Cannabis Grow

However, most published studies have focused only on adolescents under the age of 18 years and do not reflect the adult population to which medical marijuana policies apply . Therefore, long-term longitudinal studies are needed to monitor the effects of marijuana legalization, marijuana initiation/ re-initiation, cigarette initiation/ reinitiation, and patterns of co-use across all age categories. Additionally, it is recommended that such studies take into account statewide variables including number of years since the policy went into effect to adequately capture any measurable changes. These data are needed to explore the growing evidence and public health concerns about the potential “gateway” effect of marijuana on cigarette initiation and nicotine dependence in adolescents and young adults in addition to the potential for re-initiation of cigarettes among former tobacco users. As more states pass marijuana policies, potential increases in co-use could have important treatment implications. Cigarette smokers who also reported current marijuana use were more likely to have nicotine dependence,commercial greenhouse benches which is a known predictor of smoking and quitting behavior . The positive link between co-use and nicotine dependence was observed across age categories but these associations differed across measures of dependence . We analyzed both NDSS and TTFC. NDSS scores might have been a better measure of nicotine dependence in our comparison across age groups since the scale addresses five aspects of dependence .

In comparison, the TTFC single-item scores might not have captured dependency, particularly in adolescent and young adult populations, who have yet to become regular and established smokers. Other studies have shown problems in using TTFC as a measure of dependence in young adults . Since our analysis included both adolescents and adults, we report both NDSS and TTFC measures of nicotine dependence. In addition, in the present study, cigarette smokers who reported ever but not current marijuana use were at greater risk of having nicotine dependence compared to never marijuana users. This finding supports that the effect of THC exposure on nicotine receptors may be irreversible . Studies are needed to further examine both short term and possibly even the long-term effects of THC and nicotine exposure on nicotine dependence and tobacco cessation. In this analysis, 12–17 year old adolescent and 50–64 year old cigarette and marijuana cousers had the highest odds of having nicotine dependence. These findings support previous studies linking co-use and nicotine dependence in adolescents and young adults and add to preliminary data that this association was also stable in adults and, surprisingly, particularly robust in 50–64 year old adults. These findings reflect evidence of a U-shaped effect between age and nicotine dependence which peaks at age 50 years due to changes in nicotinic receptors and nicotine-associated metabolism with age , and suggest that this relationship was stable among co-users. Studies are needed to determine the extent to which THC exposure and/or current marijuana use add to this effect . Additionally, 50–64 year olds may represent a unique birth cohort who spent their formative years during the 1960’s and 1970’s with minimal tobacco regulations coupled with a counterculture that promoted marijuana use among a large population . More studies on the Baby Boomer generation, specifically, their perceptions about marijuana, current marijuana use including purpose of use , modality, cigarette co-use, and health outcomes could provide a glimpse into the future as continued legalization will likely influence social norms across the general population .

As more states adopt liberal marijuana policies, more studies are needed to understand co-use including the relationship between THC and nicotine in addition to other individual-level factors such as genetics and personality traits that might influence dependence and cessation . We found higher percentages of non-Hispanic Whites and Blacks/ African-Americans in states where medical marijuana was illegal. In this study, these results may be attenuated since our analysis comparing nicotine dependence depended on exclusion of blunt use. The American Civil Liberties Union report data from the NSDUH and Uniform Crime Reporting Data showing that Black males were no more likely to report marijuana use, but 4-times more likely to be incarcerated for marijuana possession compared to their non-Hispanic White male counterparts . Epidemiologic data have shown a linear increase in cigarette and marijuana co-use in Whites, Blacks/ African-Americans, and Hispanics with the fastest rate of increase among Blacks/ African-Americans . Among Blacks/ African-Americans, it is possible that statewide legalization of medical marijuana could help to reduce marijuana-related incarcerations, and at the same time, influence the rate of couse. We are cognizant of the many layers that add to the complexities around the issue of marijuana legalization that are well beyond the scope of our study. We recommend future research will assess potential and actual benefits/ costs of marijuana legalization to society at large, and in states where marijuana is legal, identify issues that can be addressed with specific regulatory measures .Study limitations include the cross sectional nature of these analyses which limits our ability to infer causality. Interpretation of our findings is limited to cigarette smokers which is distinct from those who reported other tobacco products . We were unable to examine statewide legalization of medical marijuana by the number of years the policy went into effect using the NSDUH to account for time lags from adoption to full implementation. The NSDUH public dataset only provides a binary categorization of states that were legal vs. illegal that lumps states that just passed the law with long-term legalization states limits our ability to detect long-term effects and may have attenuated our findings. Further study is needed to examine the effect of combusted vs. non-combusted marijuana use on nicotine given increasing prevalence of edible and aerosolized delivery of marijuana with vaporizers .

At present, the NSDUH does not ask respondents to indicate whether use was combusted and/ or non-combusted and we recommend that future surveys collect information on marijuana modality to elucidate the relationship between various forms of marijuana intake and nicotine and/ or THC dependence. Data on combusted vs. non-combusted THC intake can also help to identify if there might be differences in health effects across marijuana use modality. In addition, the present study did not examine population density which might be a potential covariate for marijuana use. Strengths of the study were use of a large national dataset representative of the U.S. population and internal validity of nicotine dependence comparisons across age categories using the same dataset,grow bench which eliminates methodological variations from one study to another. Medical marijuana legalization was positively associated with cigarette and marijuana couse and co-users were at greater risk for nicotine dependence. Long-term longitudinal data across age groups are needed to elucidate these results. In the meantime, it is recommended that stakeholders in tobacco control participate in policy discussions involving marijuana legalization including regulatory measures to prevent further co-use and develop novel cessation treatments to help co-users who may have a harder time with quitting. Marijuana use is legal for medical or recreational purposes in 33 states and Washington, DC . Recreational legalization has ushered in rapid commercialization . Both Colorado and Washington—the first 2 states to legalize marijuana for recreational use—have seen retail sales exceed a billion dollars annually . States with recreational marijuana have been inundated with mass marketing promoting marijuana use, and also an increase in novel marijuana products with tetrahydrocannabinol content at concentrations not evaluated for safety in humans . Given the absence of federal regulations in managing the commercial marijuana market, individual states are developing regulations governing marijuana advertising, production, and sale . In US adults, rates of recreational marijuana use and cannabis use disorders have increased considerably over the last several years .Legalization for medical purposes has been accompanied with increased daily use and marijuana use disorders among US adults . Approximately 15% of the US adult population used marijuana in some form in 2017 . Between 2016 and 2017, past-month use of marijuana increased nearly 2% among adults aged 18 to 25 years and 1.2% among adults 26 years and older . Additionally, national surveys suggest the perception of ‘‘great risk’’ from weekly marijuana use dropped from 50.4% in 2002 to 33.3% in 2014 and has dropped further since . Recent national surveys also demonstrate that the public attributes benefits to marijuana that are not supported by existing scientific evidence, such as relief from anxiety, stress, and depression, improved appetite, and improved sleep . It is unknown whether adult residents of states where marijuana has been commercialized for recreational use are more likely to attribute benefits to marijuana use.

Given the growing body of evidence that adverse consequences are associated with regular marijuana use , determining whether residents of recreational states perceive marijuana use differently than residents of states without commercial legalization is an important consideration and may inform the needs for more investment in communications of potential risks to the public. In this study, we examine the differences in beliefs about marijuana use and rates of use across states defined by their marijuana legalization status .Survey questions were developed by identifying gaps in existing federally funded national surveys, including the National Survey on Drug Use and Health and Monitoring the Future , and drafting questions to address those gaps. Questions were refined through interviews with marijuana industry professionals, dispensary staff, marijuana distributors, and mental health and substance use disorder experts. Survey items developed included individual opinions on the risks and benefits of marijuana use, comparisons of risks and benefits of marijuana to other psychoactive substances, and the form, amount, and frequency with which individuals use marijuana. In total, the survey included 29 questions assessing beliefs about the risks and benefits of marijuana and 54 questions assessing marijuana use. Answer options for all opinion questions used Likert scales to allow participants to respond with the answer most closely aligned with their beliefs. All questions were written at an 8th-grade reading level and were tested on a convenience sample of 40 adults to ensure readability and construct validity. Full details on survey development have been previously published . The survey tool is available in the supplementary material .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 . 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. Participants are provided with no more than 6 surveys a month and are expected to complete an average of four surveys a month . Sampling was stratified by legalization status of marijuana in the state of residence . California residents and young adults aged 18 to 26 years old were over sampled to facilitate a future investigation into the role of recreational legalization on use patterns among young adults in California. Sampling weights were provided by GfK.The response rate, determined using methods outlined by the American Association for Public Opinion Research, was the ratio of respondents to all potential participants . Characteristics of the survey respondents were weighted using weights provided by GfK to approximate the US population based on age, sex, race, ethnicity, education, household income, home ownership, and metropolitan area. All analyses used weighting commands using the weight variable provided by GfK to generate national estimates. We first compared the sociodemographic characteristics of our respondents to that of the NSDUH—an annual, federally funded epidemiologic survey . We then compared views and forms of marijuana use of residents across recreational, medical, and nonlegal states using chi-square statistics. Finally, we reported the prevalence of different forms of use stratified by legalization status of states and the associated 95% confidence interval . In supplementary analyses, using logistic regression, we examined views of residents of recreational states compared with other states after adjusting for baseline demographic characteristics including age, sex, race, employment status, and household size.

To assess volume of activation within posterior parietal and left prefrontal ROIs, we calculated the number voxels showing significantly greater activation during SWM relative to vigilance for each participant within each ROI. We then performed regression analyses to predict volume of activation from age, gender, and their interaction. To determine whether movement during fMRI scanning might affect results, we examined relationships between age and bulk motion in two ways. Both total number of removed repetitions and average movement in each direction throughout the task were examined in relation to age and gender using correlational analyses. The number of repetitions removed for excessive motion during the task declined with age . However, in brain regions demonstrating a relationship between SWM response and age, number of removed repetitions did not significantly relate to brain response , and the relationship between age and brain response in each cluster remained significant after controlling for number of removed repetitions. Mean rotational and translational motion were not significantly related to age. The average rotational movement throughout the task was 0.07, 0.22, and 0.09 degrees for roll, pitch, and yaw, respectively; the average translational movement was 0.16, 0.06, and 0.09 mm for superior, left, and posterior, respectively. There were no significant gender differences for number of repetitions removed for movement or on any directional movement parameter, with the exception of males demonstrating significantly greater rotational motion than females in the pitch direction = -2.08, p < .05.Age positively predicted SWM brain response in bilateral medial portions of superior frontal gyrus ; left superior and middle frontal gyri ;inferior aspects of the left precuneus and angular gyrus ; and a cluster encompassing the right inferior parietal lobule, postcentral gyrus, and insula .

A negative relationship between age and SWM response was observed in the left superior frontal gyrus ; left precuneus and superior parietal lobule ; superior portions of the right inferior parietal lobule ; and the right lingual gyrus . Exploratory follow-up analyses revealed that in the medial superior frontal cluster, teens evidenced less response during SWM than during vigilance,commercial drying racks with younger youths showing greater vigilance response than older teens. Further, in the right lingual gyrus, youths demonstrated less response during SWM than during rest , with older teens showing a greater decrease in SWM response relative to fixation than younger teens . In the left superior frontal gyrus , most participants showed no significant response to SWM relative to vigA significant age × gender interaction was observed in the right fronto polar superior frontal gyrus , in the same location as the gender difference described above . In this cluster, males showed a negative relationship between age and SWM response, but females showed a positive relationship.To understand whether age and gender related differences in BOLD response could be accounted for by task performance , we examined mediational models using a series of regressions . As vigilance reaction time was the only task performance index related to age or gender, regression analyses examined whether it mediated the relationship between age or gender and BOLD response in any of the clusters listed in Table 2. Vigilance reaction time was not significantly related to brain response in any region that was related to age or gender, and therefore did not mediate the relationship between age or gender and BOLD response. The posterior parietal ROI encompassing areas demonstrating significant activation to SWM relative to vigilance in young and old youths was 96,876 microliters, and spanned bilateral portions of the precuneus and superior and inferior parietal lobules. Within that cluster, ROI analyses demonstrated that activation for young adolescents was mostly in superior regions of parietal cortex, while response for old teens was mostly in inferior parietal areas .

Although age and the age × gender interaction did not predict volume of parietal activation within the combined parietal ROI, males demonstrated larger volumes of activations than females =5.62, p<.025. Further, males showed a significant negative relationship between age and volume of activation , while females showed no relationship between age and volume of response. Within the left prefrontal ROI , age significantly predicted the volume of activation, with larger volumes of activation demonstrated by older teens =4.95, p=.03. There was no significant effect of gender or the age × gender interaction on the volume of left prefrontal activation. This cross-sectional study examined the effects of age and gender on brain response during a SWM task among 12- to 17-year-olds. In general, we observed comparable task performance across the age range and between genders, and all teens showed typical response patterns for SWM, with activation in bilateral prefrontal and posterior parietal cortices. This pattern parallels adult activation during spatial working memory tasks and supports occipitoparietal, or “dorsal stream,” processing of spatial locations , suggesting that, in general, teens use similar working memory and spatial processing strategies as adults. However, specific localization and intensity of response varied across the adolescent age range, and males and females showed slightly different activations. These differential patterns emerged despite similar task performance across the age range and between genders, suggesting that developmental changes in SWM brain response are driven by factors other than task performance.In contrast to the literature suggesting that SWM abilities on n-back tasks improve across the adolescent age range , we did not observe age related improvements in performance on our SWM task. While this was likely due to the low difficulty level of the task used which approached ceiling effects, it is a benefit to the neuroimaging component of this study as it prevented confounding performance effects on the neural activation patterns observed across this age range.

Although task performance was not related to age, teens performed more accurately on vigilance than SWM, yet reaction times were faster on SWM. Our previous studies using this task demonstrated similar findings, with slightly faster performance on SWM than vigilance . While the reason for this difference is unclear, it could be that the small visual discrimination necessary for dot detection during vigilance blocks is more time consuming than the broader location detection required during SWM blocks. Future studies should attempt to eliminate this difference in reaction times between experimental and control conditions, perhaps by designing a task with easier visual discrimination .As hypothesized, age positively predicted SWM activation within the prefrontal cortex. Specifically,rolling grow table age was positively associated with both the intensity and extent of brain response in the left middle and superior frontal gyri . This cluster spanned frontopolar cortex but also encompassed parts of dorsolateral prefrontal cortex. Frontopolar prefrontal cortex activation has been associated with subgoal processing and evaluation of internally generated information . Thus, older teens may invoke more self-generated strategies, including rule induction or more efficient retrieval processes. This cluster also included portions of dorsolateral prefrontal cortex, which has been consistently implicated in working memory tasks. Adult studies have suggested that prefrontal activation is often left-lateralized during verbal working memory tasks ; thus the greater left prefrontal response among older teens may suggest that older teens employ more verbal rehearsal strategies during the task than younger adolescents. While the task was designed to minimize verbal encoding, older teens may have imagined the eight possible stimulus locations as positions on a clock face, facilitating verbal labeling and resulting in greater left prefrontal activity. Also consistent with our hypotheses, we observed a positive relationship between age and SWM response in posterior parietal regions. However, while we detected a positive relationship between age and SWM activation in bilateral inferior parietal regions, including inferior aspects of the right precuneus and left inferior parietal lobule, our data also revealed a negative relationship between age and brain response in bilateral superior parietal cortex, comprising superior portions of the right inferior parietal lobule, left precuneus and left superior parietal lobule. Exploratory ROI analyses confirmed these findings, demonstrating that while both young and old teens evidenced overlapping posterior parietal activation, younger youths showed activation mostly in superior regions, but older teens showed activation mostly in inferior regions. Together, these results indicate a shift from superior to inferior parietal areas utilized during SWM across adolescence. Previous fMRI studies of SWM have suggested that parietal activation intensity increases across adolescence , yet small sample sizes and different task designs may have prevented the observation of additional negative relationships identified in the current study. While functional parcellation of parietal involvement in sub-components of spatial working memory is largely unknown, some researchers of adult populations have suggested that superior parietal regions may be important for spatial rehearsal during working memory , while inferior parietal regions may be implicated in short-term storage during working memory .

Therefore, the superior to inferior shift in parietal activation across adolescence could represent a change in spatial working memory strategies. Younger adolescents may rely more on spatial rehearsal, which could become more automated throughout adolescence, requiring less superiorparietal activation. Along those lines, older adolescents may be better able to engage inferior parietal regions involved with spatial storage, and rely less on spatial rehearsal. Moreover, if older adolescents are employing greater verbal rehearsal strategies as discussed above, then spatial rehearsal may be less efficient, and therefore utilized to a lesser degree. In addition, stage of pubertal development was negatively associated with response in the superior right inferior parietal lobule cluster, above and beyond the effects of chronological age. Previous literature has demonstrated the impact of sex hormones on the development of cerebral lateralization , and pubertal timing has been related to functional asymmetry . Similarly, in this study, right parietal maturation appears linked to pubertal stage while left parietal development is not, suggesting asymmetrical cortical development that may be hormonally influenced. This finding points to the importance of individual variation in biological maturation that may not be accounted for by chronological age, and suggests that indices of pubertal development may further characterize neural maturation and help explain changes in SWM brain response patterns and cognitive strategies across adolescence. As well as showing changing fMRI response patterns to SWM tasks across adolescence , previous adolescent research has also demonstrated age-related increases in the spatial extent of frontal and parietal SWM activation . We found a greater number of significantly activated voxels in left prefrontal cortex with increasing adolescent age, suggesting that in some regions, both the magnitude of response and the volume of significant activation increase across adolescence. However, the results of our spatial extent analysis in posterior parietal cortex showed no significant relationship between age and volume of significant SWM response. Taken together with our results demonstrating age-related regional changes in the intensity of activation, these findings suggest that in late developing frontal brain regions, intense and more widespread activation emerges, while in slightly earlier developing posterior parietal networks, there is a focal shift in localization of activity. When examined in light of the adult working memory literature, adolescent age-related changes in frontal and parietal networks involved in SWM support the evolution of more efficient cognitive strategies. In the lingual gyrus, we observed deactivation that increased with age. Increasing occipital deactivation across adolescence could represent enhanced priming, and therefore greater recognition and reprocessing of repeated spatial locations among older youths. Teens in this study also demonstrated less response during SWM than during the vigilance condition in medial superior frontal cortex, yet this discrepancy dissipated across adolescence, such that this area was no longer “under active” in older teens. Medial frontal cortex is highly active at rest, during which it is involved in attentional monitoring of various internal and external stimuli . Medial frontal cortex underactivation during a cognitive task may represent reallocation of limited attentional resources to areas directly involved in task performance . Thus, SWM task demands may be more difficult for younger youths, who require greater attentional allocation to maintain performance, and therefore greater under-activation of medial frontal cortex. fMRI Response and Gender This is the first known fMRI study to attempt to examine the role of gender in relation to the neural substrates involved in SWM across adolescent development. While our findings do not entirely support the hypothesis that females would evidence more mature SWM response patterns than males, several interesting gender specific findings suggest that males and females utilize slightly different brain regions to perform well on a SWM task. Specifically, females demonstrated more right anterior cingulate response during the vigilance condition than did males.

After further review, involving the Planning Commission and additional public hearings, the county voted to permit marijuana production and processing establishments if they complied with notice provisions and strict zoning restrictions. On the eastern side of Washington state, the Spokane County Commission imposed a moratorium on outdoor marijuana farms in November 2016, because of residential complaints about odors from marijuana agriculture and processing; at the time of the moratorium, over 160 marijuana growers and processors were operating in the county.By the end date of our data collection , some counties still had moratoriums in place and stated in policy documents they were awaiting additional information to make a permanent decision. A few counties permitted marijuana facilities but integrated policy mechanisms to address residential concerns about matters such as odors, bright lighting, and increased traffic and perceived risk of crime associated with these establishments. For example, Chelan County, Washington, required marijuana producers and processors to register and pay a fee for an enforcement fund to ensure regulatory compliance.Although policy communication and advocacy strategies varied by county and state, we found patterns during our qualitative review of ordinance and newspaper article data related to primary county-level policy stakeholders, and arguments in support of or opposition to local marijuana policy. Primary stakeholders and advocates involved in county-level marijuana policy debates were similar in both states and included elected county officials tasked with decision making , law enforcement ,greenhouse rolling benches individual marijuana growers/farmers, marijuana business license applicants, parents, and other residents.

Several ordinances also named county voters as stakeholders who would be impacted by policy decisions. Some ordinances named specific county government departments , zoning authorities/commissions, or public entities as local stakeholders. We did not identify any specific advocacy groups or advocates external to the county involved in county-level policy debates.Our analysis of arguments in support of or opposition to specific policies revealed that many counties pointed to local public opinion as a basis for decision making, as well as local election results from the statewide 2012 marijuana legalization referendum, as evidence of either support of or opposition to legalizing marijuana facilities. For example, in 2012, The Wall Street Journal quoted the chair of the Douglas County, Colorado, Board of Commissioners as saying the local election results supported a prohibition: “Our county has never passed or supported anything regarding the legalization of marijuana…we tend to be very conservative,” he said.35 Alternatively, in Jefferson County, Washington, the ordinance included the quote, “Whereas, some 65% of voters in Jefferson County voted yes on Initiative 502,”to support allowing marijuana facilities. Similarly, a local newspaper article about a 2015 proposal to extend a moratorium in Huerfano County, Colorado, quoted a resident as saying, “Legalization of marijuana passed by 60 percent of the vote here in Huerfano, and we need to respect the will of the people.”Table 2 presents the main policy arguments, including public opinion, to support or oppose county-level marijuana policy positions in Colorado and Washington. Frequent arguments in Colorado and Washington in favor of allowing some or all commercial marijuana facilities focused on economic gain, reduced criminality, and potential health benefits. Economic arguments from proponents of legalized marijuana varied and included mention of local revenue increases for the municipality, increased employment opportunities , expanded tourism, and personal financial gain for local residents involved in marijuana cultivation, processing, or retail. In Whitman County, Colorado, opponents of a moratorium said it “would prohibit people from getting jobs and cause the county to lose out on revenue from the industry.”

During a community forum in Wahkiakum County, Washington, a resident pointed to local revenue for a nearby county in support of allowing marijuana retail facilities, “I am merely speaking from a financial perspective…The county is in financial difficulty, and this is a legal revenue source available to the county.”Criminality arguments—less commonly mentioned by marijuana legalization proponents than economic arguments—pointed to the potential reduction of illegal marijuana markets and activity as a benefit.We identified a wider variety of arguments used in support of prohibitions, including economic loss arguments to counter economic gain arguments from proponents of legalized marijuana. Economic loss arguments pointed to the possible loss of tax revenues , increased lawsuits, and increased cost of law enforcement to enforce regulations. Concerns about public health, safety, and welfare were among the most frequently mentioned arguments against permitting local marijuana establishments. Residents noted concerns about environmental hazards , increased addiction, increased traffic issues around retailers, and the risk of accidental poisoning or overdose among minors. Additionally, elected local officials and residents pointed to federal law, namely the illegality of the cultivation, possession, sale, and use of marijuana under federal criminal statutes, as a deterrent for allowing marijuana facilities.The article noted residents’ concerns about illegal growing of marijuana in Colorado, which was described as a substantial challenge for local and federal law enforcement. Three minor arguments included land use concerns, proximity to states where marijuana was illegal, and possible harm to the county’s reputation or local identity. For the latter, proponents of bans/moratoriums pointed to potential harm or loss of neighborhood character/identity, suggesting that allowing a marijuana facility would be detrimental to the status quo. In the following sections, we provide illustrative examples of four counties to describe in more detail the various county policy environments and highlight policy changes within the counties.

These counties are classified using a brief version of the rural-urban categories from the USDA Economic Research Service’s Rural-Urban Continuum Codes: metropolitan; non-metropolitan, urban; and rural.40On November 12, 2013, Mason County, a non-metropolitan, urban county located in western Washington , initially adopted an ordinance allowing licensed marijuana producers, processors, and retailers.Meeting minutes from a county commissioner meeting on June 24, 2014, highlighted resident concerns about marijuana producers and processors during a public comment period when a majority of marijuana opponents,greenhouse bench top self-identified as county residents who lived near a licensed marijuana production or manufacturing facility,expressed NIMBY sentiments about these facilities. Noted concerns included possible criminal activity, safety issues, odor problems, environmental risks , decreased property value, and law enforcement implementation issues.On July 1, 2014, the board of commissioners enacted a six-month moratorium prohibiting building or land use related to the production and processing of marijuana, allowing these activities solely in agricultural and industrial zones .On July 22, 2014, opponents of the moratorium said they felt specifically targeted and residents should “be concerned about meth, heroin and other drugs, not legal marijuana,” in addition to expressing concerns about financial losses. Proponents reiterated arguments about the potential crime impact, loss of property value, and marijuana being “against federal law.” Some residents asked for more time for public input and recommended revising the ordinance to address residential concerns but allow legal cultivation, processing, and sales.On October 21, 2014, the board of commissioners voted to repeal the moratorium and simultaneously issued code amendments to address certain residential concerns .Costilla County is a rural county located along the southern Colorado border with New Mexico. It presents a unique local jurisdiction that enacted policies to allow cultivation and retail facilities.Permitting these facilities contrasted with other rural counties that opted to prohibit all marijuana facilities. Costilla County spans 1,227 square miles with a large swath of high desert land lacking in building or residential infrastructure. The low cost of land, coupled with state legalization of recreational marijuana, led to an influx of outsiders interested in purchasing property for commercial marijuana cultivation and production.Marijuana facility licensing and license renewal fees were sources of revenue in addition to the distribution of the retail marijuana state sales tax. Local governments could also implement their own local sales or excise taxes. Concerns included the increased use of educational and social service resources for new families from out of state and lack of local licensing and enforcement personnel. The Denver Post quoted a county commissioner as saying there was a shift in the jail population from local residents to a majority “from outside,” but also said there was not a spike in crime in these early years.A local disagreement between a proposed marijuana cultivation facility and a museum in early 2015 highlighted the conflict between marijuana business license owners and other establishments in the county. Supporters of the museum opposed issuing a license for the proposed facility, mentioning the facility’s potential impact on minors and other museum visitors.Ultimately, the business owner decided to change the location of the marijuana cultivation facility to “be a good neighbor” and ameliorate possible odor and lighting concerns. The owner, however, continued to pursue a license for a retail facility and medical marijuana dispensary next to the museum.Chaffee County, located in the central part of Colorado, is an example of a non-metropolitan, urban county with a restrictive marijuana policy environment.

In September 2013, the county unanimously approved ordinance 2013-02, which temporarily banned new recreational marijuana establishments in the county through December 31, 2014.However, the county permitted recreational marijuana cultivation licenses in industrial zones, and some facilities that cultivated medical marijuana or manufactured medical marijuana–infused products were exempt from the ban—if such facilities were in good standing in the county and met the state and county licensing standards, they were allowed to convert to recreational facilities.Grandfathered facilities could apply for recreational facility licenses .Ordinance 2014-02,unanimously adopted in 2014, amended the prior ban through December 31, 2015, to include all marijuana facilities and limit the number of facilities in the county to six. An exception was again made for cultivation or manufacturing facilities in good standing with the county and state; these facilities were grandfathered and could renew their licenses or expand their operations and the number of plants within their existing parcel allotted. The moratorium banning any type of recreational marijuana establishment, with the exception of certain grandfathered facilities, was reissued multiple times .Benton County, Washington, is a metropolitan county bordering Oregon. This county was a unique case: it transitioned from permitting marijuana businesses to enacting moratoriums, ordinances, and zoning limitations, and eventually banning new recreational marijuana facilities while grandfathering in existing operations in response to local concerns and complaints. Benton County did not start out with a ban. In 2015, recreational marijuana retail facilities opened in the county.Later that year, multiple residents expressed concern over a marijuana grow site, which led to an emergency moratorium and public hearing process.The controversy continued, and, in late 2017, a local newspaper article featured alternate perspectives from two longtime county residents expressed during the county’s public hearing: one had concerns about the odor associated with marijuana facilities and the effect on children, whereas the other, a landowner who rented property to a marijuana producer, opposed a ban.Opponents of a ban mentioned economic empowerment and access to medicinal marijuana, whereas supporters mentioned odor and enforcement issues. The county opted to ban new marijuana retail facilities and enact a moratorium on new producers and processors. In April 2018, the board of county commissioners voted two to one to permanently ban all new marijuana production and processing operations in all unincorporated zoning districts, although over 50 licensed operators in these areas were allowed to continue operating.The commission also expanded the sheriff’s authority to enforce nuisance and odor rules in response to residential complaints.Our county-level recreational marijuana policy surveillance study reveals a patchwork of local policies in place by 2019 in the two earliest states to legalize recreational marijuana. Our findings add to existing literature that suggests state marijuana legalization policies are nuanced and complex, varying at the local level.Our policy change results highlight the importance of ongoing policy surveillance research to examine changes to local marijuana policy over time. We found 40 counties across both states prohibited all marijuana facilities by early 2019, either through temporary or permanent bans. Prior research in Washington similarly found 10 counties had moratoriums or permanent bans in effect on retail recreational cannabis outlets as of mid- 20147 and mid-2016,3 compared to 9 counties at the end of our study period . To our knowledge, this is the first study to report county-level recreational marijuana policies in Colorado. We found that nearly half of Colorado counties had prohibited all types of marijuana facilities by the end of our study period. This is a high percentage compared to Washington. Of note, the lower percentage in Washington may be partially explained by Washington’s merger of recreational and medical marijuana markets in 2015.

Although we report complete data for all analyses, meta analyses of cross-sectional studies examining cough, sputum production, and wheezing were limited by heterogeneity. Heterogeneity was likely related to the lack of uniform assessment of marijuana use and outcome ascertainment . Our current understanding of the long-term health effects of marijuana could be improved by standardized assessment tools for marijuana use and studies with larger samples of marijuana-only users and longer follow-up times. Low-strength evidence indicates that smoking marijuana is associated with cough, sputum production, and wheezing. Current understanding of marijuana’s effect on pulmonary function tests and development of obstructive lung disease is insufficient and is limited by low exposure and young study populations. Given rapidly expanding use, we need large scale longitudinal studies examining the long-term pulmonary effects of daily marijuana use.Marijuana is one of the most widely used illicit substances world-wide. Although it has been reported that marijuana use rate has stabilized or even decreased in recent years in most high-income countries, the continuing high prevalence of use among adolescents and young adults is a cause for concern. Such emerging trends have heightened interest in the link between mental health problems and adolescent marijuana use to inform policy and prevention efforts. Understanding the comorbidity between psychopathology and marijuana use is complicated. Marijuana use is associated with numerous different psychiatric disorders, cannabis drying racks commercial each of which tend to co-occur with one another. Additionally complicating matters is the potential bidirectional nature of this association, with evidence that marijuana use may both predict and result from poor zmental health.

A parsimonious explanation of this comorbidity may be that a small set of transdiagnostic psychopathological vulnerabilities that give rise to numerous mental health conditions may also contribute to and result from marijuana use. Such transdiagnostic vulnerabilities may account for the pervasive patterns of psychiatric comorbidity with use of marijuana and other substances. One such transdiagnostic vulnerability is anhedonia— diminished capacity to experience pleasure in response to rewards. As a subjective manifestation of deficient reward processing capabilities, anhedonia is believed to result from hypoactive brain reward circuitry. While anhedonia is a core feature in a DSM-defined major depressive episode, it has also been linked to other psychopathologies comorbid with drug use, including psychosis, borderline personality disorder , social anxiety, attention deficit hyperactivity disorder and post-traumatic stress disorder and has therefore been proposed to be a transdiagnostic process. Departing from its consideration as a ‘symptom’ of a disease state as in DSM-defined major depression, anhedonia has also been conceptualized as a continuous dimension, upon which there are substantial inter individual differences. Individuals at the lower end of the anhedonic spectrum experience high levels of pleasure and experience robust affective responses to pleasurable events, whereas those at the upper end of this spectrum exhibit more prominent deficits in their pleasure experience.Anhedonia operates as a ‘trait like’ dimension that is stable yet malleable, which is empirically and conceptually distinct from other emotional constructs, such as reward sensitivity , alexithymia and emotional numbing , sadness and negative affect. Recent literature documents a consistent association between anhedonia and substance use in adults.

To the best of our knowledge, there has been only prior study of the association between anhedonia and marijuana use in youth, which found higher anhedonia levels among treatment-seeking marijuana users than healthy controls in a cross-sectional analysis of 62 French adolescents and young adults. Given the absence of longitudinal data, it is unclear whether anhedonia is a risk factor for or consequence of adolescent marijuana use. Because youth with higher anhedonia levels experience little pleasure from routine rewards they may seek out drugs of abuse, such as marijuana, which stimulate neural circuitry that underlie pleasure pharmacologically. Alternatively, repeated tetrahydrocannabinol exposure during adolescence produces enduring deficits in brain reward system function and anhedonia-like behavior in rodent models. In observational studies of adults, heavy or problematic marijuana use is associated with subsequent anhedonia and diminished brain reward region activity during reward anticipation. Consequently, it is plausible that anhedonia may both increase risk of marijuana use and result from marijuana use. Because early adolescence is a period in which risk of marijuana use uptake is high and the developing brain may be vulnerable to cannabinoid-induced neuroadaptations, this study estimated the strength of bidirectional longitudinal associations between anhedonia and marijuana use among adolescents during the first 2 years of high school. The primary aim was to test the following hypotheses: greater baseline anhedonia would be associated with a faster rate of escalation in marijuana use across follow-up periods; and more frequent use of marijuana at baseline would be associated with increases in anhedonia across follow-ups. A secondary aim was to test whether these putative risk pathways were amplified or suppressed among pertinent sub-populations and contexts.

Associations of affective disturbance and other risk factors with adolescent substance use escalation have been reported to be amplified among girls, early- onset substance users and those with substance-using peers.We therefore tested whether associations between anhedonia and marijuana use were moderated by gender, history of marijuana use prior to the study surveillance period at baseline and peer marijuana use at baseline.To characterize trajectories of anhedonia and marijuana use across time, latent growth curve modeling was applied to estimate a baseline level and linear slope for both anhedonia and marijuana use. Univariate latent growth curve models were first fitted for marijuana use and anhedonia separately to determine the shape and variance of trajectories. A two-process parallel latent growth curve model was then fitted, which simultaneously included growth factors for anhedonia and marijuana use after adjusting for covariates listed above and including within-construct level-to-slope associations. The parallel process model was constructed to test: bidirectional longitudinal associations by including directional paths from baseline anhedonia level to marijuana use slope as well as baseline marijuana use level to anhedonia slope; and non-directional correlations between baseline levels of anhedonia and marijuana use and between anhedonia slope and marijuana use slope. Significant directional longitudinal paths between anhedonia and marijuana use in the overall sample were tested subsequently in moderation analyses of differences in the strength of paths across subsamples stratified by moderator status using a multi-group analysis. Analyses were performed using Mplus with the complex analysis function to adjust parameter standard errors due to clustering of the data by school. To address item- and wave-level missing data, full information maximum likelihood estimation with robust standard errors was applied. Continuous and categorical ordinal scaled outcomes were applied for anhedonia and marijuana use, vertical grow racks cost respectively. The Akaike information criterion and the Bayesian information criterion were used to gauge model fit in which lower values represent better-fitting models. For moderator analyses, χ2 differences were calculated using log-likelihood values and the number of free parameters contrasting the model fit with equality constraints on the anhedonia–marijuana use path of interest across groups stratified by the moderator variable. Standardized parameter estimates and 95% confidence intervals are reported. Significance was set at α = 0.05 .Youth with higher levels of anhedonia at baseline were at increased risk of marijuana use escalation during early adolescence in this study. In addition, levels of anhedonia and marijuana use reported at the beginning of high school were associated cross-sectionally with each other.

To the best of our knowledge, the only prior study on this topic found higher levels of anhedonia in 32 treatment-seeking marijuana users than 30 healthy controls in a cross-sectional analysis of French 14–20-year-olds who did not adjust for confounders. The current data provide new evidence elucidating the nature and direction of this association in a large community-based sample, which advances a literature that has addressed the role of anhedonia predominately in adult samples. The association of baseline anhedonia with marijuana use escalation was observed after adjustment of numerous possible confounders, including demographic variables, symptom levels of three psychiatric syndromes linked previously with anhedonia and alcohol and tobacco use. Consequently, it is unlikely that anhedonia is merely a marker of these other psychopathological sources of marijuana use risk or a non-specific proclivity to any type of substance use. The temporal ordering of anhedonia relative to marijuana was addressed by the overarching bidirectional modeling strategy, which showed evidence of one direction of association and not the other direction . Ordering was confirmed further in moderator tests showing that the association of anhedonia with subsequent marijuana use did not differ by baseline history of marijuana use. Thus, differences in risk of marijuana use between adolescents with higher anhedonia may be observed in cases when anhedonia precedes the onset of marijuana use. Why might anhedonia be associated uniquely with subsequent risk of marijuana use escalation in early adolescence? Anhedonic individuals require a higher threshold of reward stimulation to generate an affective response and therefore may be particularly motivated to seek out pharmacological rewards to satisfy the basic drive to experience pleasure, as evidenced by prior work linking anhedonia to subsequent tobacco smoking escalation. Among the three most commonly used drugs of abuse in youth , marijuana may possess the most robust mood-altering psychoactive effects in young adolescents. Consequently, marijuana may have unique appeal for anhedonic youth driven to experience pleasure that they may otherwise be unable to derive easily via typical non-drug rewards. The study results may open new opportunities for marijuana use prevention. Brief measures of anhedonia that have been validated in youth, such as the SHAPS scale used here, may be useful for identifying teens at risk who may benefit from interventions. If anhedonia is ultimately deemed a causal risk factor, targeting anhedonia may prove useful in marijuana use prevention. Interventions promoting youth engagement in healthy alternative rewarding behaviors without resorting to drug use have shown promise in prevention, and could be useful for offsetting anhedonia-related risk of marijuana use update. Moderator results raise several potential scientific and practical implications. The association was stronger among adolescents with friends who used marijuana, suggesting that expression of a proclivity to marijuana use may be amplified among teens in environments in which marijuana is easily accessible and socially normative. The association of anhedonia with marijuana use escalation did not differ by gender or baseline history of marijuana use. Thus, preventive interventions that address anhedonia may: benefit both boys and girls , aid in disrupting risk of onset as well as progression of marijuana use following initiation and be particularly valuable for teens in high-risk social environments. While anhedonia increased linearly over the first 2 years of high school on average, the rate of change in anhedonia was not associated with baseline marijuana use or changes in marijuana use across time. Given that anhedonia is a manifestation of deficient reward activity, this finding is discordant with pre-clinical evidence of THCinduced dampening of brain reward activity and prior adult observational data, showing that heavy or problematic marijuana use is associated with subsequent anhedonia and diminished brain reward region activity during reward anticipation. Perhaps the typical level and chronicity of exposure to marijuana use in this general sample of high school students was insufficient for detecting cannabinoid-induced manifestations of reward deficiency. Longer periods of follow-up may be needed to determine the extent of marijuana exposure at which cannabinoid-induced reward functioning impairment and resultant psychopathological sequelae may arise. Strengths of this study include the large and demographically diverse sample, repeated-measures follow-up over a key developmental period, modeling of multi-directional associations, rigorous adjustment of potential confounders, high participation and retention rates and moderator tests to elucidate generalizability of the associations. Future work in which inclusion of biomarkers and objective measures is feasible may prove useful. Prevalence of heavy marijuana use was low in this sample, which precluded examination of clinical outcomes, such as marijuana use disorder. Students who did complete the final follow-up had lower baseline marijuana use and anhedonia, which might impact representativeness. Further evaluation of the impact of family history of mental health or substance use problems as well as use of other illicit substances, which was not addressed here, is warranted.Medical marijuana has moderate-to-high-quality evidence to treat conditions including chronic pain, neuropathic pain, spasticity due to multiple sclerosis, and chemotherapy associated nausea and vomiting .

It appears that the gaps in hospitalizations involving marijuana dependence and abuse were continuously widened between the states adopting and non-adopting medical marijuana policies with states adopting medical marijuana policies increased more sharply. Throughout the study period, the states with medical marijuana policies continuously had higher rates of hospitalizations related to opioid dependence or abuse. Hospitalization rates related to OPR overdose were originally higher in the states with medical marijuana policies, but increased less rapidly compared to the states without medical marijuana policies. Table 1 reports the associations of hospitalizations to the indicator of medical marijuana policy implementation, controlling for time-varying marijuana-related policies, state-level socioeconomic factors, and state and year fixed effects. The implementation of medical marijuana policies did not have any significant associations with hospitalizations related to marijuana dependence or abuse. However, it was associated with a 23% reduction in hospitalizations related to opioid dependence or abuse and a 13% reduction in hospitalizations related to OPR overdose . In Table 2, the first column for each outcome variable evaluates the indicator of medical marijuana dispensaries. Relative to generic implementation of medical marijuana legalization,plant growing rack the operation of medical marijuana dispensaries had comparable associations with hospitalizations related to opioid dependence or abuse and OPR overdose .

The second column for each outcome variable reports results including both the indicator of medical marijuana policy and the indicator of medical marijuana dispensaries. Medical marijuana dispensaries alone did not have any independent associations with any hospitalization outcomes after indicators for medical marijuana policy implementation were also included in the regressions. In Table 3, we explored if any policy effects could be detected in the periods prior to the implementation year of medical marijuana policies. We found no evidence that hospitalization rates of any category differed between states adopting and non-adopting medical marijuana policies in the pre-policy periods. Table 3 also assesses the presence of dynamic policy effects after the implementation year. We found that the reduction in hospitalizations related to opioid dependence or abuse was most salient after 1 year of policy implementation , whereas the reduction in hospitalizations related to OPR overdose was observed in the third year after policy implementation . With respect to other policy and socioeconomic covariates, uninsured rate was associated with increased OPR overdose hospitalizations. Other covariates including marijuana decriminalization, prescription drug monitoring program, and pain management clinic regulations were generally not associated with any hospitalization outcomes. Using state-level administrative hospitalization data during 1997–2014, we found no convincing evidence that the implementation of medical marijuana policies was associated with a subsequent increase in marijuana-related hospitalizations. This result was robust to the key policy dates defined in different ways. In conjunction with the studies that demonstrated negative or null associations of medical marijuana policies to substance abuse treatment admissions , suicide rates , and crime rates , our study counters the arguments about the severe health consequences that legalizing medical marijuana may bring to the public health.

It should be noted that this study does not necessarily contradict some prior research that reported an increase in marijuana use prevalence in association with medical marijuana policies . It just appears that, even if legalization resulted in an increase in the prevalence, it did not contribute to the severe health consequences that concern the public the most. Whether such findings hold in the long term needs further monitoring and investigations. This study demonstrated significant reductions in OPR-related hospitalizations associated with the implementation of medical marijuana policies. These findings were supported by the recent studies that reported reduced prescription medications , OPR overdose mortality , opioid positivity among young and middle aged fatally injured drivers , and substance abuse treatment admissions in association with medical marijuana legalization. As mentioned earlier, using marijuana can lead to either an increase or a reduction in OPR use depending on the use purposes and the underlying assumptions. This study appears to support the hypothesis that patients prescribed with OPR substitute OPR with marijuana, but it is not directly testable in our data. An alternative explanation for the results reported in this study is that states with medical marijuana legalization may also have tough OPR prescription regulations. However, this hypothesis was not supported by the null associations of OPR prescription regulations estimated in this study. Future empirical evaluations are warranted to explore the use pattern of OPR and marijuana and substantiate the substituting and gateway effects of the two drugs. Consistent with prior research , policy effects reported in this study were not static. We found reductions in OPR-related hospitalizations immediately after the year of policy implementation as well as delayed reductions in the third post-policy year. Nonetheless, the availability of medical marijuana dispensaries was not independently associated with hospitalizations as suggested by other studies .

A possible interpretation is that only 1 state in our data legalized medical marijuana but did not have operating medical marijuana dispensaries; a few other states opened medical marijuana dispensaries within only 1–2 years after the legalization of medical marijuana. The lack of variations in policy adoption and timing limited our ability to detect independent effects of detailed policy provisions of medical marijuana legalization. The 300% increase in hospitalization rates related to marijuana is striking. In contrast, the past-month prevalence of marijuana use increased at a much slower rate from 6% in 2002 to 7.5% in 2013 . It is unclear what factors have been driving the huge discrepancies between the trends of use prevalence and the trends of hospitalization rates. Although quite a few states legalized medical marijuana or decriminalized marijuana, this study suggested that they did not contribute to the rise of marijuana-related hospitalizations. One alternative hypothesis is the escalation in marijuana potency , which has tripled from 4% in 1995 to 12% in 2014 in the U.S. . Nonetheless, empirical evidence again did not find any associations between the potency increase and the legalization of medical marijuana . Studies to understand the growing market share of high-potency marijuana and its associations with marijuana-related hospitalizations are urgently needed. The unprecedented increase in OPR-related hospitalization rates and other related health outcomes has become a major public health crisis. Compared to the limited research on marijuana, OPR abuse and overdose epidemic has been relatively well studied. It is largely driven by the liberalization of OPR prescription for the treatment of chronic non-cancer pain . Despite lack of evidence in this study,indoor vertical garden system prescription drug monitoring programs and pain management clinic regulations have shown promises to tackle the OPR crisis in some other studies . If the causal relationship indicated in this study can be substantiated in future research, medical marijuana legalization and regulationmay be considered as an alternative strategy to reduce OPR-related hospitalizations without aggravating the adverse consequences related to marijuana.

Our study was subject to several limitations, most of which were related to the data used. First, some states included hospitalization records in the SID from non-community hospitals such as psychiatric facilities and Veterans Affairs hospitals, but some states did not . States may also vary on ICD-9-CM coding practice particularly for drug dependence, abuse, and overdose cases. The coding of opioid dependence or abuse may include heroin cases. The inclusion of state fixed effects should to some extent alleviate these biases in the reporting. Second, the aggregate SID data represented the total number of discharges but not the total number of patients because a patient may be admitted to hospital more than once in a year. The public-use SID were not available before 1997 and not all states participated in the SID during the study period. The findings may not be generalizable to the states that were excluded from this study. Particularly, the results may be inapplicable to California, which has the longest history of medical marijuana legalization as well as the largest population of registered medical marijuana patients and the largest number of medical marijuana dispensaries. Third, although no statistical differences in hospitalization rates between states adopting and non-adopting medical marijuana policies were revealed before policy implementation, we cannot rule out policy endogeneity issues that may be caused by time-varying unobserved factors and were not captured by the two-way fixed effects models. In addition, we were not able to examine detailed policy provisions of medical marijuana legalization such as home cultivation and requirement of patient registry because of small sample size and lack of variations. We were not able to assess OPR-related policies that were adopted by a few states most recently, such as requirements of following OPR prescribing guidelines and mandatory checking prescription drug monitoring program data by providers. This limitation, however, is unlikely to influence the study findings significantly because these policies were not adopted until the very end of the study period or after the study period. Finally, the study findings do not apply to recreational marijuana legalization. In fact, the findings are likely to alter if marijuana for recreational purpose is indeed a gateway drug to OPR. Examinations on the most recent regulations of recreational marijuana are warranted. Laws and social norms around marijuana use are changing rapidly in the United States. Twenty-four states and Washington D.C. have legalized some form of medical marijuana, four additional states have decriminalized marijuana possession, and four states with medical marijuana policies recently voted to legalize retail marijuana.To inform policy efforts around marijuana, it is important to monitor the sociodemographic and psychosocial correlates of marijuana use. Nationally, young adults have the highest rates of past 30 day marijuana use, with 18.9% of 18–25 year olds using in 2013, compared to 7.1% of 12–17 year olds and 5.5% of adults 26 years old and older.In California marijuana use rates are even higher among young adults , and about 7% higher than cigarette use.However, rates of use may differ across race/ethnicity, sex, sexual orientation, socioeconomic status and region. National data show past 30 day marijuana use is highest among non-Hispanic Native Hawaiian/Pacific Islander young adults ages 18–25, followed by non-Hispanic American Indians , blacks , whites and Latinos .Men in this age range are also estimated to use marijuana at slightly higher rates , as are young adults with less than a high school education .However in longitudinal studies of adolescents, including those accounting for cannabis use disorders, non-Hispanic black adolescents and young adults and those identifying with two or more racial categories appear to be at greater risk.Furthermore, as local data may differ significantly from findings in national data sets, closer examination of sociodemographic associations with marijuana use in a diverse population of young adults may suggest unique targets for intervention. Young adulthood is a time of transition, in which people are navigating new roles and identities; it can also be a time of great stress.Past research has found that adolescents and young adults identify stress as a motive for using marijuana as they perceive it to be an effective coping method.Young adults who report using marijuana as a coping mechanism demonstrate poorer mental health outcomes and greater risk for marijuana dependence and other substance use, such as alcohol and tobacco,and some studies report Black and multiracial young people co-use marijuana with tobacco and alcohol more frequently.Psychological distress has also been shown to be related to use of marijuana in adults,but there is limited research on the relationship between psychological distress and marijuana use in young adults.At a population level distress is an especially useful measure as it quantifies sub-clinical incidence of mental illness and may provide additional insight as to how and why young adults use marijuana.Young adults’ who are transitioning in social roles may experience heightened feelings of loneliness, or a perceived deficit in the quality or quantity of their social relationships.Loneliness has been found to be positively related to alcohol and marijuana use, but not consistently.Conversely, perceived social support, or the idea that there are people in someone’s life who can provide emotional support and help with problems,might be associated with a lower probability of using marijuana. However at least one study among adolescents found social support to predict an increase in substance use while others have found inverse associations.

Participants were invited to participate in 9 in-person clinic examinations over the study period . We used data from the first seven visits, up to year 20, as ECG measures were available at baseline, visit Year 7, and 20. Each participant who attended the examination received non-monetary gifts and monetary reimbursement to cover expenses. All study protocols were approved by the institutional review boards at each site. Multiple marijuana use variables are available for all visits . Current marijuana use was assessed by the following survey question: ‘During the last 30 days, on how many days did you use marijuana?’. We defined daily use as 30 days of use in the last 30 days. Direct self-reported lifetime exposure was assessed by the question: ‘About how many times in your lifetime have you used marijuana?’ We used current use and baseline lifetime use to compute marijuana-years, with 1 year of exposure equivalent to 365 days of marijuana use. We assumed that current use at each visit reflected the average number of days of use during the months before and after each visit. We estimated the cumulative lifetime use by adding the total number of days using marijuana during followup. We adjusted our estimate upwards whenever participants self-reported higher lifetime use than we compute for each visit. Marijuana use was not legal in the cities at this time. Standard 12-lead electrocardiogram was recorded at baseline, Year 7 and Year 20 visits,weed growing systems as described extensively elsewhere. All abnormalities were coded according to Minnesota Code Manual of Electrocardiographic Findings . The MC is used for population research and clinical trials and standardizes coding of ECG abnormalities.

We classified abnormalities according to MC . We also built composite categories of major and minor abnormalities: If an individual’s ECG contained any abnormality on the major list , the ECG was classed as composite major. If it contained only abnormalities on the minor list , we classed the ECG as composite minor. This allowed us to study both composite major and minor abnormalities and specific ECG abnormalities.Tobacco cigarette smoking behavior was evaluated at each in-person CARDIA examination and at yearly phone follow-up between CARDIA examinations. We used these data to estimate cumulative lifetime exposure to tobacco cigarettes in terms of pack-years. We estimated alcohol consumption as drink-years . Education was the highest educational grade reached by the participant by examination Year 20. We measured physical activity at every visit with the CARDIA physical activity history questionnaire. Our cardiovascular risk factor measurements included blood pressure, blood cholesterol, body mass index , binge drinking and diagnosis of diabetes mellitus, which were collected at each CARDIA examination . We observed prevalent abnormalities at visit 0, 7, and 20, but focused on Year 20 since major ECG abnormalities are expectedly more prevalent later in life and since cumulative marijuana exposure rises with time. Based on the number of computed cumulative marijuana-years and data on current use, we divided participants into four categories: never used marijuana; past use and moderate cumulative lifetime marijuana use, up to or 0.5 marijuana-years; past use and higher cumulative lifetime marijuana use, above 0.5 marijuana-years; and, current users , no matter the level of their cumulative use. We first analyzed the association between marijuana use and ECG abnormalities in unadjusted logistic regression models, separately at visit 0, 7, and 20. We then adjusted for demographic variables and then further adjusted for potential confounders such as tobacco cigarette smoking, alcohol consumption, and physical activity and BMI .. We decided to restrict main multi-variable adjusted models to the previously mentioned variables because of low event number in specific ECG abnormalities, but still performed exploratory analyses with fully adjusted models including cardiovascular health variables , presented in the online supplement.

To account for deaths and informative censoring in later examinations , we used inverse probability of attrition weights . We separately fit a model for loss to follow-up caused by the death, and a separate model for censoring due to reasons other than death, computed in one score. We used last-value-carried-forward and backward imputation for missing covariables and verified results using multiple imputation. The first considered only cumulative use and not current use; the second compared daily marijuana use to less frequent, past use, and never use. Finally, we stratified our results to see if they varied by sex and race because prevalence of ECG abnormalities, and distribution of exposure and covariables differs between Black and white, and male and female participants. We restricted regression analyses to ECG abnormalities that occurred in at least 50 of each race-sex stratum. We also fitted models with marijuana-years modelled as a restricted cubic spline as covariable for state of marijuana use . We further modelled incident abnormalities between Year 0 and Year 20. We included specific major and minor abnormalities at Year 20 that were not already identified in these categories in Year 0. For example, a specific minor abnormality detected at Year 0 that evolved into a major abnormality detected at Year 20 was coded as an incident major abnormality at Year 20. We applied a series of unadjusted and multi-variable adjusted models to analyze the association between current and lifetime cumulative marijuana use on incident ECG abnormalities. Tests of statistical significance were two-tailed; alpha level was 0.05. All statistical analyses were performed on Stata version 14.2 . We hypothesized that cumulative marijuana use was not associated with ECG abnormalities, but that current use might be associated with unspecific changes in ECG.

Various small experimental studies suggested immediate effects after using marijuana, with measured parameters returning to pre-exposure levels after ceasing marijuana use. The primary research question and analysis plan were submitted to the CARDIA Presentation & Publication Committee, before obtaining and analysing the data. However, they were not pre-registered on a publicly available platform and the results should thus be considered exploratory. Table 2 shows that at the Year 20 examination, 173 participants had composite major abnormalities and 944 had composite minor abnormalities . Composite and specific major abnormalities at Year 20 did not vary with status of marijuana use, but showed a tendency towards fewer events in current marijuana users: when we compared current marijuana use to never use,indoor farming systems the unadjusted OR for composite major ECG abnormalities was 0.77 . After multi-variable adjustment, the OR was 0.55 . Tables 2 and 3 show that in the unadjusted model, composite minor abnormalities and some specific minor abnormalities were more common among current marijuana users . These differences were attenuated after adjustment for demographic variables . The odds ratios stayed similar between categories of marijuana use after multi-variable adjustment and use of IPAW . Figure 1 illustrates that after multi-variable adjustment, no specific ECG abnormality or composite major or minor abnormalities was significantly associated with marijuana use. Current marijuana users had a multi-variable adjusted OR of 0.34 for major ST-T abnormalities, with a p-value across categories of 0.17. Past users with a cumulative use of over 0.5 marijuana-years had a multi-variable adjusted OR of 2.06 for minor ST-T abnormalities, with a pvalues across categories of 0.044. At Year 0 and Year 7, no ECG abnormality was significantly associated with marijuana use . Further adjusting for cardiovascular risk factors did not change results . We found no association between alternative categorizations of marijuana use and prevalent major or minor ECG abnormality after multi-variable adjustment. When we compared >2 marijuana-years of cumulative use to never use, the multi-variable adjusted OR for composite major ECG abnormalities was 0.70 and the multi-variable adjusted OR for composite minor ECG abnormalities was 1.03 . When we compared daily marijuana use to never use, the proportion of composite major abnormalities was no higher . Because few daily users had major abnormalities , we did not fit logistic regression models. For composite minor ECG abnormalities, the multi-variable adjusted OR was 1.72 . We found no association between current marijuana use and ECG abnormalities after adjusting for cumulative marijuana use . In stratified analyses by sex and race, black women with 0.5 to 2 marijuana-years of cumulative exposure had a multi-variable adjusted OR of minor ST-T abnormalities of 2.40 , with a p-value across categories of 0.10 . We found no abnormality associated with cumulative marijuana use in stratified analyses by sex and race at baseline or Year 7 . Current use at Year 20 was not associated with prevalent or incident ECG abnormalities in stratified analyses . Whether we applied IPAW or not, and used LVCBF or multiple imputation, results were virtually unchanged.

We found no evidence that current or lifetime cumulative use of marijuana was associated with a higher prevalence or incidence of major or minor ECG abnormalities in this cohort including black and white participants, although major ECG abnormalities seemed to be less frequent in current marijuana users. In this population, we also observed the tendency towards more minor ECG abnormalities compared to never marijuana users. Whether participants used marijuana daily, in the last 30 days or intermittently over a lifetime, marijuana use was not associated with an increase in prevalent or incident specific ECG abnormalities by middle-age. Applying different classifications of marijuana use did not change our results. Our findings did not vary by sex and race. Unlike some small experimental studies from the USA in the late 1970’s that, in samples of around 10 people, suggested marijuana was associated with some specific ECG abnormalities, we found these were just as frequent in current or cumulative marijuana users as in never users. The small sample size, brief exposure of participants to THC, short follow-up, and inclusion only of young, healthy men makes it difficult to draw useful conclusions on the population level from these experimental studies. We found multiple case-reports from the early 2010’s about ECG abnormalities following marijuana use. In our large biracial 20-year cohort of women and men participants who reported a broad variety of marijuana use habits, from never users to daily users, we found no evidence to support an association between marijuana and any ECG abnormality, incident abnormalities in new marijuana users, or abnormalities that would indicate past, ongoing, or future myocardial infarction. Our findings align with earlier epidemiological research on thousands of participants from Europe and the USA, including participants of the same cohort, that found no association between marijuana and CVD, mortality or other measure of sub-clinical atherosclerosis. When we stratified results by sex and race, we noticed that black participants presented with a higher proportion of ECG abnormalities than white participants, regardless of their marijuana use habits, but black marijuana users had no more ECG abnormalities than black never users; likewise, white marijuana users had no more ECG abnormalities than white never users. The assessment of exposure to marijuana was not validated by biological markers, so we rely on participant self-reports. Marijuana use was illegal during the whole course of the study and we cannot exclude social desirability bias, but because participants were queried about marijuana and other illegal drug use at each clinical visit, we could track past exposures. With this method, 84% percent of participants reported any past marijuana, suggesting that possible social desirability bias was mitigated by the trust participants had in the study personnel to report their true exposure. The low number of daily marijuana users at Year 20 in CARDIA limited our ability to fit multi-variable adjusted models, and our results should be carefully interpreted in this population. Future studies with higher sample size will be better equipped to assess the association between daily marijuana use and ECG abnormalities. Also, immediate effects of marijuana might not be reflected on resting ECGs performed hours or days after its use. We rely on marijuana use information provided by participants every 2 to 5 years, and participants only reported on how many days they had used marijuana within the last 30 days. Our analyses cannot inform on the acute effects of marijuana use on ECG. Previous experiments suggested acute effects of marijuana use on ECGs, with conflicting results . Further experimental studies, especially among people with underlying risk of CVD, are needed to test the effects of acute marijuana use on ECG abnormalities. Though the cohort we studied was racially diverse and spanned 20 years, our analysis was limited mainly to a middle-aged population, where CVD is not yet common.

Current research indicates that secondhand marijuana smoke contains many of the same chemicals as secondhand tobacco smoke and some in greater concentrations with recent studies demonstrating that secondhand marijuana smoke has negative cardiovascular effects similar to tobacco smoke.Non-smokers exposed to secondhand marijuana smoke had detectable levels of THC and metabolites, with levels increasing when higher potency marijuana was used.Non-smokers exposed to cannabis smoke for 60 min in an unventilated room had detectable levels of THC in blood following the exposure, increased heart rate, mild to moderate self-reported sedative drug effects and performed worse on a cognitive test.As normalisation of marijuana use continues, it is important to monitor the effects of normalisation on tobacco use, perceptions and smoke-free spaces. Smoke-free policies should cover all products, including combustible marijuana and electronic vaporisers for tobacco and marijuana. Signs and information signalling smoke-free policies should be adapted to clearly include marijuana smoke where applicable. Information about harmful effects of secondhand tobacco smoke was found to be a deterrent to smoking initiation and a motivator for cessation for youth.Studies should explore messaging around the negative effects of secondhand marijuana smoke. As a qualitative study,hydroponic rack system our relatively small sample provides insight into how some young adults in Colorado integrate tobacco, marijuana and vaporiser use.

While these experiences may not be representative, this work begins to shed light on how these products are used and made sense of alongside one another. Further in-depth qualitative work is needed to document the complexities of perceptions of tobacco and marijuana in distinct legal contexts , and examine differences between perceptions of medical and retail marijuana in relationship to tobacco. More work is also needed to understand those who primarily vaporise nicotine, those who vaporise marijuana and those who use both. The SCTC research initiative addresses high-priority gaps in tobacco control research through collaboration between academic researchers and local tobacco control agencies and community organisations. Legalisation of marijuana is one area that is highly salient for many state and community tobacco programmes because of its potential to affect use and perceptions of tobacco. Moreover, tobacco control experts within agencies are frequently tasked with recommending marijuana policies or educating citizens about rules of use and potential health effects. Tobacco, marijuana and vaporisers are most effectively studied together and future research should address perceptions of comparative harm of these products; social, political and health effects of their use; and adequate measurement of use patterns, especially when products are combined. Finally, tobacco programmes and policies should take into account emerging research on the complexities of this triangulum, particularly in the context of marijuana legalisation.Following recreational marijuana legalization and commercialization in the US, marijuana dispensaries have served as a major venue for marijuana retail sales in neighborhoods.

Nonetheless, research on the impacts of marijuana dispensaries on public health remains limited . Availability, accessibility, and point-of-sale marketing of retail outlets have been associated with attitudes, perceptions, and health behaviors in tobacco and alcohol literature . Marijuana dispensaries may impact marijuana-related outcomes in a similar manner. They may increase availability and accessibility of marijuana , promote greater awareness and consumption through marketing activities , increase product appeal such as through increased quality and potency , diversify product variation such as vaping devices and edibles , reduce prices through mass production and introduction of competition , and shape social norms favorable of marijuana use . A major challenge in understanding the availability and retail environments of marijuana dispensaries is identifying a complete and accurate list of marijuana dispensaries in neighborhoods. In a state operating a statewide licensing system, one can obtain the official licensing directories from government databases. Nonetheless, most of these directories are updated infrequently. More importantly, they do not reflect the operation status of dispensaries in reality or capture unlicensed dispensaries that are common in areas with weak law enforcement. Business directories provided by commercial providers are commonly used to identify tobacco, alcohol, and food retail outlets when state licensing directories are unavailable or unsatisfactory . Unfortunately, these commercial databases had not systematically gathered information on marijuana dispensaries by the time of this study. One can also conduct a field census with direct search and observation to enumerate a certain type of business in a geographic area. It is considered to be the best practice in outlet identification and often used to validate the business lists obtained from commercial databases . The limitation of field census is obvious: the required efforts and resources increase exponentially as the geographic area of interest expands.

Due to practical and budget concerns, most tobacco, alcohol, and food outlet studies that adopted this method searched retail outlets in smaller regions such as a county . State-level field censuses, especially in a large state like California, are nearly nonexistent. In light of the challenges of using conventional approaches to identify marijuana dispensaries, existing studies have primarily relied upon a single or a few online crowd sourcing platforms, such as Weedmaps, Leafly, and Yelp, to obtain dispensary information voluntarily submitted by dispensary owners and marijuana users . Because these platforms serve as online communities to promote dispensaries, products, and share experiences, they are perceived to be more up-to-date and comprehensive than official licensing directories. Particularly, these platforms provide data on both licensed and unlicensed dispensaries. Despite the increasingly common use of online crowd sourcing platforms in marijuana research, the validity of this approach has not been comprehensively assessed at statewide level. To date, only two studies have conducted validation in a single county , one before recreational marijuana commercialization and one after the commercialization in California. In this study, we examined the validity of using secondary data sources, including the state licensing directory and commonly used online crowd sourcing platforms, in enumerating brick-and-mortar marijuana dispensaries across the entire state of California. California is the most populous state with the longest history of medical marijuana legalization in the US. In November 2016 California legalized recreational marijuana and in January 2018 California initiated retail sale of recreational marijuana in dispensaries. California now has the largest legal marijuana market in the world,rolling tables grow with sales rising from $2.5 billion in 2018 to $3.1 billion in 2019 . Although California allows delivery services, in this study, we concentrated only on brick-and-mortar marijuana dispensaries because delivery-only providers do not have storefronts to showcase and promote products. In addition, the wide geographic coverage of delivery services contributes little variation in marijuana availability at neighborhood level. We offered a protocol for identifying dispensaries that can be replicated in other large geographic regions with marijuana retail sales. We aimed to answer two research questions. The first question was to what extent online crowd sourcing platforms are valid in enumerating licensed brick-and-mortar dispensaries. The motivation was that many dispensaries in California operated without a license. Even for licensed dispensaries, how they operate in practice may not agree with what was approved in the license. Findings from the first question will provide quantifiable evidence on the level of agreement between state licensing directory and online crowd sourcing platforms, add surveillance data point on the operation of unlicensed dispensaries, and inform policymakers regarding the validity of using online crowd sourcing platforms as alternatives when state licensing directory is not publicly accessible or licensing information is inadequate . The second question was to what extent state licensing directory and online crowd sourcing platforms are valid in enumerating the universe of active brick-and-mortar dispensaries. The motivation was that a single data source may not capture all active dispensaries in California and the information in a data source may not agree with how dispensaries operate in practice. Findings from the second question will provide quantifiable evidence on the strengths and weaknesses of each data source, inform surveillance and research regarding how to best strategize data use when resources are limited, and demonstrate the need for combining multiple data sources and verifying information to obtain the universe of dispensaries in a large geographic area. Because recreational-only, medical-only, and recreational & medical dispensaries co-existed in California, we also assessed validity measures by dispensary category.

Dispensaries may tend to promote themselves on online crowd sourcing platforms in larger counties with keen competition, we hence further assessed validity measures by county population size. From May to July 2019, eight trained research associates aged 21 or older called the 2,121 unique businesses to verify their street address, operation status, category of business, and presence of storefronts . Each call took fewer than 5 minutes on average. As commonly done in compliance check inspections of tobacco product retailers, the research associates did not reveal the research purpose of the calls. Instead, they identified themselves as interested customers who were considering a visit in near future. To determine dispensary category, researchers asked if a doctor’s recommendation or a patient registration card was required to enter the dispensary and make purchase. An affirmative response indicated the dispensary category to be medical only. If the response was negative yet customers with a doctor’s recommendation or a patient registration card were eligible for reduced tax rates, the dispensary was categorized as recreational & medical. The remaining dispensaries were considered to be recreational only. Up to five calls were made to each business in different business hours and/or on different business days to determine operation status. If a dispensary could not be reached after five call attempts, researchers checked its recent online activities on Weedmaps, Leafly, Yelp, and Google Map Reviews. If the dispensary had any online activity within the past month , it would be considered active1 . After removing inactive businesses, businesses not selling marijuana, and businesses without storefronts during the verification procedure, the 2,121 unique records were reduced to 826 businesses . These 826 dispensaries constituted the call-verified, combined database of active brick-and-mortar dispensaries in California. Validity statistics, including sensitivity, specificity, positive predictive value , and negative predictive value were computed for each of the four secondary data sources when applicable. Definitions and calculations were described in Technical Note S1. To compute validity statistics, a gold standard must be defined that can identify the “true positive” and the “true negative”. However, it is infeasible in this study due to budget and time constraints for a statewide census. Two gold standards were adopted alternatively to answer the two research questions. To answer the first question regarding the validity of online crowd sourcing platforms in enumerating licensed brick-and-mortar marijuana dispensaries, the first gold standard was whether a record was listed in the BCC state licensing directory . To answer the second question regarding the validity of state licensing directory and online crowd sourcing platforms in enumerating active brick-and-mortar marijuana dispensaries, the second gold standard was whether a record was included in the call-verified, combined database of active dispensaries . We must also define a test that can identify the “positive test” and the “negative test” in validity statistics calculations. Two tests were conducted. The first test was whether a record was present in a given data source after online data cleaning . We used this test to examine the validity of using a single data source with simple online data cleaning for dispensary identification, an approach requiring moderate resources. The second test was whether a record passed call verification; in other words, whether the record was verified to be an active brick-and-mortar dispensary . We used this test to examine the validity of using a single data source with simple online data cleaning plus call verification for dispensary identification, an approach requiring much more resources. To illustrate these validity statistics in the context of this study, we provide an example below . In this example, the data source of interest is Weedmaps, the gold standard is whether a record on Weedmaps was present in the BCC state licensing directory, and the test is whether a record was present on Weedmaps after online data cleaning. Sensitivity measures the probability of a record present on Weedmaps conditional on the record being included in the BCC directory, calculated as the number of records that were present on both Weedmaps and the BCC directory divided by the number of records present on the BCC directory. Specificity measures the probability of a record absent on Weedmaps conditional on the record being excluded from the BCC directory, calculated as the number of records that were neither present on Weedmaps nor present on the BCC directory divided by the number of records excluded from the BCC directory.

In a few instances the difference between these two variables is zero, which appears to be a reporting error as they reported all their usage in the last 30 days and yet that they started at a young age. We code them as a zero at t1 under the presumption that this earlier usage was very limited, and perhaps experimental. However, if the difference is non-zero, since the In-School Survey was conducted at least six months before the wave-1 In-Home Survey, we divide this difference by 5 to average over five months [i.e., /5]. Those with values less than 1 were categorized as non-users at t1 , those with values between 1 and 10 were categorized as light users and those with values above 10 were categorized as heavy users . Light users comprised about 16% of adolescents in Sunshine High and 17% of adolescents in Jefferson High. Likewise, heavy users comprised about 5% of the adolescents in Sunshine High and 8% of the adolescents in Jefferson High. Overall, this reconstruction strategy enabled us to estimate a three-wave SAB model for each of the two samples without discarding any data. The last step of the reconstruction procedure for the heavy marijuana users is not perfectly accurate and might mistakenly categorize a few light users as heavy users, since they could have used marijuana outside of the last five months. The proportion of cases that might have been misclassified is less than 10%. Furthermore, sensitivity tests in which the level of marijuana use for these uncertain cases was randomly assigned to “light” or “heavy” use exhibited similar results over a large number of samples .Our estimated SAB models include gender , grade , race , and parental education level . Depressive symptoms are included as a factor score based on 19 ordinal items modified from the Center for Epidemiologic Studies Depression Scale . Parental support and parental monitoring are constructed as standardized factor scores through confirmatory factor analysis,horticulture trays with Root Mean Squared Error of Approximation about .05 and Comparative Fit Index greater than .95, which both suggest a good fit.

Parental support is based on how adolescents rated their parents in 6 aspects: whether they communicated well, were “warm and loving”, had a “good relationship” , and whether the adolescents felt cared about, felt close , and discussed personal problems with their parents . Parental monitoring is based on 9 items: whether parents were home before school , after school , at bedtime , present during dinnertime , and whether adolescents were allowed to decide their weeknight bedtime, weekend curfew, people they hung around with, and how much television and which television program they watched .Regarding missing data, for students in Sunshine High the response rates were 76% at t1 , 82% at t2 , and 75% at t3 . In Jefferson High the response rates were 79% , 81% , and 74% across the three waves. We imputed missing network data using the technique described in Wang et al. given the evidence that failing to do so can result in in biased estimates. Other actor attributes at t1 were imputed using the multiple imputation system of chained equations implemented in Stata. For the later waves, missing data is handled within the Stochastic Actor-Based models in RSiena software as suggested by Huisman and Steglich and Ripley et al. . The 501 and 166 students who graduated at t3 and were no longer in the network are treated as structural zeros in the Stochastic Actor-Based models at the last wave.Network statistics are measured at three waves. As shown in Table 1, in both school samples the number of out-going ties decreased over time due to limited nomination restrictions, graduation, moving, dropping out, and sample attrition/non-response/missing network data. The reciprocity index is the proportion of ties that were reciprocal. The proportion of reciprocal ties over all out-going ties was 4% to 10% higher in Jefferson High than in Sunshine High ateach wave. The transitivity index is the proportion of 2-paths that were transitive , which is similar in the two schools. The Jaccard index measures the network stability between consecutive waves.

There were substantial changes in friendship ties across waves, with the Jaccard index staying at .16 in Sunshine High and ranging from .21 to .22 in Jefferson High. Due to a survey implementation error in Add Health, some adolescents could only nominate one female and one male friend at t2 and t3 . Most limited nomination restrictions happened at wave 2, and involved less than 5% in the two schools. With respect to smoking behavior, there were between 69% and 78% non-smokers in Sunshine High over the three waves, and between 7% and 10% heavy-smokers . In Jefferson High, there were between 42% and 53% non-smokers and between 26% and 32% heavy smokers. Sunshine High also had more non-drinkers than Jefferson High , and more non-users of marijuana . The descriptive statistics of covariates are reported in the lower part of Table 1.As shown in Table 2, our estimated SAB model includes a smoking behavior equation, a drinking behavior equation, a marijuana use equation, and a network equation. Based on the smoking behavior equation, those who were one point higher on the marijuana scale are 25% [exp = 1.25] and 15% [exp = 1.15] more likely to increase their own smoking behavior at the next time point in Sunshine High and Jefferson High, respectively. Those who drank alcohol did not smoke more over time. There is no evidence of cross substance influence, as having more friends who drank or used marijuana did not impact a respondent’s own smoking over time. In ancillary models, we measured average level of drinking or marijuana use for friends and these effects were also statistically insignificant. These results are shown in S1 Table. Regarding the other measures in the smoking behavior equation, we detect a negative smoking behavior linear shape parameter in both school samples along with a positive smoking behavior quadratic shape parameter. This suggests that adolescents were inclined to adopt lower levels of smoking behavior over time, but they also tended to stay as or become non-smokers or escalate to heavy-drinkers due to a pull towards extreme values of this scale. Turning to the peer influence effect, we find that adolescents’ own smoking levels were affected by that of their best friends in both schools.

There is no evidence that parental support or monitoring reduced levels of smoking over time in either sample. African Americans and Latinos smoked less than Whites in Sunshine High. Depressive symptoms were found to increase smoking behavior in Jefferson High. In the drinking behavior equation, we find that an adolescent who was one point higher on the marijuana use measure was 22% and 16% more likely to increase their own alcohol use at the next time point in Sunshine High and Jefferson High, respectively. However,sliding grow tables respondents’ drinking was not related to their greater cigarette use. There is no evidence that friends’ smoking behavior or marijuana use affected respondents’ drinking behavior. This was the case whether measured as the number of friends who smoked or used marijuana, or as the average level of such behaviors. A negative linear shape effect and a positive quadratic shape effect are also confirmed regarding drinking behavior. An adolescents’ drinking level was positively predicted by that of one’s best friends. Whereas there is no evidence in these two networks that high levels of parental support impacted drinking levels of adolescents, we do see that higher levels of parental monitoring were associated with lower levels of drinking behavior over time in Jefferson High. In Sunshine High, African Americans were found to drink less than Whites, and depressive symptoms were found to increase drinking levels. The marijuana use equation suggests no evidence that increasing usage of the other two substances leads to increasing marijuana use. We once again see no evidence of cross-substance influence, as the number of friends who smoked or drank or the average smoking or drinking level of friends is not related to ego’s marijuana use levels over time. A negative linear shape effect and a positive quadratic shape effect are also detected on marijuana use behavior. Across both samples there is very strong evidence of a peer influence effect from anadolescent’s best friends’ marijuana use to an individual’s own marijuana use. Higher levels of parental support or monitoring were not found to reduce levels of marijuana use over time. For all three substance use behaviors, there was no evidence that adolescents who are more “popular” were any more likely to increase their substance use over time. In the network equation the expected patterns are detected regarding the endogenous network structural effects across samples.

At the dyadic level, adolescents did not randomly nominate peers as friends, since friendship ties inherently require the investment of time and energy, as indicated by the negative out-degree parameters; instead, adolescents tended to nominate peers who had already nominated them as friends previously, as indicated by the positive reciprocity parameters. At the triadic level, adolescents tended to nominate a friend’s friend as a friend but avoided ending in 3-person cyclic relationships. The negative out-degree/in-degree popularity parameters and the out-out degree assortativity parameters suggest that adolescents were less likely to befriend peers who have already made/received many friendship nominations or have similar out-degrees. Instead, they were more likely to befriend peers with similar in-degrees, as indicated by the positive in-in degree assortativity parameters. We also find that adolescents were more likely to nominate peers as friends if they were of the same gender, race , and grade. Grade is a particularly strong effect, as adolescents were 86% and 77% more likely to nominate a friend if they were in the same grade than if they were in a different grade in Sunshine High and Jefferson High, respectively. Lastly, the limited nomination parameter shows that for adolescents who encountered the administrative error of being limited to nominate only one male or one female friend, their odds of nominating friends is re-adjusted by the SAB models to be 132% larger in Sunshine High and 297% larger in Jefferson High than those with no such problem.Whereas our initial models tested the relationship between interdependent substance use behavior, they assumed that these effects are symmetric: that is, usage of one substance equally increases or decreases usage of another substance. In our next set of models, we relax this assumption and test whether usage of one substance increases behavior of another substance or decreases behavior , or both. These models were estimated separately as the combined model exhibited extreme collinearity. As shown in Table 3, there is a significantly positive creation function from marijuana use to drinking in both samples, implying that respondents’ marijuana use increased their odds of drinking initiation. Thus, one unit higher marijuana use made a nondrinker 62% and 60% more likely to start drinking rather than stay as a non-drinker at the next time point in Sunshine High and Jefferson High, respectively. On the other hand, the endowment function from marijuana use to drinking is not statistically significant at either school, implying that marijuana use does not affect the likelihood of stopping drinking behavior. We detect a statistically significant creation function in Sunshine High: a one unit increase in marijuana use increases the odds 62% that adolescent non-smoker will initiate smoking rather than stay as a non-smoker. There was no evidence of a statistically significant endowment function in Sunshine High. On the other hand, the pattern is reversed in Jefferson High with a statistically significant endowment function but a statistically insignificant creation function. Thus, in Jefferson High although marijuana use does not impact respondent’s likelihood of smoking initiation, one unit higher marijuana use made smokers 27% more likely to stay as smokers rather than quit smoking at the next time point. To understand the magnitude of these effects , we engaged in a small simulation study in which we omitted some of the effects from the SAB model shown in Table 2 and assessed the consequences for the level of substance use behavior in the schools. That is, we changed a particular parameter value from the one estimated in the model to zero, and then simulated the networks and behaviors forward 1000 times. We then assessed the average level of smoking, drinking, and marijuana use in the network at the end of the simulation runs.

Few group differences in cortical thickness were observed; the majority of areas were no longer significant after controlling for lifetime alcohol use. Thicker medial temporal lobe estimates were found in the user group at baseline and follow-up when ICV and lifetime alcohol use were controlled for. We observed subtle decreasing thickness estimates from baseline to follow-up in seven regions, but no interactions were identified. We found quite divergent relationships between cumulative lifetime alcohol and marijuana use and cortical thickness in the user group. More reported marijuana use was related to thinner cortices in temporal and frontal regions, and more lifetime alcohol use was related to thicker cortices in all four lobes of the cortex bilaterally. Improvement in cognitive functioning over time in both the users and controls was observed, given the short retest interval and anticipated gains in performance. We did not see greater improvement in cognition in the user group following abstinence or, surprisingly, consistent group differences in cognition across the five domains as suggested in the literature and previous studies in our laboratory . MJ + ALC did not improve in their performance on the Complex Figure Copy sub-test; however, this pattern of performance was not found across the other tests. In general, thinner cortices were related to better global cognitive performance in the larger sample with the exception of the right entorhinal cortex and cognitive functioning in the user group at baseline, in which thicker cortices were associated with better attentional processing. Lopez-Larson and colleagues cross-sectionally examined cortical thickness in teens, ages 16–19 years, with heavy cannabis vertical farming use histories. They found decreased thickness in frontal regions and the insula, along with increased thickness in lingual, temporal, and parietal regions .

Our findings are similar given that we found increased thickness in temporal and posterior regions, such as the entorhinal cortex , and relationships showing thinner cortices associated with increased severity of use in frontal regions. The authors discuss multiple pathways for tissue disruption, including altered neurodevelopmental trajectories and/or tissue loss or remodeling. Similarly, Mata and colleagues found flattening and thinning of the sulciin frontal regions in adolescent marijuana users, suggesting chronic cannabinoid exposure may link to atypical trajectories of the gyral folding process. Marijuana may interfere with the cannabinoid system by altering patterning, plasticity, and connectivity during neurodevelopment, and trigger neurochemical and protein activity in response to neural injury . Macrostructural findings typically focus on structures with a high density of cannabinoid type 1 receptors , and findings show larger structural volume in areas such as the anterior cerebellum and amygdala , whereas others show decreased gray matter volume and density . Cousijn and colleagues found that amygdala and hippo campal volume negatively correlated with weekly cannabis use, as more use was related to smaller limbic structures. In our study, cortical thickness differences persisted after controlling for alcohol in the entorhinal cortex. Given the high concentration of CB1 receptors in temporal lobe structures , marijuana use may be particularly influencing developmental events in this anatomical region. Positive associations between thickness in the entorhinal cortex and cognitive functioning is in contrast to the finding that thinner cortices are related to better global cognitive performance in the larger sample.

However, greater reported marijuana use and earlier age of initiation were also associated with thinner cortices in frontal and temporal brain regions. We suggest that endocannabinoid system alterations or marijuana-related toxicity may trigger developmental consequences such as premature cortical thinning and subsequent declines in cognitive functioning. It is unclear why associations between age of initiation and thickness estimates were observed at follow-up only. Subtle neural architectural changes may be occurring over the abstinence period, and the acute impact on neural development may not be fully captured at baseline for this predictor. Neural recovery is likely to extend past 28 days and well into the year following cessation of use, as residual effects of marijuana use have been reported in cognitive and neuroimaging markers from days to months following cessation of use . Pre-existing structural differences are also likely to contribute, as smaller orbitofrontal cortex volume predicted initiation of cannabis use by age 16 . The positive dose-dependent relationships with lifetime alcohol use are particularly notable given the sub-clinical heavy episodic drinking patterns reported by the sample. Thickness estimates in these regions were related to number of heavy drinking episodes reported, which is particularly concerning given the consistently high rates of heavy episodic drinking reported by adolescents in the United States . The cerebral cortex is highly vulnerable to the effects of alcohol . Squeglia and colleagues found that female heavy episodic drinkers had thicker cortices in frontal brain regions compared to female controls, and thicker cortices were associated with worse cognitive functioning for both males and females. However, thinner cortices were identified for male heavy episodic drinkers compared to controls , similar to recent prospective findings showing decreased thickness estimates in adolescents who transitioned into sub-clinical heavy episodic drinking .

Our findings suggest widespread increases in cortical thickness with increased lifetime alcohol use and heaving drinking episodes. The present study, combined with Squeglia et al. , suggests that more often thicker cortices are associated with worse neurobehavioral performance. Despite the quantity of reported alcohol use in our users being more modest compared to that of treatment seeking individuals, differences in these studies of adolescents reporting similar alcohol use patterns may be attributed to methodological design or an interaction between co-occurring alcohol and marijuana use. Neuroprotective properties of marijuana may modulate neurotransmission and mitigate ethanol-induced neural injury; however, marijuana may trigger neurotoxic chemical cascades leading to changes in endocannabinoid signaling, altered developmental trajectories, increased alcohol administration, and worse psychosocial outcomes in the developing brain . Although initial cross-sectional studies in our laboratory suggested evidence for white matter neuroprotection in those using cannabis drying rack and alcohol , poorer outcomes for co-occurring use from adolescence to young adulthood were found after a 3-year follow-up . The mechanism of alcohol-related toxicity on the cerebral cortex remains unclear. Alcohol may interfere with temporal sequences of neurodevelopment , myelination, and/or generation and survival of cortical cells ; overall, the unanticipated associations with alcohol found in this study underscore the deleterious impact adolescent alcohol use likely has on neurodevelopment when used independently or concomitantly with marijuana. Our sample was predominantly male . Therefore, it is unlikely that excluding our female participants would have changed the observed relationships. However, gender may moderate these findings . Studies have found gender to play a significant role in gray and white matter neural architecture and neurocognition in both healthy adolescents/ young adults and those engaging in substance use . We suspect that findings represent a “longer-term” impact of marijuana and alcohol use, given the monitored abstinence period. The majority of findings present at baseline were present at follow-up, but this was not the case for all regions . The prospect of neural recovery after cessation of use is understudied in the adolescent literature, although there is some suggestion that brain structural changes can occur within the initial 24 months of abstinence from alcohol in an adult sample of former heavy alcohol users reporting 1 month to 26 years of abstinence . Our preliminary findings need to be replicated and expanded upon. Longer follow-up periods are necessary to understand changes in marijuana use trajectories over time and differences in residual versus acute effects. Given the preliminary and exploratory nature of this work, large number of analyses conducted, and modest effect sizes, replication of findings is crucial. Subtle alterations in neurodevelopmental trajectories may have long-term consequences for cognition and daily functioning. To identify how alcohol and marijuana use leads to brain changes, disentangling pre-existing, substance-related, and acute versus residual effects is important. The present findings raise concern for adolescent alcohol and marijuana users and provide more evidence for a longer-term effect on neural tissue development. Our future work will integrate longer follow-up periods with pre- and post-initiation data to understand how such commonly used substances affect the developing brain.Young adulthood is a critical developmental period that commonly includes multiple important life changes . This period is also marked by increased access and susceptibility to risky behaviors, including tobacco and other drug use.

Recent national data on 18-24 year old young adults indicate past-month prevalence of 22-25% for marijuana and 29.1% for cigarettes . In both cases rates were higher than those of older adults. While increasing use of marijuana and tobacco each raise public health concerns, recent escalation of concurrent use of both is evident in the literature. This trend has ignited interest in exploring whether marijuana use may potentiate exposure to tobacco-related harms. Recent analyses of national data suggest rates of co-use of tobacco and marijuana increased by 18.2% from 2003 to 2012, and 40.6 % of adults aged 18-25 reported past-month use of both products in 2012 . Our own analyses of Population Assessment of Tobacco and Health data suggest users of combustible tobacco products, e-cigarettes, and multiple tobacco products were 4-8 times more likely to report current marijuana use, and concurrent users of tobacco and marijuana were less likely to attempt tobacco cessation . National data indicate that co-use is particularly common among daily marijuana users and non-daily tobacco smokers . Experimentation with marijuana among tobacco smokers and experimentation with tobacco among marijuana users may in part be facilitated by product modifications that allow for consumption of both products simultaneously . Relatedly, advertisements for tobacco products may be designed to indicate that the products can be used to consume marijuana . Some initial findings suggest using tobacco products to deliver marijuana may both increase and normalize young adults’ use of tobacco products . Use of both products may potentiate smoking-related disease by not only increasing exposures to two sources of harmful constituents but by potentiating persistent use. Frequency of marijuana use has been linked consistently to greater nicotine dependence and more persistent tobacco use . Users of both products perceive marijuana as safer , report low interest in quitting both marijuana and tobacco , and are less likely to successfully quit using tobacco . Thus, young adults who use both products may be disproportionately vulnerable to doing so chronically. Evidence for overlapping negative health consequences of tobacco and marijuana smoking suggest high priority for understanding and preventing use in young adulthood. The context of both tobacco and marijuana use have changed dramatically in the past decade as a result of increasing availability, perceived safety and acceptability of non-cigarette tobacco products, and growing legalized access to marijuana . While a number of studies have demonstrated cross-sectional links between use of both products, less is known about the interplay between use of both over time among young adults. This potential bidirectional relationship may be especially important in the context of non-daily tobacco smoking. Pooled data from multiple national surveys show that adults aged 18-24 are more likely to be non-daily smokers than older adults . Further, preliminary evidence suggests a link between non-daily cigarette smoking and recent increases in daily cannabis use from 2.8% to 8.0% between 2002-2014 . However, the extent to which trajectories of marijuana and tobacco use may interact is unknown, and examination of young adult non-daily cigarette smokers provides an opportunity to identify risk factors for tobacco progression. Thus, the first goal of this study was to test the hypothesis that, among 18-24 year old non-daily cigarette smokers, greater frequency of marijuana use over two years would be positively associated with cigarette quantity and frequency, frequency of non-cigarette tobacco product use, and likelihood of poly tobacco use over time. Second, we tested for the existence of a bidirectional relationship, hypothesizing that more frequent use of tobacco would predict heavier marijuana use. Participants were recruited primarily via paid Facebook posts that were targeted by age and location. Clicking on these posts led to the study website, where eligibility was determined. Interested and eligible individuals provided informed consent and completed the baseline assessment on the website. They completed additional quarterly electronic assessments 3, 6, 9, 12, 15, 18, 21 and 24 months later via SurveyMonkey . At the baseline, 12, and 24 month time points, assessments consisted of a single survey that was typically completed in 15-20 minutes and for which participants received $25 compensation. At the 3, 6, 9, 15, 18, and 21 month time points, participants completed brief daily assessments for 9 consecutive days, and were compensated with $4 per day completed plus an additional $4 if all 9 days were completed . Because evidence suggests young adults smoke more cigarettes on weekend days , each 9-day period began on a Friday to standardize the number of weekend days included.

In other words, workforce participation at age 23 is associated with lower marijuana-use rate over subsequent years for both males and females. This finding supports key concepts from the life-course theory which emphasizes salient life events such as employment or marriage to explain both continuity in childhood deviant behavior and changes during the life-course, and highlights the need for integrated drug programs that provide substance abuse treatment in conjunction with occupational trainings. Third, marijuana-use during the initial observation period was negatively correlated with slope of employment trajectory for males, indicating that marijuana-use is associated with decreased levels of workforce participation and has an adverse consequence on subsequent career growth. Clearly, the implication is that the harm of marijuana-use on users’ socioeconomic aspects of life is long term and chronic. One possible explanation for why this is uniquely found for males may lie in the dissimilarity of level of workforce participation and level of marijuana consumption. As illustrated in Figure 1, on average, females appear to be employed less time than males and tend to engage in marijuana-use to a lesser degree. Kaestner calls to attention the importance of including demographic contexts such as educational achievement, marital status and number of dependent children in examining the association of marijuana-use and employment. These demographic factors have been reported as important determinants of work participation and also show influences on level of marijuana consumption . Therefore, indoor grow rack further studies that simultaneously examine the association of employment with drug use as well as the demographic contexts are recommended.

Finally, slope of employment trajectory is not significantly correlated with slope of marijuana-use trajectory for either gender, indicating that the association between changes in marijuana-use and work participation over age are not systematic. The lack of consistency in the association between the two longitudinal trajectories suggests that the magnitude and direction of relationship between employment and drug use are not consistent over age, and that the direction of causality is complicated and uncertain. Again, this is consistent with other longitudinal studies , and it highlights the need for sophisticated causal inference approaches in future studies, especially given the limitations of empirical analyses on providing credible evidence for causal relationships. Despite significant findings, the present study has several limitations. The sample used for the analysis was a subset of the National Longitudinal Survey of Youth 1979 cohort who completed the 2004 follow-up survey. While utmost care was taken in examining participants’ demographic characteristics for any possible systematic missing patterns of subjects, the generalizability of the findings from this study sample to the entire NLSY79 cohort may be limited. Furthermore, the measures used in NLSY to record substance use are relatively coarse. A dichotomous measure of marijuana-use are relative crude and may differ drastically from person to person when identifying patterns of marijuana-use that affect employment. The current approach to estimating a BRISM is also limited in its ability to incorporate time varying covariates , as these would need to be treated as additional longitudinal trajectories within a multivariate random intercept and slope model. In addition, inclusion of quadratic, or higher order terms, within the bivariate longitudinal model resulted in a lack of model convergence. Work is currently ongoing to develop methods that allows for the incorporation of time-varying covariates.

One possible solution to be explored in future studies include taking a Bayesian approach to fitting the model with informative prior distributions that are derived from empirical studies . In sum, our results highlight the cross-correlational longitudinal effects of substance use and employment outcomes for young adults, while properly accounting for dynamic interdependencies between two concurrent repeated-measures outcomes. Additional research is encouraged to determine whether the findings endure with other data sets, different types of drugs and different employment variables. In particular, future research should closely examine how these two concurrent longitudinal outcomes may differ by race/ ethnicity groups through assessment of their interaction effect with the inter-dependent trajectories. Marijuana continues to be legalized in many states, generally with limited public health input. Although valid medicinal applications exist, the National Academies of Science, Engineering, and Medicine concluded that substantial evidence suggests that marijuana use is also associated with significant harms, including psychosis, schizophrenia, problem marijuana use, motor vehicle collisions, low birth weight, and respiratory symptoms.Evidence is emerging regarding the association of marijuana use with youths’ cognition and cardiovascular disease,as well as other areas, and the 2019 vaping epidemic demonstrated the hazards of rapid product innovation without due evaluation of safety.With widespread lifetime and adolescent use of marijuana, reaching 43.6% of 12th-grade students nationally, and 51.5% of 18- to 25-year-olds in 2018, even modest increases in risk may have a significant effect on population health.

Vaping of marijuana in the past 30 days, which typically involves high-potency concentrates, increased from 5% of 12th-grade students in 2017 to 14% in 2019, with 3.5% vaping near daily in 2019.10 The potential magnitude of mental health effects associated with the growing market of high-potency marijuana products is evidenced by estimates of the population-attributable fraction of first-episode psychosis due to use of high potency marijuana at 12% in 11 primarily European cities studied, and by elevated risk for first-episode psychosis found in individuals using these products daily. Treatment data also suggest reason for concern. In 2014, marijuana was the leading drug used by clients entering drug treatment in a study of 22 European countries, representing 46% of all new clients, up from 29% in 2003.Both marijuana-related new clients and daily users in treatment more than doubled between 2003 and 2014. Prior to legalization of adult use of marijuana in California, as legalization advanced nationally, identification of key policy concerns and calls for caution emerged. Barry and Glantz recommended that “to protect public health, marijuana should be treated like tobacco, legal but subject to a robust demand reduction program modeled on evidence-based tobacco control programs before a large industry develops and takes control of the market and regulatory environment.”Authors noted that the transition from small-scale marijuana growers and retailers to large-scale industrial consolidation and marketing would bring risks, including aggressive lobbying, campaign contributions, and efforts to create favorable regulation. Richter and Levy noted the parallels between modern trends in marijuana product diversification and past transformations of tobacco to a deadly industrialized product designed to boost nicotine delivery and enhance addictive potential and palatability. Volkow et al at the National Institutes of Health raised concerns over the potential effects of rising product potency and of use on the developing brain.Subsequently, in 2019, Ayers et al called attention to emerging patterns of marijuana branding, marketing health claims,indoor farming equipment lack of health warnings, and appeals to youths and called for federal regulation. California’s tobacco control oversight experts called for broad application of lessons learned from tobacco control to commercial marijuana.Others called for legalization processes to intentionally advance social equity through criminal justice policy, offering economic opportunity to communities hard hit by the war on drugs, and reinvesting revenues in those communities.In November 2016, a California ballot initiative, Proposition 64, successfully legalized production and sale of marijuana for adult use, 20 years after legalization of medicinal use of marijuana in the state. An important part of that initiative was the assurance that local control would be preserved and cities and counties would have broad discretion to allow legal marijuana commerce, or not, and to regulate its practice.

In 2017 and 2018, the state created a regulatory framework for legal cultivation, manufacturing, and retailing of marijuana, which generally prioritized facilitating the shift from the illegal market to the legal market rather than demand reduction strategies. The first legal marijuana dispensaries for adult use in California opened January 2018. Three marijuana industry behaviors—extensive increases in potency , manufacturing of products to attract youths, and aggressive marketing—that were directly adopted from tobacco industry practices became immediately evident across the state. Despite the threat to public health, state regulations failed to constrain these practices, even though California has led tobacco control efforts in the United States and pioneered tobacco control policies such as public smoking bans, flavored product bans, and electronic cigarette bans. Cities and counties are often “laboratories” of innovation in public policy, and, notably, in tobacco control. Because California law allowed significant local control, we therefore asked: To what extent have recommendations from the public health community and potential lessons from tobacco control and other legal, but harmful, products been adopted in the marijuana legalization process?In a cross-sectional study with data collection and analysis from February 1 to November 30, 2019, we studied laws and regulations in California to understand the extent to which public health recommendations and tobacco control best practices, and in some cases, alcohol control best practices with evidence of effectiveness and potential relevance, had been incorporated into marijuana legislation by January 31, 2019. We followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline. The jurisdiction law review reported here was determined not to be human participant research by the Public Health Institute Institutional Review Board.Selected practices were identified during an earlier literature review and national consultation with 62 stakeholders during key informant interviews conducted from 2017 to 2019 with experts in marijuana, tobacco, and alcohol regulation, the First Amendment, tobacco and alcohol law, local government, community organizing, criminal justice, and substance abuse and marijuana research, as well as marijuana industry participants. Collectively, the results of these interviews informed production of 2017 model ordinances for marijuana retailing, marketing, and taxation for California local government.Potential best practices identified included restrictions on retail outlets, buffer zones, certain product types, delivery, marketing, and conflicts of interest, as well as requiring preservation of smoke-free air, health warnings, pricing and taxation measures, and equity policies in licensing, hiring, and revenue capture.Although only practices considered legally defensible were recommended, descriptions of local measures were collected regardless of whether they went beyond recommendations .Of 147 jurisdictions allowing medical or adult use storefront commerce, 93 limited the number of dispensaries, with a mean of 1 store for every 19 058 residents . The state imposed no limits on the number of dispensaries or delivery businesses that could be licensed. Forty-two jurisdictions imposed a buffer between retailers and schools greater than the stater equired 600 feet, yet 6 jurisdictions allowed retailers to locate closer to schools than the state’s requirement, at a mean of 258 feet. More than 100 jurisdictions added establishments to the state’s list of “sensitive use” sites from which storefront dispensaries must be distanced, which consisted of kindergarten through grade 12 schools, day care centers, or youth centers. Locally adopted examples included colleges, public beaches, libraries, tutoring centers, and recreation centers. More than one-third of jurisdictions imposed buffers between retail locations, with a median of 600 feet.Provisions to promote economic equity and diversity in marijuana licensing were limited to 5 of the largest cities. Oakland, Long Beach, and the city of Los Angeles gave a defined class of “equity” applicants priority in licensing and a reduction in certain costs, and required that certain percentages of employees be low-income, local, or transitional workers. Sacramento also had equity licensing priority and reduced costs, and San Francisco had equity licensing priority and employee requirements. The state did not establish an equity licensing system. Proposition 64 established the right to expunge certain past marijuana convictions, and state legislation subsequently approved a process for automatic expungement, reducing barriers for eligible individuals to benefit.Among jurisdictions allowing retail sale, only 8 imposed restrictions on types of marijuana products for sale, beyond state regulations. One jurisdiction, Contra Costa County, pioneered the prohibition of sale of flavored products for combustion or inhalation, 3 jurisdictions prohibited the sale of marijuana-infused beverages resembling “alcopops” , 5 jurisdictions restricted products appealing to youths, and 5jurisdictions imposed restrictions on edible marijuana products beyond state regulations. No jurisdictions limited the potency of products sold, although 1 jurisdiction established a potency linked tax. The state did not limit or tax potency, except for establishing a maximum 10-mg THC dose for edible marijuana products , nor did they limit manufacturing or sale of flavored products, such as flavored vaping liquids or prerolled cigarettes, although state regulations did create restrictions on products resembling existing foods or with characteristics that were particularly attractive to children.