Hemp Growing

For the entire experiment, 304 samples were processed for DNA extraction including 19 controls, 45 “BE” samples, 92 gill swabs, 92 skin swabs, and 56 digesta swabs .After swabbing the BE and fish mucosal sites, individual swab heads were broken off into a 2 ml PowerSoil tube and then stored at −20°C for 2 weeks until DNA extraction to preserve microbiome integrity . All molecular processing was done according to the standard Earth Microbiome Project protocols . Lysis in single tubes were used to minimize noise from well-to-well contamination . A serial dilution of a positive control, Escherichia coli isolate , along with negative control blanks were included to estimate the limit of detection of the assay . By using the Katharoseq method, we empirically calculated the read count used to exclude samples . For library preparation, DNA samples of equal volume were processed using the EMP 16S rRNA 515F /806R primers with 12 bp golay barcodes at a miniaturized PCR reaction volume of 5 μl reactions in triplicate . After PCR, equal volumes of each library were pooled and processed through the MinElute PCR purification kit followed by a 1× Ampure cleanup. The final library was sequenced using a MiSeq 2 × 250 bp kit . Sequences were uploaded, demultiplexed, and processed in Qiita , using the Qiime2 commands . Specifically, sequences from the first read were trimmed to 150 bp following the EMP protocol, and processed through the deblur pipeline and SEPP to generate Amplicon Sequence Variants “ASVs” . ASVs were rarified to 5,000 reads per sample. General Alpha and Beta diversity measures were generated in Qiita. Microbial Alpha diversity comparisons were calculated for richness, Shannon diversity ,pot drying and Faith’s Phylogenetic Diversity . For statistical analysis, grouped comparisons were compared using Kruskal–Wallis test with Benjamini Hochberg FDR 0.05 .

To compare the age of fish with alpha diversity metrics, both linear regression and Spearman correlation were used using PRISM 9.0 . Beta diversity measures were calculated using both Unweighted UniFrac and Weighted normalized UniFrac . Categorical group comparisons of beta diversity were calculated using PERMANOVA tests . Lastly, to quantify the effects or sources of microbes from the BE onto the fish mucus, we applied the microbial source tracking software SourceTracker2 . Prior to SourceTracker2 analysis, ASVs which had less than 100 total counts across the dataset were removed to reduce sparsity and improve performance of the microbial source tracking. Skin samples did not differ in microbial diversity based on rearing type. In the BE, water generally was highest in microbial diversity, while both air stones and air diffusers had the lowest diversity across all sample types. When comparing the water communities of the FT and RAS tanks, the richness and phylogenetic diversity trended higher in RAS . Interestingly, the inlet pipe biofilms were highly variable across the FT and RAS systems with the FT tank having a very high microbial diversity compared to RAS systems. The tank side biofilms were generally higher in microbial diversity in the RAS tanks as compared to the FT tank. When comparing beta diversity, the largest compositional differences were due to the feed vs all other sample types, with most feed pellet communities highly differentiated from the BE and fish mucus with the exception of live rotifer feeds. Many chloroplasts ASVs were present in the pellet feeds, likely from plant ingredients, which likely drove this separation. Upon chloroplast removal, read counts for feed samples drop to levels which would largely exclude them from analysis thus suggesting that feed samples have very low proportions of microbes. The second largest driver in microbial community composition was the fish body sites for both Weighted and Unweighted UniFrac .

For individual body sites, the tank systems also had a moderate impact with gill samples being more differentiated across tank systems . Specifically, for gill samples, the tank rearing system had an impact on the microbial community for both Unweighted Unifrac distance and Weighted normalized Unifrac distances . Pairwise comparisons of Unweighted Unifrac distances revealed that gill microbiomes of RAS reared fish were also differentiated but in general less differentiated as compared to the FT reared fish . Pairwise comparisons of Weighted normalized Unifrac distances revealed the same pattern, with fish reared in different RAS systems having a differentiated community but more even more differentiated when compared to fish reared in FT systems . Skin microbial communities were only influenced by the rearing method when comparing Unweighted Unifrac but not with Weighted normalized Unifrac. When comparing YTK from the same age and genetic cohort reared in three different conditions, gill microbial communities were more influenced by the environmental conditions than the skin, while microbial communities of the BE were highly variable across tank systems.After quantifying the variation which existed across tank systems at a single age of fish, we next wanted to evaluate the extent by which mucosal microbiomes varied with fish age. Specifically, we sought to investigate factors governing the randomness vs. deterministic mechanisms for microbial colonization in marine fish over time. Fish were sampled at three age points including 43, 137, and 430 dph. At 430 dph, fish were either collected from an offshore sea pen or from the indoor environment. The indoor fish at 430 dph had been in the sea pen but were transferred back to the indoor environment to be used as broodstock . These fish were in the indoor tanks for 79 days before sampling. Fish from 43 to 137 dph were always reared in cannabis indoor systems. At each body site: gill , skin , and digesta , microbial diversity was compared across fish ages. Additionally, fish from 430 dph were separated by either indoor or ocean net pen. When comparing richness measures, all three body sites were influenced by age with the gill being most influenced followed by digesta and then skin . A similar pattern was observed for Faith’s PD, which takes into account microbial phylogenetic diversity with all three body sites being influenced by age.

The gill was most influenced followed by digesta and lastly skin . Shannon diversity had the same pattern with gill , digesta , and skin all being influenced by fish age in the same order of impact. When comparing only samples at 430 dph, gill diversity was larger for fish which were transferred from the ocean net pen back into the indoor environment as compared to ocean net pen reared fish. This effect was also seen in the skin, but to a much smaller degree. To model age and microbial diversity across the body sites, we performed a regression and Spearman correlation for each diversity measure. For this analysis, we excluded ocean net pen reared fish from 430 dph to compare only indoor fish . For richness, both gill and skin samples were positively associated with fish age while digesta samples were negatively associated with fish age . For Faith’s PD, both gill and skin again were positively associated with fish age . Lastly for Shannon diversity, skin was positively associated with fish age while digesta was negatively associated with fish age . These cumulative results suggest a general mechanism for alpha diversity changes in the marine fish YTK, S. lalandi, whereby alpha diversity may continue to increase over time in the gill and skin surfaces while digesta samples start highly diverse but then adapt or reduce in complexity over time. Next we wanted to understand how the composition of microbial diversity changed over time and to also determine if there was evidence for succession. To determine if age was associated with microbial niche differentiation across body sites, we compared the fish body site microbiome independently at each of the four ages or conditions including 43 dph , 137 dph , 430 dph “indoor tank” , and 430 dph “seapen” . Body sites at each age group, even as early as 43 dph, had unique microbial communities measured using Unweighted and Weighted normalized Unifrac distance metrics . For Weighted normalized Unifrac, based on the F-statistic, body site microbial communities were most differentiated at 430 dph, especially in the open sea pens. This result suggests that body site microbial communities continue to differentiate throughout the lifetime of the fish. We then sought to answer the question if certain body sites are more influenced by age. To do this, we compared microbiome differences of age and tank type within each body site independently . For both Unweighted and Weighted normalized Unifrac distance comparisons, the gill microbiome samples were more differentiated across ages as compared to the skin and digesta . Furthermore, when observing the gill samples, the 430 dph fish reared in the indoor tank and ocean net pen were divergent on the PCoA . In addition, fish at 43 dph were also differentiated. Next, we evaluated if overall fish mucosal microbiome similarity to the BE changed with age and if it did, which BE or water sample types were most influential . For indoor reared fish at 43, 137, and 430 dph, we compared the microbiome of the gill, skin, and gut to various hatchery components including tank side, water from the tank, the inlet pipe into the tank, air stones, air diffusers, and feed. For feed, we evaluated 12 different feed types that were used throughout the production schedule ranging from days 1–12 until harvest. The first feed type consistently had a more similar microbial community to the gill, skin, and digesta samples across the different ages thus we used these samples for the feed comparison in the broader BE comparison.

When including all possible BE sample types, a noticeable trend emerged where at the earliest age , the microbial communities across all body sites were generally more similar to the BE . Whereas at later ages, the microbiome of the gill and skin communities generally become more dissimilar from the inlet pipe and feeds, but became more similar to the air diffuser. The digesta samples , however, consistently became more differentiated from the BE samples over time suggesting a stronger niche differentiation in the gut. To quantify this, we included only BE sample comparisons which were consistent in all ages – water, inlet pipe, and first feeds – and compared how the mucosal microbiomes of the fish disperse or converge toward the BE. For both gill and skin samples, the total differentiation of fish mucosal site to the three BE samples was least at 43 dph but increased with age . The gill and skin samples were both more similar to the inlet pipe at 43 dph and became more divergent from the inlet pipe over time . Digesta samples became more differentiated from all BE surfaces equally over time . To estimate the total impact of these differences, we calculated the effect size . For the gill, the dissimilarity differences across the BE samples explained 34.5% of the variation at 43 dph but then increased to 68.8% of the variation explained at 137 dph. For the skin, the largest jump in effect size occurred between 137 dph and 430 dph . These results indicate that niche differentiation occurs at varying rates depending on body site and that some BE microbial sources continue to have an influence on the fish mucosal microbiome throughout the lifespan of the fish, whereas other environmental sources may only be influential during early ontogeny. To identify the extent by which the BE contributes to the mucosal microbiome of the fish, we applied the popular microbial source tracking program SourceTracker2 which uses Bayesian statistics to estimate contributions of features from various sources to sink communities. SourceTrackr2 determined that contributions of the BE varied widely depending on both the body site and the age of the fish. At 43 dph, the tank side biofilm and air stones were the biggest sources of microbes to the gill and skin of the fish larvae, while the majority of microbes in digesta samples were from unknown or unsampled sources . Rotifer feeds also contributed to the gill, skin, and gut microbiomes, but to a lesser extent compared to airstone and tank side . At 137 dph, gill was again influenced by the airstone and air diffusers in the BE, while higher frequencies of skin and digesta samples were colonized by microbes from feeds . However, microbes from unknown sources had the largest overall contribution at 137 dph across all body sites .

In the work of Arterberry et al. , frequency of marijuana use was determined by asking participants how often they used marijuana in the last year, and responses were coded on a scale from 0 to 10 . In contrast, our participants reported how often they used marijuana within the past month by indicating a specific number of days from 0–30. Second, all participants in our study were current cigarette smokers, while the NESARC sample included both cigarette smokers and non-smokers. There are several study limitations. Though the TABS II used a random sample, it is not nationally representative. All data is self-reported, and there are no biomarkers for verification of tobacco, marijuana, or PPR use. The cross-sectional nature of the analysis prevents causal inference. As there were no non-smokers included in the analysis to compare with the current smokers, further analysis of the present study is warranted. We do not have data on participants’ reason for PPR use. Some participants could have medical providerissued prescriptions for pain, yet it is possible that even patients with valid prescriptions may not actually “need” a prescription pain reliever. We did not have any information on the presence, absence, or type of pain. Studies have shown that simply asking a primary care physician for a narcotic by brand name significantly increases the likelihood of being prescribed a medication and being prescribed a strong narcotic . Because pain is subjective, a definitive conclusion as to what level pain a patient is experiencing is not possible. Therefore, the only data available in a large data base is whether or not the participant had a prescription for use. We also did not measure the frequency of PPR use within the past 30 days, as we did with marijuana,container for growing weed nor did we ask respondents about the type of PPRs they used. In the future, questions about these details of PPR use would be beneficial to include, as they would enable a more nuanced analysis of the data.

Concerning our frequency analysis, it is possible that our sample of current marijuana users was too small to capture a significant effect between frequency of marijuana use and PPR use. Finally, despite random sampling, our sample was predominantly Caucasian and older, limiting generalizability. To frame conclusions about the presence of a complementary effect between marijuana and PPR use and to identify a potential causal relationship between use of these two substances, future studies should be longitudinal, with larger and more diverse samples that include both smokers and non-smokers. The incorporation of unique aspects of MMLs into future models would be useful to more accurately determine the effects of such laws. The buildings in which we live and work may be inadvertently detrimental to our chronic health. There is an obvious trade off here. In environments with epidemic disease, it is essential that we remove ourselves from waste streams that can propagate the infective agents. As people have always liked to gather into ever-larger villages, towns and cities, it has become necessary to build infrastructure that delivers clean water and food, and takes away waste products. In the absence of these services, and especially if people are ignorant of the problems caused by poor sanitation, disease will spread as people dispose of waste and use available water without taking appropriate precautions to reduce negative health consequences. Over the last few hundred years, especially since the mid-19th century, the public and government came to realize that disease was being spread by dangerous microorganisms, and therefore that more public works were needed to eradicate them. Over the last 100 years that common sense has transmuted into an out-and-out war on microbes, with most people’s motto, especially in the healthcare industry, being ‘The only good microbe, is a dead microbe.’ This ideological approach to ‘hygiene’ filtered down into everyone’s lives, and has been exemplified by an advertising industry keen to sell products that would facilitate the public’s germophobia. This has reached a peak, whereby it is now possible to buy microbe-killing cleaning agents, surface materials that kill bacteria on site, and even air-filters that are designed to ensure sterility.

There is a surprising lack of data to suggest that any of these precautions have significant health benefit when employed in countries that have adequate health care, vaccination rates and public works that provide clean water, food and removal of sewage. There is, however, a large body of evidence growing that overt cleanliness may be detrimental to our health. With our clean, bleached homes, antimicrobials, and processed air and where even our water is chlorinated and food prewrapped and sterilized, it is no shock that our immune systems, which have been expecting continuous bombardment, are now over-reacting. One place we should expect cleanliness and hygiene is our hospitals. People with communicable disease or poorly functioning immune systems need to be kept in an environment where microbes are held at bay. But even here there is need to consider ways to stimulatethe immune system to help people heal. Despite expectation, many infections that occur after surgery may be due to bacteria or viruses that were already present in the patient. To prevent these infections, we may not need antibiotics, instead we may want to consider ways to manage the microbiome to prevent it from expressing a virulent lifestyle. In addition, when a patient is healing post treatment, it is possible that controlled microbial exposure may actually help to promote recovery . So even in hospitals maybe, we should be considering how to manage microbial exposure, rather than attempting to eliminate it entirely. The purpose of this perspective was not to review the evidence, but to postulate potential solutions that may require a research renaissance to develop. One research anecdote on which we have been working recently comes from studying the microbial exposure of agrarian peoples, and the impact of disrupted exposure on the development of asthma. We have demonstrated previously that Amish families whose children grow up on farms, interacting with animals and the farming environment on a regular basis, have a substantially lower rate of asthma compared with the Hutterites who live a similar technology-free life, but whose children are not allowed on the farm . Currently, we are proposing intervention studies whereby we expose Hutterite children to farm animals and observe whether this helps to protect against asthma.

The hypothesis is that bacteria, viruses and fungi associated with the animals help to train the immune system,cannabis square pot as well as helping to build a microbiome in the gut that protects children against asthma development. Similar interventions could be put in place for other children, for example, those in urban settings who develop atopy, including food allergies, eczema, etc. Through our studies of bacteria, fungi and viruses in the built environment, and through efforts to compare the microbiome of homes between families with atopy and those without , it will be possible to identify microorganisms that may protect against atopy onset. But, how would exposure best be mediated. We know that animal exposure can be beneficial; even dogs can provide protection , with evidence that this is mediated through microbial exposure . So the simplest way may be to just expose children to more animals. However, in our modern world this is not always possible. Children growing up in urban environments are unlikely to have ready access to animals, and unless there is a shift in how urban environments are managed it is unlikely this will be an effective means of exposure. One way that we have been investigating is to impregnate building materials with microorganisms; essentially to create living walls, ceilings and floors, even carpet materials. Many bacteria can survive in a quiescent state, or as spores, waiting for the right conditions to germinate. Germination or rapid growth usually occurs following the introduction of water, but it can also happen if a person acquires the microbe so it can flourish in their warm, moist body. How could microbially active materials actually work? First, microbial cells or particles in the case of viruses, can be dried down and added as a coated layer to most materials, or alternatively can be integrated at some stage of material development so that the material is suffused with these agents. If we can design materials with a three-dimensional structure, to include pores and spaces that could house these organisms, then the material itself would become a source of microbes. We would need to be very careful to select microorganisms that will not lead to serious health complications; although, all houses are already replete with human-derived bacteria, fungi and viruses derived from the occupants. The majority of microbes from animals, plants or soil are unlikely to be overtly pathogenic, and so further selection among these would be relatively simple. Adding microbes from animals, plants, or soil, into this environment in a way that means that the animals, plants or soil themselves do not need to be present, would enable new buildings to be ‘probiotic’, or ‘healthy promoting’. In addition, including microbes that can either out-compete or directly inhibit the growth of less-desirable organisms would provide additional benefits. For example, certain bacteria found in soil, as well as those in animals and plants, can inhibit the germination of fungi. Therefore, having walls impregnated with these bacteria could help to inhibit fungal growth during periods of water incursion. Mold and fungal growth after flooding in homes has serious health complications that require expensive cleanup or demolition of the structure. Water incursion into a material would activate spores or quiescent strains of anti-fungal bacteria, inducing them to produce metabolites that would suppress fungal germination and thereby inhibit growth.

For example, methicillin-resistant Staphylococcus aureus can be outcompeted in the human nares by Staphylococcus lugdunensis . Therefore, adding S. lugdunensis to building materials may help to reduce the spread of MRSA. However, there is a complication, in that this common skin-associated bacteria has been associated with infections following cuts or skin injury, albeit ones that can be readily treated. However, it is possible that there are other bacteria or viruses that are more suitable, or that the metabolites produced by these organisms could be isolated and added to materials so that the biological organism need not be present. The situation in hospitals in far more complex. Yet, it is still possible to imagine recovery rooms that have bioactive surface materials that could passivelyencourage healing in patients through immune stimulation. The alternative may be just to provide relevant probiotics through oral consumption or skin application as a cream. But, as some of the main routes of microbial acquisition and immune system activation are not fully understood, attempts to recreate a microbial exposure environment that provides a less direct association with the probiotic may prove to be beneficial. We are already exploring ways to augment the environment of microbes in the human colon during surgery to reduce nutrient stress and thereby reduce virulence activation. In animal studies, adding nutrients to the gut, that remain unavailable to the microbes, but allow them to sense the availability of nutrients, has been shown to substantial improve recovery rates . This paradigm of treating not only just the patient, but also the patients’ microbiome, could become one of the changes in surgical practice over the next 5–10 years that has the biggest impact on beneficial outcomes. Microbiologically inspired design and biotechnology has so many potential avenues for improving human health. And when it comes to altering our world to improve the health of our children, all options should be on the table. Determining the most effective way forward will require some very well designed experiments, and even a cultural shift in how we see microbes. Intervention studies that demonstrate health improvements from biologically active building materials will be the gold standard, but until then engineers, architects and policy makers need to collaborate with microbiologists and clinicians to identify the strategic way-marker that need to be reached in order to determine the relevancy of such interventions. The paradigm of bringing the microbial world to the people, rather than the people to the microbial world, requires a Kuhnian shift that the public and policy makers may not be ready for, but that scientists and engineers needs to explore nevertheless.The policies and attitudes toward marijuana use are changing in the United States . As of January 2018, 30 states and the District of Columbia have passed medical marijuana laws , and fewer adults perceive non-medical marijuana use as risky .

The majority of respondents were cognizant of the risks of maternal marijuana use for fetuses and infants, yet sizeable proportions disagreed with or were neutral about the validity of these risks. For beliefs about marijuana use during pregnancy, the evidenced based risks of preterm birth and low birth weight received the lowest levels of agreement and thus warrant high prioritization in health communications, educational materials, and guidelines. For beliefs about marijuana use while breastfeeding, attention and learning problems along with detrimental effects on intellectual capabilities received the lowest endorsement. Given the growing evidence for these cognitive consequences and their adverse impacts throughout the lifespan, public health communications should aim to increase awareness of their potential while reporting the current status of empirical support. The findings suggest that ever users, and not just recent users, are important targets for health information about maternal marijuana use. Specifically, prior cannabis users reported more supportive beliefs about its benefits and more lenient beliefs about its risks. These patterns were found not only for recent marijuana users but also for those who had ever used marijuana. These findings suggest that prenatal and postnatal providers could ask about the history of any prior marijuana use to become more informed about one maternal feature that could increase the propensity to be open to using marijuana. Similarly, the findings suggest that males are particularly likely to benefit from education about the risks of maternal marijuana use. Important targets include the risks of preterm birth and low birth weight,vertical grow rack the lack of evidence about its benefits in reducing pain, the potential harms for breastfeeding babies despite marijuana being plant-based, its lack of impact on milk supply, and the potential for infant THC addiction.

Latino and non-Latino respondents differed on only four beliefs about maternal marijuana use, although these differences were in the predicted direction and consistent with evidence that Latinos tend to hold more cautious beliefs about marijuana harms and benefits relative to other ethnic groups . Latino respondents held more cautious beliefs about its benefits in reducing pain and nausea during pregnancy and calming the baby when breastfeeding, and the risks of child attention and learning deficits with use while breastfeeding. The findings provide no support that, in rural, Latinomajority, disadvantaged communities, Latino residents are less well-informed than non-Latino residents on these health issues. Having Spanish-fluent research assistants and promotores, who are highly trusted in these communities, recruit residents and offer Spanish versions of the survey materials likely protected against ethnic group differences arising from poor comprehension or survey engagement. Few differences between parents and non-parents emerged although they were consistent with predictions that parents would have relatively more cautious beliefs about the benefits and risks of maternal marijuana use. This pattern is consistent with findings from a prior study that parents perceived marijuana use by pregnant women as more risky and less beneficial . Importantly, however, this prior study included a generic measure of risk and thus lacked the specificity afforded by the items in the present study. The present findings thus provide a more nuanced and detailed framework of specific risk and benefits beliefs, highlighting the few that are likely to be strong contributors to parental status differences in general risk perceptions of maternal marijuana use.

Key strengths and limitations of the study warrant attention in contextualizing the findings and highlighting future research directions. The study successfully recruited a large community sample with strong representation of Latino, female, and Spanish-preference residents of disadvantaged, rural communities and thus contributes to efforts to build the scientific evidence base with data from these and other under-represented communities . However, the sample included a limited number of male, pregnant, and breastfeeding residents. Further, the relatively high rates of missing values for reports of education, pregnancy status, and breastfeeding status contribute modest uncertainty about these sample characteristics. Social desirability concerns could inhibit motivations to report these characteristics. In addition, information about social ties with pregnant and breastfeeding women other than partner status was not collected. Future studies can extend the current research by using a sample with a larger number of male residents as well as measures that minimize social desirability and that clearly identify pregnant and breastfeeding women and those who are partners, family members, health workers, and influencers in their social networks. In addition, research utilizing a nationally representative sample is needed to provide a common benchmark for interpreting and contextualizing regional and social group patterns of beliefs. There is also a need for more research on how partners, family members, friends, and members of the broader community influence decisions to use marijuana while pregnant and breastfeeding. This research can inform the development of health communications and tools that enable influential people in social networks and communities to engage with women to promote and support their informed decisions on marijuana use. Future research could include measures of health care advice and education about marijuana use during pregnancy and breastfeeding to discern how it is associated with beliefs about its harms and benefits.

As the U.S. and other nations transition to legalizing recreational marijuana, there is a growing need for science based guidelines for counseling and public education efforts to increase awareness about the health effects of marijuana use. Pregnant and breastfeeding women, along with their partners, families, and friends,cannabis grow racks highly desire healthy infants and want to learn about potential risks and ways to reduce them . These findings provide insights into beliefs about maternal marijuana use held by members of the diverse, rural regions in California. These belief frameworks can inform the development of health communications and guidelines about risks of use during pregnancy and breastfeeding so that they target common misperceptions and are tailored to demographic and social groups.Approximately eighteen percent of the population has been diagnosed with an anxiety disorder. There are also many people who suffer from anxiety but are not qualified for a diagnosis. This greatly increases the percentage of people suffering from anxiety throughout the United States. There are a few different types of treatment for anxiety such as psychotherapy, cognitive-behavioral therapy, and medication. Most patients are recommended a combination of therapy sessions and medication. There are various medications that can be prescribed to a patient with an anxiety disorder but one of the most controversial forms of medication across the United States is cannabis. Although there is research that identifies the medical benefits of cannabis use among patients suffering from anxiety, some research suggests that long-term use can lead to potential mental health risks and it can also result in memory loss thus questioning the medical benefits. The legalization of cannabis is a highly debated topic in the United States. As of June 2014, twenty-three states have legalized marijuana for medical purposes. While almost half of the United States has legalized cannabis for medical purposes, it still remains completely illegal throughout the other half and under federal law. Cannabis contains two imperative cannabinoids that makes it a legal source of medication in certain states. One is cannabidiol also known as CBD and the other is delta- 9-tetrahydrocannabinol also known as THC. Even though the properties of marijuana were carefully examined before it was legalized as a form of medication, there is still some data that suggests there are negative effects from regular use of cannabis. For instance, the CBD cannabinoid content in marijuana is the primary source that activates the anxiety relieving symptoms in the hypothalamus of the brain. The study “Effects of Cannabidiol on Regional Blood Flow” conducted by de Souza Crippa, Zuardi, Garrido, Wichert-Ana, Guarnieri, Ferrari, Azevedo-Marques, Hallak, McGuire, and Busatto experimented on subjects to see if CBD had any correlations with anxiety. This study involved two groups of healthy people who were given both CBD and placebo. One group was given the CBD first while the other received the placebo first.

The results show that “the administration of CBD was associated with significantly decreased subjective anxiety and increased mental sedation, while placebo was not” . When the people were given both CBD and placebo, the CBD was the chemical that reduced anxiety symptoms. From the SPECT scans done on the subjects, it was clear that this activity was occurring in the hypothalamus. The hypothalamus is a significant brain structure that facilitates the effects of anxiety. When one experiences high levels of anxiety, it increases activity in the hypothalamic area. In the study they concluded, “Thereduced hypothalamic activity that we observed is thus consistent with the anxiolytic effect of CBD” . Since the levels of anxiety correlate with hypothalamic activity, this study proves that consuming CBD will decrease hypothalamic activity and reduce anxiety. This is a major reason why medical marijuana is prescribed to patients with anxiety. However, while CBD drives to reduce anxiety, THC on the other hand contributes to increasing anxiety. Although THC plays a role in treating many serious medical conditions such as insomnia and anorexia, it is also involved in increasing paranoia and anxiety. The article “Can Marijuana Treat Anxiety Disorders” discusses the different effects of the THC and CBD cannabinoids. It mentions, “THC seems to have opposite effects on anxiety levels depending on the dosage, with THC acting to decrease anxiety at lower doses yet increasing anxiety at higher doses” . When marijuana has a higher dosage of THC than CBD, it is identified as a sativa strand and it raises anxiety symptoms. Patients who suffer from anxiety are referred medical marijuana with a higher quantity of CBD and a less THC in order to relieve anxiety; this is recognized as an indica strand. Since THC is the main active chemical in marijuana, patients can often feel an increase in anxiety with regular use. This is where the controversy lies. Another significant issue with medical marijuana is that it can lead to substance dependence. Although marijuana does not contain any addictive elements, in regular users “abstinence leads to a withdrawal syndrome characterized by negative mood , muscle pain, chills, sleep disturbance and decreased appetite” . This means that eventually when a regular cannabis user stops using cannabis, they will experience a withdrawal syndrome that includes many negative moods. The study “Neural Effects of Positive and Negative Incentives during Marijuana Withdrawal” by Filbey, Dunlop, and Myers confirmed, “growing evidence support a marijuana withdrawal syndrome that may drive the high rate of relapse in marijuana dependent individuals” . When this marijuana withdrawal occurs, most patients decide to resume using cannabis to refrain from dealing with the negative effects. This then makes the patient dependent on cannabis leading to long-term usage, which can result in mental health risks such as depression, cognitive impairments, and memory loss. Many patients who suffer from anxiety begin to feel depressed and an increase in anxiety after long-term use of medical marijuana. In Marijuana and Madness by Degenhardt, Hall, Lynsskey, Coffey, and Patton, conducted studies to investigate if there were any connections between long-term marijuana use and depression. One of the studies established that “regular cannabis use increases the risk of depression” . Although the risk of developing depression depends on the amount of time one has been consuming marijuana, the percentage of people who develop depression is still incredibly high. One important study that was mentioned in this text was the Bovasso study. This study gathered important information on cannabis users and depression. “Approximately 67% of those with cannabis abuse but no depressive symptoms at baseline developed depression after 14-16 years” . This means that a majority of people who use cannabis for fourteen to sixteen years will develop depression even if they never had depressive symptoms to begin with. It is harmful to ones mental health to regularly use medical marijuana for more than fourteen years because it can result in depression. Another negative effect for longterm users of cannabis is that they can experience memory loss and cognitive impairments. The excerpt “How marijuana relievesanxiety” by Lecia Bushak reports some important findings by Dr. Sachin Patel. He asserts, “Though the short-term effects may be relaxing, the long-term effects may not have an influence in affecting anxiety. Instead, long-term use of the drug can lead to memory loss and cognitive impairment” .

The rapidly evolving environment poses considerable concerns about children’s exposure to marijuana and related marketing and creates significant challenges for pediatricians preventing, treating, and educating about marijuana-related harms among children. As stated in its most recent policy statement about marijuana commercialization, the American Academy of Pediatrics “strongly recommends strict enforcement of rules and regulations that limit access and marketing and advertising to youth”. The presence of RMDs in neighborhoods and point-of-sale marketing such as advertising and promotional activities in RMDs might increase the visibility and awareness of marijuana products among children, whose perceptions and behaviors may be influenced. A study in Oregon found that dispensary storefront was the most common source of advertising seen after commercialization. Self-reported exposure to medical marijuana advertising was found to be related to higher levels of use and intentions of future use among children in California schools. Products, packages, and advertisements that are designed to be appealing to children are particularly concerning. Tobacco and alcohol literature repeatedly suggested that children are common targets of marketing. Despite the fact that all the states with marijuana commercialization have some form of prohibitions on child-appealing products and marketing, it remains undocumented as to what extent the marijuana industry is complying. This study is the first to comprehensively assess point-of-sale marketing practices in RMDs with a focus on those relevant to children. Unlike previous marijuana research relying on individual self-reported exposure measures, we adopted the direct and objective observation approach that has been commonly used in tobacco and alcohol studies on retail outlets. We audited RMDs near a representative and large sample of schools in California,cannabis grow setup the largest legal retail market in the US where over 10 million children can be potentially influenced.

We identified product and packaging characteristics, advertising and promotional activities, and access restrictions in these dispensaries. This was a cross-sectional and observational study conducted in June-September in 2018. We obtained a list of public schools in California that participated in the 2017–18 California Student Tobacco Survey . California was first stratified into 22 regions. Schools within each region were then randomly selected, proportional to the number of students enrolled within the region. A total of 623 schools across California were sampled and invited, with 403 schools agreeing to participate. Among these 403 schools, 44 schools opted out before the survey was conducted, and 26 schools participated in the survey but were excluded from CSTS data due to low response rate. The final effective school sample size was 333, among which 256 were high schools and 77 were middle schools. The total number of students participating in the survey was 151,404, making it the largest school-based surveys in California. Our study focused on RMDs near these 333 schools.Six trained field workers audited retail environments in RMDs in closest proximity to the 333 schools . We first identified dispensaries using crowd sourced online websites, including Weedmaps, Wheresweed, Leafly, and Yelp. State licensing records were not used because they could not provide a complete list of dispensaries at the time of data collection. Specifically, 1) Marijuana commercialization in California took effect in January 2018. During the study period, California was in a transition stage when annual licenses were just issued, and most were not approved. 2) The licensing policy in California was not enforced, with a large portion of dispensaries operating without licenses. 3) For licensed dispensaries, the registered and actual business name and address often mismatched.

Alternatively, we utilized crowd sourced databases, which were considered as reliable, up-to-date, and comprehensive sources of dispensary directories. To identify the dispensary closest to a school, field workers entered school zip code in the online searchable databases. The street addresses of all the dispensaries with the school zip code were geocoded and mapped in ArcGIS to compute their distances to the school. Field workers then called the dispensary with the shortest distance to verify its address and operational status. These procedures were repeated if a dispensary was permanently closed or not verifiable via multiple calls until an active dispensary was identified. The primary focus was RMDs. Yet, medical marijuana dispensaries that require a doctors’ recommendation or state patient ID cards coexisted in California in 2018. During call verifications, if dispensary staff indicated that a doctors’ recommendation or a patient ID was required to enter the dispensary and make purchase, the dispensary was categorized as a MMD.i Fieldworkers also verified dispensary classification during the subsequent auditing. For those verified as MMDs, we repeated the aforementioned procedures until an active RMD was identified. The six trained workers in teams of two audited verified RMDs.ii On average, each RMD visit took 10–15 minutes. The 103 RMDs had unique RMD-school pairs and the 60 RMDs were the closest ones to two or more schools out of the remaining 230 schools. In the main analysis, we reported observations in the unique RMDs . In the secondary analysis, we reported observations on RMDs using school as the unit of analysis . The 60 RMDs shared by two or more schools were counted multiple times or over-weighted in the secondary analysis, reflecting their potential to influence children in multiple schools. The Human Research Protections Program at the University of California San Diego deemed this research non-human-subject and required no review.

California bans products and marketing “attractive to children”, “designed to be appealing to children or easily confused with commercially sold candy or foods”, or “in a manner to encourage persons under 21 years of age to consume”. Because these regulatory texts are rather vague, the definition and operationalization of what child-appealing is in this study were primarily informed by specific details in laws from other states, particularly Nevada and Washington . Specifically, we defined child-appealing products, packages, paraphernalia, and advertisements as those characterized by promotional characters , shaped like commercially sold products usually consumed by children , or using bright colors or bubble-like fonts . We examined the overall availability as well as the availability by dispensary-to-school distance.These measures included general practices not specifically relevant to children: availability and types of promotions , branded marketing materials, health promotional or warning messages related to marijuana, and images or wording indicative of marijuana. Field workers also visually measured the size of the biggest exterior advertisement. Although California does not restrict size of advertisements in RMDs, some other states do. For instance, Washington requires advertisements to be no larger than 1,600 square inches. This study demonstrated that, in the early stage of marijuana commercialization in California, point-of-sale marketing practices that are appealing to children were minimal on the exterior of the RMDs around schools. However, such practices were abundant on the interior. Marketing practices not specifically appealing to children were common on both the interior and exterior of the RMDs. Given the age limit, RMDs’ exterior marketing might be the most concerning source of exposure for children. It is reassuring that child-appealing marketing was rarely observed on the exterior of the RMDs around schools. Yet, three quarters of the RMDs had some form of child-appealing marketing on the interior,outdoor cannabis grow which violated the California laws. Although children should have little direct access to the interior, child-appealing items may be available to children through indirect pathways and should not be overlooked. For instance, children’s social networks such as older relatives, peers, or caregivers are their important sources of drugs. A study reported that almost three quarters of underage users obtained marijuana from friends, relatives, or family members. Child-appealing products, paraphernalia, or promotional materials could then be made available to children through these adults who are eligible for marijuana purchase. Particularly, about 30% RMDs violated the California law to offer free samples, which could be taken out of the dispensaries and given away to children. These child-appealing items in RMDs could be also resold to children in illicit markets by street dealers. Research on tobacco and alcohol have suggested that children are exposed to and influenced by tobacco and alcohol products and point-of sale marketing despite the age limit for purchase . Whether and how the marketing activities inside of RMDs impact children’s perceptions and behaviors should be examined in future research. Meanwhile, exterior retail environments not specifically relevant to children still warrant further attention. For instance, 63% RMDs had image or wording indicative of marijuana on the exterior. One third of the RMDs had generic advertisements, and some advertisements were of a relatively big size. Marijuana could be smelled outside of 25% RMDs. All of these might potentially increase perceived presence of RMDs in the neighborhoods and shape children’s social norms.

Approximately half of schools had RMDs located within a 3-mile distance that is reachable to children by walking, cycling, or driving. Some RMDs were located further away, especially in suburban or rural areas. Nonetheless, children are not free from exposure to RMDs even if RMDs are located more than 3 miles away from schools. In 2009, the average travel distance from home to school among all school children was 4.4 miles; among highschool students, the average distance was even longer . The travel distance was also increasing over time. An interesting exploratory observation indicated that, compared to RMDs located further away from schools, a larger proportion of RMDs in reachable distance to schools had interior child-appealing marketing. It is possible that RMDs intentionally targeted children if they were in closer proximity of schools. Unfortunately, our study was not able to test this hypothesis directly. Almost all the audited RMDs followed California rules on age verification. If continuous monitoring and enforcements are not in place, however, children might get access to abundant child-appealing marketing practices inside of the dispensaries, the consequences of which could be grave. Furthermore, exterior signs of age limit were absent in over 80% RMDs and security personnel were only observed in 40% RMDs. These might increase the risks of accidental or even intentional attempts of children to enter RMD premises, who would be then exposed to interior marketing in waiting area. Compared to laws in other states, California regulations on child-appealing marketing seem to be vague and less comprehensive during the study period. Because content restrictions are inherently subjective, it might be challenging for California RMDs to comply and for regulators to enforce without objective, operationalizable measures of “child-appealing”. Fortunately, after this study was completed, California released new regulations in January 2019 on child-relevant products and marketing. Specifically, marijuana products and packages “shall not use any depictions or images of minors” and “shall not contain the use of objects, such as toys, inflatables, movie characters, cartoon characters, or include any other display, depiction, or image designed in any manner likely to be appealing to minors”. These texts are expected to provide clearer guidance to law compliance and enforcements. In addition to prohibitions in laws, California could also consider screening content materials such as packages before they are available in RMDs. For instance, Massachusetts allows manufacturers to submit artwork to a regulatory board for review to ensure non-child-appealing packaging. Standardized packaging might be another alternative, which has shown effectiveness in tobacco control outside of the US. This study has limitations. First, this study used a cross-sectional design to capture a snapshot in summer 2018, approximately half a year after California’s commercialization of marijuana. This unique transition period was characterized with a lack of law enforcement, delay of dispensary licensing, and inadequate understanding of laws. As the legal market matures and government makes endeavors on law interpretation and enforcement, we might expect a stronger compliance with laws and possibly a reduction in marketing practices. The findings may not be generalizable to other time points in California. Second, our observations were largely constrained within the regulatory regime in California and may not be generalizable to other states where different regulatory measures are in place. Third, frequency or quantity measures in each marketing category would be more informative than simple binary indicators for availability. Unfortunately, a dispensary often displays hundreds or even thousands of products, packages, paraphernalia, and advertisements. Obtaining frequency or quantity information requires the field workers to spend a considerably longer time evaluating the RMD environment, which is infeasible in practice. Fourth, California laws lacked specific details related to children during the study period.

Prior research examining secondary effects of alcohol BMIs have noted a decrease in marijuana use when there was also a decrease in alcohol consumption . It could be that factors that result in students’ experiencing fewer alcohol-related consequences without changing their drinking differ from ones that would lead to reductions in alcohol or marijuana use. Although our study did not include a measure of marijuana-related consequences, future research should examine changes in marijuana consequences to investigate whether changes in alcohol-related consequences correspond with changes in marijuana consequences following alcohol-focused BMIs. Second, a lack of effects may be due to the fact that our BMI was focused solely on changing alcohol-related behaviors and did not discuss the participant’s marijuana use. Future research should examine process coding in BMIs that do discuss marijuana use to explore possible in-session processes that may be related to changes in marijuana use and can be targeted in future interventions3 . Similarly, although alcohol and marijuana use share similar predictors , they may differ in their mechanisms of change. For example, the underlying motives that drive these two behaviors may vary so changing one will not ultimately lead to changes in the other and existing BMIs may not be targeting or altering both. Third, the referral incident in this study may not have been severe enough to warrant an overall re-evaluation of substance use, as may have been the case for those who required a visit to the ED as a result of their alcohol use . Marijuana users may require a more focused intervention or a supplemental session that targets alternative substance free activities to facilitate changes in marijuana use . Finally,cannabis grow tray with growing trends in decriminalization and legalization of marijuana in the US, the perceived risk of marijuana has decreased among college students .

Marijuana use may be more entrenched in the college social environment and more difficult to change without a targeted marijuana specific intervention. The results of this study should be interpreted within the context of its limitations. First, our study is restricted by our measure of marijuana use, which was limited to frequency and did not assess for marijuana-related consequences. Future studies may include assessments of quantity, days smoked, and consequences to get a better of understanding of the severity of participants’ marijuana use. Although daily marijuana use is on the rise, with almost 6% of college students reporting daily use , marijuana users in our study were using about 13.7 times in the past month. This is fairly low compared to those seeking treatment for marijuana use or being seen in an emergency department. Findings may be different in those populations where marijuana use is greater. For example, Metrik et al. found that compared to lighter users, those who reported weekly marijuana use demonstrated a significant decrease in use following treatment. Furthermore, our measure of pBAC was derived from participants’ reported heaviest drinking event and may not be the best way to capture peak BAC levels. Additionally, the study sample was predominantly white which may limit our ability to generalize findings to other populations of interest. Finally, we relied on self-reported data collection that did not include corroborating measures. Research using collateral informants indicated that mandated students may under-report alcohol use . Despite these limitations, this study adds to the existing literature on the secondary effects of alcohol-focused BMIs. To our knowledge it is the first study to examine the influence of two different alcohol interventions on marijuana use in the context of stepped care. Furthermore, findings indicate that heavy drinking college students who also use marijuana may still benefit from alcohol treatment especially in reducing their alcohol related consequences.

From a theoretical perspective, our results suggest that changing one behavior does not necessarily mean changes in another will occur, at least with respect to marijuana. However, future work should examine other health behaviors that might change as a result of reducing alcohol consequences. For example, it may be that increases in substance free activities like exercising, volunteering, or academic related behaviors occur alongside changes in alcohol-related behaviors . Future research examining marijuana focused interventions of different intensity implemented in a stepped care approach may enhance our understanding of which interventions are most effective for college students with varying levels of involvement with marijuana.Marijuana is the third most commonly used drug after alcohol and tobacco, with approximately half of US adults having ever used marijuana and 10% having used marijuana in the past month. Marijuana has been subject to ongoing legal and social debates, including its use for medical therapies and recreational use. As a medical therapy, marijuana is used to reducechemotherapy-induced nausea and vomiting and chronic neuropathic pain, although it increases risk of cardiovascular disease, respiratory illness and metabolic disorders. Marijuana use has also increased over the past several decades, coincident with laws and regulations. Due to the increase in use and increasing number of states legalizing recreational marijuana, studies are needed to evaluate its health effects, in particular its cumulative effects on health. While previous studies observed associations between marijuana and age-related health outcomes, the effect of marijuana on the aging process at a molecular level has not received suffcient attention. Several molecular markers have been proposed to quantify biological age, including epigenetic age as estimated from age-related DNA methylation biomarkers . Moreover, the discrepancy between chronological age and epigenetic age is used to calculate epigenetic age acceleration , where a higher value represents an older epigenetic age relative to one’s chronological age and vice versa.

Several epigenetic age and EAA metrics have been developed, including those by Horvath, Hannum, Levine, and Lu, and have been associated with multiple age-related outcomes, such as disease, physical functionality, and mortality. Lifestyle factors, such as alcohol consumption, tobacco smoking, physical activity, and diet, have been shown to accelerate or decelerate epigenetic aging relative to chronological age. For example, cumulative alcohol consumption was positively associated with EAA, whereas recent consumption exhibited inverse associations, suggesting possible difference in effects of cumulative and recent exposures on EAA. However, studies examining the effect of marijuana, both cumulative and recent use, on epigenetic aging remain limited. Given the limited data on marijuana age-related epigenetic changes, we investigated the association between marijuana and EAA in the Coronary Artery Risk Development in Young Adults Study, in which marijuana has been longitudinally collected.Marijuana use was obtained at baseline and at each follow-up examination by asking participants “Have you ever used marijuana?”, “About how many times in your lifetime have you used marijuana?”, and “During the last 30 days, on how many days did you use marijuana?” We considered four variables to capture cumulative and recent use of marijuana at Y15 and Y20. Two binary marijuana variables indicated if a participant has ever used marijuana and used in the last 30 days . A continuous variable quantified the number of days of marijuana use in the last 30 days . We also estimated a continuous variable capturing cumulative marijuana use, i.e. ‘marijuana-years’,vertical grow systems for sale as previously described. Briefly, we assumed marijuana use in the last 30 days refflected use during the time period between examinations, where a marijuana-year is equivalent to 365 days of marijuana use. We then estimated cumulative marijuana-years by summing the total number of days of marijuana use from baseline to Y15 and Y20 separately and dividing by 365.Lastly, Lu’s age, GrimAge acceleration , was estimated from 1,030 CpGs and is associated with lifespan. The DNA-methylation epigenetic age estimates were calculated using the publicly available online calculator . EAA was calculated from the residuals from a linear regression model for each epigenetic age regressed on chronological age.We observed positive associations between cumulative and recent marijuana use and GAA in young adults. We observed ever use of marijuana and each additional marijuana-year were associated with a 6-month and 2.5- month higher GAA average, respectively. Additionally, any recent use, which exhibited the largest effect estimate, and each additional day of recent use were associated with a 20-month and 1-month higher GAA average, respectively. We also observed statistical interactions between cumulative and recent marijuana use and alcohol consumption on GAA, with nondrinkers exhibiting a higher average in GAA compared to heavy drinkers. These findings provide novel insights into the association between marijuana use and epigenetic age acceleration as estimated by GAA. As a DNA-methylation-based measure of biological age, GrimAge is a composite biomarker of seven DNA methylation surrogates. Several of these surrogates of GAA have been associated with components of the endocannabinoid system, including leptin, GDF1, cystatin C, and PAI1. We observed similar, albeit weak, correlations between several GrimAge surrogate biomarkers of blood plasma proteins and marijuana in our study, suggesting the association between marijuana and GAA may occur through DNA methylation changes related to these specific plasma proteins. When comparing correlations between the GrimAge surrogate biomarkers of blood plasma proteins and marijuana use and cumulative packs of cigarettes, we note despite only a modest correlation between these variables , their correlations with surrogate biomarkers were generally consistent in direction but smaller in magnitude for marijuana use. This suggests marijuana and tobacco use may operate via similar pathways.

The associations between marijuana and GAA remained robust even after adjustment for cumulative packs of cigarettes, suggesting epigenetic age-related changes are independent of cigarette smoking. Additionally, the observed variation in associations between the four EAA metrics may be due to the methodological differences in the development of these measures, which capture different aspects of the aging process. Together, the current and previous results demonstrate marijuana may modulate DNA methylation-based surrogate biomarkers associated with lifespan and may negatively impact the aging process. Given the movement to legalize marijuana, interventions to limit marijuana use may aid in slowing the aging process and potentially, hinder age-related conditions and improve longevity. However, further studies examining marijuana and its effect on GAA and corresponding blood plasma proteins may provide new mechanistic insight into the molecular effects of this health-related behavior and its effects on disease risk. The magnitude of effect of marijuana on age-related epigenetic changes appear to differ by the period of exposure to marijuana. Although recent use of marijuana exhibited a three times greater gain in GAA compared to ever use of marijuana during GEE analysis, marijuana years exhibited a greater gain in GAA compared to the number of days of recent use, suggesting the large effect of recent exposure is also transient . This may reflect the pharmacokinetics of cannabis where plasma concentrations of metabolites, such as tetrahydrocannabinol, are highest after use and decrease over time. The higher concentration and rapid decline in blood tetrahydrocannabinol concentration with recent use may result in temporary epigenetic alterations that subsequently resolve over time. However, prolonged use may lead to the accumulation of marijuana metabolites in adipose tissue that are released into the blood and subsequently, exert sustained effects on blood DNA methylation. As such, behavioral modifications to limit use of marijuana may aid in limiting both short- and long-term impacts on the aging process as captured through DNA methylation. Marijuana is the most commonly used controlled substance among those who consume alcohol. We observed cumulative and recent marijuana use were associated with a higher GAA among nondrinkers compared to drinkers, who exhibited a smaller GAA gain with increasing alcohol intake. While these findings suggest statistical interactions between marijuana and alcohol, the biological mechanism for this interaction remains unclear. Alcohol consumption has previously been shown to increase cytokine production and subsequent peripheral inflammation and damage to organ, and cannabis may exert anti-inflammatory properties and mitigate inflammation from alcohol consumption, suggesting opposing effects of cannabis and alcohol on inflammatory pathways. Inflammatory marker IL-6 was previously found to be positively associated with alcohol consumption and further analysis identified a statistical interaction between alcohol consumption and marijuana use, where a significant positive association was observed among non-users and a non-significant negative association was observed among users, demonstrating marijuana may modulate inflammatory cytokines induced by alcohol. Studies have also observed cannabinoids may reduce alcohol-induced oxidative stress and autophagy related damage. In sum, our statistical findings are consistent with proposed mechanisms and findings demonstrating opposing effects of marijuana use in the context of alcohol consumption.

As expected in these models, average cigarette use, marijuana use, and alcohol use frequency were all significantly associated with likelihood to smoke cigarettes on a given day . Neither sex, age, ethnicity, nor source study were significant covariates, and their inclusion in the model had no effect on these reported outcomes. The present study was the first to examine event-level, daily patterns of co-use of marijuana, alcohol, and cigarettes in a sample of non-treatment seeking individuals. Similarly, any cigarette smoking increased the probability of same-day alcohol or marijuana co-use, and marijuana use also increased the odds of same-day alcohol or cigarette co-use. Additionally, we found generally additive effects of simultaneous co-use on the likelihood of using a third substance ; the co-use of alcohol with cigarettes and marijuana with cigarettes increased the odds of same day marijuana and alcohol use by over five times, respectively. When taken together, these results indicate that the use of either marijuana, alcohol, or tobacco substantially increases the probably of the co-use one of the two other substances, and if two of these substances are co-used, the likelihood of a using the third is further amplified. Our results may aid in the understanding of how simultaneous co-use of marijuana with alcohol and/or tobacco relates to the etiology, maintenance, and treatment of AUD, CUD, and tobacco use disorder . Our event-level findings that marijuana, alcohol, and/or cigarette use substantially increased odds of simultaneous co- and tri-use in non-treatment seeking,mobile vertical grow rack regular substance users support epidemiological data that describe highly prevalent concurrent and simultaneous couse of these three substances. The behavioral mechanisms underlying the relationship between alcohol and tobacco co-use have been well characterized and may be applicable to understanding co-use of each substance with marijuana.

As reviewed in detail elsewhere , the underlying motivation for simultaneous co-administration of alcohol and tobacco appears to be predominantly driven by cue-conditioned cross-reactivity, in which each substance elicits cue-induced craving for the other via Pavlovian conditioning, and the additive or synergistic reinforcing effects of the drugs when used in combination. The findings of the present study may suggest that individuals are simultaneously using marijuana with alcohol and/or tobacco due to similar mechanisms. Such motives for co-or tri-use would be consistent with the majority of preclinical and clinical studies examining the combined effects or patterns of co-use of marijuana with alcohol or tobacco. For example, tobacco and marijuana co-users have reported simultaneously using both substances because each drug increases craving for the other, tobacco enhances the subjective effects of marijuana, and simultaneous co-use produces additive subjective effects . Furthermore, the majority of molecular and behavioral pharmacology studies in rodents and humans suggest additive, or even synergistic, reinforcing as well as impairing effects of combined marijuana and alcohol . Interestingly, a recent study found that alcohol consumption was positively associated with being open to experiment with tobacco or marijuana co-use in different places and with different people, suggesting a contextual or social influence on couse in addition to the pharmacological factors discussed above . If the pattern of simultaneous co- and tri-use observed in this study is representative of a chronic behavior, we speculate that additive co-reinforcement and cue-cross-reactivity, as well as the likely development of cross-tolerance due to overlapping neurobiological effects , could lead to escalation of substance use to hazardous levels and underlie the development of comorbid or even trimorbid CUD, AUD, and/or TUD. This proposed progression would be consistent with epidemiological literature indicating that the simultaneous use of marijuana with tobacco or alcohol is associated with psychological and physiological harm, negative social consequences, high risk substance use, development of dependence, more severe dependence levels, and poorer treatment outcomes above and beyond both concurrent and single drug use .

Yet, there is a sizeable literature suggesting marijuana is sometimes used as a substitution for alcohol or cigarettes. Individuals who use marijuana concurrently with alcohol or tobacco report using marijuana in place of both drugs . Furthermore, cessation studies have also shown that as marijuana use declines, craving and use of alcohol or tobacco may rise, which indirectly supports a substitution pattern of use . Indeed, some have argued for marijuana to be positioned as a substitute for alcohol and other illicit drug abuse as a harm reduction strategy . Marijuana may have a superior safety profile to alcohol or tobacco , but the concept of drug substitution as a harm reduction strategy is predicated on the idea that use of the substituted drug decreases rather than increases the likelihood of target drug use. Although marijuana use strongly augmented the odds of same-day drug co-use in our sample, we also observed that the co-use of alcohol and marijuana was associated with a decrease in the odds of cigarette consumption compared with non-drinking days. One possible interpretation of this result is that individuals were substituting marijuana for cigarettes in this particular co-use event. Despite this single sub-additive result, our findings when taken as a whole suggest additive co-use effects and indicate further research of event level, simultaneous co-use in both treatment-seeking and non-treatment-seeking populations is needed before considering marijuana as a harm reduction strategy for AUD or TUD. Sex was a significant moderator of several of the observed patterns of co- and tri-use between marijuana, alcohol, and tobacco. The effect of alcohol and cigarette use independently increasing the odds of same-day marijuana co-use was stronger in men than women. This finding is broadly consistent with epidemiological data showing that men, vs. women, have higher rates of marijuana, alcohol, and cigarette use, start using these substances at a younger age,vertical grow rack use them in greater quantities, and have greater prevalence of dependence . More specifically, men have higher rates of marijuana co-use with each alcohol and tobacco and display a more rapid escalation in the frequency of this co-administration than women, both of which directly support the patterns of co-use observed in the present study .

Interestingly, while men had stronger relationships of single drug use predicting simultaneous marijuana co-use, women were more likely to have drug co-use turn into triuse. We observed that the odds of alcohol use after simultaneous cigarette and marijuana couse and marijuana use after cigarette and alcohol co-use were greater in women than men. An event-level pattern of tri-use such as this, i.e., with greater odds of progressing from simultaneously using two substances to co-using three substances in an event, could plausibly be related to more severe consequences from substance use in women even if they consumed less overall quantity than men. While men use marijuana, tobacco, and alcohol more heavily and have higher rates of dependence than women, women often experience more severe consequences from use. Some, but not all , studies have demonstrated that women display “telescoping” in the development of AUD and CUD. That is, while men have higher rates of the disorders, women tend to enter treatment for CUD and AUD after fewer years and quantity of use than men . Additionally, women are at greater risk for lost productivity, alcohol-induced blackouts, more severe neurocognitive impairment, brain atrophy, and a variety of physiological problems due to alcohol abuse despite drinking less and for a shorter amount of time than men . Sex differences in patterns of co- and tri-use could inform sex specific treatment and intervention of comorbid substance use disorders. However, given the exploratory nature of our comparison of sex differences and the paucity of studies that have included sex as a variable when examining event-level patterns of co-use, our sex-related results should be viewed as preliminary and are in need of replication in independent samples. As the original purpose for collecting the data that was analyzed in this manuscript was participant screening, and the analysis presented in this manuscript was ad hoc, there are several important limitations that should be considered when interpreting our results. The primary study limitation is the potential for low external validity due to the very specific composition of our sample. The recruitment goals of the four parent studies were in part to screen individuals who were regular-to-heavy-drinkers but who did not have other serious psychiatric disorders or medical conditions. Additionally, one of the parent studies only enrolled individuals of East Asian descent . The resultant sample in the present study is reflective of these parameters; that is, one with a higher than expected percentage of Asian Americans who are very hazardous drinkers, have low nicotine dependence, have borderline hazardous marijuana use, and report having no serious medical or psychiatric conditions. However, as outlined in the introduction, individuals from the general population who simultaneously co-administer alcohol, marijuana, and/or cigarettes on a regular basis would likely present with comorbid psychiatric disorders and serious health problems, and this may be especially true for treatment-seeking populations. Further, individuals of East Asian descent generally report lower alcohol consumption and have reduced risk of AUD development than other ethnicities , so it is potentially unlikely that this ethnic background would be responsible for 36% of the individuals who use alcohol, marijuana, and cigarettes in the real world. Although controlling for ethnicity in all analyses increases external validity and confidence in the presented results, it is still unclear how the presented results may generalize to both the general population of substance using adults as well as those seeking treatment for AUD, CUD, and/or TUD. Additional limitations may be related to the use of the TLFB to retrospectively assess patterns of drug co-use. When compared to same-day assessment, the use of the TLFB interview to retrospectively record drug use introduces a risk of recency bias . However, this recency effect appears to be mostly related to underreporting measurements of consumption levels rather than accuracy in dichotomously assessing whether any drug was consumed on a given day , which would mitigate any negative influence of recall bias on the present results. Also, because our standard procedures for TLFB administration was to assess marijuana use as a dichotomous “Yes/No” variable, no information was collected onthe route, formulation, or quantity of marijuana that was consumed on a given day. For example, in our data we have no ability to distinguish whether a single “hit” from a vaporizer, 30 mg of marijuana extract taken orally, or three entire blunts was consumed in a day; all could feasibly be coded identically in our dataset. Furthermore, while we do interpret the self-report of co-use within a day as simultaneous rather than concurrent use, we do not have data directly indicating that all substances were consumed during a single drug-use event. It is conceivable, albeit unlikely, that an individual would regularly use one drug in the morning and a second in the evening, for example. Yet, we believe we are warranted to interpret same-day co-use as simultaneous given prior findings indicating that poly drug users simultaneously co-administer drugs the far majority of the time and that marijuana is commonly self-reported as being used simultaneously with alcohol or tobacco . Lastly, although overall a clear strength of our study, our data only allows us to examine couse within a given day. Thus, we are unable to determine causal pathways underlying specific sequences of co-use, and future studies, for example with ecological momentary assessment methods, should examine the temporal relationship between marijuana, alcohol, and tobacco use within a given drug-use episode. Religion and spirituality play complex roles in the health of sexual minorities. For example, they may support positive coping with challenging life circumstances. However, many major religious traditions are non-affirming of same sex attractions and behaviors , thereby contributing to stigma and oppression that undermine the potential health and psychological benefits often associated with religion and spirituality. For example, one U.S. study found that exposure to religious prejudice was associated with negative health outcomes among sexual minorities, including higher levels of stress, anxiety, shame, harmful alcohol use, and more instances of experiencing physical and verbal abuse . Similarly, findings from systematic reviews and meta-analyses suggest that while some sexual minorities find social support and refuge in religious traditions, others report religious affiliation and religion as a source of stigma and stress .

It is important that consumers receive accurate information on whether or not a product is organic. The European Union prohibits the use of “organic” on any product label where less than 95% by weight of ingredients of agricultural origin are not organic, or any product produced with or containing genetically-modified organisms. Similar provisions are included in US product labeling law. Due to federal prohibition of marijuana in the United States, however, marijuana companies are not allowed to market marijuana products as ‘organic’. If federal prohibition is lifted, companies may use the term ‘organic’ in marijuana packaging and marketing to increase product appeal. More research is needed to understand how organic labeling on marijuana packages impacts risk perceptions and use, and what public health messages could counteract misperceptions of risk. Young adults may be receptive to health-related content on tobacco warnings and this could be translated to warning labels for hookah, e-cigarettes, and marijuana. While these findings should be validated with a larger sample, for many participants chemicals, toxins, and additives were associated with greater harm. Warnings for hookah, e-cigarettes, and marijuana could address chemicals and toxins found in the organic matter, as well as chemicals used to produce marijuana concentrates and e-liquids. Warning labels could address the misperception that a product is safer because it contains, or was extracted with, water. Warning labels reflecting the novel themes identified in this study might be more effective if they follow the current state of the art for tobacco warning labels, including use of images in addition to text,ebb flow tray and if warning label messages are reinforced by mass media educational campaigns. The opioid epidemic is a major public health concern in the United States . The number of opioids prescribed in 2015 was approximately three times as high as in 1999 . At least 11.8 million adolescents and adults misused opioids, and 2.1 million had an opioid use disorder , in 2016 . The opioid-related hospitalizations increased by 64%, and emergency department visits doubled, in 2005-2014 .

Over the past decade, concerted policy efforts have been made to restrict the prescribing of opioids . Expanding access to effective treatments for OUD is essential to reduce its burden . Historically, medications for treating OUD, such as methadone and buprenorphine, were provided only in opioid treatment programs, and, therefore, only a fraction of patients were willing and able to access these medications . To expand the clinical ability to treat OUD, the US Drug Addiction Treatment Act of 2000 waived the requirement of obtaining a Drug Enforcement Administration registration as an opioid treatment program for physicians providing buprenorphine treatment in their offices. Physicians can acquire DATA-2000 waivers if they had a board certification in addiction medicine or psychiatry or completed required training . Since 2010, there has been a dramatic increase in the number of DATA-2000 waivered providers . These providers might be more likely to begin prescribing buprenorphine in areas with higher opioid-related mortality rates . It was hoped that expanding the capacity of buprenorphine treatment could improve access to OUD treatment. The expansion of buprenorphine treatment affected opioid-related outcomes at the population level has remained unexplored. Parallel with the opioid epidemic, marijuana legalization has expanded throughout the US. As of November 2018, in addition to the District of Columbia, 33 states have legalized marijuana use for medical purposes, 10 of which further legalized marijuana use for recreational purposes. First, marijuana use may exacerbate opioid use. Second, marijuana use may substitute for opioid use . The rationale for the first hypothesis was that marijuana may precede use of opioids, and individuals who used marijuana may share risk factors with individuals who used opioids . As demonstrated by a cohort study, recreational marijuana use was associated with increased likelihoods of opioid misuse and OUD . But the data of this study were collected before any states have legalized recreational marijuana use.

The evidence on the impact of state recreational marijuana laws on opioid-related outcomes remained scarce, and no positive associations have been documented . The rationale for the second hypothesis was the potential therapeutic effects of cannabinoids and smoked marijuana on pain symptoms, which were supported bysystematic reviews of randomized controlled trials . Chronic or severe pain was, therefore, the most commonly approved condition in the states that legalized medical marijuana. Several ecological studies consistently suggested that state-wide medical marijuana laws were associated with considerable reductions in opioid prescriptions, misuse, overdose deaths, and related hospitalizations at state level . However, these ecological studies above were not supported by a recent individual-level prospective cohort study in Australia which found no evidence that marijuana use was associated with reduced opioid use among pain patients . But in this study, the majority of participants used illicitly obtained marijuana. It is still unknown to what extent the findings can be generalized to the current legal environment in the US. The availability of marijuana dispensaries and DATA-2000 waivered providers varied substantially across neighborhoods within a state, but its associations with opioid-related outcomes in a neighborhood was unknown . To fill the knowledge gap, we examined the associations of neighborhood availability of marijuana dispensaries and DATA-2000 waivered providers with hospital stays related to opioids, using hospital records from January through June in 2016 in Washington. We hypothesized that the availability of recreational and medical marijuana dispensaries was associated with a higher and lower risk of hospital stays related to opioids, respectively. According to availability theory, increased access to marijuana may lead to increased marijuana use among the local population .

Thus, increased availability of recreational marijuana dispensaries may result in increased marijuana use for recreational purposes which may lead to increased opioid or OUD-related health outcomes, while increased availability of medical marijuana dispensaries may results in elevated marijuana use for medical purposes which may lead to alleviated opioid or OUD-related health outcomes. We also hypothesized that the availability of DATA-2000 waivered providers was associated with a lower risk of hospital stays related to opioids. According to the Andersen’s behavioral model of health services use, individuals living in areas with more available health care resources were more likely to visit a provider . One study reported that living in neighborhoods with more DATA-2000 waivered providers was associated with an increased likelihood of being treated with buprenorphine for OUD . Thus, increased availability of DATA-2000 waivered providers may lead to improved opioid- or OUD-related health comes through more accessible OUD treatment. To analyze the potential differential associations with recreational and medical marijuana dispensaries,flood and drain tray we took advantage of the unique policy context in Washington in early 2016, a time when recreational marijuana and medical marijuana dispensaries coexisted. Washington passed the laws to legalize medical marijuana in 1998 and recreational marijuana in 2012. Before recreational marijuana was legalized, medical marijuana dispensaries in Washington largely operated without regulations. Unlike other states such as Colorado that built its recreational marijuana industry and regulations on top of the existing medical marijuana system, Washington chose to abandon its medical marijuana system and start recreational marijuana regulations from scratch. In 2015, Washington passed the Cannabis Patient Protection Act requiring that all marijuana dispensaries operate as licensed recreational marijuana dispensaries and obtain a medical marijuana endorsement if they opt to specialize in medical marijuana . As a result, between July 2014 when the first recreational marijuana dispensary opened and July 2016 when SB 5052 took effect, the old medical marijuana dispensaries that exclusively served medical marijuana patients and the newly licensed recreational marijuana dispensaries that might serve both patients and recreational users operated at the same time in Washington. This is a cross-sectional ecological study using secondary de-identified data, and the ethics approval and consent were not needed.

We obtained inpatient and observation stay discharge records in all the community hospitals between January 1, 2016 and June 30, 2016 from Washington Comprehensive Hospital Abstract Reporting System administered by the State Department of Health. The records included detailed information on patient demographics, zip code of patient’s home address, as well as up to 25 International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis and procedure codes. Patients younger than 12 years of age or living outside of Washington were excluded from the analyses. The final study sample included 264,013 inpatient stay records and 12,621 observation stay records. Directories and point locations of marijuana dispensaries with physical storefronts in Washington were obtained between March and June in 2016 from a crowd sourced website . Weedmaps provides detailed and up-to-date dispensary information contributed by dispensary owners and users. Its data have been validated and used in previous research . Notably, each dispensary on weedmaps self reports whether it is a medical or recreational marijuana dispensary. This is the only source to differentiate recreational and medical marijuana dispensaries during our study period, as official records for medical marijuana dispensaries were not available until they were regulated in July 2016. Directories and point locations of DATA-2000 waivered providers in Washington were obtained in August 2016 from the Substance Abuse and Mental Health Services Administration. Tobacco and alcohol outlet locations were obtained from business list provider reference USA and other contextual factors were obtained from the US Census and the American Community Survey.The patient-level outcome variables were opioid related hospital stays, including inpatient stays and observation stays. Inpatient stays were hospital stays after patients were formally admitted to a hospital. Observation stays were short-term hospital stays for patients who were not well enough to go home but not sick enough to be admitted right away. Observation stays usually lasted for less than 24 hours and rarely exceeded 48 hours. Patients were either discharged or admitted as inpatients after observation stays. In CHARS, if a patient was transferred to inpatient care after an observation stay, this patient would only be recorded as an inpatient. In other words, observation stay discharge records in CHARS captured patients who were discharged after observation stays. To construct opioid-related hospital stays, we first used ICD-10-CM diagnosis codes to identify OUD and opioid overdose . A hospital stay with OUD or opioid overdose in all-listed diagnoses, including principal diagnoses as well as secondary diagnoses, was defined as an opioid-related hospital stay. Accordingly, three dichotomized indicators were created to represent inpatient stays involved with OUD, inpatient stays involved with opioid overdose, and observation stays involved with OUD. Observation stays involved with opioid overdose were not analyzed because of insufficient sample size. The primary explanatory variables of interest were the availability of marijuana dispensaries and DATA-2000 waivered providers in a neighborhood defined by zip code tabulation area . Measures for recreational and medical marijuana dispensaries were constructed separately. All the point locations were geocoded using ArcGIS and aggregated to zip code level. Availability was measured by the density of marijuana dispensaries or DATA-2000 waivered providers per square mile. In sensitivity analyses, we altered the operationalization of primary explanatory variables to test the robustness of our results. First, we used the total density of recreational and medical marijuana dispensaries. Second, we used three dichotomous variables indicating the presence of any recreational marijuana dispensaries, medical marijuana dispensaries, or DATA-2000 waivered providers because the majority of zip codes did not have any of them. Third, we used three categorical variables to represent 0, 1, and 2+ recreational marijuana dispensaries, medical marijuana dispensaries, or DATA-2000 waivered providers in a zip code, as few zip codes had more than two of them. Patient-level covariates included age , sex , primary payer , and race/ethnicity . Zip code level covariates included proportion of population under age 21 , whether the population were predominantly racial and ethnic minority , median household income in thousand dollars of 2016, number of tobacco and alcohol outlets per square mile, and population density . The descriptive and regression analyses were conducted in STATA 14 . We conducted multilevel logistic regressions with random intercepts at the zip code level to examine the associations of the availability of DATA-2000 waivered providers and marijuana dispensaries with opioid-related inpatient or observation stays, controlling for other patient and neighborhood covariates. Multilevel models were used to account for within-neighborhood correlations, as patients nested within zip codes shared the same zip code level explanatory variables of interest and covariates.

The George Washington University Committee on Human Research, Institutional Review Board determined that this study did not meet the definition of human subjects research. Table 3 presents the predicted probabilities of past-month marijuana use at each level of risk and by reasons for use. We also present marginal effects, which represent the change in the probability of each outcome as the risk factors change, while holding all other characteristics constant. Adjusted relative risk ratios from the multinomial logistic regression model are presented in supplemental Table S3 in the online version of this article. Overall, the average predicted probabilities of marijuana use for medical, recreational, and both reasons were 28.6%, 38.2%, and 33.1%, respectively. Several covariates were associated with marijuana users’ reported reasons for use including, but not limited to, gender, age, race, education status and employment. Associations between both health status and frequency of use and reasons for use were large in magnitude and statistically significant. For example, respondents who reported 14+ versus no days of poor mental health had significantly increased chances of reporting marijuana use for medical reasons or both reasons and a significantly decreased probability of reporting recreational reasons . This same pattern—that is, greater probabilities of reporting medical reasons or both reasons among those in poor health—was also evident for physical health. Marijuana users who reported daily use had a 6.3% increased probability of reporting medical reasons and a 15.6% increased probability of reporting both reasons. Daily users had a significantly reduced likelihood of reporting recreational reasons for use . Estimates from the logistic regression model showed similar patterns. See Supplemental Table S4 in the online version of this article.Among respondents in fully legal states, the chance of reporting recreational reasons was 5.5% lower than in illegal states ,grow table hydroponic but the chance of reporting both reasons was 7.0% higher . While the predicted probability of reporting medical reasons was lower with states’ liberalization of marijuana, differences were not statistically significant. Our user profiles confirmed these patterns .

We deliberately modeled illustrative profiles possessing characteristics associated with medical and recreational reasons for use based on our results: an older woman in poor mental and physical health who is a daily marijuana user and a young adult male who is an infrequent marijuana user and a binge drinker. In the first case, we found that the average predicted probabilities of reporting medical, recreational, and both reasons did not significantly vary by states’ legal environments. For the second user profile, we found that the predicted probability of reporting recreational reasons was 5.3% lower in a fully legal state than in a fully illegal state. Conversely, the predicted probability of reporting both reasons was 5.0% higher in a fully legal than in an illegal state. The difference in the probabilities of reporting medical reasons in legal versus illegal states was not statistically significant.Our study makes two, unique contributions. First, we estimated the change in probability associated with incremental changes in risk factors on each reason for use and created user profiles to illustrate these relationships. Second, we used three years of BRFSS data to examine marijuana users’ reasons for use, data which we do not believe has been previously used for this purpose. We found that the prevalence of past-month marijuana use in our sample was 11.2%, which was similar to, but not the same as, rates reported from other U.S. sample surveys. For example, two studies based on BRFSS data from 2016 and 2016-2017, respectively, found prevalence rates of 9.1% and 13.6% . The National Survey on Drug Use and Health reported rates of past-month marijuana use among persons aged 12 or older from 11.2% in 2017 to 13.0% in 2019 , 2018, 2019, 2020. Findings from a study that used 2005-2018 National Health and Nutrition Examination Survey data reported a 14.4% past-month marijuana prevalence rate . Differences in these estimates could be attributable to time trends, study inclusion criteria, and/or differences in each survey’s design and data collection procedures.

Like other studies , we found that adults were most likely to report recreational reasons for use followed by both reasons and medical reasons . The characteristics we identified as being associated with marijuana users’ reasons for use—gender, age, race, education, health status, and frequency of use—also comport with prior research . Consistent with prior studies , we found that medical marijuana users were less likely to report smoking marijuana and more likely to eat/drink, vape, and use other routes of administration. This pattern is consistent with evidence, which has found that vaporization is a commonly used mode of delivery among medical marijuana users because of its relative health advantages over smoking and the flexibility, portability, efficiency, and ease of use that accompanies vaporization devices . It is possible that eating/drinking offers similar benefits. Additional studies are needed to explore this phenomenon. We found that being a woman increased the odds of reporting medical reasons for past month marijuana use . While prior studies have found that women were more likely to report using marijuana for medical reasons compared to recreational ones , in only one of these studies was the difference statistically significant . Our findings complement a recent study , which explored gender differences in medical marijuana use and found that women were more likely than men to use medical marijuana for a variety of symptoms including pain, anxiety, and nausea—conditions which commonly qualify patients for medical use . Finally, we found that past-month marijuana users who reported medical reasons for use were more likely to be older and in poorer health, and they were more likely to be daily users. Prior studies have produced similar findings . Notably, we also found that marijuana users who reported using for both medical and recreational reasons were the most likely to be daily users. Because the frequency and quantity of marijuana consumed have been associated with marijuana dependence and other adverse effects , persons who report using for medical and/or both reasons could bear greater risks. Studies that compare daily and intermittent marijuana users’ reasons for use and adverse outcomes, and which draw on diverse populations, are needed to better understand these relationships and optimize generalizability .

Our findings regarding the association between legalization and marijuana users’ reported reasons for use were unexpected. We found that in fully legal states , the predicted probability of reporting recreational use was significantly lower while the probability of reporting both reasons was significantly higher. While these findings are counter intuitive, restrictive recreational marijuana laws and higher tax rates incentivize medical use . These forces could drive recreational users, especially those who use marijuana to self-medicate , 2020c, towards using and reporting medical reasons or both reasons for use, which might explain our findings. Alternatively, states that have legalized marijuana for recreational use tend to hold liberal positions on other issues and attract residents who share those values. It is entirely possible that persons who hold such liberal values are more likely to view marijuana favorably, recognize its therapeutic benefits,grow table and attribute at least some of their use to a medical need. Our findings should be placed within the context of data and study limitations. BRFSS data are self-reported, which could introduce reporting bias. While we combined the three most recent years of BRFSS data, we effectively limited our sample to 20 states and 18,925 marijuana users. This diminishes the generalizability of our results. We know that marijuana prevalence varies by state , 2020c, at least in part, because of states’ policies regarding marijuana use, possession, and sales . If the subset of states that opted to include the BRFSS marijuana module also had different use rates or reasons for marijuana use, confounding could have been introduced. Further, we excluded 294,751 respondents from our sample . These respondents differed from those retained on many characteristics. For example, respondents in our study sample were more likely to be male, older, report using tobacco and binge drinking and less likely to be married. If this same pattern existed for the variables of greatest interest, our estimates could be biased.Additionally, the BRFSS’ asks respondents about their reasons with three responses: medical, recreational, or both. In reality, marijuana users’ reasons for use may be more nuanced; users might not fit “neatly” into these three categories. For example, some may view marijuana as having general health benefits—helping with relaxation or enhancing wellness. This is supported by studies, which have found users describing marijuana as a “natural” alternative to or substitute for other prescription medications and the marijuana industry’s marketing of it as a lifestyle product . In these cases, it is unclear how a user might answer the BRFSS question. Further, the BRFSS does not ask respondents about several potentially important predictors of marijuana users’ reasons for use, including quantity, duration of use or dose. Our coding of states’ policy environments as a categorical variable could be an imprecise reflection of how states’ policy environments influence consumption patterns . Given that local government entities have enacted additional policies that further regulate marijuana markets, studies that also account for local policies and other factors such as retail availability of marijuana are needed. Finally, our analysis offers insight into the correlates of reasons for marijuana use, but because of the BRFSS’ cross-sectional nature, causal inferences cannot be made.

Recent national data showed that 27.1% of high school students reported past 30-day use of any tobacco1 and 19.8% reported past 30-day use of any marijuana.Furthermore, several studies indicated that past 30-day co-use of two substances was higher than use of either tobacco or marijuana only among adolescents,and higher than use of marijuana only among young adults.The use of these substances during neurodevelopmental stages exposes AYA to numerous adverse health consequences and societal impacts .Understanding tobacco and marijuana co-use among AYA has become more important given the proliferation of new products. The tobacco landscape has shifted from conventional cigarettes to cigars/cigarillos, hookah, smokeless tobacco, and more recently, e-cigarettes.Marijuana is also available in a variety of combustible , vaporized , and edible products .Co-use can refer to use of both substances separately across the aforementioned products during the past 30 days, or at the same time or in the same product, such as with blunts .Recent studies have demonstrated a transformation of tobacco use patterns with the use of non-cigarette products now surpassing conventional cigarette use among adolescents.In contrast, despite the appeal of newer marijuana products, combustible marijuana remains the most common form used across all age groups.Therefore, it is critical to characterize tobacco and marijuana co-use in light of specific products. Past research on tobacco and marijuana co-use among AYA has predominantly focused on examining the overall relationship between tobacco and marijuana using “blanket terms” , or only co-use of combustible forms and blunts.Newer co-use research among AYA has taken into account specific products.However, these studies have examined only the co-use of individual tobacco products with “any marijuana,” which limits our understanding on specific marijuana products co-used with tobacco. Only two recent studies have assessed couse of specific products for both substances. One study among 1,420 high school students compared past 30-day use of cigarettes, cigars, hookah, and e-cigarettes between current blunt and combustible marijuana users.This study, however, focused on only combustible forms of marijuana; thus, it did not fill an important gap in the literature on alternative forms of marijuana .The other study among 2,668 adolescents assessed the relationship between previous use of tobacco products and subsequent use of marijuana products .Although this study provided more insights by including noncombustible marijuana products, it did not directly provide data on past 30-day co-use of tobacco and marijuana products, and more importantly, did not include a sample of young adults, a group with the highest risk of co-use relative to other age groups.National data indicated co-use prevalence among young adults in 2014–2015 was 21.3%,4 and nearly half of adult co-users were between 18–25 years old.To our knowledge, there has been no research among young adults considering specific products for both tobacco and marijuana. Given the current era of marijuana legislative reform nationwide as well as the proliferation of proposed national and state tobacco regulations, more research on co-use of tobacco and marijuana is needed to inform these actions.

To continue, when participants were asked about whether or not they have noted marijuana recovery programs within their counties or the state, they responded that they have not heard of marijuana recovery programs nor have they seen visual representations/advocacy associated with these programs. Because the respondents conveyed that they were unaware of marijuana recovery programs within South Central LA, this finding suggests that the existence marijuana use recovery programs have been obfuscated not only due to the acceptance of marijuana legalization, but also due to ineffective community educational programming tailored to the promotion of marijuana recovery programs in South Central LA. It was implied that more visual flyers and media promotion must be available to shed light onto current highly regulated community educational programming directed towards marijuana mis-use prevention/recovery strategies. In alignment with Goldstick’s argument, education in the form of disseminating “health outcomes” is crucial in informing decision making/intervention strategies. Dissemination of health outcomes through increased production of visual aids may establish presence of these interventions, while reinforcing their benefits in lowering marijuana dependency. It can also be inferred that ineffective community educational programming may not only hinder the development of these intervention strategies, but also may create nebulousness around substance mis-use recovery program compilation as well as subsequent dissemination. For adults, the implementation and uncovering of medical marijuana education programs, assisted in the reduction of adult marijuana mis-use instances, in comparison to loosely regulated marijuana prevention programs. Consequently, the presence of tight marijuana educational programming, whether its medical marijuana education or marijuana mis-use prevention,mobile vertical grow tables can be beneficial in lowering youth ingestion of marijuana alongside decreasing their inclination to access marijuana within outlets.

With this study finding bolstered by Goldstick’s and Lemstra’s argument as well as supported by Williams, it further coalesces with the research question: “can effective educational programming spur awareness on marijuana mis-use rates amongst adolescents of color, while emphasizing the presence of marijuana mis-use recovery initiatives amidst the indoctrinating marketing behind marijuana outlet formation and decreasing youth consumption of marijuana?”Towards the end of the focus group discussion, respondents provided their opinions on types of community educational programs which could be beneficial towards educating youth communities of color on the dangers of marijuana mis-use as well as challenge the effects of social disorganization factors such as lacking educational programming materials which could prompt increases in marijuana mis-use rates. Specifically, respondents proposed that increased community intervention programming, related to long-term deleterious effects of marijuana as well as marijuana outlet development prevention, that are implemented in middle schools and high schools may steer youth from mis-using marijuana. These programs that were suggested by respondents were also said to be effective in encouraging middle/high schoolers to turn to marijuana recovery programs if addiction behaviors prevail. This finding suggests that effective educational programming in the form of informative presentations with subtopics such as short/long-term effects of marijuana, perceptions behind marijuana use, as well as statistics that show target groups mostly affected by marijuana use, can not only educate youth on marijuana mis-use prevention, but also it can prevent marijuana outlet construction and sensationalized marketing towards youth. Continuing this, according to a systematic review conducted by Lemstra, researchers unraveled that school-based marijuana prevention programs helped adolescents reduce marijuana usage per month by 7 days.

This result is validated by Lemstra’s argument and further implicates that effective educational curricula development tailored to youth attunement on marijuana mis-use can potentially attenuate the likelihood of youth accessing marijuana outlet concentrations within South Central LA. When I attended Drug Take Back Day in South Central LA, SCPC volunteers and I encouraged event attendees to fill out a survey that asked them to strongly agree or disagree with statements related to the effectiveness of these events as a means to shed light on increased substance mis-use practices. Ninety percent of survey respondents who participated in active prescription drug disposal and who filled out the survey agreed that safe drug disposal events are beneficial for the community. This result suggests that the creation of events, similar to the engaging Drug Take Back Day event, can be developed to not only incorporate marijuana mis-use education/safe substance prevention practices in South Central LA, but also empower attendees to resist adverse drug marketing/outlet construction community indicators. Ultimately, this ties into the research inquiry presented above in that this finding provides support for whether the effectiveness of educational programming in a community event format can dismantle substance mis-use behaviors spurred by social disorganization indicators. Within the study data collection tools and methodology, study limitations were identified. During the focus group interview, eight individuals participated in this interview and received a monetary award for their participation. To strengthen evidence for the research question as well as effectively seal the gaps presented in the literature review, the focus group interview could have included a total of 12-15 individuals and more focus group interviews could have been conducted if time permitted. Additionally, the decreased quantity of focus group interviews tailored toward the exploration of increased marjiuana mis-use as well as contributing causes to marijuana mis-use amongst adolescents, posed as a limitation to this community engaged research.

This study utilized one focus group interview session that spanned 2 hours, however, if more focus group interviews were conducted with different sets of 12 participants each, then the credibility of the results as well as the application of the results to the research goals could have been improved. By including a few more participants and focus group interview sessions, more responses to the questions asked could have been analyzed and validated the use of the data tools in corroborating research goals as well as the overarching research question. Lastly, during the Drug Take Back day event, program evaluation surveys were devised and administered to participating individuals within South Central LA. With only nine participants filling out the Drug Take Back Day survey pertaining to the disadvantages and advantages in holding the Drug Take Back Day structured event,mobile vertical farm this small sample size overall alongside limited outreach about the event to community members may present another limitation to this community engaged research project. Therefore, increasing the sample size from 9 to around 50 participants may authenticate participants responses to questions pertaining to whether the Drug Take Back day event goals/practices or events similar to this were beneficial as well as may substantiate whether general perceptions suggest that more events similar to Drug Take Back day construction will sideline the historically based propulsion of substance outlet development. Future research that could be undertaken would be creating surveys before and after marijuana mis-use presentation materials to further record whether participants agreed with statements regarding marijuana mis-use before/after the presentation as well as evaluate whether they were/are aware of the information presented. Additionally, the responses from these surveys can be used to substantiate community programming/marijuana prevention programming development within South Central LA. Further research can also investigate strategy based proposals in regards to reducing the prevalence of marijuana outlet presence/development in South Central LA. Data collection tools that may be used for this would be focus group interviews, participant observation, and literature tables which can be coined to highlight current perceptions on marijuana outlet presence in middle school/high school areas as well as review policy rhetoric that underscores marijuana outlet development permissibility. Lastly, as the open-coding process was completed and yielded substantial themes for discussion, a few themes were omitted from the study as they weren’t affiliated with the research topic nor did they directly contribute to the address al of the gap. Themes from this study that could be further explored through outlet density mapping and interviewing are: pop culture promotion and commercialization of marijuana. Next steps include further collaborating with my community partner to contest potential data limitations by setting up more focus group interviews with disparate respondent choice, creating program evaluation surveys for Drug Take Back day which consists of multiple questions regarding program structure areas of strengths/weaknesses instead of just 1-2 questions for a larger audience, and generating eclectic substance mis-use education materials which address substance use misconceptions/community social disorganization causes.

Ultimately, these steps can be enacted to ascertain which programming materials have the potency to combat historically known causes that propel substance mis-use and whether these materials can effectively implore individuals to abstain from substance use/refer individuals to substance prevention programming. TOBACCO USE, INCLUDING CIGARETTE smoking, remains the single most preventable cause of morbidity and mortality in the United States, accounting for approximately one in five deaths, or 440,000 deaths per year . In the United States, the prevalence of cigarette smoking has declined among adults since 1983. However, the cigarette smoking prevalence among young adults ages 18–25 years has remained stable, with 34% reporting having smoked in the past month in 2010 . More than 90% of cigarette smokers become regular users before age 18 . Tobacco industry documents reveal that young adults ages 18–24 years constitute the largest segment of targeted tobacco-marketing efforts , which are associated with smoking initiation . Compared with other age groups, young adults are less likely to use behavioral or pharmacotherapy interventions for smoking cessation . Marijuana is the most commonly used illicit substance among young adults, with approximately 19% of those ages 18–25 reporting marijuana use in the past month . Rates of marijuana use among young adults have increased since 2008 and are highest among those ages 18–25, compared with any other age group. Young adulthood is an important developmental stage for understanding use patterns of cigarettes and marijuana .In 2009, 35% of cigarette smokers ages 18–25 had used marijuana in the past month, almost three times the rate of the general adult population . Tobacco use has been implicated as a gateway drug to the use of marijuana and other illicit drugs . Cigarette smoking is associated with the initiation and the extent of marijuana use in young adulthood. One study demonstrated that young adults ages 18–25 are 10 times more likely to have ever used marijuana if they also have a history of smoking cigarettes . Research also documents a reverse gateway effect, whereby those who smoke marijuana in early young adulthood are more likely to initiate tobacco use and to have a greater likelihood of developing nicotine dependence than their nonsmoking peers . Those who go on to have problems with the use of illicit drugs, including but not limited to marijuana, are more likely to be heavy smokers, to be nicotine dependent, and to experience greater difficulty with quitting smoking . The mechanisms by which tobacco and marijuana use are related include shared genetic factors, a similar route of administration , and co-administration . Possible manifestations of such commonalities in use are a substitution effect, whereby using marijuana causes smokers to smoke fewer cigarettes than they otherwise would have, or a facilitation effect, whereby smoking marijuana increases the intensity of use and is associated with reduced motivation, reduced abstinence goals, and increased barriers to quitting tobacco. For example, in one study among college students who reported smoking both marijuana and tobacco, 65% had smoked tobacco and marijuana in the same hour, and 31% reported that tobacco prolonged and sustained the effects of marijuana . Additionally, a qualitative study reported that youth were most likely to relapse to tobacco use while smoking marijuana . A recent review of clinical outcomes of tobacco and marijuana co-use found that, relative to tobacco use only, co-occurring use was not associated with a greater likelihood of tobacco use disorder, psychosocial problems, or poorer tobacco-cessation outcomes . However, many of the studies reviewed included marijuana use as a dichotomous variable , limiting the ability to detect a relationship between heavier marijuana use and tobacco use outcomes. As others have noted, most substance use interventions target risk behaviors individually . However, interventions that have targeted tobacco use in the context of treatment for other substance dependence have demonstrated significant post treatment effects on tobacco use and even improved long-term sobriety . Given the high rate of tobacco and marijuana co-use among young people, information is needed on patterns and processes of tobacco and marijuana use to determine the best way to tailor interventions to this population.

As marijuana use was considered illegal for most yearly examinations in CARDIA, use may have been under reported. However, at each examination, marijuana use was self-reported , collected at a research site , and participants’ responses were confidential. The route of administration of marijuana can also affect the onset, intensity, and duration of the psychoactive effects, as well as organ systems. Investigations into marijuana use via other routes of administration may provide novel additional insights, including the latter, which was not present during the time points in the current study but is becoming more widely used. Additionally, this study examined acute exposure to marijuana , compared to hyperacute exposure and investigations into DNA methylation changes due to hyperacute exposure may provide further insight into the acuity of exposure on epigenetic factors. And lastly, although CARDIA is a diverse cohort, Black and White participants were sampled from four centers across the US. As such, additional studies from more diverse populations across different geographical locations will enable for better generalizability of the findings presented here.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,indoor weed growing accessories 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 KnowledgePanel —a nationally representative panel of civilian, non-institutionalized US adults aged 18 years and older that has been used to survey public opinion since 1999 .

GfK created a representative sample of US adults by random sampling of addresses . The address-based sampling covers 97% of the country and encompasses a statistical representation of the US population. Households without internet access are provided with an Internet connection and a tablet to ensure participation. All participants in the panel are sampled with a known probability of selection. No one can volunteer to participate. 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. All analyses were performed with R statistical software .

The response rate of the survey was 56.3% and did not vary by status of legalization in state of residence . The rate of missing or refused questions ranged from 0% to 3.9%. The sample was 52% female, 64% white, 12% black, 16% Hispanic, and 8% other race with a mean age of 48 years. Residents of the 3 state types did not differ by age. The residents of recreational states were predominantly white and less diverse than other state types . The residents of recreational states had higher rates of education and higher income levels compared with other state types. Sociodemographic characteristics of the respondents were largely similar to those of NSDUH,cannabis trimming though our sample had a slightly higher average income .Overall, residents of states where marijuana was legalized for recreational purposes were more likely to endorse the belief that marijuana had benefits compared with residents of other states . Specifically, residents in recreationally legal states were more likely to believe marijuana could be beneficial for pain management ; provide relief from stress, anxiety, or depression ; and improve appetite . Pain management was endorsed as the most important benefit regardless of state of residence . Residents of nonlegal states were more likely to endorse the belief that marijuana had no benefits compared with those in recreationally legal states . Multivariate analyses confirmed that residents of recreational states were less likely to believe marijuana had ‘‘no benefits’’ and more likely to believe that marijuana use had benefits in pain management, helped with reducing or stopping other medications, provided relief from stress, anxiety, and depression, improved sleep and appetite, and improved creativity compared with residents of medical and nonlegal states after adjusting for baseline characteristics .The belief that marijuana use was associated with the development of addiction was similar across states . Residents of recreational, medical, and nonlegal states all endorsed addiction as the most important risk associated with use . Multivariate analyses revealed that residents of recreational states were more likely to believe that marijuana use impaired memory, and also caused a decrease in intelligence and energy compared with residents of other medically legal and nonlegal states after adjusting for baseline characteristics .Residents in recreational states were significantly more likely to believe that smoking one marijuana joint a day is somewhat or much safer than smoking 1 cigarette a day . Residents of recreationally and medically legal states were more likely to believe second-hand marijuana smoke was somewhat or much safer than second-hand tobacco smoke .

Opinions regarding other relevant public health concerns were largely similar across states: most residents, regardless of legal status in state of residence, agreed that it is unsafe for children and adults to be exposed to second-hand marijuana smoke, and that marijuana use was unsafe for pregnant women. Multivariate analyses confirmed that residents of recreational states were more likely to believe that smoking 1 marijuana joint a day was safer than smoking 1 cigarette a day compared with residents of other medically legal and nonlegal states after adjusting for baseline characteristics . Residents of recreational states were also more likely to believe second-hand smoke from marijuana was safer than second-hand smoke from tobacco compared with residents of other medical and nonlegal states after adjusting for baseline characteristics .In this national study, we found that residents of states that had legalized recreational marijuana use more commonly attributed some benefit to marijuana than residents of medically legal or nonlegal states. We also found that the perception of risks from marijuana use was similar across states. In addition, we found that residents of states where marijuana was legalized were more likely to believe that marijuana smoke was less harmful than tobacco smoke. Finally, use of all forms and multiple forms of marijuana was more common among residents of recreationally legal states. Several national surveys, including the NSDUH and MTF, assess individual risk perception of marijuana use among national samples, and recent research suggests that risk perception has decreased nationwide . Previous research demonstrates that marijuana legalization is associated with decreases in risk perception, as evident from studies examining California pre and post medical legalization in 1999 . More recent research supports this assertion , and while research into the role of recreational legalization specifically is limited, initial data in adolescents suggest recreational legalization has been associated with a considerable decrease in risk perception . While such surveys have adequately examined the decrease in risk perception associated with marijuana, there exists no detail on the types of risks individuals associate with marijuana use or potential benefits individuals assign to marijuana use. Our results show that residents of states where marijuana has been legalized for recreational use have an overall more favorable view towards potential benefits of marijuana use and were more likely to attribute benefits to marijuana use that are not supported by evidence. For example, a majority of respondents endorsed pain relief as a benefit of marijuana use, despite only limited evidence supporting its effect in managing chronic neuropathic pain and no evidence in treating other types of chronic pain . There is no evidence currently available that suggests second-hand marijuana smoke is safer than tobacco smoke and some evidence suggesting it is toxic . When taken in context with previous research demonstrating the decrease in risk perception associated with marijuana use, our findings are significant as they illustrate the need for targeted public health campaigns to combat misinformation specifically in states with recreational marijuana legalization. We found that residents of recreationally legal states expressed less concern regarding second-hand marijuana smoke compared with second-hand tobacco smoke, and were more likely to believe that smoking marijuana is somewhat or much safer than smoking tobacco. These differences in perception are concerning, given the evidence that inhalation of particulate matter in any form is associated with increased cardiovascular risk . The perception that marijuana smoke is relatively safe compared with tobacco smoke has been perpetuated by the spread of inaccurate information on the internet . Some highly frequented internet sites suggest that smoking marijuana has many health benefits, such as improvement of lung health or the slowing of Alzheimer symptoms . There is currently no data to suggest that smoking marijuana improves lung health. On the contrary, recent evidence demonstrates smoking marijuana is associated with coughing, wheezing, and sputum production .