 ,
• Robin M Murray , professor 39 ,
• David Castle , professor 40 41 ,
• Jae Il Shin , professor 42 43 ,
• Elena Dragioti , associate professor 18 44
• 1 Department of Psychiatry, University of Ottawa, Ontario, ON, Canada
• 2 On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ontario, ON, Canada
• 3 Ottawa Hospital Research Institute, Clinical Epidemiology Program, University of Ottawa, Ottawa, ON, Canada
• 4 School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
• 5 Early Psychosis: Interventions and Clinical detection Lab, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK
• 6 Centre for Innovation in Mental Health-Developmental Lab, School of Psychology, University of Southampton, and NHS Trust, Southampton, UK
• 7 Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
• 8 Psychiatry Unit, Veris Delli Ponti Scorrano Hospital, Department of Mental Health, ASL Lecce, Lecce, Italy
• 9 Yonsei University College of Medicine, Seoul, South Korea
• 10 Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
• 11 Physiotherapy Department, South London and Maudsley NHS Foundation Trust, London, UK
• 12 Centre of Chronic Illness and Ageing, University of Greenwich, London, UK
• 13 Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
• 14 Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
• 15 Neurosciences Department, Padua Neuroscience Center, University of Padua, Italy
• 16 Mental Health Department, AULSS 3 Serenissima, Mestre, Venice, Italy
• 17 Department of Mental Health, AULSS 7 Pedemontana Veneto, Italy
• 18 Pain and Rehabilitation Centre, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
• 19 Department of Mental Health, Asl Salerno, Salerno, Italy
• 20 European Biomedical Research Institute of Salerno, Salerno, Italy
• 21 Department of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
• 22 Mental Health Department, ULSS 6 Euganea, Padova, Italy
• 23 Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
• 24 Section of Psychiatry, Department of Neuroscience, University School of Medicine Federico II, Naples, Italy
• 25 Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
• 26 Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
• 27 WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
• 28 Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA
• 29 Meta-Research Innovation Center Berlin, Berlin Institute of Health, Charité Universitätsmedizin, Berlin, Germany
• 30 Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, USA
• 31 IMPACT - The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Deakin University, Geelong, VIC, Australia
• 32 Institut d'Investigacions Biomediques August Pi i Sunyer, CIBERSAM, Instituto de Salud Carlos III, University of Barcelona, Barcelona, Spain
• 33 Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
• 34 Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
• 35 Clinical and Experimental Sciences (Central Nervous System and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
• 36 Solent NHS Trust, Southampton, UK
• 37 Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
• 38 Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, New York, NY, USA
• 39 Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College of London, London, UK
• 40 Department of Psychiatry, University of Tasmania, Sandy Bay, TAS, Australia
• 41 Co-Director, Centre for Mental Health Service Innovation, Department of Health, Tasmania, Australia
• 42 Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
• 43 Severance Underwood Meta-research Center, Institute of Convergence Science, Yonsei University, Seoul, South Korea
• 44 Research Laboratory Psychology of Patients, Families and Health Professionals, Department of Nursing, School of Health Sciences, University of Ioannina, Ioannina, Greece
• Correspondence to: M Solmi msolmi{at}toh.ca
• Accepted 27 June 2023

Objective To systematically assess credibility and certainty of associations between cannabis, cannabinoids, and cannabis based medicines and human health, from observational studies and randomised controlled trials (RCTs).

Design Umbrella review.

Data sources PubMed, PsychInfo, Embase, up to 9 February 2022.

Eligibility criteria for selecting studies Systematic reviews with meta-analyses of observational studies and RCTs that have reported on the efficacy and safety of cannabis, cannabinoids, or cannabis based medicines were included. Credibility was graded according to convincing, highly suggestive, suggestive, weak, or not significant (observational evidence), and by GRADE (Grading of Recommendations, Assessment, Development and Evaluations) (RCTs). Quality was assessed with AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews 2). Sensitivity analyses were conducted.

Results 101 meta-analyses were included (observational=50, RCTs=51) (AMSTAR 2 high 33, moderate 31, low 32, or critically low 5). From RCTs supported by high to moderate certainty, cannabis based medicines increased adverse events related to the central nervous system (equivalent odds ratio 2.84 (95% confidence interval 2.16 to 3.73)), psychological effects (3.07 (1.79 to 5.26)), and vision (3.00 (1.79 to 5.03)) in people with mixed conditions (GRADE=high), improved nausea/vomit, pain, spasticity, but increased psychiatric, gastrointestinal adverse events, and somnolence among others (GRADE=moderate). Cannabidiol improved 50% reduction of seizures (0.59 (0.38 to 0.92)) and seizure events (0.59 (0.36 to 0.96)) (GRADE=high), but increased pneumonia, gastrointestinal adverse events, and somnolence (GRADE=moderate). For chronic pain, cannabis based medicines or cannabinoids reduced pain by 30% (0.59 (0.37 to 0.93), GRADE=high), across different conditions (n=7), but increased psychological distress. For epilepsy, cannabidiol increased risk of diarrhoea (2.25 (1.33 to 3.81)), had no effect on sleep disruption (GRADE=high), reduced seizures across different populations and measures (n=7), improved global impression (n=2), quality of life, and increased risk of somnolence (GRADE=moderate). In the general population, cannabis worsened positive psychotic symptoms (5.21 (3.36 to 8.01)) and total psychiatric symptoms (7.49 (5.31 to 10.42)) (GRADE=high), negative psychotic symptoms, and cognition (n=11) (GRADE=moderate). In healthy people, cannabinoids improved pain threshold (0.74 (0.59 to 0.91)), unpleasantness (0.60 (0.41 to 0.88)) (GRADE=high). For inflammatory bowel disease, cannabinoids improved quality of life (0.34 (0.22 to 0.53) (GRADE=high). For multiple sclerosis, cannabinoids improved spasticity, pain, but increased risk of dizziness, dry mouth, nausea, somnolence (GRADE=moderate). For cancer, cannabinoids improved sleep disruption, but had gastrointestinal adverse events (n=2) (GRADE=moderate). Cannabis based medicines, cannabis, and cannabinoids resulted in poor tolerability across various conditions (GRADE=moderate). Evidence was convincing from observational studies (main and sensitivity analyses) in pregnant women, small for gestational age (1.61 (1.41 to 1.83)), low birth weight (1.43 (1.27 to 1.62)); in drivers, car crash (1.27 (1.21 to 1.34)); and in the general population, psychosis (1.71 (1.47 to 2.00)). Harmful effects were noted for additional neonatal outcomes, outcomes related to car crash, outcomes in the general population including psychotic symptoms, suicide attempt, depression, and mania, and impaired cognition in healthy cannabis users (all suggestive to highly suggestive).

Conclusions Convincing or converging evidence supports avoidance of cannabis during adolescence and early adulthood, in people prone to or with mental health disorders, in pregnancy and before and while driving. Cannabidiol is effective in people with epilepsy. Cannabis based medicines are effective in people with multiple sclerosis, chronic pain, inflammatory bowel disease, and in palliative medicine but not without adverse events.

Study registration PROSPERO CRD42018093045.

Funding None.

Introduction

Cannabis contains over 100 cannabinoids, of which Δ 9 -tetrahydrocannabinol and cannabidiol are the most clinically relevant. Tetrahydrocannabinol is a partial agonist at CB1 and binds CB2 receptors. CB1 is widely expressed by central and peripheral neurones but also by immune cells and other type of cells in the brain and in the periphery, and when it binds with tetrahydrocannabinol, a so-called high is induced, which is responsible for potential misuse. CB2 receptors are also expressed by neurons, but less than CB1, and are most abundantly expressed in immune cells. 1 2 3 Cannabidiol, however, does not produce the high and thus does not carry the same potential for substance misuse. 4 Furthermore, cannabidiol does not seem to promote psychosis inducing effects. 5 Cannabis use can evolve into cannabis use disorder, broadly defined as an inability to quit cannabis use, continuous use despite harmful consequences (eg, cannabinoid hyperemesis syndrome 6 ), or functional impairment. 7 8

According to the Global Burden of Disease 2019 study, more than 23.8 million people have cannabis use disorder globally, 9 and cannabis use ranks third worldwide among consumed substances of misuse, after alcohol and tobacco. 10 11 12 13 Cannabis use disorder is more common in men and high income countries. The prevalence of cannabis use disorder in the USA has been estimated to be around 6.3% in a lifetime and 2.5% for 12 months, and in Europe, around 15% of people aged 15-35 years reported cannabis use in the previous year. 14 Of those using cannabis, one in three developed problems related to cannabis use that impaired functioning, 13 and 10% used cannabis on a daily basis. 15 Cannabis use disorder can affect up to 50% of people who use cannabis daily. 16

In Europe, over the past decade, self-reported use of cannabis within the past month has increased by almost 25% in people aged 15-34 years, and more than 80% in people who are 55-64 years. 17 Cannabis or products containing tetrahydrocannabinol (cannabinoids) are widely available and have increasingly high tetrahydrocannabinol content. 18 For instance, in Europe, tetrahydrocannabinol content increased from 6.9% to 10.6% from 2010 to 2019. 17 Evidence has suggested that cannabis may be harmful, for mental 19 20 and physical health, 21 as well as driving safety, 22 across observational studies but also in experimental settings. 23 Conversely, more than a decade ago, cannabidiol was proposed as a candidate drug for the treatment of neurological disorders such as treatment-resistant childhood epilepsy. Furthermore, it has been proposed that this substance might be useful for anxiety and sleep disorders, and even as an adjuvant treatment for psychosis. 24 Moreover, cannabis based medications (ie, medications that contain cannabis components) have been investigated as putative treatments for several different conditions and symptoms. 23

The multifarious nature of cannabis’s main active components, contrasting evidence from observational studies reporting detrimental effects of cannabis, and therapeutic findings of cannabis based medicines from interventional studies, is reflected in different legislative approaches. Thus, in most countries cannabis use is illegal, but in a small and growing number of countries and states cannabis is legally sold without the need for a medical prescription. 25 26 27

Publication of meta-analyses investigating the effects of cannabinoids on health and other outcomes have substantially increased. However, most meta-analytical findings synthesised data from observational studies and are prone to several sources of bias. 28 29 To date, no umbrella review has systematically evaluated the evidence around cannabis, cannabinoids and cannabis nased medicines and health outcomes in humans from meta-analyses encompassing both observational studies and randomised controlled trials. Thus, this work aimed to systematically evaluate the breadth, quality, credibility, and certainty of associations between cannabis, cannabinoids, cannabis based medicines, and human health. We aimed to use established quantitative criteria, account for several sources of bias, 30 31 32 and identify converging findings from different study designs.

Searches and inclusion criteria

We conducted an umbrella review of meta-analyses of observational studies (ie, case-control and cohort studies) and randomised controlled trials that reported on any outcome associated with cannabis and cannabinoids use in humans. We followed an a-priori protocol (PROSPERO CRD42018093045). We adhered to PRIOR and PRISMA 2020 guidelines (adapting PRISMA to the abstract of an umbrella review; supplementary tables 1-2). 33 34 Two of the authors independently screened literature that was retrieved systematically by searching PubMed, Embase, and PsycINFO from database inception up to 9 February 2022, without language restrictions, and extracted data into a spreadsheet. The search key is available in the supplementary methods. We also manually searched the Cochrane Library. When two or more meta-analyses examined the same association, we selected only the one that included the largest number of studies. We excluded systematic reviews without a meta-analysis, meta-analyses of risk factors for cannabinoids use, meta-analyses of cross-sectional studies only, pooled analyses of studies identified without a systematic search, and individual studies.

The co-primary outcomes were the efficacy and safety of cannabinoids on target symptoms (eg seizures in epilepsy) in meta-analyses of randomised controlled trials. The secondary outcomes were any outcome reported in the meta-analyses of observational studies.

Data extraction and quality assessment

Extracted information from meta-analyses and individual studies included in meta-analyses were the bibliographic identifiers of the publication (ie, PubMed-Indexed for Medline or the digital object identifier), first author, year of publication, design of included studies (ie, cohort, case-control, randomised controlled trial), number of included studies in the meta-analysis, specific population under investigation (ie, general population, pregnant women, or people with medical disorders), the exposure and comparison definitions (eg author defined marijuana use v no use or heavy use of cannabis v no use), the outcomes, and their effect size and dispersion measure (when adjusted and unadjusted effect sizes were reported, we selected the adjusted ones). We also extracted what factors analyses were adjusted for. The methodological quality of each included meta-analysis was assessed by two independent investigators using A Measurement Tool to Assess Systematic Reviews version 2 (AMSTAR 2). 35

Data analysis

For each association from observational studies (ie, between exposure to cannabis or cannabinoids and outcomes), we extracted the effect sizes of individual studies reported in each meta-analysis, recalculating the pooled effect sizes and 95% confidence intervals, using random effects models. Specifically, we re-analysed each eligible association under the random effects model with DerSimonian and Laird method if included studies were equal or more than 10, 36 and Hartung, Knapp, Sidik, and Jonkman if less than 10. 37 We transformed the initial effect sizes or modified the direction of associations presented by the original authors to present comparable estimates (ie, equivalent odds ratio; supplementary methods). 38 Heterogeneity was tested with the I 2 and Tau statistics. 39 I 2 measures the proportion of the total variability due to heterogeneity, Tau measures true heterogeneity as an absolute measure of heterogeneity, instead. Moreover, 95% prediction intervals for the summary random effect sizes were computed to estimate the possible range in which the effect sizes of future studies were anticipated to fall. 40 We calculated prediction intervals using both the estimated between-study heterogeneity variance given from tau2 as well as the standard error of the pooled effect. We then examined small study effect bias (ie, whether smaller studies generated larger effect sizes compared with larger studies). 38 41 42 43 44 45 46 Small study effect was deemed present when both the Egger regression asymmetry test indicated publication bias (P value ≤0.10), and the random effects summary effect size was larger than the effect size of the largest study contributing to that association. 42 44 45 46 Finally, we evaluated significance bias using an updated method to detect the publication selection of statistically significant findings based on observable excess statistical significance. 47 48 We computed the test of excess statistical significance and the proportion of statistical significance, which have adequate control for type I errors and high statistical power. The presence of excess significance bias for individual meta-analyses was considered if either excess statistical significance or proportion of statistical significance were greater than 1.645. 47

All analyses were conducted in Stata/SE, version 17.0.

Assessment of the credibility of evidence

In accordance with previous umbrella reviews, 49 50 51 52 eligible associations from observational studies were classified into five levels according to the strength of the evidence of potential environmental risk or protective factors: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), and not significant. Briefly, credibility of evidence from observational studies is rated on the basis of the number of events developing the outcome of interest, P value of the association, small study effect, excess of significance bias, prediction intervals, statistical significance of the largest study, and heterogeneity. The specific criteria are exhaustively reported in the supplementary methods. We used sensitivity analyses on all levels of evidence, removing the criterion of more than 1000 cases, and on adjusted estimates and cohort studies on class I and II evidence only (supplementary methods).

We classified evidence from meta-analyses of randomised controlled trials, updating a previously proposed framework, classifying certainty of evidence as high, moderate, low, or very low, 53 based on GRADE (Grading of Recommendations, Assessment, Development and Evaluations). 32 GRADE is a transparent framework that is widely used to develop and present evidence synthesis, providing a set of explicit criteria across different domains to assess level of evidence, and making clinical practice recommendations. As recommended by GRADE, the level of evidence was determined by risk of bias, inconsistency, indirectness, imprecision, and publication bias (supplementary methods).

Patient and public involvement

This study was author funded and we did not involve patients and the public in this work, but we will apply for funding to involve them in the knowledge translation of present findings. Knowledge translation activities will include, but will not be limited to, dissemination of findings via personal and institutional social media, education of health professional trainees, and continuous medical education activities for health professionals. We will involve patient and public representatives in creating a plain language summary of findings to be distributed to the clinical population with mental health disorders, and pregnant women, informing policy makers across different countries with written communications.

Literature search

Starting from 6657 records after duplicate removal, we excluded 5941 studies at title and abstract screening stage, and 599 at full-text level, resulting in 101 publications included. Studies identified by manual search had already been identified from the systematic search. The list of studies excluded after full-text assessment, with reason for exclusion, is reported in supplementary table 3, and the article selection flow is reported in figure 1 . 33 Of the 101 articles, 50 were meta-analyses of observational studies (215 meta-analytical associations), 21 22 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 and 51 were meta-analyses of randomised controlled trials 23 74 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 (364 meta-analytical associations) Of note, one meta-analysis reported on both observational and randomised controlled trials ( table 1 , table 2 , supplementary material 2). 74

Study selection flow. References of excluded studies after full text assessment available in supplementary table 3. *One meta-analysis included both observational and randomised controlled trials

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Characteristics of included meta-analyses of observational studies, or non-randomised studies

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Characteristics of included meta-analyses of randomised controlled trials

Meta-analyses of randomised controlled trials

The eligible meta-analyses of randomised controlled trials were published between 2008 and 2022. The quality of included meta-analyses according to AMSTAR 2 was high in 20 meta-analyses, moderate in seven, low in 21, and critically low in four ( table 2 ). The median number of studies included in meta-analyses was five (interquartile range 3-9, range 2-42) and the median number of participants was 540 (251-1276, 37-4243).

Cannabidiol was specifically evaluated in seven meta-analyses, while others considered different combinations of cannabis, cannabinoids, tetrahydrocannabinol, and cannabis-based medicines including nabiximols, dronabinol, nabilone, levonantradol, and CT3. Overall, 364 unique meta-analytical associations were identified reporting on acceptability or tolerability of physical adverse events (n=213), psychiatric or psychological related outcomes (n=54), pain related outcomes (n=39), cognitive related (n=20), euphoria or feeling high (n=5), quality of life (n=5), and other various outcomes (n=28). Supplementary table 4 (associations with low or very low certainty) shows the summary effects of the unique meta-analyses or associations for randomised controlled trials.

Summary of associations

Based on the GRADE approach, 14 statistically significant meta-analytical associations (3.8%) met the high certainty criteria, 92 (25.3%) moderate certainty, 200 associations (55.0%) met the low certainty, and 58 associations (16.9%) met the very low certainty. The table detailing the classification of the level of evidence is presented in the supplementary material (supplementary table 4). In the following sections, we principally described the associations with high and moderate GRADE by subgroup of populations.

GRADE of evidence of cannabinoids and outcomes

Mixed chronic pain conditions.

Among the 34 associations in this population, cannabis-based medicines or cannabinoids reduced pain by 30% (equivalent odds ratio 0.59 (95% confidence interval 0.37 to 0.93)), but for pain relief no effect emerged (equivalent odds ratio not calculable, mean difference −0.09 (95% confidence interval −0.30 to 0.10)) with high certainty. An additional seven beneficial effects were supported by moderate certainty, including analgesic efficacy (n=5), pain reduction (n=1), and change in pain scores (n=1), yet no effect emerged on patient global impression much or very much improved (n=1), and 50% pain reduction (n=1) ( fig 2 supplementary table 4). Two other associations with harmful effects were supported by moderate certainty, including psychological distress (n=1) and withdrawals due to adverse events (n=1). Low (n=17) or very low (n=3) certainty were found for the remaining associations (supplementary table 5).

Moderate and high certainty evidence according to Grading of Recommendations, Assessment, Development and Evaluations (GRADE), from randomised controlled trials on outcomes of cannabis based medications in people with chronic pain, multiple sclerosis, inflammatory bowel disease, and cancer. Only associations for which an eOR was available are displayed. Results are displayed in descending order of level of evidence and effect size. CBM=cannabis based medications eOR=equivalent odds ratio; H=high; M=moderate

Multiple sclerosis and paraplegia

None of the 18 associations in this population was supported by high certainty. Two beneficial effects of cannabis based medicines were supported by moderate certainty, including pain reduction (n=1), and spasticity (subjective; n=1) ( fig 2 , supplementary table 4). An additional four harmful effects were supported by moderate certainty, including dizziness (n=1), dry mouth (n=1), nausea (n=1) and somnolence (n=1) ( fig 2 , supplementary table 4). Low (n=10) or very low (n=2) certainty were found for the remaining associations (supplementary table 4).

Inflammatory bowel or Crohn’s disease

Among the three associations in this population one between cannabinoids and better quality of life ( fig 2 , supplementary table 4) presented high certainty (equivalent odds ratio 0.34 (95% confidence interval 0.22 to 0.53)). Low (n=1) or very low (n=1) certainty were found for the remaining associations (supplementary table 5).

None of the 60 associations in this population was supported by high certainty. A beneficial effect emerged on sleep disturbances (n=1), as well as an increased risk of adverse events of gastrointestinal disorders (n=1), nervous system disorders (n=1), serious adverse events (n=1), tolerability (n=1), nausea (n=1), and no effect on daily breakthrough opioid dosage (n=1), constipation (n=1), pain (n=4), risk of psychiatric disorder (n=1), vomiting (n=1), or withdrawal due to adverse events (n=1), with moderate certainty ( fig 2 , supplementary table 4).

Among the 46 associations in this population one between cannabidiol and diarrhoea presented high certainty with harmful effects (equivalent odds ratio 2.25 (95% confidence interval 1.33 to 3.81)), and no effect on sleep disruption (equivalent odds ratio not calculable, mean difference −0.29 (95% confidence interval −0.88 to 0.30)). Moderate certainty emerged for seven harmful effects, namely any adverse event (n=2), decreased appetite (n=1), diarrhoea (n=1), serious adverse events (n=1), somnolence (n=1), treatment related adverse events (n=1), as well as for 10 beneficial effects: seizures reduction (n=7), global impression improvement (n=2), and quality of life (n=1). No effect was noted, with moderate certainty, for gastrointestinal side effects (n=1), quality of life in children (n=1), status epilepticus (n=1), upper respiratory infection (n=1), vomiting (n=1), pyrexia (n=1). Low (n=16) or very low (n=3) certainty were reported for the remaining associations ( fig 3 , supplementary table 4).

Moderate and high certainty evidence according to Grading of Recommendations, Assessment, Development and Evaluations (GRADE), from randomised controlled trials on outcomes of cannabis based medications (CBD) in people with epilepsy. Results are displayed in descending order of level of evidence and effect size; only associations for which an eOR was available are displayed. eOR=equivalent odds ratio; H=high; M=moderate

Mixed conditions

Among the 140 associations in this population three between cannabis-based medicines and various adverse events (supplementary figure 1, supplementary table 4) presented high certainty with harmful effects (equivalent odds ratio 2.84 (95% confidence interval 2.16 to 3.73) for central nervous system adverse events; 3.07 (1.79 to 5.26) for psychological adverse events, and 3.00 (1.79 to 5.03) for vision related adverse events). Moderate certainty supported a beneficial effect on nausea or vomit reduction (n=1), pain reduction (n=3), spasticity reduction (global impression of change) (n=1), an increased risk of feeling high (n=1), gastrointestinal adverse events (n=2), gastrointestinal disorder (non-serious; n=1), emerging psychiatric disorder (n=1), somnolence (n=1), and withdrawal due to adverse events (n=1), while no associations were reported with application site discomfort (n=1), cardiac adverse events (n=1), headache (n=1), musculoskeletal and connective disorder (n=1) and musculoskeletal adverse events (n=1), quality of sleep (n=1), renal urinary disorder (n=1), respiratory disorder (n=1), spasticity reduction (n=1), or weakness (n=1).

Two other beneficial effects of cannabidiol were noted with high certainty, on seizures (equivalent odds ratio 0.59 (95% confidence interval 0.38 to 0.92) for 50% seizure reduction and 0.59 (0.36 to 0.96) for seizure events; supplementary figure 1, supplementary table 4), but moderate evidence supported an increased risk of pneumonia (n=1), somnolence (n=1), gastrointestinal hyperactivity events (n=1), and withdrawal due to adverse events (n=1) (supplementary figure 1, supplementary table 4).

Low (cannabis-based medicines, n=63; cannabis, n=28) or very low (cannabis based medicines, n=9; cannabidiol, n=12) certainty were found for the remaining associations (supplementary table 5).

General population

Among the 23 associations in this population, two between cannabis and emerging psychiatric symptoms presented at high certainty with harmful effects (positive psychotic symptom severity, equivalent odds ratio 5.21 (3.36 to 8.01) and total psychiatric symptoms, equivalent odds ratio 7.49 (5.31 to 10.42)). An additional 12 harmful effects were supported by moderate certainty, including negative symptom severity (n=1), and cognitive outcomes (n=11) (supplementary figure 2, supplementary table 5). Low (n=7) or very low (n=2) certainty were found for the remaining associations (supplementary table 4).

Healthy people

Among the three associations in this population two between cannabinoids and pain outcomes presented high certainty with beneficial effects (equivalent odds ratio 0.74 (95% confidence interval 0.59 to 0.91) for pain threshold and 0.60 (0.41 to 0.88) for pain unpleasantness). Low (n=1) certainty was noted for the remaining association (supplementary table 4).

Mental health disorders, dementia, Alzheimer’s, and Parkinson’s disease

None of the 37 associations in various neuropsychiatric populations (ie, psychiatric disorders, dementia, Alzheimer’s disease, Parkinson’s disease, opioid use disorder, and cannabis use disorder) was supported by either high or moderate certainty. Low (n=26) or very low (n=11) certainty was found for all the associations (supplementary table 4).

Meta-analyses of observational studies

The eligible meta-analyses of observational studies were published between 2002 and 2022. The quality of included meta-analyses according to AMSTAR 2 was high in 13 meta-analyses, moderate in 24, low in 12, and critically low in one ( table 1 ). The median number of individual studies included in the meta-analyses was 6 (interquartile range 4-13, range 2-69), the median number of participants was 1063 (526-4414, 44-5 962 412), and the median number of cases was 814 (447-2078, 126-8060).

The meta-analyses of observational studies reported a wide range of meta-analytical associations between cannabinoids and related health outcomes (supplementary table 5): cognitive, neuropsychological (n=81), brain function, volume (n=38), maternal and neonatal (n=12), psychosis symptoms and relapse (n=15), cancer (n=14), motor vehicle accidents (n=7), suicide (n=6), depression (n=4), behavioural inhibition (n=5), adherence to antipsychotic treatment (n=4), liver fibrosis (n=3), physical dating violence (n=2), and others (n=24). The 215 meta-analytical associations included 878 individual estimates from individual studies: 375 were derived from cohort studies, 493 from case-control studies, and 10 from mixed study designs.

Of the 215 examined meta-analytical associations, 109 (51%) had a nominally statistically significant effect (P≤0.05) under the random-effects models, but only 14 of those (7%) reached a P value of 10 −6 or less. Only 15 meta-analytical associations (7%) had more than 1000 cases and none had more than 20 000 participants for continuous outcomes. Sixty-eight meta-analytical associations (32%) exhibited large heterogeneity (I 2 >50%), and only 12 of them (6%) had a 95% prediction interval that excluded the null value. Additionally, small study effects were found for 13 meta-analytical associations (6%) and excess significance bias was found for 15 (7%).

Only two associations (1%) showed a convincing level of evidence (class I), and one (<1%) showed highly suggestive evidence (class II). Of the remaining associations, four (2%) showed suggestive evidence (class III), 102 (47%) weak evidence (class IV), and 106 (49%) had no evidence (not significant). The table detailing the classification of the level of evidence is presented in the supplementary material (supplementary table 5). In the following sections, we principally described the associations with the highest classes (I-convincing, II-highly suggestive, III-suggestive) of the evidence in the main and general sensitivity analysis by subgroup of populations.

Credibility of evidence of associations between cannabinoids and outcomes

Pregnant women.

Among the 19 associations in this population only two outcomes ( fig 4 , supplementary table 5) presented convincing evidence with harmful effects of cannabinoids (marijuana use and low birth weight, equivalent odds ratio 1.43 (95% confidence interval 1.27 to 1.62)) and marijuana and small for gestational age (1.61 (1.41 to 1.83); both unadjusted estimates). Class III evidence emerged for two other associations with harmful effects: one between marijuana use and preterm delivery (1.32 (1.14 to 1.54)) and one between marijuana and neonatal intensive care unit admission (1.41 (1.15 to 1.71); both unadjusted estimates). After removing the criterion of number of cases of more than 1000 in the sensitivity analysis, no change was reported in the level of class I and III evidence, however, one additional association was upgraded from weak (class IV) to suggestive evidence (class III; mean birth weight, unadjusted; supplementary table 5). No evidence was found for the remaining associations (supplementary table 5).

Observational meta-analytical associations between cannabis and outcomes in pregnant women, drivers, and people with psychosis supported by convincing, highly suggestive, or suggestive evidence in main or sensitivity analysis. Results are displayed in descending order of level of evidence and effect size; only associations for which an eOR was available are displayed. n=cases; N=population; CE=class of evidence (convincing (I), highly suggestive (II), suggestive (III), weak (IV)); CES=class of evidence after removing the n>1000 cases criterion; eOR=equivalent odds ratio; NR=not reported

The association between marijuana use and low birth weight was downgraded to no evidence using only adjusted estimates or cohort studies. The association between marijuana and small for gestational age remained at the same level (ie. convincing) using only cohort studies (adjusted sensitivity analysis not possible). The association with preterm delivery remained suggestive in analyses of only cohort studies, but the level was downgraded to no evidence with use of only adjusted estimates (supplementary table 5).

None of the seven associations in this population was supported by convincing or highly suggestive evidence (class I and II) ( fig 4 , supplementary table 5). Evidence was weak (class IV) for the seven associations between cannabis use and driving outcomes with harmful effects (supplementary table 4). In the sensitivity analysis, after removing the criterion of number of studies as more than 1000, two associations were upgraded from weak (class IV) to convincing evidence (class I) for tetrahydrocannabinol and harmful effects of car crash and culpability (adjusted estimates). Two other associations between cannabis use and car death after car crash (unadjusted) and unfavourable traffic events related to cars (unadjusted) were upgraded from weak (class IV) to highly suggestive evidence (class II). Two additional associations between cannabis use and car collision and car injury (both unadjusted estimates) were upgraded from weak (class IV) to suggestive evidence (class III) ( fig 4 , supplementary table 5).

None of the 50 associations in this population was supported by convincing or highly suggestive evidence (class I and II) ( fig 4 , supplementary table 5). Weak evidence (class IV) was available for 13 associations with harmful effects, whereas no evidence was found for the remaining associations (supplementary table 5). After removing the criterion of more than 1000 cases in the sensitivity analysis, five associations of cannabinoids with harmful effects (ie, working memory, psychosis relapse, premorbid IQ (unadjusted), poor adherence to antipsychotics (two associations adjusted)) were upgraded from weak (class IV) to suggestive evidence (class III).

Among the 119 associations in this population, only one between cannabis and psychosis ( fig 5 , supplementary table 5) presented highly suggestive evidence with harmful effects of cannabinoids in adolescents (equivalent odds ratio 1.71 (95% confidence interval 1.47 to 2.00); no information on adjustments). Evidence was suggestive (class III) for two other associations with harmful effects: one between heavy use of cannabis and suicide attempt (3.20 (1.72 to 5.94)) and one between most frequent use of cannabis and psychotic symptoms (2.18 (1.45 to 3.27); both adjusted estimates). Weak or no evidence were found for the remaining associations (supplementary table 5). After removing the criterion of more than 1000 cases in the sensitivity analysis, the level of class II and III evidence did not change. However, one additional association with harmful effects between tetrahydrocannabinol and increased heart rate (unadjusted) was upgraded from weak (class IV) to highly suggestive evidence (class II). Additionally, eight associations with harmful effects were upgraded from weak (class IV) to suggestive evidence (class III) including mania symptoms (adjusted), depression (adjusted), testicular cancer (three associations), orbitofrontal cortex volume (medial, total), and physical dating violence (supplementary table 5). The association with cannabis and psychosis also remained highly suggestive (table 2), but the level of evidence was upgraded to convincing when only cohort studies were included (adjusted sensitivity analysis not possible).

Observational meta-analytical associations between cannabis and outcomes in the general population and healthy people supported by convincing, highly suggestive, or suggestive evidence in main or sensitivity analysis excluding 1000 cases criterion. Results are displayed in descending order of level of evidence and effect size; only associations for which an eOR was available are displayed. n=cases; N=population; CE=class of evidence (convincing (I), highly suggestive (II), suggestive (III), weak (IV)); CES=class of evidence after removing the n>1000 cases criterion; eOR=equivalent odds ratio; NR=not reported

Healthy people who use cannabis

None of the eight associations in healthy people who use cannabis was supported by convincing or highly suggestive evidence (class I and II) ( fig 5 , supplementary table 5). Only weak evidence (class IV) was noted for eight associations between cannabis use and cognitive outcomes with harmful effects (supplementary table 5). After removing the criterion of more than 1000 cases in the sensitivity analysis, four associations with harmful effects (ie, visual immediate recall, prospective memory, verbal learning, and verbal delayed recall) were upgraded from weak (class IV) to highly suggestive evidence (class II). Additionally, three associations (ie, verbal immediate recall, verbal recognition, and working memory) were upgraded from weak (class IV) to suggestive evidence (class III) ( fig 5 , supplementary table 5).

Other populations

Across people with cannabis use disorder, insomnia, chronic pain, mixed conditions, hepatitis C virus or non-alcoholic fatty liver disease, and central nervous system malignant disease, none of the 12 associations was supported by convincing or highly suggestive evidence (class I and II, supplementary table 5). Weak evidence (class IV) was noted for five associations between cannabis use with harmful effects, whereas no evidence was found for the remaining associations (supplementary table 5). After removing criterion of more than 1000 cases in the sensitivity analysis, three associations of cannabinoids with beneficial effects (namely sleep quality or quantity improvement, pain relief, and hepatic steatosis (all unadjusted)) were upgraded from weak (class IV) to suggestive evidence (class III).

Other details of cannabis use, adjustment of analyses, and quality of individual studies

Details on type of cannabis, route of administration, use, variables that analyses were adjusted for, and quality or risk of bias of individual studies included in eligible meta-analyses are reported in supplementary material 3.

Of the 512 individual studies included in the eligible meta-analyses, 325 were observational studies and 187 were randomised controlled trials. Among the 325 observational studies (cohort n=160, cross-sectional n=97, case-control n=68), 211 reported on cannabis, 108 on marijuana, two on dronabinol, two on nabilone, one on cannabidiol, and one on tetrahydrocannabinol and cannabidiol. Of these, 312 focused on recreational use of cannabinoids, 12 on medical use, and one on both; 292 studies did not report the route of administration, which was inhaled in 28 studies and oral in five studies. Overall analyses were unadjusted in 79 studies and adjusted or matched in the remaining studies. The median Newcastle-Ottawa score of case-control and cohort studies was 7 (interquartile range 7-9).

Among the 187 randomised controlled trials, 64 reported on tetrahydrocannabinol, 32 on nabilone, 26 on nabiximols, 22 on cannabis, 18 on cannabidiol, and the remaining on various combinations of cannabis-based medicines, or other individual cannabis based medicines. Of these, 186 focused on medical use of cannabinoids, and one on recreational use; the route of administration was oral in 121, oral spray in 29, inhaled in 21, intravenous in six, intramuscular in four, oral and inhaled in three, and transdermal in two studies. The risk of bias was high in 79 randomised controlled trials, unclear in 55, low in 48, and moderate in five.

Principal findings

This umbrella review grades the credibility and certainty of evidence on the effect of cannabinoid use, encompassing observational and interventional evidence.

Regarding harmful outcomes, among all meta-analytical associations supported by at least suggestive evidence in observational studies and moderate certainty in randomised controlled trials, converging evidence supports an increased risk of psychosis associated with cannabinoids in the general population. Specifically, cannabis use was associated with psychosis in adolescents (highly suggestive credibility, convincing certainty in main sensitivity analyses) and adults (suggestive credibility, suggestive certainty), and with psychosis relapse in people with a psychotic disorder (weak credibility, suggestive certainty). Use of cannabinoids in adult non-clinical and clinical populations was associated with positive (high certainty) and negative (moderate certainty) psychotic symptoms in randomised controlled trials.

Evidence from observational studies (weak credibility, suggestive certainty) and randomised controlled trials (high credibility, moderate certainty) show an association between cannabis and general psychiatric symptoms, including depression and mania, as well as detrimental effects on prospective memory, verbal delayed recall, verbal learning, and visual immediate recall (weak credibility, highly suggestive in observational evidence, moderate certainty in randomised controlled trials). Across different clinical and non-clinical populations, observational evidence suggests an association between cannabis use and motor vehicle accidents (weak credibility, convincing certainty). Additionally, evidence from randomised controlled trials shows an association with somnolence (cannabinoids (moderate certainty) and cannabidiol (high certainty)), 103 and cannabis based medicines and visual impairment (high certainty), disorientation, dizziness, sedation, and vertigo (moderate certainty), among others.

These associations are of particular concern given the epidemiology and age pattern of cannabis use disorders, and the population attributable fraction of cannabis for schizophrenia, which is almost 10%. 152 According to the Global Burden of Disease 2019, cannabis use disorders are associated with 690 000 (95% uncertainty interval 421 000-1 080 000) disability adjusted life years per 100 000 individuals globally. 9 Prevalence and disability related to cannabis start to be measurable at ages 10-14 years (11 900 disability adjusted life years), peak at ages 20-24 years (163 000 disability adjusted life years), then gradually decrease. 9 12 153 The age pattern of cannabis use disorders coincide with the peak age at onset of mental health disorders. According to the largest meta-analysis on the age at onset of mental disorders published to date, which pooled 192 studies and 708 561 individuals, around 34.6% of mental health disorders have onset by age 14 years, 48.4% by 18 years, and 62.5% by 25 years; the age that any mental health disorder onset peaks is at 14.5 years. 154 For cannabis use disorders, 66% of people will have onset by age 25 years, with age of peak onset 20.5 years. Of note, age at peak onset of schizophrenia spectrum disorders is also in the early 20s, with a slightly lower proportion of people with onset by 25 years (47.8%). In addition to the association between cannabis and psychosis, cannabis is also associated with a worse outcome after onset, including poorer cognition, 87 lower adherence to antipsychotics, 56 and higher risk of relapse. 85 In other words, use of cannabis when no psychotic disorder has already occurred increases the risk of its onset, and using cannabis after its onset, worsens clinical outcomes. Mood disorders also have their peak of onset close to that for cannabis use, which is of concern given the associations shown in this work between cannabis and depression, mania, and suicide attempt. Moreover, high tetrahydrocannabinol content cannabis could serve as a so-called gateway to other substances, particularly in younger people: this effect has been shown in humans 155 and animal models, 156 157 strengthening the recommendation to avoid cannabis use in adolescents and young adulthood.

Evidence suggests detrimental effects on cognition, an association with motor vehicle accidents, together with the age pattern of cannabis use (disorder), and related burden, which raise two additional matters. Firstly, given the adverse effects of cannabis on verbal delayed recall, verbal learning, visual immediate recall, and mental health, negative effects on scholastic or academic performance are reasonably expected, particularly in people who heavily use. Secondly, psychiatric symptoms such as suicide ideation and attempt, mania, and poor cognition, among other adverse events (eg, somnolence, disorientation, dizziness, sedation, vertigo, and visual impairment) might mediate the association between cannabis and increased risk of motor vehicle accidents. According to the DRUID project (driving under the influence of drugs, alcohol, and medicines in Europe), tetrahydrocannabinol ((0.5-2.2), measured as tetrahydrocannabinol or carboxy-tetrahydrocannabinol, in oral fluid or blood) is the second most frequent compound detected in seriously injured drivers, after alcohol (14.1-30.2%), then cocaine and amphetamines. 158

Numerous observational associations indicated harmful outcomes, but they were either isolated without converging evidence from different study designs, supported by weak evidence only, or downgraded to not significant. Downgrading applied to the association between cannabis and low birth weight, and preterm delivery, 100 which might be mediated by smoking.

Regarding the therapeutic potential of cannabis-based medicines, cannabidiol was beneficial in reducing seizures in certain forms of epilepsy in children and adults, including Lennox-Gastaut syndrome, Dravet syndrome, or other types of epilepsy. Cannabis based medicines were beneficial for pain and spasticity in multiple sclerosis, as well as for chronic pain in various conditions, and in palliative care, yet not without adverse events. However, cannabidiol and other cannabis-based medicines were associated with lower acceptability and tolerability than placebo in children and adults, and cannabis based medicines were also associated with psychiatric adverse events, as stated previously. These findings must be put into a clinical perspective to be fully appreciated and compared with available alternatives. Regarding epilepsy, established anticonvulsants are not free from adverse events, including sedation, weight gain, cognitive impairment, and psychiatric symptoms. 159 160 161 Regarding chronic pain, excessive use of prescribed opioid medications has contributed to the opioid crisis, indicating the need for novel pharmacological and non-pharmacological treatment options for chronic pain 162 to reduce prescribed opioid medications abuse. Regarding multiple sclerosis, botulinum toxin seems to be the only pharmacological alternative to cannabis based medicines for spasticity. 110 163 Finally, the clinical populations included in eligible meta-analyses had treatment resistant or chronic conditions or were being treated in the context of palliative care and ongoing chemotherapy, and other treatment options had not proven effective. Thus, cannabis-based medicines could be reasonable options for chronic pain in different conditions, muscle spasticity in multiple sclerosis, and for nausea and vomiting in mixed clinical populations, and for sleep in people with cancer. Importantly, in patients with chronic pain, evaluation of the clinical effects considering the whole clinical presentation (several of the included reviews question the clinical value), the effects of prolonged use of cannabinoids still needs to be tested because current findings only come from short term randomised controlled trials. Also, active comparisons between cannabidiol and available options for epilepsy, as well as between cannabis-based medicines and other pain medications, other treatments for muscle spasticity in multiple sclerosis, or treatments for sleep in persons with cancer are needed, with a focus on both efficacy and safety, to inform future guidelines.

Overall, a mismatch is manifest between the legislation ruling cannabinoids versus alcohol use, considering both the well-known harms of alcohol on physical and mental health, in any age group, 164 and the epidemiological figures. According to Global Burden of Disease 2019, alcohol use disorders were associated with 17 000 000 (95% uncertainty interval 13 500 000-21 500 000) disability adjusted life years per 100 000 individuals, 9 roughly 25 times higher than for cannabis. Also, disability related to cannabis was largely limited to individuals aged 10-24 years, whereas alcohol is associated with disability from early stages of life, increasing continuously to 2 120 000 disability adjusted life years at age 35-39 years, and very slowly decreasing to less than 200 000 disability adjusted life years only after age 80 years. 9 If cannabis use prevalence increased in the younger portion of the population due to large scale legalisation, whether the gap described previously would diminish is unclear. Moreover, to the best of our knowledge alcohol has no role as a medical treatment, whereas our research shows that cannabinoids can have beneficial effects in specific clinical conditions. The (scientific) reasoning behind extreme or ideological legislative approaches, namely complete legalisation and commercialization of cannabis even in young adults versus complete prohibition, and the different legislative requirements between cannabis and alcohol in disclosing to consumers the associated risks remains unclear. 9

Strengths and limitations

The main strength of this work is that we pool evidence from different sources of evidence and deliberately consider convergence of results from different study designs. Also, this umbrella review is the first to pool observational and interventional studies on the effects of cannabinoids on humans.

Our results should be interpreted with caution. Firstly, the evidence from observational studies has ecological validity with regards to the type of cannabis available in the legal or illegal market on a large scale. However, tetrahydrocannabinol content and other cannabinoids on which no meta-analytical evidence was included can vary considerably among legal and illegally sold products. At the individual level, this variation can mean the difference between harmful or neutral or beneficial effects. Moreover, evidence from more than a decade ago, might not be representative of the cannabis that can be purchased nowadays illegally and legally, which is rich in tetrahydrocannabinol. This means that findings of this work might be underestimating harmful effects of cannabis. Also, the clinical effect of tetrahydrocannabinol on GABA and glutamate signalling via partial agonism on CB1 receptors depends on the concentration and distribution of CB1 receptors in the brain of each individual. 10 As such, not all individuals will experience the same effects of cannabis on their mental health and cognition. Nonetheless, a crossover trial of 64 volunteers found that short term detrimental effects of 10 mg of inhaled tetrahydrocannabinol on psychological measures and cognition was not influenced by the co-administration of up to 30 mg of cannabidiol, potentially mitigating potential concerns with a role of tetrahydrocannabinol or cannabidiol ratio as a confounder of findings of this work. 165 However, this trial was limited by a very short follow-up (90 min) and high loss to follow-up (28%). Furthermore, cannabidiol products that contain either no tetrahydrocannabinol, or subclinical amounts, are unlikely to result in psychological or cognitive impairment. Similarly, cannabis use disorder seems to have similar rates of people who use recreationally versus medically. 166 Secondly, another reason to be cautious is that umbrella reviews neglect evidence from individual cohort studies or randomised controlled trials that have not been previously pooled in meta-analyses. However, individual studies need replication, are frequently exploratory, and need to be pooled in systematic reviews (and ideally meta-analyses) so that a comprehensive understanding of a given association or intervention can be appraised. Hence, any evidence that was not included in this umbrella review, even if potentially relevant, could be exploratory or preliminary. Thirdly, confounding factors could drive associations in observational evidence. However, we have applied stringent criteria, as confirmed by downgrading convincing evidence to non-significant on the association between cannabis and pregnancy outcomes. The quantitative criteria we applied to grade evidence from observational evidence accounted for selection and publication bias, excess of significance driven by small studies with larger effect sizes than the largest study in the meta-analysis, or marginal statistical significance driven by large sample sizes. Also, we have discussed findings from observational evidence in the context of converging evidence from different sources of evidence. For instance, observational studies might be affected by confounding factors, whereas randomised controlled trials might not be representative of the real-world population and affected by selection bias instead. We believe that converging evidence from these two study designs strengthens the ecological and methodological credibility of our findings. Fourthly, excess of significance bias testing might have been underpowered in meta-analyses with few studies, which could arguably apply to all meta-analyses included in this umbrella review, yet, a specific threshold of number of studies to set adequate power of excess of significance bias has not been established. Fifthly, we could have included meta-analyses based on their quality instead of the number of studies. However, that would have introduced a selection bias, leaving out a large portion of evidence. Sixthly, to harmonise effect sizes, we calculated the equivalent odds ratio as a measure of strength of the association. Yet, the harmonisation comes at the cost of losing information on time-to-event analyses and of course any association should be considered more in depth considering the frequency of each outcome, and the follow-up duration and time to event occurrence in each of the included studies. Additionally, the number of cases over the overall population of included studies does not reflect the prevalence of outcomes of interest. For instance, the global prevalence of preterm birth is 11.3% 167 versus 9.9% reported in this work and varies across regions and countries’ income levels. The global prevalence of small for gestational age is 27% 167 versus 9.1% reported here, with large variations across regions. The prevalence of testicular cancer is 0.04% versus 29.5% reported in the included meta-analysis of case-control studies. Lastly, and most importantly, the results of this work aim to inform future guidelines. These guidelines should account for additional aspects such as cost-effectiveness considerations, clinical relevancies (eg, numbers needed to treat for benefit), long term effects of cannabinoids on which evidence is lacking, and stakeholders, including patients and family members perspectives.

Future research assessing use of cannabis should clearly report what type of cannabis that patients used, how cannabis was administered, the content of tetrahydrocannabinol and cannabidiol, and the amount of cannabis consumed. The dose of exposure to different cannabinoids is needed to infer any causal relation between cannabis and outcomes.

Conclusions

Convincing or converging evidence supports that cannabis use is associated with poor mental health and cognition, increased the risk of car crashes, and can have detrimental effects on offspring if used during pregnancy. Cannabis use should be avoided in adolescents and young adults (when neurodevelopment is still occurring), when most mental health disorders have onset and cognition is paramount for optimising academic performance and learning, as well as in pregnant women and drivers. Conversely, cannabidiol could be considered a potential beneficial treatment option in epilepsy across age groups to reduce seizures. Cannabis based medicines could also be considered for chronic pain across different conditions, such as multiple sclerosis, spasticity in multiple sclerosis, for nausea and vomiting in people with mixed conditions and for sleep in cancer. However, clinical relevance must be considered before a possible incorporation into clinical guidelines; for example, including numbers needed to treat for benefit, risk to benefit ratios, comparative efficacy and safety with existing treatment options, and development of patient information concerning potential adverse events. Cannabidiol appears to be safe regarding psychiatric symptoms, but more research needs to be conducted before this drug can be recommended for the treatment of any psychiatric disorder. The remaining associations between cannabis and health outcomes are not supported by converging or convincing evidence.

Law and public health policy makers and researchers should consider this evidence synthesis when making policy decisions on cannabinoids use regulation, and when planning a future epidemiological or experimental research agenda, with particular attention to the tetrahydrocannabinol content of cannabinoids. Future guidelines are needed to translate current findings into clinical practice, while involving stakeholders.

What is already known on this topic

Observational evidence reported that cannabinoids were associated with numerous health outcomes and have been tested for several conditions in randomised controlled trials

Credibility and coherence of findings from different sources of evidence on the same outcomes have not been assessed to date

What this study adds

Most outcomes associated with cannabinoids are supported by weak evidence (observational studies), low to very low certainty (randomised controlled trials), or are not significant (observational studies, randomised controlled trials)

Convincing or converging evidence recommends avoiding cannabis during adolescence and early adulthood in people prone to have or have mental health disorder, who are pregnant, and while driving

Cannabidiol is effective for epilepsy, notably in children, while other cannabinoids can be effective in use for multiple sclerosis, chronic pain, inflammatory bowel disease, and palliative care

Ethics statements

Ethical approval.

Ethical approval for this study was not required.

Data availability statement

The whole dataset is available from authors on request.

Contributors: MS, MDT, and JYK are joint first authors. JIS and MS contributed equally as corresponding authors. ED, MS, and JIS designed and supervised the study. FB, LB, MJC, GC, MDT, ED, JYK, SM, AM, FM, and MS screened the literature and extracted the data. MDT, ED, and MS drafted the manuscript. ED conducted the analyses, and MS had full access to data. All authors approved the project design, and critically reviewed, contributed to the final version of this work, and approve it. ED, MS, and MDT are guarantors of this work. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: None.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: MS received honoraria/has been a consultant for AbbVie, Angelini, Lundbeck, Otsuka. DC has received grant monies for research from Eli Lilly, Janssen Cilag, Roche, Allergen, Bristol-Myers Squibb, Pfizer, Lundbeck, Astra Zeneca, Hospira; Travel Support and Honoraria for Talks and Consultancy from Eli Lilly, Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Cilag, Pfizer, Organon, Sanofi-Aventis, Wyeth, Hospira, Servier, Seqirus; and is a current or past Advisory Board Member for Lu AA21004: Lundbeck; Varenicline: Pfizer; Asenapine: Lundbeck; Aripiprazole LAI: Lundbeck; Lisdexamfetamine: Shire; Lurasidone: Servier; Brexpiprazole: Lundbeck; Treatment Resistant Depression: LivaNova; Cariprazine: Seqirus. He is founder of the Optimal Health Program, currently operating as Optimal Health Australia; and is part owner of Clarity Healthcare. He is on the scientific advisory of The Mental Health Foundation of Australia. He does not knowingly have stocks or shares in any pharmaceutical company. EV has received grants and served as consultant, advisor or CME speaker for the following entities: AB-Biotics, AbbVie, Angelini, Biogen, Boehringer-Ingelheim, Celon Pharma, Dainippon Sumitomo Pharma, Ferrer, Gedeon Richter, GH Research, Glaxo-Smith Kline, Janssen, Lundbeck, Novartis, Orion Corporation, Organon, Otsuka, Sage, Sanofi-Aventis, Sunovion, Takeda, and Viatris, outside of the submitted work. CUC has been a consultant or advisor to or have received honoraria from: AbbVie, Acadia, Alkermes, Allergan, Angelini, Aristo, Boehringer-Ingelheim, Cardio Diagnostics, Cerevel, CNX Therapeutics, Compass Pathways, Darnitsa, Gedeon Richter, Hikma, Holmusk, IntraCellular Therapies, Janssen/Johnson & Johnson, Karuna, LB Pharma, Lundbeck, MedAvante-ProPhase, MedInCell, Merck, Mindpax, Mitsubishi Tanabe Pharma, Mylan, Neurocrine, Newron, Noven, Otsuka, Pharmabrain, PPD Biotech, Recordati, Relmada, Reviva, Rovi, Seqirus, SK Life Science, Sunovion, Sun Pharma, Supernus, Takeda, Teva, and Viatris. He provided expert testimony for Janssen and Otsuka. He served on a Data Safety Monitoring Board for Lundbeck, Relmada, Reviva, Rovi, Supernus, and Teva. He has received grant support from Janssen and Takeda. He received royalties from UpToDate and is also a stock option holder of Cardio Diagnostics, Mindpax, and LB Pharma.

All authors declare manuscript is an honest, accurate, and transparent account of the study being reported; no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned (and, if relevant, registered) have been explained in published protocol.

Dissemination to participants and related patient and public communities: Our dissemination of findings will be shared via personal and institutional social media, the education of health professional trainees, continuous medical education activities for health professionals, and co-produced plain language summary of findings to be distributed to the clinical population with mental health disorders and to pregnant women. We will inform policy makers across different countries with written communications.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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• Open access
• Published: 10 December 2019

Benefits and harms of medical cannabis: a scoping review of systematic reviews

• Misty Pratt 1 ,
• Adrienne Stevens 1 , 2 ,
• Micere Thuku 1 ,
• Claire Butler 1 , 3 ,
• Becky Skidmore 4 ,
• L. Susan Wieland 5 ,
• Mark Clemons 6 , 7 ,
• Salmaan Kanji 6 , 8 , 9 &
• Brian Hutton   ORCID: orcid.org/0000-0001-5662-8647 1 , 6  

Systematic Reviews volume  8 , Article number:  320 ( 2019 ) Cite this article

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There has been increased interest in the role of cannabis for treating medical conditions. The availability of different cannabis-based products can make the side effects of exposure unpredictable. We sought to conduct a scoping review of systematic reviews assessing benefits and harms of cannabis-based medicines for any condition.

A protocol was followed throughout the conduct of this scoping review. A protocol-guided scoping review conduct. Searches of bibliographic databases (e.g., MEDLINE®, Embase, PsycINFO, the Cochrane Library) and gray literature were performed. Two people selected and charted data from systematic reviews. Categorizations emerged during data synthesis. The reporting of results from systematic reviews was performed at a high level appropriate for a scoping review.

After screening 1975 citations, 72 systematic reviews were included. The reviews covered many conditions, the most common being pain management. Several reviews focused on management of pain as a symptom of conditions such as multiple sclerosis (MS), injury, and cancer. After pain, the most common symptoms treated were spasticity in MS, movement disturbances, nausea/vomiting, and mental health symptoms. An assessment of review findings lends to the understanding that, although in a small number of reviews results showed a benefit for reducing pain, the analysis approach and reporting in other reviews was sub-optimal, making it difficult to know how consistent findings are when considering pain in general. Adverse effects were reported in most reviews comparing cannabis with placebo (49/59, 83%) and in 20/24 (83%) of the reviews comparing cannabis to active drugs. Minor adverse effects (e.g., drowsiness, dizziness) were common and reported in over half of the reviews. Serious harms were not as common, but were reported in 21/59 (36%) reviews that reported on adverse effects. Overall, safety data was generally reported study-by-study, with few reviews synthesizing data. Only one review was rated as high quality, while the remaining were rated of moderate ( n = 36) or low/critically low ( n = 35) quality.

Conclusions

Results from the included reviews were mixed, with most reporting an inability to draw conclusions due to inconsistent findings and a lack of rigorous evidence. Mild harms were frequently reported, and it is possible the harms of cannabis-based medicines may outweigh benefits.

Systematic review registration

The protocol for this scoping review was posted in the Open Access ( https://ruor.uottawa.ca/handle/10393/37247 ).

Peer Review reports

Interest in medical applications of marijuana ( Cannabis sativa ) has increased dramatically during the past 20 years. A 1999 report from the National Academies of Sciences, Engineering, and Medicine supported the use of marijuana in medicine, leading to a number of regulatory medical colleges providing recommendations for its prescription to patients [ 1 ]. An updated report in 2017 called for a national research agenda, improvement of research quality, improvement in data collection and surveillance efforts, and strategies for addressing barriers in advancing the cannabis agenda [ 2 ].

Proponents of medical cannabis support its use for a highly varied range of medical conditions, most notably in the fields of pain management [ 3 ] and multiple sclerosis [ 4 ]. Marijuana can be consumed by patients in a variety of ways including smoking, vaporizing, ingesting, or administering sublingually or rectally. The plant consists of more than 100 known cannabinoids, the main ones of relevance to medical applications being tetrahydrocannabinol (THC) and cannabidiol (CBD) [ 5 ]. Synthetic forms of marijuana such as dronabinol and nabilone are also available as prescriptions in the USA and Canada [ 6 ].

Over the last decade, there has been an increased interest in the use of medical cannabis products in North America. It is estimated that over 3.5 million people in the USA are legally using medical marijuana, and a total of USD$6.7 billion was spent in North America on legal marijuana in 2016 [ 7 ]. The number of Canadian residents with prescriptions to purchase medical marijuana from Health Canada–approved growers tripled from 30,537 in 2015 to near 100,000 in 2016 [ 8 ]. With the legalization of recreational-use marijuana in parts of the USA and in Canada in October 2018, the number of patients using marijuana for therapeutic purposes may become more difficult to track. The likely increase in the numbers of individuals consuming cannabis also necessitates a greater awareness of its potential benefits and harms.

Plant-based and plant-derived cannabis products are not monitored as more traditional medicines are, thereby increasing the uncertainty regarding its potential health risks to patients [ 3 ]. While synthetic forms of cannabis are available by prescription, different cannabis plants and products contain varied concentrations of THC and CBD, making the effects of exposure unpredictable [ 9 ]. While short-lasting side effects including drowsiness, loss of short-term memory, and dizziness are relatively well known and may be considered minor, other possible effects (e.g., psychosis, paranoia, anxiety, infection, withdrawal) may be more harmful to patients.

There remains a considerable degree of clinical equipoise as to the benefits and harms of marijuana use for medical purposes [ 10 , 11 , 12 , 13 ]. To understand the extent of synthesized evidence underlying this issue, we conducted a scoping review [ 14 ] of systematic reviews evaluating the benefits and/or harms of cannabis (plant-based, plant-derived, and synthetic forms) for any medical condition. We located and mapped systematic reviews to summarize research that is available for consideration for practice or policy questions in relation to medical marijuana.

A scoping review protocol was prepared and posted to the University of Ottawa Health Sciences Library’s online repository ( https://ruor.uottawa.ca/handle/10393/37247 ). We used the PRISMA for Scoping Reviews checklist to guide the reporting of this report (see Additional file 1 ) [ 15 ].

Literature search and process of study selection

An experienced medical information specialist developed and tested the search strategy using an iterative process in consultation with the review team. Another senior information specialist peer-reviewed the strategy prior to execution using the PRESS Checklist [ 16 ]. We searched seven Ovid databases: MEDLINE®, including Epub Ahead of Print and In-Process & Other Non-Indexed Citations, Embase, Allied and Complementary Medicine Database, PsycINFO, the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects, and the Health Technology Assessment Database. The final peer-reviewed search strategy for MEDLINE was translated to the other databases (see Additional file 2 ). We performed the searches on November 3, 2017.

The search strategy incorporated controlled vocabulary (e.g., “Cannabis,” “Cannabinoids,” “Medical Marijuana”) and keywords (e.g., “marijuana,” “hashish,” “tetrahydrocannabinol”) and applied a broad systematic review filter where applicable. Vocabulary and syntax were adjusted across the databases and where possible animal-only and opinion pieces were removed, from the search results.

Gray literature searching was limited to relevant drug and mental health databases, as well as HTA (Health Technology Assessment) and systematic review databases. Searching was guided by the Canadian Agency for Drugs and Technologies in Health’s (CADTH) checklist for health-related gray literature (see Additional file 3 ). We performed searches between January and February 2018. Reference lists of overviews were searched for relevant systematic reviews, and we searched for full-text publications of abstracts or protocols.

Management of all screening was performed using Distiller SR Software ® (Evidence Partners Inc., Ottawa, Canada). Citations from the literature search were collated and de-duplicated in Reference Manager (Thomson Reuters: Reference Manager 12 [Computer Program]. New York: Thomson Reuters 2011), and then uploaded to Distiller. The review team used Distiller for Levels 1 (titles and abstracts) and 2 (full-text) screening. Pilot testing of screening questions for both levels were completed prior to implementation. All titles and abstracts were screened in duplicate by two independent reviewers (MT and MP) using the liberal accelerated method [ 17 ]. This method requires only one reviewer to assess an abstract as eligible for full-text screening, and requires two reviewers to deem the abstract irrelevant. Two independent reviewers (MT and MP) assessed full-text reports for eligibility. Disagreements during full-text screening were resolved through consensus, or by a third team member (AS). The process of review selection was summarized using a PRISMA flow diagram (Fig. 1 ) [ 18 ].

PRISMA-style flow diagram of the review selection process

Review selection criteria

English-language systematic reviews were included if they reported that they investigated harms and/or benefits of medical or therapeutic use of cannabis for adults and children for any indication. Definitions related to medical cannabis/marijuana are provided in Table 1 . We also included synthetic cannabis products, which are prescribed medicines with specified doses of THC and CBD. Reviews of solely observational designs were included only in relation to adverse effects data, in order to focus on the most robust evidence available. We considered studies to be systematic reviews if at least one database was searched with search dates reported, at least one eligibility criterion was reported, the authors had assessed the quality of included studies, and there was a narrative or quantitative synthesis of the evidence. Reviews assessing multiple interventions (both pharmacological and complementary and alternative medicine (CAM) interventions) were included if the data for marijuana studies was reported separately. Published and unpublished guidelines were included if they conducted a systematic review encompassing the criteria listed above.

We excluded overviews of systematic reviews, reviews in abstract form only, and review protocols. We further excluded systematic reviews focusing on recreational, accidental, acute, or general cannabis use/abuse and interventions such as synthetic cannabinoids not approved for therapeutic use (e.g., K2 or Spice).

Data collection and quality assessment

All data were collected electronically in a pre-developed form using Microsoft Excel software (Microsoft Corporation, Seattle, USA). The form was pilot tested on three included reviews by three people. One reviewer (MP or CB) independently extracted all data, and a second reviewer (MT) verified all of the items collected and checked for any omitted data. Disagreements were resolved by consensus and consultation with a third reviewer if necessary. A data extraction form with the list of included variables is provided in Additional file 4 . All collected data has also been made available in the online supplemental materials associated with this report.

Quality assessment of systematic reviews was performed using the AMSTAR-2 [ 20 ] tool. One reviewer (MP or CB) independently assessed quality, while a second reviewer (MT) verified the assessments. Disagreements were resolved by consensus and consultation with a third reviewer if necessary. The tool consists of 16 items in total, with four critical domains and 12 non-critical domains. The AMSTAR-2 tool is not intended to generate an overall score, and instead allows for an overall rating based on weaknesses in critical domains. Reviews were rated as high (no critical flaws with zero or one non-critical flaw), moderate (no critical flaws with ≥ 1 non-critical flaw), low (one critical flaw with/without non-critical weakness), or critically low (> 1 critical flaw with/without non-critical weakness) quality.

Evidence synthesis

We used a directed content analytic approach [ 21 ] with an initial deductive framework [ 22 ] that allowed flexibility for inductive analysis if refinement or development of new categorization was needed. The framework used to categorize outcome data results is outlined in Table 2 . Where reviews had a mix of narrative and quantitative data, results from meta-analyses were prioritized over count data or study-by-study data. The extraction and reporting of data results was performed at a high level and did not involve an in-depth evaluation, which is appropriate for a scoping review [ 14 ]. Review authors’ conclusions and/or recommendations were extracted and reported narratively.

Changes from the study protocol

For feasibility, we decided to limit the inclusion of systematic reviews of only observational study designs to those that addressed adverse events data. All other steps of the review were performed as planned.

Search findings

The PRISMA flow diagram describing the process of review selection is presented in Fig. 1 . After duplicates were removed, the search identified a total of 1925 titles and abstracts, of which 47 references were located through the gray literature search. Of the total 1925 citations assessed during Level 1 screening, 1285 were deemed irrelevant. We reviewed full-text reports for the 640 reviews of potential relevance, and of these, 567 were subsequently excluded, leaving a total of 72 systematic reviews that were included; the associated data collected are provided in Additional file 5 . A listing of the reports excluded during full-text review is provided in Additional file 6 .

Characteristics of included reviews

There were 63 systematic reviews [ 4 , 19 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ] and nine guidelines with systematic reviews [ 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ]. Overall, 27 reviews were performed by researchers in Europe, 16 in the USA, 15 in Canada, eight in Australia, two in Brazil, and one each in Israel, Singapore, South Africa, and China. Funding was not reported in 29 (40%) of the reviews, and the remaining reviews received funding from non-profit or academic ( n = 20; 28%), government ( n = 14; 19%), industry ( n = 3; 4%), and mixed ( n = 1; 1%) sources. Five reviews reported that they did not receive any funding for the systematic review. Tables 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , and 13 provide an overview of the characteristics of the 72 included systematic reviews.

The reviews were published between 2000 and 2018 (median year 2014), and almost half (47%) were focused solely on medical cannabis. Four (6%) reviews covered both medical and other cannabis use (recreational and substance abuse), 19 (26%) reported multiple pharmaceutical interventions (cannabis being one), six (8%) reported various CAM interventions (cannabis being one), and nine (13%) were mixed pharmaceutical and CAM interventions (cannabis being one). Multiple databases were searched by almost all of the reviews (97%), with Medline/PubMed or Embase common to all.

Cannabis use

Figure 2 illustrates the different cannabis-based interventions covered by the included reviews. Plant-based cannabis consists of whole plant products such as marijuana or hashish. Plant-derived cannabinoids are active constituents of the cannabis plant, such as tetrahydrocannabinol (THC), cannabidiol (CBD), or a combination of THC:CBD (also called nabiximols, under the brand name Sativex) [ 3 ]. Synthetic cannabinoids are manufactured rather than extracted from the plant and include drugs such as nabilone and dronabinol.

Review coverage of the various cannabis-based interventions

Twenty-seven reviews included solely interventions from plant-derived cannabinoids, 10 studied solely synthetic cannabinoids, and eight included solely studies on plant-based cannabis products. Twenty-four reviews covered a combination of different types of cannabis, and the remaining three systematic reviews did not report which type of cannabinoid was administered in the included studies.

The systematic reviews covered a wide range of conditions and illnesses, the most notable being pain management. Seventeen reviews looked at specific types of pain including neuropathic [ 31 , 42 , 62 , 69 , 85 , 90 ], chronic [ 26 , 32 , 52 , 58 , 80 ], cancer [ 84 , 87 ], non-cancer [ 41 , 68 ], and acute [ 38 ] types of pain (one review covered all types of pain) [ 65 ]. Twenty-seven reviews (38%) also focused on management of pain as a symptom of conditions such as multiple sclerosis (MS) [ 6 , 23 , 27 , 43 , 46 , 52 , 63 , 85 , 92 ], injury [ 29 , 35 , 36 , 69 ], cancer [ 37 , 43 , 65 , 88 ], inflammatory bowel disease (IBD) [ 28 ], rheumatic disease (RD) [ 49 , 51 , 73 ], diabetes [ 68 , 69 , 70 ], and HIV [ 48 , 53 , 67 ]. In Fig. 3 , the types of illnesses addressed by the set of included reviews are graphically represented, with overlap between various conditions and pain. Some systematic reviews covered multiple diseases, and therefore the total number of conditions represented in Fig. 3 is greater than the total number of included reviews.

Conditions or symptoms across reviews that were treated with cannabis. IBD inflammatory bowel disease, MS multiple sclerosis, RD rheumatic disease

One review included a pediatric-only population, in the evaluation of marijuana for nausea and vomiting following chemotherapy [ 54 ]. Although trials in both adult and child populations were eligible for thirteen (18%) reviews, only two additional reviews included studies in children; these reviews evaluated cannabis in cancer [ 60 ] and a variety of conditions [ 25 ]. Many of the reviews ( n = 25, 35%) included only adults ≥ 18 years of age. Almost half of the reviews ( n = 33, 46%) did not report a specific population for inclusion.

Cannabis was prescribed for a wide range of medical issues. The indication for cannabis use is illustrated in Fig. 4 . Pain management ( n = 27) was the most common indication for cannabis use. A number of reviews sought to address multiple disease symptoms ( n = 12) or explored a more holistic treatment for the disease itself ( n = 11). After pain, the most common symptoms being treated with cannabis were spasticity in MS, movement disturbances (such as dyskinesia, tics, and spasms), weight or nausea/vomiting, and mental health symptoms.

Indications for cannabis use across included reviews

Figure 5 summarizes the breadth of outcomes analyzed in the included reviews. The most commonly addressed outcomes were withdrawal due to adverse effects, “other pain,” neuropathic pain, spasticity, and the global impression of the change in clinical status. Many outcomes were reported using a variety of measures across reviews. For example, spasticity was measured both objectively (using the Ashworth scale) and subjectively (using a visual analog scale [VAS] or numerical rating scale [NRS]). Similarily, outcomes for pain included VAS or NRS scales, reduction in pain, pain relief, analgesia, pain intensity, and patient assessment of change in pain.

Quality of the systematic reviews

Quality assessments of the included reviews based upon AMSTAR-2 are detailed in Additional file 7 and Additional file 8 . Only one review was rated as high quality [ 45 ]. All other reviews were deemed to be of moderate ( n = 36) or low/critically low ( n = 35) methodological quality. Assessments for the domains deemed of critical importance for determining quality ratings are described below.

Only 20% of reviews used a comprehensive search strategy; another 47% were given a partial score because they had not searched the reference lists of the included reviews, trial registries, gray literature, and/or the search date was older than 2 years. The remaining reviews did not report a comprehensive search strategy.

Over half of the reviews (51%) used a satisfactory technique for assessing risk of bias (ROB) of the individual included studies, while 35% were partially satisfactory because they had not reported whether allocation sequence was truly random and/or they had not assessed selective reporting. The remaining reviews did not report a satisfactory technique for assessing ROB.

Most reviews (71%) could not be assessed for an appropriate statistical method for combining results in a meta-analysis, as they synthesized study data narratively. Approximately 19% of reviews used an appropriate meta-analytical approach, leaving 10% that used inappropriate methods.

The final critical domain for the AMSTAR-2 determines whether review authors accounted for ROB in individual studies when discussing or interpreting the results of the review. The majority of reviews (83%) did so in some capacity.

Mapping results of included systematic reviews

We mapped reviews according to authors’ comparisons, the conditions or symptoms they were evaluating, and the categorization of the results (see Table 2 ). In some cases, reviews contributed to more than one comparison (e.g., cannabis versus placebo or active drug). As pain was the most commonly addressed outcome, we mapped this outcome separately from all other endpoints. This information is shown for all reviews and then restricted to reviews of moderate-to-high quality (as determined using the AMSTAR-2 criteria): cannabis versus placebo (Figs. 6 and 7 ), cannabis versus active drugs (Figs. 8 and 9 ), cannabis versus a combination of placebo and active drug (Figs. 10 and 11 ), one cannabis formulation versus other (Figs. 12 and 13 ), and cannabis analyzed against all other comparators (Fig. 14 ). Details on how to read the figures are provided in the corresponding figure legends. The median number of included studies across reviews was four, and ranged from one to seventy-nine (not shown in figures).

Cannabis vs. placebo. Authors’ presentations of the findings were mapped using the categorization shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. placebo, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. active drugs. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. active drugs, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. placebo + active drug. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. placebo + active drug, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

One cannabis formulation vs. other. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

One cannabis formulation vs. other, high and moderate quality reviews. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis vs. all comparators combined. Authors’ presentations of the findings were mapped using the categorizations shown in Table 2 . According to the reviews’ intended scope for the condition being treated, outcomes were mapped into “pain,” “non-pain outcomes,” and “adverse events.” For each condition and outcome pair (i.e., each row in the grid), the number of reviews reporting findings is shown according to the results categorization. For pain, reviews numbered in different categories signal discordant findings across those reviews. For non-pain outcomes, reviews presenting findings in the different categories would signal different results for different outcomes, as well as discordant findings within and across reviews. Adverse events are grouped as a whole and “favors intervention” would be interpreted as a decrease in events with cannabis when compared with the control group. Favors int = favors intervention; Favors Ctrl = favors control; Not stat sig = not statistically significant

Cannabis versus placebo

Most reviews (59/72, 82%) compared cannabis with placebo. Of these reviews, 34 (58%) addressed pain outcomes and 47 (80%) addressed non-pain outcomes, with most outcomes addressed by three reviews or fewer (Fig. 6 ). Some reviews had a mix of quantitative syntheses and study-by-study data reported (13/59, 22%), while another group of reviews (14/59, 24%) only reported results study-by-study. Overall, 24% (14/59) of the cannabis versus placebo reviews had only one included study.

Pain outcomes

Reviews focused on addressing pain across conditions. In most cases, findings were discordant across reviews for the pain outcomes measured. For chronic non-cancer pain, however, two reviews favored cannabis over placebo for decreasing pain. One review assessing acute pain for postoperative pain relief found no difference between various cannabinoid medications and placebo. The distribution of findings was similar when restricting to moderate-to-high-quality reviews.

Reviews focused on treating a condition or family of related conditions . Various results were observed for pain. For MS and HIV/AIDS, one review each reported quantitative results favoring cannabis for decreased pain but with other reviews reporting results study-by-study, it is difficult to know, broadly, how consistent those findings are. For cancer, two reviews reported results favoring cannabis for decreased pain. For rheumatic disease, findings are discordant between two reviews, and another two reviews reported results study-by-study. One review that included studies of MS or paraplegia found no difference in pain between groups. For treating injury, one review showed that the placebo group had less pain and one review reported data study-by-study. No reviews addressed pain in movement disorders, neurological conditions, and IBD.

For those reviews assessing pain as part of a focus on treating a range of conditions, two showed cannabis reduced pain [ 43 , 52 ], but one showed mixed results depending on how pain was measured [ 43 ]. These reviews covered several different conditions, including injury, chronic pain, rheumatoid arthritis, osteoarthritis, fibromyalgia, HIV/AIDS, cancer, and MS or paraplegia.

When restricting to moderate-to-high-quality reviews, only one review each in multiple sclerosis and HIV/AIDS with a study-by-study analysis on pain remained. One review on cancer favored cannabis for pain reduction. Findings remained the same for MS or paraplegia and rheumatic disease. No review for injury and paint outcomes was of higher quality.

Non-pain outcomes

The types of non-pain outcomes included in the reviews varied by condition/illness. The most commonly reported outcomes (see Fig. 5 for overall outcomes) when comparing cannabis to placebo included muscle- or movement-related outcomes ( n = 20), quality of life ( n = 14), and sleep outcomes ( n = 10).

There was no consistent pattern for non-pain outcomes either within or across medical conditions. Many ( n = 24, 33%) reviews assessing non-pain outcomes reported the results of those analyses study-by-study. Conflicting results are observed in some cases due to the use of different measures, such as different ways of quantifying spasticity in patients with multiple sclerosis [ 56 , 91 ]. One review each addressing neurological conditions [ 50 ] (outcome: muscle cramps) and MS/paraplegia [ 27 ] (outcomes: spasticity, spasm, cognitive function, daily activities, motricity, and bladder function) showed no difference between groups.

Adverse effects

Adverse effects were reported in most reviews comparing cannabis with placebo (49/59, 83%). Most adverse events were reported study-by-study, with few reviews ( n = 16/59, 27%) conducting a narrative or quantitative synthesis. Serious adverse effects were reported in 21/59 (36%) reviews, and minor adverse effects were reported in 30/59 (51%) reviews. The remaining reviews did not define the difference between serious and minor adverse events. The most commonly reported serious adverse events included psychotic symptoms ( n = 6), severe dysphoric reactions ( n = 3), seizure ( n = 3), and urinary tract infection ( n = 2). The most commonly reported minor adverse events included somnolence/drowsiness ( n = 28), dizziness ( n = 27), dry mouth ( n = 20), and nausea ( n = 18). Many reviews ( n = 37/59, 63%) comparing cannabis to placebo reported both neurocognitive and non-cognitive adverse effects. Withdrawals due to adverse events were reported in 22 (37%) reviews.

Of the moderate-/high-quality reviews, adverse effect analyses were reported in reviews on pain, multiple sclerosis, cancer, HIV/AIDS, movement disorders, rheumatic disease, and several other conditions. Two reviews on pain showed fewer adverse events with cannabis for euphoria, events linked to alternations in perception, motor function, and cognitive function, withdrawal due to adverse events, sleep, and dizziness or vertigo [ 58 , 90 ]. One review on MS showed that there was no statistically significant difference between cannabis and placebo for adverse effects such as nausea, weakness, somnolence, and fatigue [ 91 ], while another review on MS/paraplegia reported fewer events in the placebo group for dizziness, somnolence, nausea, and dry mouth [ 27 ]. Within cancer reviews, one review found no statistically significant difference between cannabis and placebo for dysphoria or sedation but reported fewer events with placebo for “feeling high,” and fewer events with cannabis for withdrawal due to adverse effects [ 40 ]. In rheumatic disease, one review reported fewer total adverse events with cannabis and found no statistically significant difference between cannabis and placebo for withdrawal due to adverse events [ 51 ].

Cannabis versus other drugs

Relatively fewer reviews compared cannabis with active drugs ( n = 23/72, 32%) (Fig. 8 ). Many of the reviews did not synthesize studies quantitatively, and results were reported study-by-study. The most common conditions in reviews comparing cannabis to active drugs were pain, cancer, and rheumatic disease. Comparators included ibuprofen, codeine, diphenhydramine, amitriptyline, secobarbital, prochlorperazine, domperidone, metoclopramide, amisulpride, neuroleptics, isoproterenol, megestrol acetate, pregabalin, gabapentin, and opioids.

Reviews focused on addressing pain across conditions. When comparing across reviews, a mix of results are observed (see Fig. 8 ), and some were reported study-by-study. One review found no statistically significant difference between cannabinoids and codeine for nociceptive pain, postoperative pain, and cancer pain [ 65 ]. Another review favored “other drugs” (amitriptyline and pregabalin) over cannabinoids for neuropathic pain [ 90 ]. The distribution of findings was similar when restricting to moderate-to-high-quality reviews.

Reviews focused on treating a condition or family of related conditions. One review on cancer compared cannabinoids and codeine or secobarbital and reported pain results study-by-study. Another review on fibromyalgia comparing synthetic cannabinoids with amitriptyline also reported pain data study-by-study [ 39 ].

Two reviews on cancer favored cannabinoids over active drugs (prochlorperazine, domperidone, metoclopramide, and neuroleptics) for patient preference and anti-emetic efficacy [ 40 , 60 ]. Non-pain outcomes were reported study-by-study for the outcome of sleep in neuropathic pain [ 90 ] and rheumatic disease [ 39 , 49 ]. In a review covering various conditions (pain, MS, anorexia, cancer, and immune deficiency), results were unclear or indeterminate for subjective measures of sleep [ 46 ].

Adverse effects were reported in 20/24 (83%) of the reviews comparing cannabis to active drugs, and only 6/20 (30%) reported a narrative or quantitative synthesis. Many reviews that reported narrative data did not specify whether adverse effects could be attributed to a placebo or active drug comparator.

Of the moderate-to-high-quality reviews, two pain reviews found no statistically significant difference for cannabis compared to codeine or amitriptyline for withdrawals due to adverse events [ 65 , 90 ]. Results from one cancer review were mixed, with fewer adverse events for cannabis (compared to prochlorperazine, domperidone, or metoclopramide) or no difference between groups, depending on the type of subgroup analysis that was conducted [ 40 ].

Cannabis + active drugs versus placebo + active drugs

Two reviews compared cannabis with placebo cannabis in combination with an active drug (opioids and gabapentin) (Figs. 10 and 11 ). Both were scored to be of moderate quality. Although one review showed that cannabis plus opioids decreased chronic pain [ 80 ], another review on pain in MS included only a single study [ 81 ], precluding the ability to determine concordance of results. Cannabis displayed varied effects on non-pain outcomes, including superiority of placebo over cannabis for some outcomes. One review reported withdrawal due to adverse events study-by-study and also reported that side effects such as nausea, drowsiness, and dizziness were more frequent with higher doses of cannabinoids (data from two included studies) [ 80 ].

Cannabis versus other cannabis comparisons

Six (8%) reviews compared different cannabis formulations or doses (Figs. 12 and 13 ). Almost all were reported as study-by-study results, with two reviews including only one RCT. One review for PTSD found only observational data [ 33 ] and another review on anxiety and depression combined data from one RCT with cross-sectional study data [ 19 ]. A single review on MS reported a narrative synthesis that found a benefit for spasticity. However, it was unclear if the comparator was placebo or THC alone [ 56 ]. Four reviews reported adverse effects study-by-study, with a single review comparing side effects from different dosages; in this review, combined extracts of THC and CBD were better tolerated than extracts of THC alone [ 56 ].

Cannabis versus all comparators

One review combined all comparators for the evaluation (Fig. 14 ). The review (combining non-users, placebo and ibuprofen) covered a range of medical conditions and was rated as low quality [ 30 ]. No adverse effects were evaluated for this comparison.

Mapping the use of quality assessment and frameworks to interpret the strength of evidence

Although 83% of reviews incorporated risk of bias assessments in their interpretation of the evidence, only 11 (15%) reviews used a framework such as GRADE to evaluate important domains other than risk of bias that would inform the strength of the evidence.

Mapping authors’ conclusions or recommendations

Most reviews (43/72 60%) indicated an inability to draw conclusions, whether due to uncertainty, inconsistent findings, lack of (high quality) evidence, or focusing their conclusion statement on the need for more research. Almost 15% of reviews (10/72) reported recommendations or conclusions that included some uncertainty. One review (1%) provided a statement of the extent of the strength of the evidence, which differed according to outcome.

Eleven reviews provided clearer conclusions (14%). Four indicated that cannabis was not effective or not cost-effective compared to placebo in relation to multiple sclerosis, acute pain, cancer, and injury. Three reviews addressing various conditions provided varying conclusions: one stated cannabis was not effective, one indicated it was modestly safe and effective, and one concluded that cannabis was safe and efficacious as short-term treatment; all reviews were of low quality. The three remaining reviews stated moderate or modest effects for improving chronic pain, compared with placebo or other analgesia; two of those reviews were of medium AMSTAR-2 quality, and one used the GRADE framework for interpreting the strength of the evidence.

The eight remaining included reviews (11%) did not provide a clear conclusion statement or reported only limitations.

Mapping authors’ limitations of the research

Several of the reviews indicated that few studies, small sample sizes, short duration of treatment, and issues related to outcomes (e.g., definition, timing, and types) were drawbacks to the literature. Some reviews noted methodological issues with and heterogeneity among studies as limitations. A few authors stated that restricting eligibility to randomized trials, English-language studies, or full publications may have affected their review results.

With the increasing use of medical cannabis, an understanding of the landscape of available evidence syntheses is needed to support evidence-informed decision-making, policy development, and to inform a research agenda. In this scoping review, we identified 72 systematic reviews evaluating medical cannabis for a range of conditions and illnesses. Half of the reviews were evaluated as being of moderate quality, with only one review scoring high on the AMSTAR-2 assessment tool.

There was disparity in the reported results across reviews, including non-synthesized (study-by-study) data, and many were unable to provide a definitive statement regarding the effectiveness of cannabis (as measured by pain reduction or other relevant outcomes), nor the extent of increased side effects and harms. This is consistent with the limitations declared in general across reviews, such as the small numbers of relevant studies, small sample sizes of individual studies, and methodological weaknesses of available studies. This common theme in review conclusions suggests that while systematic reviews may have been conducted with moderate or high methodological quality, the strength of their conclusions are driven by the availability and quality of the relevant underlying evidence, which was often found to be limited.

Relatively fewer reviews addressed adverse effects associated with cannabis, except to narratively summarize study level data. Although information was provided for placebo-controlled comparisons, none of the comparative effectiveness reviews quantitatively assessed adverse effects data. For the placebo-controlled data, although the majority of adverse effects were mild, the number of reviews reporting serious adverse effects such as psychotic symptoms [ 25 , 42 ] and suicidal ideation [ 68 , 85 ] warrants caution.

A mix of reviews supporting and not supporting the use of cannabis, according to authors’ conclusions, was identified. Readers may wish to consider the quality of the reviews, the use of differing quality assessment tools, additional considerations covered by the GRADE framework, and the potential for spin as possible reasons for these inconsistencies. It is also possible that cannabis has differing effects depending on its type (e.g., synthetic), dose, indication, the type of pain being evaluated (e.g., neuropathic), and the tools used for outcome assessment, which can be dependent on variations in condition. Of potential interest to readers may be a closer examination of the reviews evaluating chronic pain, in order to locate the source(s) of discordance. For example, one review was deemed of moderate quality, used the GRADE framework, and rated the quality of evidence for the effectiveness of cannabis for reducing neuropathic pain as moderate, suggesting that further investigation of cannabis for neuropathic pain may be warranted [ 80 ]. The exploration aspects outlined in this paragraph are beyond the purview of scoping review methodology; a detailed assessment of the reviews, including determining the overlap of included studies among similar reviews, potential reasons for the observed discordance of findings, what re-analysis of study-by-study analyses would yield, and an undertaking of missing GRADE assessments would fall outside the bounds of a scoping review and require the use of overview methodology [ 14 ].

Our findings are consistent with a recently published summary of cannabis-based medicines for chronic pain management [ 3 ]. This report found inconsistent results in systematic reviews of cannabis-based medicines compared to placebo for chronic neuropathic pain, pain management in rheumatic diseases and painful spasms in MS. The authors also concluded that cannabis was not superior to placebo in reducing cancer pain. Four out of eight included reviews scored high on the original AMSTAR tool. The variations between the two tools can be attributed to the differences in our overall assessments. Lastly, the summary report included two reviews that were not located in our original search due to language [ 93 ] and the full-text [ 94 ] of an abstract [ 95 ] that was not located in our search.

This scoping review has identified a plethora of synthesized evidence in relation to medical cannabis. For some conditions, the extent of review replication may be wasteful. Many reviews have stated that additional trials of methodologically robust design and, where possible, of sufficient sample size for precision, are needed to add to the evidence base. This undertaking may require the coordination of multi-center studies to ensure adequate power. Future trials may also help to elucidate the effect of cannabis on different outcomes.

Given authors’ reporting of issues in relation to outcomes, future prospective trials should be guided by a standardized, “core” set of outcomes to strive for consistency across studies and ensure relevance to patient-centered care. Development of those core outcomes should be developed using the Core Outcome Measures in Effectiveness Trials (COMET) methodology [ 96 ], and further consideration will need to be made in relation to what outcomes may be common across all cannabis research and which outcomes are condition-specific. With maturity of the evidence base, future systematic reviews should seek and include non-journal-published (gray literature) reports and ideally evaluate any non-English-language papers; authors should also adequately assess risk of bias and undertake appropriate syntheses of the literature.

The strengths of this scoping review include the use of an a priori protocol, peer-reviewed search strategies, a comprehensive search for reviews, and consideration of observational designs for adverse effects data. For feasibility, we restricted to English-language reviews, and it is unknown how many of the 39 reviews in other languages that we screened would have met our eligibility criteria. The decision to limit the inclusion of reviews of observational data to adverse effects data was made during the process of full-text screening and for pragmatic reasons. We also did not consider a search of the PROSPERO database for ongoing systematic reviews; however, in preparing this report, we performed a search and found that any completed reviews were already considered for eligibility or were not available at the time of our literature search. When charting results, we took a broad perspective, which may be different than if these reviews were more formally assessed during an overview of systematic reviews.

Cannabis-based medicine is a rapidly emerging field of study, with implications for both healthcare practitioners and patients. This scoping review is intended to map and collate evidence on the harms and benefits of medical cannabis. Many reviews were unable to provide firm conclusions on the effectiveness of medical cannabis, and results of reviews were mixed. Mild adverse effects were frequently but inconsistently reported, and it is possible that harms may outweigh benefits. Evidence from longer-term, adequately powered, and methodologically sound RCTs exploring different types of cannabis-based medicines is required for conclusive recommendations.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

Abbreviations

Canadian Agency for Drugs and Technologies in Health

Complementary and alternative medicine

Cannabidiol

Grading of Recommendations Assessment, Development and Evaluation

Human immunodeficiency virus

Inflammatory bowel disease

Multiple sclerosis

Numeric rating scale

Randomized controlled trial

Rheumatic disease

Risk of bias

Tetrahydrocannabinol

Visual analog scale

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Research reported in this publication was supported by the National Center for Complementary and Integrative Health of the National Institutes of Health under award number R24AT001293. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Misty Pratt, Adrienne Stevens, Micere Thuku, Claire Butler & Brian Hutton

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MP, AS, and BH drafted the initial version of the report. BS designed and implemented the literature search. MP, MT, and CB contributed to review of abstracts and full texts as well as data collection. MP, AS, and BH were responsible for analyses. All authors (MP, AS, MT, CB, BS, SW, MC, SK, BH) contributed to interpretation of findings and revision of drafts and approved the final version of the manuscript.

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Additional file 1..

PRISMA Scoping Review Extension Completed Checklist.

Additional file 2.

Literature Search Strategies.

Additional file 3.

Grey Literature Sources.

Additional file 4.

Listing of Data Extraction Items.

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Data extractions from included studies.

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Listing of Studies Excluded During Full Text Screening.

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AMSTAR Scoring Outline.

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Pratt, M., Stevens, A., Thuku, M. et al. Benefits and harms of medical cannabis: a scoping review of systematic reviews. Syst Rev 8 , 320 (2019). https://doi.org/10.1186/s13643-019-1243-x

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CALL FOR PAPERS : Special Issue Cannabis in Canada in the Post-Legalization Period

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October 2023 marks the 5-year anniversary of Canada’s legalization of recreational cannabis, and it remains the only G7 nation to have taken this step. To coincide with this milestone and further expand Cannabis’s international scope, we are announcing the publication of a special issue focused on cannabis in Canada during the post-legalization period. For this national natural experiment, the impacts of legalization and post-legalization patterns of recreational and medical use are important for Canadian public health but also can help to inform other countries as they consider legalization policies. 

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Empirical evaluations of impacts of recreational cannabis legalization

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Changes in recreational and medical use over time

Cannabis use in special populations, including veterans, indigenous individuals, and older adults

Patterns of overlap between cannabis use and other psychoactive drug use, as well as psychiatric co-morbidities

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Research with underrepresented populations (e.g., sexual and gender minority individuals, racial and ethnic minority individuals, intersections of identities)

Innovations in prevention and treatment of cannabis misuse and cannabis use disorder

SUBMISSIONS DEADLINE PASSED EXPECTED PUBLICATION DATE: NOVEMBER 2024

• Introduction
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A, The y-axis corresponds to the cannabinoid composition of medical cannabis prescriptions (balanced, CBD-dominant, THC-dominant). The x-axis represents time in years over the sample period (December 2018 to May 2022). The solid fitted lines are locally estimated scatterplot smoothing curves with bandwidths of 0.9 and 2-sided 95% CIs around the smooths. B, Raincloud plots for the daily dose amounts of CBD and THC (x-axis) across the 3 main cannabinoid composition categories (y-axis) are shown. Each dot in the panel corresponds to a single patient-consult dose recording (measured in mg), whereas the boxplot showcases the associated means (denoted by the x), medians (middle line of the box), first and third quartiles (left and right hinges), and 1.5 times the interquartile range left and right of the first and third quartiles, respectively (left and right whiskers), for both CBD and THC. Finally, the split-violin plot visualizes the distribution density of CBD/THC dosing behavior. C, The y-axis represents the daily dose of CBD and THC taken, while the x-axis denotes the number of consultations since commencing treatment. Error bars show 95% CI. CBD indicates cannabidiol; THC, delta-9-tetrahydrocannabinol.

Mean scores on the y-axes correspond to the respective 0 to 100 subscales for general health (A), bodily pain (B), physical functioning (C), and role-physical (D) from the SF-36, respectively. The follow-up on the x-axes represents the number of consultations since commencing treatment. Mean levels of the 4 domain scores are computed for each follow-up consult. The red horizontal lines show the respective pretreatment means at baseline. The gray horizontal lines illustrate the associated means reported by individuals in the 2015 wave of the Household, Income and Labour Dynamics in Australia survey (see reference in text). Error bars show 95% CIs.

Mean scores on the y-axes correspond to the respective 0 to 100 subscales for mental health (A), role-emotional (B), social functioning (C), and vitality (D) from the SF-36, respectively. The follow-up on the x-axes represents the number of consultations since commencing treatment. Mean levels of the 4 domain scores are computed for each follow-up consult. The red horizontal lines show the respective pre-treatment means at baseline. The gray horizontal lines illustrate the associated mean reported by individuals in the 2015 wave of the Household, Income and Labour Dynamics in Australia survey (see reference in text). Error bars show 95% CIs.

eTable 1. Data Availability on Quality of Life (SF-36) Measures by Follow-up

eTable 2. OLS Regression Results, Estimating General Health (Increasing From 0 to 100)

eTable 3. OLS Regression Results, Estimating Bodily Pain (Decreasing From 0 to 100)

eTable 4. OLS Regression Results, Estimating Physical Functioning (Increasing From 0 to 100)

eTable 5. OLS Regression Results, Estimating Role-Physical (Decreasing From 0 to 100)

eTable 6. OLS Regression Results, Estimating Mental Health (Increasing From 0 to 100)

eTable 7. OLS Regression Results, Estimating Role-Emotional (Decreasing From 0 to 100)

eTable 8. OLS Regression Results, Estimating Social Functioning (Increasing From 0 to 100)

eTable 9. OLS Regression Results, Estimating Vitality (Increasing From 0 to 100)

eTable 10. Reported Adverse Events Across Different Severity Levels

eFigure. Flow of Patients Through the Study of the Association of Medicinal Cannabis With Health-Related Quality of Life

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Arkell TR , Downey LA , Hayley AC , Roth S. Assessment of Medical Cannabis and Health-Related Quality of Life. JAMA Netw Open. 2023;6(5):e2312522. doi:10.1001/jamanetworkopen.2023.12522

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Assessment of Medical Cannabis and Health-Related Quality of Life

• 1 Centre for Human Psychopharmacology, Swinburne University of Technology, Melbourne, Victoria, Australia
• 2 Institute for Breathing and Sleep (IBAS), Austin Hospital, Melbourne, Victoria, Australia
• 3 Department of Economics, University of Western Australia, Crawley, Western Australia, Australia
• 4 Emyria, Leederville, Western Australia, Australia

Question   Is medical cannabis treatment associated with improvements in health-related quality of life?

Findings   In this case series of 3148 patients, significant improvements were reported on all 8 domains of the 36-Item Short Form Health Survey health-related quality of life assessment after commencing treatment with medical cannabis. Improvements were largely sustained over time.

Meaning   These findings suggest that medical cannabis treatment may be associated with improvements in health-related quality of life among patients with a range of health conditions.

Importance   The use of cannabis as a medicine is becoming increasingly prevalent. Given the diverse range of conditions being treated with medical cannabis, as well as the vast array of products and dose forms available, clinical evidence incorporating patient-reported outcomes may help determine safety and efficacy.

Objective   To assess whether patients using medical cannabis report improvements in health-related quality of life over time.

Design, Setting, and Participants   This retrospective case series study was conducted at a network of specialist medical clinics (Emerald Clinics) located across Australia. Participants were patients who received treatment for any indication at any point between December 2018 and May 2022. Patients were followed up every mean (SD) 44.6 (30.1) days. Data for up to 15 follow-ups were reported. Statistical analysis was conducted from August to September 2022.

Exposure   Medical cannabis. Product types and cannabinoid content varied over time in accordance with the treating physician’s clinical judgement.

Main Outcomes and Measures   The main outcome measure was health-related quality of life as assessed using the 36-Item Short Form Health Survey (SF-36) questionnaire.

Results   In this case series of 3148 patients, 1688 (53.6%) were female; 820 (30.2%) were employed; and the mean (SD) age was 55.9 (18.7) years at baseline before treatment. Chronic noncancer pain was the most common indication for treatment (68.6% [2160 of 3148]), followed by cancer pain (6.0% [190 of 3148]), insomnia (4.8% [152 of 3148]), and anxiety (4.2% [132 of 3148]). After commencing treatment with medical cannabis, patients reported significant improvements relative to baseline on all 8 domains of the SF-36, and these improvements were mostly sustained over time. After controlling for potential confounders in a regression model, treatment with medical cannabis was associated with an improvement of 6.60 (95% CI, 4.57-8.63) points to 18.31 (95% CI, 15.86-20.77) points in SF-36 scores, depending on the domain (all P  < .001). Effect sizes (Cohen d ) ranged from 0.21 to 0.72. A total of 2919 adverse events were reported, including 2 that were considered serious.

Conclusions and Relevance   In this case series study, patients using medical cannabis reported improvements in health-related quality of life, which were mostly sustained over time. Adverse events were rarely serious but common, highlighting the need for caution with prescribing medical cannabis.

Medical cannabis was legalized in Australia in November 2016.Aside from Sativex and Epidiolex, all other cannabinoid products are considered unapproved therapeutic goods at the time of this writing. Physicians must obtain regulatory approval to prescribe via one of several special access pathways. These approvals have increased rapidly over the last 2 years and now total more than 332 000. 1 Most approvals have been for chronic pain (55%), followed by anxiety (23%) and insomnia and/or sleep disorders (6%). 2 Major reviews have generally concluded there is evidence for cannabinoid efficacy in the treatment of several conditions: pain in adults, chemotherapy-induced nausea and vomiting, and spasticity associated with multiple sclerosis. 3 - 5 Moderate evidence exists for cannabinoid efficacy in treating secondary sleep disturbances, and there is limited, insufficient, or absent evidence for other conditions. Despite this, enrollment in medical cannabis programs increased 4.5-fold in the US between 2016 and 2020, 6 and a recent survey conducted in the US and Canada found that 27% of all respondents (n = 27 169) had used cannabis for medical purposes at some point. 7

The term medical cannabis encompasses a vast array of products (eg, dried flower, oils, edibles) containing multiple bioactive constituents including, but not limited to, delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Because patients are using these products to manage such a wide range of health conditions—in addition to the paucity of evidence from randomized clinical trials—clinical evidence incorporating patient-reported outcomes is becoming increasingly recognized as a vital source of safety and efficacy data. 8 , 9 Validated health-related quality of life measures can help provide important, global insights into associations between medical cannabis treatment and daily functioning, physical mobility, and mental health among patients with various and disparate conditions. Here, we examine changes in health-related quality of life over time in a cohort (n = 3148) of Australian patients receiving medical cannabis treatment between 2018 and 2022.

We conducted a retrospective case series analysis of patients prescribed medical cannabis through Emerald Clinics, a network of specialist medical clinics across Australia. After providing informed written consent, patients presenting to Emerald Clinics first undergo a comprehensive consultation with a physician, who reviews their medical history and determines suitability for cannabinoid treatment. In addition to meeting Australia’s regulatory requirements for access to unapproved products (physicians must provide a suitable clinical justification for the use of medical cannabis, including reasons why products included in the Australian Register of Therapeutic Goods are not suitable for treatment of the patient), patients are also required to have exhausted other treatment options for the clinical indication(s) they are presenting with. Moreover, site-specific contraindications for treatment include: (1) urine positive for carboxy-THC (THC-COOH), (2) pregnant and/or breastfeeding, (3) serious cardiac disease, or (4) serious mental health conditions, such as suicidal ideation or a history of psychosis. Patients are instructed to slowly increase their dose via a “start low, go slow” principle. The target dose is determined on a case-by-case basis and is subject to regular reviews by the prescribing physician to assess treatment efficacy and side effects, including any interactions with concomitant medication. Although no official prescribing guidelines exist in Australia, clinical judgement of appropriate dose and product type may be influenced by various factors such as health condition, age, concomitant medications, comorbidities, dose form, and the cost of treatment. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

In accordance with Australia’s National Statement on Ethical Conduct in Human Research (2007) requirements for exemption from review, data collection commenced in December 2018 and remains ongoing. For this study, we included every observation available (as of May 5, 2022) comprising baseline and up to and including the first 15 follow-up consultations of each patient. We limited the number of follow-ups to 15 as patient numbers become much smaller thereafter (n <80). Besides providing detailed clinical and demographic information (such as age, gender, employment status, and any other medications currently being used), at each consultation patients were also asked to complete several validated questionnaires, including the 36-Item Short Form Health Survey (SF-36) which is the focus of this study. eTable 1 in Supplement 1 presents a consult-by-consult overview of data availability for each measure used in our analysis, but also the mean (SD) time elapsed between consultations. On average, patients attended a mean of (SD) 5.6 (4.9) consultations with a mean (SD) time between consultations of 44.6 (30.1) days.

The primary outcome was change from baseline in patient scores on the SF-36, 10 , 11 a widely used measure of health-related quality of life. The SF-36 includes 36 items which form 8 distinct scales, including: (1) limitations in physical activities due to health problems; (2) limitations in social activities due to physical or emotional problems; (3) limitations in usual role activities due to physical health problems; (4) bodily pain; (5) general mental health (psychological distress and well-being); (6) limitations in usual role activities due to emotional problems; (7) vitality (energy and fatigue); and (8) general health perceptions. Scores can range from 0 to 100, with higher values indicating better outcomes. A recent review considered a 10-point change to be the minimally clinically important difference. 12 Finally, as an additional outcome we also report any reported adverse events.

Our analysis followed a conventional ordinary least squares model. We first estimated a univariate regression using a binary treatment indicator for taking medical cannabis as the sole estimator for each of the 8 domain scores. We then moved to a more complete framework, estimating each score y for patient i at consult t with: y i,t  = β 1 Treatment t + β 2 X i,t + β 3 Z i + ε i,t (equation 1). The coefficient associated with β 1 represents the effect of commencing with the treatment on a patient’s quality of life. X i,t represents a set of control variables that could potentially influence y i,t . These include the number of medications a patient takes daily (at the time of consult), binary indicators for both 8 medication categories (simple analgesics, opioids, antidepressants, benzodiazepines, GABA analogues, antipsychotic medications, compound analgesics, and other pain medications) and 4 primary diagnosis categories (pain, psychiatric, neurological, or other), the number of other comorbidities reported, the patient’s age, gender, and employment status, and a nonlinear treatment trend (equal to the reciprocal of the number of follow-up consults since commencing treatment), as well as month- and year-fixed effects. Furthermore, Z i incorporates patient-fixed effects and ε i,t corresponds to the usual error term. Note that throughout all estimations, 95% CIs were clustered at the patient level while statistical significance was tested at the 5% level ( P  = .05). We then reestimated the same regression analysis displayed in equation 1 for the separate treatment categories, focusing on whether a patient was using a balanced (40% to <60% CBD content), CBD-dominant (≥60% CBD content), or THC-dominant (≥60% THC content) treatment as the main regressors of interest. Effect sizes equivalent to Cohen d were calculated by dividing the associated treatment coefficients in our patient fixed-effects model by the SDs of the respective SF-36 scores at baseline. All analyses were performed in R 4.2.2 (R Project for Statistical Computing) using the lfe package from August to September 2022.

Among the 3148 patients included in this data set, 1688 (53.6%) were female; 820 (30.2%) were employed; and the mean (SD) age was 55.9 (18.7) years at baseline before treatment. Table 1 summarizes the demographics and characteristics of the 3148 patients included in this study. Chronic non-cancer pain was the most common indication for treatment (68.6% [2160 of 3148]), followed by cancer pain (6.0% [190 of 3148]), insomnia (4.8% [152 of 3148]), and anxiety (4.2% [132 of 3148]). Number of comorbidities ranged from 0 to 36, with a mean (SD) of 5.2 (3.9). On average, patients were taking a mean (SD) of 6.58 (4.58) medications a day prior to commencing treatment. The most common medications included simple analgesics (54.1% [1703 of 3148]), opioid analgesics (48.4% [1523 of 3148]), antidepressants (44.5% [1401 of 3148]), benzodiazepines (34.4% [1084 of 3148]), and GABA analogues (22.0% [693 of 3148]). Except for the mental health measure (mean [SD]: 54.06 [22.27]), all mean (SD) pretreatment SF-36 scores were well below the halfway mark on the respective 0 to 100 scales: 40.22 (22.40) for general health; 29.85 (24.16) for bodily pain; 40.99 (30.49) for physical functioning; 14.02 (28.99) for role-physical; 28.37 (37.30) for role-emotional; 36.57 (26.84) for social functioning; and 30.19 (20.83) for vitality.

Figure 1 A shows the percentage of prescriptions by cannabinoid category across the sample period. Prescriptions for CBD-dominant treatments increased consistently from February 2019, and accounted for approximately 80% of all monthly prescriptions (compared with 7.5% and 12.5% for balanced and THC-dominant categories, respectively) at the end of the data collection period. Most of these prescriptions were for orally administered products including oils (n = 14 779 [90.1%]) and capsules (n = 631 [3.8%]). There were only a small number of prescriptions for dried flower for inhalation either alone (n = 244 [1.5%]) or in combination with an oil (n = 168 [1.0%]). Figure 1 B compares daily THC and/or CBD doses across categories. For balanced treatments, the mean (SD) CBD dose was 18.8 (19.2) mg and the mean (SD) THC dose was 18.8 (19.0) mg. For CBD-dominant treatments, the mean (SD) CBD dose was 97.1 (155.0) mg and the mean (SD) THC dose was 8.7 (12.2) mg. For THC-dominant treatments, the mean (SD) CBD dose was 5.0 (6.9) mg while the mean (SD) THC dose was 35.9 (71.6) mg. As Figure 1 C illustrates, the mean (SD) daily CBD dose initially increased from 51.4 (128.4) mg at follow-up 1 (approximately 45 days after treatment initiation) to 72.2 (217.6) mg at follow-up 2 (approximately 90 days after treatment initiation), but then stayed relatively stable across subsequent consults. The mean (SD) daily THC dose, on the other hand, increased steadily over time from 6.5 (8.2) mg at follow-up 1 to 25.8 (23.6) mg at follow-up 15 (approximately 675 days after treatment initiation).

Figure 2 and Figure 3 display mean scores for all SF-36 domains across 15 follow-up consults, with the red horizontal line showing the mean score at baseline as a pretreatment reference point. The gray line provides a comparison to the mean Australian score as reported in the 2015 wave of the Household, Income and Labour Dynamics in Australia survey. 13 As can be seen in Figure 2 , patients reported an increase relative to baseline on all 4 physical component domains, yet scores remain substantially lower than the mean Australian score. For physical functioning ( Figure 2 C), mean scores regressed toward baseline at follow-up 10, but did not decrease beyond this point. For all other physical domains, gains relative to baseline were maintained across all 15 follow-ups. For bodily pain (Figure 2B) and role-physical ( Figure 2 D), the change from baseline was statistically significant across all time points ( P  < .05). Figure 3 shows a similar if not greater (relative to physical component domains) improvement in mental health domain scores. We observed pronounced and statistically significant improvements on all 4 domains across all 15 follow-ups ( P  < .01). For both Figure 2 and Figure 3 , wider 95% CIs at later time points (ie, longer treatment duration) reflect smaller patient numbers.

Table 2 reports the ordinary least squares regression results for all 8 SF-36 domain scores. Here, we only display the primary coefficient of interest with the corresponding 95% CIs, R 2 value, and effect size (Cohens d ). The complete regression output can be found in eTables 2 to 9 in Supplement 1 . Our complete regression model accounts for a relatively high proportion of variance (41% to 79%) in SF-36 domain scores. Overall ( Table 2 ), treatment with medical cannabis was associated with improvements on all physical and mental health domain scores: general health (β = 8.42; 95% CI, 6.73-10.11; P  < .001); bodily pain (β = 17.34; 95% CI, 15.41-19.27; P  < .001); physical functioning (β = 6.60; 95% CI, 4.57-8.63; P  < .001); role-physical (β = 16.81; 95% CI, 13.58-20.04, P  < .001); mental health (β = 11.00; 95% CI, 9.32-12.68; P  < .001); role-emotional (β = 14.19; 95% CI, 10.01-18.36; P  < .001); social functioning (β = 18.31; 95% CI, 15.86-20.77; P  < .001); and vitality (β = 12.91; 95% CI, 11.02-14.79; P  < .001). Effect sizes were small-moderate in magnitude, ranging from 0.21 to 0.72. For all domains except for physical functioning and role-physical, balanced products were associated with marginally greater improvements than either CBD-dominant or THC-dominant products. CBD-dominant products were associated with largest improvements on the role-physical domain, while THC-dominant products were associated with largest improvements on the physical functioning domain.

A total of 2919 adverse events were reported over the sampling period (eTable 10 in Supplement 1 ). Most were either mild (n = 1905) or moderate (n = 922); 86 were severe. Two adverse events were considered serious, including 1 incidence of hallucination. In order of frequency, adverse events included sedation and/or sleepiness (13.1% of patients), dry mouth (11.4%), lethargy and/or tiredness (7.4%), dizziness (7.1%), difficulty concentrating (6.4%), nausea (6.3%), diarrhea and/or loose stools (4.9%), feeling high (4.7%), increased appetite (3.7%), headache (3.2%), anxiety and/or panic attack (2.7%), vivid dreams (1.7%), hallucination (1.4%), and impaired coordination (1.3%). The incidence of adverse events did not differ significantly across cannabinoid composition categories.

In this retrospective case series, patients reported improvements on all 8 health-related quality of life domains assessed by the SF-36 after commencing treatment with medical cannabis. In our most complete regression model, observed treatment effects suggest improvements relative to baseline (pretreatment) ranging from 6.60 to 18.31 points. Even though the mean daily THC/CBD dose differed considerably across the balanced (18.8 mg THC; 18.8 mg CBD), CBD-dominant (8.7 mg THC; 97.1 mg CBD) and THC-dominant (35.9 mg THC; 5.0 mg CBD) treatment categories, estimated treatment effects were very similar. The mean daily THC dose increased consistently across the sample period from 6.5 mg at follow-up 1 to 25.8 mg at follow-up 15, consistent with a standard dose titration protocol. The mean CBD dose, on the other hand, stayed relatively stable across the sample period after reaching 72.2 mg at follow-up 2.

Commensurate with the Therapeutic Goods Administration data reflecting broader prescription patterns across Australia, 2 chronic noncancer pain was by far the most common primary diagnosis in this sample population (n = 2160), followed by cancer pain (n = 190), insomnia (n = 152), and anxiety (n = 132). As might be expected given the high incidence of pain conditions, almost half of all patients were using simple and/or opioid analgesics at baseline. Patient-reported bodily pain and physical functioning scores at baseline were more than 40% below the Australian mean score, while patient-reported role-physical scores (limitations in usual role activities due to physical health problems) were more than 70% below the Australian mean. Patient-reported social functioning and role-emotional (limitations in usual role activities due to emotional problems) were also more than 40% below the Australian mean. Considering this, the estimated treatment effects reported here (ranging from 6.60 to 18.31 points) suggest substantial absolute gains across all functional domains, although it is important to contextualize the magnitude of these changes within the broader literature.

In a recent systematic review and meta-analysis of randomized clinical trials of medical cannabis for chronic pain (n = 32 trials with 5174 patients), oral medical cannabis was associated with a 4% increase in the proportion of patients experiencing an improvement of more than 10 points (the minimally clinically important difference) on the physical functioning scale of the SF-36 relative to placebo. 12 No evidence was found for improvements on the role-emotional, role-physical, or social functioning scales; however, the median follow-up time was only 50 days (maximum: 154 days), and there was considerable variability in active drug type and route of administration. Here, clinically important improvements (>10 points) were observed for the role-emotional, role-physical, and social functioning scales, with associated effect sizes (0.38 to 0.68), suggesting considerable clinical gains over the long term.

Pritchett et al 14 reported significant improvements on 5 SF-36 domains when comparing scores prior to commencing medical cannabis with posttreatment scores. In a sample of 2183 patients in Florida, large mean differences of 43.64, 35.15 and 26.55 points were noted for the social functioning, bodily pain, and physical functioning scales. However, pretreatment scores were retrospectively reported by patients, which limits their reliability, and only a single posttreatment measure was obtained. To better determine the long-term effects of medical cannabis treatment, Safakish et al 15 examined changes on the SF-12 (a short-form version of the SF-36) over 12 months in 751 patients with chronic pain commencing medical cannabis treatment. While statistically significant improvements were seen on both the physical and mental health domains, these changes were notably smaller than those seen here. Nevertheless, patients did experience a clinically important reduction in pain severity of 2.09 points on the brief pain inventory.

Pain severity was also significantly reduced in 274 patients with chronic pain when assessed 6 months after treatment, as was pain interference and most social and emotional disability scores on the S-TOPS. 16 An analysis of 190 patients with chronic pain in the UK Medical Cannabis Registry likewise revealed improvements on a range of scales (including the EQ-5D, Sleep Quality Scale, General Anxiety Disorder-7) at 1, 3, and 6 months relative to baseline. 17 Changes in EQ-5D scores after 6 weeks of treatment were less consistent in a study involving 214 Canadian patients commencing medical cannabis treatment; improvements were seen for patients with anxiety and PTSD, but not for patients with arthritis and other rheumatic disorders or sleep disorders. 18 Despite an improvement in quality of life among patients with anxiety, there were no significant changes in the anxiety subscale of the Depression, Anxiety and Stress Scale. These data suggest that treatment with medical cannabis may, in some circumstances, improve quality of life without reducing the severity of the underlying condition.

A recent study by Aviram et al 19 provides some evidence to support this notion. In a sample of 429 patients who consumed medical cannabis via inflorescence inhalation and were followed up monthly over 6 months, there was no change over time in the least, average, and worst weekly pain intensities, or in pain frequency. There was, however, an increase in the proportion of patients reporting better quality of life on the EQ-5D and a decrease in the proportion reporting consumption of analgesic medications at subsequent time points. There was also a reduction in the mean (SD) morphine equivalent dose of opioid analgesics from 21 (91) mg at baseline to 5.2 (27) mg at 6 months, suggesting a possible opioid-sparing association with medical cannabis, consistent with several other recent studies.( 20 - 22 ) These data are also supported by epidemiological evidence for reduced state-level opioid overdose mortality rates in US states with medical cannabis laws, 23 although as Noori et al 24 caution in a recent review, 24 extant evidence from randomized and observational studies is of very low certainty.

This study is limited by the use of a retrospective case series design without a control, which restricts what conclusions can be drawn around treatment efficacy, and limits generalizability to other clinical populations. Given the ongoing increase in medical cannabis prescribing, other clinics should strongly consider implementing a similarly rigorous clinical data collection protocol in order to monitor clinical safety and patient-reported outcomes associated with medical cannabis use. As most patients began treatment at some point during the sampling period, patient numbers at later consults (ie, reflecting longer treatment periods) are lower than patient numbers at earlier consults. As a result, mean SF-36 domain scores show considerably greater variability at later consults and should be interpreted with caution. We intend to conduct a follow-up study in the future with larger patient numbers and a longer follow-up period. Furthermore, patients were not required to complete the questionnaires described here, and so these data may be biased upwards if patients experiencing a positive effect of medical cannabis were more likely to respond. Finally, the clinical care model used by Emerald Clinics may have also contributed to perceived improvements in quality of life.

This study suggests a favorable association between medical cannabis treatment and quality of life among patients with a diverse range of conditions. However, clinical evidence for cannabinoid efficacy remains limited, and further high-quality trials are required. While we cannot exclude the possibility that adverse events may have been caused in whole or part by the disease state and concomitant medications, the relatively high incidence of adverse events still affirms the need for caution with THC prescribing and careful identification of patients with contraindications.

Accepted for Publication: March 27, 2023.

Published: May 9, 2023. doi:10.1001/jamanetworkopen.2023.12522

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License . © 2023 Arkell TR et al. JAMA Network Open .

Corresponding Author: Thomas R. Arkell, PhD, Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia ( [email protected] ).

Author Contributions: Dr Roth had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Arkell, Downey, Hayley.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Arkell, Downey, Hayley.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Roth.

Administrative, technical, or material support: Downey, Hayley, Roth.

Supervision: Downey, Hayley, Roth.

Conflict of Interest Disclosures: Dr Arkell reported receiving personal fees from Althea, personal fees from bod, personal fees from NUBU Pharmaceuticals, personal fees from the International College of Cannabinoid Medicine, and grants from Barbara Dicker Foundation outside the submitted work. Dr Downey reported receiving grants from National Health & Medical Research Council, grants from Cannvalate, and grants from Barbara Dicker Foundation outside the submitted work. Dr Hayley reported receiving grants from Cannvalate, grants from Rebecca L. Cooper Foundation for the Al and Val Rosenstrauss Fellowship (F2021894), grants from Barbara Dicker Foundation, and grants from Road Safety Innovation Fund outside the submitted work. No other disclosures were reported.

Funding/Support: Emyria funded the collection of data for this study from 2018 to 2022, and Dr Roth conducted statistical analysis as a paid employee of the company. Funding for development of the manuscript was provided to Drs Arkell and Hayley, and Prof Downey via a grant from Emyria to Swinburne University.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The funder (Emyria) did have a role in the collection and management of the data (from 2018 to 2022).

Data Sharing Statement: See Supplement 2 .

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Sustainable approach to weed management: the role of precision weed management.

1. Introduction

2. search strategy, 3. weeds: threat or benefit, 4. weed management and the need for a new paradigm, 4.1. conventional weed control strategies, 4.1.1. herbicide control, 4.1.2. mechanical control, 4.2. non-conventional weed control strategies, 4.2.1. mulching, 4.2.2. cover crops and living mulches, 4.2.3. soil solarization, 4.2.4. thermal weed control, 4.2.5. weed control through livestock grazing, 4.3. limitations of conventional and non-conventional weed control strategies, 5. precision weed management, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Monteiro, A.; Santos, S. Sustainable Approach to Weed Management: The Role of Precision Weed Management. Agronomy 2022 , 12 , 118. https://doi.org/10.3390/agronomy12010118

Monteiro A, Santos S. Sustainable Approach to Weed Management: The Role of Precision Weed Management. Agronomy . 2022; 12(1):118. https://doi.org/10.3390/agronomy12010118

Monteiro, António, and Sérgio Santos. 2022. "Sustainable Approach to Weed Management: The Role of Precision Weed Management" Agronomy 12, no. 1: 118. https://doi.org/10.3390/agronomy12010118

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• v.42(3); 2017 Mar

Medicinal Cannabis: History, Pharmacology, And Implications for the Acute Care Setting

The authors review the historical use of medicinal cannabis and discuss the agent’s pharmacology and pharmacokinetics, select evidence on medicinal uses, and the implications of evolving regulations on the acute care hospital setting.

INTRODUCTION

Medicinal cannabis, or medicinal marijuana, is a therapy that has garnered much national attention in recent years. Controversies surrounding legal, ethical, and societal implications associated with use; safe administration, packaging, and dispensing; adverse health consequences and deaths attributed to marijuana intoxication; and therapeutic indications based on limited clinical data represent some of the complexities associated with this treatment. Marijuana is currently recognized by the U.S. Drug Enforcement Agency’s (DEA’s) Comprehensive Drug Abuse Prevention and Control Act (Controlled Substances Act) of 1970 as a Schedule I controlled substance, defined as having a high potential for abuse, no currently accepted medicinal use in treatment in the United States, and a lack of accepted safety data for use of the treatment under medical supervision. 1

Cannabis is the most commonly cultivated, trafficked, and abused illicit drug worldwide; according to the World Health Organization (WHO), marijuana consumption has an annual prevalence rate of approximately 147 million individuals or nearly 2.5% of the global population. 2 In 2014, approximately 22.2 million Americans 12 years of age or older reported current cannabis use, with 8.4% of this population reporting use within the previous month. 3 , 4 General cannabis use, both for recreational and medicinal purposes, has garnered increasing acceptance across the country as evidenced by legislative actions, ballot measures, and public opinion polls; an October 2016 Gallup poll on American’s views on legalizing cannabis indicated that 60% of the population surveyed believed the substance should be legalized. 5 Further, a recent Quinnipiac University poll concluded 54% of American voters surveyed would favor the legalization of cannabis without additional constraints, while 81% of respondents favored legalization of cannabis for medicinal purposes. 6 Limited data suggest that health care providers also may consider this therapy in certain circumstances. 7 – 9 In the United States, cannabis is approved for medicinal use in 28 states, the District of Columbia, Guam, and Puerto Rico as of January 2017. 10

The use and acceptance of medicinal cannabis continues to evolve, as shown by the growing number of states now permitting use for specific medical indications. The Food and Drug Administration (FDA) has considered how it might support the scientific rigor of medicinal cannabis claims, and the review of public data regarding safety and abuse potential is ongoing. 11 , 12 The purpose of this article is to review the historical significance of the use of medicinal cannabis and to discuss its pharmacology, pharmacokinetics, and select evidence on medicinal uses, as well as to describe the implications of evolving medicinal cannabis regulations and their effects on the acute care hospital setting.

HISTORICAL SIGNIFICANCE

Cannabis is a plant-based, or botanical, product with origins tracing back to the ancient world. Evidence suggesting its use more than 5,000 years ago in what is now Romania has been described extensively. 13 There is only one direct source of evidence (Δ 6 -tetrahydrocannabinol [Δ 6 -THC] in ashes) that cannabis was first used medicinally around 400 ad . 14 In the U.S., cannabis was widely utilized as a patent medicine during the 19th and early 20th centuries, described in the United States Pharmacopoeia for the first time in 1850. Federal restriction of cannabis use and cannabis sale first occurred in 1937 with the passage of the Marihuana Tax Act. 15 , 16 Subsequent to the act of 1937, cannabis was dropped from the United States Pharmacopoeia in 1942, with legal penalties for possession increasing in 1951 and 1956 with the enactment of the Boggs and Narcotic Control Acts, respectively, and prohibition under federal law occurring with the Controlled Substances Act of 1970. 1 , 17 , 18 Beyond criminalization, these legislative actions contributed to creating limitations on research by restricting procurement of cannabis for academic purposes.

In 1996, California became the first state to permit legal access to and use of botanical cannabis for medicinal purposes under physician supervision with the enactment of the Compassionate Use Act. As previously stated, as of January 1, 2017, 28 states as well as Washington, D.C., Guam, and Puerto Rico will have enacted legislation governing medicinal cannabis sale and distribution; 21 states and the District of Columbia will have decriminalized marijuana and eliminated prohibition for possession of small amounts, while eight states, including Alaska, California, Colorado, Maine, Massachusetts, Nevada, Oregon, and Washington, as well as the District of Columbia, will have legalized use of marijuana for adult recreation. 10 , 19

THE MEDICINAL CANNABIS DEBATE

As a Schedule I controlled substance with no accepted medicinal use, high abuse potential, concerns for dependence, and lack of accepted safety for use under medical supervision—along with a national stigma surrounding the potential harms and implication of cannabis use as a gateway drug to other substances—transitioning from a vilified substance to one with therapeutic merits has been controversial. The United States Pharmacopoeia and the FDA have considered the complexities of regulating this plant-based therapy, including the numerous compounds and complex interactions between substances in this product, and how it might fit into the current regulatory framework of drugs in United States. 11 , 12 , 17

The emergence of interest in botanical medicinal cannabis is thought by many to be a collateral effect of the opioid abuse epidemic; public perception surrounding the use of medicinal cannabis suggests that this plant-based therapy is viewed as not much different than a botanical drug product or supplement used for health or relief of symptoms if disease persists. Like some herbal preparations or supplements, however, medicinal cannabis may similarly pose health risks associated with its use, including psychoactive, intoxicating, and impairing effects, which have not been completely elucidated through clinical trials. Proponents argue that there is evidence to support botanical medicinal cannabis in the treatment of a variety of conditions, particularly when symptoms are refractory to other therapies; that beneficial cannabinoids exist, as evidenced by single-entity agents derived from cannabis containing the compounds THC and cannabidiol (CBD); that cannabis is relatively safe, with few deaths reported from use; that therapy is self-titratable by the patient; and that therapy is relatively inexpensive compared with pharmaceutical agents. 20 – 22 Opponents of medicinal cannabis use argue, in part, that well-designed randomized trials to confirm benefits and harms are lacking; that it has not been subject to the rigors of the FDA approval process; that standardization in potency or quantity of pharmacologically active constituents is absent; that adverse health effects relate not only to smoking cannabis but to unmasking mental health disorders, impairing coordination, and affecting judgment; that standardization does not exist for product packaging and controls to prevent inadvertent use by minors or pets; that there is a potential for dependence, addiction, and abuse; and that costs pose a potential burden. 23 – 25

Regardless of personal views and perceptions, to deny or disregard the implications of use of this substance on patient health and the infrastructure of the health care system is irresponsible; clinicians must be aware of these implications and informed about how this therapy may influence practice in a variety of health care settings, including acute care.

PHARMACOLOGY

Endocannabinoids (eCBs) and their receptors are found throughout the human body: nervous system, internal organs, connective tissues, glands, and immune cells. The eCB system has a homeostatic role, having been characterized as “eat, sleep, relax, forget, and protect.” 26 It is known that eCBs have a role in the pathology of many disorders while also serving a protective function in certain medical conditions. 27 It has been proposed that migraine, fibromyalgia, irritable bowel syndrome, and related conditions represent clinical eCB deficiency syndromes (CEDS). Deficiencies in eCB signaling could be also involved in the pathogenesis of depression. In human studies, eCB system deficiencies have been implicated in schizophrenia, multiple sclerosis (MS), Huntington’s disease, Parkinson’s disease, anorexia, chronic motion sickness, and failure to thrive in infants. 28

The eCB system represents a microcosm of psycho-neuroimmunology or “mind–body” medicine. The eCB system consists of receptors, endogenous ligands, and ligand metabolic enzymes. A variety of physiological processes occur when cannabinoid receptors are stimulated. Cannabinoid receptor type 1 (CB 1 ) is the most abundant G-protein–coupled receptor. It is expressed in the central nervous system, with particularly dense expression in (ranked in order): the substantia nigra, globus pallidus, hippocampus, cerebral cortex, putamen, caudate, cerebellum, and amygdala. CB 1 is also expressed in non-neuronal cells, such as adipocytes and hepatocytes, connective and musculoskeletal tissues, and the gonads. CB 2 is principally associated with cells governing immune function, although it may also be expressed in the central nervous system.

The most well-known eCB ligands are N-arachidonyl-ethanolamide (anandamide or AEA) and sn-2-arachidonoyl-glycerol (2-AG). AEA and 2-AG are released upon demand from cell membrane phospholipid precursors. This “classic” eCB system has expanded with the discovery of secondary receptors, ligands, and ligand metabolic enzymes. For example, AEA, 2-AG, N-arachidonoyl glycine (NAGly), and the phytocannabinoids Δ 9 -THC and CBD may also serve, to different extents, as ligands at GPR55, GPR18, GPR119, and several transient receptor potential ion channels (e.g., TRPV1, TRPV2, TRPA1, TRPM8) that have actions similar to capsaicin. 28 The effects of AEA and 2-AG can be enhanced by “entourage compounds” that inhibit their hydrolysis via substrate competition, and thereby prolong their action through synergy and augmentation. Entourage compounds include N-palmitylethanolamide (PEA), N-oleoylethanolamide (SEA), and cis-9-octadecenoamide (OEA or oleamide) and may represent a novel route for molecular regulation of endogenous cannabinoid activity. 29

Additional noncannabinoid targets are also linked to cannabis. G-protein–coupled receptors provide noncompetitive inhibition at mu and delta opioid receptors as well as norepinephrine, dopamine, and serotonin. Ligand-gated ion channels create allosteric antagonism at serotonin and nicotinic receptors, and enhance activation of glycine receptors. Inhibition of calcium, potassium, and sodium channels by noncompetitive antagonism occurs at nonspecific ion channels and activation of PPARα and PPARγ at the peroxisome proliferator-activated receptors is influenced by AEA. 30

THC is known to be the major psychoactive component of cannabis mediated by activation of the CB 1 receptors in the central nervous system; however, this very mechanism limits its use due to untoward adverse effects. It is now accepted that other phytocannabinoids with weak or no psychoactivity have promise as therapeutic agents in humans. The cannabinoid that has sparked the most interest as a nonpsychoactive component is CBD. 31 Unlike THC, CBD elicits its pharmacological effects without exerting any significant intrinsic activity on CB 1 and CB 2 receptors. Several activities give CBD a high potential for therapeutic use, including antiepileptic, anxiolytic, antipsychotic, anti-inflammatory, and neuroprotective effects. CBD in combination with THC has received regulatory approvals in several European countries and is under study in registered trials with the FDA. And, some states have passed legislation to allow for the use of majority CBD preparations of cannabis for certain pathological conditions, despite lack of standardization of CBD content and optimal route of administration for effect. 32 Specific applications of CBD have recently emerged in pain (chronic and neuropathic), diabetes, cancer, and neurodegenerative diseases, such as Huntington’s disease. Animal studies indicate that a high dose of CBD inhibits the effects of lower doses of THC. Moreover, clinical studies suggest that oral or oromucosal CBD may prolong and/or intensify the effects of THC. Finally, preliminary clinical trials suggest that high-dose oral CBD (150–600 mg per day) may exert a therapeutic effect for epilepsy, insomnia, and social anxiety disorder. Nonetheless, such doses of CBD have also been shown to cause sedation. 33

PHARMACOKINETICS AND ADMINISTRATION

The three most common methods of administration are inhalation via smoking, inhalation via vaporization, and ingestion of edible products. The method of administration can impact the onset, intensity, and duration of psychoactive effects; effects on organ systems; and the addictive potential and negative consequences associated with use. 34

Cannabinoid pharmacokinetic research has been challenging; low analyte concentrations, rapid and extensive metabolism, and physicochemical characteristics hinder the separation of compounds of interest from biological matrices and from each other. The net effect is lower drug recovery due to adsorption of compounds of interest to multiple surfaces. 35 The primary psychoactive constituent of marijuana—Δ 9 -THC—is rapidly transferred from lungs to blood during smoking. In a randomized controlled trial conducted by Huestis and colleagues, THC was detected in plasma immediately after the first inhalation of marijuana smoke, attesting to the efficient absorption of THC from the lungs. THC levels rose rapidly and peaked prior to the end of smoking. 36 Although smoking is the most common cannabis administration route, the use of vaporization is increasing rapidly. Vaporization provides effects similar to smoking while reducing exposure to the byproducts of combustion and possible carcinogens and decreasing adverse respiratory syndromes. THC is highly lipophilic, distributing rapidly to highly perfused tissues and later to fat. 37 A trial of 11 healthy subjects administered Δ 9 -THC intravenously, by smoking, and by mouth demonstrated that plasma profiles of THC after smoking and intravenous injection were similar, whereas plasma levels after oral doses were low and irregular, indicating slow and erratic absorption. The time courses of plasma concentrations and clinical “high” were of the same order for intravenous injection and smoking, with prompt onset and steady decline over a four-hour period. After oral THC, the onset of clinical effects was slower and lasted longer, but effects occurred at much lower plasma concentrations than they did after the other two methods of administration. 38

Cannabinoids are usually inhaled or taken orally; the rectal route, sublingual administration, transdermal delivery, eye drops, and aerosols have been used in only a few studies and are of little relevance in practice today. The pharmacokinetics of THC vary as a function of its route of administration. Inhalation of THC causes a maximum plasma concentration within minutes and psychotropic effects within seconds to a few minutes. These effects reach their maximum after 15 to 30 minutes and taper off within two to three hours. Following oral ingestion, psychotropic effects manifest within 30 to 90 minutes, reach their maximum effect after two to three hours, and last for about four to 12 hours, depending on the dose. 39

Within the shifting legal landscape of medical cannabis, different methods of cannabis administration have important public health implications. A survey using data from Qualtrics and Facebook showed that individuals in states with medical cannabis laws had a significantly higher likelihood of ever having used the substance with a history of vaporizing marijuana (odds ratio [OR], 2.04; 99% confidence interval [CI], 1.62–2.58) and a history of oral administration of edible marijuana (OR, 1.78; 99% CI, 1.39–2.26) than those in states without such laws. Longer duration of medical cannabis status and higher dispensary density were also significantly associated with use of vaporized and edible forms of marijuana. Medical cannabis laws are related to state-level patterns of utilization of alternative methods of cannabis administration. 34

DRUG INTERACTIONS

Metabolic and pharmacodynamic interactions may exist between medical cannabis and other pharmaceuticals. Quantification of the in vitro metabolism of exogenous cannabinoids, including THC, CBD, and cannabinol (CBN), indicates hepatic cytochrome 450 (CYP450) isoenzymes 2C9 and 3A4 play a significant role in the primary metabolism of THC and CBN, whereas 2C19 and 3A4 and may be responsible for metabolism of CBD. 40 Limited clinical trials quantifying the effect of the exogenous cannabinoids on the metabolism of other medications exist; however, drug interaction data may be gleaned from the prescribing information from cannabinoid-derived pharmaceutical products such as Sativex (GW Pharmaceuticals, United Kingdom) and dronabinol (Marinol, AbbVie [United States]). 41 , 42 Concomitant administration of ketoconazole with oromucosal cannabis extract containing THC and CBD resulted in an increase in the maximum serum concentration and area under the curve for both THC and CBD by 1.2-fold to 1.8-fold and twofold, respectively; coadministration of rifampin is associated with a reduction in THC and CBD levels. 40 , 41 In clinical trials, dronabinol use was not associated with clinically significant drug interactions, although additive pharmacodynamic effects are possible when it is coadministered with other agents having similar physiological effects (e.g., sedatives, alcohol, and antihistamines may increase sedation; tricyclic antidepressants, stimulants, and sympathomimetics may increase tachycardia). 41 Additionally, smoking cannabis may increase theophylline metabolism, as is also seen after smoking tobacco. 40 , 42

ADVERSE EFFECTS

Much of what is known about the adverse effects of medicinal cannabis comes from studies of recreational users of marijuana. 43 Short-term use of cannabis has led to impaired short-term memory; impaired motor coordination; altered judgment; and paranoia or psychosis at high doses. 44 Long-term or heavy use of cannabis, especially in individuals who begin using as adolescents, has lead to addiction; altered brain development; cognitive impairment; poor educational outcomes (e.g., dropping out of school); and diminished life satisfaction. 45 Long-term or heavy use of cannabis is also associated with chronic bronchitis and an increased risk of chronic psychosis-related health disorders, including schizophrenia and variants of depression, in persons with a predisposition to such disorders. 46 – 48 Vascular conditions, including myocardial infarction, stroke, and transient ischemic attack, have also been associated with cannabis use. 49 – 51 The use of cannabis for management of symptoms in neurodegenerative diseases, such as Parkinson’s, Alzheimer’s, and MS, has provided data related to impaired cognition in these individuals. 52 , 53

A systematic review of published trials on the use of medical cannabinoids over a 40-year period was conducted to quantify adverse effects of this therapy. 54 A total of 31 studies evaluating the use of medicinal cannabis, including 23 randomized controlled trials and eight observational studies, was included. In the randomized trials, the median duration of cannabinoid exposure was two weeks, with a range between eight hours and 12 months. Of patients assigned to active treatment in these trials, a total of 4,779 adverse effects were reported; 96.6% (4,615) of these were not deemed by authors to be serious. The most common serious adverse effects included relapsing MS (9.1%; 15 events), vomiting (9.8%; 16 events), and urinary tract infections (9.1%; 15 events). No significant differences in the rates of serious adverse events between individuals receiving medical cannabis and controls were identified (relative risk, 1.04; 95% CI, 0.78–1.39). The most commonly reported non-serious adverse event was dizziness, with an occurrence rate of 15.5% (714 events) among people exposed to cannabinoids. 54

Other negative adverse effects reported with acute cannabis use include hyperemesis syndrome, impaired coordination and performance, anxiety, suicidal ideations or tendencies, and psychotic symptoms, whereas chronic effects may include mood disturbances, exacerbation of psychotic disorders, cannabis use disorders, withdrawal syndrome, and neurocognitive impairments, as well as cardiovascular and respiratory conditions. 52 Long-term studies evaluating adverse effects of chronic medicinal cannabis use are needed to conclusively evaluate the risks when used for an extended period of time.

MEDICINAL USES

Cannabis and cannabinoid agents are widely used to alleviate symptoms or treat disease, but their efficacy for specific indications is not well established. For chronic pain, the analgesic effect remains unclear. A systematic review of randomized controlled trials was conducted examining cannabinoids in the treatment of chronic noncancer pain, including smoked cannabis, oromucosal extracts of cannabis-based medicine, nabilone, dronabinol, and a novel THC analogue. 55 Pain conditions included neuropathic pain, fibromyalgia, rheumatoid arthritis, and mixed chronic pain. Fifteen of the 18 included trials demonstrated a significant analgesic effect of cannabinoids compared with placebo. Cannabinoid use was generally well tolerated; adverse effects most commonly reported were mild to moderate in severity. Overall, evidence suggests that cannabinoids are safe and moderately effective in neuropathic pain with preliminary evidence of efficacy in fibromyalgia and rheumatoid arthritis. 55

While there is not enough evidence to suggest routine use of medicinal cannabis for alleviating chemotherapy-related nausea and vomiting by national or international cancer societies, therapeutic agents based on THC (e.g., dronabinol) have been approved for use as an antiemetic in the United States for a number of years. Only recently has the efficacy and safety of cannabis-based medicines in managing nausea and vomiting due to chemotherapy been evaluated. In a review of 23 randomized, controlled trials, patients who received cannabis-based products experienced less nausea and vomiting than subjects who received placebo. 56 The proportion of people experiencing nausea and vomiting who received cannabis-based products was similar to those receiving conventional antiemetics. Subjects using cannabis-based products experienced side effects such as “feeling high,” dizziness, sedation, and dysphoria and dropped out of the studies at a higher rate due to adverse effects compared with participants receiving either placebo or conventional antiemetics. In crossover trials in which patients received cannabis-based products and conventional antiemetics, patients preferred the cannabis-based medicines. Cannabis-based medications may be useful for treating chemotherapy-induced nausea and vomiting that responds poorly to conventional antiemetics. However, the trials produced low to moderate quality evidence and reflected chemotherapy agents and antiemetics that were available in the 1980s and 1990s.

With regard to the management of neurological disorders, including epilepsy and MS, a Cochrane review of four clinical trials that included 48 epileptic patients using CBD as an adjunct treatment to other antiepileptic medications concluded that there were no serious adverse effects associated with CBD use but that no reliable conclusions on the efficacy and safety of the therapy can be drawn from this limited evidence. 57 The American Academy of Neurology (AAN) has issued a Summary of Systematic Reviews for Clinicians that indicates oral cannabis extract is effective for reducing patient-reported spasticity scores and central pain or painful spasms when used for MS. 58 THC is probably effective for reducing patient-reported spasticity scores but is likely ineffective for reducing objective measures of spasticity at 15 weeks, the AAN found; there is limited evidence to support the use of cannabis extracts for treatment of Huntington’s disease, levodopa-induced dyskinesias in patients with Parkinson’s disease, or reducing tic severity in Tourette’s. 58

In older patients, medical cannabinoids have shown no efficacy on dyskinesia, breathlessness, and chemotherapy-induced nausea and vomiting. Some evidence has shown that THC might be useful in treatment of anorexia and behavioral symptoms in patients with dementia. The most common adverse events reported during cannabinoid treatment in older adults were sedation-like symptoms. 59

Despite limited clinical evidence, a number of medical conditions and associated symptoms have been approved by state legislatures as qualifying conditions for medicinal cannabis use. Table 1 contains a summary of medicinal cannabis indications by state, including select disease states and qualifying debilitating medical conditions or symptoms. 10 , 60 , 61 The most common conditions accepted by states that allow medicinal cannabis relate to relief of the symptoms of cancer, glaucoma, human immunodeficiency virus/acquired immunodeficiency syndrome, and MS. A total of 28 states, the District of Columbia, Guam, and Puerto Rico now allow comprehensive public medical marijuana and cannabis programs. 10 The National Conference of State Legislatures uses the following criteria to determine if a program is comprehensive:

Medicinal Cannabis Indications for Use by State 10 , 60 , 61

1 = State law additionally covers any condition where treatment with medical cannabis would be beneficial, according to the patient’s physician

2 = State law covers any severe condition refractory to other medical treatment

3 = Additional restrictions on the use for this indication exist in this state

4 = State law requires providers to certify the existence of a qualifying disease and symptom

HIV/AIDS = human immunodeficiency virus/acquired immunodeficiency syndrome

Table adapted with permission from the Marijuana Policy Project; 60 table is not all-encompassing and other medical conditions for use may exist. The reader should refer to individual state laws regarding medicinal cannabis for specific details of approved conditions for use. In addition, states may permit the addition of approved indications; list is subject to change.

• Protection from criminal penalties for using marijuana for a medical purpose;
• Access to marijuana through home cultivation, dispensaries, or some other system that is likely to be implemented;
• Allows a variety of strains, including more than those labeled as “low THC;” and
• Allows either smoking or vaporization of some kind of marijuana products, plant material, or extract.

Some of the most common policy questions regarding medical cannabis now include how to regulate its recommendation and indications for use; dispensing, including quality and standardization of cultivars or strains, labeling, packaging, and role of the pharmacist or health care professional in education or administration; and registration of approved patients and providers.

REGULATORY IMPLICATIONS OF MEDICINAL CANNABIS

The regulation of cannabis therapy is complex and unique; possession, cultivation, and distribution of this substance, regardless of purpose, remain illegal at the federal level, while states that permit medicinal cannabis use have established individual laws and restrictions on the sale of cannabis for medical purposes. In a 2013 U.S. Department of Justice memorandum to all U.S. attorneys, Deputy Attorney General James M. Cole noted that despite the enactment of state laws authorizing marijuana production and sale having a regulatory structure that is counter to the usual joint efforts of federal authorities working together with local jurisdictions, prosecution of individuals cultivating and distributing marijuana to seriously ill individuals for medicinal purpose has not been identified as a federal priority. 62

There are, however, other regulatory implications to consider based on the federal restriction of cannabis. Physicians cannot legally “prescribe” medicinal cannabis therapy, given its Schedule I classification, but rather in accordance with state laws may certify or recommend patients for treatment. Medical cannabis expenses are not reimbursable through government medical assistance programs or private health insurers. As previously described, the Schedule I listing of cannabis according to federal law and DEA regulations has led to difficulties in access for research purposes; nonpractitioner researchers can register with the DEA more easily to study substances in Schedules II–V compared with Schedule I substances. 63 Beyond issues related to procurement of the substance for research purposes, other limitations in cannabis research also exist. For example, the Center for Medicinal Cannabis Research at the University of California–San Diego had access to funding, marijuana at different THC levels, and approval for a number of clinical research trials, and yet failed to recruit an adequate number of patients to conduct five major trials, which were subsequently canceled. 64 Unforeseen factors, including the prohibition of driving during the clinical trials, deterred patients from trial enrollment. The limited availability of clinical research to support or refute therapeutic claims and indications for use of cannabis for medicinal purposes has frequently left both state legislative authorities and clinicians to rely on anecdotal evidence, which has not been subjected to the same rigors of peer review and scrutiny as well-conducted, randomized trials, to validate the safety and efficacy of medicinal cannabis therapy. Furthermore, although individual single-entity pharmaceutical medications, such as dronabinol, have been isolated, evaluated, and approved for use by the FDA, a plant cannot be patented and mass produced by a corporate entity. 65 Despite this limitation, some corporations, including GW Pharmaceuticals, are mass producing cannabis plants and extracting complex mixtures or single cannabinoids for clinical trials. 65 The complex pharmacology related to the numerous substances and interactions among chemicals in the cannabis plant coupled with environmental variables in cultivation further complicate regulation, standardization, purity, and potency as a botanical drug product.

RELEVANCE TO HOSPITAL PRACTITIONERS

Although the public has largely accepted medicinal cannabis therapy as having a benefit when used under a provider’s supervision, the implications of the use of this substance when patients transition into the acute care setting are additionally complex and multifaceted. The Schedule I designation of cannabis causes hospitals and other care settings that receive federal funding, either through Medicare reimbursement or other federal grants or programs, to pause to consider the potential for loss of these funds should the federal government intercede and take action if patients are permitted to use this therapy on campus. Similarly, licensed practitioners registered to certify patients for state medicinal cannabis programs may have comparable concerns regarding jeopardizing their federal DEA registrations and ability to prescribe other controlled substances as well as jeopardizing Medicare reimbursements. In 2009, U.S. Attorney General Eric Holder recommended that enforcement of federal marijuana laws not be a priority in states that have enacted medicinal cannabis programs and are enforcing the rules and regulations of such a program; despite this, concerns persist.

The argument for or against the use of medicinal cannabis in the acute care setting encompasses both legal and ethical considerations, with the argument against use perhaps seeming obvious on its surface. States adopting medical cannabis laws may advise patients to utilize the therapy only in their own residence and not to transport the substances unless absolutely necessary. 66 Further, many acute care institutions have policies prohibiting smoking on facility grounds, thus restricting the smoking of cannabis, regardless of purpose or indication. Of note, several Canadian hospitals, including Montreal’s Jewish General Hospital and Quebec’s Centre Hospitalier Universitaire de Sherbrooke, have permitted inpatient cannabis use via vaporization; the pharmacy departments of the respective institutions control and dispense cannabis much like opioids for pain. Canada has adopted national regulations to control and standardize dried cannabis for medical use. 67 , 68 There are complicated logistics for self-administration of medicinal cannabis by the patient or caregiver; in particular, many hospitals have policies on self-administration of medicines that permit patients to use their own medications only after identification and labeling by pharmacy personnel. The argument can be made that an herb- or plant-based entity cannot be identified by pharmacy personnel as is commonly done for traditional medicines, although medicinal cannabis dispensed through state programs must be labeled in accordance with state laws. Dispensing and storage concerns, including an evaluation of where and how this product should be stored (e.g., within the pharmacy department and treated as a controlled substance, by security personnel, or with the patient); who should administer it, and implications or violations of federal law by those administering treatment; what pharmaceutical preparations should be permitted (e.g., smoked, vaporized, edible); and how it should be charted in the medical record represent other logistical concerns. Inpatient use of medicinal cannabis also carries implications for nursing and medical staff members. The therapy cannot be prescribed, and states may require physicians authorizing patient use to be registered with local programs. In a transition into the acute care setting from the community setting, a different clinician who is not registered could be responsible for the patient’s care; that clinician would be restricted in ordering continuation of therapy.

Despite the complexities in the logistics of continuing medicinal cannabis in the acute care setting, proponents of palliative care and continuity of care argue that prohibiting medicinal cannabis use disrupts treatment of chronic and debilitating medical conditions. Patients have been denied this therapy during acute care hospitalizations for reasons stated above. 69 Permission to use medicinal cannabis in the acute care setting may be dependent on state legislation and restrictions imposed by such laws. Legislation in Minnesota, as one example, has been amended to permit hospitals as facilities that can dispense and control cannabis use; similar legislative actions protecting nurses from criminal, civil, or disciplinary action when administering medical cannabis to qualified patients have been enacted in Connecticut and Maine. 70 – 73 Proposed legislation to remove restrictions on the certification of patients to receive medicinal cannabis by doctors at the Department of Veterans Affairs was struck down in June; prohibitions continue on the use of this therapy even in facilities located in states permitting medicinal cannabis use. 74

Despite lingering controversy, use of botanical cannabis for medicinal purposes represents the revival of a plant with historical significance reemerging in present day health care. Legislation governing use of medicinal cannabis continues to evolve rapidly, necessitating that pharmacists and other clinicians keep abreast of new or changing state regulations and institutional implications. Ultimately, as the medicinal cannabis landscape continues to evolve, hospitals, acute care facilities, clinics, hospices, and long-term care centers need to consider the implications, address logistical concerns, and explore the feasibility of permitting patient access to this treatment. Whether national policy—particularly with a new presidential administration—will offer some clarity or further complicate regulation of this treatment remains to be seen.

Disclosures: The authors report no commercial or financial interests in regard to this article.