research paper on marijuana use

• Research article
• Open access
• Published: 04 February 2020

Marijuana legalization and historical trends in marijuana use among US residents aged 12–25: results from the 1979–2016 National Survey on drug use and health

• Xinguang Chen 1 ,
• Xiangfan Chen 2 &
• Hong Yan 2  

BMC Public Health volume  20 , Article number:  156 ( 2020 ) Cite this article

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Marijuana is the most commonly used illicit drug in the United States. More and more states legalized medical and recreational marijuana use. Adolescents and emerging adults are at high risk for marijuana use. This ecological study aims to examine historical trends in marijuana use among youth along with marijuana legalization.

Data ( n  = 749,152) were from the 31-wave National Survey on Drug Use and Health (NSDUH), 1979–2016. Current marijuana use, if use marijuana in the past 30 days, was used as outcome variable. Age was measured as the chronological age self-reported by the participants, period was the year when the survey was conducted, and cohort was estimated as period subtracted age. Rate of current marijuana use was decomposed into independent age, period and cohort effects using the hierarchical age-period-cohort (HAPC) model.

After controlling for age, cohort and other covariates, the estimated period effect indicated declines in marijuana use in 1979–1992 and 2001–2006, and increases in 1992–2001 and 2006–2016. The period effect was positively and significantly associated with the proportion of people covered by Medical Marijuana Laws (MML) (correlation coefficients: 0.89 for total sample, 0.81 for males and 0.93 for females, all three p values < 0.01), but was not significantly associated with the Recreational Marijuana Laws (RML). The estimated cohort effect showed a historical decline in marijuana use in those who were born in 1954–1972, a sudden increase in 1972–1984, followed by a decline in 1984–2003.

The model derived trends in marijuana use were coincident with the laws and regulations on marijuana and other drugs in the United States since the 1950s. With more states legalizing marijuana use in the United States, emphasizing responsible use would be essential to protect youth from using marijuana.

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Introduction

Marijuana use and laws in the united states.

Marijuana is one of the most commonly used drugs in the United States (US) [ 1 ]. In 2015, 8.3% of the US population aged 12 years and older used marijuana in the past month; 16.4% of adolescents aged 12–17 years used in lifetime and 7.0% used in the past month [ 2 ]. The effects of marijuana on a person’s health are mixed. Despite potential benefits (e.g., relieve pain) [ 3 ], using marijuana is associated with a number of adverse effects, particularly among adolescents. Typical adverse effects include impaired short-term memory, cognitive impairment, diminished life satisfaction, and increased risk of using other substances [ 4 ].

Since 1937 when the Marijuana Tax Act was issued, a series of federal laws have been subsequently enacted to regulate marijuana use, including the Boggs Act (1952), Narcotics Control Act (1956), Controlled Substance Act (1970), and Anti-Drug Abuse Act (1986) [ 5 , 6 ]. These laws regulated the sale, possession, use, and cultivation of marijuana [ 6 ]. For example, the Boggs Act increased the punishment of marijuana possession, and the Controlled Substance Act categorized the marijuana into the Schedule I Drugs which have a high potential for abuse, no medical use, and not safe to use without medical supervision [ 5 , 6 ]. These federal laws may have contributed to changes in the historical trend of marijuana use among youth.

Movements to decriminalize and legalize marijuana use

Starting in the late 1960s, marijuana decriminalization became a movement, advocating reformation of federal laws regulating marijuana [ 7 ]. As a result, 11 US states had taken measures to decriminalize marijuana use by reducing the penalty of possession of small amount of marijuana [ 7 ].

The legalization of marijuana started in 1993 when Surgeon General Elder proposed to study marijuana legalization [ 8 ]. California was the first state that passed Medical Marijuana Laws (MML) in 1996 [ 9 ]. After California, more and more states established laws permitting marijuana use for medical and/or recreational purposes. To date, 33 states and the District of Columbia have established MML, including 11 states with recreational marijuana laws (RML) [ 9 ]. Compared with the legalization of marijuana use in the European countries which were more divided that many of them have medical marijuana registered as a treatment option with few having legalized recreational use [ 10 , 11 , 12 , 13 ], the legalization of marijuana in the US were more mixed with 11 states legalized medical and recreational use consecutively, such as California, Nevada, Washington, etc. These state laws may alter people’s attitudes and behaviors, finally may lead to the increased risk of marijuana use, particularly among young people [ 13 ]. Reported studies indicate that state marijuana laws were associated with increases in acceptance of and accessibility to marijuana, declines in perceived harm, and formation of new norms supporting marijuana use [ 14 ].

Marijuana harm to adolescents and young adults

Adolescents and young adults constitute a large proportion of the US population. Data from the US Census Bureau indicate that approximately 60 million of the US population are in the 12–25 years age range [ 15 ]. These people are vulnerable to drugs, including marijuana [ 16 ]. Marijuana is more prevalent among people in this age range than in other ages [ 17 ]. One well-known factor for explaining the marijuana use among people in this age range is the theory of imbalanced cognitive and physical development [ 4 ]. The delayed brain development of youth reduces their capability to cognitively process social, emotional and incentive events against risk behaviors, such as marijuana use [ 18 ]. Understanding the impact of marijuana laws on marijuana use among this population with a historical perspective is of great legal, social and public health significance.

Inconsistent results regarding the impact of marijuana laws on marijuana use

A number of studies have examined the impact of marijuana laws on marijuana use across the world, but reported inconsistent results [ 13 ]. Some studies reported no association between marijuana laws and marijuana use [ 14 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], some reported a protective effect of the laws against marijuana use [ 24 , 26 ], some reported mixed effects [ 27 , 28 ], while some others reported a risk effect that marijuana laws increased marijuana use [ 29 , 30 ]. Despite much information, our review of these reported studies revealed several limitations. First of all, these studies often targeted a short time span, ignoring the long period trend before marijuana legalization. Despite the fact that marijuana laws enact in a specific year, the process of legalization often lasts for several years. Individuals may have already changed their attitudes and behaviors before the year when the law is enacted. Therefore, it may not be valid when comparing marijuana use before and after the year at a single time point when the law is enacted and ignoring the secular historical trend [ 19 , 30 , 31 ]. Second, many studies adapted the difference-in-difference analytical approach designated for analyzing randomized controlled trials. No US state is randomized to legalize the marijuana laws, and no state can be established as controls. Thus, the impact of laws cannot be efficiently detected using this approach. Third, since marijuana legalization is a public process, and the information of marijuana legalization in one state can be easily spread to states without the marijuana laws. The information diffusion cannot be ruled out, reducing the validity of the non-marijuana law states as the controls to compare the between-state differences [ 31 ].

Alternatively, evidence derived based on a historical perspective may provide new information regarding the impact of laws and regulations on marijuana use, including state marijuana laws in the past two decades. Marijuana users may stop using to comply with the laws/regulations, while non-marijuana users may start to use if marijuana is legal. Data from several studies with national data since 1996 demonstrate that attitudes, beliefs, perceptions, and use of marijuana among people in the US were associated with state marijuana laws [ 29 , 32 ].

Age-period-cohort modeling: looking into the past with recent data

To investigate historical trends over a long period, including the time period with no data, we can use the classic age-period-cohort modeling (APC) approach. The APC model can successfully discompose the rate or prevalence of marijuana use into independent age, period and cohort effects [ 33 , 34 ]. Age effect refers to the risk associated with the aging process, including the biological and social accumulation process. Period effect is risk associated with the external environmental events in specific years that exert effect on all age groups, representing the unbiased historical trend of marijuana use which controlling for the influences from age and birth cohort. Cohort effect refers to the risk associated with the specific year of birth. A typical example is that people born in 2011 in Fukushima, Japan may have greater risk of cancer due to the nuclear disaster [ 35 ], so a person aged 80 in 2091 contains the information of cancer risk in 2011 when he/she was born. Similarly, a participant aged 25 in 1979 contains information on the risk of marijuana use 25 years ago in 1954 when that person was born. With this method, we can describe historical trends of marijuana use using information stored by participants in older ages [ 33 ]. The estimated period and cohort effects can be used to present the unbiased historical trend of specific topics, including marijuana use [ 34 , 36 , 37 , 38 ]. Furthermore, the newly established hierarchical APC (HAPC) modeling is capable of analyzing individual-level data to provide more precise measures of historical trends [ 33 ]. The HAPC model has been used in various fields, including social and behavioral science, and public health [ 39 , 40 ].

Several studies have investigated marijuana use with APC modeling method [ 17 , 41 , 42 ]. However, these studies covered only a small portion of the decades with state marijuana legalization [ 17 , 42 ]. For example, the study conducted by Miech and colleagues only covered periods from 1985 to 2009 [ 17 ]. Among these studies, one focused on a longer state marijuana legalization period, but did not provide detailed information regarding the impact of marijuana laws because the survey was every 5 years and researchers used a large 5-year age group which leads to a wide 10-year birth cohort. The averaging of the cohort effects in 10 years could reduce the capability of detecting sensitive changes of marijuana use corresponding to the historical events [ 41 ].

Purpose of the study

In this study, we examined the historical trends in marijuana use among youth using HAPC modeling to obtain the period and cohort effects. These two effects provide unbiased and independent information to characterize historical trends in marijuana use after controlling for age and other covariates. We conceptually linked the model-derived time trends to both federal and state laws/regulations regarding marijuana and other drug use in 1954–2016. The ultimate goal is to provide evidence informing federal and state legislation and public health decision-making to promote responsible marijuana use and to protect young people from marijuana use-related adverse consequences.

Materials and methods

Data sources and study population.

Data were derived from 31 waves of National Survey on Drug Use and Health (NSDUH), 1979–2016. NSDUH is a multi-year cross-sectional survey program sponsored by the Substance Abuse and Mental Health Services Administration. The survey was conducted every 3 years before 1990, and annually thereafter. The aim is to provide data on the use of tobacco, alcohol, illicit drug and mental health among the US population.

Survey participants were noninstitutionalized US civilians 12 years of age and older. Participants were recruited by NSDUH using a multi-stage clustered random sampling method. Several changes were made to the NSDUH after its establishment [ 43 ]. First, the name of the survey was changed from the National Household Survey on Drug Abuse (NHSDA) to NSDUH in 2002. Second, starting in 2002, adolescent participants receive $30 as incentives to improve the response rate. Third, survey mode was changed from personal interviews with self-enumerated answer sheets (before 1999) to the computer-assisted person interviews (CAPI) and audio computer-assisted self-interviews (ACASI) (since 1999). These changes may confound the historical trends [ 43 ], thus we used two dummy variables as covariates, one for the survey mode change in 1999 and another for the survey method change in 2002 to control for potential confounding effect.

Data acquisition

Data were downloaded from the designated website ( https://nsduhweb.rti.org/respweb/homepage.cfm ). A database was used to store and merge the data by year for analysis. Among all participants, data for those aged 12–25 years ( n  = 749,152) were included. We excluded participants aged 26 and older because the public data did not provide information on single or two-year age that was needed for HAPC modeling (details see statistical analysis section). We obtained approval from the Institutional Review Board at the University of Florida to conduct this study.

Variables and measurements

Current marijuana use: the dependent variable. Participants were defined as current marijuana users if they reported marijuana use within the past 30 days. We used the variable harmonization method to create a comparable measure across 31-wave NSDUH data [ 44 ]. Slightly different questions were used in NSDUH. In 1979–1993, participants were asked: “When was the most recent time that you used marijuana or hash?” Starting in 1994, the question was changed to “How long has it been since you last used marijuana or hashish?” To harmonize the marijuana use variable, participants were coded as current marijuana users if their response to the question indicated the last time to use marijuana was within past 30 days.

Chronological age, time period and birth cohort were the predictors. (1) Chronological age in years was measured with participants’ age at the survey. APC modeling requires the same age measure for all participants [ 33 ]. Since no data by single-year age were available for participants older than 21, we grouped all participants into two-year age groups. A total of 7 age groups, 12–13, ..., 24–25 were used. (2) Time period was measured with the year when the survey was conducted, including 1979, 1982, 1985, 1988, 1990, 1991... 2016. (3). Birth cohort was the year of birth, and it was measured by subtracting age from the survey year.

The proportion of people covered by MML: This variable was created by dividing the population in all states with MML over the total US population. The proportion was computed by year from 1996 when California first passed the MML to 2016 when a total of 29 states legalized medical marijuana use. The estimated proportion ranged from 12% in 1996 to 61% in 2016. The proportion of people covered by RML: This variable was derived by dividing the population in all states with RML with the total US population. The estimated proportion ranged from 4% in 2012 to 21% in 2016. These two variables were used to quantitatively assess the relationships between marijuana laws and changes in the risk of marijuana use.

Covariates: Demographic variables gender (male/female) and race/ethnicity (White, Black, Hispanic and others) were used to describe the study sample.

Statistical analysis

We estimated the prevalence of current marijuana use by year using the survey estimation method, considering the complex multi-stage cluster random sampling design and unequal probability. A prevalence rate is not a simple indicator, but consisting of the impact of chronological age, time period and birth cohort, named as age, period and cohort effects, respectively. Thus, it is biased if a prevalence rate is directly used to depict the historical trend. HAPC modeling is an epidemiological method capable of decomposing prevalence rate into mutually independent age, period and cohort effects with individual-level data, while the estimated period and cohort effects provide an unbiased measure of historical trend controlling for the effects of age and other covariates. In this study, we analyzed the data using the two-level HAPC cross-classified random-effects model (CCREM) [ 36 ]:

Where M ijk represents the rate of marijuana use for participants in age group i (12–13, 14,15...), period j (1979, 1982,...) and birth cohort k (1954–55, 1956–57...); parameter α i (age effect) was modeled as the fixed effect; and parameters β j (period effect) and γ k (cohort effect) were modeled as random effects; and β m was used to control m covariates, including the two dummy variables assessing changes made to the NSDUH in 1999 and 2002, respectively.

The HAPC modeling analysis was executed using the PROC GLIMMIX. Sample weights were included to obtain results representing the total US population aged 12–25. A ridge-stabilized Newton-Raphson algorithm was used for parameter estimation. Modeling analysis was conducted for the overall sample, stratified by gender. The estimated age effect α i , period β j and cohort γ k (i.e., the log-linear regression coefficients) were directly plotted to visualize the pattern of change.

To gain insight into the relationship between legal events and regulations at the national level, we listed these events/regulations along with the estimated time trends in the risk of marijuana from HAPC modeling. To provide a quantitative measure, we associated the estimated period effect with the proportions of US population living with MML and RML using Pearson correlation. All statistical analyses for this study were conducted using the software SAS, version 9.4 (SAS Institute Inc., Cary, NC).

Sample characteristics

Data for a total of 749,152 participants (12–25 years old) from all 31-wave NSDUH covering a 38-year period were analyzed. Among the total sample (Table  1 ), 48.96% were male and 58.78% were White, 14.84% Black, and 18.40% Hispanic.

Prevalence rate of current marijuana use

As shown in Fig.  1 , the estimated prevalence rates of current marijuana use from 1979 to 2016 show a “V” shaped pattern. The rate was 27.57% in 1979, it declined to 8.02% in 1992, followed by a gradual increase to 14.70% by 2016. The pattern was the same for both male and female with males more likely to use than females during the whole period.

Prevalence rate (%) of current marijuana use among US residents 12 to 25 years of age during 1979–2016, overall and stratified by gender. Derived from data from the 1979–2016 National Survey on Drug Use and Health (NSDUH)

HAPC modeling and results

Estimated age effects α i from the CCREM [ 1 ] for current marijuana use are presented in Fig.  2 . The risk by age shows a 2-phase pattern –a rapid increase phase from ages 12 to 19, followed by a gradually declining phase. The pattern was persistent for the overall sample and for both male and female subsamples.

Age effect for the risk of current marijuana use, overall and stratified by male and female, estimated with hierarchical age-period-cohort modeling method with 31 waves of NSDUH data during 1979–2016. Age effect α i were log-linear regression coefficients estimated using CCREM (1), see text for more details

The estimated period effects β j from the CCREM [ 1 ] are presented in Fig.  3 . The period effect reflects the risk of current marijuana use due to significant events occurring over the period, particularly federal and state laws and regulations. After controlling for the impacts of age, cohort and other covariates, the estimated period effect indicates that the risk of current marijuana use had two declining trends (1979–1992 and 2001–2006), and two increasing trends (1992–2001 and 2006–2016). Epidemiologically, the time trends characterized by the estimated period effects in Fig. 3 are more valid than the prevalence rates presented in Fig. 1 because the former was adjusted for confounders while the later was not.

Period effect for the risk of marijuana use for US adolescents and young adults, overall and by male/female estimated with hierarchical age-period-cohort modeling method and its correlation with the proportion of US population covered by Medical Marijuana Laws and Recreational Marijuana Laws. Period effect β j were log-linear regression coefficients estimated using CCREM (1), see text for more details

Correlation of the period effect with proportions of the population covered by marijuana laws: The Pearson correlation coefficient of the period effect with the proportions of US population covered by MML during 1996–2016 was 0.89 for the total sample, 0.81 for male and 0.93 for female, respectively ( p  < 0.01 for all). The correlation between period effect and proportion of US population covered by RML was 0.64 for the total sample, 0.59 for male and 0.49 for female ( p  > 0.05 for all).

Likewise, the estimated cohort effects γ k from the CCREM [ 1 ] are presented in Fig.  4 . The cohort effect reflects changes in the risk of current marijuana use over the period indicated by the year of birth of the survey participants after the impacts of age, period and other covariates are adjusted. Results in the figure show three distinctive cohorts with different risk patterns of current marijuana use during 1954–2003: (1) the Historical Declining Cohort (HDC): those born in 1954–1972, and characterized by a gradual and linear declining trend with some fluctuations; (2) the Sudden Increase Cohort (SIC): those born from 1972 to 1984, characterized with a rapid almost linear increasing trend; and (3) the Contemporary Declining Cohort (CDC): those born during 1984 and 2003, and characterized with a progressive declining over time. The detailed results of HAPC modeling analysis were also shown in Additional file 1 : Table S1.

Cohort effect for the risk of marijuana use among US adolescents and young adults born during 1954–2003, overall and by male/female, estimated with hierarchical age-period-cohort modeling method. Cohort effect γ k were log-linear regression coefficients estimated using CCREM (1), see text for more details

This study provides new data regarding the risk of marijuana use in youth in the US during 1954–2016. This is a period in the US history with substantial increases and declines in drug use, including marijuana; accompanied with many ups and downs in legal actions against drug use since the 1970s and progressive marijuana legalization at the state level from the later 1990s till today (see Additional file 1 : Table S2). Findings of the study indicate four-phase period effect and three-phase cohort effect, corresponding to various historical events of marijuana laws, regulations and social movements.

Coincident relationship between the period effect and legal drug control

The period effect derived from the HAPC model provides a net effect of the impact of time on marijuana use after the impact of age and birth cohort were adjusted. Findings in this study indicate that there was a progressive decline in the period effect during 1979 and 1992. This trend was corresponding to a period with the strongest legal actions at the national level, the War on Drugs by President Nixon (1969–1974) President Reagan (1981–1989) [ 45 ], and President Bush (1989) [ 45 ],and the Anti-Drug Abuse Act (1986) [ 5 ].

The estimated period effect shows an increasing trend in 1992–2001. During this period, President Clinton advocated for the use of treatment to replace incarceration (1992) [ 45 ], Surgeon General Elders proposed to study marijuana legalization (1993–1994) [ 8 ], President Clinton’s position of the need to re-examine the entire policy against people who use drugs, and decriminalization of marijuana (2000) [ 45 ] and the passage of MML in eight US states.

The estimated period effect shows a declining trend in 2001–2006. Important laws/regulations include the Student Drug Testing Program promoted by President Bush, and the broadened the public schools’ authority to test illegal drugs among students given by the US Supreme Court (2002) [ 46 ].

The estimated period effect increases in 2006–2016. This is the period when the proportion of the population covered by MML progressively increased. This relation was further proved by a positive correlation between the estimated period effect and the proportion of the population covered by MML. In addition, several other events occurred. For example, over 500 economists wrote an open letter to President Bush, Congress and Governors of the US and called for marijuana legalization (2005) [ 47 ], and President Obama ended the federal interference with the state MML, treated marijuana as public health issues, and avoided using the term of “War on Drugs” [ 45 ]. The study also indicates that the proportion of population covered by RML was positively associated with the period effect although not significant which may be due to the limited number of data points of RML. Future studies may follow up to investigate the relationship between RML and rate of marijuana use.

Coincident relationship between the cohort effect and legal drug control

Cohort effect is the risk of marijuana use associated with the specific year of birth. People born in different years are exposed to different laws, regulations in the past, therefore, the risk of marijuana use for people may differ when they enter adolescence and adulthood. Findings in this study indicate three distinctive cohorts: HDC (1954–1972), SIC (1972–1984) and CDC (1984–2003). During HDC, the overall level of marijuana use was declining. Various laws/regulations of drug use in general and marijuana in particular may explain the declining trend. First, multiple laws passed to regulate the marijuana and other substance use before and during this period remained in effect, for example, the Marijuana Tax Act (1937), the Boggs Act (1952), the Narcotics Control Act (1956) and the Controlled Substance Act (1970). Secondly, the formation of government departments focusing on drug use prevention and control may contribute to the cohort effect, such as the Bureau of Narcotics and Dangerous Drugs (1968) [ 48 ]. People born during this period may be exposed to the macro environment with laws and regulations against marijuana, thus, they may be less likely to use marijuana.

Compared to people born before 1972, the cohort effect for participants born during 1972 and 1984 was in coincidence with the increased risk of using marijuana shown as SIC. This trend was accompanied by the state and federal movements for marijuana use, which may alter the social environment and public attitudes and beliefs from prohibitive to acceptive. For example, seven states passed laws to decriminalize the marijuana use and reduced the penalty for personal possession of small amount of marijuana in 1976 [ 7 ]. Four more states joined the movement in two subsequent years [ 7 ]. People born during this period may have experienced tolerated environment of marijuana, and they may become more acceptable of marijuana use, increasing their likelihood of using marijuana.

A declining cohort CDC appeared immediately after 1984 and extended to 2003. This declining cohort effect was corresponding to a number of laws, regulations and movements prohibiting drug use. Typical examples included the War on Drugs initiated by President Nixon (1980s), the expansion of the drug war by President Reagan (1980s), the highly-publicized anti-drug campaign “Just Say No” by First Lady Nancy Reagan (early 1980s) [ 45 ], and the Zero Tolerance Policies in mid-to-late 1980s [ 45 ], the Anti-Drug Abuse Act (1986) [ 5 ], the nationally televised speech of War on Drugs declared by President Bush in 1989 and the escalated War on Drugs by President Clinton (1993–2001) [ 45 ]. Meanwhile many activities of the federal government and social groups may also influence the social environment of using marijuana. For example, the Federal government opposed to legalize the cultivation of industrial hemp, and Federal agents shut down marijuana sales club in San Francisco in 1998 [ 48 ]. Individuals born in these years grew up in an environment against marijuana use which may decrease their likelihood of using marijuana when they enter adolescence and young adulthood.

This study applied the age-period-cohort model to investigate the independent age, period and cohort effects, and indicated that the model derived trends in marijuana use among adolescents and young adults were coincident with the laws and regulations on marijuana use in the United States since the 1950s. With more states legalizing marijuana use in the United States, emphasizing responsible use would be essential to protect youth from using marijuana.

Limitations

This study has limitations. First, study data were collected through a household survey, which is subject to underreporting. Second, no causal relationship can be warranted using cross-sectional data, and further studies are needed to verify the association between the specific laws/regulation and the risk of marijuana use. Third, data were available to measure single-year age up to age 21 and two-year age group up to 25, preventing researchers from examining the risk of marijuana use for participants in other ages. Lastly, data derived from NSDUH were nation-wide, and future studies are needed to analyze state-level data and investigate the between-state differences. Although a systematic review of all laws and regulations related to marijuana and other drugs is beyond the scope of this study, findings from our study provide new data from a historical perspective much needed for the current trend in marijuana legalization across the nation to get the benefit from marijuana while to protect vulnerable children and youth in the US. It provides an opportunity for stack-holders to make public decisions by reviewing the findings of this analysis together with the laws and regulations at the federal and state levels over a long period since the 1950s.

Availability of data and materials

The data of the study are available from the designated repository ( https://nsduhweb.rti.org/respweb/homepage.cfm ).

Abbreviations

Audio computer-assisted self-interviews

Age-period-cohort modeling

Computer-assisted person interviews

Cross-classified random-effects model

Contemporary Declining Cohort

Hierarchical age-period-cohort

Historical Declining Cohort

Medical Marijuana Laws

National Household Survey on Drug Abuse

National Survey on Drug Use and Health

Recreational Marijuana Laws

Sudden Increase Cohort

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Additional file 1: table s1..

Estimated Age, Period, Cohort Effects for the Trend of Marijuana Use in Past Month among Adolescents and Emerging Adults Aged 12 to 25 Years, NSDUH, 1979-2016. Table S2. Laws at the federal and state levels related to marijuana use.

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Yu, B., Chen, X., Chen, X. et al. Marijuana legalization and historical trends in marijuana use among US residents aged 12–25: results from the 1979–2016 National Survey on drug use and health. BMC Public Health 20 , 156 (2020). https://doi.org/10.1186/s12889-020-8253-4

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

Misty pratt.

1 Knowledge Synthesis Group, Ottawa Methods Centre, Ottawa Hospital Research Institute, The Ottawa Hospital, General Campus, 501 Smyth Road, Ottawa, Ontario K1H 8 L6 Canada

Adrienne Stevens

2 TRIBE Graduate Program, University of Split School of Medicine, Split, Croatia

Micere Thuku

Claire butler.

3 Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec H3A 2B4 Canada

Becky Skidmore

4 Ottawa, Canada

L. Susan Wieland

5 Center for Integrative Medicine, University of Maryland School of Medicine, Baltimore, MD USA

Mark Clemons

6 School of Epidemiology and Public Health, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8 M5 Canada

7 Division of Medical Oncology and Department of Medicine, University of Ottawa, Ottawa, Canada

Salmaan Kanji

8 Department of Pharmacy, The Ottawa Hospital, Ottawa, Canada

9 Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

Brian Hutton

Associated data.

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

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 ).

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 – 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. ​ (Fig.1) 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 ​ Table1. 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.

Context for the use of cannabis-related terms during the review selection process

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 ​ Table2. 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.

Outcome result categorization

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. ​ Fig.1. 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 – 83 ] and nine guidelines with systematic reviews [ 84 – 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 ​ Tables3, 3 , ​ ,4, 4 , ​ ,5, 5 , ​ ,6, 6 , ​ ,7, 7 , ​ ,8, 8 , ​ ,9, 9 , ​ ,10, 10 , ​ ,11, 11 , ​ ,12, 12 , and ​ and13 13 provide an overview of the characteristics of the 72 included systematic reviews.

Multiple sclerosis

MS multiple sclerosis, NICE National Institute for Health and Care Excellence, No . number, NR not reported, NRS numerical rating scale, QoL quality of life, RMI Rivermead Mobility Index, SBS study-by-study, VAS visual analog scale

*A colon indicates that there were separate analyses for each comparator

Movement disorders

HD Huntington’s disease, MS multiple sclerosis, NR not reported, PD Parkinson’s disease, SBS study-by-study, SCL-90R Symptoms Checklist-90 Revised, QoL quality of life, STSSS Shapiro Tourette Syndrome Severity Scale, THC tetrahydrocannabinol, TS-CGI Tourette Syndrome Clinical Global Impressions, TSSL Tourette’s Syndrome Symptom List (patient rated), VAS visual analog scale, YGTSS Yale Global Tic Severity Scale

AE : adverse effect, NICE National Institute for Health and Care Excellence, NNT numbers needed to treat, NP neuropathic pain, NR not reported, QoL quality of life, QST quantitative sensory testing, SBS study-by-study, VAS visual analog scale

*A colon indicates that there were separate analyses for each comparator; a “+” sign indicates placebo was combined with another comparator

AE adverse effect, NP neuropathic pain, NR not reported, NRS numerical rating scale, QoL quality of life, THC tetrahydrocannabinol, SIGN Scottish Intercollegiate Guidelines Network, SBS study-by-study

Rheumatic disease

AE adverse event, FM fibromyalgia, NR not reported, NRS numerical rating scale, OA osteoarthritis, RA rheumatoid arthritis, SBS study-by-study

NP neuropathic pain, NR not reported, QoL quality of life, SBS study-by-study

Mental health

PTSD posttraumatic stress disorder, SBS study-by-study

NP neuropathic pain, NR not reported, SBS study-by-study

Neurological conditions

AE adverse effect, ALS amyotrophic lateral sclerosis, CADTH Canadian Agency for Drugs and Technologies in Health, NR not reported

Various conditions

AE adverse effect, AD Alzheimer’s disease, ALS amyotrophic lateral sclerosis, CADTH Canadian Agency for Drugs and Technologies in Health, CGI-C Clinical Global Impression of Change scale, COPD Chronic Obstructive Pulmonary Disease, FIQ fibromyalgia impact questionnaire, FM fibromyalgia, HD Huntington’s disease, IBD inflammatory bowel disease, MS multiple sclerosis, NP neuropathic pain, NR not reported, PD Parkinson’s disease, PTSD posttraumatic stress disorder, RA rheumatoid arthritis, SBS study-by-study, SCI spinal cord injury

Other conditions

CADTH Canadian Agency for Drugs and Technologies in Health, IBS irritable bowel syndrome, NR not reported, QoL quality of life, SBS study-by-study, VAS visual analog scale

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 ​ Figure2 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 – 70 ], and HIV [ 48 , 53 , 67 ]. In Fig. ​ Fig.3, 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. ​ Fig.3 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. ​ Fig.4. 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 ​ Figure5 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 ​ Table2). 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. ​ (Figs.6 6 and ​ and7), 7 ), cannabis versus active drugs (Figs. ​ (Figs.8 8 and ​ and9), 9 ), cannabis versus a combination of placebo and active drug (Figs. ​ (Figs.10 10 and ​ and11), 11 ), one cannabis formulation versus other (Figs. ​ (Figs.12 12 and ​ and13), 13 ), and cannabis analyzed against all other comparators (Fig. ​ (Fig.14). 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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 ​ Table2. 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. ​ (Fig.6). 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.

• i. 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.
• ii. 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.

• 2. Non-pain outcomes

The types of non-pain outcomes included in the reviews varied by condition/illness. The most commonly reported outcomes (see Fig. ​ Fig.5 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.

• 3. 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. ​ (Fig.8). 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.

• i. Reviews focused on addressing pain across conditions. When comparing across reviews, a mix of results are observed (see Fig. ​ Fig.8), 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.
• ii. 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 ].
• Non-pain outcomes

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. ​ (Figs.10 10 and ​ and11). 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. ​ (Figs.12 12 and ​ and13). 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. ​ (Fig.14). 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.

Supplementary information

Acknowledgements.

Not applicable.

Abbreviations

Authors’ contributions.

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.

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.

Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

BH has previously received honoraria from Cornerstone Research Group for provision of methodologic advice related to the conduct of systematic reviews and meta-analysis. All other authors declare that they have no conflicts of interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Misty Pratt, Email: ac.irho@ttarpim .

Adrienne Stevens, Email: ac.irho@snevetsda .

Micere Thuku, Email: ac.irho@ukuhtm .

Claire Butler, Email: ac.irho@reltublc .

Becky Skidmore, Email: moc.sregor@eromdiksb .

L. Susan Wieland, Email: moc.liamg@dnaleiwsl .

Mark Clemons, Email: ac.hot@snomelcm .

Salmaan Kanji, Email: ac.hot@ijnaks .

Brian Hutton, Email: ac.irho@nottuhb .

Supplementary information accompanies this paper at 10.1186/s13643-019-1243-x.

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Regions & Countries

Most americans favor legalizing marijuana for medical, recreational use, legalizing recreational marijuana viewed as good for local economies; mixed views of impact on drug use, community safety.

Pew Research Center conducted this study to understand the public’s views about the legalization of marijuana in the United States. For this analysis, we surveyed 5,140 adults from Jan. 16 to Jan. 21, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report and its methodology .

As more states pass laws legalizing marijuana for recreational use , Americans continue to favor legalization of both medical and recreational use of the drug.

An overwhelming share of U.S. adults (88%) say marijuana should be legal for medical or recreational use.

Nearly six-in-ten Americans (57%) say that marijuana should be legal for medical and recreational purposes, while roughly a third (32%) say that marijuana should be legal for medical use only.

Just 11% of Americans say that the drug should not be legal at all.

Opinions about marijuana legalization have changed little over the past five years, according to the Pew Research Center survey, conducted Jan. 16-21, 2024, among 5,14o adults.

The impact of legalizing marijuana for recreational use

While a majority of Americans continue to say marijuana should be legal , there are varying views about the impacts of recreational legalization.

About half of Americans (52%) say that legalizing the recreational use of marijuana is good for local economies; just 17% think it is bad and 29% say it has no impact.

More adults also say legalizing marijuana for recreational use makes the criminal justice system more fair (42%) than less fair (18%); 38% say it has no impact.

However, Americans have mixed views on the impact of legalizing marijuana for recreational use on:

• Use of other drugs: About as many say it increases (29%) as say it decreases (27%) the use of other drugs, like heroin, fentanyl and cocaine (42% say it has no impact).
• Community safety: More Americans say legalizing recreational marijuana makes communities less safe (34%) than say it makes them safer (21%); 44% say it has no impact.

Partisan differences on impact of recreational use of marijuana

There are deep partisan divisions regarding the impact of marijuana legalization for recreational use.

Majorities of Democrats and Democratic-leaning independents say legalizing recreational marijuana is good for local economies (64% say this) and makes the criminal justice system fairer (58%).

Fewer Republicans and Republican leaners say legalization for recreational use has a positive effect on local economies (41%) and the criminal justice system (27%).

Republicans are more likely than Democrats to cite downsides from legalizing recreational marijuana:

• 42% of Republicans say it increases the use of other drugs, like heroin, fentanyl and cocaine, compared with just 17% of Democrats.
• 48% of Republicans say it makes communities less safe, more than double the share of Democrats (21%) who say this.

Demographic, partisan differences in views of marijuana legalization

Sizable age and partisan differences persist on the issue of marijuana legalization though small shares of adults across demographic groups are completely opposed to it.

Older adults are far less likely than younger adults to favor marijuana legalization.

This is particularly the case among adults ages 75 and older: 31% say marijuana should be legal for both medical and recreational use.

By comparison, half of adults between the ages of 65 and 74 say marijuana should be legal for medical and recreational use, and larger shares in younger age groups say the same.

Republicans continue to be less supportive than Democrats of legalizing marijuana for both legal and recreational use: 42% of Republicans favor legalizing marijuana for both purposes, compared with 72% of Democrats.

There continue to be ideological differences within each party:

• 34% of conservative Republicans say marijuana should be legal for medical and recreational use, compared with a 57% majority of moderate and liberal Republicans.
• 62% of conservative and moderate Democrats say marijuana should be legal for medical and recreational use, while an overwhelming majority of liberal Democrats (84%) say this.

Views of marijuana legalization vary by age within both parties

Along with differences by party and age, there are also age differences within each party on the issue.

A 57% majority of Republicans ages 18 to 29 favor making marijuana legal for medical and recreational use, compared with 52% among those ages 30 to 49 and much smaller shares of older Republicans.

Still, wide majorities of Republicans in all age groups favor legalizing marijuana at least for medical use. Among those ages 65 and older, just 20% say marijuana should not be legal even for medical purposes.

While majorities of Democrats across all age groups support legalizing marijuana for medical and recreational use, older Democrats are less likely to say this.

About half of Democrats ages 75 and older (53%) say marijuana should be legal for both purposes, but much larger shares of younger Democrats say the same (including 81% of Democrats ages 18 to 29). Still, only 7% of Democrats ages 65 and older think marijuana should not be legalized even for medical use, similar to the share of all other Democrats who say this.

Views of the effects of legalizing recreational marijuana among racial and ethnic groups

Substantial shares of Americans across racial and ethnic groups say when marijuana is legal for recreational use, it has a more positive than negative impact on the economy and criminal justice system.

About half of White (52%), Black (53%) and Hispanic (51%) adults say legalizing recreational marijuana is good for local economies. A slightly smaller share of Asian adults (46%) say the same.

Criminal justice

Across racial and ethnic groups, about four-in-ten say that recreational marijuana being legal makes the criminal justice system fairer, with smaller shares saying it would make it less fair.

However, there are wider racial differences on questions regarding the impact of recreational marijuana on the use of other drugs and the safety of communities.

Use of other drugs

Nearly half of Black adults (48%) say recreational marijuana legalization doesn’t have an effect on the use of drugs like heroin, fentanyl and cocaine. Another 32% in this group say it decreases the use of these drugs and 18% say it increases their use.

In contrast, Hispanic adults are slightly more likely to say legal marijuana increases the use of these other drugs (39%) than to say it decreases this use (30%); 29% say it has no impact.

Among White adults, the balance of opinion is mixed: 28% say marijuana legalization increases the use of other drugs and 25% say it decreases their use (45% say it has no impact). Views among Asian adults are also mixed, though a smaller share (31%) say legalization has no impact on the use of other drugs.

Community safety

Hispanic and Asian adults also are more likely to say marijuana’s legalization makes communities less safe: 41% of Hispanic adults and 46% of Asian adults say this, compared with 34% of White adults and 24% of Black adults.

Wide age gap on views of impact of legalizing recreational marijuana

Young Americans view the legalization of marijuana for recreational use in more positive terms compared with their older counterparts.

Clear majorities of adults under 30 say it is good for local economies (71%) and that it makes the criminal justice system fairer (59%).

By comparison, a third of Americans ages 65 and older say legalizing the recreational use of marijuana is good for local economies; about as many (32%) say it makes the criminal justice system more fair.

There also are sizable differences in opinion by age about how legalizing recreational marijuana affects the use of other drugs and the safety of communities.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Marijuana and Cannabinoids: Health, Research and Regulatory Considerations (Position Paper)

Executive summary.

Marijuana and related substance misuse are complex issues impacting family medicine, patient health, and public health. The American Academy of Family Physicians (AAFP) believes family physicians are essential in addressing all forms of inappropriate substance use. The AAFP urges its members to be involved in the diagnosis, treatment, and prevention of substance use, as well as secondary diseases impacted or caused by use. The World Health Organization (WHO) reports approximately 2.5% of the global population uses cannabis annually, making it the most commonly used drug worldwide. 1  Simultaneously, the AAFP acknowledges preliminary evidence indicates marijuana and cannabinoids may have potential therapeutic benefits, while also recognizing subsequent negative public health and health outcomes associated with cannabis use. 2

During the 20 th  century, law enforcement and public policy activities have undermined opportunities for scientific exploration. Barriers to facilitating both clinical and public health research regarding marijuana is detrimental to treating patients and the health of the public. The lack of regulation poses a danger to public health and impedes meaningful, patient-centered research to exploring both therapeutic and negative impacts of marijuana and cannabinoids.

Relevant AAFP Policy

Marijuana Possession for Personal Use The American Academy of Family Physicians (AAFP) opposes the recreational use of marijuana. However, the AAFP supports decriminalization of possession of marijuana for personal use. The AAFP recognizes the benefits of intervention and treatment for the recreational use of marijuana, in lieu of incarceration, for all individuals, including youth. 3

The AAFP also recognizes that several states have passed laws approving limited recreational use and/or possession of marijuana. Therefore, the AAFP advocates for further research into the overall safety and health effects of recreational use, as well as the effects of those laws on patient and societal health. 4

It should be noted that cannabis and marijuana are not interchangeable terms. In this position paper, cannabis is an overarching term used to refer to the plant  Cannabis sativa . Substances derived from the cannabis   plant include marijuana, hemp, and cannabinoids.

Call to Action Family physicians have a vested interest in policies that advance and protect the health of their patients and the public. The regulatory environment surrounding cannabis, medical and recreational marijuana, and cannabidiol (CBD) is rapidly changing, along with the retail environment. This shift has not been accompanied by robust scientific research regarding the health effects of cannabis, both therapeutic or detrimental. The AAFP recognizes the need for substantial clinical, public health, and policy evidence and research regarding cannabis, marijuana, cannabinoids, and CBD to inform evidence-based practice and the impact on public health.

• The AAFP promotes a society which is free of substance misuse, including alcohol and drugs. 3
• The AAFP recognizes there is support for the medical use of marijuana and cannabinoids, but advocates that usage be based on high-quality, evidence-based public health, policy, and patient-centered research, including the impact on vulnerable populations. 3
• The AAFP advocates for further studies into the use of medical marijuana and related compounds. This process should also ensure appropriate funding allocated for this research.
• The AAFP calls for decreased regulatory barriers to facilitate clinical and public health cannabis research, including reclassifying cannabis from a Schedule I controlled substance. 3
• The AAFP advocates for further research into the overall safety and health effects of recreational use, as well as the impact of legal recreational marijuana use laws on patient and societal health. 4
• The AAFP advocates for robust regulation regarding labeling and child-proof packaging of all marijuana and cannabinoid products.
• The AAFP opposes the recreational use and legalization of marijuana, but supports decriminalization of marijuana for personal use. The AAFP recognizes the benefits associated with intervention and treatment, in lieu of incarceration. 4
• The AAFP advocates for regulation regarding marketing claims, labeling, and advertising of all marijuana and cannabinoid products.
• The AAFP supports requirements testing current marijuana and cannabinoid products for safety, dosing, and product consistency.

In the Exam Room

• The AAFP urges its members to be involved in the diagnosis, treatment, and prevention of substance use, as well as the secondary diseases impacted by use.
• The AAFP calls for family physicians to discuss the health consequences of marijuana and cannabis use, as well as prevention strategies to prevent use and unintended consequences of marijuana exposure in at-risk populations.

Cannabis use, both medically and recreationally, is prevalent throughout history. Extensive evidence indicates cannabis was used by ancient civilizations, dating back more than 5,000 years ago. 1  In the U.S. in the 19th and early 20th centuries, cannabis was frequently used for medicinal purposes, often prescribed by clinicians. 1,5  Cannabis was first listed in the  United States Pharmacopoeia  in 1851, indicating use as an analgesic, hypnotic, and anticonvulsant agent. 5  After the 1937  Marihuana Tax Act , in 1942, cannabis was removed from the  United States Pharmacopoeia . 5

Attitudes and perceived risk of marijuana use have changed with the varying levels of legalization in the U.S. Surveying marijuana use is essential to gauge public health implications of increased access to marijuana, cannabinoid, and cannabis products. According to the 2018 National Institute on Drug Abuse (NIDA) Monitoring the Future Survey (MTF), daily, past month, past year, and lifetime marijuana use among 8 th  graders has declined, and remained unchanged in 10 th  and 12 th  graders, when compared to the 2013 MTF survey. 6  Despite the changing landscape of marijuana regulations nationwide, past year use of marijuana reached and maintained its lowest levels in more than two decades in 2016 among 8 th  and 10 th  graders. 6  However, marijuana vaping did significantly increase between 2017 and 2018, mirroring trends in youth tobacco use. 6  The NIDA 2017 National Survey on Drug Use and Health indicates nearly 53% of adults between the ages of 18-25 have tried marijuana at some point in their lifetime, 35% have used marijuana within the past year, and 22% within the past month. 7  While the lifetime use remains relatively stable for this cohort, from 2015-2017, past year and past month use increased 2.7% and 2.3%, respectively. 7  Nearly half of adults 26 or older reported using marijuana at some point in their lifetime. 7  Although adults ages 26 and up report the highest percentage of lifetime use, this age group has a significantly lower past year use (12%) and past month use (8%). 7

Forms and Use of Cannabis The cannabis plant,  Cannabis sativa , is comprised of both non-psychoactive and psychoactive chemicals called cannabinoids. 5  The cannabinoid commonly known for its psychoactive properties is delta-9-tetrahydrocannabinol (THC). 5  CBD is the most abundant cannabinoid in cannabis, and is considered to be largely non-psychoactive. 5  The biological system responsible for the synthesis and degradation of cannabinoids in mammals is referred to as the endocannabinoid system, which is largely comprised of two g-coupled protein receptors (GPCRs). 8  The GPCRs—CB1 and CB2—are found throughout many bodily tissues. However, CB1 is most concentrated in the neural tissues. 5,8  CB2 receptors are found in the brain, but are mostly found in immune cells, like macrophages, microglia, osteoclasts, and osteoblasts. 5,8

There are many forms of, and products derived from, the  Cannabis sativa  plant, including hemp, CBD, and marijuana.  Cannabis sativa  with less than 0.3% THC is considered industrial hemp, and can be used for industrial agriculture cultivation. 9,10  Industrial hemp can be harvested and used for many things, including fibers for textiles, food products, and building materials. 11,12  CBD, the non-psychoactive cannabinoid, is extracted from the flower of industrial hemp. 13  Marijuana and hemp, technically speaking, are the same plant. 13  However, the hemp variety of cannabis contains no more than 0.3% THC, while the marijuana variety contains 5-20% THC. 13

Marijuana and CBD are most commonly used via inhalation, ingestion, and topical absorption. 5  Inhalation can be through combustible mechanisms using dried flowers, including the use of a pipe, rolled joints, blunts, and water pipes (also called bongs). 14  Vaping marijuana and CBD concentrates are an increasingly popular inhalation method. 5,6  Concentrates, the concentrated form of marijuana and CBD, come in various forms, including oil, butter, or a dark sticky substance often referred to as shatter. 15  Concentrates can be both smoked or vaporized, and may also be used as additives or cooking agents for ingestion. 5,15  There are many different ways to ingest cannabinoids. Food products—called edibles—like brownies, gummies, cookies, and candies are common forms of cannabis ingestion, as well as liquid forms like juices, soda, and tea. 5,16  Tinctures are liquid, ultra-concentrated alcohol-based cannabis extracts commonly applied in and absorbed through the mouth. 17  Topical cannabis is applied to, and absorbed through, the skin in a cream or salve form. 18

Routes or methods of administration affect cannabis delivery. When cannabis is smoked or vaporized, onset of effect is within 5-10 minutes with a duration of 2-4 hours. 19  When ingested, effect is within 60-180 minutes with a duration of 6-8 hours. 19  The oromucosal route has an onset of 15-45 minutes and a duration of 6-8 hours. 19  Topical administration of cannabis or cannabinoids has variable onset and duration. 19  The smoked or vaporized method offers the more rapid activity for acute symptoms with the topical preparations offering less systemic effects. 19

Health Effects of Cannabis

Although there is preliminary evidence indicating cannabinoids may have some therapeutic benefit, a large portion of the evidence is very limited for many reasons. These include small sample sizes, lack of control groups, poor study design, and the use of unregulated cannabis products. There are also clear negative health and public health consequences that must be considered, as well as the need for a significant increase in evidence. More research is needed to create a robust evidence base to weigh the potential therapeutic benefits against potential negative impacts on health and public health. Currently, there are three medical formulations of cannabis approved for use in the U.S.; dronabinol, nabilone, and epidiolex. 20  Nabiximols is approved for use in the United Kingdom. 21  Dronabinol is delta-9 THC and ingested as either an oral solution or an oral capsule. 22  Nabilone is an oral capsule containing synthetic THC. 23  Epidiolex is a CBD oral solution. 24  Nabiximols is an oral mucosa spray containing the cannabinoids THC and CBD. 25

In 2015, Whiting, et al, performed a meta-analysis and systematic review of research on the medical use of cannabis. 25  This systematic review served as the basis for many recommendations in 2017 by the National Academy of Science, Engineering, and Health Report on medical marijuana. 5  Dronabinol, nabilone, and nabiximols were included in the studies. However, other cannabis formulations were found in research trials, including CBD, marijuana, and other cannabinoids. 26  Evidence is most substantial for nausea and vomiting associated with chemotherapy, chronic pain treatment, multiple sclerosis spasticity, and intractable seizures associated with Dravet syndrome and Lennox-Gastaut syndrome. 27  There is moderate evidence for the use of cannabinoids for sleep and limited evidence for use in psychiatric conditions, such as post-traumatic stress disorder, depression, anxiety, and psychosis; appetite stimulation and weight gain; and no evidence for cancer treatment. 5

Dronabinol and nabilone were both approved in 1985 for use in treating refractory chemotherapy-induced nausea and vomiting. 5,23  Dronabinol is approved by the Food and Drug Administration (FDA) for appetite stimulation and weight gain, despite limited and often inconclusive evidence that it or other cannabinoids are effective. 22  This drug has traditionally been used in human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients to mitigate weight loss and to treat anorexia-cachexia syndrome associated with cancer and anorexia nervosa. 5,22

Cannabinoids have been assessed for chronic pain management. Many forms of chronic pain management were studied, including cancer and chemotherapy-induced pain, fibromyalgia, neuropathic pain, rheumatoid arthritis, non-cancer pain, and musculoskeletal pain. Several studies indicate smoked THC and nabiximols were both associated with pain reduction. 5,25,26  There is limited, mixed evidence regarding the viability of cannabinoids for some forms of chronic pain management. 5  However, limitations exist with these studies, including the variable doses of THC and CBD; unregulated, non-FDA approved products; and conflicting evidence. Studies assessing cannabinoids in treating the spasticity due to multiple sclerosis or paraplegia have mixed results. The cannabinoids nabiximols, dronabinol, and TCH/CBD have all been associated with decreased spasticity. Nabilone and nabiximols were the only drugs with statically-significant decreases. 2,25

In 2018, the FDA approved a cannabidiol oral solution called epidiolex for the treatment of refractory seizures associated with Dravet syndrome and Lennox-Gastaut syndrome. 28  Epidiolex was associated with significant seizure reduction when compared to placebo. 29–31  Dravet syndrome and Lennox-Gastaut syndrome are disorders associated with severe seizures, impaired cognitive skills and development, and uncontrollable muscle contractions. 29–31

Moderate evidence exists for the use of cannabis for sleep. Nabilone and nabiximols have been associated with improvement in sleep from a baseline and sleep restfulness. 2,5,25  Improved sleep was also considered a secondary outcome when evaluating other conditions (chronic pain, multiple sclerosis) with various cannabinoids. 2,5,25

There is limited evidence for the use of cannabis or cannabinoids for the treatment of post-traumatic stress disorder (PTSD), anxiety, depression, or psychosis. Of the limited evidence, nabilone was associated with a decrease in PTSD related nightmares. 5,25  One small study indicated CBD improved public speaking anxiety. 5  There are no studies directly evaluating the effectiveness of cannabis in the treatment of depression. However, some studies measured depression as a secondary outcome, but indicated no difference in depression when compared to placebo. 25  Limited evidence (two studies) have shown no difference in treating psychosis with CBD, amisulpride, or placebo. 25  Evidence indicates individuals who use marijuana are more likely to experience temporary psychosis and chronic mental illness, including schizophrenia. 5,32

There was no evidence or insufficient evidence for the use of cannabis or cannabinoids in the treatment of cancer; neurodegenerative disorders like Huntington’s chorea, Parkinson’s disease, or amyotrophic lateral sclerosis; irritable bowel syndrome; or addiction. 5

Cannabis overdose is rare in adults and adolescents. 33  Children who experience acute intoxication from cannabis generally ingest marijuana or other cannabinoids through experimentation. 33  When compared to adults and adolescents, children are more likely to experience life-threatening symptoms of acute cannabis intoxication, which may include depressed respiration rates, hyperkinesis, or coma. 33  Management consists of supportive care dependent on the manifestation of symptoms. 33  Adults and adolescents may experience increased blood pressure and respiratory rates, red eyes, dry mouth, increased appetite, and slurred speech. 33

Negative health effects are also associated with marijuana and cannabinoid use. Frequent marijuana use has been associated with disorientation. In teens, it has been linked with depression, anxiety, and suicide. 5,32  However, this is not a proven causal relationship. Lung health can also be negatively impacted depending on the delivery mechanism. 34  Smoking marijuana can cause lung tissue scarring and damage blood vessels, further leading to an increased risk of bronchitis, cough, and phlegm production. 34  This generally decreases when users quit. 34

Secondhand smoke is a serious issue associated with marijuana use. However, there is limited evidence on how it impacts heart and lung health. 34  Detectable THC has been found in children who live in the home or have a caretaker who use marijuana, subjecting children to developmental risks of THC exposure. 35  Fetal, youth, and adolescent exposure to THC is associated with negative health effects, including impacting brain development. 34  There is inconsistent, insufficient evidence to determine the long-term effects of marijuana and cannabinoid use while breastfeeding. 36  However, THC has been detected in breast milk for up to six days post-cannabinoid use, and exposure to cannabinoids is known to impact development in children. 37  Evidence also suggests cannabis use during pregnancy may be linked with preterm birth. 38  Cardiovascular health may be impacted by smoked marijuana use. However, the negative health effects are associated with the harmful chemicals in smoke similar to tobacco smoke. 34

Approximately 9% of all individuals who use marijuana develop an addiction, which is variable by age of first use and frequency of use. 34  That number for addiction jumps to 17% for individuals who begin using marijuana as teenagers and 25-50% of those who smoke marijuana daily. 34  Marijuana use does not typically lead to harder drug use, like cocaine and heroin, in most individuals. 39  Further research is needed to evaluate any potential gateway effect. 39

Mental health outcomes associated with marijuana use include an increased risk of anxiety and depression. Marijuana has been linked to schizophrenia, psychoses, and advancing the trajectory of the disease, particularly in individuals with pre-existing genetic indicators. 5,34  Global research also suggests daily use of high-potency marijuana increases risk for psychotic episodes among individuals with no underlying mental health condition. 40  While it is widely accepted that marijuana acutely impairs cognitive function, studies suggest differential outcomes regarding short- versus long-term cognitive impairment. 34

Research Considerations

The regulatory environment surrounding cannabis, marijuana, and cannabinoid research creates barriers detrimental to facilitating meaningful medical, public health, policy, and public safety research. Approval for research expands beyond institutional review boards. Due to the Schedule I classification by the Drug Enforcement Agency (DEA), researchers seeking to investigate health effects associated with cannabis must follow a regimented application process. 41  Applicants must submit an Investigational New Drug (IND) application to the FDA, which will then be reviewed to determine scientific validity and research subjects’ rights and safety. 42  Researchers must also follow the NIDA regulatory procedures for obtaining cannabis for research purposes. 41  Researchers may only use cannabis supplied by the University of Mississippi, the single NIDA-approved source for cannabis research. 41  Requiring research to rely on one source of cannabis limits availability and the variety of products. While the University of Mississippi cultivates different strains of cannabis, it is unable to supply the vast array of strains of cannabis found in the evolving retail environment with varying levels of THC, CBD, and cannabinoid content. 5  Substantial funding and capacity is required for researchers to obtain all regulatory approval and remain in compliance while conducting cannabis-related research. The required processes and procedures present a serious burden, dissuading researchers from pursuing cannabis-related projects. This has led to a lack of empirical evidence regarding a myriad of health-related issues, including potential therapeutic benefits of cannabis, public health impact, health economics, and the short- and long-term health effects from cannabis use.

In order to address the research gaps associated with both beneficial and harmful effects of cannabinoids used in both medical and recreational capacities, the AAFP calls for a comprehensive review of processes and procedures required to obtain approval for cannabis research.  

The AAFP encourages the appropriate regulatory bodies, such as the DEA, NIDA, FDA, Department of Health and Human Services (DHHS), National Institutes of Health (NIH), and the Centers for Disease Control and Prevention (CDC), to collaborate with non-governmental stakeholders to determine procedures to decrease the burden of cannabis-related research while maintaining appropriate regulatory safety guards. This should include a reclassification of marijuana from Schedule I to facilitate clinical research. The AAFP calls for increased funding from both public and private sectors to support rigorous scientific research to address gaps in evidence regarding cannabis to protect the health of the public and inform evidence-based practices. 3  Future research should address the impact of cannabis use on vulnerable and at-risk populations.

Regulatory Considerations

While cannabis was federally regulated in 1906 for consumer and safety standards and labeling requirements, the  Marihuana Tax Act  of 1937 was the first federal regulation to impose a fine or imprisonment for non-medical use and distribution of cannabis. 5  The tax act also regulated production, distribution, and use of cannabis, further requiring anyone dealing with cannabis to register with the federal government. 5  In 1970, the DEA classified marijuana as a Schedule I drug, which is defined as a drug with no current acceptable medical use and a high potential for abuse. 43  Other Schedule 1 drugs include heroin, lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (ecstasy), methaqualone, and peyote. 43  Since this class of substances is determined as having no medical usage, they cannot be legally prescribed and thus, there is no medical coverage for them.

Marijuana is illegal under federal law. Penalties cover possession, sale, cultivation, and paraphernalia. However, the Agriculture Improvement Act of 2018 included a U.S. Department of Agriculture (USDA) Hemp Production Program, removing hemp from the Controlled Substances Act. 10,44  As a result, CBD  sourced from hemp plants containing no more than 0.3% THC is legal to produce. 10,44  The FDA has approved three medications containing cannabinoids: epidiolex (CBD), dronabinol, and nabilone (synthetic cannabinoids). 5  No other forms of cannabis are currently regulated by the FDA. The AAFP calls upon the FDA to take swift action to regulate CBD and cannabinoid products now legal in order to protect the health of the public.

States have separate marijuana, cannabinoid, and cannabis laws, some of which mirror federal laws, while others may be more harsh, or have decriminalized and even legalized marijuana and cannabis. 45  In 1996, California was the first state to legalize the medical use of marijuana. 46  States have subsequently decriminalized and/or legalized cannabinoids, medical marijuana, and recreational marijuana. 46  As of August 2019, 30 states, along with the District of Columbia, Guam, and Puerto Rico have legalized marijuana in varying forms. 46  Decriminalization laws may include reduction of fines for possession of small amounts of marijuana, reclassification of criminal to civil infractions, excluding the infraction from criminal records and expunging prior offenses and convictions related to marijuana. 47  Thirty-three states, along with the District of Columbia, Guam, Puerto Rico, and the U.S. Virgin Islands have a comprehensive, publicly-available medical marijuana/cannabis program, and 13 of these states have also removed jail time for possessing small amounts of non-medical marijuana. 47  Adult recreational marijuana use is legal in 13 states and the District of Columbia. 47  Vermont and the District of Columbia, however, do not allow the sale of marijuana for recreational purposes. This means it is not a crime to use and possess marijuana recreationally, but commercial sales are not allowed. 47  States have also authorized the sale of products that have low levels of THC, but high levels of CBD. These products are widely available in retail locations, but are highly unregulated. 47  The benefits of CBD touted by the public and retailers are largely anecdotal. The vast majority of these claims are not substantiated by valid research.

Decriminalizing and legalizing marijuana can decrease the number of individuals arrested and subsequently prosecuted for possession and/or use. 48  However, evidence suggests that these practices are not applied equitably. People of color are more likely to be arrested and prosecuted for marijuana possession despite overall decreased arrest rates. 48  Incarceration impacts health. People who are incarcerated have significantly higher rates of disease than those who are not, and are less likely to have access to adequate medical care. 49

The AAFP “opposes the recreational use of marijuana. However, the AAFP supports decriminalization of possession of marijuana for personal use. The AAFP recognizes the benefits of intervention and treatment for the recreational use of marijuana, in lieu of incarceration, for all individuals, including youth.” 4  The AAFP calls for family physicians to advocate to prevent unnecessary incarceration by diverting eligible people from the justice system to substance abuse and/or mental health treatment. 49

There are many public health considerations when regulating cannabis products. Serious public health concerns include impaired driving, youth exposure to advertisements, and accidental poisoning in children. Second to alcohol, marijuana is the most common illicit drug associated with impaired driving and accidents. 34  Marijuana slows reaction time and decision making, substantially increasing risk for traffic accidents. 50  Some states have a zero-tolerance policy, where there is no allowable detectable level of THC while driving, while other states have set five nanograms per milliliter or higher limits of THC, or minimally-detectable amounts of THC. 51

Evidence indicates adolescents who are exposed to medical marijuana advertising are more likely to have positive views of and subsequently use marijuana. 52  Those exposed to medical marijuana advertising were more likely to report past use and expectant future use. 52  These adolescents also reported agreeing with statements like, marijuana helps people relax and get away from their problems. 52  Adolescent exposure to medical marijuana advertising was also associated with self-reporting negative consequences associated with marijuana use, including missing school and concentration issues. 52  The AAFP calls for immediate regulation of advertising of all marijuana and cannabinoid products to decrease youth exposure to aid in preventing initiation and subsequent use of marijuana.

Children are most susceptible to severe effects associated with marijuana poisoning, including decreased coordination, lethargy, sedation, difficulty concentrating, and slurred speech. 53  Exposure may also include serious, potentially life-threatening symptoms like respiratory distress and coma. 33  Unintentional exposures to marijuana in children have increased each year since 2012, likely due to legalization policies across the U.S. and popularity of edibles. 53  Edibles often look exactly like their non-THC counterparts, and come in brightly colored packaging appealing to children, often mimicking candy products. 53  Effective legislation requiring childproof packaging for edible products can help mitigate and prevent unintentional exposure in children. 54  Family physicians should discuss safe storage of all cannabis products with their patients who live with children. 54  Under the Child Abuse Prevention and Treatment Act (CAPTA), physicians are mandated reporters of suspected child abuse and neglect. 55  The 2010 law requires states to enact laws for reporting substance use-exposed infants to child protective services. 55

Family physicians play a key role in addressing marijuana, cannabinoid, and cannabis product use; reducing barriers to research; and advocating for appropriate policy to protect the health of patients and the public.

Family physicians can address the inappropriate use of marijuana, cannabinoid, and cannabis products. Family physicians should discuss safe storage of all cannabis products with patients who live with or serve as primary caregivers for children to prevent unintended exposure. 56  It is important to discuss the developmental and negative impacts of marijuana and cannabis products with individuals who are or can become pregnant, children, and adolescents. Family physicians should also emphasize the serious consequences of impaired driving and marijuana intoxication.

It is essential to decrease barriers to research all forms of marijuana, cannabis, and cannabinoids, including a reclassification of cannabis as a Schedule I drug. High-quality research regarding the impact on patients, public health, society, and health policy are essential to providing patient-centered care and promoting evidence-based public health practices. Immediate regulations for marijuana and cannabinoid products, including CBD, like product safety and consistency safeguards, child-proof packaging, labeling, marketing claims and advertising, and impairment standards are vital for consumer safety and injury prevention. Regulatory measures focused on preventing youth initiation of marijuana and cannabinoid product use must be prioritized to prevent a public health epidemic.

The health benefits associated with intervention and treatment of recreational marijuana and cannabinoid use, in lieu of incarceration, is an important policy consideration.

Utilizing an interdisciplinary, evidence-based approach to addressing both medical and recreational marijuana and cannabis use is essential to promote public health, inform policy, and provide patient-centered care. Family physicians, in partnership with public health and policy professionals, can play an imperative role in addressing the changing landscape of marijuana and cannabis products.

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• 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(21):2011-2020.
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• Volkow ND, Swanson JM, Evins AE, et al. Effects of cannabis use on human behavior, including cognition, motivation, and psychosis: a review.  JAMA Psychiatry . 2016;73(3):292-297.
• Wang GS. Cannabis (marijuana): acute intoxication. UpToDate. Accessed August 20, 2019.
• Volkow ND, Baler RD, Compton WM, Weiss SRB. Adverse health effects of marijuana use.  N Engl J Med . 2014;370(23):2219-2227.
• Wilson KM, Torok MR, Wei B, et al. Detecting biomarkers of secondhand marijuana smoke in young children.  Pediatr Res . 2017;81(4):589-592.
• Reece-Stremtan S, Marinelli KA. ABM Clinical Protocol #21: Guidelines for Breastfeeding and Substance Use or Substance Use Disorder, Revised 2015.  Breastfeed Med . 2015;10(3):135-141.
• Bertrand KA, Hanan NJ, Honerkamp-Smith G, Best BM, Chambers CD. Marijuana use by breastfeeding mothers and cannabinoid concentrations in breast milk.  Pediatrics . 2018;142(3):e20181076.
• Corsi DJ, Walsh L, Weiss D, et al. Association between self-reported prenatal cannabis use and maternal, perinatal, and neonatal outcomes.  JAMA . 2019;322(2):145-152.
• Centers for Disease Control and Prevention. Does marijuana use lead to other drug use?  Accessed August 20, 2019.
• Di Forti M, Quattrone D, Freeman TP, et al. The contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicentre case-control study.  The Lancet Psychiatry . 2019;6(5):427-436.
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• National Institute on Drug Abuse (NIDA). NIDA’s role in providing marijuana for research. www.drugabuse.gov/drugs-abuse/marijuana/nidas-role-in-providing-marijuana-research. Accessed August 20, 2019.
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• U.S. Food and Drug Administration. Statement from FDA Commissioner Scott Gottlieb, M.D., on signing of the Agriculture Improvement Act and the agency's regulation of products containing cannabis and cannabis-derived compounds. Accessed August 20, 2019.
• American Bar Assocation. Conflicting state and federal marijuana laws create ethical complications for lawyers. Accessed August 20, 2019.
• National Conference of State Legislatures. Marijuana laws. www.ncsl.org/bookstore/state-legislatures-magazine/marijuana-deep-dive.aspx. Accessed August 20, 2019.
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(July 2019 BOD)

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Research explores liberalization of medical marijuana and mental health in the US

T he approval of marijuana for medical use has had little effect on the mental health of the general population in the US. But legalization for therapeutic purposes does benefit those for whom it is intended. This is the conclusion of a study by researchers at the University of Basel. The research is published in the journal Health Economics, Policy and Law .

In the US, access to marijuana has been facilitated in most states since the mid-1990s—whether through medical clearance or through decriminalization of recreational use. However, liberalization is still controversial, and the effects on the well-being of specific groups and the therapeutic value of marijuana remain debated.

While some fear negative consequences from addiction, others highlight the potential medical benefits for people suffering from chronic pain, nausea or convulsions.

In a new study, researchers from Basel have now investigated whether medical cannabis legislation in the U.S. is improving the situation for sick people and whether it has a negative impact on the mental health of the overall population.

Probability-based analysis

For their analysis, the researchers combined two large datasets. They used data from almost eight million people who took part in telephone surveys between 1993 and 2018 as part of the Behavioral Risk Factor Surveillance System, which collects data about mental well-being, among other things. But they also used data from the National Survey on Drug Use and Health, which collects information on health-related issues such as drug use in the United States.

The researchers formed different groups using statistical assignment. They include individuals who are highly likely to abstain from using marijuana, to use marijuana as a recreational drug or to use it for medical reasons. It was also possible to identify individuals with a high probability of chronic pain. Mental health was measured using self-assessment, in which respondents reported the number of days they had had mental health problems in the previous month.

Positive effects of therapeutic use

Using statistical methods, the researchers were able to estimate the impact of the legal approval of marijuana for medical use. The result: Easier access improves the mental health of individuals who use marijuana for medical reasons. The same applies to people who are very likely to suffer from pain. The study authors estimate that these two groups spend 0.3 days less per month in poor mental health due to the change in the law.

At the same time, the researchers found no effect on the mental health of recreational users or on younger populations.

"Overall, our results show that medical cannabis legislation in the U.S. benefits the people it is intended for without harming other groups," summarizes the study leader, Prof. Alois Stutzer from the University of Basel.

More information: Jörg Kalbfuss et al, Medical marijuana laws and mental health in the United States, Health Economics, Policy and Law (2024). DOI: 10.1017/S1744133124000033

Provided by University of Basel

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Title: mapping the increasing use of llms in scientific papers.

Abstract: Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in their academic writing, and to what extent this tool might have an effect on global scientific practices. However, we lack a precise measure of the proportion of academic writing substantially modified or produced by LLMs. To address this gap, we conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time. Our statistical estimation operates on the corpus level and is more robust than inference on individual instances. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers (up to 17.5%). In comparison, Mathematics papers and the Nature portfolio showed the least LLM modification (up to 6.3%). Moreover, at an aggregate level, our analysis reveals that higher levels of LLM-modification are associated with papers whose first authors post preprints more frequently, papers in more crowded research areas, and papers of shorter lengths. Our findings suggests that LLMs are being broadly used in scientific writings.

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Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

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Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don’t fully grasp how they work.

In an effort to better understand what is going on under the hood, researchers at MIT and elsewhere studied the mechanisms at work when these enormous machine-learning models retrieve stored knowledge.

They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts. Moreover, the model uses the same decoding function for similar types of facts. Linear functions, equations with only two variables and no exponents, capture the straightforward, straight-line relationship between two variables.

The researchers showed that, by identifying linear functions for different facts, they can probe the model to see what it knows about new subjects, and where within the model that knowledge is stored.

Using a technique they developed to estimate these simple functions, the researchers found that even when a model answers a prompt incorrectly, it has often stored the correct information. In the future, scientists could use such an approach to find and correct falsehoods inside the model, which could reduce a model’s tendency to sometimes give incorrect or nonsensical answers.

“Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them. This is one instance of that,” says Evan Hernandez, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper detailing these findings .

Hernandez wrote the paper with co-lead author Arnab Sharma, a computer science graduate student at Northeastern University; his advisor, Jacob Andreas, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author David Bau, an assistant professor of computer science at Northeastern; and others at MIT, Harvard University, and the Israeli Institute of Technology. The research will be presented at the International Conference on Learning Representations.

Finding facts

Most large language models, also called transformer models, are neural networks . Loosely based on the human brain, neural networks contain billions of interconnected nodes, or neurons, that are grouped into many layers, and which encode and process data.

Much of the knowledge stored in a transformer can be represented as relations that connect subjects and objects. For instance, “Miles Davis plays the trumpet” is a relation that connects the subject, Miles Davis, to the object, trumpet.

As a transformer gains more knowledge, it stores additional facts about a certain subject across multiple layers. If a user asks about that subject, the model must decode the most relevant fact to respond to the query.

If someone prompts a transformer by saying “Miles Davis plays the. . .” the model should respond with “trumpet” and not “Illinois” (the state where Miles Davis was born).

“Somewhere in the network’s computation, there has to be a mechanism that goes and looks for the fact that Miles Davis plays the trumpet, and then pulls that information out and helps generate the next word. We wanted to understand what that mechanism was,” Hernandez says.

The researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely complex, the models decode relational information using a simple linear function. Each function is specific to the type of fact being retrieved.

For example, the transformer would use one decoding function any time it wants to output the instrument a person plays and a different function each time it wants to output the state where a person was born.

The researchers developed a method to estimate these simple functions, and then computed functions for 47 different relations, such as “capital city of a country” and “lead singer of a band.”

While there could be an infinite number of possible relations, the researchers chose to study this specific subset because they are representative of the kinds of facts that can be written in this way.

They tested each function by changing the subject to see if it could recover the correct object information. For instance, the function for “capital city of a country” should retrieve Oslo if the subject is Norway and London if the subject is England.

Functions retrieved the correct information more than 60 percent of the time, showing that some information in a transformer is encoded and retrieved in this way.

“But not everything is linearly encoded. For some facts, even though the model knows them and will predict text that is consistent with these facts, we can’t find linear functions for them. This suggests that the model is doing something more intricate to store that information,” he says.

Visualizing a model’s knowledge

They also used the functions to determine what a model believes is true about different subjects.

In one experiment, they started with the prompt “Bill Bradley was a” and used the decoding functions for “plays sports” and “attended university” to see if the model knows that Sen. Bradley was a basketball player who attended Princeton.

“We can show that, even though the model may choose to focus on different information when it produces text, it does encode all that information,” Hernandez says.

They used this probing technique to produce what they call an “attribute lens,” a grid that visualizes where specific information about a particular relation is stored within the transformer’s many layers.

Attribute lenses can be generated automatically, providing a streamlined method to help researchers understand more about a model. This visualization tool could enable scientists and engineers to correct stored knowledge and help prevent an AI chatbot from giving false information.

In the future, Hernandez and his collaborators want to better understand what happens in cases where facts are not stored linearly. They would also like to run experiments with larger models, as well as study the precision of linear decoding functions.

“This is an exciting work that reveals a missing piece in our understanding of how large language models recall factual knowledge during inference. Previous work showed that LLMs build information-rich representations of given subjects, from which specific attributes are being extracted during inference. This work shows that the complex nonlinear computation of LLMs for attribute extraction can be well-approximated with a simple linear function,” says Mor Geva Pipek, an assistant professor in the School of Computer Science at Tel Aviv University, who was not involved with this work.

This research was supported, in part, by Open Philanthropy, the Israeli Science Foundation, and an Azrieli Foundation Early Career Faculty Fellowship.

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Researchers at MIT have found that large language models mimic intelligence using linear functions, reports Kyle Wiggers for  TechCrunch . “Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them,” writes Wiggers. 

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• Jacob Andreas
• Language and Intelligence Group
• Computer Science and Artificial Intelligence Laboratory
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• Computer Science and Artificial Intelligence Laboratory (CSAIL)
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SCI, Carnegie Library of Pittsburgh present ALSC 2024 Children’s Literature Lecture

April 4, 2024

"We are proud to host the Children’s Literature Lecture of the Association for Library Service to Children with the Carnegie Library of Pittsburgh,” said Mary Kay Biagini, chair of the Department of Information Culture and Data Stewardship. “It honors the roots of our Master of Library and information Science program that began in 1901 in the basement of the Carnegie Library as the first program in America to educate librarians to work with children and youth. For more than 120 years, we have collaborated with the Carnegie Library to continue this proud tradition of educating public and school librarians.”

Last held in Pittsburgh in 1989, the ALSC Children’s Literature Lecture is an annual event geared toward adults who work with children and/or who have an interest in children’s literature. This prestigious honor features an author, critic, librarian, historian or educator presenting a paper considered as a significant contribution to children’s literature. 

This event is free and open to the public. The 2024 lecture is hosted in cooperation with Allegheny County Library Association, August Wilson African American Cultural Center and United Black Book Clubs of Pittsburgh. 

Event Details: