All analyses were performed with R statistical software .Overall, residents of states where marijuana was legalized for recreational purposes were more likely to endorse the belief that marijuana had benefits compared with residents of other states . Specifically, residents in recreationally legal states were more likely to believe marijuana could be beneficial for pain management ; provide relief from stress, anxiety, or depression ; and improve appetite . Pain management was endorsed as the most important benefit regardless of state of residence . Residents of nonlegal states were more likely to endorse the belief that marijuana had no benefits compared with those in recreationally legal states . Multivariate analyses confirmed that residents of recreational states were less likely to believe marijuana had ‘‘no benefits’’ and more likely to believe that marijuana use had benefits in pain management, helped with reducing or stopping other medications, provided relief from stress, anxiety, and depression, improved sleep and appetite, and improved creativity compared with residents of medical and nonlegal states after adjusting for baseline characteristics .Residents of recreational, medical, and nonlegal states all endorsed addiction as the most important risk associated with use . Multivariate analyses revealed that residents of recreational states were more likely to believe that marijuana use impaired memory, and also caused a decrease in intelligence and energy compared with residents of other medically legal and nonlegal states after adjusting for baseline characteristics .Residents in recreational states were significantly more likely to believe that smoking one marijuana joint a day is somewhat or much safer than smoking 1 cigarette a day . Residents of recreationally and medically legal states were more likely to believe second-hand marijuana smoke was somewhat or much safer than second-hand tobacco smoke .

Opinions regarding other relevant public health concerns were largely similar across states: most residents,plant nursery benches regardless of legal status in state of residence, agreed that it is unsafe for children and adults to be exposed to second-hand marijuana smoke, and that marijuana use was unsafe for pregnant women. Multivariate analyses confirmed that residents of recreational states were more likely to believe that smoking 1 marijuana joint a day was safer than smoking 1 cigarette a day compared with residents of other medically legal and nonlegal states after adjusting for baseline characteristics . Residents of recreational states were also more likely to believe second-hand smoke from marijuana was safer than second-hand smoke from tobacco compared with residents of other medical and nonlegal states after adjusting for baseline characteristics .In this national study, we found that residents of states that had legalized recreational marijuana use more commonly attributed some benefit to marijuana than residents of medically legal or nonlegal states. We also found that the perception of risks from marijuana use was similar across states. In addition, we found that residents of states where marijuana was legalized were more likely to believe that marijuana smoke was less harmful than tobacco smoke. Finally, use of all forms and multiple forms of marijuana was more common among residents of recreationally legal states. Several national surveys, including the NSDUH and MTF, assess individual risk perception of marijuana use among national samples, and recent research suggests that risk perception has decreased nationwide . Previous research demonstrates that marijuana legalization is associated with decreases in risk perception, as evident from studies examining California pre and post medical legalization in 1999 . More recent research supports this assertion , and while research into the role of recreational legalization specifically is limited, initial data in adolescents suggest recreational legalization has been associated with a considerable decrease in risk perception .

While such surveys have adequately examined the decrease in risk perception associated with marijuana, there exists no detail on the types of risks individuals associate with marijuana use or potential benefits individuals assign to marijuana use. Our results show that residents of states where marijuana has been legalized for recreational use have an overall more favorable view towards potential benefits of marijuana use and were more likely to attribute benefits to marijuana use that are not supported by evidence. For example, a majority of respondents endorsed pain relief as a benefit of marijuana use, despite only limited evidence supporting its effect in managing chronic neuropathic pain and no evidence in treating other types of chronic pain . There is no evidence currently available that suggests second-hand marijuana smoke is safer than tobacco smoke and some evidence suggesting it is toxic . There is no data suggesting that marijuana is an effective and safe treatment for insomnia . When taken in context with previous research demonstrating the decrease in risk perception associated with marijuana use, our findings are significant as they illustrate the need for targeted public health campaigns to combat misinformation specifically in states with recreational marijuana legalization. We found that residents of recreationally legal states expressed less concern regarding second-hand marijuana smoke compared with second-hand tobacco smoke, and were more likely to believe that smoking marijuana is somewhat or much safer than smoking tobacco. These differences in perception are concerning, given the evidence that inhalation of particulate matter in any form is associated with increased cardiovascular risk . The perception that marijuana smoke is relatively safe compared with tobacco smoke has been perpetuated by the spread of inaccurate information on the internet . Some highly frequented internet sites suggest that smoking marijuana has many health benefits, such as improvement of lung health or the slowing of Alzheimer symptoms .

There is currently no data to suggest that smoking marijuana improves lung health. On the contrary, recent evidence demonstrates smoking marijuana is associated with coughing, wheezing, and sputum production . The lack of a clear federal public health response to the growing legalization of marijuana and proliferation of pro-marijuana marketing has left a vacuum that is filled by commercial interests . Unlike the tobacco industry, the marijuana industry has remained largely unchallenged by a coordinated regulatory response, and is aggressively advertising its product in states with rapidly expanding commercial markets . Over half of adults living in states with recreational marijuana are frequently exposed to pro-marijuana advertising in numerous forms , and research indicates that greater exposure to pro-marijuana advertising is associated with heavier marijuana use among adolescents and heavier use among adult persons who use . More stringent regulations of marijuana product marketing, and also a cohesive public health messaging campaign, are necessary to combat misinformation and communicate the potential risks associated with marijuana use so consumers can make informed choices about use. With the exception of smoking rates, which were roughly equivalent among residents in recreationally legal and medically legal states, prevalence of use among all forms of marijuana and use of multiple forms of marijuana was higher among residents in recreationally legal states. This is not surprising, given that novel forms of marijuana are more accessible in states with robust recreational markets. For example,metal greenhouse benches in the first year with an active recreational marijuana market, Colorado dispensaries sold 4.81 million units of edible cannabis product . The popularity of marijuana products in forms other than smoking is a cause for some concern as such products are increasingly available with THC content at high levels not yet studied. Previous research suggests that some edible products exceed state-mandated THC thresholds and can reach as high as 7000 mg per package . Given the growing popularity of marijuana in forms like edibles or extracts, increased focus should be directed towards understanding the health effects of THC at such concentrated levels. In the absence of evidence of harms, states may be reluctant to more stringently regulate the form and content of edible products. There are several limitations to this study. The generalizability of our results may have been limited by the use of an internet survey as the population who choose to join an ongoing internet panel may be different from individuals who choose not to participate. However, GfK’s KnowledgePanel has demonstrated no evidence of non-response bias in the panel on core demographic and socioeconomic variables . We did not conduct reliability testing of the survey items. As a result, it is possible the interpretation of our questions might differ between participants. For example, though pain management was endorsed as the most important benefit across residents of all states, we did not distinguish between types of chronic pain, and this may have been interpreted differently between participants. Additionally, we did assess the extent of individual marijuana use among participants, medical reasons for use among marijuana users, and sources of information regarding beliefs about marijuana. However, the data were not sufficiently relevant when stratified by state legalization status.

Furthermore, it is important to note that we did not differentiate between state legal status beyond the designation of ‘‘recreationally legal, medically legal, or nonlegal,’’ and marijuana accessibility can vary greatly within states with the same legal status due to differences in state-based implementation. Nonetheless, we found clear differences in opinions of residents of recreationally legal states compared with other states. Finally, the study was cross-sectional. Therefore, it is unknown if people in states where marijuana was legalized for recreational use developed their beliefs before legalization, which then led to legalization in their state, or if the opinions assessed in this survey were a result of recreational legalization of marijuana.The prevalence of marijuana use has increased in the past decade in the United States population and worldwide. In tandem with increasing marijuana use, there has also been a substantial drop in the public’s perception of risks from marijuana use in the US and other Western countries.Moreover, the marijuana industry has experienced tremendous growth in the past decade and is projected to exceed $57 billion in annual revenue within the next decade.In tandem with the growth in marijuana marketing, sales, and use, there has been a proliferation of misinformation.National surveys suggest the perception of Bgreat risk^ from weekly marijuana use has dropped from 50.4% in 2002 to 33.3% in 2014 and has dropped further since.A recent national survey demonstrated that the public attributes benefit to marijuana without any evidence to support such beliefs . Moreover, recent data also suggests that many Americans believe that marijuana has no risks and that it prevents health problems.The main drivers of this favorable perception in the US are unclear, but it is likely multi-factorial and includes the liberalization of medical marijuana laws and promotion by advocacy organizations and business interests. For example, Business Insider, a website with an audience of over 100 million visitors a month,recently touted the ability of marijuana to Breverse carcinogenic effects of tobacco and improve lung health.^ The source research article cited by Business Insider did not support such a claim.In addition, legalization has been accompanied by the commercialization of marijuana, with projections estimating that marijuana sales will exceed $25 billion by 2025.There is overt marketing to consumers of marijuana on the Internet and social media with inadequate regulatory oversight.It is possible that sources of information are playing a role in furthering misinformation among the public, which in turn is resulting in decreases in risk perception, particularly among adolescents.Determining the public’s main sources of information about marijuana use is essential to curbing misinformation and improving public health outcomes. In this national survey of US adults, we examined where US adults receive information about marijuana. We also examined whether the sources of information were associated with believing unsupported claims about marijuana.We conducted a survey of a nationally representative sample of 16,280 US adults on risks and benefits of marijuana use. The survey was conducted using Knowledge Panel , a nationally representative panel of civilian, non-institutionalized US adults aged 18 years and older that has been used to survey public opinion since 1999. GfK created a representative sample of US adults by random sampling of addresses. The address-based sampling covers 97% of the country and encompasses a statistical representation of the US population. Households without Internet access are provided with an Internet connection and a tablet to ensure participation. All participants in the panel are sampled with a known probability of selection. No one can volunteer to participate and are instead selected randomly by GfK based on address.

The proportion of counties in Colorado with prohibitions was also greater than that found in a policy surveillance study from California.Our results add to existing reports that numerous counties are opting to prohibit recreational marijuana facilities in states where recreational marijuana can be legally purchased and consumed . We found local stakeholders were publicly engaged in county policy decisions. We did not identify stakeholders who represented industry groups or external entities, which appear to play a limited public role in local debates. It is feasible that industry groups or other external advocates were involved in private lobbying efforts as opposed to public facing advocacy strategies. Our findings about the predominance of local stakeholders in policy debates are similar to research examining a local medical marijuana policy debate in California involving internal actors, and the arguments namely reflected a struggle between identity and social order within the region.The involvement of local stakeholders also suggests marijuana commercialization is currently a cottage industry. In the absence of targeted regulatory policy to retain small-scale growers and retailers, a large marijuana industry could emerge, prioritizing lobbying and aggressive advertising strategies.Tobacco companies may potentially enter marijuana policy debates given their long-standing interest in entering into legalized marijuana markets based on the sales potential.Based on our qualitative data, whereas some stakeholders were likely to support allowing marijuana facilities as a way of attracting outside tourism, other stakeholders used concerns about legal liability or additional crime from outsiders as rationale to prohibit these facilities. Future work should further explore how county policymakers balance local-level politics and perspectives with state policy directives and policies over time. Additional research could examine factors influencing enactment of local recreational marijuana policies and closely examine the effect of population density and county-state borders on local marijuana policy decisions.

Arguments in favor of permissive cannabis dry rack county ordinances included economic benefits 60 and increased access to medicinal and therapeutic treatment. Early evidence on cannabis markets in Washington suggests increased cannabis retail access is associated with more frequent use among adults in the state.Sales data from Washington indicate a large and growing marijuana market, yielding a substantial source of state revenue allocated to the general fund, basic health, local municipalities, prevention and education, research, and other issues. In fiscal year 2019, Washington collected $395.5 million in legal marijuana taxes and license fees.62 During the COVID-19 pandemic, monthly recreational marijuana sales in Colorado were $183 million in July 2020 alone.As state and local governments continue to be impacted by the economic losses associated with COVID-19, economic gain arguments might become increasingly attractive to offset diminishing revenues from other sources. The gradient of local marijuana policy is more nuanced than these three categories because some counties allowing all types of marijuana facilities also have restrictive zoning conditions, limit the maximum number of facilities, or limit acreage for cultivation or processing facilities. Additionally, our study did not closely examine other local marijuana policy elements such as minimum buffer distances, business hour limits, public health messaging, or advertising restrictions.Another study limitation is the exclusion of cities and towns from the sample. It is important to note that counties are limited in their regional authority and can include cities with marijuana policies that differ from the county policy. Prior studies on similar topics and geographies have examined marijuana policy in Washington cities with more than 3,000 residents3 and all cities in California.6 Given our additional focus on policy stakeholders, arguments, and advocacy efforts, we elected to solely concentrate on county-level policies. A key strength of our study is that it is the first to consider local policy variation across two states that legalized recreational marijuana use at the same time. The typology we created and employed can serve as the basis for future legal epidemiology work to examine the effects of policy on public health, health, and social outcomes.

Other strengths are the inclusion of qualitative research and newspaper article data for the purpose of providing rich descriptions of the county policy environments and change. Prior cross-sectional policy surveillance studies lacked these elements. Further, our findings convey the value of ongoing local policy surveillance data because four counties identified as having moratoriums/bans in place as of July 2014 allowed recreational marijuana facilities by mid-2019, indicating considerable local policy change processes occurred within these jurisdictions in the span of a few years.Since 2012, several other states have legalized recreational marijuana for adults and have permitted local-level jurisdictions to regulate local markets. States may benefit from the results of our findings as they create their own frameworks to regulate marijuana and take into consideration similar fundamental local government concerns, such as public safety, health, and environmental impact issues. Opponents of permissive marijuana policies point to perceived public health, safety, and welfare issues related to marijuana facilities and use, including addiction, increased crime, and detrimental health effects for minors. Early results from Colorado suggest legalization has led to increased marijuana-related hospitalizations, an initial surge in poison control center calls mentioning marijuana, and increased DUIs for which marijuana was identified as the primary impairing substance.In Colorado and Washington, commercial marijuana legalization is significantly associated with increased rates of fatal motor vehicle accidents.There is also evidence on the harms of marijuana secondhand smoke on cardiovascular health.Use of high-potency cannabis concentrate products has also been associated with negative consequences like psychosis and emergency department visits.These public health outcomes should be considered and used to inform policy decisions. Research about associations between crime and legal recreational marijuana facilities and local markets can also help address perceived concerns about increased crime. Findings from existing studies are mixed, likely due in part to challenges in interpreting law enforcement data.

We found that advocates of allowing local marijuana facilities claimed legalization could reduce local marijuana black market activities; however, opponents were concerned about additional enforcement or implementation costs, as well as crime. Cost-effectiveness studies comparing the cost of additional enforcement or implementation mechanisms to local revenues are needed. Potential associations between local marijuana policy and health effects and crime have gone largely unexplored due to restrictions and limitations on federal funding for marijuana research. Studies on the implementation and management of marijuana prevention and control programs have also been constrained by a lack of federal funding. However, interest in the public health and safety impact of local marijuana businesses expressed by local stakeholders in our study indicates a critical demand for policy effectiveness research to inform local decisions. Youth access to and use of marijuana, driving under the influence of marijuana, marijuana dependence and addiction,botanicare rolling benches unwanted contaminants in marijuana products, uncertain potency of marijuana products, and concurrent use of marijuana and alcohol have previously been identified as important topics for public health research and evaluation to inform policy.Public concern about noxious odors was a frequent argument against permitting marijuana cultivation and processing facilities in local jurisdictions, as well as a reason to overturn permissive marijuana ordinances. This is an issue that policymakers and marijuana producers need to address and mitigate with regulatory policy and practice. As the legal market continues to grow, more research is needed on the impact of operating marijuana cultivation and processing facilities on the environment,including studies of the use of pesticides and cannabis cultivation and regulation standards.Concerns have been raised regarding the concentration of cultivation licenses in agricultural areas and environmental health concerns for vulnerable populations,who often disproportionately face environmental injustices. Research is also needed to explore the role of state and local public health departments in regulating marijuana in states where recreational marijuana is legal, since a public health framework would designate them as the lead regulatory agency.Studying the impact of marijuana legalization on public health departments’ scope of work and health education and promotion efforts in marijuana prevention and control programs is also important. States such as Washington that have earmarked revenue for prevention, control, and research may have made further progress than counterparts in this area for developing educational campaigns .Co-occurring marijuana and tobacco use have been associated with increased rates of psychiatric disorders and psychosocial problems . Further, co-use has been linked to more days of past month marijuana use and higher rates of marijuana dependence relative to marijuana only smokers . Despite the negative health related consequences of tobacco and marijuana co-use, a recent review found only three studies focusing on co-use prevention and two studies on treatment . Since that review, a few treatment studies for marijuana and tobacco co-use have been published . However, additional research is needed to inform the effective development of prevention and treatment interventions for tobacco and marijuana co-users. Identifying interaction expectancies might help identify barriers and strategies that should be used in the development of prevention and treatment interventions for co-users .

High expectations may pose barriers to cessation of either substance, therefore underlying low motivation to quit. The Nicotine and Other Substance Use Interaction Expectancies Questionnaire was developed by Rohsenow et al. to investigate expectations regarding the relationship between smoking and other substance use held by treatment seeking substance users. Participants reported that substance use almost always increased their smoking or urges to smoke, but smoking only increased their substance use or urges about half of the time . More recently, Ramo et al. adapted the NOSIE to examine cigarette and marijuana interaction expectancies . In a non-treatment seeking sample of young adult marijuana and tobacco co-users, Ramo et al. found that young adults who used more tobacco and marijuana held higher expectancies regarding the interaction of the two substances. In addition, days of past month marijuana use and thoughts about abstinence, significantly predicted responses to the NAMIE scales. The current study examines presence and predictors of tobacco and marijuana interaction expectancies among African American young adults participating in an anonymous online survey. African Americans were chosen as the population of interest, as co-use is considerably high among this population of young adults and they experience higher rates of negative drug-related consequences of substance use comparable to other racial/ethnic groups . Further, African Americans are more likely to smoke blunts than other racial/ethnic groups , and little is known about whether this form of co-use may be associated with unique patterns of interaction expectancies compared to other forms of co-use. The purpose of the current study was to determine if there are differences in tobacco and marijuana co-use expectations among blunt and cigarette co-users [hereafter referred to as blunt co-users] and non-blunt and cigarette co-users [non-blunt co-users] and determine if expectancies among blunt co-users and non-blunt co-users were associated with variables that have been linked with expectations about drug use in previous studies , including age at first tobacco use, age at first marijuana use, days of past month marijuana use, and days of past month tobacco use.Participants were African American young adults who completed an anonymous online survey. The survey was designed to assess patterns of and factors associated with marijuana and tobacco co-use among non-treatment seeking young adults. Participants were recruited via flyers distributed in predominately African American communities , word of mouth, and a free campaign on Craigslist in a Midwestern city in the United States. The survey link, along with a Quick Response code, was included on the flyer, which directed participants to the consent form, screening questions, and a secure survey within Qualtrics. Participants who met the following criteria were eligible to participate in the survey: were between the ages of 18–29, self-reported as being a non-Hispanic African American or Black woman or man, self-reported smoking marijuana at least four times in the past month, self-reported smoking tobacco at least 20 of the past 30 days, were not enrolled in outpatient or residential substance abuse treatment for marijuana or not engaged in formal smoking cessation treatment in the past year and had a personal email account to receive an electronic gift card. Eligible participants were invited to complete a brief survey, and received a $20 online gift card for their time and effort. Of the 185 participants who completed the survey screener, 162 participants met eligibility criteria to complete the survey. Of those, 144 completed the survey. For the current study, we used data from participants who reported past month marijuana and cigarette use .

There has been growing interest in standardized trials to determine efficacy and side effects using standardized dosing . Marijuana is also increasingly recognized as a promising therapeutic target in various digestive disorders including inflammatory bowel disease, irritable bowel syndrome, secretion and motility related disorders. Cross-sectional studies  have reported a correlation between daily marijuana use and increase in liver fibrosis and steatosis among hepatitis C patients. However, several subsequent cohort studies did not confirm the association between marijuana use and accelerated progression to fibrosis or cirrhosis in HCV or HIV/HCV coinfected patients. Whether marijuana use should be a contraindication for liver transplant is unclear. In a recent survey of transplant physicians, 47% identified marijuana use as a “controversial characteristic”. In fact, there is little consensus within the LT community whether marijuana users should be eligible for transplant listing at all . Despite this debate, there are few reports on overall survival among chronic liver disease patients who use marijuana. Consequently, marijuana use among LT candidates remains a controversial topic . Yet, in July 2015, California adopted Assembly Bill 258, the Medical Cannabis Organ Transplant Act, which prohibits transplant institutions from denying transplantation to medical marijuana users based on their use of marijuana alone. In fact, 6 other states have adopted similar measures protecting medical cannabis users: Arizona, Delaware, Illinois, Minnesota, New Hampshire and Washington. In addition, 6 states passed legislation or ballot measures to legalize medical marijuana in 2016 alone, bringing the total number of states with legalization of marijuana to twenty-eight in addition to the District of Columbia. Given the increasing trend towards legalization and protection of medical marijuana,indoor grow lights shelves understanding the impact of marijuana use on LT outcomes is not only practical but also essential. In the only prior study assessing LT-related outcomes among marijuana users, Ranney et al found no survival difference among LT candidates whether they consumed marijuana or not.

However, in this study, more than a third of eligible patients on the LT wait list were excluded due to missing tobacco, toxicology or psychiatric history. This large proportion of patients with missing toxicology data is likely to include many substance users who might be reluctant to undergo drug screening for fear of delisting or had poor follow up. Despite exclusion of these potentially high-risk patients, marijuana users were significantly less likely to receive LT . Given the proportion of missing data, it is plausible that this study may not have adequately captured adverse outcomes like death or delisting among marijuana users on the LT wait list. In light of the limited data on LT wait list outcomes, in the present study, we aimed to assess several outcomes among historical marijuana users who were evaluated for LT at our institution, including death or delisting on the LT waiting list and probability of receiving LT. In addition, we also sought to evaluate the prevalence of and factors associated with marijuana use among all patients undergoing LT evaluation at our center to guide future studies among this population. All adults presenting for a LT evaluation at University of California, San Francisco over a 2-year period, from January 1, 2012 through December 31, 2013, were included in this retrospective cohort study. The study was reviewed and approved by the UCSF institutional review board. During the study period, the UCSF LT program had a policy of not listing patients with active marijuana use. Prior to listing, patients were required to abstain from marijuana use and, therefore, all marijuana use among listed patients is likely to be historical. Marijuana use was defined as ‘recent’ if subjects self-reported ongoing marijuana use at the time of first LT evaluation and/or had positive drug toxicology on screening laboratory evaluation. These patients were generally asked to abstain from marijuana use before being listed for LT. ‘Prior’ use refers to self-reported historical use of marijuana. Similarly, tobacco, alcohol and illicit substance use was defined by combination of self-report and urine toxicology and further categorized as ‘prior’ and ‘recent’. Patient demographic and clinical data were collected by individual health record review and/or programmed capture from electronic medical record databases .

Substance use, including marijuana, information was obtained from review of detailed psychosocial assessment conducted by trained social workers at the time of first LT evaluation. Statistical analyses were performed using STATA versions 12 and 14 software . Marijuana use was defined as combination of self-report on psychosocial assessment or positive urine toxicology during initial LT evaluation and work up. Urine drug screening was performed at the discretion of the LT team based on perceived risk of drug use on the LT wait list. Our study includes the initial urine drug screen with further data captured at the end of the study period via final LT status . Marijuana use was further categorized as ‘prior’ or ‘recent’ at the time of first LT evaluation, as described previously. Factors associated with marijuana use were analyzed using a multi-variable log-link Poisson regression with robust standard errors to estimate adjusted incidence rate ratios . Using this method, the calculated IRR approximates the prevalence ratio. All risk factors with p-values of less than 0.05 were retained in the multi-variable model. Among those listed for LT, we calculated the cumulative incidence of death or delisting within strata of marijuana use. Similarly, we also calculated the cumulative incidence of receiving LT within strata of marijuana use. Observation time was measured from date of first LT evaluation to the first of dropout, wait list death, or transplant. Cumulative incidence estimates accounted for competing events and patients remaining on the waiting list were censored at the last known date on the list. Using Fine and Gray competing risk regression 19 we estimated the hazard ratios and 95% CI for risk of the 2 outcomes of interest, wait list death or delisting and receiving transplant on the wait list. Factors with a univariate p<0.2 and the primary explanatory variable, marijuana use, were included in the multi-variable modeling process. The final multi-variable models were selected by backward elimination with p>0.05 for removal while retaining marijuana use. Of all 884 LT candidates, 585 were listed for LT. Among them, 205 died or were delisted while 287 received LT. Among never users of marijuana, 69% were listed for LT. While, 65% and 51% of prior and recent users of marijuana were listed for LT, respectively. Listing and wait list outcomes for all participants are outlined in Figure 2. Among those listed for LT, there was no statistically significant difference in the cumulative incidence of death or delisting on the LT waiting list within strata of marijuana use .

Similarly, there was no statistically significant difference in the cumulative incidence of receiving LT within strata of marijuana use . Reasons for delisting from the LT wait list are presented in Table 4. The most common reasons for delisting included ‘too sick for transplant’ and ‘death’ . There were isolated cases of ‘substance abuse relapse’ with no significant differences between recent, prior and never users of marijuana . Our study presents a comprehensive assessment of marijuana use among LT candidates. We found no statistically significant association between the risk of wait list removal or death and historical marijuana use. On the other hand, notably,vertical indoor growing system a history of recent illicit drug use was associated with higher risk of death or delisting. This finding could be related to a number of possibilities including recidivism to drug abuse which would prompt delisting from LT wait list, or perhaps higher rate of medical illness from complications of drug use resulting in death or delisting. Illicit drug use may also reflect worse social circumstances and lack of support leading to delisting. On the other hand, a similar association with marijuana use was not found. This observation supports major differences in the impact of a history of marijuana use vs. illicit drug use among LT candidates. Similarly, in unadjusted and adjusted competing risk regression, we were unable to detect a statistically significant association between receiving LT and history of marijuana use. Factors associated with higher chance of receiving LT included MELD score ≥20 and HCC – both of which are consistent with and reflect current LT allocation practices . Marijuana use was highly prevalent among LT candidates at our institution. Almost half of all evaluated patients had a history of marijuana use and a considerable proportion were recent users at the time of evaluation. Among users, 13% had a self-reported history of weekly use while 16% had been daily users. Substance use, beyond marijuana, was a common feature among LT candidates – we found high prevalence of historical tobacco use , alcohol use , illicit drug use and prescription opiate/BDZ use . More than half of all alcohol users had a history of heavy use/abuse, and a significant proportion of candidates were recent users of illicit substances. We also found that almost a quarter of evaluated patients had recent opiate/BDZ prescriptions. Though detailed and systematic data about substance abuse among all LT candidates are limited, our findings are similar to prior reports, including those assessing patients with alcoholic liver disease .

We also identify several factors associated with marijuana use, including younger age and white race. Marijuana use was closely associated with other substance use – persons with alcoholic and HCV cirrhosis were more likely to have been marijuana users compared to those with HBV cirrhosis. Tobacco use, both prior and recent, was also associated with higher prevalence of marijuana use. Similarly, prior and recent illicit drug users had higher prevalence of marijuana use. Notably, never users of alcohol had much lower prevalence of marijuana use – this likely reflects a small proportion of LT candidates who have been abstinent or had very limited exposure to any substance use. There have been prior conflicting reports regarding an association between marijuana use and lower BMI. In univariate analysis, marijuana users were less likely to be obese compared to overweight , though this association was not significant after multi-variable adjustment . This is the first study to present detailed data on prevalence and multi-variable adjusted factors associated with marijuana use among LT candidates. Our findings are consistent with limited prior reports of marijuana use patterns among LT candidates. Recent national drug use surveys 27 have found that 6.5% of adults older than 25 had active marijuana use. These nationally representative estimates are in close agreement with our finding of 7% recent marijuana use among LT candidates. It is also important to note that marijuana use was not just limited to those with a history of substance abuse but was rather distributed across the spectrum of substance use, as demonstrated in Figure 1. Yet, in our study, despite noting a high prevalence of marijuana use and its associations with other substance use, we were unable to detect worse outcomes with historical marijuana use itself; whereas, illicit substance use did confer higher risk of death or delisting on the wait list. In a recent study, Greenan et al 28 also found that isolated recreational marijuana was not associated with poorer outcomes among kidney transplant patients. Though most patients underwent urine drug screening in addition to psychosocial evaluation to identify marijuana use in our cohort, we assessed for differences in sensitivity of marijuana use assessment between urine drug screening and psychosocial screening. Most marijuana users had positive urine toxicology – among ‘recent’ marijuana users 80% had positive urine toxicology while an additional 20% were identified based on self-report alone. Therefore, sensitivity of drug screening alone was 80% while that of self-report alone was 62% . We also assessed for differences in outcomes between those who tested negative for marijuana and those who were not tested with urine drug screening. A similar proportion of subjects with and without urine drug screen were positive for marijuana use – 48% and 46% , respectively, with no statistical difference detected . When comparing subjects without marijuana use by presence or absence of the urine test, risk of death/delisting and LT failed to differ statistically. Regardless of screening method, a similar proportion of patients were positive for marijuana use and wait list outcomes were similar.

Gaining better understanding how psychological distress and social supports or deficits relate to marijuana use among young adults may inform tailored intervention development. Marijuana has become more socially acceptable in California since medical marijuana laws passed in 1996, and acceptability is likely to increase further if marijuana is legalized. A 2015 study of Northern California teens reported that marijuana was perceived as more socially acceptable, normative, and safer than cigarettes.However, it remains unclear how this social context may interact with sociodemographic and psychosocial characteristics in marijuana use among young adults. Although the evidence base for understanding the health effects of marijuana is limited as a result of the difficulties with conducting research on a drug classified as Schedule 1 under the United States Controlled Substances Act, early studies have indicated associations between long-term heavy marijuana use and increased risk of addiction, chronic bronchitis, cognitive impairment and psychosis disorders in people predisposed to them.Additionally, studies of marijuana use among adolescents have demonstrated associations with decreased academic achievement and increases in use of other illicit drugs as well as suicide attempts.Marijuana use has further been associated with risky health behaviors, such as tobacco use and binge drinking, and secondhand marijuana smoke has been shown to compromise vascular function similar to secondhand cigarette smoke.30 This study’s objective is to describe the sociodemographic correlates of marijuana use in a diverse population-based sample of young adults residing in San Francisco and Alameda Counties, and to investigate associations between psychological distress, social support,clone rack loneliness and marijuana use. Specifically, we hypothesize that: 1) there will be differences in marijuana use by sociodemographic characteristic; 2) psychological distress will be positively associated with use; and 3) social support and loneliness will moderate the association between psychological distress and marijuana use.

We additionally control for known correlates of marijuana use among young adults, including perceived harms of marijuana, sleep quality, cigarette smoking, and alcohol use.Data for this study are from the 2014 San Francisco Bay Area Young Adult Health Survey, a probabilistic multi-mode household survey of 18–26 year old young adults, stratified by race/ethnicity, in Alameda and San Francisco Counties in California. Potential respondent households were identified in two ways – first from address lists obtained from Marketing Systems Group wherein there was an approximately 40% chance that an eligible young adult resided at the selected addresses , and second using 2009–2013 American Community Survey and 2010 decennial census data in a multistage sampling design to identify Census Block Groups and subsequently Census Blocks in which at least 15% of residents were adults in the eligible age range; address lists were compiled for each selected block and households were randomly selected from these lists for face-to-face visits . The survey was conducted using three modes – mail/web, telephone, and face-to-face interviews. Mailings, including the survey questionnaire, informed consent document, and $2 incentive were sent to each of the 15,000 addresses identified, followed by two additional mailings. Respondents could return the questionnaire by mail, or complete it online. Subsequently, the ~13,000 households for which we had phone numbers and from which we had not already received a response received up to three calls to determine eligibility and attempt questionnaire completion. Finally, a random selection of addresses was drawn from the households lacking a mail or phone response, and research assistants visited each of these households, as well as all of the randomly selected housing units from the block sample, in person up to three times. The final number of observations was 1,363 for a survey response rate of 30%. Approximately 2/3 of respondents replied via mail or online with most of the remaining responses completed in the face-to face phase; only a handful of questionnaires were completed via telephone. Individual sample and post-stratification adjustment weights were constructed after data collection.

We assessed respondent age, sex, race/ethnicity, sexual orientation, maternal education and marital status. Age is a continuous variable in years; self-reported race/ethnicity is measured as a categorical indicator and the remaining measures are dichotomous. Respondents were given the opportunity to select multiple race categories, and those who selected more than one category and were not Latino were classified as “multi-race.” Sex was coded as ‘1’ if the respondent was male, 0 otherwise; LGBT was coded as ‘1’ if the respondent identified as homosexual or bisexual; mother’s college education was coded as ‘1’ if the respondent’s mother had at least graduated college; married was coded as ‘1’ if the respondent was currently married. Psychological distress was measured with the K6 scale,which asks respondents how frequently in the past month they felt nervous, hopeless, restless, depressed, worthless and like everything was an effort. Participants were classified as having serious psychological distress if the K6 value was equal to or greater than the validated cutoff of 13.Social support is a composite variable based on five items, derived from the Multidimensional Scale of Perceived Social Support, indicating agreement with the following statements: 1) there are people I can count on when I need help; 2) there is a special person in my life who cares about my feelings; 3) my family really tries to help me; 4) my friends really try to help me; and 5) I get the emotional support I need from people in my life. Responses were on a five-point Likert scale from strongly disagree to strongly agree. Loneliness was measured using two items, derived from the Loneliness Scale,employing the same Likert response scale: 1) no one really knows me well; and 2) my interests and ideas are not shared by those around me. Each of the two composite variables were standardized, and higher response values indicate greater levels of social support and loneliness. These items were based on longer versions of validated scales; and we conducted confirmatory factor analyses to ensure that the abbreviated scales each conform to one factor . Perceived harm of marijuana use was measured continuously with respondents indicating how harmful they think marijuana is to general health, from “not at all” harmful to “extremely” harmful .

We assessed three other health behaviors possibly related to marijuana use: current cigarette smoking, number of days respondents engaged in binge drinking and respondent sleep quality. Current smoking was coded as ‘1’ if the respondent smoked a cigarette at least one day in the previous. Days binge drinking was measured continuously as the number of days respondents reported drinking five or more alcoholic beverages “within a few hours” in the past 30 days. Sleep quality was also measured continuously; respondents rated their sleep quality from “very bad” to “very good” .We used Stata 13 to examine bivariate relationships between each of our predictors and marijuana use , followed by a hierarchical logistic regression, reflected in Table 2, using the “svyset” command to adjust for the complex sampling design. Data for the analysis were retained for 1324 of the total 1363 observations. Twenty-seven respondents were missing values for marijuana use, and the remaining observations were dropped due to incomplete data across several of the independent variables in the model. In conducting the hierarchical regression,hydroponic shelves we first assessed relationships between exogenous and socioeconomic characteristics and marijuana use, before entering serious psychological distress, followed by social support and loneliness and adding perceived harm and other health behaviors in the final model. The series of models addressed how covariates and serious psychological distress were independently associated with marijuana use and whether social support or loneliness moderated those associations. We hypothesized that marijuana use would have a positive association with marijuana use and that social support would diminish this association while loneliness would amplify it. Weighted characteristics for the study sample are shown in Table 1. Average age in the young adult sample was 22.7 years and approximately half of respondents were male. The race/ethnic distribution of the sample closely mirrors the distribution found in the young adult population in Alameda and San Francisco Counties.Approximately 10% of respondents identified as LGBT and 3.2% were married. Almost 9% of young adult respondents were classified as having serious psychological distress, which is substantially higher than the three percent of 18–64 year old adults estimated to be distressed in the most recent National Health Interview Survey.Approximately 15% reported current cigarette smoking, consistent with national estimates of smoking in this age group, but higher than the California adult prevalence rate of 12%.Marijuana users were more likely than non-users to be male and identify as LGBT . Non-Hispanic Asian/Pacific Islanders were the least likely race/ethnic group to use marijuana with only 10% reporting past 30 day use. Marijuana users were also more likely to have a mother with at least a bachelors degree , and had higher rates of serious psychological distress , much higher smoking rates and more reported days of binge drinking .Non-Hispanic Asian/Pacific Islander young adults had significantly lower odds of using marijuana, while non-Hispanic multiracial young adults had twice the odds of using marijuana compared to non-Hispanic White young adults. This is relatively consistent with prior studies showing lower substance use among Asians and the limited research that exists on multiracial adults indicates higher rates of substance use in this group.Few studies evaluate outcomes specifically for multiracial populations, often because the data are unavailable or the sample is too small, yet people who identify as multi-race are one of the fastest growing populations in the United States.

Younger populations in particular increasingly identify as multi-race, and it will be important to gain better understanding of how health behavior manifests among these individuals. Maternal education also demonstrated a significant positive correlation with marijuana use, a finding counter intuitive to prior health research suggesting protective effects of maternal education and family socioeconomic status on health outcomes.However, at least one prior study has found that those in the middle income range may be most likely to use marijuana, and given how little information is currently available on the characteristics and effects of marijuana use it may be premature to expect that marijuana use behavior shares the same features as more demonstrated problematic health behaviors, such as smoking and heavy alcohol use.While we cannot account for other measures of family SES, in our sample marijuana use peaked among young adults whose mothers had a bachelor’s degree. We did not find support for our second hypothesis; although positive, the relationship between psychological distress and marijuana use was not significant. We could locate only one other study that assessed serious psychological distress in relation to marijuana use among young adults, and studies of this relationship in adolescents had mixed findings.As social norms around marijuana change, reasons for using marijuana may also be changing in ways not directly measured in our study. Rather than to cope with psychological distress, young adults may use marijuana for recreational purposes or to mitigate physical pain, and these and other explanatory factors warrant further investigation. We found mixed support for our third hypothesis as social support and loneliness did not appear to moderate any association of psychological distress on marijuana use, but they both demonstrated significant positive correlations with marijuana use. While the finding concerning loneliness lends support to the idea that marijuana may be used as a coping method, the positive association with social support is less straightforward. Social capital, broadly conceived, is generally thought to be inversely associated with poor health outcomes and potentially risky health behavior, but some research in recent years has called this into question, arguing that peers and social networks may influence substance use, including that of marijuana.The extent to which the association between social support and marijuana use may be a social network effect in our analysis is unknown. Another possibility is that marijuana use, unlike cigarette smoking, does not carry a distinct stigma, and young adults may not use marijuana in similar ways to tobacco, alcohol or other substances. Similar to prior research,we found lower perceived harm of marijuana to be significantly related to marijuana use. Marijuana legalization advocates argue that portraying marijuana as safer than alcohol both in terms of health risks and societal harms is the strategy that brought the ballot victory to the legalization attempt in Colorado, and the same tactics were repeated successfully in Alaska campaign.It is likely that the same arguments will be used to push for legalization in other states and may lead to reduced perceptions of harm and increased use. Lastly, current cigarette smoking was associated with four times greater odds of using marijuana, while an additional day of binge drinking correlated to a 13% greater likelihood of using marijuana.

Given the high number of analyses performed across subgroups and multiple ECG abnormalities studied, there is high risk of a statistically significant false positive finding. Minor ECG abnormalities are common in healthy black people and associated with physical activity; we might have not been able to fully adjust for that. Minor abnormalities have not been associated with future CVD in young adults. While these elements inherently limit our confidence in the measures of association, CARDIA is to our knowledge the only cohort with ECG data which assessed marijuana exposure over such a long time and in such a large cohort. While the study of daily marijuana users is of evident interest, few participants use marijuana daily, and we still need to know if marijuana use is associated with changes in ECG, even at lower exposures or from past exposure. The strength of the CARDIA dataset lies in the possibility to study the full spectrum of marijuana use intensity typical of the exposure in the general population. No other longitudinal study assessed the association between marijuana use and ECG. In this study, we did not find an association between cumulative or past use of marijuana and ECG abnormalities. Compared to the previous studies assessing the association between marijuana use and ECG, most of them published more than 4 decades ago and including only very few participants, we can now report results from a cohort more than 100 times larger than previous studies. We were able to adjust for a rich set of covariables repeatedly measured at 7 examinations and a dozen phone follow-ups. We cannot exclude the possibility of informative censoring, which is a source of bias,vertical farming equipment suppliers but addressed that issue by using IPAW, as stated in the methods section.The District of Columbia and 15 States – Alaska, Arizona, California, Colorado, Illinois, Maine, Massachusetts, Michigan, Montana, Nevada, New Jersey, Oregon, South Dakota, Vermont, and Washington have legalized recreational marijuana use.

As a result, involuntary exposure to secondhand marijuana smoke has become much more common in everyday settings across the country. Studies have shown that secondhand exposure close to tobacco smoking or vaping is substantially higher than farther away – this “proximity effect” will also be an issue near marijuana smoking or vaping. The initial research investigating the proximity effect and spatial variation of exposure near a source used a tracer gas to mimic the transport of emitted air pollutants. For example, McBride et al released carbon monoxide as a tracer in a residential living room while using 12 real time CO monitors to measure concentrations at different indoor positions. Acevedo-Bolton et al deployed a larger monitoring array in the same residential living room to characterize exposure as a function of the distance from a continuous CO source. Klepeis et al measured real-time CO concentrations at up to 36 points in a residential backyard to consider the proximity effect outdoors near a building. These tracer gas studies provided insight into how different environmental conditions influence the proximity effect; however, they did not account for the characteristics of real smoking or vaping emissions, such as the exhalation of mainstream smoke and the buoyancy of sidestream smoke that can also affect proximity exposure greatly. Studies involving real human smoking or vaping were conducted mostly in prescribed settings. Acevedo-Bolton et al performed controlled experiments inside 2 homes and 16 outdoor locations, using a small group of investigators wearing personal exposure monitors to measure PM2.5 exposure close to prescribed tobacco cigarette smoking. Ott et al used a similar small-group monitoring approach to measure PM2.5 exposure near prescribed tobacco cigarette smoking at 6 outdoor bus stops on California roadways. Zhao et al measured indoor PM2.5 concentrations at 4 different distances from volunteers performing e-cigarette vaping, using a standardized puff frequency indoors in an 80 m3 patient room in a clinical research center. Using a heated mannequin, Martuzevicius et al measured indoor particle exposures at 3 different distances from e-cigarette vaping, adopting the same 30 s puff frequency.

Nguyen et al investigated particle concentrations at personal-space, social-space, public-space distances from non-prescribed vaping activities in California vaping shops. These studies provided valuable data for the levels of exposure close to tobacco smoking or vaping in real-world indoor and outdoor settings. Marijuana is most often smoked in homes . Using a commercial real-time sensor , a recent research study monitored particle number concentrations in ~300 California residences. This study provided the first set of data on particle levels inside real homes with marijuana smoking. However, this large-scale study did not allow spatial measurement of exposure inside a home or accurate mass concentration measurements based on gravimetric calibration. Little is known about the PM2.5 exposure close to a marijuana smoker. There also is virtually no knowledge of how different source types and environments affect the proximity effect. Our first goal was to examine, for the first time, PM2.5 exposure close to a marijuana smoker and how the exposure can be reduced by increasing the distance from the source; we measured real-time PM2.5 concentrations at 1, 2, and 3 m distances from marijuana emissions in a smoker’s home and assessed both the level and frequency of exposure versus distance. Our second goal was to investigate whether choosing a different source type, a different location, or a different environmental setting can reduce the proximity exposure; we tested two common marijuana source types along with their corresponding exhalation patterns in an indoor and an outdoor location under different ventilation and air mixing conditions. Given the collected exposure data, an additional goal of our research was to explore data analysis methods that can potentially be useful for evaluating the recommended physical distance from marijuana sources to minimize involuntary exposure. We performed field research inside a residential property in San Jose, CA . This single-family home has two stories and a private backyard, and the marijuana smoker is the only occupant in this property. Five AM510 SidePakTM monitors were deployed near the indoor chair in the 4.3×3.7×2.4 m family room or the outdoor chair in the backyard where the participant normally smokes or vapes marijuana . Both chairs backed up to a wall, and the outdoor chair had a small table 0.7 m high to its immediate left.

The 5 SidePak monitors were placed radially with 15o angle spacing at an equal distance from the source in each session , measuring PM2.5 concentration every 1 s; they were facing the front of the smoker to account for the worst-case exposure. Three monitors were placed at 1 m height ,grow lights shelves whereas two monitors were at 1.5 m height to consider typical adult breathing heights while sitting and standing, respectively . The actual measured breathing heights of the smoker sitting on the indoor and outdoor chairs were 1.2 m and 1.1 m, respectively. Using these monitoring settings, we performed 35 experiments . For the indoor experiments, 17 were performed with all windows and interior or exterior doors closed in the house – “base case” while 3 involved opening the family-room door and two dining room windows while running the fan of the centralized HVAC system – “alternative case”. For outdoor experiments, 12 were carried out with a fully-opened outdoor umbrella above the smoker – “base case” – while 3 were carried out with this umbrella fully closed – “alternative case”. We hypothesize opening or closing the umbrella would noticeably affect the air mixing and proximity effect close to the source. For the base-case experiments, all 5 monitors were underneath the umbrella when placed at 1 m distance from the smoker. We used the VelociCalc 8386 anemometer to measure and log the indoor and outdoor air velocities near the smoking or vaping locations every 2 s during each experiment. This instrument has a 6-mm diameter sensor probe with a 25 mm long anemometer at its tip, and its minimum detectable air speed is 0.01 m/s. It was not possible to release carbon monoxide or sulfur hexafluoride tracer gas in the participant’s house. As a way to estimate the magnitude of ventilation, we burned matches inside the house while using the Optical Particle Sizer 3330 to measure the particle number concentrations every 1 min. The air change rate was estimated by the log linear regression between concentration of the smallest particle size range and time after the well-mixed condition was reasonably achieved. Given the timescale of the experiments , diffusional and gravitational losses of particles within this size range were expected to be negligible compared with air exchange; this method has been used to estimate ACH in a residence where tracer gas releases were not feasible . These air change rate tests were performed outside the regular smoking or vaping experiments, because they involved particle emissions. We investigated two types of marijuana sources regularly used by the participant: a cigarette-like marijuana joint with 0% CBD and 9.6% THC, and an electronic vaping pen with the “Care by Design” 2:1 cartridge . A standardized smoking or vaping protocol that consisted of 5 puffs over a 5-minute period was used. After inhaling, the participant exhaled at the starting point of every minute ; we defined the 5 min period as the source period. This protocol was intended to enable comparisons between experiments with different source types or source distances based on the same exhalation or emission frequency . Zhao et al and Martuzevicius et al have adopted this approach but with a different frequency for ecigarette vaping. In our study, the participant chose the 1-min time interval for the 5-puff sequence to not exceed his normal habit of smoking and vaping. We did not choose a specific volume and duration for each puff, since we wanted to preserve the behavioral differences embedded in each puff for different source types and to investigate how they may affect the spatial variation of exposure close to a source.

The participant did not permit sensors to be used in contact with his body; therefore, puff topography or spirometry measurement involving sensor mouthpiece breathing was not conducted in this study. As a surrogate approach, we placed the VelociCalc anemometer in front of the smoker during the 5-min source period at 0.1 m horizontal distance from the mouth position to record the “exhalation peak velocity” – the maximum air velocity produced by each exhalation . This approach enabled us to investigate human exhalation via air environment measurement. We discovered the temporal fluctuations of air velocities outdoors were comparable to the magnitudes of exhalation peak velocities. Therefore, we were not able to measure the exhalation peak velocities in the outdoor experiments. The durations of the exhalation were measured by the participant using a stopwatch. A test examining how consistently exhalation peak velocities can be produced and measured by the environmental sensing method is available in the Supplementary Material . PM2.5 Calibration. To ensure consistent measurements between monitors, we conducted a separate quality assurance study in which we placed 17 SidePak monitors inside a car chamber with a smoke source, simultaneously measuring PM2.5 concentrations every 1 min. After the emission stopped and well-mixed condition was reasonably achieved , the exponentially decaying measurements of the SidePak monitors were compared by linear regression with our reference SidePak monitor, giving R 2 > 0.999 for the 5 SidePak monitors used . The SidePak monitors measure PM2.5 concentration based on light scattering properties, which are affected by the particle size and composition. To accurately represent the actual PM2.5 concentration, the calibration factor – the ratio of gravimetrically-to-optically-measuredPM2.5 concentration is needed for each source type . In a previously published paper, we determined the CFs for the two marijuana source types: 0.35 for joint smoking and 0.44 for vaping for the reference SidePak monitor; they were applied along with the inter-monitor slopes to rescale all PM2.5 measurement in this study where i = 1-5. Jiang et al found that CFs for SidePak monitors remained relatively constant over time; for a 16-month period, the average difference was ~3%. The particle zero filter was attached to the inlet of each SidePak monitor immediately before each experiment for zero calibration. The PM2.5 measurements at the 3 different distances were collected from separate experiments, not simultaneously.

PPV measures the probability of a record included in the BCC directory conditional on the record being present on Weedmaps, calculated as the number of records that were present on both Weedmaps and the BCC directory divided by the number of records present on Weedmaps. NPV measures the probability of a record excluded from the BCC directory conditional on the record being absent on Weedmaps, calculated as the number of records that were neither present on Weedmaps nor present on the BCC directory divided by the number of records being absent on Weedmaps. You will notice that specificity and NPV cannot be calculated in this example, because we were not able to identify a “true negative”, a record that was excluded from Weedmaps and also absent in the BCC directory. In fact, not all validity statistics were applicable to a combination of a gold standard and a test with the current study design . Following tobacco outlet research , we considered validity statistics 0-0.2 to be poor, 0.21-0.4 to be fair, 0.41-0.6 to be moderate, 0.61-0.8 to be good, and 0.81-1.0 to be very good. R Version 3.5.3 was used to calculate 95% confidence intervals for all the validity statistics. We computed overall statistics as well as the statistics by dispensary category and county population size . Locations of call-verified active brick-and-mortar dispensaries in California were mapped with ArcGIS Version 10.5. A total of 2,121 business records were combined from BCC and the three online crowd sourcing platforms after online data cleaning. BCC, Weedmaps, Leafly, and Yelp had 630, 811, 535, and 1,468 records included in the combined database, respectively. The overlaps across the data sources were presented in Figure S1. Only 240 records were present in all four data sources. Following call verification, the 2,121 records were reduced to 826,pipp grow rack which were confirmed to be active brick-and-mortar dispensaries. Among the 1,295 records removed during call verification, 56.0% were closed, 4.2% were not open yet, 38.0% were not selling marijuana, and 1.8% had no storefronts .

BCC, Weedmaps, Leafly, and Yelp had 486, 659, 459, and 471 records included in these 826 verified dispensaries, respectively. The overlaps across the data sources were presented in Figure S2. The 826 records included 77 recreational-only, 65 medical-only, and 684 recreational & medical dispensaries. The dispensary category was based on self-reporting by dispensary staff in call verification. Table 1 reports validity statistics using the BCC licensing directory as the gold standard. When the test was whether being present on each online crowd sourcing platform after online data cleaning, Leafly had good sensitivity and Weedmaps and Yelp had moderate sensitivity . It indicated that 70% of the BCC licensing directory could be found on Leafly. Leafly also had very good PPV , yet Yelp’s PPV was only fair . It indicated that 83% of Leafly records were included in the BCC licensing directory. When the test was whether passing call verification, Leafly still had the highest sensitivity and PPV , and Yelp had the highest specificity and NPV . It indicated that, call-verified Leafly records performed the best for identifying truly licensed dispensaries and call-verified Yelp records performed the best for identifying truly unlicensed dispensaries in this scenario. Table 2 reports validity statistics using the call-verified, combined database as the gold standard. When the test was whether being present in each data source after online data cleaning, Weedmaps had the highest sensitivity and BCC, Leafly, and Yelp all had moderate level of sensitivity ranging from .56 to .59. It indicated that 80% of the call-verified, combined database of active dispensaries could be found on Weedmaps. Leafly and Weedmaps had very good PPV , and Yelp’s PPV was only fair . It indicated that 86% of Leafly records were included in the call-verified, combined database of active dispensaries. When the test was whether passing call verification, sensitivity statistics remained the same as when the test was whether being present in each data source. This was because call-verified businesses in each data source were a subset of the businesses included in each data source before call verification, such that the numerators and denominators for sensitivity calculation remained the same. Yelp had the highest NPV and Leafly had the lowest NPV . Table 3 reports the agreement between BCC, online crowd sourcing platforms, and call verification in terms of the category of the 630 licensed dispensaries.

Approximately 25% of the licensed dispensaries on Weedmaps and 29% of the licensed dispensaries on Leafly posted their category that disagreed with what was approved in the BCC license. Approximately 12% of the call-verified, licensed dispensaries stated their category in call verification that disagreed with what was approved in the BCC license. Most of the businesses that stated an unapproved category on online crowd sourcing platforms and/or in call verification claimed themselves to be recreational & medical when they were only licensed for recreational-only or medical-only. Table S3 quantifies category-specific validity statistics when the gold standard was whether being present in the BCC licensing directory. Leafly had the highest sensitivity in recreational-only and recreational & medical categories and Weedmaps had the highest sensitivity in medical-only category, regardless of the definition of a test. Table S4 quantifies category-specific validity statistics when the gold standard was whether being present in the call verified, combined database. When the test was whether being present in each data source after online data cleaning, Weedmaps had the highest sensitivity in identifying recreational-only and medical-only dispensaries, yet BCC had the highest sensitivity in identifying recreational & medical dispensaries. When the test was whether passing call verification, Weedmaps overall had the highest sensitivity in all three categories. In 2019, California had 16 counties with a population size above one million and 42 counties with a population size below one million. Table S5 reports validity statistics by county population size when the gold standard was whether being present in the BCC licensing directory. Leafly had the highest sensitivity regardless of test definition and county population size. Table S6 reports validity statistics by county population size when the gold standard was whether being present in the call-verified, combined database. Regardless of test definition, Weedmaps had the highest sensitivity in more populated counties and BCC had the highest sensitivity in less populated counties.This study is the first to assess the validity of secondary data sources in identifying brick and-mortar marijuana dispensaries across a large state.

We reported the validity of online crowd sourcing platforms in enumerating licensed dispensaries and the validity of state licensing directory and online crowd sourcing platforms in enumerating active dispensaries. Regarding the validity of using online crowd sourcing platforms in identifying the BCC licensing directory, all three online crowd sourcing platforms were able to include over 50% records in the BCC directory, with Leafly containing the largest number of licensed dispensaries . These findings suggested that the online crowd sourcing platforms could serve as a reasonable proxy for the licensing directory. It evidences the validity for many existing and future studies to utilize online crowd sourcing platforms for dispensary identification, especially if a licensing system is not open to the public or is updated infrequently. It should be noted, however, that the dispensary category registered in the BCC directory may be mismatched with the “de facto” category in which dispensaries operated. Over 25% licensed dispensaries on online crowd sourcing platforms posted their category that disagreed with the BCC license and over 10% call-verified,4×8 botanicare tray licensed dispensaries stated their category in call verification that disagreed with the BCC license. Particularly, most of such dispensaries claimed themselves to be recreational & medical while they were only licensed for recreational only or medical only. Such disagreement might be intentionally used as a means of attracting customers or be reflective of how dispensaries operate in practice. Regarding the validity of using the state licensing directory in identifying active brick and-mortar dispensaries, over 20% licensed dispensaries did not pass call verification. This indicated that business licenses may not accurately represent businesses’ operation status in reality. For instance, a business may have been closed before its license is expired and a business may not be open yet even though its license has been approved. In the final 826 call-verified dispensaries, 58.8% were included in the BCC licensing directory. This indicated that the BCC directory failed to capture unlicensed dispensaries, which accounted for over 40% of the total active dispensaries in California. Solely relying on a state licensing directory would overestimate active, licensed dispensaries whereby overlook active, unlicensed dispensaries. Regarding the validity of using online crowd sourcing platforms in identifying active brick-and-mortar dispensaries, Weedmaps had a nearly very good sensitivity; it contributed 80% of the records in the final call-verified, combined database. It had the highest sensitivity in identifying recreational-only and medical-only dispensaries. It was also the most sensitive database in identifying dispensaries in more populated counties, which were mostly urban areas. The high concentration of dispensaries and intense competition in urban areas may motivate more businesses to promote themselves on this highly visible and popular platform .

Leafly had the lowest sensitivity in identifying active dispensaries. It also had the lowest sensitivity in identifying all three dispensary categories. It is likely because the costs of advertising on Leafly were substantially higher than other online crowd sourcing platforms specialized in marijuana . Only 32% of the businesses listed on Yelp were verified to be active brick-and-mortar dispensaries. This is not surprising because Yelp, which provides a general business listing service not specifically designed for marijuana industry, had more records irrelevant to marijuana dispensary than Weedmaps and Leafly. Taken together, no single secondary data source could provide a reasonably complete and accurate list of active brick-and-mortar dispensaries in a large state like California. We recommend surveillance and research to consider their unique strengths and weaknesses when a single data source is used to minimize required resources. When resources are available, we recommend the integration of multiple secondary data sources, preferably including a licensing directory and multiple online crowd sourcing platforms, as well as verification through phone calls such as what has been done in this study or through even better approaches such as a field census. The verification could considerably improve the accuracy of the data compiled from secondary data sources. Our findings were overall consistent with the two smaller-scale studies conducted in California, both in Los Angeles County. One was conducted in 2016-2017, before recreational marijuana dispensaries were allowed to open . This study obtained medical marijuana dispensary information from five online crowd sourcing platforms. Weedmaps was suggested to be the most accurate and up-to-date platform, contributing to 95% of the final records. Call verification was conducted in 10% of the dispensaries and found to generally align with the information posted on online crowd sourcing platforms. The other study was conducted in 2018-2019, after recreational marijuana dispensaries were allowed to open . It extracted data from Weedmaps and Yelp and verified dispensary information through site visits. About 80% dispensaries that were determined to be active through online data cleaning were confirmed to be active in site visits, and licensed dispensaries accounted for roughly 40% of the active dispensaries. Neither study reported validity statistics for each specific data source. Our study expanded on the prior research by covering a much larger geographic region, computing detailed validity statistics for each data source by dispensary category and county population size, and by using two gold standards and two tests to demonstrate validities in different scenarios and for different purposes. This study has limitations. First, due to the lack of feasibility of conducting a field census in such a large geographic region, phone calls were made to verify information obtained from secondary data sources. While this approach was cost effective, businesses not listed in these secondary data sources were excluded from the analysis, potentially the smaller, unlicensed dispensaries that did not intend to promote themselves on online crowd sourcing platforms because of cost and law enforcement concerns. Future research using field census approach is warranted to assess to what extent unlicensed dispensaries were underrepresented in our study.

To save space, we only present the results for Sunshine High; see S2 File for the Jefferson High results, which were similar.The highest level of smoking is observed when we set to zero the influence effect of friends on smoking behavior, as the percentage of non-smokers drops from 72% in the original model to 63%, and the percentage of heavy-smokers increases from 11% to 18% . The pattern was similar in Jefferson High, with analogous values of 48% to 42%, and 31% to 35%. This corroborates the findings in previous simulation research that peer influence has a protective effect on smoking and drinking adoption. The lowest levels of smoking are observed in the hypothetical scenario in which marijuana use has no effect on one’s own smoking behavior, as the percentage of non-smokers rises from 72% to 81%, and the percentage of heavy-smokers decreases from 11% to 5%. The analogous values in Jefferson High were 48% to 54%, and 31% to 25%. Regarding drinking behavior, we see that the effect of one’s own marijuana use is particularly important as setting this effect to zero results in a decrease in drinking behavior . In the scenario of no effect of marijuana use on drinking behavior the percentage of nondrinkers rises from 50% to 59% and the percentage of heavy drinkers falls from 13% to 7%. The analogous values in Jefferson High were 35% to 42% and 16% to 10%. It is notable that setting the influence effect of friends’ drinking on one’s own drinking behavior to zero reduces drinking somewhat . In Jefferson High, the number of heavy drinkers rises from 16% to 20%. For marijuana usage, very pronounced strong effects are observed for friends’ influence . Setting this influence effect to zero results in a sharp decrease in non-marijuana users from 62% to 47%, and a parallel large increase in heavy users from 19% to 32%. In Jefferson High, the analogous values were 61% to 43% and 18% to 33%. In sum, when the effect from marijuana use to cigarette use is turned off,indoor grow trays more non-smokers and fewer heavy-smokers are expected in both schools. When the peer influence effect with regard to each substance use is turned off, fewer non-users and more heavy-users of each substance are expected in both schools.

In the scenarios in which we set other parameters to zero, the simulation results indicated that the substance use distribution was not altered in either school.Overall, our findings indicate some evidence of sequential substance use, as adolescent marijuana use increased subsequent smoking and drinking behavior in our two school samples. Whereas some existing research has found evidence that marijuana use leads to use of these substances, an important contribution of our study was simultaneously taking into account the substance use behavior of adolescents’ peer networks and other social processes occurring in networks. Our findings are partially consistent with Pearson et al., who found that that marijuana users smoked cigarettes more over time. Our findings are suggestive that marijuana use increases both alcohol and cigarette use. In addition, we made a distinction between whether interdependent substance use going from marijuana to cigarettes and alcohol results in initiation, cessation, or both. We found that marijuana use resulted in drinking initiation in both samples, and smoking initiation in Sunshine High. In contrast, marijuana use decreased the likelihood of smoking cessation in Jefferson High. Previous literature suggests that alcohol use is not a prerequisite for the initiation of marijuana use and the effect of alcohol use on the onset of marijuana use has declined while that of marijuana use on the onset of alcohol use has increased since 1965, and our findings are consistent with this prior literature. Moreover, we tested cross-substance influence effects, which assessed whether the substance use behavior of one’s friends on a particular substance affected an individual’s own use of the other two substances. We found no evidence that such effects exist in our samples. We did, however, find peer influence effects for each specific substance, which is consistent with multiple past studies. Note, however, that whereas one implication is that having more friends who use marijuana, for example, results in greater marijuana use behavior on the part of the individual, another implication is that having more friends who do not use marijuana results in less marijuana use behavior.

This relative symmetry of influence effects is sometimes overlooked when interpreting influence results, and our simulation results confirmed that this influence effect is in fact more likely to have a negative effect on substance use behavior. These results are similar to an earlier simulation study that found that increasing the amount of peer influence in two high schools diminished school level smoking and drinking behavior. These results are consistent with theoretical insights from the Dynamic Social Impact Theory, which would predict that youth in friendship networks would adopt the same substance use behaviors through peer influence pathways, likely through social proximity and consolidation of youths’ attitudes and behaviors in adolescent networks. This highlights that the presumption that influence effects will always increase behavior is not necessarily accurate. In fact, we might expect that the dominant norms in a context will drive the direction of influence effects: in a school with little substance use, the greater number of non-users will push adolescents towards non-use, whereas in a school with high levels of substance adolescents are more likely pushed towards greater use. Given the complexity of our agent-based network models, we demonstrated the relative magnitude of the effects by combining a small-scale simulation with a strategy in which we constructed hypothetical models that set certain key effects to zero and simulated the networks and behaviors forward. A key finding was that in a simulated world in which one’s own marijuana use did not affect smoking or drinking behavior, there would be a notable decrease in overall levels of smoking and alcohol usage in these schools, even controlling for the complexity of these models. We also saw that marijuana use operates as a mechanism between friends’ marijuana use and one’s own smoking and drinking behavior, as adolescents’ use of marijuana is impacted by their friends’ marijuana use, and this then affects the adolescent’s level of cigarette and alcohol use. Furthermore, one of the strongest effects detected was the influence effect of friends’ marijuana usage, as this has a particularly strong relationship to adolescents’ own marijuana use. Our findings highlight the importance of understanding interdependence in the use of multiple substances in adolescence, particularly those which operate through peer influence effects within friendship networks.

Another notable finding was that depressive symptoms increased smoking behavior in Jefferson High. This high school has a relatively high average level of substance use compared to Sunshine High. Perhaps in a social milieu with a high average level of drug use, adolescents reporting higher levels of depressive symptoms may be more likely to display higher levels of cigarette smoking as compared to those who report lower level of depressive symptoms, given that past studies link depression and adolescent smoking.There are some limitations to note in this study. First, the time lags between the two sets of waves are not equal . Although it is preferable to have equal time periods, we performed a post hoc time heterogeneity test to ensure that the co-evolution of substance use behaviors and friendship networks was not significantly different across the three waves,2 tier grow rack or two time periods. Second, our SAB model specification is data intensive and can only be estimated for the two large schools among the 16 saturated schools in Add Health which are feasible for this type of analysis. This limits generalizability and does not allow assessing why the interdependent effect from marijuana use to smoking is different across the two schools. Third, we had indirect information about marijuana use at time one, for a large percentage of the sample. Using this indirect information allowed us to avoid discarding a large amount of information at t1, however with a relatively small amount of potentially misclassified cases. Fourth, while the data are relatively old, we are aware of no evidence that the mechanisms of in person friendship formation, as captured in these Add Health network data,have changed significantly since the mid-nineties. In the current study, friendship networks were constructed through name generator items instead of real-time communication technology such as cell phone use. While future studies are needed to leverage existing technology such as cell phone usage for collecting adolescent social network data, these in person network data are likely still meaningful. Moreover, research suggests that cell phones help reinforce and reproduce existing social roles and structures rather than alter them. That said, future studies are needed to collect nationally representative contemporary data from US adolescents and investigate how the findings herein would be different if such technology was considered.Our findings have important implications for future studies. First, our findings suggest both feasibility and merit in exploring concurrent or sequential substance use behaviors across multiple time periods. Interdependence in substance use should be studied within one single model framework with multiple simultaneous on-going processes to reduce the risk of over-estimation of each process due to the auto correlation among them. Second, further explication of the interdependent effects from marijuana use to smoking and drinking is a useful direction for future research. Third, given smoking rates among adolescent youth have decreased significantly since the mid-1990s, more recent data are required to test whether our findings from these two Add Health large schools can be replicated in future research. Our findings also have practical implications for health behavior change interventions targeting adolescent substance use.

Studies have found evidence of a protective effect of social network ties for adolescent substance use. Moreover, other research indicates that social networks can be leveraged for health behavior change interventions and may even be superior to non-network based interventions. Peer network based interventions targeting adolescent substance use might address the possibility that marijuana use increases alcohol and cigarette use. One approach to do this would be to disseminate tailored messages through adolescent peer networks to modify norms favoring the concurrent use of these substances, and therefore alter peer influences condoning the use of one of these substances or the concurrent use of two these substances. Such messages could act as cues to action to halt peer influences facilitating the progression from use of one substance to using both, concurrently. Lastly, a policy-relevant implication of our finding that marijuana use appears to lead to more cigarette and alcohol use is that there may be unintended consequences for adolescent substance use from the legalization of marijuana in states. If such legalization leads to greater marijuana use among adolescents, our results suggest that more cigarette smoking and alcohol drinking behavior among adolescents might occur concurrently. This is a possibility that has received some research attention and should be given more consideration in future work. Pain is one of the most distressing symptoms of cancer and can considerably impact quality of life. While 40% of patients with early-intermediate stage cancer and 90% of patients with advanced cancer experience moderate to severe pain , up to 70% of patients with cancer-related pain do not receive adequate pain relief and thus experience a lower quality of life . More than 289 million opioid prescriptions are written in the US each year6 , and these analgesics are a mainstay in the effective treatment of cancer-related pain. However, opioids are also associated with risk of misuse and dependence; there has been a doubling of the rate of opioid-overdose related inpatient hospitalizations between 2000 and 20129 . Per the 2016 Surgeon General’s report, the US is facing an opioid epidemic, as opioid overdose accounted for 61% of 47,055 drug overdose deaths in 2014 – more than any previous year on record. The financial burden of opioid abuse, misuse, and overdose is also substantial, around $78.5 million in aggregate costs. Cancer patients, in particular, may be at higher risk for opioid use disorders than non-cancer patients, and previous studies have reported increased risk of long-term prescription opioid use after cancer-related surgeries.

Links to surveys were sent to participants’ email addresses and smartphones. Staff reminded participants to complete assessments via text message, telephone, and email. All procedures were approved by the University of California, San Diego Institutional Review Board. Demographic characteristics including age, sex, race, ethnic background, and student status were measured at baseline by self-report. Student status was collapsed into a dichotomous variable comparing full-time students to all other participants. Cigarette and other tobacco use were assessed at each of the 9 time points. At baseline and 12 and 24 months later, the Timeline Follow Back  was used to evaluate number of cigarettes smoked, as well as whether participants had used each of marijuana, alcohol, ecigarettes, hookah tobacco, and any other tobacco product , on each of the 14 days preceding the day of assessment receipt. At the 3, 6, 9, 15, 18 and 21 month assessments, participants reported whether they had used marijuana, alcohol, e-cigarettes, hookah tobacco, and OTPs in the past 24 hours on each of 9 consecutive days. Raw data for each of the days assessed were aggregated to create variables reflecting quantity of cigarettes smoked over 9 or 14 days of each assessment period , and frequency or number of days on which marijuana , cigarettes , e-cigarettes , hookah tobacco , and OTPs were used during each assessment period. We created a count variable that reflected the number of days at each time point on which participants reported using any tobacco product , and a binary variable that assessed whether or not they reported use of multiple tobacco products at each time point . The marijuana days variable was used to calculate a time-varying variable that measured cumulative number of time points,grow trays 4×4 up to and including the one being assessed, at which marijuana days was greater than 0. For example, if a participant reported 1 marijuana day at baseline, 0 at 3 months, and 4 at 6 months, his or her values for marijuana frequency at those time points would be 1 , 1 , and 2 , respectively.

The purpose of this variable was to capture cumulative marijuana use aggregated over the full two years, rather than within each assessment period. We assumed that if marijuana use is a predictor of heavier tobacco use, individuals who use marijuana more frequently over the entire study period would be most vulnerable to this association. Thus, we believed that this variable would better capture marijuana use over time relative to a variable that evaluated marijuana frequency at each assessment but did not account for previous use. Consequently, analyses included cumulative marijuana frequency as a predictor of tobacco outcomes over 2 years. Similar variables were calculated to reflect cumulative frequencies of cigarette use, overall tobacco use, poly tobacco use, and alcohol use. Because time points varied in the number of days on which use was assessed, we also created a time-varying variable that measured number of days on which use was assessed at each time point.All analyses were conducted using Stata 15.0 , with α=.05. We used bivariate tests to evaluate relationships between demographic, predictor, and outcome variables. Tests of associations between cumulative marijuana frequency and tobacco use over time were conducted by testing separate models of the association of the predictor with each time-varying outcome . Each model included cumulative alcohol frequency and assessment days as covariates, as well as terms for both linear and quadratic time and their interactions with marijuana frequency. Non-significant interaction terms that were removed and the model refit. Count outcomes were evaluated via longitudinal negative binomial regression, using Stata’s xtnbreg module, because that was a better fit to the data than linear or Poisson models. Polytobacco use, as a time-varying binary outcome, was analyzed using a longitudinal logistic regression model via the generalized estimating equations approach using xtgee in Stata. Tests of whether tobacco frequency was associated with marijuana use over time were conducted by fitting separate models of the associations of each predictor with marijuana days over time, again utilizing negative binomial models.

The proportion of the sample completing each post-baseline assessment generally decreased over time: 94% at 3 months, 88% 6 months, 85% at 9 months, 89% at 12 months, 84% at 15 months, 82% at 18 months, 78% at 21 months and 81% at 24 months. Having missing data at a specific time point was not significantly associated with predictor or outcome variables at the previous assessment. Quantity and frequency of cigarette and marijuana use over time are shown in Table 2. Bivariate assessments indicated that women, full-time students, and Asian Americans smoked fewer cigarettes than others , and therefore sex, student status, and race/ethnicity were included as covariates in subsequent hypothesis tests.The final model is shown in Table 3. All interactions were non-significant, indicating the association between marijuana frequency and total cigarettes was consistent over time; these terms were excluded from the final model. There was a significant main effect of marijuana frequency [Incidence Rate Ratio =1.11 , p<.001]. The effect size indicates that each additional time point at which recent marijuana use was reported was associated with an 11% increase in number of cigarettes. Put another way, if Participant A reported never using marijuana through the first 5 assessments, and Participant B reported recent marijuana use at each of these assessments, Participant B would be expected to report 55% more cigarettes at the 5th assessment than Participant A. The models of cigarette and tobacco frequency are shown in Table 3. Both yielded similar results as the first analysis. Cumulative marijuana frequency was a significant predictor of cigarette [IRR=1.09 , p<.001] and overall tobacco [IRR=1.09 , p<. 001] frequencies. In both cases, the association was stable over time. These analyses suggest that each additional assessment period with recently marijuana use predicted a 9% increase in both the number of cigarette days and in the number of days on which any tobacco product was used. The GEE model indicated poly tobacco use was more common among men but did not vary by race/ethnicity or student status.

There were significant interactions between marijuana use and time, suggesting the impact of marijuana frequency on poly tobacco use changed over time. More specifically, the interaction between cumulative marijuana use and time2 was a significant predictor of likelihood of concurrent use of multiple tobacco products over time . To better understand this interaction, we calculated odds ratios indicating the association between cumulative marijuana frequency and odds of poly tobacco use at each individual time point, accounting for all covariates in the original model. A plot of these odds ratios indicates the association between cumulative marijuana use and poly tobacco use was highest at baseline, when the possible values for the former were 0 and 1 . At baseline, participants who used marijuana recently were 65% more likely to report use of multiple tobacco products than those who reported no recent marijuana use. This association decreased over time as a function of the increasing range of possible values of cumulative marijuana frequency. More specifically, at each time point, the odds ratio reflects change in odds of recent multiple product use with a one-point change in the cumulative marijuana predictor. As the range of cumulative marijuana frequency increased over time, a one-point change became relatively smaller. Over the second year of observation,horticulture products each additional time point of marijuana use was associated with a 10-21% increase in the odds of poly tobacco use. The model examining cumulative frequency of cigarette smoking on marijuana frequency over time yielded a significant main effect [IRR=1.20 , p < .001; Table 4] that did not vary over time. When we modeled the association between cumulative all tobacco use and marijuana frequency, we found a significant main effect [IRR=1.22 , p<. 001] that did not vary with time. Similarly, analyses showed a significant main effect of cumulative frequency of poly tobacco use on marijuana frequency [IRR=1.19 , p<.001] but no interaction with time. These results indicate that each additional time point at which participants reported any tobacco use or poly tobacco use predicted 22% and 19% more days of marijuana use, respectively.The aim of this study was to examine whether cumulative frequency of recent marijuana use at quarterly assessments over 2 years would be associated with quantity and frequency of tobacco use among young adults who were non-daily cigarette smokers at baseline. Additionally, we sought to examine whether cumulative tobacco use over time predicted frequency of marijuana use. As expected, we found a dose-response relationship, such that participants with greater marijuana use reported greater quantity and frequency of cigarette use, and greater frequency of use of any tobacco product. Cumulative marijuana use also predicted likelihood of use of multiple tobacco products at single time points over time. Each additional time point of recent marijuana use was generally associated with a 10-20% increase in tobacco quantity/frequency. Similarly, non-daily cigarette smokers who used multiple tobacco products more frequently also reported more frequent use of marijuana. Each additional time point at which participants used cigarettes, all tobacco, or multiple tobacco products was associated with approximately 20% greater marijuana frequency.

These findings are consistent with cross-sectional studies suggesting substantial overlap between marijuana and tobacco use . However, our data also meaningfully extend previous work by demonstrating that longer-term use of marijuana is associated with greater tobacco consumption and vice versa. These associations were comparable in magnitude, suggesting a bidirectional relationship in which either may be the initial substance of interest. Given decreasing legal barriers to marijuana use, the fact that cumulative marijuana use was associated with increasing tobacco frequency in a sample of non-daily cigarette smokers is concerning, as it indicates that marijuana use may promote tobacco progression, increasing risk of poor health outcomes.For example, more frequent simultaneous use of marijuana and tobacco would lead one substance to serve as a behavioral cue for the other, and possibly to increased use of both. Additionally, learned cognitions may play a role, as demonstrated in a study examining expectancies of interactions between marijuana and tobacco effects . Higher expectations that marijuana use increases tobacco use and urges have been positively associated with tobacco and marijuana frequency, severity of marijuana use, and proportion of days of marijuana and tobacco co-use . Thus, individuals who hold these expectancies and use marijuana may experience more tobacco urges, leading to increased tobacco use over time. Further mechanisms are suggested by a recent review of neurobiological mechanisms underlying co-use . One proposed mechanism centers on synergistic effects or functional interactions, whereby use of one substance enhances the reinforcing effects of the other. Currently, the few studies that directly addressed this question have yielded conflicting findings. Some have supported the notion that nicotine enhances the effects of marijuana, while others have failed to support this relationship . As such, further study of this relationship is warranted. Another mechanism centers on compensatory effects, whereby use of one substance alleviates negative effects of the other. This hypothesis derives from evidence that marijuana withdrawal effects may be ameliorated by nicotine and vice versa . In support of this mechanism, a study of expectancies for the interactive effects of nicotine and marijuana found that higher expectations of smoking as a means to cope with marijuana urges were associated with greater marijuana cravings . Whether this influences the progression of marijuana and tobacco co-use is currently unknown and merits exploration. In all, multiple active mechanisms are likely contributing to this overlap, consistent with our finding of a bidirectional relationship. Exploration of such mechanisms and trajectories of co-use have clear clinical implications, especially in the context of smoking cessation. Evaluations of the influence of cannabis use on cessation outcomes have primarily comprised secondary analyses of cessation trials. Similarly, knowledge about the impact of tobacco on cannabis cessation is based on secondary analyses. There is preliminary evidence that pharmacotherapy and behavioral therapy may be effective treatments for cooccurring marijuana and tobacco use. Our findings converge with this evidence to encourage further systematic exploration into how marijuana-tobacco relationships impact clinical outcomes and into what may be effective at treating concurrent use. Clinically, these findings also reinforce the importance of evaluating use of both products even for intermittent users, and of incorporating evaluation outcomes into efforts to quit using one or both products. This study has some limitations.

Although the state required only a limited health warning in hard-to-read 6-point font on packages, whose text was defined in the ballot initiative, jurisdictions required additional health warnings in stores and 4 jurisdictions required additional health warnings on packages. No jurisdiction required warnings on advertising. Although the state prohibited only misleading or unsubstantiated health claims, 1 county, Mono, prohibited all health-related claims on marijuana labels, any advertising or marketing, and in retailer names.Seventy-four jurisdictions limited advertising in some way, primarily through limited business signage. Fourteen jurisdictions prohibited billboards and other outdoor advertising. Five jurisdictions limited advertising on television, on the radio, online, or in print, and 5 jurisdictions prohibited advertisements attractive to youths more explicitly than the state’s prohibition. The state did not require warnings on advertisements and used regulation to weaken Proposition 64’s prohibition on billboards on state and interstate highways that cross state borders, limiting its application to roads within 15 miles of the state border.State law does require that advertisements be 1000 feet from schools, daycare centers, playgrounds, or youth centers, and that advertising and marketing not be designed to appeal to underage consumers.Twenty-seven jurisdictions allowed on-site consumption of marijuana in some form at retail locations, all of which allowed either smoking , vaping , or both on the premises, 3 of which allow use by staff only. Thirteen jurisdictions explicitly established a permit system for marijuana-related temporary events,farming shelving while 21 jurisdictions banned them and most jurisdictions were silent. The state allows both on-site consumption and marijuana-related temporary events if locally permitted. Although California laws prohibit smoking marijuana in most workplaces or in any place where smoking cigarettes is prohibited by law, these local exceptions are now in effect.

Of jurisdictions legalizing any commercial marijuana activity , 154 did not tax marijuana activity locally; 119 passed a “general” tax, which in California is a tax that the governing authority can use for any purpose;passed a general tax with an advisory committee guiding revenue use; 3 passed a tax that earmarked revenue, dedicated in different cases to police and law enforcement, fire services, parks and recreation, repairing city streets, or enhancing community centers; and 6 passed “fees.” Cathedral City taxed the highest-potency marijuana concentrates, such as “shatter” , at 8 times the price of lower-potency products. Little local revenue was captured for prevention or reinvestment in low-income communities. Only 5 jurisdictions prohibited discounting, such as redemption of coupons, discount days, or other promotions, and none implemented a minimum price law, all of which are price policies that have been used in tobacco control. The state levied a 15% excise tax on retail sales in addition to a cultivation tax, much of which is slated for investment in prevention of substance use by youths and in communities but did not constrain discounting other than prohibiting distribution of free products, nor did it create a floor price.Fifty-three jurisdictions added some form of prescriber conflict of interest rule, such as no marijuana prescribers may work as staff or be owners or employees in retail outlets. The state prohibited those involved in marijuana regulation, enforcement, or appeals from holding marijuana licenses or financial interest, and persons licensed for testing laboratories may not hold other marijuana licenses. Neither state nor local government prohibited those with marijuana financial interests from participation in advisory bodies, and such participation is occurring.Our review reveals important gaps in the regulatory scheme for marijuana in California cities and counties. Many fundamental lessons from tobacco control to reduce demand, limit harm, and prevent marijuana use by youths have gone largely ignored, leaving state law setting the standard. Nevertheless, in communities that have opted to legalize marijuana, examples are emerging of local policy innovation for reducing demand and protecting youths. Limits on retail outlets are the most common. The first prohibition on flavored products was passed in 2018, as was the first ban on vaped marijuana later in 2019.

However, limitations of high-potency or flavored marijuana product types, industry practices associated with risk of addiction and psychosis, and risk of youth initiation have received little local attention. Most state residents are exposed to aggressive marketing practices such as prominent billboards promoting marijuana use. They are not informed by clear and salient health warnings such as those used on marijuana products in Canada or tobacco products in the United States. Local onsite consumption permits have been associated with smoke-filled lounges and outdoor marijuana events, such as legal sales at concerts, fairs, or park events, which may threaten decades of progress in smoke-free workplaces and outdoor air. State laws and regulations neglected to limit retail outlets. State, like local, provisions on marketing and advertising are relatively weak, even when taking into account protections on commercial speech. The state does tax and invests some tax revenue in prevention of substance use and other community-based investments. State law and regulation does not restrict manufacturing or sale of flavored products—a well-recognized industry strategy to attract youths—despite promoting a large-scale “Flavors Hook Kids” campaign for tobacco products in the same time period.It allowed products of any potency, even those with more than 90% THC, as well as marijuana-infused sodas mimicking “alcopops” and a wide range of edible marijuana products. The entire legal marijuana market is being permitted by state and local regulators to shift to high-potency flower and concentrates in California and elsewhere.Similar manipulation of nicotine content to increase addiction was a tobacco industry strategy condemned in the landmark 2006 decision US v Philip Morris. This strategy has permitted, for example, even products such as a grape-flavored vaping cartridge in a hot pink memory stick–like device, with the equivalent of 78 unmetered “standard” 5-mg THC doses37 in 1/50th of 1 ounce to be sold legally. These, like flavored electronic cigarettes, may increase the risk of addiction in youths. Many California communities reacted to legalization of marijuana by delaying or rejecting local commercial activity. The state then partially overrode voter-approved Proposition 64 guarantees of local control, promulgating regulation allowing any delivery licensee to deliver marijuana products anywhere in the state.This measure was challenged by local government and continues in the courts. These conflicts may reflect disparate visions for legalization: one prioritizing industry growth, revenue, and elimination of illicit sales; a second rejecting legalization or wishing it to occur elsewhere; and a third allowing legal commerce but prioritizing public health and demand reduction.This study has certain strengths, including the near-complete coverage of California jurisdictions,hemp drying racks as well as providing the first snapshot of California local law. Findings are also consistent with recent work on local policy in Washington state.Nevertheless, limitations should also be noted. This study describes local regulations 1 year after adult-use sales of marijuana began.

Regulation continues to evolve, and we will assess change annually. Second, we examined only local marijuana laws. Other local laws addressing issues such as zoning, advertising, or smoke-free air may include relevant provisions such as global bans on billboards that were not captured. Frameworks for legalization and local control vary widely between states, and these findings cannot be generalized. However, the concerns identified and potential best practices may be broadly relevant for national, state, and local marijuana regulation, even where local authority for adoption is absent. Importantly, the fundamental questions of whether legalization leads to net public health benefit or harm and whether these “best practices” work remain unanswered. These early descriptive data provide a valuable basis for future research on health and social outcomes in association with variations in the rigor or laxity of local policy after state legalization.The effect of marijuana use on lung health has not been extensively studied, with most data coming from cross sectional and several longitudinal studies. While a significant association of marijuana smoking with symptoms of chronic bronchitis has been reported in most studies, associations with changes in lung function or other aspects of lung health over time, especially in those at risk of or with diagnosed chronic obstructive pulmonary disease , has been less studied. One found a small forced expiratory volume in 1 second decrement over a 20-year period in the relatively small number of heavy marijuana smokers, i.e., ≥20 joint-years 13 and one found a decrement in FEV1 only in former but not current marijuana smokers.Analyzing a subgroup derived from the Canadian Cohort Obstructive Lung Disease study,Tan et al found that marijuana smoking was associated with worse FEV1 decline over a median of 5.9 years in comparison with tobacco-only smokers.While the latter finding mostly related to individuals with a heavy marijuana smoking history , the design of this study might have influenced the results. In a cross-sectional analysis of participants with COPD in the Sub-Populations and Intermediate Outcome Measures In COPD Study with a tobacco smoking history of ≥20 pack years, Morris et al10 reported higher values for FEV1 percentage predicted and a lower percentage of emphysema on HRCT images in both former marijuana smokers and current marijuana smokers , compared with never marijuana smokers who smoked tobacco only, after adjustments for relevant variables. In a preliminary analysis focused on lung function change, we recently showed that ever marijuana smoking among SPIROMICS participants with ≥3 spirometry visits did not have a deleterious impact on FEV1 decline over time nor on the risk for developing spirometry-defined COPD in tobacco smokers without COPD at baseline.However, in order to examine the impact of marijuana smoking on the progression of respiratory symptoms, health status, HRCT metrics, or frequency of exacerbations in addition to the change in lung function, we analyzed a larger subgroup of SPIROMICS participants with ≥2 spirometry visits and an ever marijuana-smoking history as well as a heavy marijuana-smoking history compared to those with ≥3 spirometry visits as previously reported.We aimed to determine whether SPIROMICS FMSs and CMSs exhibit higher rates of change in respiratory symptoms, HRCT metrics, and lung function over time compared to NMSs and whether the reported cumulative lifetime exposure to marijuana would affect these changes. In addition, we evaluated whether self-reported marijuana smoking among SPIROMICS participants without spirometric evidence of COPD at baseline would affect the subsequent development of COPD.SPIROMICS is a prospective cohort study aiming to identify new COPD subgroups and intermediate markers of disease progression.Participants were followed annually over 3 years in SPIROMICS I and had an additional in-person visit in SPIROMICS II. Enrolled participants were 40–80 years old and had either normal spirometry and no tobacco-smoking history or had ≥20 pack years of tobacco smoking; the latter subgroup was further divided based on a post-bronchodilator FEV1 to forced vital capacity ratio ≥0.70 or <0.70. Current asthma was an exclusionary criterion. SPIROMICS was approved by the institutional review boards of each individual site prior to the enrollment of participants. All participants provided informed consent. For the present analysis, data were obtained from ever tobacco-smoking SPIROMICS participants who had spirometry at the baseline visit and at least one followup visit, reported marijuana use or nonuse at the baseline visit, and had no missing covariate information . These participants were divided into the following 3 groups based on their self-reported history of marijuana use: NMSs , FMSs, i.e., no marijuana smoking within the last 30 days , or CMSs, i.e., marijuana smoking within the last 30 days . Based on their baseline frequency and duration of marijuana use, participants were further categorized by their cumulative lifetime history of marijuana smoking defined in terms of joint years, calculated as the number of joints smoked per day times the number of years that marijuana was regularly smoked. Recognizing that marijuana is smoked using a variety of devices, we equated a bowlful of marijuana smoked via a pipe or a bong to one joint. Participants were not asked at the baseline visit about alternative modalities of inhaled marijuana such as vaping, hookah, and “dabbing.” Patients were also categorized into 4 joint-year groups as follows: 0 ; >0–<10 ; 10–<20 ; and ≥20 joint years. Longitudinal data over a period of at least 52 weeks were compared between the 3 groups defined by marijuana smoking status as well as between the 4 subgroups defined by the number of joint years.