research paper on marijuana use

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The Impact of Recreational Cannabis Legalization on Cannabis Use and Associated Outcomes: A Systematic Review

Kyra n farrelly.

1 Department of Psychology, York University, Toronto, ON, Canada

2 Peter Boris Centre for Addictions Research, St. Joseph’s Healthcare Hamilton, McMaster University, Hamilton, ON, Canada

Jeffrey D Wardell

3 Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada

4 Department of Psychiatry, University of Toronto, Toronto, ON, Canada

Emma Marsden

Molly l scarfe, peter najdzionek, jasmine turna.

5 Michael G. DeGroote Centre for Medicinal Cannabis Research, McMaster University & St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada

James MacKillop

6 Homewood Research Institute, Guelph, ON, Canada

Background:

Recreational cannabis legalization has become more prevalent over the past decade, increasing the need to understand its impact on downstream health-related outcomes. Although prior reviews have broadly summarized research on cannabis liberalization policies (including decriminalization and medical legalization), directed efforts are needed to synthesize the more recent research that focuses on recreational cannabis legalization specifically. Thus, the current review summarizes existing studies using longitudinal designs to evaluate impacts of recreational cannabis legalization on cannabis use and related outcomes.

A comprehensive bibliographic search strategy revealed 61 studies published from 2016 to 2022 that met criteria for inclusion. The studies were predominantly from the United States (66.2%) and primarily utilized self-report data (for cannabis use and attitudes) or administrative data (for health-related, driving, and crime outcomes).

Five main categories of outcomes were identified through the review: cannabis and other substance use, attitudes toward cannabis, health-care utilization, driving-related outcomes, and crime-related outcomes. The extant literature revealed mixed findings, including some evidence of negative consequences of legalization (such as increased young adult use, cannabis-related healthcare visits, and impaired driving) and some evidence for minimal impacts (such as little change in adolescent cannabis use rates, substance use rates, and mixed evidence for changes in cannabis-related attitudes).

Conclusions:

Overall, the existing literature reveals a number of negative consequences of legalization, although the findings are mixed and generally do not suggest large magnitude short-term impacts. The review highlights the need for more systematic investigation, particularly across a greater diversity of geographic regions.

Introduction

Cannabis is one of the most widely used substances globally, with nearly 2.5% of the world population reporting past year cannabis use. 1 Cannabis use rates are particularly high in North America. In the U.S., 45% of individuals reported ever using cannabis and 18% reported using at least once annually in 2019. 2 , 3 In Canada, approximately 21% of people reported cannabis use in the past year use in 2019. 4 In terms of cannabis use disorder (CUD), a psychiatric disorder defined by clinically significant impairment in daily life due to cannabis use, 5 ~5.1% of the U.S. population ages 12+ years met criteria in 2020, with ~13.5% of individuals ages 18 to 25 years meeting criteria. 6

Overall, rates of cannabis use have shown long-term increasing trends among several age groups in North America. 7 - 9 Moreover, research has revealed recent cannabis use increases in at risk populations, such as individuals with depression and pregnant women. 10 , 11 Parallel to increased cannabis use over time, rates of cannabis-related consequences have also increased across Canada and the U.S., including cannabis dependence and CUD, 8 , 12 crime rates (eg, increased possession charges), 8 and cannabis-impaired driving (and, lower perception of impairment and risk from cannabis use). 11 , 13 , 14 Further, cannabis use poses a risk for early-onset or use during adolescence as there is evidence that cannabis use in adolescence is linked with poorer cognitive performance, psychotic disorders, and increased risk of mood and addictive disorders. 15 With the rates of negative consequences from cannabis use increasing, particularly in North America where cannabis has become legal in many parts of the US and all of Canada, understanding the role of cannabis legalization in these changes is crucial to inform ongoing changes in cannabis policies worldwide.

The legal status of cannabis varies widely across countries and regions. Although cannabis is largely illegal at the global level, policies surrounding cannabis use are becoming steadily liberalized. Decriminalization (reduced penalties for self-use but not distribution) is more widespread worldwide, including in the Netherlands, Portugal, and parts of Australia. Medical legalization is also seen in Peru, Germany, New Zealand, the Netherlands and across many U.S. states. To date, Canada, Uruguay, and Malta are the only 3 countries to legalize recreational cannabis use at the national level. Further, individual U.S. states began legalizing recreational cannabis in 2012, with nearly half of U.S. states having legalized recreational cannabis by 2023. As national and subnational recreational legalization continues to gain support and take effect, understanding the consequences of such major regulatory changes is crucial to informing ongoing policy changes.

There are arguments both for and against recreational cannabis legalization (RCL). Common pro-legalization arguments involve increasing regulatory control over product distribution, weakening organized crime, reducing burden and inequality in the criminal justice system, and generating economic benefits such as tax revenues and commercial activity. 16 Furthermore, as cannabis obtained from illicit markets is of varying and unknown potency, 17 cannabis legalization may help better regulate the potency and quality of cannabis products. 18 On the other hand, there are anti-legalization arguments such as the possibility of legalization leading to increased use among youth and increased cannabis-impaired driving. 16 A nationally representative survey in the U.S. found that pro-legalization arguments were perceived to be more persuasive than public health anti-legalization arguments in a U.S. nationally representative survey, 19 suggesting policymaker concerns regarding RCL do not seem to hold as much weight in the general public. However, while research may be increasing surrounding the impacts of RCL, the general consensus of if RCL leads to more positive or negative consequences is unclear.

With RCL becoming more prevalent globally, the impacts it may have on a variety of health-related outcomes are of critical importance. Prevalence of cannabis use is of course a relevant issue, with many concerned that RCL will cause significant spikes in rates of cannabis use for a variety of groups, including youth. However, current studies have revealed mixed evidence in the U.S., 20 , 21 thus there is a need to synthesize the extant literature to better understand the balance of evidence and potential impacts of RCL across different samples and more diverse geographic areas. Another common question about RCL is whether it will result in changes in attitudes toward cannabis. These changes are of interest as they might forecast changes in consumption or adverse consequences. Similarly, there are concerns surrounding RCL and potential spill-over effects that may influence rates of alcohol and other substance use. 22 Thus, there remains a need to examine any changes in use of other substance use when studying effects of RCL.

Beyond changes in cannabis and other substance use and attitudes, health-related impacts of RCL are important to consider as there are links between cannabis use and adverse physical and mental health consequences (eg, respiratory and cardiovascular diseases, psychosis). 23 Additionally, emergency service utilization associated with cannabis consumption is a frequent concern associated with RCL, particularly due to the spikes in admissions following RCL in Colorado. 24 However, the rates of cannabis-related emergency service admissions more globally (eg, in legal countries like Canada and Uruguay) have not been fully integrated into summaries of the current literature. Finally, another health-related consequence of RCL is potential impacts on opioid use. While opioid-related outcomes can fall into substance use, they are considered health-related for this review as much of the discussion surrounding RCL and opioids involve cannabis substituting opioid use for medicinal reasons or using cannabis as an alternate to prescription opioids in the healthcare system. The current opioid crisis is a global public health problem with serious consequences. While there is evidence that medicinal cannabis may reduce prescription opioid use 25 and that cannabis may be a substitute for opioid use, 26 the role of recreational cannabis legalization should also be examined as the 2 forms of cannabis use are not interchangable 27 and have shown unique associations with prescription drug use. 28 Thus, there is a need to better understand how and if RCL has protective or negative consequences on opioid-related outcomes.

Due to the impairing effects of cannabis on driving abilities and the relationship with motor vehicle accidents, 29 another important question surrounding RCL is how these policy changes could result in adverse driving-related outcomes. An understanding of how RCL could influence impaired driving prevalence is needed to give insight into how much emphasis jurisdictions should put on impaired driving rates when considering RCL implementation. A final consequence of RCL that is often debated but requires a deeper understanding is how it impacts cannabis-related arrest rates. Cannabis-related arrests currently pose a significant burden on the U.S. and Canadian justice system. 30 , 31 Theoretically, RCL may ease the strain seen on the justice system and have positive trickle-down effects on criminal-related infrastructure. However, the overall implications of RCL on arrest rates is not well understood and requires a systematic evaluation. With the large number of RCL associated outcomes there remains a need to synthesize the current evidence surrounding how RCL can impact cannabis use and other relevant outcomes

Present review

Currently, no reviews have systematically evaluated how RCL is associated with cannabis-use changes across a variety of age groups as well as implications on other person- or health-related outcomes. The present review aims to fill an important gap in the literature by summarizing the burgeoning research examining a broad range of consequences of RCL across the various jurisdictions that have implemented RCL to date. Although previous reviews have considered the implications of RCL, 32 , 33 there has recently been a dramatic increase in studies in response to more recent changes in recreational cannabis use policies, requiring additional efforts to synthesize the latest research. Further, many reviews focus on specific outcomes (eg, parenting, 34 adolescent use 35 ). There remains a need to systematically summarize how RCL has impacted a variety of health-related outcomes to develop a more comprehensive understanding of the more negative and positive outcomes of RCL. While a few reviews have examined a broad range of outcomes such as cannabis use, related problems, and public health implications, 32 , 33 some reviews have been limited to studies from a single country or published in a narrow time window. 32 Thus, a broader review is necessary to examine multiple types of outcomes from studies in various geographic regions. Additionally, a substantial amount of the current literature examining the impact of RCL relies on cross-sectional designs (eg, comparing across jurisdictions with vs without recreational legalization) which severely limit any conclusions about causal associations. Thus, given its breadth, the current systematic review is more methodologically selective by including only studies with more rigorous designs (such as longitudinal cohort studies), which provide stronger evidence regarding the effects of RCL. In sum, the aim of the current review was to characterize the health-related impacts of RCL, including changes in these outcomes in either a positive or negative direction.

The review is compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 36 ). Full-text extraction was initiated immediately following article search, therefore the protocol was not registered with PROSPERO. Relevant articles on cannabis legalization were principally identified using the Boolean search terms (“cannabis” OR “marijuana” OR “THC” OR “marihuana”) AND “legalization” AND (“recreational” OR “non-medical” OR “nonmedical”) AND (“longitudinal” OR “pre-post” OR “prospective” OR “timeseries” OR “cohort”). The search was conducted using PubMed/MEDLINE, EMBASE, and PsycINFO through November 2022. Relevant studies identified through secondary means (eg, prior knowledge of a relevant publication, articles brought to the authors’ attention) were also included for screening. Titles and abstracts resulting from the initial search were screened in Covidence (Veritas Health Innovation Inc) by 2 reviewers for suitability for full-text review and final inclusion. Conflicts were discussed by both reviewers and a final decision was made by consensus. Following screening, reviewers read and extracted relevant data. To be included, an article was required to meet the following criteria: (i) an original empirical research article published in a peer-reviewed journal; (ii) written in (or available in) English; (iii) RCL serves as an independent variable; (iv) quantitative study design that clearly permitted the evaluation of the role of RCL with a more rigorous non-cross-sectional study design (eg, pre- vs post-legalization, longitudinal, cohort, interrupted time series, etc.); and (v) reports on health-related outcomes (ie, changes in consumption or attitudes, as opposed to changes in price or potency).

RCL related outcomes that were considered were those specifically involving the behavior, perceptions, and health of individuals. Population-level outcomes (eg, health-care utilization or impaired driving) were considered eligible for inclusion as they involve the impacts that legalization has on individual behavior. Thus, economic- or product-level outcomes that do not involve individual behavior (eg, cannabis prices over time, changes in cannabis strain potency) were considered out of scope. The outcome groups were not decided ahead of time and instead 5 main themes in outcomes emerged from our search and were organized into categories for ease of presentation due to the large number of studies included.

Studies that examined medicinal cannabis legalization or decriminalization without recreational legalization, and studies using exclusively a cross-sectional design were excluded as they were outside the scope of the current review. The study also excluded articles that classified RCL as the passing of legal sales rather than implementation of RCL itself as RCL is often distinct from introduction of legal sales, or commercialization. Thus, we excluded studies examining commercialization as they were outside the scope of the current review.

Characteristics of the literature

The search revealed 65 relevant articles examining RCL and related outcomes (see Figure 1 ). There were 5 main themes established: cannabis use and other substance use behaviors ( k  = 28), attitudes toward cannabis ( k  = 9), health-related outcomes ( k  = 33), driving related impacts ( k  = 6), and crime-related outcomes ( k  = 3). Studies with overlapping themes were included in all appropriate sections. Most studies (66.2%) involved a U.S. sample, 32.3% examined outcomes in Canada, and 1.5% came from Uruguay. Regarding study design, the majority (46.2%) utilized archival administrative data (ie, hospital/health information across multiple time points in one jurisdiction) followed by cohort studies (18.5%). The use of administrative data was primarily used in studies examining health-related outcomes, such as emergency department utilization. Studies examining cannabis use or attitudes over time predominantly used survey data. Finally, both driving and crime related outcome studies primarily reported findings with administrative data.

Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) study flow diagram.

Changes in cannabis and other substance use

Cannabis and other substance use changes represented the second largest number of studies, with 28 articles identified. Studies examining changes in cannabis use behaviors were divided by subpopulation (ie, adolescents, young adults, general population adults, clinical populations, and maternal use; see Table 1 ). Finally, we separately summarized studies reporting changes in concurrent use of other substances, and routes of cannabis administration.

Studies investigating the role of recreational cannabis legalization on cannabis and other substance consumption.

Author, author of article; Year, publication year of article; Location, jurisdiction article data was collected in; Date of Legalization, year legalization was enacted in jurisdiction; Sample, total N of article sample; RCL, Recreational Cannabis Legalization.

Cannabis use changes in adolescents (~12-17)

Ten studies examined changes in cannabis use among adolescents and found that changes in the rates of use were inconsistent following RCL. Gunadi et al 37 found an association between RCL and more pronounced transition from non-use to cannabis use when compared to states with no legalization and those with medical cannabis legalization ( P  ⩽ .001) combined, but not when compared to states with medical cannabis legalization only. Another study found that in states with RCL adolescents who never used cannabis but used e-cigarettes were more likely to use cannabis at follow-up than those living in states without RCL (aOR = 18.39, 95% CI: 4.25-79.68vs aOR = 5.09, 95% CI: 2.86-9.07, respectively) suggesting a risk of cannabis initiation among legal states. 38 Among adolescents reporting recent alcohol and cannabis co-use, one study found a significant increase in the frequency of past 30-day cannabis use following RCL ( b  = 0.36, SE = 0.07, P  ⩽ .001). 39 In a Canadian study using a repeated cross-sectional design as well as a longitudinal design to examine changes in cannabis use, results revealed that adolescents had increased odds of ever using cannabis in the year following RCL in the cross-sectional data ( P  = .009). 40 However, the longitudinal sample revealed no significant differences in the odds of ever use, current use, and regular use of cannabis post-legalization. There is also evidence of RCL impacts on adolescent cannabis use consequences, as a Washington study found a significant indirect effect of RCL on cannabis consequences through perceived risk as a mediator ( B  = 0.37, P  ⩽ .001). 41

On top of the above evidence, there were multiple studies examining cannabis use changes over time among adolescents in Washington and Oregon that found higher rates of cannabis use associated with cohorts examined during RCL compared to non-legal cohorts, 42 - 44 although the differences across legal cohorts were not significant in all cases. 42 Furthermore, in another study, RCL did not impact initiation of use, but for current users the RCL group had significantly greater increased rates of cannabis use compared to the pre-RCL group (RR = 1.26, 95% CI = 1.10, 1.45). 43 For the final study, cannabis use increased in the post-RCL group but patterns of use (frequency; daily vs weekly use) were similar across groups. 44 Overall, the preceding 8 studies reveal some evidence that RCL was associated with increasing rates of cannabis use in adolescent. However, 5 studies point to some inconsistent associations of RCL and cannabis use and suggest that overall relationship of RCL and adolescent cannabis as mixed.

Three studies add to these inconsistent findings and point to lack of an association between RCL and changes in cannabis use among adolescents. Two studies found no significant increase in the frequency of or prevalence of cannabis use following RCL. 41 , 45 Finally, a study examining trends of adolescent cannabis use and associations with period effects (ie, external world events that could influence use) suggests laws and regulations associated with RCL were not associated with cannabis use changes. 46 The current research reveals conflicting evidence about the role of RCL on adolescent cannabis use.

Cannabis use changes in young adults (~18-25)

Young adulthood, typically defined as ages 18 to 25 and also known as emerging adulthood, is commonly associated with decreased parental supervision, increased availability of substances, and greater substance experimentation making it a key developmental period for the onset of cannabis use. 47 Four studies examined the impact of RCL on cannabis use among young adults, 2 of which found significant associations between RCL and increased cannabis use in college students. 47 , 48 Barker and Moreno 48 found the rate of students ever using cannabis did not change. However, in those who had used cannabis prior to RCL, the proportion of students using in the past 28-days increased faster following RCL in Washington (legal-state) when compared with the rate of increase in Wisconsin (non-legal state; P  ⩽ .001). 48 Further, in college students from Oregon, rates of cannabis use increased significantly from before to after RCL ( P  = .0002). 47 Another study looked at changes in cannabis use in a sample of young adults from the U.S. who had never vaped cannabis at the time of recruitment. 49 Results revealed that cannabis use in the past year did not differ in states with or without RCL, although, those living in states with RCL did show a larger increase in rates of cannabis vaping across time, compared to those in non-RCL states. Finally, in a sample of youth from Oregon and Washington, RCL predicted a higher likelihood of past-year cannabis use ( P  = .001). 50 In contrast to the adolescent literature, studies examining cannabis use in young adult samples fairly consistently point to an association between RCL and increasing rates of cannabis use.

Cannabis use changes in general population adults

Five studies examined changes in cannabis use in adults (without further age subclassification) associated with RCL. Four of these studies suggested higher rates of cannabis use in adults for RCL jurisdictions compared to non-legal states post-RCL, or increased use following RCL. 37 , 45 , 51 , 52 Past 30-day cannabis use increased significantly 1-month post-RCL and remained elevated 6-months post-RCL (ps = 0.01) in a sample of adults from California. 51 Another study found an association between RCL and transition from non-users to cannabis users and non-users to weekly users when compared to states with no medical legalization or RCL ( P  ⩽ .001) and states with no legalization combined with those with medical cannabis legalization ( P  ⩽ .001). 37 Meanwhile, in Canada, a significant increase in prevalence of cannabis use was observed following RCL. 45 Additionally, in those reporting no cannabis use prior to RCL in Canada, there were significant increases in cannabis use frequency, quantity of cannabis used, and severity of cannabis misuse following RCL. 52 The opposite pattern was seen for those reporting cannabis use prior to RCL, with significant decreases in frequency of use, quantity, and misuse. 52 However, not all studies found RCL was associated with increased cannabis use. For instance, a repeated cross-sectional study of adult in the U.S. found no association between RCL and frequency of cannabis use. 53

A benefit of the extant literature examining general population cannabis use is that it covers a variety of jurisdictions and study designs, albeit with some heterogeneity and mixed findings. On balance, the evidence within the current literature, generally suggests an increase in cannabis use for adults in the general population following RCL with 80% of the reviewed studies supporting this conclusion.

Maternal use

Three studies examined whether rates of cannabis use during pregnancy have increased following RCL. Two studies suggested increased cannabis use during pregnancy associated with RCL. In one study urine screen-detected cannabis use during pregnancy increased from 6% to 11% following RCL in California ( P  = .05). 54 Another study in a sample of women participating in an intensive case management program for heavy alcohol and/or drug use during pregnancy, examined cannabis use among those exiting from the program before versus after RCL. Findings revealed women exiting after RCL were more likely to report using cannabis in the 30 days prior to exit compared to those pre-RCL (OR = 2.1, P  ⩽ .0001). 55 One study revealed no significant difference in cannabis or alcohol use associated with RCL in women living with HIV during pregnancy or the postpartum period. 56 Overall, the evidence from these three studies suggests there may be increases in perinatal cannabis use following RCL, but the small number of studies and unique features of the samples suggests a need for more research.

Clinical populations use

Six studies examined cannabis use in clinical populations. One study investigated use and trauma admissions for adults and pediatric patients in California. 57 Results showed an increase in adult trauma patients with THC+ urine tests from pre- to post-RCL (9.4% to 11.0%; P  = .001), but no difference for pediatric trauma patients. A study based in Colorado and Washington, found that cannabis use rates in inflammatory bowel disease patients significantly increased from 107 users to 413 ( P  ⩽ .001) pre to post-RCL. 58 A Canada-based study of women with moderate-to-severe pelvic pain found an increase in the prevalence of current cannabis use following RCL (13.3% to 21.5%; P  ⩽ .001). 59 Another Canadian study showed an increase in the prevalence of current cannabis use after RCL among cancer patients (23.1% to 29.1%; P  ⩽ .01). 60 Finally, two studies examined changes in cannabis use among individuals receiving treatment for a substance use disorder. In a sample of Canadian youth in an outpatient addictions treatment program, there was no change in the rate of cannabis use following RCL. 61 Further, in a sample of individuals receiving treatment for opioid use disorder, cannabis use was compared for those recruited 6 months before or after RCL with no significant changes in the prevalence or frequency of self-reported ( P  = .348 and P  = .896, respectively) or urine screen-detected ( P  = .087 and P  = .638, respectively) cannabis use following RCL. 62 Although these studies only represent a small number of observations, their findings do reveal associations between RCL and increasing cannabis use within some clinical samples.

Changes in polysubstance and other substance use

One study examined simultaneous cannabis and alcohol use among 7th, 9th, and 11th grade students in the U.S. 39 This study found that RCL was associated with a 6% increase in the odds of past 30-day alcohol and cannabis co-use. The association was even stronger in students with past 30-day alcohol use and heavy drinking. However, among past 30-day cannabis users, RCL was associated with a 24% reduction in co-use. This study suggests at least a modest association between RCL and concurrent cannabis and alcohol use among adolescents.

Numerous studies examined changes of alcohol and other substance use pre to post RCL. With regard to alcohol, one study from Colorado and Washington found a decrease in alcohol consumption among adolescents following RCL, 42 whereas another Washington study found RCL predicted a higher likelihood of alcohol use among youth. 50 A Canadian study also found no significant effect of RCL on rates of alcohol or illicit drug use among youth. 61 Finally, in a sample of trauma patients in California the findings around changes in substance use were mixed. 57 In adult patients, the rates of positive screens for alcohol, opiates, methamphetamine, benzodiazepine/barbiturate, and MDMA did not change following RCL, but there was an increase in positive screens for cocaine. In pediatric patients, increases were seen in positive screens for benzodiazepine/barbiturate, but positive screens for alcohol, opiates, methamphetamine, and cocaine did not change. 57 The current evidence is divided on whether RCL is associated with increased alcohol and other substance use, with 40% of studies finding an association and 60% not observing one or finding mixed results.

In the case of cigarettes, Mason et al 42 did find significant cohort effects, where the post-RCL cohort was less likely to consume cigarettes compared to the pre-RCL one (Coefficient: − 2.16, P  ⩽ .01). However, these findings were not echoed in more recent studies. Lack of an effect for cigarette use is supported by an Oregon study that found RCL was not associated with college student’s cigarette use. 47 Similarly, RCL was not significantly associated with past-year cigarette use in a sample of young adults from Oregon and Washington. 50 On balance, there is little evidence that RCL is linked with changes in cigarette smoking.

Route of administration

The increase in smoke-free alternative routes of cannabis administration (eg, vaping and oral ingestion of edibles) 63 , 64 make method of cannabis consumption an important topic to understand in the context of RCL. Two studies examined differences in route of cannabis consumption as a function of cannabis policy. One study examined changes in the number of different modes of cannabis use reported by high school students in Canada. 65 Results showed that from pre-to-post RCL 31.3% of students maintained a single mode of use, 14.3% continued to use cannabis in multiple forms, while 42.3% expanded from a single mode to multiple modes of administration and 12.1% reduced the number of modes they used. Another study found that smoking, vaping, and edibles (in that order) were the most frequent modes of cannabis use pre- and post-RCL in California, suggesting minimal impact of RCL on mode of cannabis use. 51 However, the least common mode of cannabis use was blunts, which did decline following RCL (13.5%-4.3%). 51 Overall, the evidence suggests RCL may be associated with changes in modes of cannabis consumption, but as the evidence is only from two studies there still remains a need for more studies examining RCL and cannabis route of administration.

Nine studies examined RCL and cannabis attitudes (see Table 2 ). Regarding cannabis use intentions, one U.S. study found that for both a non-RCL state and a state that underwent RCL, intention to use in young adults significantly increased post-RCL, suggesting a lack of RCL specific effect, 48 and that aside from the very first time point, there were no significant differences between the states in intention to use. Further, attitudes and willingness to use cannabis, between the RCL and non-RCL state remained similar overtime ( P s ⩾ .05), although both states reported significantly more positive attitudes toward cannabis following RCL ( P  ⩽ .001). 48 However, another study U.S. from found differences in adolescent use intentions across RCL, whereby those in the RCL cohort in jurisdictions that allowed sales were less likely to increase intent to use cannabis ( P  = .04), but the RCL cohort without sales were more likely to increase intent to use ( P  = .02). 43 The pre-RCL cohort in communities that opted out of sales were also less likely to increase willingness to use compared to the cohort with legal sales ( P  = .02). 43 Both studies reveal contrasting findings surrounding RCL’s relationship with cannabis use intentions and willingness to use.

Studies examining recreational cannabis legalization and attitudes surrounding cannabis.

Looking at cannabis use motives, one study found a non-significant increase in recreational motives for cannabis use post-RCL. 60 Similarly following RCL in Canada, 24% of individuals previously reporting cannabis use exclusively for medical purposes declared using for both medical and non-medical purposes following RCL, and 24% declared use for non-medical purposes only, 66 suggesting RCL can influence recreational/nonmedicinal motivations for cannabis use among those who previously only used for medical reasons.

In studies examining perceived risk and perceptions of cannabis use, one U.S. study found an indirect effect between RCL and increased consequences of use in adolescents through higher perceived risk ( P  ⩽ .001), but no association with frequency of use. 41 Another U.S. study revealed mixed results and found that RCL was not associated with perceived harm of use in youth. 50 Further, youth in one study did not report differences in perceptions of safety of cannabis, ease of accessing cannabis use or on concealing their use from authority, 61 which contrasts with another study finding increased reports of problems accessing cannabis post-RCL ( P  ⩽ .01). 60 Regarding health perceptions, a California study found that cannabis use was perceived as more beneficial for mental health, physical health, and wellbeing in adults at 6 months post-RCL compared to pre-RCL and 1-month post-RCL ( P  = .02). 51 Mental health perceptions of cannabis use increased from being perceived as “slightly harmful” pre-RCL to perceived as “slightly beneficial” at 6 months post-RCL. 51 However, in a sample of treatment seeking individuals with an opioid use disorder, the vast majority of participants reported beliefs that RCL would not impact their cannabis use, with no difference in beliefs pre- to post-RCL (85.9% reported belief it would have no impact pre-RCL and 85.7%, post-RCL). 62 The combined results of the studies suggest potential associations of RCL with risk and benefit perceptions of cannabis use, however as 55% of studies suggest a lack of or inconsistent association with RCL, on balance the literature on RCL’s impact on cannabis attitudes is mixed.

Health-related outcomes

We identified 33 articles that examined various health-related outcomes associated with RCL (see Table 3 ). The largest number involved hospital utilization (ie, seeking emergency services for cannabis-related problems such as unintentional exposure, CUD, and other harms). Other health-care outcomes included opioid-related harms, mental health variables, and adverse birth outcomes.

Studies investigating the relationship of recreational cannabis legalization and health-related outcomes.

Author, Author of article; Year, Publication year of article; Location, Jurisdiction article data was collected in; Date of Legalization, Year legalization was enacted in jurisdiction; Sample, Total N of article sample; CDC, Center for Disease Prevention; WONDER, Wide-Ranging Online Data for Epidemiologic Research; RCL, Recreational Cannabis Legalization.

Emergency service utilization

Seventeen studies examined the association between RCL and use of emergency services related to cannabis (eg, hospital visits, calls to regional poison centers). Regarding emergency service rates in youth, a Colorado study found the rate of pediatric cannabis-related emergency visits increased pre- to post-RCL ( P  ⩽ .0001). 67 Similarly, cannabis-related visits requiring further evaluation in youth also increased. 67 This increasing need for emergency service related to cannabis exposure in youth following RCL was supported in 4 other U.S. studies. 68 - 71 A Canadian study supported the U.S. studies, finding a 2.6 increase in children admissions for cannabis poisonings post-RCL. 72 In contrast, overall pediatric emergency department visits did not change from pre- to post-RCL in Alberta, Canada, 73 but there was a non-significant increase of the rate and proportion of children under 12 presenting to the emergency department. However, unintentional cannabis ingestion did increase post-RCL for children under 12 (95% CI: 1.05-1.47) and older adolescents (1.48, 95% CI: 1.21-1.81). 74 Taken together, these studies do suggest a risk for increasing cannabis-related emergency visits in youth following RCL, with 75% of studies finding an association between RCL and increasing emergency service rates in youth.

There is also evidence of increased hospital utilization in adults following RCL. Five studies found evidence of increased emergency service utilization or poison control calls from cannabis exposure associated with RCL in the U.S. and Canada. 24 , 69 , 74 - 76 Finally, a Colorado study saw an increase in cannabis involved pregnancy-related hospital admissions from 2011 to 2018, with notable spikes after 2012 and 2014, timeframes associated with state RCL. 77

However, some evidence points to a lack of association between RCL and emergency service utilization. A chart review in Ontario, Canada found no difference in number of overall cannabis emergency room visits pre- versus post-RCL ( P  = .27). 78 When broken down by age group, visits only increased for those 18 to 29 ( P  = .03). This study also found increases in patients only needing observation ( P  = .002) and fewer needing bloodwork or imaging services (both P s ⩽.05). 78 Further in a California study that found overall cannabis exposure rates were increasing, when breaking these rates down by age there was no significant change in calls for those aged 13 and up, only for those 12 and under. 69 An additional Canadian study found that rates of cannabis related visits were already increasing pre-RCL. 79 Following RCL, although there was a non-significant immediate increase in in cannabis-related emergency visits post-RCL this was followed a significant drop off in the increasing monthly rates seen prior to RCL. 79 Another Canadian study that examined cannabis hyperemesis syndrome emergency visits found that rates of admissions were increasing prior to RCL and the enactment of RCL was not associated with any changes in rates of emergency admissions. 80 As this attenuation occurred in Canada prior to commercialization where strict purchasing policy was in place, it may suggest that having proper regulations in place can prevent the uptick in cannabis-related emergency visits seen in U.S. studies.

Other hospital-related outcomes examined included admissions for cannabis misuse and other substance use exposure. One study found decreasing CUD admission rates over time (95% CI: −4.84, −1.91), with an accelerated, but not significant, decrease in Washington and Colorado (following RCL) compared to the rest of the U.S. 81 In contrast, another study found increased rates of healthcare utilization related to cannabis misuse in Colorado compared to New York and Oklahoma ( P s ⩽.0005). 82 With respect to other substance use, findings revealed post-RCL increases in healthcare utilization in Colorado for alcohol use disorder and overdose injuries but a decrease in chronic pain admissions compared to both controls ( P  ⩽ .05). 82 However, two Canadian studies found the rate of emergency department visits with co-ingestant exposure of alcohol, opioid, cocaine, and unclassified substances in older adolescents and adults decreased post-RCL. 73 , 77 Another Canadian study found no change in cannabis-induced psychosis admissions nor in alcohol- or amphetamine-induced admissions. 83

Finally, three studies examined miscellaneous hospital-related outcomes. A study examining hospital records in Colorado to investigate facial fractures (of significance as substance impairment can increase the risk of accidents) showed a modest but not significant influence of RCL. 84 The only significant increases of facial trauma cases were maxillary and skull base fracture cases ( P s ⩽ .001) suggesting a partial influence of RCL on select trauma fractures. The second study found increased trauma activation (need for additional clinical care in hospital) post-RCL in California ( P  = .01). 57 Moreover, both adult and pediatric trauma patients had increased mortality after RCL ( P  = .03; P  = .02, respectively). 57 The final study examining inflammatory bowel disease (IBD) outcomes in the U.S. found more cannabis users on total parenteral nutrition post-RCL (95% CI: 0.02, 0.89) and lower total hospital costs in users post-RCL (95% CI: −15 717, −1119). 58 No other IBD outcomes differed pre- to post-RCL (eg, mortality, length of stay, need for surgery, abscess incision and drainage).

Overall, these studies point to increased cannabis-related health-care utilization following RCL for youth and pediatrics (75% finding an increase). However, the impact of legalization on adult rates of cannabis-related emergency visits is mixed (44% finding lack of an association with RCL). As findings also varied across different countries (ie, Canada vs the U.S.), it suggests the importance of continually monitoring the role of RCL across different jurisdictions which may have different cannabis regulations in place. These studies also suggest there may be other health consequences associated with RCL. Further research should be done to examine trends of other emergency service use that could be influenced by RCL.

Two studies reported a weak or non-existent effect of RCL on opioid related outcomes. 85 , 86 First, a U.S. administrative study found no association of RCL and opioid prescriptions from orthopedic surgeons. 85 The second study found that, of U.S. states that passed RCL, those that passed policies before 2015 had fewer Schedule III opioid prescriptions ( P  = .003) and fewer total doses prescribed ( P  = .027), 86 but when compared to states with medicinal cannabis legislation, there were no significant differences. However, 3 studies suggested a potential protective effect of RCL, with one study finding a significant decrease for monthly opioid-related deaths following RCL (95% CI: –1.34, –0.03), compared to medical cannabis legalization and prohibition. 87 A Canadian study examining opioid prescription claims also found an accelerated decline in claims for public payers post-RCL compared to declines seen pre-RCL ( P  ⩽ .05). 88 Next a study examining women with pelvic pain found that post-RCL patients were less likely to report daily opioid use, including use for pain ( P  = .026). 59 These studies indicate some inconsistencies in relationships between RCL, opioid prescriptions and use indicators in the current literature, while the literature on balance points to a potential relationship with RCL (60%), the overall evidence is still mixed as 40% of studies support a weak association with RCL.

Adverse birth outcomes

Changes in adverse birth outcomes including small for gestational age (SGA) births, low birth weight, and congenital anomalies were examined in two studies. The first study, which examined birth outcomes in both Colorado and Washington, found that RCL was associated with an increase in congenital anomaly births for both states ( P  ⩽ .001, P  = .01 respectively). 89 Preterm births also significantly increased post-RCL, but only in Colorado ( P  ⩽ .001). Regarding SGA outcomes, there was no association with RCL for either state. 89 Similarly, the second study did find an increase in the prevalence of low birth weight and SGA over time, but RCL was not directly associated with these changes. 90 Although the current literature is small and limited to studies in Washington and Colorado, the evidence suggests minimal changes in adverse birth outcomes following RCL.

Mental health outcomes

Six studies examined mental health related outcomes. A Canadian study examining psychiatric patients did not see a difference in rates of psychotic disorders pre- to post-RCL. 45 Similarly, another Canadian study did not see a difference in hospital admissions with schizophrenia or related codes post-RCL. 83 However, the prevalence of personality disorders and “other” diagnoses was higher post-RCL ( P  = .038). 45 In contrast, another Canadian study found that rates of pediatric cannabis-related emergency visits with co-occurring personality and mood-related co-diagnoses decreased post-RCL among older adolescents. 73 A U.S. study examining the relationship between cannabis use and anxious mood fluctuations in adolescents found RCL had no impact on the association. 91 Similarly, another Canadian study found no difference in mental health symptomology pre- to post-RCL. 61 In contrast, anxiety scores in women with pelvic pain were higher post-RCL compared to pre-RCL ( P  = .036). 59 The small number and mixed findings of these studies, 66.7% finding no association or mixed findings and 33.3% finding an association but in opposite directions, identify a need for further examination of mental health outcomes post-RCL.

Miscellaneous health outcomes

Three studies examined additional health-related outcomes. First, a California study examined changes in medical cannabis status across RCL. Post-RCL, 47.5% of medical cannabis patients remained medical cannabis patients, while 73.8% of non-patients remained so. 92 The transition into medical cannabis patient status post-RCL represented the smallest group (10%). Cannabis legalization was the most reported reason for transition out of medical cannabis patient status (36.2%). 92 Next, a study examining pelvic pain in women found that post-RCL patients reported greater pain catastrophizing ( P  ⩽ .001), less anti-inflammatory ( P  ⩽ .001) and nerve medication use ( P  = .027), but more herbal pain medication use ( P  = .010). 59 Finally, a Canadian study that examined cannabinoids in post-mortem blood samples reported that post-RCL deaths had higher odds of positive cannabis post-mortem screens compared to pre-RCL (95% CI: 1.09-1.73). 93 However, the majority of growth for positive cannabinoid screens took place in the two years prior to RCL implementation. In sub-group analyses, only 25- to 44-year-olds had a significant increase in positive cannabinoid screens (95% CI: 0.05-0.19). Additional post-mortem drug screens found an increase in positive screens for amphetamines ( P  ⩽ .001) and cocaine ( P  = .042) post-RCL. These additional health outcomes demonstrate the wide-ranging health impacts that may be associated with RCL and indicate a continued need to examine the role of RCL on a variety of outcomes.

Driving-related outcomes

Six studies examined rates of motor vehicle accidents and fatalities (see Table 4 ). Two U.S. studies found no statistical difference in fatal motor vehicle collisions associated with RCL. 94 , 95 Further, a California-based study examining THC toxicology screens in motor vehicle accident patients, did find a significant increase in positive screens, but this increase was not associated with implementation of RCL. 96 However, three studies suggest a negative impact of RCL, as one U.S. study found both RCL states and their neighboring states had an increase in motor vehicle fatalities immediately following RCL. 97 Additionally, a Canadian study did find a significant increase in moderately injured drivers with cannabis positive blood screens post-RCL. 98 Finally, a study in Uruguay found RCL was associated with increased immediate fatal crashes for cars, but not motorcycles; further investigation suggested this effect was noticeable in urban areas, but not rural areas. 99 While the overall evidence was inconsistent, current evidence does suggest a modest increase, seen in two studies, in motor vehicle accidents associated with RCL. Further longitudinal research in more jurisdictions is needed to understand the long-term consequences of RCL on motor vehicle accidents.

Studies looking at recreational cannabis legalization and driving related outcomes.

Crime-related outcomes

Three studies explored crime-related outcomes associated with RCL (see Table 5 ). A Washington study examining cannabis-related arrest rates in adults did find significant drops in cannabis-related arrests post-RCL for both 21+ year olds (87% drop; P  ⩽ .001) and 18 to 20-year-olds (46% drop; P  ⩽ .001). 100 However, in another study examining Oregon youth this post-RCL decline for arrests was not seen; cannabis-related allegations in youth actually increased following RCL (28%; 95% CI = 1.14, 1.44). 101 Further, declines in youth allegations prior to RCL ceased after RCL was implemented. In contrast, a Canadian study did find significant decreases in cannabis-related offenses in youth post RCL ( P  ⩽ .001), but rates of property and violent crime did not change across RCL. 102 These studies highlight the diverse effects of RCL across different age groups. However, there remains a need for a more comprehensive evaluation on the role of RCL on cannabis-related arrests.

Studies investigating recreational cannabis legalization and crime related outcomes.

Author, Author of article; Year, Publication year of article; Location, Jurisdiction article data was collected in; Date of Legalization, Year legalization was enacted in jurisdiction; Sample, Total N of article sample; RCL, Recreational Cannabis Legalization.

Notably, two studies also examined race disparities in cannabis-related arrests. For individuals 21+ relative arrest disparities between Black and White individuals grew post-RCL. 100 When looking at 18 to 20-year-olds, cannabis-related arrest rates for Black individuals did slightly decrease, albeit non-significantly, but there was no change in racial disparities. 100 In youth ages 10 to 17, Indigenous and Alaska Native youth were more likely than White youth to receive a cannabis allegation before RCL (95% CI: 2.31, 3.01), with no change in disparity following RCL (95% CI: 2.10, 2.81). 101 On the other hand, Black youth were more likely to receive a cannabis allegation than White youth prior to RCL (95% CI: 1.66, 2.13), but the disparity decreased following RCL (95% CI: 1.06, 1.43). 101 These studies suggest improvements in racial disparities for cannabis-related arrests following RCL, although there ware only two studies and they are limited to the U.S.

The aim of this systematic review was to examine the existing literature on the impacts of RCL on a broad range of behavioral and health-related outcomes. The focus on more rigorous study designs permits greater confidence in the conclusions that can be drawn. The literature revealed five main outcomes that have been examined: cannabis use behaviors, cannabis attitudes, health-related outcomes, driving-related outcomes, and crime-related outcomes. The overall synthesizing of the literature revealed heterogenous and complex effects associated with RCL implementation. The varied findings across behavioral and health related outcomes does not give a clear or categorical answer as to whether RCL is a negative or positive policy change overall. Rather, the review reveals that while a great deal of research is accumulating, there remains a need for more definitive findings on the causal role of RCL on a large variety of substance use, health, attitude-related, driving, and crime-related outcomes.

Overall, studies examining cannabis use behavior revealed evidence for cannabis use increases following RCL, particularly for young adults (100%), peri-natal users (66%), and certain clinical populations (66%). 47 , 54 , 59 While general adult samples had some mixed findings, the majority of studies (80%) suggested increasing rates of use associated with RCL. 51 Of note, the increasing cannabis use rates found in peri-natal and clinical populations are particularly concerning as they do suggest increasing rates in more vulnerable samples where potential adverse consequences of cannabis use are more pressing. 103 However, for both groups the overall literature revealed only a few studies and thus requires further examination. Further, a reason to caution current conclusions surround RCL impacts on substance use, is that there is research suggesting cannabis use rates were increasing prior to RCL in Canada. 104 Thus, there still remains a need to better disentangle causal consequences of RCL on cannabis use rates.

In contrast to studies of adults, studies of adolescents pointed to inconsistent evidence of RCL’s influence on cannabis use rates, 38 , 45 with 60% of studies finding no change or inconsistent evidence surrounding adolescent use following RCL. Thus, a key conclusion of the cannabis use literature is that there is not overwhelming evidence that RCL is associated with increasing rates of cannabis among adolescents, which is notable as potential increases in adolescent use is a concern often voiced by critics of RCL. 16 This might suggest that current RCL policies that limit access to minors may be effective. However, a methodological explanation for the discrepancy between findings for adolescents and adults is that adults may be more willing to report their use of cannabis following RCL as it is now legal for them to use. However, for adolescents’ cannabis use remained illicit, which may lead to biased reporting from adolescents. Thus, additional research using methods to overcome limitations of self-reports may be required.

With regard to other substance use, primarily alcohol and cigarettes, there is little evidence that RCL is associated with increased use rates and may even be associated with decreased rates of cigarette use. 42 , 61 The lack of a relationship with RCL and increasing alcohol and other substance use, seen in 60% of studies, is relevant due to concerns of RCL causing “spill-over” effects to substances other than cannabis. However, the decreasing rates on cigarette use associated with RCL seen in 33% of studies may also suggest a substitution effect of cannabis. 105 It is possible that RCL encourages a substitution effect where cannabis is used to replace use other substances such as cigarettes, but 66% of studies found no association of RCL and cigarette use so further research examining a potential substitution effect is needed. In sum, the literature points to a heterogenous impact of RCL on cannabis and other substance use rates, suggesting complex effects of RCL on use rates that may vary across age and population. However, the review also highlights that there are still limited studies examining RCL and other substance use, particularly a lack of multiple studies examining the same age group.

The current evidence for the impact of RCL on attitudes surrounding cannabis revealed mixed or limited results, with 44% studies finding some sort of relationship with attitudes and RCL and 55% studies suggest a lack of or inconsistent relationship. Studies examining cannabis use attitudes or willingness to use revealed conflicting evidence whereas some studies pointed to increased willingness to use associated with RCL, 43 and others found no change or that changes were not specific to regions that implemented RCL. 48 For attitude-related studies that did reveal consistent findings (eg, use motivation changes, perceptions of lower risk and greater benefits of use), the literature was limited in the number of studies or involved heterogenous samples, making it difficult to make conclusive statements surrounding the effect of RCL. As cannabis-related attitudes (eg, perceived risk, intentions to use) can have implications for cannabis use and consequences 106 , 107 it is interesting that current literature does not reveal clear associations of cannabis-related attitudes and RCL. Rather, this review reveals a need for more research examining changes in cannabis-attitudes over time and potential impacts of RCL.

In terms of health outcomes, the empirical literature suggests RCL is associated with increased cannabis-related emergency visits 24 , 67 , 70 , 76 and other health consequences (eg, trauma-related cases 57 ). The literature also suggests there may be other potential negative health consequences associated with RCL, such as increasing adverse birth outcomes and post-mortem cannabis screens. 45 , 89 Synthesizing of the literature points to a well-established relationship of RCL and increasing cannabis-related emergency visits. While some extant literature was mixed, on balance most studies included in the review (70.6%) found consistent evidence of increased emergency service use (eg, emergency department admissions and poison control calls) for both adolescents and adults with only 31% of studies finding mixed or no association with RCL. This points to a need for stricter RCL policies to prevent unintentional consumption or hyperemesis such as promoting safe or lower risk use of cannabis (eg, using lower THC products, avoiding deep inhales while smoking), clearer packaging for cannabis products, and safe storage procedures.

However, the literature on health outcomes outside of emergency service utilization is limited and requires more in-depth evaluations to be fully understood. Additionally, not all health-outcomes indicated negative consequences associated with RCL. There is emerging evidence of the potential of RCL to help decrease CUD and multiple substance hospital admissions 74 , 82 Furthermore, while some findings were mixed and the number of studies limited, 60% of studies found potential for RCL to have protective effects for opioid-related negative consequences. 87 , 88 However, opioid-related findings should be considered in the context of population-level changes in opioid prescriptions and shifting opioid policy influence. 108 Thus, findings may be a result of changes driven by the response to the opioid epidemic rather than RCL, and there remains a need to better disentangle RCL impacts on opioid-related consequences. It is also worth noting that some opioid and cannabis studies are underwritten by the cannabis industry, so the findings should be interpreted with caution due to potential for conflicts of interest. 88 In sum, the overall literature suggests that RCL is associated with both negative and positive health-related consequences and reveals a need to examine the role of RCL across a wide range of health outcomes.

The findings from the driving-related literature do suggest RCL is associated with increased motor vehicle accidents (50% of studies) although the literature was quite evenly split as higher accident rates were not seen across all studies (50% studies). These results point to potential negative consequence associated with RCL and may indicate a need for better measures to prevent driving while under the influence of cannabis in legalized jurisdictions. However, as the evidence was split and predominately in the U.S. additional studies spanning diverse geographical jurisdictions are still needed.

On the other hand, the findings from crime-related outcomes showed some inconsistencies. While one study did suggest minimal decreases for substance-use related arrests in adults, the findings were not consistent across the two studies examining arrest-rates in youth. 100 - 102 These potential decreases in arrest rates for adults can have important implications as cannabis-related crime rates make up a large amount of overall crime statistics and drug-specific arrests. 30 , 31 This discrepancy in youth findings between a U.S. and Canadian study are notable as Canadian RCL policies do include stipulations to allow small scale regulations in youth. Thus, it suggests RCL policies that maintain prohibition of use among underage youth do not address issues related to arrests and crime among youth. In fact, the current literature suggests that cannabis-related charges are still being enforced for youth under the legal age of consumption in the U.S. Another important outcome revealed is racial disparities in cannabis-related arrests. Previous evidence has shown there are racial disparities, particularly between Black, Indigenous, and Hispanic individuals compared to White counterparts, in cannabis-related charges and arrests. 109 , 110 Regarding racial disparities and RCL, there was very little evidence of decreases in disparities for cannabis-related arrests following RCL. 100 , 101 This racialized arresting is significant as it can be associated with additional public health concerns such as physical and mental health outcomes, harm to families involved, and to communities. 111 This finding is particularly concerning as it suggests racialized arrests for cannabis are still occurring despite the intentions of liberalization of cannabis policies to help reduce racial disparities in the criminal justice system. However, it is important to note that there were only 2 studies of racial disparities in cannabis-related arrests and both were conducted in the U.S. Thus, additional research is required before drawing any firm conclusions about the ability of RCL to address systemic issues in the justice system.

Limitations

The findings should be considered within context of the following limitations. The research was predominately from North America (U.S. and Canada). While both countries have either federal or state RCL, findings only from two countries that are geographically connected may not reflect the influence of RCL across different cultures and countries globally. The majority of studies also relied on self-report data for cannabis-related outcomes. Thus, there is a risk that any increases in use or other cannabis-related outcomes may be due to an increased comfort in disclosing cannabis use due to RCL.

Given the large number of studies on multiple outcomes, we chose to focus on implementation of RCL exclusively, rather than related policy changes such as commercialization (ie, the advent of legal sales), to allow for clearer conclusions about the specific impacts on RCL. However, a limitation is that the review does not address the impact of commercialization or changes in product availability. While outside the scope of the current review, it does limit the conclusions that can be drawn about RCL overall as some jurisdictions implemented features of commercialization separately from legalization. For example, in Ontario, Canada, storefronts and edible products became legal a year after initial RCL (when online purchase was the exclusive modality), which may have had an additional impact on behavioral and health-related outcomes. Additionally, the scope of the review was limited to recreational legalization and did not consider other forms of policy changes such as medicinal legalization or decriminalization, as these have been summarized more comprehensively in prior reviews. 112 - 114 Further, this review focused on behavioral and health outcomes; other important outcomes to examine in the future include economic aspects such as cannabis pricing and purchasing behaviors, and product features such as potency. Finally, as this review considered a broad range of outcomes, we did not conduct a meta-analysis which limits conclusions that can be drawn regarding the magnitude of the associations.

Conclusions

The topic of RCL is a contentious and timely issue. With nationwide legalization in multiple countries and liberalizing policies across the U.S., empirical research on the impacts of RCL has dramatically expanded in recent years. This systematic review comprehensively evaluated a variety of outcomes associated with RCL, focusing on longitudinal study designs and revealing a wide variety of findings in terms of substance use, health, cannabis attitudes, crime, and driving outcomes examined thus far. However, the current review highlights that the findings regarding the effects of RCL are highly heterogenous, often inconsistent, and disproportionately focused on certain jurisdictions. With polarizing views surrounding whether RCL is a positive or negative policy change, it is noteworthy that the extant literature does not point to one clear answer at the current time. In general, the collective results do not suggest dramatic changes or negative consequences, but instead suggest that meaningful tectonic shifts are happening for several outcomes that may or may not presage substantive changes in personal and public health risk. Furthermore, it is clear that a more in-depth examinations of negative (eg, frequent use, CUD prevalence, ‘gateway’ relationships with other substance use), or positive consequences (eg, therapeutic benefits for mental health and/or medical conditions, use of safer products and routes of administration), are needed using both quantitative and qualitative approaches.

Acknowledgments

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding support from the Peter Boris Chair in Addictions Research and a Canada Research Chair in Translational Addiction Research (JM). Funders had no role in the design or execution of the review.

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: James MacKillop discloses he is a principal and senior scientist in Beam Diagnostics, Inc, and a consultant to ClairvoyantRx. No other authors have disclosures.

Author Contributions: The author’s contribution is as follows: study conceptualization and design: KF, JW, JT, JM; data collection and interpretation: KF, EM, MS; manuscript writing and preparation: KF, EM, MS, PN; manuscript reviewing and editing: JW, JT, JM. All authors have reviewed and approved the final manuscript.

• 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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BY designed the study, collected the data, conducted the data analysis, drafted and reviewed the manuscript; XGC designed the study and reviewed the manuscript. XFC and HY reviewed the manuscript. All authors read and approved the final version of the manuscript.

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

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

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

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

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

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

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

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

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

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

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

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

eTable 10. Reported Adverse Events Across Different Severity Levels

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

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

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Assessment of Medical Cannabis and Health-Related Quality of Life

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Accepted for Publication: March 27, 2023.

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

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

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

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

Concept and design: Arkell, Downey, Hayley.

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

Drafting of the manuscript: Arkell, Downey, Hayley.

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

Statistical analysis: Roth.

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

Supervision: Downey, Hayley, Roth.

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

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

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

Data Sharing Statement: See Supplement 2 .

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• About Cannabis
• Health Effects
• Cannabis FAQs
• Facts and Stats
• Journal Articles
• MMWR Articles
• Related Websites

Cannabis Facts and Stats

At a glance.

A variety of information sources are available to monitor the prevalence and trends of cannabis use in the United States. The resources below cover cannabis-related issues, including data around use, emergency department visits, substance use and misuse, policy measures, and other related tools.

• Cannabis is the most commonly used federally illegal drug in the United States; 52.5 million people, or about 19% of Americans, used it at least once in 2021. 1
• Recent research estimated that approximately 3 in 10 people who use cannabis have cannabis use disorder. 2
• The risk of developing cannabis use disorder is even greater for people who begin to use it before age 18. 3
• Cannabis use directly affects the parts of the brain responsible for memory, learning, attention, decision-making, coordination, emotion, and reaction time. 4 5
• Infants, children, and teens (who still have developing brains) are especially susceptible to the adverse effects of cannabis. 4 5
• Long-term or frequent cannabis use has been linked to increased risk of psychosis or schizophrenia in some users. 6
• Using cannabis during pregnancy may increase the person's risk for pregnancy complications. Pregnant and breastfeeding persons should avoid cannabis. 7

National Surveys That Collect Information About Cannabis Use

Cdc sponsored surveys.

Behavioral Risk Factor Surveillance System (BRFSS)

World's largest, continuously conducted telephone survey that tracks health behaviors, chronic diseases, and preventive health practices among noninstitutionalized adults in the United States.

Youth Risk Behavior Surveillance System (YRBSS)

Monitors six categories of priority health risk behaviors, including cannabis use, among high school youth at national, state, and local levels.

Pregnancy Risk Assessment Monitoring System (PRAMS)

Collects state-specific, population-based data on cannabis use before, during, and shortly after pregnancy.

National Health and Nutrition Examination Survey (NHANES)

Assesses the health and nutritional status of adults and children, aged 12 years and older, in the United States. The survey is unique in that it combines interviews and physical examinations. Voluntary drug use questions ask lifetime cannabis use, age of first use, age when starting to use cannabis regularly, amount used, frequency of use, and time since last use. These data are available from 2005-2007 survey period onward.

Other National Surveys

National Survey on Drug Use and Health (NSDUH)

Ongoing and long-term system, sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA) NSDUH is the primary source of information on the prevalence, patterns, and consequences of alcohol, tobacco, and illegal drug use and abuse in the general U.S. civilian noninstitutionalized population, ages 12 and older.

Monitoring the Future Survey

Ongoing and long-term system, sponsored by the National Institute on Drug Abuse (NIDA) that collects data on the behaviors, attitudes, and values regarding substance use of American teens, college students, and adults. Each year a total of approximately 50,000 students in 8th, 10th, and 12th grades are surveyed about substance use, including cannabis, and a subset are sent follow-up questionnaires through age 45 years.

National Drug Early Warning System (NDEWS)

NDEWS monitors drug use trends in 12 sentinel communities across the United States. Sentinel Site profiles describing drug abuse trends and emerging issues are available on NDEWS website.

National Programs That Collect Information About Cannabis Policies

Alcohol Policy Information System (APIS)

A policy monitoring system sponsored by the National Institute on Alcohol Abuse and Alcoholism (NIAA) that provides detailed information on a wide variety of alcohol-related policies in the United States at both state and federal levels. The system was expanded in 2016 to include policies related to legalizing the cultivation, sale, or use of cannabis for prohibitions and restrictions on such practices.

State Cannabis Policy Enactment Database

A policy monitoring system sponsored by the National Conference of State Legislatures that provides up-to-date information on cannabis legislation that has been enacted in the 50 states, District of Columbia, and its territories. The database is sortable by state, topic, keyword, and primary sponsor.

• Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2021 National Survey on Drug Use and Health (HHS Publication No. PEP22-07-01-005, NSDUH Series H-57). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. 2022. https://www.samhsa.gov/data/report/2021-nsduh-annual-national-report . Accessed on February 9, 2024.
• Hasin DS, Saha TD, Kerridge BT, et al. Prevalence of marijuana use disorders in the United States between 2001-2002 and 2012-2013. JAMA Psychiatry. 2015 Dec;72(12):1235-1242. doi: 10.1001/jamapsychiatry.2015.1858.
• Winters KC, Lee C-YS. Likelihood of developing an alcohol and cannabis use disorder during youth: Association with recent use and age. Drug Alcohol Depend. 2008;92(1-3):239-247. doi: 10.1016/j.drugalcdep.2007.08.005.
• National Academies of Sciences, Engineering, and Medicine. The health effects of cannabis and cannabinoids: the current state of evidence and recommendations for research. Washington, DC: The National Academies Press; 2017. https://nap.nationalacademies.org/catalog/24625/the-health-effects-of-cannabis-and-cannabinoids-the-current-state. Accessed February 8, 2024.
• Giedd JN. The teen brain: Insights from neuroimaging. J Adolesc Health. 2008;42(4):335–343. doi: 10.1016/j.jadohealth.2008.01.007.
• 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 Mar;73(3):292-297. doi: 10.1001/jamapsychiatry.2015.3278.
• Ryan SA, Ammerman SD, O’Connor ME, et al. Marijuana use during pregnancy and breastfeeding: Implications for neonatal and childhood outcomes. Pediatrics. 2018;142(3):e20181889. doi: 10.1542/peds.2018-1889.

Cannabis and Public Health

Cannabis—which can also be called marijuana —is the most commonly used federally illegal drug in the United States.

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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Why do religious teens engage in less risky behavior? A psychologist explains

Professor of Psychology, University of Florida

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James Shepperd receives funding from the Templeton Foundation (Grant No. 12829).

University of Florida provides funding as a founding partner of The Conversation US.

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Researchers have long known that religious teens are less likely to engage in risky behavior . My team’s research explains why.

We surveyed multiple times the religious beliefs and risk behavior of over 1,400 teens from Florida between 2010 and 2012. Although the majority of our sample self-identified as Christian, many of the teens identified as belonging to other religious groups or as nonreligious.

Our work has focused on risky behaviors – such as using alcohol, drugs and tobacco – that are socially unacceptable, potentially harmful and often illegal for teens.

Why it matters

We identified four conditions that can reduce risky behavior: low opportunity, appeal, acceptability and a high level of self-control.

Take drinking alcohol, as an example. Teens are less likely to drink if they lack opportunities or if they view drinking to be unappealing , perhaps because the people who are important to them view drinking unfavorably . Teens are also less likely to drink alcohol if they find drinking to be morally unacceptable . Finally, teens are less likely to drink if they can control their impulses and resist the temptation or peer pressure .

These four conditions overlap. For example, peer disapproval can reduce both the appeal and the moral acceptability of using alcohol. In addition, circumstances such as parent supervision that limit opportunity may also communicate that the behavior is morally unacceptable or unappealing.

Although religions differ in their beliefs, they all share three features that can affect the four conditions that deter risky behavior.

First, all religions offer people a worldview , which is a set of beliefs that addresses questions such as why people exist, how they should behave and what happens after they die.

Worldviews provide guidelines that can influence the appeal and moral acceptability of risky behavior. My research team found that religious teens – that is, teens who express stronger religious beliefs and display more frequent religious behavior – possess a stronger sense of meaning and a clear understanding of what is right versus wrong . These benefits of a worldview were also linked to lower rates of smoking, drinking and marijuana use.

Second, religions often revolve around belief in an omniscient entity or God that monitors and can punish or reward behavior. Belief in God, in turn, can promote self-monitoring, self-control and ultimately less risky behavior .

Third, religions are not just a set of beliefs; they represent communities of people who can influence thought and behavior. They can limit opportunities to engage in risky behavior. They can convey values , such as the idea that using alcohol is wrong, that influence the appeal and moral acceptability of risky behavior. And they can offer support and feelings of belongingness that can help youth with impulse control.

What still isn’t known

Most research exploring the effects of religion on risk behavior examines Christians in the U.S. and Europe. We need more research from other cultures and other religions. It is noteworthy that our research suggests that the link between greater religiousness and less risky behavior is generally the same for boys and girls and across religious and racial groups .

Research suggests that a sense of meaning and clear understanding of what is right and wrong is linked to engaging in less risky behavior even among nonreligious teens . Our interpretation is that having a strong worldview matters more than the source of the worldview.

In addition, secular communities lack belief in an all-powerful God. But the larger point is that monitoring and rewards from authority figures can influence risky behavior . Secular communities may be able to reduce risky behavior in teens through greater monitoring and rewards and by adapting the other features of religion that appear to deter risky behavior in religious adolescents.

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The North America legal cannabis market size reached US$ 35.6 Billion in 2023. Looking forward, the market is projected to reach US$ 298.5 Billion by 2032, exhibiting a growth rate (CAGR) of 26.65% during 2023-2032.

The growing use for recreational use, wide availability through various distribution channels, and increasing utilization to treat chronic diseases represent some of the key factors driving the market.

Legal cannabis refers to a drug derived from dried leaves, stems, seeds, and flowers of a plant called Cannabis sativa. It has various components, such as cannabidiol (CBD) and tetrahydrocannabinol (THC). It can be easily smoked, vaped, and ingested while having psychoactive properties that provide relaxing and calming effects in the body. It offers an enhanced sense of hearing, vision, and taste and improves sleeping problems. It prevents relapse into drug and alcohol addiction, treats anxiety disorders, and lowers blood pressure. It assists in relieving chronic pains that are caused by numerous diseases among adults.

It aids in chemotherapy-induced nausea and multiple sclerosis spasticity symptoms. Besides this, it increases appetite and reduces the risk of migraine, inflammation, and seizures. As it is also consumed to treat asthma and glaucoma, as an antidepressant and anticonvulsant and anti-spasmodic, the demand for legal cannabis is rising across the North American region.

North America Legal Cannabis Market Trends

At present, the increasing consumer preference from conventional treatment to cannabis-based treatments represents one of the key factors impelling the growth of the market in the North American region. Besides this, the growing demand for cannabis to reduce stress, anxiety, and depression among individuals is offering a positive market outlook across the region. In addition to this, several banks are approving loans for marijuana businesses to enhance the production process, which is supporting the growth of the market in the region.

Moreover, the increasing utilization of legal cannabis to treat several chronic diseases, such as epileptic seizures, chronic pain, and Alzheimer's, among individuals is propelling the growth of the market in the North American region. Apart from this, the wide availability of legal cannabis through various distribution channels, such as regulated dispensaries, recreational stores, and stand-alone retailers, is positively influencing the market. Additionally, there is a rise in the demand for cannabis for recreational use among the masses.

This, coupled with the increasing utilization of hemp-derived cannabidiol (CBD) in the skincare and the cosmetic industry, is offering lucrative growth opportunities to industry investors in the region. Furthermore, the rising demand for marijuana oils and tinctures for treating vomiting and nausea problems among individuals is strengthening the growth of the market in the North American region.

The report has also provided a comprehensive analysis of the competitive landscape in the North America legal cannabis market. Competitive analysis such as market structure, market share by key players, player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Key Market Segmentation

This report provides an analysis of the key trends in each sub-segment of the North America legal cannabis market report, along with forecasts at the regional and country level from 2024-2032. The report has categorized the market based on products derived and distribution channel.

The report has provided a detailed breakup and analysis of the North America legal cannabis market based on the products derived. This includes marijuana, industrial hemp, and others. According to the report, marijuana represented the largest segment.

A detailed breakup and analysis of the North America legal cannabis market based on the distribution channel has also been provided in the report. This includes regulated dispensary, recreational stores, stand-alone retailers, and others. According to the report, regulated dispensary accounted for the largest market share.

Key Questions Answered in This Report

• How big is the North America legal cannabis market?
• What is the expected growth rate of the North America legal cannabis market during 2024-2032?
• What are the key factors driving the North America legal cannabis market?
• What has been the impact of COVID-19 on the North America legal cannabis market?
• What is the breakup of the North America legal cannabis market based on the products derived?
• What is the breakup of the North America legal cannabis market based on the distribution channel?
• What are the key regions in the North America legal cannabis market?
• Who are the key players/companies in the North America legal cannabis market?

Key Attributes:

Key Players

• 22nd Century Group Inc.
• Medical Marijuana Inc
• Axim Biotechnologies Inc.
• Arena Pharmaceuticals Inc.
• Canopy Growth Corporation
• Aphria Inc.
• Aurora Cannabis Inc.
• Abcann Medicinals Inc.

For more information about this report visit https://www.researchandmarkets.com/r/hd9gjt

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• Published: 08 May 2024

Accurate structure prediction of biomolecular interactions with AlphaFold 3

• Josh Abramson   ORCID: orcid.org/0009-0000-3496-6952 1   na1 ,
• Jonas Adler   ORCID: orcid.org/0000-0001-9928-3407 1   na1 ,
• Jack Dunger 1   na1 ,
• Richard Evans   ORCID: orcid.org/0000-0003-4675-8469 1   na1 ,
• Tim Green   ORCID: orcid.org/0000-0002-3227-1505 1   na1 ,
• Alexander Pritzel   ORCID: orcid.org/0000-0002-4233-9040 1   na1 ,
• Olaf Ronneberger   ORCID: orcid.org/0000-0002-4266-1515 1   na1 ,
• Lindsay Willmore   ORCID: orcid.org/0000-0003-4314-0778 1   na1 ,
• Andrew J. Ballard   ORCID: orcid.org/0000-0003-4956-5304 1 ,
• Joshua Bambrick   ORCID: orcid.org/0009-0003-3908-0722 2 ,
• Sebastian W. Bodenstein 1 ,
• David A. Evans 1 ,
• Chia-Chun Hung   ORCID: orcid.org/0000-0002-5264-9165 2 ,
• Michael O’Neill 1 ,
• David Reiman   ORCID: orcid.org/0000-0002-1605-7197 1 ,
• Kathryn Tunyasuvunakool   ORCID: orcid.org/0000-0002-8594-1074 1 ,
• Zachary Wu   ORCID: orcid.org/0000-0003-2429-9812 1 ,
• Akvilė Žemgulytė 1 ,
• Eirini Arvaniti 3 ,
• Charles Beattie   ORCID: orcid.org/0000-0003-1840-054X 3 ,
• Ottavia Bertolli   ORCID: orcid.org/0000-0001-8578-3216 3 ,
• Alex Bridgland 3 ,
• Alexey Cherepanov   ORCID: orcid.org/0000-0002-5227-0622 4 ,
• Miles Congreve 4 ,
• Alexander I. Cowen-Rivers 3 ,
• Andrew Cowie   ORCID: orcid.org/0000-0002-4491-1434 3 ,
• Michael Figurnov   ORCID: orcid.org/0000-0003-1386-8741 3 ,
• Fabian B. Fuchs 3 ,
• Hannah Gladman 3 ,
• Rishub Jain 3 ,
• Yousuf A. Khan   ORCID: orcid.org/0000-0003-0201-2796 3 ,
• Caroline M. R. Low 4 ,
• Kuba Perlin 3 ,
• Anna Potapenko 3 ,
• Pascal Savy 4 ,
• Sukhdeep Singh 3 ,
• Adrian Stecula   ORCID: orcid.org/0000-0001-6914-6743 4 ,
• Ashok Thillaisundaram 3 ,
• Catherine Tong   ORCID: orcid.org/0000-0001-7570-4801 4 ,
• Sergei Yakneen   ORCID: orcid.org/0000-0001-7827-9839 4 ,
• Ellen D. Zhong   ORCID: orcid.org/0000-0001-6345-1907 3 ,
• Michal Zielinski 3 ,
• Augustin Žídek   ORCID: orcid.org/0000-0002-0748-9684 3 ,
• Victor Bapst 1   na2 ,
• Pushmeet Kohli   ORCID: orcid.org/0000-0002-7466-7997 1   na2 ,
• Max Jaderberg   ORCID: orcid.org/0000-0002-9033-2695 2   na2 ,
• Demis Hassabis   ORCID: orcid.org/0000-0003-2812-9917 1 , 2   na2 &
• John M. Jumper   ORCID: orcid.org/0000-0001-6169-6580 1   na2  

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• Drug discovery
• Machine learning
• Protein structure predictions
• Structural biology

The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2–6 . In this paper, we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates significantly improved accuracy over many previous specialised tools: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than nucleic-acid-specific predictors, and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer v2.3 7,8 . Together these results show that high accuracy modelling across biomolecular space is possible within a single unified deep learning framework.

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These authors contributed equally: Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore

These authors jointly supervised this work: Victor Bapst, Pushmeet Kohli, Max Jaderberg, Demis Hassabis, John M. Jumper

Authors and Affiliations

Core Contributor, Google DeepMind, London, UK

Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J. Ballard, Sebastian W. Bodenstein, David A. Evans, Michael O’Neill, David Reiman, Kathryn Tunyasuvunakool, Zachary Wu, Akvilė Žemgulytė, Victor Bapst, Pushmeet Kohli, Demis Hassabis & John M. Jumper

Core Contributor, Isomorphic Labs, London, UK

Joshua Bambrick, Chia-Chun Hung, Max Jaderberg & Demis Hassabis

Google DeepMind, London, UK

Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexander I. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B. Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Kuba Perlin, Anna Potapenko, Sukhdeep Singh, Ashok Thillaisundaram, Ellen D. Zhong, Michal Zielinski & Augustin Žídek

Isomorphic Labs, London, UK

Alexey Cherepanov, Miles Congreve, Caroline M. R. Low, Pascal Savy, Adrian Stecula, Catherine Tong & Sergei Yakneen

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Corresponding authors

Correspondence to Max Jaderberg , Demis Hassabis or John M. Jumper .

Supplementary information

Supplementary information.

This Supplementary Information file contains the following 9 sections: (1) Notation; (2) Data pipeline; (3) Model architecture; (4) Auxiliary heads; (5) Training and inference; (6) Evaluation; (7) Differences to AlphaFold2 and AlphaFold-Multimer; (8) Supplemental Results; and (9) Appendix: CCD Code and PDB ID tables.

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Abramson, J., Adler, J., Dunger, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature (2024). https://doi.org/10.1038/s41586-024-07487-w

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Published : 08 May 2024

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