Cannabis Grow

While federal law has remained unchanged throughout years of state experimentation with marijuana liberalization, federal enforcement in these states has varied widely. Before 2009, the federal government made direct threats toward MML states, stating that even users and suppliers in compliance with state policy would remain subject to federal prosecution . However, between 2009 and 2012, two federal memos dramatically altered perceived federal enforcement in medical marijuana states. The Ogden Memo, announced on October 19, 2009, formalized guidelines for federal prosecutors in MML states. The memorandum maintained the government’s commitment to prosecuting significant traffickers of marijuana, but emphasized that “prosecution of individuals with cancer or other serious illnesses who use marijuana as part of a recommended treatment regimen consistent with applicable state law, or those caregivers in clear and unambiguous compliance with existing state law who provide such individuals with marijuana, is unlikely to be an efficient use of limited federal resources” . In sum, the Ogden Memo de-prioritized the federal government’s involvement in prosecuting medical marijuana users and suppliers in states with MMLs. On June 29, 2011, the US government reversed this stance by issuing the Cole Memo as a response to the government’s perceived “increase in the scope of commercial cultivation, sale, and distribution and use of marijuana for purported medical purposes” . The Cole Memo stated that individuals involved in the business of medical cannabis growing equipment sales and distribution would be subject to federal enforcement action.

In the months leading up to and following the memo, the Drug Enforce-ment Administration stepped up raids on medical marijuana producers . If changes in the risk of federal prosecution shift production costs, then both state variation in supply restrictions and time-variation in federal policy will determine the size of the legal market. Moreover, in states where legal and illegal markets for marijuana co-exist, policy changes that shift production costs in the legal market may affect price and availability in the illicit market. To better understand the effects of costs associated with state restrictions and federal enforcement, I outline a simple model of supplier behavior. The purpose of the model is to provide theoretical predictions of how supplier responses to the changes in federal enforcement should differ depending on state MML regulations. I can then link these predictions to newly collected empirical evidence on the size of the legal market. The model also provides intuition for the regulatory variables used in the empirical analysis and clarifies the key assumptions upon which my approach relies. As the illegal market for marijuana is not directly observed, modeling the interaction between the legal and illegal markets is helpful in motivating the appropriate empirical framework to estimate how legalizing medical marijuana affects recreational consumption. The model describes the market for marijuana in an MML state as composed of consumers and suppliers who respond to changes in the probability of being prosecuted by state or federal enforcement. Suppliers operating in the legal medical marijuana market also face capacity constraints that vary by how strictly the state regulates producers. Each state’s market is assumed to operate in isolation.

Most MML states have mandatory medical marijuana registration programs: laws requiring medical marijuana users to register in order to receive protection from state arrest.An individual who wants medical marijuana must first obtain a physician’s certification that the individual has a medical condition which could benefit from the use of marijuana. The patient then must submit an application to the state authority, along with a registration fee. If the application is approved, the patient receives documentation providing access to dispensaries and protection from state prosecution. By definition, all other consumption is illegal. The number of registered medical marijuana patients provides an observable measure of the size of the legal market. A limitation faced by previous research has been that state records of registered patient counts are not readily available and are not maintained similarly across states. To overcome this limitation, I collected monthlydata from a number of sources, including contact with state officials, state department websites, news articles, and academic papers. Since my outcome variables are annual state-level prevalence measures, I linearly interpolate missing end-of-year registration rates using the nearest available months of registered patient counts. The final measure of market size is the annual registration rate, calculated as the percent of adults registered as medical marijuana patients at the end of December in a given year. I include the voluntary registration data available from California after 2005, but due to lack of data, Maine and Washington are excluded.From this data, the medical marijuana market in 2013 is estimated to consist of about 1,139,098 legal medical marijuana patients,and industry reports estimate annual retail sales of legal marijuana in 2013 at $1.43 billion .While the legal market size is dwarfed by the estimated $25-$40 billion size of the illicit market , growth in the legal marijuana market over the past decade is twice that of the illicit market.

The estimate of the number of registered patients in 2013 represents a more than 300% increase in the number of medical marijuana users from 2007 compared to only a 150% increase in the number of total adult past-month users . The legal market for marijuana is thus rapidly expanding. To study how changes in the legal market affect the illegal market, it is important to first understand the factors driving legal market growth.To isolate the supply-side drivers of growth in legal medical marijuana markets,cannabis drying trays the ideal policy variation would be such that production costs were exogenously shifted in some MML states but unchanged in others. Based on the model outlined in Section 2.2.2, the Ogden and Cole Memos approximated this ideal by shifting production costs differentially more in MML states with lax supply restrictions compared to those states with strict supply restrictions. Figure 2.1 connects the model’s propositions to the data by documenting registration rate trend breaks at the Ogden and Cole Memos for a sub-sample of states that exemplify the different state production restrictions. Hawaii and New Mexico serve as examples of more restrictive production states; Colorado and Montana are representative of MML states with looser supply restrictions. For all four states, few patients register during the first few years following MML passage. The initial slow growth in medical marijuana take-up suggests that, conditional on federal enforcement remaining high, reduced state risk following MML enactment had little effect on the size of the legal market. The empirical evidence from Figure 2.1 is also consistent with the model’s prediction that the Ogden and Cole Memos had far greater effect in states with loose production limits. In Hawaii, where suppliers had strict limits and could only serve a single patient, the Ogden Memo had very little effect on registration rate trends. Correspondingly, there is also little break in trend following the Cole Memo. In New Mexico, which allowed state licensed dispensaries that had higher production limits but faced heavy regulation, the Ogden and Cole Memos also seem to have had small effects. In contrast, the states with lax production limits saw dramatic changes in registration rates with the Ogden and Cole Memos.After the Ogden Memo, Colorado experienced the “Green Rush,” a proliferation of dispensaries that arose following decreased fears of federal intervention.Alongside the expansion of unregulated dispensaries in Colorado, patient registration rates spiked . Similarly, in Montana, where caregivers were permitted to produce for an unlimited number of patients and receive compensation for their services, the number of caregivers providing for 20 or more patients increased seven-fold within one year following the Ogden Memo. This was accompanied by a nearly six-fold increase in registered patients . In line with the propositions from the model, the Cole Memo reversed this trend.

The decline in registration rates in Montana is particularly dramatic because, concurrent with the Cole Memo, Montana’s legislature passed Senate Bill 423 which effectively dismantled the medical marijuana supply industry by establishing caregiver patient limits and preventing caregivers from receiving compensation.To examine this relationship for all states, I exploit the model’s predicted trend reversal between the Ogden period and the Cole period in the relationship between a state’s laxness of medical marijuana supply regulation and registration rates. Intuitively, in states with loose medical marijuana production limits, the Ogden Memo decreased marginal costs over a broader range of production , inducing producer entry and supply increases by existing producers. Column reports regression results that allow federal enforcement changes to also affect MML states with strict restrictions on supply. Consistent with the model predictions, both federal policies had far larger effects on registration rate trends in states where marijuana suppliers were relatively unrestricted. Between the Ogden and Cole Memos, states with strict production limits on average saw an additional 0.2% of the adult population register as medical marijuana patients , which is statistically significant but an order of magnitude smaller than the increases seen in MML states with loose supply regulations . There is no effect of the Cole Memo in strictly regulated MML states, consistent with evidence that federal enforcement related to the Cole Memo targeted large-scale production. Limiting the sample to only those states with MMLs by 2012 in columns and yields coefficients of similar magnitude but with slightly larger standard errors. Unlike initial MML enactment, the federal memos substantially altered the size of the legal market, with larger effects in states with looser supply regulations. The differential response of registration rates to the federal government’s policies in states with lax compared to strict producer restrictions suggests that patient registration rates are driven primarily by supply-side shifters. Therefore, to estimate the causal effect of changes in medical marijuana supply on recreational consumption, my empirical strategy uses the timing of the federal memos and differences in initial MML supply restrictions as instruments. The main threat to identification is that registration rates and illegal use may be jointly determined by unobservables affecting demand. State and year fixed effects control for time-invariant state characteristics and national trends. Time-varying state covariates included that potentially affect recreational marijuana use can be categorized as: demographics influencing recreational marijuana use, economic characteristics, and substance-related policies influencing marijuana consumption. A full listing of covariates is provided in Table 2.3. For all specifications, to account for heteroskedasticity and serial correlation, robust standard errors are clustered at the state level . To account for potential violation of the parallel trends assumption, specifications including state-specific linear trends are also presented as robustness checks in section 2.6. Even after controlling for state and year fixed effects and state-year covariates, identification of β is challenging due to concerns of endogeneity between recreational marijuana use and registration rates. While fixed effects will account for issues of cross sectional endogeneity, β could still be biased due to some omitted state-level time-varying variable that affects both registered users and illicit users. For instance, β will be biased upward if changes in local perceptions regarding the health risks of marijuana use led to changes in both medical and recreational use. To account for potential endogeneity, I instrument for registration rates by way of two-stage-leastsquares using equation 2.6 as the first-stage specification. The instrumental variable estimates are valid as long as the exclusion restriction is satisfied. In the context of equations 2.6 and 2.7, this occurs if E[εjtZjt|uj , vt , Xjt] = 0, where Zjt is the vector of instruments including the interaction of MML supply restrictions with trend breaks based on the exogenous timing of the Ogden and Cole Memos. As state and year fixed effects are included, the identification is not threatened by level differences between states or by national trends in marijuana consumption . However, the exclusion restriction will be violated if changes in federal enforcement following the Ogden and Cole Memos had differential effects on demand in states with loose compared to strict production restrictions through any channel other than supply. Evidence validating the exclusion restriction is presented in section 2.6. The measures of marijuana consumption come from the National Survey of Drug Use and Health . The NSDUH is an annual survey funded by the Substance Abuse and Mental Health Services Administration of the US population over twelve years of age.

Given that many smoking studies use CO as an endpoint for bio-verifying tobacco use status, researchers will need to determine how to use this information.It is unknown whether excluding marijuana use was done to isolate cigarette smoking as a source of CO for bio-verifying smoking reduction, or whether this exclusion was a strategy to exclude drug use. For example, dependence on chemicals other than nicotine is common exclusion criteria for e-cigarette switching studies. Interventionists should be aware that excluding cigarette smokers who use marijuana use will significantly decrease study generalizability. Researchers who wish to include combustible marijuana users in their study sample are advised to conduct a nuanced measurement of product use and account for combustible marijuana use when bio-verifying tobacco use status.The study includes several important limitations. The study was conducted in two geographical locations in the United States and among two different racial/ethnic groups and findings may not generalize to other populations. Additionally, the product characteristics and frequency patterns of marijuana use came from a relatively small sample that may not generalize to other populations of marijuana users. Findings must also be taken in light of a sample of smokers willing to switch to e-cigarettes and provided e-cigarettes at no cost for 6 weeks.

Self-reported marijuana use was higher at the San Diego site than the Kansas City site. Although participants were told their responses were deidentified and confidential, Kansas City participants could have felt pressure to conceal marijuana use due to the state’s medicinal-only status, compared with the San Diego site where recreational marijuana use is legal. In addition to decreased comfort of reporting due to associated legal risks,ebb and flow flood table there are also practical considerations that may contribute to less use such as accessibility to purchasing and exposure to advertising. Prevalence of marijuana use has been reported highest in states with legal recreational use.To address reporting bias in a multi-site setting, measuring a biomarker of cannabis use is recommended. It is also possible that the younger age of San Diego study participants compared with Kansas City participants contributed to differential marijuana use, as age is associated with marijuana use.Finally, study outcomes were taken at 6-week post baseline and the long-term impact of marijuana use on cigarette reduction and related health effects is an important topic for future study. While this study captured a majority of combustible marijuana use, understanding if there are reinforcing sensory components of vaping marijuana and vaping nicotine e-liquid remains to be studied.As of 2015, twenty-four U.S. jurisdictions have medical marijuana laws , which provide legal protection for individuals who use marijuana for medical purposes, physicians who recommend marijuana to patients with certain medical conditions, and growers and distributors who supply these patients. Past studies have exploited state-time variation in MML enactment to estimate the effects of liberalization on recreational marijuana use,1 traffic fatalities , obesity , suicides , and crime .

However, interventions may not attain full steady-state effectiveness immediately upon implementation , and estimation based on the timing of MML passage will likely not capture the full effects of these policies as medical marijuana markets evolve. While MML enactment alone may signal a shift in governmental acceptance of the drug, effects on marijuana availability and price will depend on the specific regulations established by MML policy and the duration of exposure to the more liberal regime. Changes in social access, perceived community approval, and spillover effects to illegal use and other public health outcomes may well vary according to the extent to which legal users and suppliers actively participate in the medical marijuana program. If recreational and medical marijuana consumption decisions are made based on similar consumer optimization problems,2 both medical marijuana participation and use in the general population will follow similar patterns. Understanding the factors that drive changes in medical marijuana participation can thus offer insight into the mechanisms by which MMLs generate spillovers to recreational use and other health outcomes. This is the first paper to investigate the determinants and dynamics of medical marijuana participation by legal users using newly collected data on medical marijuana patient registration rates. While some recent research has examined data on registered patients, these studies have either been descriptive , cross-sectional , or limited to one state . By collecting data on registered patient counts from both administrative and non-administrative sources, this paper presents the most comprehensive state-level monthly panel dataset to date on medical marijuana participation to date. Data was obtained from 1999-2014 for the sixteen states3 that required individuals to register as medical marijuana patients in order to receive the legal protections afforded by the MML.

The data show that there have been dramatic changes in medical marijuana participation over the last two decades. Registered patient counts were relatively low until 2009, when they sharply increased. The number of registered patients continued to climb until mid-2011, when they leveled before resuming an upward trend a few years later. These trend breaks in medical marijuana patient registration rates coincide with the timing of federal enforcement policy changes that have been widely ignored by past research. While federal law has strictly prohibited the use and distribution of marijuana since 1937 regardless of state policy, a federal statement of non-enforcement in MML states was released in October 2009 ; in June 2011, another federal statement was issued to clarify that this did not apply to large-scale producers . To understand the factors responsible for driving these changes in medical marijuana take-up, I first outline a conceptual framework whereby individuals apply to register with the medical marijuana program if the expected utility from registering exceeds that of not registering. Costs to patients include transaction costs associated with registration fees and finding a doctor to provide a recommendation, as well as perceived risk from state and federal enforcement. Benefits include access to legitimate sources of marijuana , which will vary depending on the production limits established by the state’s specific MML regulation. The federal memos are predicted to affect medical marijuana participation through changing the perceived risk associated with federal enforcement for both registered patients and state-legal producers. The empirical results confirm the descriptive evidence showing that the federal memos significantly affected medical marijuana take-up. Controlling for state-level demographic and economic variables or time-invariant state characteristics dampens the magnitude of these effects slightly, but they remain large and statistically significant. However,hydroponic drain table the federal memos did not affect all MML states equally. Interacting the federal memo changes with state-specific supply-side regulations shows that the magnitude of their effects was significantly larger in states that imposed relatively lax restrictions on legal producers. These findings imply that medical marijuana participation is primarily driven by the expected benefits associated with access to legal supply. Additional robustness checks support that the extent of medical marijuana participation is highly responsive to supply-side changes in the legal market. This paper builds on recent work recognizing that heterogeneity in the specifics of state MML regulations may generate heterogeneous effects , and it contributes to a broader economic literature showing that the effects of regulatory changes depend largely on the specifics of their design, implementation, and enforcement.Section 1.2 details the history of marijuana regulation in the U.S. and provides background on modern MMLs and changes in federal enforcement policy. Section 1.3 outlines a conceptual framework to suggest the factors determining medical marijuana participation.

Section 1.4 presents the data, empirical framework, and results for the determinants of medical marijuana participation. Finally, section 1.5 places these results in the context of the existing literature on MMLs, and section 1.6 concludes. The first federal regulation of marijuana was introduced with the Marijuana Tax Act of 1937. The Marijuana Tax Act did not criminalize the possession or use of marijuana, as this was a potential violation of the Tenth Amendment’s limitation on federal power, but instead made it illegal to grow or distribute marijuana unless the grower obtained a federal stamp. Since stamps were largely unavailable and there was no application process, the Act effectively served as federal prohibition . Marijuana use remained limited until the mid-1960s, when the baby boom generation reached adolescence. Thicker drug markets associated with this larger youth cohort resulted in a significant increase in illicit drug use among college and high school students . In 1970, Congress responded by passing the Controlled Substances Act , which repealed the Marijuana Tax Act5 but classified marijuana as a Schedule I substance with high potential for abuse and no accepted medical value.6 The CSA criminalized the manufacture, distribution, and possession of marijuana for both recreational and medicinal purposes, and it provided the system of federal penalties and enforcement that remains in place today. Despite federal prohibition, marijuana use continued to rise in the United States. By the early 1970s, eight million people were using marijuana regularly, at least half a million people were consuming it daily, and 421,000 people were arrested for marijuana offenses annually . In 1972, the National Commission on Marijuana and Drug Abuse, which had been created as part of the CSA, released a comprehensive report based on surveys of health experts and law enforcement officials. The report recommended the removal of criminal penalties for marijuana possession and advocated further scientific research on the substance’s potential medicinal value. President Nixon rejected the Commission’s recommendations, but the 1972 report helped trigger a push toward liberalization policies . In 1972, the National Organization for the Reform of Marijuana Laws filed the first petition to reschedule marijuana. In 1975, the federal government established the Individual Patient Investigational New Drug program, which enabled participating physicians to prescribe marijuana to enrolled patients. The federal program was designed to accept patients with serious illnesses and directly provide them with marijuana through the National Institute on Drug Abuse. While the IND ostensibly established a legal channel by which patients could obtain marijuana, the application process was complicated and burdensome, and only six patients were accepted into the program during its first decade of operation . Still, this signaled a movement toward federal acceptance of marijuana’s medicinal value, and many states began to adopt their own legislation allowing the use of cannabis for medical purposes under specified conditions . Figure 1.1 graphs the number of proposed state-level medical marijuana initiatives from 1972-1995. Statutes are classified as therapeutic research programs , rescheduling provisions, or physician prescription laws. While these laws demonstrated increasing state recognition of marijuana’s therapeutic value, they had little practical significance . The federal approval process for state TRPs was complicated and costly. In the few states that obtained the necessary federal permissions, enrollment was highly restrictive and largely dependent on receiving marijuana from the federal government. In theory, physician prescription laws and rescheduling provisions allow physicians to legally prescribe marijuana for medicinal purposes outside of a TRP.7 However, since the federal CSA classification of marijuana as Schedule I supersedes any state CSA, physicians who prescribe marijuana outside of an officially recognized state TRP risk facing federal sanctions. Additionally, even should a patient obtain a physician’s prescription, these statutes did not establish any legitimate supply channel for patients to obtain marijuana. By 1984, the wave of state medical marijuana initiatives had quickly come to an end. This shift occurred in large part due to the spread of the crack-cocaine epidemic and increased federal emphasis on drug policy enforcement, seizures, and interdictions under the Reagan and Bush Administrations . It became increasingly unlikely that NORML’s 1972 petition would result in federal rescheduling of marijuana, and the government’s IND program was suspended in 1991 and discontinued one year later. State policy mirrored the federal stance, and by 1990 a number of the existing decriminalization and medical cannabis statutes expired or were repealed. The discovery of naturally occurring cannabinoid receptors in the human brain in the early 1990s led to a resurgence of medical interest in the potential therapeutic value of marijuana .8 There was increasing evidence that smoked marijuana offered significant benefits for patients suffering from symptoms of cancer and HIV/AIDS, and in 1995 the Journal of the American Medical Association ran a commentary supporting the use of marijuana for medicinal purposes and calling for increased research. In 1996, with the passage of Proposition 215, California became the first state to establish an effective medical marijuana law that removed criminal penalties for the use, possession, and cultivation of medical marijuana by qualifying patients and their primary caregivers.

COMT, along with monoamine oxidase, modified the L-tyrosine-derived catecholamine neurotransmitter dopamine for excretion in the urine. In the years following, similar to the case of endogenous DMT biosynthesis, several studies identified enzymes in mammalian tissues that could catalyze the chemical transformations of dopamine-related metabolites 3- methoxytyramine into 3-methoxy-5-hydroxytyramine and 3,5-dimethoxytyramine into , although no endogenous could be identified from mammalian organisms.Several mechanisms for biosynthesis in peyote and related cacti have been proposed by metabolite isolation and radiolabeled feeding studies.One proposed pathway by Lundström is shown in Fig. 21.The proposed biosynthesis begins with hydroxylation of L-tyrosine to 3-hydroxy-Ltyrosine by tyrosine hydroxylase , followed by decarboxylation catalyzed by DOPA decarboxylase to yield . Alternatively, may also be converted to tyramine through a decarboxylation catalyzed by tyrosine decarboxylase , followed by aromatic hydroxylation to by an unknown enzyme. From either route, can be converted into 3-methoxytyramine, which has been isolated from mescaline producing plants, by the enzyme catechol O-methyltransferase using SAM as the methyl donor. The final intermediates towards mescaline production 3-methoxy-5-hydroxytyramine and 3,5-dimethoxytyramine have been determined to be naturally occurring in mescaline producing plants by inverse isotope dilution,hydroponic table but neither have been isolated from plants. These are likely to be on pathway intermediates since they are incorporated into mescaline to a greater extent than other possible intermediates.

While the biosynthesis of in peyote has yet to be elucidated, Ibarra-Laclette et al. recently generated two cDNA libraries of the L. williamsii transcriptome, one for button and one forroot, using RNA-seq.From this data set, the authors identified putative genes that may encode bio-synthetic enzymes for mescaline production including DOPA decarboxylases, hydroxylases, and O-methyltransferases based on phylogenetic analysis.Careful in vitro experiments will be required to finally ascertain the mescaline bio-synthetic pathwayIbotenic acid , a nonproteinogenic amino acid with a hydroxylated isoxazole ring, and its decarboxylated form, muscimol , are the main psychoactive constituents of the toadstool, Amanita muscaria, commonly known as fly agaric .Similar to Psilocybe sp., recreational consumption of Amanita sp. rose in popularity in the 1960s. However, contrary to other fungal psychoactives that target the serotonin receptor, these compounds are γ- aminobutyric acid type A receptor agonists.GABAA receptors are found in multiple regions of the brain and thus and can alter the activity of the cerebral cortex and cerebellum leading to alterations in sensory processing and motor function, respectively. A. muscaria is classified as poisonous, which can in part be attributed to the neurotoxicity of 72. Its structural similarity to L-glutamic acid allows to act as an agonist towards the N-methyl-D-aspartate receptor resulting in electrolytic lesions in the brain and naturally occur in low concentrations in the cap and stem of A. muscaria. Minimal dosage for psychedelic effects are estimated as low as 6 mg for 46 and 30–60 mg for 72. 226 Interestingly, A. muscaria and its constituents are not regulated by the United States federal government, in contrast to 1 and 42 from Psilocybe sp. While 72 was first isolated over 50 years ago, its biosynthesis remained elusive.

Recently, Obermaier and Muller identified a gene cluster encoding 72 and 73 biosynthesis in A. muscaria. The key to locating this cluster was the identification of a glutamate hydroxylase, an enzyme first implicated in 72 biosynthesis over 50 years ago, but never found. This enzyme, a nonheme, iron and α-ketoglutarate-dependent dioxygenase named IboH, hydroxylates L-glutamate 36 at the C3 position resulting in the formation of 3- hydroxy-L-glutamic acid 74. Treatment-seeking individuals with an alcohol use disorder exhibit a range of neurocognitive and inhibitory control deficits. A recent review describes deficits related to working memory, visuospatial functions, inhibition, and executive-based functions such as mental flexibility, problem solving, divided attention , and cognitive control . AUD also exhibit worse cognitive efficiency than controls . Of clinical relevance, inhibitory control deficits are greater in actively drinking alcoholics compared to controls and they predict relapse in AUD . Research has also noted deleterious effects on neurocognition from chronic cigarette smoking, the most common substance use comorbidity in AUD, with rates in treatment seeking populations estimated at 60–90% . Greater smoking severity in AUD predicted worse executive function , and smoking AUD performed worse than nonsmoking AUD on domains of auditory-verbal learning and memory, processing speed, cognitive efficiency, and working memory at one week and four weeks of abstinence . Furthermore, smoking was shown to significantly hinder recovery of visuospatial learning and processing speed in AUD . A large proportion of treatment-seeking AUD have a concurrent substance use disorder , with 1.3 million people in the United States alone in 2013 ; therefore, this group is better described as “polysubstance users” , a term used in the literature to describe AUD who meet dependence criteria for additional substances .

Given the cognitive and inhibitory control deficits observed in AUD studies, it is not surprising that recently detoxified individuals with a substance use dependence diagnosis on any combination of heroin, alcohol, methamphetamine, and/or cannabis also performed worse than controls on several measures of executive function, including working memory, response inhibition, cognitive flexibility, and on inhibitory control measures of decision making . Individuals with both alcohol and stimulant dependence performed worse than controls on cognitive efficiency ,complex attention and memory as well as delayed discounting . Furthermore, poorer executive function in abstinent abusers of several substances has been related to the amount of cocaine and cannabis consumed , suggesting clinically relevant consequences of chronic substance use. Despite extensive research into the neurocognitive correlates of substance abuse, only few studies investigated neurocognition in PSU relative to the more extensively studied AUD, and then only on specific tasks. Short-term abstinent alcohol and stimulant dependent individuals performed worse than AUD on immediate and delayed recall conditions of a verbal memory task , but they did not differ from AUD on cognitive efficiency tasks of visual perception and category sorting . Another study found treatment-seeking abusers of multiple substances to perform moderately worse than AUD on executive function tasks of verbal fluency, working memory, planning, and multi-tasking. Neurocognitive functions recover at least partially in AUD during sustained abstinence, and some evidence suggests that additional use of other substances by people with AUD impact neurocognitive recovery negatively . Very few longitudinal studies have explicitly investigated changes in neurocognition or inhibitory control in abstinent PSU. In one study,grow rack individuals with a combined alcohol and cocaine use disorder demonstrated significant improvements on measures of immediate memory over six months of abstinence , while another described improvements in verbal short-term memory over three to four months of abstinence from multiple substances .Intact neurocognition and inhibitory control are important for addiction treatment efficacy, retention , and maintenance of abstinence during treatment . Recent evidence has shown an association between better treatment response and longitudinal cognitive recovery in AUD . Identifying the specific neurocognitive and inhibitory control deficits that differentiate PSU and AUD may provide helpful insights into the specific clinical needs of this understudied , albeit highly prevalent population of PSU in substance use treatment centers today; such deficits potentially differ from those in the more extensively studied AUD population and therefore may require more tailored treatment approaches to increase treatment effectiveness. Our recent reports of different neurobiological abnormalities in AUD and a subset of the PSU cohort presented here further supports the view that neurocognition may also differ between AUD and PSU populations.

Accordingly, the main goals of this study were to determine the degree to which one-month-abstinent PSU and AUD differ on neurocognitive functioning and inhibitory control, and if cigarette smoking affects neurocognition in PSU, similar to what has been reported in AUD. A secondary goal was to explore if PSU exhibit improvements of neurocognitive function and inhibitory control between one and four months of abstinence from all substances except tobacco. Thirty-six treatment-seeking polysubstance users and 69 treatment-seeking alcohol users were recruited from substance abuse treatment programs at the San Francisco VA Medical Center and Kaiser Permanente for two different research projects on alcohol and substance use disorders. Table 1 displays group demographics and relevant substance use characteristics. At baseline, PSU and AUD were abstinent from all substances except tobacco for approximately 29 days. Seventeen PSU were restudied after 128 ± 29 days of sustained abstinence from all substances except tobacco. The 19 PSU not restudied at follow-up either self-reported relapse to any amount of substance use after baseline , were found to have relapse notes in their medical charts, or were lost to follow-up. All participants provided written informed consent according to the Declaration of Helsinki prior to participation. Study procedures were approved by the local Committee on Human Research. All 105 participants met DSM-IV-TR criteria for an alcohol use disorder. In addition, all 36 PSU met DSM-IV-TR criteria for at least one other substance use disorder: 27 with cocaine use disorder; 12 with amphetamine use disorder; 7 with cannabis use disorder; 5 with opioid use disorder; 1 with anxiolytic use disorder; and 1 with hallucinogen use disorder. Not considering cigarette smoking, nine PSU had two or more substance use disorders in addition to an alcohol use disorder. Specifically, of these nine, five participants met criteria for cocaine, amphetamine, and cannabis use disorder and one also met criteria for opioid and hallucinogen use disorders; two participants met criteria for amphetamine and cannabis use disorder ; one participant met criteria for cocaine and opioid use disorders, and another met criteria for opioid and anxiolytic use disorders. Nonsmoking participants smoked fewer than 20 cigarettes in their lifetime, with no cigarette use in the 10 years prior to study and no history of use of other tobacco products. Smoking participants were actively smoking at the time of the baseline assessment and smoked at least 10 cigarettes per day for 5 years or more, with no periods of smoking cessation greater than 1 month in the 5 years prior to enrollment. None of the PSU studied longitudinally changed their smoking status or severity between assessments. Medical exclusion criteria were a current or past history of intrinsic cerebral tumors, human immunodeficiency virus or acquired immune deficiency syndrome, cerebrovascular accident, aneurysm, insulin dependent diabetes, chronic obstructive pulmonary disease, nonalcohol related seizures, significant exposure to known neurotoxins, demylenating and neurodegenerative diseases, Wernicke-Korsakoff Syndrome, alcohol-induced persisting dementia, and traumatic brain injury resulting in loss of consciousness for more than 15 minutes. Psychiatric exclusion criteria included schizophrenia or other thought disorders, bipolar disorder, dissociative disorders, posttraumatic stress disorder, obsessive compulsive disorder, and panic disorder , Hepatitis C, type-2 diabetes, hypertension, and unipolar mood disorders, were not exclusionary given their high prevalence in substance use disorders . At baseline and follow-up , each participant completed the Structured Clinical Interview for DSM-IV Axis I Disorder Patient Edition, Version 2.0, as well as questionnaires assessing depressive and anxiety symptoms and Y-2 , STAI. Lifetime alcohol consumption was assessed at baseline with the Lifetime Drinking History semi-structured interview . We derived the average number of standard alcoholic drinks consumed per month, both one year before enrollment and over lifetime. Substance use history of PSU participants was assessed at baseline with a semi-structured interview developed in-house . For each substance for which a PSU participant met criteria for a current or past substance use diagnosis, date of last use, frequency of use, and quantity of use were gathered. Abstinence was assessed with self-report, and confirmed via medical chart review, mandating negative urine toxicology and blood alcohol concentration tests conducted weekly as part of routine clinical care. The Fagerstrom Tolerance Test for Nicotine Dependence was used to assess level of nicotine dependence, total years of cigarette smoking, and average number of daily cigarettes currently smoked. A comprehensive neurocognitive battery was administered to each participant at baseline and again to PSU participants at follow-up. The battery included measures of executive function, general intelligence, auditory-verbal learning/memory, visuospatial learning/ memory/skills, processing speed, working memory, cognitive efficiency, and fine motor skills. Neurocognitive domains and constituent measures are presented in Table 2. Alternate forms for Brief Visuospatial Memory Test – Revised and California Verbal Learning Test-II were used at follow-up assessments for PSU. Premorbid verbal intelligence was estimated with the American National Adult Reading Test at baseline only . All measures are well normed and commonly used in clinical and/or research settings .

The enzymology of these enzymes has been well-studied and the reader can refer to other reviews for more information.Here we will briefly summarize a few enzyme-catalyzed or enzyme-mediated reactions that will be found throughout the review. The aromatic amino acids L-tryptophan , L-tyrosine and to a less extent, L-phenylalanine, are commonly used precursors for alkaloid natural product biosynthesis. For example, the indole ring in L-tryptophan is preserved in compounds such as psilocybin and ibogaine; while the parahydroxybenzene side chain in L-tyrosine can be found in mescaline and morphine. The terminal amine-containing L-lysine and L-ornithine are also used as precursors. Relevant to this review, the four-carbon side chain of L-ornithine is required for the formation of pyrrolidines and tropanes. The first step in the utilization of these amino acids for alkaloid biosynthesis is decarboxylation to give the corresponding primary amines, although in lysergic acid biosynthesis L-tryptophan is used without decarboxylation. The decarboxylation products of L-tryptophan, L-tyrosine and L-ornithine are tryptamine , tyramine , and putrescine , respectively . In the case of tyramine , hydroxylation of one of the meta positions in the para-phenol ring gives the metabolite dopamine. Dopamine is a natural product building block, but also a neurotransmitter in mammals. The chemical logic for the early decarboxylation is straightforward: to facilitate intra- and intermolecular Mannich reactions with aldehydes and ketones using the nucleophilic amine .

This decarboxylation-Mannich two step rapidly sets up the -heterocyclic scaffold of many alkaloidal natural products. The decarboxylation reactions are catalyzed by dedicated amino acid decarboxylases. For example,plant bench indoor in the case of L-tryptophan, a tryptophan decarboxylase is involved. These enzymes typically use the PLP cofactor, as expected for many enzymes that perform Cα, Cβ and Cγ modifications on amino acids.The mechanism of the reaction is shown in Fig. 2B. The aldehyde of PLP modifies an active site lysine to form the resting aldimine in the decarboxylase active site. A transaldimination step takes place next in which the amine of the substrate amino acid attacks the aldimine and forms the amino acid–PLP aldimine. The PLP then serves as an electron sink in the enzyme-catalyzed cleavage of the Cα-COO− bond via a quinonoid species. Reprotonation of the Cα then generates the product aldimine, which can undergo another transaldimination with the active site lysine to release the product amine and regenerate the resting aldimine. Following decarboxylation of the amino acids to the corresponding primary amines, a common next step is the Mannich reaction involving the primary amine. The Mannich reaction is a two-step reaction that yields an alkylated amine.In the first step, the primary amine reacts with either an aldehyde or a ketone to form the Schiff base. The C=N double bond is then attacked by a carbon nucleophile, such as the acidic Cα of a carbonyl to form the β-amino-carbonyl product. Two examples of an intramolecular Mannich reaction can be found in the formation of the tropane unit in cocaine.Starting from putrescine , methylation of one of the primary amines gives the intermediate N-methylputrescine ; oxidation and hydrolysis of the other amine yields N-methylaminobutanal , which is in equilibrium with the cyclic N-methylpyrrolinium . Attack of the imine by the enolized 3-oxo-glutaric acid yields the adduct pyrrolidine tropane scaffold precursor .

A subsequent dehydrogenation generates a new pyrrolinium species that can be attacked with Cα of the 1,3-diketo unit in a second Mannich reaction . One variation of the Mannich reaction that is central to the biosynthesis of plant alkaloids is the Pictet-Spengler reaction involving β-arylethylamines such as tryptamine and dopamine. In the PS reaction, after the amine reacts with an aldehyde or ketone to form the Schiff base, a carbanion resonance structure of the indole in tryptamine or the parahydroxy phenol ring in dopamine can attack the imine to form the new C–C bond. This can be followed by rearrangements to form the stable tricyclic tetrahydro-β-carboline or bicyclic tetrahydroisoquinoline, respectively. The tryptamine-derived tetrahydro-β-carboline is found in harmala alkaloids and iboga alkaloids . To generate the harmala family of compounds, tryptamine is condensed with pyruvic acid , followed by attack of the imine by C3 from the indole ring to form a spirocycle, which collapses via single bond migration to complete the PS reaction .Similarly, the condensation between the aldehyde donor secologanin and tryptamine is catalyzed by a dedicated PictetSpenglerase, yielding strictosidine, the universal precursor to monoterpene indole alkaloids including ibogaine.In the biosynthesis of benzylisoquinoline alkaloids such as morphine , the PS reaction takes place between dopamine and 4- hydroxyphenylacetaldehyde , both oxidation products of tyramine , to form the key intermediate S-norcoclaurine , precursor to R-reticuline and morphine.Group transfer reactions are widely used by Nature in the biosynthesis of natural products. Functional groups that are frequently transferred from donor molecules to bio-synthetic intermediates include methyl, acetyl, small, medium and long alkyl-substituted acyl chains, isoprenyl, glucosyl, etc.

These reactions serve a multitude of purposes, including i) increasing the size and complexity of the molecules; ii) changing the lipophilicity of molecules; iii) altering the reactivity of functional groups; iv) serving as a transient chemical protection group for downstream modifications; v) acting as leaving groups in elimination reactions; and vi) changing the biological properties of the natural product. Hence, these reactions are indispensable to the structural diversity of natural products that have been isolated to date. The donor molecules, those that “carry” the groups to be transferred, are kinetically stable and thermodynamically activated: the molecules are high in energy and therefore releasing the groups is a highly exergonic reaction; yet the molecules are stable under cellular conditions and enzyme catalysis is required to overcome the kinetic barriers. We recently reviewed eight such molecules that power cellular metabolism, which include ATP, NADH, acetyl-CoA, SAM, carbamoyl phosphate, isoprenyl pyrophosphate, UDP-glucose and molecular oxygen.NADH and molecular oxygen are involved in the redox reactions and will be summarized in the next section. Among the remaining six, carbamoylphosphate is involved in nitrogen metabolism and is not directly involved in natural product biosynthesis. The remaining five, however, are frequently used group transfer donor molecules,greenhouse rolling racks and examples can be found throughout the review. ATP, the universal cellular energy currency, is the donor in the transferring of phosphate groups to nucleophilic oxygen in the presence of a phosphotransferase. This reaction is ubiquitous in primary metabolism but is quite rare in natural product biosynthesis . One such example can be found in the psilocybin pathway . Acetyltransferases catalyze the transfer of acetyl groups from the acetyl-CoA thioester to a variety of O and N nucleophiles . SAMdependent methyltransferases use S-adenosylmethionine to transfer a methyl group from the trivalent sulfonium group to C, O, N, and S nucleophiles in an SN2 type substitution reaction . This reaction can be found in the majority of bio-synthetic pathways described herein. For example, iterative N-methylation of tryptamine yields the psychoactive molecule N,N-dimethyltryptamine . UDP-glucose is an activated glucose donor in cells for the assembly of oligosaccharides and polysaccharides. UDP-glucose is thermodynamically activated but kinetically stable in the absence of glucosyltransferases.In the presence of glucosylating enzymes, UDP dissociates via cleavage of the C–O bond in an SN1 fashion to yield a C1 oxocarbonium ion, which can be attacked by incoming nucleophiles . A notable example of substrate glucosylation is in the bio-synthetic pathway of strictosidin, the precursor to ibogaine . The enzyme 7DLGT glucosylates the hemiacetal in 7-deoxyloganetic acid to give 7-deoxyloganic acid.The glucose moiety serves as a protecting group to prevent formation of the aldehyde, and remains in strictosidine . In order to transform strictosidineinto different scaffolds, a glucosidase removes the glucose moiety, unmasking the aldehyde and leading to subsequent rearrangements towards structurally diverse monoterpene indole alkaloids. The final group transfer reaction that is relevant to this review is the transfer of prenyl groups from isoprenyl pyrophosphate to different nucleophiles in small molecules. These reactions are catalyzed by a family of enzymes known as prenyltransferases.

The prenyl unit that is transferred from the pyrophosphorylated donor to the substrate can be as small, as in the five-carbon dimethylallyl , or the more elongated oligoprenyl groups such as the ten-carbon geranyl, fifteen-carbon farnesyl, etc. In the enzyme active site, the Δ2 – prenyl pyrophosphate donors can undergo C–O bond cleavage to yield the C1 carbocation, which is stabilized by delocalization of the positive charge. Attack of the carbocation by a nucleophile carbon forges the new bond and completes the prenyl transfer reaction . Electron rich aromatic rings, such as hydroxybenzenes and indoles can serve as nucleophiles to attack the allyl cation to perform in essence an electrophilic aromatic substitution. Two examples in this review illustrate this reaction. The first is the dimethylallyl tryptophan synthase in lysergic acid biosynthesis, which prenylates the C4 position in L-tryptophan to give 4-dimethylallyl-L-tryptophan .This modification introduces an olefin-containing five carbon unit into L-tryptophan, which can be further oxidized and cyclized into the hallucinogenic lysergic acid. The mechanism of this reaction has been thoroughly studied, and is likely a two-step reaction.The C3 position of the indole ring is the most nucleophilic due to resonance with the indole nitrogen lone pair. Attack on the allyl cation can occur at either C1 or C3; this attack is proposed to take place at the more stable C3 position of the allyl cation. This generates a “reverse”-prenylated product that is proposed to undergo a nonenzymatic sigmatropic Cope rearrangement to yield the “forward”-prenylated 4-DMAT. In addition to serving as the starting point for lysergic acid , indole prenylation of early pathway intermediates is commonly observed in the biosynthesis of other fungal indole alkaloids. The second notable pathway that involves prenyl transfer is in cannabinoid biosynthesis .Starting with the first intermediate in the pathway, olivetolic acid which is a resorcinol derivative, the aromatic prenyltransferase transfers the ten-carbon geranyl group from geranyl pyrophosphate to the C3 position in the ring to give cannabigerolic acid . As in the lysergic acid example, the introduced ten-carbon unit can undergo oxidative intramolecular cyclization, providing a variety of cannabinoids . Natural product bio-synthetic pathways employ powerful redox enzymes to modify the intermediates en route to the final product. The redox modification can directly modify the molecular scaffolds, or trigger rearrangement cascades, to introduce considerable structural complexities.On the reductive side, the NADH utilizing enzymes dominate as one would expect. These include ketoreductases, short-chain dehydrogenase/reductases , ene-reductases, and imine reductases, etc. The two-electron reduction of C=C, C=O or C=N bonds are initiated through the attack by a hydride equivalent from either the dihydropyridine ring of NADH or the hydroquinone form of flavin adenine dinucleotide . On the oxidative side, aerobic organisms use an assortment of enzymes and molecular oxygen as the oxidant to perform a dazzling array of chemical modifications.Both single electron and two electron manifolds are used by enzymes. These enzymes include the large family of hemedependent cytochrome P450 monooxygenases that are abundant in plants and fungi; nonheme, iron and α-ketoglutarate dependent oxygenases, copper-dependent oxidases , and flavin-dependent monooxygenases and oxidases. In two-electron oxidation of substrates catalyzed by oxidases, molecular oxygen is reduced to hydrogen peroxide. In monooxygenases where oxygen is reduced fully to water , the substrate undergoes a two-electron oxidation, while NADPH is oxidized to NADP+. Here, the substrate can incorporate one of the oxygen atoms via hydroxylation or epoxidation, or alternatively the substrate can be oxidized without incorporation of oxygen atoms. Hence, depending on the mechanism of the redox enzyme, the outcome of the reaction can be very different. This topic has been extensively reviewed in the literature, and will not be discussed in detail here. However, we will highlight two reactions to illustrate the enzymatic prowess of the P450s, a staple of the plant bio-synthetic pathways. P450 enzymes use heme as a coenzyme to bind molecular oxygen. Binding of molecular oxygen and electron transfer from the Fe and CPR leads to a hydroperoxy Fe–O–O–H species. Cleavage of the O–O bond and the loss of water generates the high valent Fe=O porphyrin cation radical, which is also referred to as Compound I. This is a highly oxidizing species that can abstract hydrogen from substrate C, O, and N atoms to generate substrate radicals, including “unactivated” sp 3 carbons. This generates the Fe– OH species also known as Compound II. Radical OH transfer to the substrate carbon radical produces the hydroxylated product in a process known as oxygen rebound.

Taken together, it appears that although marijuana use may stimulate appetite and increase caloric intake, it is not associated with increased BMI; on the contrary, it is dose dependently associated with lower BMI and lower hemoglobin A1C levels. Our study has some limitations. Information on marijuana use and other behaviors, including alcohol, smoking, and drug use, were collected via self-report and vulnerable to under reporting due to social desirability bias. Relatedly, there was no biological quantification of marijuana constituents or mode of marijuana use from participants. The imprecise measurement of marijuana use in our study could be a source of measurement error and may have contributed to the wide confidence intervals we observed. Potential confounders, such as physical activity, diet, and waist circumference, were not included in the analysis. Additionally, data from the WIHS were from predominantly black and Hispanic women, while the MACS comprises mostly non-Hispanic white men, which may signal disparities in reliable access to health care. Recent changes in the marijuana environment in the United States include decreased restrictions on use , decreased perception of marijuana as harmful , and increased adult prevalence . As of January 1, 2020, recreational use was legal in 11 states, and decriminalized in 15 others; medical use was legal in 33 . A primary concern about legalization is increased use among youth, but this concern has not been realized to date . However,greenhouse grow tables there is evidence of increased marijuana use and cannabis use disorders among adults that appears to be concentrated among adults aged 26+ .

While extant research suggests that legalization has not led to increased overall use among youth, some recent findings suggest possible increases among young adults . Repeated cross-sectional data suggest increased frequency of use among Oregon undergraduates after legalization in that state in 2015 . Additionally, early marijuana use may lead to cigarette smoking and to poor occupational and educational outcomes in young adulthood. It is also important to note that previous research on the impact of changing marijuana restrictions is largely limited to repeated cross-sectional data. There is a need for longitudinal cohort studies to identify not only trajectories of use but also predictors and correlates. Additionally, it is unclear whether loosening restrictions may have a differential impact on 18–20 year old young adults, for whom use remains illegal, compared with 21–24 year olds. Despite decreasing public apprehension, there are reasonable concerns about young adult marijuana use. Acutely, marijuana increases risk for accidents , emergency department visits , and psychosis . Persistent use predicts poor psychosocial outcomes and neuropsychological and cognitive decline in humans , and animal models suggest cognitive deficits and social anxiety . Persistent use is associated with addiction , including withdrawal . However, continued movement toward increased access makes it important to understand potential public health effects. A further concern is whether legalization modulates tobacco use. Marijuana users appear less likely to quit tobacco , and changes in use of either product are positively associated with changes in the other . Marijuana legalization could send the message that tobacco is also less dangerous than previously believed. In other words, legalization may undermine negative perceptions of tobacco use due to similar routes of administration, use of similar devices, and frequency of co-use . This risk is particularly important during emerging adulthood, when tobacco initiation peaks and patterns of long-term use are established .California was the first state to legalize medical marijuana in 1996.

At that time marijuana use was already high compared with other states, and the impact on prevalence was minimal . However, in 2016 California legalized recreational marijuana use, including possession of up to 1 ounce and individual cultivation beginning in November 2016, with commercial sales permitted as of January 1, 2018 . . State authorities began to plan for regulation of recreational sales in late 2016, and began issuing licenses for cultivation, manufacturing, distribution, testing, and retail sales at the beginning of January, 2018 . Local jurisdictions may still prohibit cultivation and sales. As of February 2018 there were 261 active retail licenses statewide; as of October 2020 that number had increased to 753, including 46 in San Diego County where this study was based . A recent report based on a survey from 2018–19 indicates that young adults generally utilize legal avenues to access marijuana and only rarely acquire it from strangers or dealers . To our knowledge, no research has evaluated changes in frequency of marijuana use in California following legalization of recreational sales, and there are few cohort studies from other states. The current study is one of the first to examine a sample of the same participants before and after legislation was implemented. Participants were at high risk for marijuana use given that all had smoked cigarettes recently at time of enrollment . This study was a secondary analysis of a study of non-daily cigarette smokers in California who were aged 18–24 when they enrolled in 2015–16 and who were followed quarterly for 3 years. Although possession and individual cultivation were permitted during 2017, we focused on legalization of sales in the belief this would have greater impact on availability based on both increased direct sales to young adults and on diversion from peer groups. Based on recent studies among young adults and on the fact that their tobacco use status likely increased risk of marijuana use, we sought to test the hypothesis that frequency of marijuana use would increase followinglegalization. A second goal was to test whether post-legalization changes in marijuana trajectories would be moderated by demographic or other substance use factors. We expected to see greater increases in use among male participants, and among those who used other substances more frequently. Finally, we explored whether changes in marijuana use frequency following legalization were related to cumulative frequency of use prior to 2018.

We recently described the relationship between marijuana and tobacco use in this sample and there is partial overlap in the data used in these studies. The primary differences are the previous study included data only from subjects’ first two years following enrollment,cannabis growing systems while the current study utilized data from all three years of follow-up, regardless of when subjects enrolled, and the previous study examined trends in use over time without regard to changes in the legal environment, while the current study explicitly examined whether frequency of marijuana use was associated with legalization of recreational sales.We recruited California residents aged 18–24 who had smoked cigarettes at least monthly for 6 months but never daily for 30+ days. Participants were recruited on a rolling basis during 2015–2016 and completed substance use assessments quarterly for three years. The present secondary analysis utilized an “intent-to-treat” approach, in which all enrolled participants were included. Average age at enrollment was 20.4 years , 51.9% were male, and most were full-time students. In terms of race/ethnicity, 41.7% identified as Caucasian, 21.0% Latinx, 19.1% Asian American, 13.1% multi-ethnic, and 5.1% from other backgrounds. Study participants completed assessments every three months for three years. All data were collected between March 2015 and October 2019. Assessments were completed online via SurveyMonkey. Compensation was $25 per annual assessment and up to $40 per quarterly assessment via electronic gift cards. Staff sent individualized survey links via email or SMS. All procedures were approved by the University of California, San Diego Institutional Review Board.. Demographics evaluated at baseline included sex, age, racial/ethnic background, and student status. Because the age range was narrow and our interest was in the potential impact of legalization, age was transformed into a time-varying binary variable reflecting whether or not participants were aged 21 at the time of each assessment. Student status was collapsed into a dichotomous variable comparing full-time students to all others. Marijuana and tobacco frequency were assessed at all 13 timepoints. At annual assessments, participants completed the Timeline Follow Back , on which they reported number of cigarettes, and whether they had used each of e-cigarettes, hookah, cigars, cigarillos, smokeless tobacco, snus, marijuana, and alcohol on each of the previous 14 days. At quarterly assessments, participants completed brief daily surveys, in which they indicated whether they had used each of the same products in the last 24 hours. We created time-varying variables representing constructs of interest. Time reflected the study timepoint, from baseline to year 3 . For all assessments, we calculated the number of days on which participants used marijuana , cigarettes , e-cigarettes , and alcohol . We counted the number of days at each timepoint at which assessment occurred to account for the fact that the maximum number of days differed for annual versus quarterly assessments, and that participants may not have provided data for all days during quarterly assessments. We created a binary legalization variable that indicated whether or not each assessment occurred prior to or after January 1, 2018. We also created a post-legalization slope variable that was coded as 0 for all pre-legalization timepoints, and to reflect time since legalization for post-legalizatin timepoints .

Finally, for each participant we calculated the total number of days prior to January 1, 2018 on which use was assessed , as well as the number of those days on which any marijuana use was reported . We used bivariate tests to evaluate whether demographic variables were related to predictors and outcomes; when associations were significant, we accounted for demographics in hypothesis tests. To test whether frequency of marijuana use changed following legalization we utilized a piecewise or segmented multilevel longitudinal regression model, an approach recommended for evaluating the impact of policy changes . This model included segments for the period prior to January 1, 2018, and for the period from that date onward. The model tested the temporal trend in frequency of marijuana use, the impact of legalization, and changes in the rate of marijuana use over time following legalization by incorporating the time, legalization, and post-legalization slope variables as predictors. Sex, race/ethnicity, and binary age were included as covariates. Second, we used multilevel longitudinal regression models to evaluate the associations of sex, race/ethnicity, binary age and frequency of alcohol, cigarette and e-cigarette use with frequency of marijuana use before and after legalization. We did so by testing for three-way interactions between the predictors of interest , time, and legalization. Significant three-way interactions would indicate that impact of legalization on the trajectory of marijuana use frequency was moderated by the predictor of interest. All demographic interaction terms were included in one model, and all substance use interaction terms in another. In both cases, non-significant interaction terms were removed in a backward manner and models refit. Finally, we used a similar multilevel modeling approach to test whether time-invariant cumulative marijuana days was associated with time-varying marijuana frequency over time post-legalization. All analyses were conducted using Stata 15.0, with alpha = .05; missing data were not imputed.The proportion of data missing was 0% for the first 3 assessments , and increased with each subsequent assessment, with 3.2% of participants missing outcome data at year 1, 10.5% at year 2, and 14.1% at the final year 3 timepoint. Missingness increased over time and was most common among participants identifying as White . Missingness was not significantly associated with sex or with frequency of cigarette, e-cigarette, or marijuana use at the previous assessment. The first 5 assesments occurred prior to legalization for all participants. The proportion of the sample for whom assessment occurred after legalization increased with each subsequent assessment, from 1.8% at the first quarterly timepoint following year 1, to 37.7% at year 2, 80.3% six months after year 2, and 100% at year 3. Bivariate analyses indicated women tended to use e-cigarettes less frequently , younger participants used marijuana more frequently , and non-White participants reported greater cigarette frequency . Consequently, sex, age, and race/ethnicity were included in subsequent analyses. Table 1 details frequency and likelihood of marijuana, alcohol and tobacco use at baseline and at each annual assessment. The proportion of days on which participants used marijuana remained relatively stable, while the number of participants who reported any marijuana use declined modestly from baseline to year 3. Alcohol use was stable across the three years of observation. Proportion of days using e-cigarettes exhibited a 50% increase, while the proportion of participants with any e-cigarette use was relatively stable. In contrast, use of cigarettes, and consequently overall use of tobacco products, decreased over time. The piecewise regression model is shown in Table 2.

The distribution of storage time for colostrum before feeding in hours underwent a square root transformation to achieve normality. If colostrum was stored for less than 2 h, zero hours were recorded for colostrum storage time. Storage temperature for colostrum was recorded as either room temperature , refrigerated, frozen, or first refrigerated then frozen. A variable for colostrum storage temperature had indicator variables for each of the 4 storage temperatures. The type of storage container for colostrum was dichotomized as solid or bags, as thawing frozen colostrum stored in bags may achieve a more uniform increase in colostrum temperature compared with colostrum stored in plastic bottles and may result in less heat damage to immunoglobulins. The variable for percent of colostrum fed that is from firstcalf heifers was dichotomized into the categories any or no colostrum from first calf heifers. The amount of colostrum fed in the first 12 h was dichotomized into <2.84 L or ≥2.84 L , as feeding 3 L of colostrum was considered a relevant biological cutpoint . Four dairies let calves nurse from their dams to meet the calves’ colostrum needs; hence, estimates for the amount of colostrum fed in the first 12 h on these dairies could not be made. In addition, a dichotomous variable for whether or not colostrum was tested for immunoglobulin content and a dichotomous variable for whether or not calves were assessed for failure of transfer of passive immunity based on serum total protein were explored in the model.Data to create variables concerning housing of calves were drawn from the questionnaire sections that captured herd-level data as well as from data collected at the individual calf level.

Questionnaire Data. Proportions of calves housed individually or in groups,rolling flood tables average group size of grouphoused calves, housing types, weaning age, age when moved from individual hutches to group housing, percent sick preweaned calves moved to a hospital pen, clinical signs to detect BRD, and treatment choices for BRD were used only for descriptive statistics purposes. This was done because these variables provide information about the whole cohort of preweaned calves and are not suitable for analyzing the association of management practices and BRD in individual calves. Data on the percent of calves raised on the premises from other dairies was dichotomized into any or no calves from other dairies on the premises. The variable feeding order by age was created with indicator variables for the answer choices youngest typically fed first , calves fed in no particular order, and oldest typically fed first. Only 1 dairy in the sample used a BRD scoring system; hence, the variable describing use of such systems was dropped from analysis. As some dairies gave multiple answers to the question about what surfaces, driven on daily, cover the roads adjacent to the calf-raising area, dichotomized variables for paved, gravel, dirt, or other surfaces were created and analyzed as separate variables. Similarly, dichotomized variables for dust-abatement procedures were created describing whether or not water, magnesium chloride, or other dust-abatement measures were implemented . For the question on how often dust was a problem in the area where preweaned calves were housed, a categorical variable was created with never as the reference and the indicator variables rarely , no week passes by without dust, daily during the nonrainy season, and daily all year round. Calf-Level Data.

Whether calves had calf-to-calf contact or not was recorded as a dichotomous variable. Hutch space was defined as the product of the width and depth, as measured during the visit. A categorical variable was created, categorizing the data as less than , greater than, or within 1 standard deviation around the mean space for wooden hutches in the sample population, and a separate category for group pens. Hutch elevation from the ground was dichotomized into elevated or not . Dichotomous variables for each of the reported floor materials were specified where absence of the specific floor material was the R [concrete, rubber, sand, Tenderfoot , dirt, grass, wood, and bedding]. In addition, dichotomous variables for slatted floors and floors that allow airflow were specified. Wall materials were categorized with indicator variables for only wood , only metal, only plastic, and a category for a mix of materials or group pens. Fourteen dairies had at least some hutches that were made of a combination of materials, such as wood and metal or metal and plastic. Calves housed in groups were added to the latter category to be able to assess the association of a single hutch material on an individually housed calf without losing group-housed calves or calves in hutches made from a mix of materials in the analysis. In addition, a dichotomous variable for solid walls or nonsolid walls was specified to explore an association with air flow in hutches and BRD. A categorical variable describing the presence and type of flush system under the calf hutches was specified with 3 levels: no flush , fresh water flush, or flush with lagoon water. A categorical variable describing the presence and type of extra shade structures above calf hutches was specified with levels no cover , partial roof, complete roof without side walls, complete roof with 1 to 3 side walls, and fully indoors.

The calving space in square meters per cow was calculated by dividing the average number of cows per pen by the dimensions of the maternity pen. Calving space was normalized using the natural log transformation. The percent of calving area that is pasture was dichotomized into any or no calving on pasture . The number of times the bedding is changed in the calving pens was dichotomized into less than or equal to 5 times and more than 5 times per month. Typical maternity pen bedding type was summarized into categories of plant fiber , recycled manure , gypsum or lime, dirt , and pasture. Those dairies that answered no maternity pen exists or no bedding in maternity pen were assigned to the category sand or dirt. Dichotomous variables were created for the presence or absence of each type of bedding,flood and drain tray as multiple dairies used more than 1 type of bedding. The data for the percent of calves removed in 1 h from the dam was dichotomized into ≥95 and <95% . Vaccine timing and frequency for cows and calves was dichotomized into whether a type of vaccine was given or not . The time since vaccination for each calf was estimated as the time between the reported age when calves receive a certain type of vaccine as stated by the interviewee in the questionnaire and the age of the calf at the time of visit. Dichotomous variables were created describing whether a certain type of vaccine was given at least 7 d before the visit to account for the fact that there is a variable lag time between vaccine administration and an immune reaction. Hygiene scores for up to 15 cows present in the maternity pen during the dairy visit were transformed into cumulative hygiene scores, which facilitated comparisons of percent of cows scoring 1, 1 and 2, or 1 and 2 and 3 versus the percent of cows scoring the respective remaining scores.Questions that could be answered with yes or no resulted in dichotomous variables including if milk was ever tested for bacterial content both before pasteurization and after pasteurization and if any medication was added to milk or milk replacer . The source of milk for each diet fed on the study dairies was recorded as a percentage of the total diet during the farm visit. For the analysis, these percentages were converted into dichotomous variables per milk source describing whether a calf’s diet consisted of >90% of one of the sources saleable milk, waste or hospital milk, unmedicated milk replacer, or medicated milk replacer for at least 7 d before the farm visit or not . No distinction was made between waste milk from fresh cows and milk from cows in a milk-withdrawal period due to medication. One dairy fed waste from a yogurt producer, which consisted of a water-yogurt mixture created during flushing of the production lines. A separate dichotomous variable for feeding of the yogurt waste product was created. The variable describing whether milk fed had been pasteurized contained a category for weaned calves to avoid exclusion of weaned calves from the model. For the number of times calves were fed milk per day, a categorical variable was created with twice feeding as the reference and levels of once daily as well as a level including 3 or 4 times or free choice feeding.

Two calves in the study were suckling from the dam based on their age and management of neonatal calves described by the producer and had no amount of milk fed or number of times fed assigned. The total volume of milk fed per day, which was calculated from the number of times calves were fed milk and the volume offered at each feeding, was categorized into ≤2.84 L , between 2.84 L and up to 5.68 L , and >5.68 L. Finally, dichotomous variables for whether or not antimicrobial drugs, vitamins, or electrolytes were added to the milk were explored. The type of medication used to treat BRD was used for descriptive statistics only.The current study is the first to evaluate BRD in preweaned calves housed on dairies throughout California and to associate BRD in these calves with management practices. Our results suggest that management relative to both housing and calf feeding practices may be the most important areas associated with the prevalence of BRD in young dairy calves on California dairies. Using lagoon water for flushing under hutches, the use of metal hutches, as well as calf-to-calf contact in older calves, and feeding Holstein calves ≤2.84 L of milk or replacer per day were all positively associated with BRD. Pasteurizing milk, feeding saleable milk, feedingJersey calves more than 5.68 L of milk or replacer per day, and providing extra shelter were all negatively associated with BRD. Interestingly, no associations were found between vaccinating dams or calves or the amount or quality of colostrum fed and BRD. Management factors significantly associated with BRD in our study may reflect some of the areas where the California dairy industry is less uniform and where changes may have the biggest effect on BRD prevalence. Current knowledge based on numerous studies underscore colostrum management and vaccinations as important components of BRD prevention; however, the current study’s dairies may have exhibited uniform colostrum and vaccination practices. The principal component analysis of a California survey on management practices related to bovine respiratory disease in preweaned dairy calves performed in 2013 identified calf housing as the component construct with the largest variation proportion . The component construct contained the variables hutch style, calf contact, flush used, and waste milk fed to heifers. Three of these variables were significantly associated with BRD in our study, supporting the idea that these are areas of highly variable management on California dairies. Although we did not observe an association between the fourth variable in the construct and BRD in our study, we did observe a negative association between BRD and feeding saleable milk for at least 7 d, which is one of the alternatives to feeding waste milk.The state-average dairy herd size in California in 2015 was 1,215 milking cows , which is less than the average herd size of 1,718 milking cows but close to the median herd size of 1,100 milking cows in our study. Herd sizes are expected to increase , so the calves enrolled in our study likely represent a growing proportion of dairy calves raised in California. Nationally, Holsteins make up 86.0% , Jerseys 7.8% , and other breeds 6.2% of US dairy cows , compared with 81.6% Holsteins, , 13.1% Jerseys , and 5.3% crossbred calves in our study. Jersey cows have increased in popularity over the last years in California due to their higher milk fat content and potential for higher feed-to-milk conversion ratios among other health related reasons . Although no official statistics for California dairy breeds are available, it may not be unreasonable to assume that Jersey cows are more common in California than nationally.

Since we largely utilize non-invasive sampling techniques, at least for the fish samples, performing quantitative measures is a challenge. Nonetheless, future studies should focus on developing non-invasive methods for accessing the quantitative measures of microbial quantities in both the BE and the fish mucous.Marijuana is one of the most commonly used drugs in the United States. It is now well known that individuals report self-treating with MJ for a number of medical and psychiatric symptoms, most commonly PTSD, pain, anxiety, and insomnia. The availability of MJ to treat these symptoms has been increasing, due in part to changes in laws related to MJ use. In the United States, MJ has been legalized in four states, decriminalized in 16 states, and there are now 23 states that have medical marijuana legalization. Many individuals using MJ medicinally or for recreational purposes use MJ for insomnia. This is despite the research suggesting that treatment seeking and non-treatment seeking 6 individuals report disturbed sleep when they stop using MJ and a only a small portion report a reduction of related symptoms as a primary benefit of use.Marijuana use is most prevalent in the United States among 18-25 year olds with approximately 32% of non-college and 35% of college-attending persons reporting past year use, and 19% of emerging adults reporting past month marijuana us. Young adults use MJ for recreational reasons, but some also use MJ for sleep difficulties. An estimated 7.3% of individuals aged 18-29 meet ICD-10 or DSM-IV criteria for the diagnosis of insomnia. In a community sample of over four thousand 18-25 year olds, 29.3% scored above the clinical cut-off on the Pittsburgh Sleep Quality Index . Thus, roll bench about 30% of individuals in this age group complain of sleep disturbance, although only one-fourth of these meet formal diagnostic criteria.

Insomnia has been associated with both self-reported impairments in daytime functioning and lost productivity. The biopsychosocial changes of young adulthood affecting sleep are well known and may contribute to MJ use. As individuals begin to live more independently, there may be fewer restrictions on sleep schedules, particularly parent-set bedtimes. Many choose to stay awake later at night to socialize or to meet academic demands. Individuals with an evening “chronotype,” who prefer to be awake late into the evening, have been shown to have more problems with reward functioning. Evening chronotypes have also demonstrated higher depression scores 16 , suicidal thoughts, more impaired work and other activities, higher paranoid symptoms, and higher anxiety, compared to a morningness-type group. Thus, the pattern of MJ use in young adults may be influenced by an interplay between changes in sleep patterns, chronotype, and mood. To date, literature on the effects of marijuana on sleep a young adult sample have been somewhat limited. While objective sleep indices have been studied in middle aged Veterans who are heavy MJ users, only one study has examined objective indices of nocturnal sleep and daytime sleepiness the following day in a community sample of young adults. In that study, 8 healthy volunteers participated in a double-blind and placebo-controlled study with either: 1) 15 mg D-9-tetrahydrocannabinol , 2) 5 mg THC combined with 5 mg cannabidiol , 3) 15 mg THC combined with 15 mg CBD or 4) placebo 22 via oromucosal spray one hour before bedtime. THC in addition to CBD was used because of the different effects associated with each compound, i.e. CBD is not as centrally activatinglike THC and has useful therapeutic/anticonvulsant properties. Participants underwent polysomnography and then sleep and morning functioning were evaluated. Nighttime sleep slightly worsened with 15 mg CBD and next day performance was impaired with 15 mg THC. This study highlights the effect of MJ on both nighttime sleep and daytime functioning.

If the relationship between MJ use and sleep disturbance in persons who are not seeking treatment for substance use disorders is substantial and documented to affect daytime function, it could have major public health significance. Health officials could publicize the relationship of MJ and sleep, primary care and behavioral health workers could highlight this information during office visits for insomnia, and drug treatment providers could meaningfully target sleep among MJ users who do seek treatment.Epidemiological studies have found associations between MJ use and insomnia over time. Adolescents, in particular, who used any illicit drug were 2.6 times more likely to report a sleep problem than those who remained substance free 24. In a nationally representative sample, adolescents with insomnia were 1.8 times as likely to report MJ use compared to adolescents without insomnia. Across ages, about one in five persons who use MJ report insomnia . This is significantly higher than the rate of insomnia diagnosed in persons not using MJ 25 which is approximately one in ten.While these epidemiological studies are informative and suggest co-occurrence, they do not inform us regarding the relationship of level of MJ use to sleep effect, or as to common versus reciprocal etiology. Participants were co-recruited from a larger study on individuals who use alcohol and marijuana between March 2012 and September 2013 through on-line advertisements in Craig’s List, Facebook, flyers, word-of-mouth, radio advertisements, and newspaper advertisements targeted at the Rhode Island/southeastern Massachusetts area. These methods are free services, widely known and used, and easy to access from a computer or smartphone. The advertisement for this study read, “Adults between 18 and 29, do you use marijuana?” You may be eligible to participate in a research study,” and asked interested persons to call the study telephone number listed. Exclusion criteria were: 1) past month cocaine, opioid, benzodiazepine, barbiturate, inhalant, PCP, hallucinogen, or stimulant use, 2) more than one episode of binge drinking in the past month.

We permitted inclusion of one binge episode because of the small number of participants who had no episodes of binge drinking in the last month, 3) night shift work, 4) self-reported diagnosis of schizophrenia, bipolar disorder, or attention deficit hyperactivity disorder, 5) lack of stable housing, 6) current suicidal ideation, and 7) past month use of sleep medication or antidepressants. We also recruited an age-matched control group who reported no MJ use in the last month. A total of 1307 persons aged 18-29 who reported using marijuana at least once a month were screened by phone for eligibility. Of these, 1052 were ineligible,drying rack cannabis due to more than one recent binge drinking episode. Other reasons for exclusion included use of other drugs , mental disorder diagnosis , currently being treated for depression , suicidal ideation , and unstable housing . Of the 146 eligible participants, 8 could not be reached to schedule a baseline appointment, 4 refused participation, and 35 did not come in for their baseline appointment. Ninety-nine consented to participate in the study; one person was excluded from participation after consent due to non-compliance with study protocols and limited cooperation with study staff. This study was approved by the Institutional Review Boards of Butler Hospital and the University of Michigan.All participants were administered the patient version of the Structured Clinical Interview for DSM-IV 27 at baseline to assess for current marijuana dependence only in the last year. During the baseline interview, a Time Line Follow Back 28was conducted to assess for MJ use over the past 4 weeks. On each day of MJ use, participants were asked how many minutes they smoked MJ. Participants were provided with the following instructions: “We would like you to look on this calendar and let us know how much time you spent using marijuana each day so we can write that in. We are also interested in knowing what time each day you used marijuana.” The day was divided into 4 six-hour quadrants. Daily smokers were defined as persons smoking MJ at least six days per week, and non-daily smokers were persons who smoked on at least 1 day in the past month to up to 5 days per week. Non-users were participants who had not smoked MJ in the last month.The basis for using minutes of marijuana as an outcome was because it is very difficult to ascertain in the naturalistic setting exactly how much THC the participant is ingesting. To clarify this measure further, we made frequency of use in days and meeting a diagnosis of dependence as our secondary measures. In looking at the validity of our chosen outcome measure, we did find support from concurrent measures of MJ use frequency. The spearman rank correlation between average minutes of MJ use during the two-week evaluation period and the percentage of days using MJ during that same period was r=0.88 .

The median was 25.18 and inter-quartile range was 12.90-62.91. The correlation between average minutes of MJ use during the twoweek evaluation period and a baseline DSM-IV diagnosis of Cannabis Dependence was r=0.33 . Although not significant statistically, those with a diagnosis of Cannabis Dependence reported an average of 42 as compared to an average of 24 among those without a diagnosis, a moderate effect size . Pittsburgh Sleep Quality Index —The Pittsburgh Sleep Quality Index is a self-rated questionnaire which assesses general sleep quality and sleep disturbances over a 1-month time period. Nineteen individual items generate seven “component” scores: subjective sleep quality; sleep latency; sleep duration; habitual sleep efficiency; sleep disturbances; use of sleeping medication and daytime dysfunction. The sum of scores for these seven components yields one global score. A global PSQI score greater than 5 has been used to define sleep disturbance.Insomnia Severity Index—The Insomnia Severity Index is a seven-item selfreport questionnaire assessing the nature, severity, and impact of insomnia in the past month. Dimensions evaluated are: severity of sleep onset, sleep maintenance, and early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, noticeability of sleep problems by others, and distress caused by the sleep difficulties. A 5-point Likert scale is used to rate each item , yielding a total score ranging from 0 to 28. We used a cut off ≥10 because this score has been shown to be optimal for detecting insomnia cases in a community sample . Epworth Sleepiness Scale—The Epworth Sleepiness Scale is an 8-item questionnaire assessing the level of daytime sleepiness. Respondents rate their likelihood to fall asleep or doze off on a scale of 0-3 in 8 different situations that may induce sleepiness . A score of 10 or greater is considered problematic daytime sleepiness, with a score of 18 signifying severe daytime sleepiness. The Morningness Eveningness Questionnaire —Smith’s MEQ is a 13- item questionnaire that assesses individual time of day preference for morning or evening activities, such as bed- and rise-times, and the clock time of becoming fully awake. This questionnaire measures behavioral temporal preference with high reliability, validity, and cross-cultural utility . Scores range from 13-52. Scores ≤22 indicate an eveningness preference, 23-43 indicated intermediate , and scores ≥44 indicate morningness.Patient Health Questionnaire-9—The PHQ9 is a 9- item questionnaire that inquires about how often mood symptoms were bothersome to the participant in the past two weeks. Participants respond on a Likert scale between 0-3, with 0 = not at all, 1=several days, 2=more than half the days, and 3=nearly every day. Total scores from 5-9 indicate mild symptoms of depression, 10-14 moderate symptoms, 15-19 moderately severe, and 20-27 severe. PHQ9 is a reliable and well validated scale.The Psychiatric Diagnostic Screening Questionnaire—To obtain information about anxiety, we used the brief 10-item PDSQ scale to measure generalized anxiety disorder. The PDSQ refers to the past 2 weeks. Participants responded to 10 questions about their anxiety with either no or yes . Higher scores reflect more anxiety symptoms.We conducted analyses on the enrolled participants that provided baseline interview responses. A hierarchical multiple regression analysis was performed to explore whether MJ use group predicted scores on sleep, daytime sleepiness, and chronotype questionnaires. Given that the high rate of unemployment in the sample may impact sleep behaviors and the possibility of gender differences in MJ use patterns, all models included gender and employment status as planned covariates. Because anxiety 40 and depression 19 are related to both marijuana use and sleep, we followed up our initial evaluations with models that controlled for these potential confounds in our analyses.

Although our baseline results showed that medical use had an adverse impact on functional impairment, these effects did not persist over the follow up period. Given the changing political landscape around marijuana, further studies focused on the potential adverse clinical effects and differences between recreational and medical marijuana users in psychiatry treatment samples will be needed to shape prevention and treatment strategies.Limitations should be noted. Data were collected from an outpatient psychiatry setting of insured patients in the San Francisco Bay Area, limiting generalizability. A PHQ-9 score of 10 indicates a positive screen for major depression, after which diagnostic assessments are required before a diagnosis of major depressive disorder can be made based on DSM criteria . Since inclusion was based on a PHQ-9 score ≥ 5, indicating at least mild depression, many participants would not have met the DSM criteria for major depressive disorder; results should be interpreted accordingly. Our marijuana status measure required medical users endorse exclusive medical use, and allowed for non-medical users to endorse non-medical use alone or in addition to medical use. As such, the data derived from this measure may underestimate medical marijuana use and overestimate non-medical marijuana use. Regarding medical marijuana use,drying racks data were not available on medical reasons for use, and future work would benefit from incorporating objective and subjective measures into the assessment.

Although adjusted multivariable analyses were used, clinical differences could present between the marijuana-use/no-use groups on unobserved factors, such as medical comorbidities and marijuana use frequency, and the results should be interpreted accordingly. Data were not available on primary marijuana compounds, Δ9 – tetrahydrocannabinol and cannabidiol , and given the potential for marijuana use to have either anxiolytic or anxiogenic effects based on the primary cannabinoid , it will be important for future work to examine the contribution of these factors to clinical outcomes in psychiatry samples. Given that several statistical tests were computed without adjustment for multiple inference testing, and all measures were based on self-report, future work would benefit from use of more robust methods and analytic procedures.Despite consistent declines in rates of cigarette use among adolescents in the last five years, rates of marijuana use have remained constant, with marijuana being the most widely used illicit drug among adolescents . Nationally representative data from Monitoring the Future show rates of conventional cigarette use among 10th graders declining significantly from 9.1% in 2013 to 7.2% in 2014; and among 12th graders from 16.3% in 2013 to 13.6% in 2014 . Rates of marijuana use have remained stable, with 16.6% of 10th graders and 21.2% of 12th graders reporting past 30-day use in 2014 . Blunts have become a common form of marijuana among adolescents, with more than half of 30-day marijuana users also reporting blunt use . Adolescents’ perceptions related to marijuana use have also changed, with the number of youth who perceive significant risk from using marijuana once or twice a week decreasing from 54.6% in 2007 to 39.5% in 2013 .

Moreover, 73.3% of 10th graders reported disapproval of occasional marijuana use in 2007, yet 62.9% reported disapproval in 2014 . Social media is a key venue for sharing marijuana-related information and attitudes, particularly among adolescents. For example, between 2012 and 2013, more adolescents than adults tweeted about marijuana, with the majority of these tweets reflecting positive attitudes about marijuana . Social acceptability and perceptions of risks and benefits, including the active sharing of these beliefs on social media, are important predictors of health behavior decision-making . Perceptions of risks generally vary by sex and age, with females and minorities tending to rate perceived risks higher than white males . Additionally, perceptions of risk related to marijuana use are known to be higher among females, non-whites, older adults, and individuals who have a family income between $20,000-49,999 . However, few studies have examined adolescents’ beliefs about specific risks and benefits related to marijuana and blunts, and studies have not examined relationships among adolescents’ perceptions, social acceptability, awareness of social media and actual marijuana use . Understanding these relationships is critical, especially since smoking marijuana places one at risk for a number of the same negative health outcomes and secondhand smoke effects as smoking conventional tobacco cigarettes . Long-term use of marijuana can lead to addiction, with initiation in adolescence associated with higher rates of addiction, negative impacts on brain development, and lower levels of school and lifetime achievement . There is also concern that using both marijuana and tobacco at the same time can reinforce the rewarding effects of both substances . Using a sample of 9th and 12th grade students recruited from California schools, this study addresses important gaps in the literature by first reporting adolescents’ rates and patterns of use of and access to marijuana, blunts, and cigarettes.

Second, this study examines and compares adolescents’ perceived prevalence, social acceptability, and risks and benefits of marijuana, blunts, and cigarettes. Lastly, this paper assesses to what extent these factors are associated with actual use of marijuana. Such information is important in order to inform the creation of better education and warning messages, especially as marijuana and blunt use increases in popularity and moves from an illicit drug to a legal drug for recreational use .This study utilized a convenience sample, in which we recruited participants from 10 large high schools throughout California. These schools were diverse with respect to geographic location , race/ethnicity, and socioeconomic status ; and were schools that were willing to participate in the study. Researchers introduced the study and invited all ninth and 12th graders to participate, during which time they provided students with consent forms for parents and students 18 and over, assent forms for students under age 18, and project information to take home and discuss with their parents/guardians. Approximately 4,000 students learned about the study,cannabis drying of whom 1,299 returned signed consent/assent forms; 405 of the consented students were disqualified from the study because of incorrect contact information, being in the wrong grade, or non-response to subsequent contact. Overall, 786 of eligible consented students completed the survey. There were some small but non-meaningful racial/ethnic differences between those who did and did not complete the survey; however, there were no differences by mother’s education. The sample size was designed to allow sufficient power to detect the contrasts of interest. The sample included 484 females and 281 males; mean age = 16.1 . Participants were ethnically diverse, with 207 White, 171 Asian/Pacific Islander, 232 Hispanic, and 168 other. Demographics of the students who participated in the study reflected the demographic make-up of their respective schools. The survey included 125 questions addressing a number of research questions; and took participants between 30 and 60 minutes to complete. Participants were allowed as much time as they wanted to complete the survey, although they were encouraged to complete the survey at one time to increase confidentiality of their responses. Only those measures related to the current study are reported here. Comprehensive results regarding the cigarette use data can be found in Roditis et al. . Many measures were derived from past surveys on adolescents’ attitudes towards substances, including those that have tested the validity of the assessments . All measures were pilot tested with adolescents of the same age and demographics of our sample. Participants indicated items that were not clear, and then we revised the survey and pilot tested it again until all measures were clear. Most items were continuously scored; the few that were dichotomized are noted below.Differences in perceptions of risks and benefits and social norms across products were assessed using a generalized linear model with the generalized estimating equation method and an exchangeable correlation matrix to adjust the variance estimates for nonindependence within school as implemented in Proc Genmod of SAS, v94. Post hoc testing utilized Tukey-Kramer tests. The relationship among marijuana use, perceptions of social norms, risks and benefits, and viewing of ads on social media was assessed using logistic regression.

The outcome variable, marijuana use, was coded into 2 categories of never used and ever used. Predictor variables included: perceived prevalence variables, perceived risk and benefit variables factor analyzed into the following categories: health and social risks, benefits, and risk of addiction, and awareness of social media attitudes and beliefs related to marijuana. Age, sex, and race/ethnicity were also included in the model; however, interactions with sex and race/ethnicity were not significant and therefore were removed in the final model. Missing data, which was negligible and varied item to item, were left missing. SPSS version 23 was used for the descriptive analyses. There were significant differences in participants’ reports of mother, father, sibling, and friend use of these products. Participants reported lower rates of marijuana and blunt use and higher rates of cigarettes use among adult figures in their lives. Conversely, participants reported much higher rates of marijuana than cigarette use among friends . They perceived significant differences in rates of use among peers, reporting that 50.92% of their peers had ever used marijuana, 42.63% had ever used blunts and 34.43% had ever used cigarettes. Participants viewed marijuana and blunts as more socially acceptable than cigarettes . Participants rated cigarettes as being overall more harmful to their health, more harmful to their friends’ health, more harmful to the environment, and more addictive than marijuana or blunts . Post-hoc analyses showed that participants perceived marijuana as more harmful to the environment than blunts, and perceived blunts as more likely to lead to addiction than marijuana. Participants viewed marijuana and blunts as similarly risky when it comes to their and their friends’ health . Generally, participants rated marijuana and blunt use as less likely to result in short-term health risks than cigarettes, with post-hoc analyses showing that they viewed marijuana and blunts as similarly risky. Participants also rated marijuana and blunt use as less likely than cigarettes to result in the short-term social risks of friends getting upset and bad breath. Participants reported no difference in the likelihood of getting in trouble from using marijuana, blunts, or cigarettes. Adolescents rated marijuana and blunts as more likely to confer social benefits of looking cool and fitting in than cigarettes, though they rated all products as equally likely to make them look mature. Participants rated marijuana and blunts as less likely to make them feel jittery or nervous, more likely to reduce stress, and more likely to make them feel high or buzzed than cigarettes. They rated all three products as equally likely to help with concentration. Use rates in this study were highest for marijuana, followed by blunts and cigarettes. Most adolescents who use these products get them from friends, use them in friends’ houses, and when they feel stressed. Adolescents perceived lower marijuana and blunt use but higher cigarette use among parents. Conversely, adolescents perceived higher use of marijuana and blunts and lower use of cigarettes among their siblings and peers. These differences in perceived use may reflect current trends in adolescent marijuana and cigarette use nationwide, in which rates of cigarette use is much lower than marijuana use, with cigarette use continuing to decline, marijuana use remaining higher , and rates of marijuana use being higher among adolescents and young adults compared to adults . While approximately a quarter of participants report having used marijuana, they thought that more than half of their friends have used marijuana. Importantly, participants who reported that their friends used marijuana had a 27% greater odds of using marijuana themselves. Previous studies also show relationships between friend drug use and adolescent drug use, and friend use is a powerful influence on adolescents’ social norms and acceptability of particular behaviors . The fact that participants report friend use rates of marijuana as double that of self-reported use may be reflective of changing social norms in which marijuana use is seen as an acceptable and common behavior, which, in turn, may be influencing decisions to use . Marijuana and blunts were generally perceived as more socially acceptable, less risky, and more beneficial than cigarettes. Despite the fact that blunts contain nicotine yet marijuana doesn’t, adolescents didn’t perceive differences in the likelihood of becoming addicted or being able to quit marijuana or blunts, although adolescents rated marijuana as more addictive than blunts. This is of particular importance, as it is possible that using both tobacco and marijuana together may actually increase the addictive potential of these products .

The models for the HIV+ group examined HIV-related clinical factors including: CD4+ cell count, detectable HIV viral load status and HAART use. In addition, for the analysis in the men in the early cohort, we estimated models for the period 2002–2013 in order to better compare the results with the men in the late cohort. Missing data for correlates were imputed using multiple imputation with chained equations . Five imputed datasets were generated for missing baseline and time-varying correlates which range from 0.2% to 14.8% and the estimates were combined according to Rubin’s rules . Because of the large sample size and number of person-visits, small prevalence ratios may be statistically significant. Thus we calculated a measure of effect size for the adjusted prevalence ratios . Cohen h or d of 0.2, 0.5 and 0.8 are small, medium and large effect sizes respectively . Throughout the analyses, P values were not adjusted for multiple comparisons. However, we highlight results where effect sizes equal or exceed the criteria for ‘small’ effects . Statistical analyses were performed using SAS version 9.4 and STATA version 11.Table 1 displays the baseline characteristics of the 5,914 participants in this study stratified by HIV serostatus and cohort enrollment. The mean age at baseline ranged from 33 years [standard deviation =6.7] among the HIV+ men in the early-cohort to 39 years among the HIV+ men in the late-cohort. The men in the early-cohort were predominantly non-Hispanic, white , cannabis vertical farming whereas the majority of the men in the late-cohort were non-Hispanic, black . At baseline, the men in the early-cohort were more educated than the men in the late-cohort .

At baseline, the prevalence of marijuana use was highest among the HIV+ men in the early-cohort and lowest among the HIV+ men in the late-cohort . However, daily marijuana use, among current users, was highest among the HIV+ men in the latecohort and lowest among the HIV− men in the early-cohort .Among the men in the early cohort, the annual prevalence of current marijuana use declined significantly from 80% in 1984 to 33% in 2013 among the HIV+ men and from 58% in 1984to 22% in 2013 among the HIV− men . The prevalence of daily marijuana use among current users increased significantly from 14% in 1984 to 32% in 2013 among the HIV+ men and from 9% in 1984 to 22% among the HIV− men . Among the men in the late cohort, prevalence of current marijuana use declined modestly from 32% in 2002 to 29% in 2013 among the HIV+ men, and decreased significantly from 37% in 2003 to 26% in 2013 among the HIV− men . However, daily marijuana use among current users increased significantly from 17% in 2002 to 37% in 2013 among the HIV+ men and from 16% in 2002 to 34% in 2013 among the HIV− men . Overall, the prevalence of daily marijuana use among all men in both the early and late cohorts were relatively stable across the followup period . The number of observations contributing to the yearly prevalence estimates for each cohort are displayed in supplemental tables S3 and S4. In this analyses of the MACS cohort, the annual prevalence of current marijuana use decreased over time among all men . However, in contrast, daily marijuana use, among those who used marijuana in the previous six months, increased among the HIV+ and HIV− men in both the early- and late- cohort enrollment: increasing by more than two-folds in nearly all groups.

Among the participants enrolled before 2001 in the MACS, the HIV+ men reported significantly higher prevalence of current and daily marijuana use as compared to the HIV− men with results reaching Cohen’s small effect size but no significant difference in marijuana use by HIV serostatus among the men enrolled after 2001. Alcohol use, particularly heavy alcohol use was significantly associated with current marijuana use and reaching Cohen’s small effect size in the analyses for both the early- and late- cohorts. Completing a graduate work or more was negatively associated with daily marijuana use and reaching Cohen’s small effect size in the analyses for both the early- and late- cohorts. The prevalence of marijuana use increased after passage of a MML in the analysis that included all men in the early-cohort but not for the men in the late-cohort, though these results did not reach Cohen’s small effect size. None of the significant associations between HAART use, CD4+ cell count and detectable HIV viral load and prevalence of marijuana use reached Cohen’s small effect size. The contrasting decline in annual prevalence of current marijuana use but increasing prevalence of daily marijuana use among users found in the current study is consistent with recent data from HIV+ women in the Women’s Interagency Study , where the authors found that between 1994 to 2010, there was a significant decrease in prevalence of current marijuana use from 21% to 14%. The most plausible explanation for the declining trend in current marijuana use may be the advancing age of participants in the MACS. One likely explanation for the increase in daily use may be that occasional marijuana users declined use over time.

The relatively stable trend in the prevalence of daily use over time supports this explanation. Between 1984 and 2013 – the period of this study – 3 of the 4 states that have MACS sites passed laws legalizing marijuana for medical purposes. In recent years, attitudes about marijuana use in the US have tempered and there has been an increase in population acceptance of marijuana use . Though not reaching Cohen’s small effect size, among the men in the early-cohort, passage of a MML was associated with an increase in the prevalence of current marijuana use in the analysis including all men, but not in the analysis that included only the HIV+ men. It is possible that the HIV+ men in the early-cohort may have already formed attitudes regarding marijuana use that passage of a MML did not influence their use. Among the men in the late-cohort, passage of MML was not significantly associated with increased marijuana use. This finding may in part be due to the short time periods pre and post enactment of the laws which may not have provided sufficient time to detect a change in their prevalence of use. Among the HIV+ men in current study,grow cannabis in containers there were few significant associations between HAART use, CD4+ cell count, detectable viral load and prevalence of marijuana use and of those that were significant none reached Cohen’s small effect size. These findings are similar to prior studies that report no significant or clinically meaningful differences in HIV viral load or CD4+ cell count among marijuana users as compared to nonusers. Yet others have found significantly lower HIV viral load and higher CD4+ count in marijuana users, although these studies differ methodologically as well as in the samples included. Taken together, these findings underscore the complex relationship between marijuana use and markers of HIV disease stage/progression and therefore warrant further study. There are some limitations to our study. We relied on self-report of marijuana use and no biological marker of marijuana use was used to confirm the self-reported data. Furthermore, data in the current analyses was collected from an ongoing longitudinal study with extended follow-up, thus attrition due to death or loss to follow-up may have influenced the prevalence estimates. This study did not assess prevalence or trends in marijuana use disorder, recreational versus medical use or other parameters of marijuana use . Also, it is important to note that the effects for passage of MML and marijuana use reported in this study should not be interpreted as causal. Our study included only four states and two states had insufficient time windows pre and post enactment of laws to provided enough information to discern a change in trend. Despite these limitations, our study has notable strengths. Our study utilized data from a large and diverse sample of HIV+ and HIV− MSM with extensive follow-up period to assess changes in prevalence of and correlates for marijuana use. In the U.S., use of prescription pain relievers , also known as prescription opioids and opioid pain relievers, has been increasing dramatically.

Worldwide, prescriptions of PPRs have almost tripled since 1990, and the U.S. is a factor in this rise, as it has the highest per capita consumption of PPRs in the past ten years . This increase has become dangerous, as opioid use carries risks that include addiction, sedation, respiratory depression, overdose and death . Between 1999 and 2010, deaths attributed to PPRs rose five times among women and 3.5 times among men . Of all prescription drug OD deaths in the U.S. in 2013, 71.3% involved PPRs . PPRs and marijuana are biologically linked; like PPRs, marijuana induces analgesia, acts on some of the same brain regions, and partly exerts its effects via opioid receptors . This connection is especially relevant due to the changing legal status of marijuana. As of August 2016, 24 states and Washington D.C. had legalized medical marijuana. Between 2007 and 2012, the number of past month marijuana users rose from 5.8 to 7.3% 2013), and between 2001 and 2013, past year adult marijuana use increased from 4.1 to 9.5% in the U.S. . Further, legalization of medical marijuana has been associated with increased odds of marijuana use among adults , though no consistent association has been determined among youth/young adults . Distinct theories attempt to explain how medical marijuana legalization affects use of substances other than marijuana. The relationship between different substances can be impacted by 1) change in cost of a substance, 2) policy alterations that influence availability of a substance, 3) shifts in legal consequences of using a substance, and/or 4) the psychoactive/pharmacological effects of a substance . More U.S. states are legalizing medical marijuana , and marijuana shares some psychoactive/pharmacological effects with PPRs. The substitution theory postulates that there is a substitution effect, whereby an increase in marijuana use coincides with a decrease in the use of other substances – in this case, PPRs . There are logical reasons why individuals would opt to use marijuana instead of PPRs. With the new legal status of medical marijuana, individuals can access it through medical dispensaries and enjoy a lower legal risk if they live in a state where it is legalized. Individuals also report switching to marijuana for pain control because when compared to prescription drugs, marijuana has fewer side effects and withdrawal symptoms . Studies supporting the substitution effect have demonstrated that either increases in the use of marijuana or the legalization of medical marijuana is associated with reductions in opioid use, hospitalizations for opioid dependence/abuse, PPR ODs, and opioid OD mortality . In contrast to the substitution effect, there may be a complementary effect, where an increase in marijuana use is associated with an increase in the use of PPRs . In support of this theory, researchers using National Survey on Drug Use and Health data found a positive association between marijuana and increased use ofPPRs . In another study, researchers focused on individuals who were prescribed long-term opioid therapy and found that those who also used medical marijuana presented with greater risk of misusing prescription opioids. Additionally, a prospective cohort study using the National Epidemiologic Survey of Alcohol and Related Conditions data determined that use of marijuana was associated with a greater risk of using nonmedical prescription opioids three years later . However, in these studies, researchers did not analyze how co-use of other substances would impact the direction and/or strength of the relationship between marijuana and opioids/PPRs. To determine if there is either a substitution or a complementary effect between marijuana use and PPR use, co-use with other substances needs to be studied. Additionally, there is a strong positive association between nicotine use and PPR use. When compared to non-smokers, tobacco smokers experience more intense and longer lasting chronic pain, as well as a higher frequency of PPR use . Studies have demonstrated an interaction between nicotine and opioids that is associated with an increase in the total consumption of the two substances and contributes to other effects of the drugs .

Most of the men in this cohort displayed a pattern of abstaining or infrequent use over time whereas approximately 10 % who used daily or near daily at their index visit continued this pattern of use over their follow-up visits. Overall, our analysis suggested that these patterns of marijuana use over time were similar for both HIV+ and HIV− participants. In the analysis among all men, HIV+ status was associated with membership across all three trajectory groups reporting any marijuana use. Among HIV+ participants, having a detectable HIV RNA over time was associated with increasing marijuana use only among the men who increased their marijuana use during the follow-up period. Self-reported ART use over time in HIV+ men was associated with reducing marijuana use in the abstainer/ infrequent and increaser groups. Overall, alcohol consumption, cigarette, stimulant/ recreational drug use and IDU over time were associated with increasing marijuana use in nearly all trajectory groups. To the best of our knowledge, we are not aware of any previous study that has examined trajectories of marijuana use among HIV+ and HIV− MSM over a long period of follow-up. Prior studies that have assessed trajectories of marijuana use have focused on adolescents transitioning into young adulthood or racial/ethnic minorities, with a few studies reporting trajectories of use covering adulthood. Direct comparisons of the results from our study with prior research may not be straightforward due to the different populations studied and age periods covered.

However, nearly all studies on trajectories of marijuana use have identified a group that abstained or used infrequently,vertical grow system with some identifying a chronic high user group and a few identifying groups that increased and decreased their use. The current study found that a HIV+ status was associated with membership in the decreaser, increaser and chronic high marijuana trajectory groups, a finding that suggests that overall HIV+ MSM in the MACS were more likely to use marijuana as compared to HIV− MSM. This finding is consistent with a number of studies reporting higher rates of marijuana use among HIV+ individuals as compared to HIV-uninfected populations. HIV+ individuals report using marijuana to alleviate symptoms related to HIV-infection as well as side effects of ART, although a substantial proportion of HIV+ individuals use marijuana recreationally. Approximately 16 % of the HIV+ men in this study reported decreasing their marijuana use over time. This pattern of decreasing substance use over time was recently observed in a study of trajectories of stimulant use among MACS participants. The authors also found that the men who decreased stimulant drug use reported significant reduction in risky sexual practices over time. Among the HIV+ MSM in this study, having a detectable HIV RNA over time was associated with increasing marijuana use among individuals in the increaser group, but not among the men in the decreaser or chronic high groups. Accordingly, we found that ART use over time was associated with decreasing marijuana use in the abstainer/infrequent and increaser groups. It is important to note that the assessment procedures used in this study make it difficult to ascertain that ART use preceded marijuana use. However, these findings provide some reassurance that there may not be an urgent need to intervene; however, there is a need to continue to study the long term effects of marijuana use on other health outcomes both in HIV+ and HIV− individuals.

In the data presented here, among the entire sample as well as HIV+ individuals, younger age was associated with membership in all marijuana trajectory groups and being nonHispanic, black was associated with membership in the decreaser and increaser groups. In addition, alcohol use, cigarette smoking, stimulants/recreational drug use, and depressive symptoms over time served to increase marijuana use within nearly all marijuana trajectory groups. This finding is consistent with previous studies that found substantial overlap between several types of drug use and other psychosocial health problems . Accordingly, any prevention approaches to mitigate these behaviors should not focus on one of these behaviors or conditions but must consider these co-occurring conditions holistically. Our study has some limitations which we highlight in order for some caution to be exercised in the interpretation of our study findings. We restricted our analysis to MACS participants who had at least 25 % or more study visits in order to estimate stable trajectory models. However, at baseline, those included in the study differed from those not included on a number of sociodemographic, clinical characteristics as well as use of substances including marijuana . Therefore, it is possible that different trajectories of marijuana use may have emerged if these participants had been included in our study. Furthermore, in the MACS, data on substance use was obtained via Audio Computer- Assisted Self-Interview . Although this method has demonstrated good accuracy in obtaining sensitive information such as drug use in studies of HIV+ individuals as well as the MSM samples, the data reported here related to substance use may be subject to social desirability bias and most likely an under reporting with a potential underestimation of the true trajectories of marijuana use. Related to this issue is the effect of other biases related to participation in a large ongoing cohort study such as the MACS, along with participant attrition due to drop outs and mortality, which may result in an underestimation of long-term marijuana use.

Indeed, in the current study, we found that men who increased their marijuana use and those with chronic high use over time were significantly more likely to die or to drop out during follow-up as compared to the abstainer/infrequent group. What this suggests is that the attrition in these groups may have precluded us from identifying what their patterns of marijuana use would have been if they had remained in the study. Also, participants in the MACS represent a highly cooperativecohort of MSM who have been retained in an ongoing cohort study; thus,cannabis grow equipment our findings may not be generalizable to the larger MSM population. Finally, the semi-parametric group based modeling approach used in this study has been criticized for its tendency to over identify trajectory groups. Accordingly, Nagin and Tremblay caution that groups extracted from the group-based trajectory models should be thought of as approximations of the more complex underlying reality of individual-level trajectories of a behavior; thus, reification of trajectory groups should be done with caution. In summary we used data from a large sample, with a long follow-up period, and utilized frequency measures of marijuana use to describe the natural history of marijuana use among HIV+ and HIV− MSM. Our study revealed different trajectories of use over time: with approximately 1 in 10 of the men emerging as chronic heavy users or increasing their use over time. Future investigations are needed determine whether long-term patterns of heavy use are associated with adverse consequences especially among HIV+ persons. Marijuana use among pregnant, breastfeeding, and reproductive-aged women has increased substantially in recent years . In the United States, national rates of use by pregnant women increased from 3.4% in 2002 to 7.0% in 2017 and particularly among younger women . In California, marijuana use by pregnant women increased from 4.2% in 2009 to 7.1% in 2016 and particularly for women ages 18 to 24, for whom use rates rose from 9.8 to 19% over this time . Comparable trends of increases in marijuana use, and especially for younger women, have been identified for new mothers and women of reproductive age in general . These increases fuel concerns about infant health consequences. The higher rates of use among young women, disadvantaged communities , and minority populations highlight the potential for disparities in infant health consequences due to maternal marijuana use. Marijuana use can be motivated by multiple psychosocial factors including peer use and efforts to cope with stress and negative affect . Beliefs about benefits and harms of marijuana use represent additional and malleable determinants of marijuana use. For example, marijuana use during pregnancy and while breastfeeding can be promoted by beliefs that it poses little or no risk to one’s infant and offers benefits such as reducing nausea or depression . Members of low-income, low-education, and minority populations, in addition to facing adversities that can exacerbate marijuana use , may be particularly likely to harbor such misperceptions due to inadequate access to health information and services .

The present study examines beliefs about the risks and benefits of marijuana use during pregnancy and breastfeeding held by residents in predominantly rural communities in California, a state that legalized recreational marijuana use in January 2018. Approximately 53.2% of the residents in this region identify as Hispanic or Latino/a/x and another 15.4% identify as a race/ethnicity other than non-Latino White . This culturally diverse region exhibits among the highest levels of income and health disparities in the U.S. . Language barriers, lack of landline phones, and low access to services in these communities contribute to their under-representation in studies using traditional survey techniques. This study employed survey methods designed to reach Latino, rural, and disadvantaged residents in this region.Growing evidence links marijuana use during pregnancy with adverse infant outcomes including stillbirth, miscarriage, preterm delivery, low birth weight, and need for neonatal intensive care . Emerging evidence also reveals its associations with deficits in attention and neurobehavioral functioning in infancy and childhood , poor intellectual performance and behavioral problems in childhood , and delinquent behaviors in adolescence . Research on how marijuana use by breastfeeding mothers affects infants is more limited, but emerging findings have stimulated concerns that it induces health harms. Its potential to affect infant physiological function is underscored by findings that tetrahydrocannabinol , the main psychoactive component, can be detected in breast milk 6 days to six weeks after use . A systematic review evaluating the safety of marijuana use while breastfeeding found that human and animal studies providing evidence supporting concerns about risks outnumber those finding no concerning evidence . In the absence of definitive evidence that it poses no health harms to infants, health organizations typically recommend a conservative approach of avoiding use while breastfeeding. The Academy of Breastfeeding Medicine also recommends that providers counsel pregnant and breastfeeding women and their family members on the risks and uncertainties surrounding marijuana use in breastfeeding . These guidelines tend to be vague and tentative about risks which, although in keeping with current scientific evidence, could limit their persuasiveness and especially for recipients with opposing beliefs about the safety and benefits of maternal marijuana use. Consultations with local health organizations have identified concerns about the public’s misperceptions regarding safety and the lack of evidence-based guidelines that enable pregnant and breastfeeding women to make informed decisions about marijuana use .Beliefs that a substance poses significant health risks are protective factors against its use and evidence, while limited, links low risk perceptions of cannabis use with substantially higher use rates among pregnant women , suggesting that health communications and guidelines that enhance beliefs in the risks of marijuana use during pregnancy or while breastfeeding could discourage their use during these times. In a sample of women receiving prenatal care, declines in marijuana use were attributed to having received educational materials on cessation of marijuana use during pregnancy . Importantly, however, acceptance of this health information will be influenced by the existing beliefs and risk perceptions held by the recipients . Little is known about how much women, their romantic partners and family members, and adults in their broader social networks hold beliefs that run counter to these guidelines and so may be resistant to accepting these recommendations. Understanding the beliefs held by romantic partners, family members, and members of one’s social network is important because these people can exert considerable influence on women’s health choices in general , during pregnancy, and while breastfeeding . Thus, understanding the beliefs held by community members more broadly is essential for identifying common misconceptions and social groups who tend to harbor them in order to develop health communications and campaigns that promote accurate beliefs for those who influence marijuana use decisions of pregnant and breastfeeding women.