Organic farms make up 6.5% of California dairies and are overrepresented in our sample ; however, this overrepresentation has the advantage of providing more accurate estimates for management practices from this sector of California’s dairy industry. The mean percentage of calves housed individually on dairies in our study of California dairieswas similar to the proportion of dairies that reported housing calves individually in the survey of California dairies by Love et al. , but larger than the mean nationally . Given the similar order of magnitude in breed distribution and other factors, such as organic status and individual calf housing, results of our study could be extrapolated to dairies in California and elsewhere that fall within the study herds’ description and climate.Case definitions for BRD vary widely between studies and, therefore, any comparisons of results should be interpreted with caution. Some studies rely on treatment records by dairy staff, whereas others use certain clinical signs for diagnosis, which may also differ between studies. Most epidemiological studies on BRD in dairy calves report incidence rates rather than prevalence. Lago et al. found a prevalence of 14.3% in 225 dairy calves housed in barns in the winter in Wisconsin and diagnosed with the Wisconsin BRD scoring system for preweaned calves. Buczinski et al. found a median prevalence of 8% in the summer and 15% in the winter for lung consolidation diagnosed by ultrasound, consistent with BRD in preweaned calves in 39 dairy herds in Québec. Indoor housing and poor ventilation are associated with BRD, so the higher prevalence in the Wisconsin and Québec studies in the winter is not unexpected . Both Buczinski et al. and Lago et al. reported a gradual increase in prevalence from birth to the sixth and seventh week of life, respectively; we observed a similar rise in prevalence,hydroponic tables canada which continued until 65 d of age . The fact that most of the calves in the current study were housed outdoors may explain the lower prevalence observed of 6.9%, comparable to what Buczinski et al. observed in the summer. Distributions of the percent of cases by age stratified by breed or region are shown in Supplemental Figures S2 and S3 , respectively.

Even though we observed lower prevalence in the NSJV region compared with the other regions of the state, region was not significantly associated with BRD when adjusting for other factors in the multivariable model. We observed lower prevalence in herds between 1,000 and 3,999 milking cows compared with herds with fewer than 250 or more than 4,000 milking cows. Likewise, we found no association between herd size and BRD in the final model. Little evidence exists in the literature to support associations of breed or herd size with BRD. A previous study found a higher risk of preweaning calf pneumonia in Ottawa for increasing number of calvings per farm per year . No interpretation for this result was offered by the authors and it may not be relevant to the large California dairy farms of today.In the current study, we observed positive associations of calves housed in hutches made from metal components and BRD. In a study based on the same data set, the prevalence of BRD observed in calves housed in wooden hutches was similar to the prevalence of calves housed in metal hutches . However, in NCA, BRD prevalence for calves housed in hutches or pens made of metal was significantly higher compared with those housed in wooden hutches . Furthermore, a longitudinal study of 11,300 preweaned calves in California showed that calves housed in hutches made of a combination of wood and metal were at higher risk of BRD compared with calves housed in hutches made of wood only . Calf-to-calf contact in the age group over 75 d was also significantly associated with BRD. Results of past studies on the effects of hutch type on calf health were mainly focused on the effect of plastic type hutches or available space. Calvo-Lorenzo et al. housed calves in wooden hutches of 3 sizes between April and July in California to assess the effect of hutch space on health, performance, and respiratory immunity. Calves were raised in conventional California-style wooden hutches and allowed either 1.23, 1.85, or 3.71 m2 /head of space. Those authors concluded that increased space may improve pulmonary immunity and health, although it was not apparent which component of the increased space allowance environment influenced the finding. We did not observe an association of hutch space with BRD in the present study, possibly because hutch material may be the more important component associated with BRD. The temperature in plastic hutches can average 5 to 10°C higher than in wooden hutches and ventilation is comparatively poor, leading to accumulation of heat, carbon dioxide, and humidity . Several studies have compared the effect of plastic hutch types on performance and health outcomes in dairy calves, and although agreement exists that plastic hutches increase heat stress, none have found significant associations with adverse health outcomes .

No references regarding the effect of metal style hutches were found in the literature, but heat stress and possibly cold stress are likely associated with metal hutches and may explain the current study findings. Specifically, 69% of metal hutches housing individual calves on 9 dairies in the current study also had metal roofs, whereas 31% on 13 dairies had no roof but were under a shed or other shade structure. The difference in health outcomes between hutch types should be researched further, ideally using an experimental study design comparing a limited number of hutch designs, including hutches made from metal components. The large number of different hutch designs in our study made it difficult to elucidate the association between hutch material and BRD and has resulted in large confidence intervals around estimates. The current study identified a positive association between calf-to-calf contact and BRD in calves older than 75 d. Callan and Garry recommend spacing hutches at least 1.2 m apart to prevent calf-to-calf contact and transmission of respiratory pathogens. A distance of 1.2 m between hutches was rarely, if at all, the case on the California dairies visited for our study, and the feasibility of this strategy and its cost in terms of land needed to raise the same number of calves may be a challenging constraint. In calves that are close to weaning age , housing- or nutrition-related factors may be less important; thus, calf-to-calf contact with animals shedding pathogens may become a more important factor for their BRD status. Lagoon flush water could be a source of noxious gases that may irritate respiratory mucous membranes, making calves susceptible for opportunistic infections with commensals. The role of lagoon water on a dairy as a source of infective agents has mainly been studied with respect to enteric pathogens, including Mycobacterium avium ssp. paratuberculosis, Salmonella spp. and Escherichia coli and its effect on antimicrobial resistance in some of the pathogens . Alhamlan et al. found members of the Flaviviridae in lagoon water samples, a family of viruses that contains bovine viral diarrhea virus . Although the viruses were not categorized to the species level in Alhamlan et al. , it is conceivable that lagoon water could be a potential source for exposure to respiratory pathogens if used as flush water, especially if aerosolized particles are created,microgreen rack for sale potentially resulting in inhalation of pathogens.

If flush water is close enough to hutch floors, calves could also be able to ingest or have direct exposure of oral or nasal mucosae to pathogens. In addition to BVDV, bovine coronavirus is shed via feces and could cause respiratory disease in calves. The significance of lagoon water as a source of pathogenicity with respect to BRD should be further evaluated. We also observed a negative association between the presence of an additional shade structure over the hutch area and BRD. Shade could reduce heat stress as well as protect from rain or frost, resulting in lower odds of BRD. The association between shade and BRD was not observed for shade structures with 1 to 4 sidewalls, which could be due to an offset of the benefits due to a lack of ventilation. Coleman et al. studied the effect of supplemental shade structures over polyethylene calf hutches in Alabama during the summer on feed consumption, growth, calf stress, and bedding contamination. Their study found no differences between groups in terms of health-related outcomes, but humidity and coliform counts in bedding were higher in shaded hutches, which may be due to the difference in climate between the 2 states, specifically the higher humidity in Alabama during the summer months.Feeding pasteurized milk versus nonpasteurized milk, including milk replacer, was associated with reduced BRD prevalence. Numerous studies have underlined the importance of pasteurizing milk fed to dairy calves for the prevention of enteric disease as well as exposure to potential respiratory pathogens . Mycoplasma spp. are mastitis pathogens that can be shed by clinically or subclinically infected cows and can colonize the nasopharynx of calves fed the milk resulting in otitis and respiratory disease . Mycoplasma and other bacterial pathogens ingested in the milk can also spread hematogenously to the lungs, where they cause respiratory disease . A study from Spain found decreased morbidity and mortality in calves with adequate transfer of passive immunity during the first 3 wk of life that were fed heat-treated colostrum and pasteurized milk versus those that received nonheat-treated colostrum and nonpasteurized milk . A separate study by Godden et al. found a higher risk of pneumonia in calves fed milk replacer than for calves fed pasteurized waste milk. Those authors argued that the improved immune function was attributable to higher energy and protein intake or the presence of medium-chain fatty acids in the whole milk, which have shown antimicrobial effects in pigs. We also observed a negative association of feeding a diet consisting of at least 90% saleable milk for at least 7 d at time of farm visit with BRD versus any other milk diet, including waste milk or milk replacer. Several studies have evaluated the effect of milk intake volume on calf performance . Medrano-Galarza et al. found no associations between within-pen prevalence of BRD and peak milk allowance in a study of majority Holstein group-housed dairy calves fed with automated milk feeders. However, calves in that study were fed at least 4 L per calf per day before and at least 6 L per calf per day after introduction to the group pen. A different study, similarly found no difference in incidence of BRD between Holstein calves fed a maximum of 6 or 8 L of milk replacer per day in a randomized trial comparing performance and health response of dairy calves offered different milk replacer allowances . Higher amount and quality of milk replacer fed was found to be associated with superior immune function in Jersey calves . The group of Holstein calves fed ≤2.84 L of milk or replacer per day in our study was fed less than the lowest amounts supplied to calves in the above-mentioned studies and were likely not able to consume enough starter to meet their nutrient requirements for growth and development if they were under 2 mo old . Results in our study showed increased odds of BRD for Holstein calves fed ≤2.84 L and decreased odds of BRD in Jersey calves fed >5.68 L of milk or replacer, which may reflect the effects of nutrition on immune function but should be interpreted with caution due to the relatively small samples in those categories, resulting in large confidence intervals for estimates. Restricting the analysis to calves ≤75 d of age did not change the sign or statistical significance of contrasts except for the comparison of >5.68 to ≤2.84 L of milk or replacer in Jersey calves, which became nonsignificant . Future research should quantify the benefits of saleable milk compared with waste milk or milk replacer further, as well as how the association with BRD is modified by pasteurization and amount of milk fed in the major dairy breeds.The current study found no significant associations between calf or dam vaccination status for respiratory pathogens and BRD in the study population.

All statistical analyses were completed using R statistical software . The purpose of this pilot study was to examine the association between non-daily and daily MJ use patterns on sleep patterns in young adults recruited from the community. Daily MJ users endorsed more sleep disturbance on the PSQI and ISI than non-daily users. These results are consistent with previous studies showing an association between sleep disturbance and heavy MJ use in adults. Of note, however, was our finding that nondaily MJ users and non-users had similar sleep indices. Daytime sleepiness and chronotype did not differ across our three groups. This study provides new information about the relationship between MJ use patterns, mood, sleep, and daytime functioning. We found that the proportion of persons reporting a clinically significant PSQI threshold of >5, which distinguishes good from poor sleepers, was lowest among non-daily users and highest among the daily users . We also found that these non-daily users tended to use MJ mostly at nighttime, whereas daily users smoked considerably more MJ on use days and used during the day and the evening. The effects of MJ on sleep in intermittent users may be similar, in part, to that of alcohol where improvements in sleep continuity measures have been reported with intermittent use, whereas daily use results in the worsening of sleep. However, methodological differences in previous marijuana studies limit definitive support. While a review of 39 research studies on the effects of cannabis on sleep revealed that cannabis may interrupt sleep patterns and result in non-restorative sleep,commercial greenhouse supplies objective polysomnography in heavy MJ users show normal sleep patterns during periods of cannabis use.

One possible explanation for the study findings is that individuals habituate to the sleep inducing effects of cannabis after continued use. In addition, our finding that non-users had similar ISI scores to non-daily users, and that a lower proportion reached clinical criteria for insomnia, suggests that sleep disturbance, which is common in this age group, may not be increased by non-daily MJ use. Because this is not a MJ administration trial, this remains speculative. The clinical significance of the lower ISI score in non-daily users is likely minimal, as all scores <10 typically reflect sub threshold insomnia. Our findings suggest that anxiety is significantly related to scores on the PSQI. Persons with anxiety may be using MJ to mitigate their sleep symptoms. This is consistent with the literature, where MJ is the most commonly used illicit substance in individuals with anxiety disorders 40 and where higher MJ use has been associated with higher rates of anxiety. Lev Ran et al. found that when adjusting for any concurrent mood disorder, there was a significant impact of regular, but not occasional, use of cannabis on mental health-related quality of life in participants with anxiety disorders. It remains possible that our ISI scores might have been higher in the daily MJ users because MJ was contributing to anxiety, which in turn may have exacerbated the severity of insomnia. We found gender effects on our PSQI and ISI scores, but not on ESS or MEQ scores. The relationship between insomnia and gender was expected as insomnia is more common in females than in males in non-substance using populations. Lev-Ran et al. found that compared to non-users, occasional MJ users had poorer mental health scores in females, but not among males. These findings suggest that MJ use patterns may affect females differently given their increased risk of both insomnia and depression. MJ has been shown to affect women more significantly than men on neuropsychological tasks.We expected that there would be more evening chronotypes in the daily MJ use category, because evening chronotypes have been shown to be more likely to use alcohol, to have poor impulse control, depression, and difficulty falling asleep.

However, our MEQ results did not differ between non-users, non-daily and, or daily users. This finding is consistent with results of one study which utilized the Horne Ostberg questionnaire to assess chronotype in MJ users and reported that there were no chronotype differences between heavy MJ users and non-users. Our study is among others to examine chronotype among persons who are primarily MJ users where the influence of concurrent heavy alcohol use has been mitigated. While the relationship between chronotype and substance use is likely multifactorial, future studies might consider exploring whether evening chronotypes may be more likely to use alcohol or MJ. Our study had several limitations. First, daily MJ users in this study were heavy users, therefore, we are unable to know if the sleep reports of daily users are a result of frequency of use or quantity of use. Future studies might try to recruit daily MJ users who smoke minimally, perhaps only at night to “treat” sleep disturbance. Second, participants selfreported MJ use and sleep indices. Third, given the cross-sectional nature of this study, we are unable to assess causality. Fourth, the size of our sample increases the risk of Type 1 error, missing associations that might have been seen in a larger study. Fifth, the absence of significant difference between the non-user and user groups may have been due to low power, as the non-user group had a lower number than user groups. Sixth, we did not quantify the use patterns beyond the past month. Therefore there may have been users in our non-user groups that could have quit more than one month ago. Seventh, the sample may not be representative of this population for two reasons. Even though we recruited MJ users and non-user controls from a larger study on alcohol and marijuana, our exclusion criteria may have been too limiting. In addition, given the high rate of unemployment and low rate of high school graduates in our study sample, this study sample may not be representative of this demographic. Eighth, the non-using control sample in this study was recruited from a larger study, which may have affected statistical assumptions for comparing the MJ user group with the HC group. Lastly, a formal diagnosis of insomnia was not conducted in this study and therefore associations with insomnia based on the ISI should be interpreted cautiously. With the increasing availability of MJ and an increasing number of individuals using it for insomnia with the belief that MJ use improves sleep, our study suggests that daily use may not in fact improve sleep, although this study cannot est. Large scale studies assessing the impact of MJ on sleep are warranted.

In 1996, California became the first state to legalize medical marijuana. Known officially as the Compassionate Use Act, Proposition 215 allowed patients and caregivers to cultivate and possess marijuana for medical use. The campaign in favor of Proposition 215 focused on the benefits for seriously ill patients. Claiming that the Proposition “sends our children the false message that marijuana is safe and healthy,” the campaign against the Proposition focused on anti-drug education . Neither side addressed potential public health consequences. If Proposition 215 led to an increase in marijuana use, for example, might it also lead to higher rates of all injury deaths , including deaths from assault , deaths from motor vehicle crashes ,cannabis dry rack and—the subject of the present study—deaths from suicide ? Such consequences assume that medical and recreational users are similar. With one exception, the evidence supports this assumption. Since most California medical users were introduced to marijuana as recreational users, for example, it is reasonable to assume that the user-types have similar socioeconomic backgrounds . Compared with recreational users, however, California’s medical users were more likely to report early health problems or disabilities that would warrant medical use . Although Proposition 215 was drafted so loosely that it effectively legalized all uses of marijuana , marijuana use by California juveniles, who were not eligible for medical marijuana certificates, did not increase following Proposition 215 . Nevertheless, at the national level, during a 15-year period when a majority of states loosened their control of medical marijuana, the U.S. suicide rate rose by 24 percent , prompting many to question how legalization and suicide might be linked. The systematic evidence connecting this trend to the availability of medical marijuana is ambiguous, however. Rylander, Valdez, and Nussbaum , for example, find no correlation between a state’s suicide rate and the number of medical marijuana cardholders in the state. Similarly, comparing suicide before and after a state enacts a medical marijuana law, Grucza et al. find no change in a state’s suicide rate. In contrast, Anderson, Rees, and Sabia report a 10.8 percent reduction in suicides averaged across all medical marijuana states. Attributing a suicide trend to the availability of medical marijuana raises questions about the potential mechanisms at play. What theoretical mechanisms could lead us to expect a relationship between the availability of medical marijuana and suicide? Could these mechanisms be more salient for certain types of suicides than others? If the expected relationship is observed, what methodological rules could be used to support a causal interpretation of the relationship? We address these questions in order. Sociological theories of suicide follow Durkheim’s dictum that “suicide varies inversely with the degree of integration of the social groups of which the individual forms a part” . Institutions that successfully integrate the individual, providing a sense of belonging to the community, inhibit suicide. When institutions break down, so do the community ties that might otherwise inhibit suicide atrophy; and suicide increases.

Durkheim used cross-sectional correlations between suicide and the strength of religious, familial, and socioeconomic institutions to demonstrate his theory. The theory has been used to investigate relationships between suicide and unemployment, , poverty and income inequality , divorce and family structure , immigration and cultural assimilation , and cohort size . Regardless of focus, research largely advances a motivational argument for understanding variation in suicide rates across place or time. Although legalization of medical marijuana is likely to affect a range of societal institutions, the indirect effects through these institutions are expected to accrue gradually. Individual-level direct effects, in contrast, are expected to be realized abruptly. A more appropriate individuallevel theory for explaining the relationship between medical marijuana and suicide posits suicide risk as the product of motivation and opportunity factors. Holding motivation constant, suicide risk responds to changes in opportunity. Holding opportunity constant, risk responds to changes in motivation. Chew and McCleary use motivation/ opportunity mechanisms to explain lifecourse changes in suicide. Kubrin and Wadsworth use motivation/opportunity mechanisms to explain the effects of socioeconomic factors and firearms availability on race-specific suicide. Wadsworth, Kubrin, and Herting use motivation/opportunity mechanisms to explain suicide trends for young Black males. Consistent with this literature, we argue that if medical marijuana affects suicide risk, it must do so through one or both pathways. Mental health theories operate through a motivation pathway. The psychiatric consensus is that suicide is related to depression, anxiety, and other treatable disorders . If marijuana alleviates the acute stress associated with these disorders, then we expect suicide risk to decrease following legalization of medical marijuana. The evidence for this is mixed, however . Whereas medical users report that alleviation of acute symptoms of these disorders was a primary motivation for permit applications , a systematic review by Walsh et al. reported that this was not consistently observed across credible studies. Of course, marijuana use itself may constitute a risk factor for suicide apart from alleviating symptoms related to depression and anxiety, at least among some populations and for some levels of suicidality .A meta-analysis by Darvishi, Farhadi, Haghtalab, and Poorolajal supports the strong consensus that alcohol use disorder “significantly increases the risk of suicidal ideation, suicide attempt, and completed suicide.” With respect to medical marijuana, of course, the theoretical prediction depends on whether marijuana is used in addition to or instead of alcohol. If marijuana and alcohol use are combined, one might expect no change in suicide risk or even an increase in suicide following legalization. If marijuana replaces alcohol, on the other hand, one might expect a decrease in suicide risk following legalization.

While perceptions of benefits and addiction were not related to use in this study, perceptions of greater health and social risks were associated with lesser odds of using marijuana. Other studies have also found risk perceptions related to use . The fact that perceptions of benefits were not related to use is surprising as other studies have found perceptions of benefits to predict use . It is possible that perceived social norms are more important drivers of adolescents’ decisions to use marijuana than perceived risks and benefits despite the fact that these constructs are linked . While perceptions of benefits of marijuana were not related to use, seeing messages about the good things or benefits of marijuana use was associated with a 6% greater odds of use. In contrast, despite adolescents seeing ads for both risks and benefits of marijuana, messages regarding risks were not related to use. It is possible that individuals who use marijuana are actively seeking and more aware of messages related to benefits of marijuana use. There are limitations to this study. The data are self-reported. Further, given the cross sectional nature of our data, we cannot suggest a causal relationship between factors associated with marijuana use and marijuana use itself. Additionally, some of the factors associated with marijuana use have a confidence interval approaching 1.0. Finally, these data were collected throughout Northern and Southern California and thus are not nationally representative. Despite these limitations, this is one of the few studies to assess perceptions of social norms, risks and benefits for marijuana, blunts, and cigarettes. Additionally, this study assessed how these factors as well as awareness of social media are related to marijuana use. Results from this study offer a number of important public health implications,drying cannabis particularly as states move towards legalization of marijuana for recreational use. As this occurs, states need to take adolescents’ perceptions of risks, benefits, social norms, and peer influences into account.

Though there is mixed evidence on how legalization impacts adolescent marijuana use, advocates for marijuana legalization argue that legalization itself does not increase use among youth . However, there is no evidence that legalization alone does anything to decrease use or access among adolescents. The results from this study have a number of implications for prevention strategies. Perceived rates of marijuana use among friends is higher than participant self-reported use rates and reported national averages of adolescent use. This finding is similar to findings in the alcohol use literature, which finds that youth and young adults tend to overestimate rates of binge drinking. Importantly, dispelling this misperception has been used effectively in a number of social norms campaigns focused on reducing binge drinking in college campuses . This suggests that using a similar social norms marketing approach, in which youth learn that rates of marijuana use among peers are much lower than they think, may be a useful strategy to prevent use. In this study, both perceived friend use and having seen positive messages about marijuana was associated with greater odds that an adolescent used marijuana. These findings also suggest the need for marketing, education and intervention strategies that specifically tackle social acceptability and peer use. This study also shows that adolescents perceive marijuana and blunts to be significantly less harmful than cigarettes, despite the fact that all of these products are combustible smoking products. Additionally, despite the fact that blunts have nicotine, adolescents did not perceive these to be more addictive than marijuana. These findings suggest that there is also a need for educational and marketing campaigns that realistically address what the risks of marijuana and blunt use are for both youth and adults, including risks of addiction. National, state, and local public health agencies should consider lessons learned from regulatory and informational strategies that have been used in tobacco control, and should implement such strategies before legalization occurs .

Aquaculture, which is the farming of aquatic organisms including algae, invertebrates, and vertebrates, has been one of the fastest growing agriculture sectors for the past 40 years . Demand for seafood has continually grown with global fish production in 2018 at around 179 million metric tons , of which 82 MMT comes from aquaculture . While 86.5% of total finfish production occurs in inland freshwater systems, with the majority in Asia , marine culture has the highest growth potential with 2% of oceans being suitable for fish farming . For marine aquaculture growth, Australia, Argentina, India, Mexico, and the United States have the greatest potential based on suitable habitat . Freshwater finfish production has primarily been driven by carp, catfish, and tilapia, while marine fish production is dominated by Atlantic salmon which has a freshwater hatchery stage. Despite the recognized opportunities for marine finfish aquaculture production, very few marine fish species have been successful compared to freshwater fish, due in part to the inability to spawn and produce quality fingerlings in captivity. This has led to the common practice of catching wild juveniles and their transfer to captive rearing environments. In recent years, however, certain high value marine species, including the yellow tail kingfish Seriola lalandi, have been successfully reared in the lab . The Seriola genus, within the family Carangidae, contains several species of yellow tail that are globally distributed across broad temperature range . S. lalandi, is reared in temperate waters across the Pacific Ocean in Japan , Australia , New Zealand , Chile , and North America . Fish, unlike mammals, are not thought to inherit their microbiome vertically. Understanding the factors which influence microbiome development in fish is an important first step in mitigating disease and promoting health. One of the primary challenges in marine fish hatcheries is poor survival rate which is often attributed to a combination of disease and nutrition . Even in the wild, the survival rate for fish larvae is 44× higher for freshwater fish as compared to marine .

Wild marine fish, particularly temperate coastal pelagics like Seriola spp. , are exposed to wide ranges in environmental variables such as temperature, oxygen, and nutrients both diurnally with vertical migration for feeding and temporally with changing seasons. The mucosal microbiome of coastal pelagics is highly differentiated across body sites, primarily in the gill, skin, digesta, and gut tissue with the microbiome on external sites most influenced by these changing environmental variables . In mammals, both phylogeny and diet influence gut microbiome development ,ebb flow whereas fish microbiomes are influenced more by environmental variables including habitat, trophic level, phylogeny, and diet . Diet also varies widely by development stage particularly in the larval to fry stages . While mammals have a significant proportion of their gut microbiome colonized or inherited vertically from the mother during birth , the initial establishment of the gut microbiome in fish is less understood. Even fewer studies have sought to identify the source colonizers of gill and skin communities. Microbial colonization throughout development of the fish is a function of both exposure and host selection. At the earliest stage, bacteria which form biofilms on the outside of the egg eventually can colonize both external and internal mucosal sites of freshly hatched larvae upon ingestion of the yolk sac . Marine fish differ from freshwater fish in that they must drink vast quantities of water to maintain osmoregulation, which in turn provides a large source of potential microbes for gut colonization . The first live feeds the larvae consume, which in hatchery settings are often artemia and rotifers, also contribute to the gut microbiome development . In larval YTK, S. lalandi, gut microbiome composition and density changes most when transitioning from a live rotifer feed to pellet based feeds around 30 days post hatch with many of the gut microbes having anti-microbial functionality . In a study assessing gut enteritis in farmed S. lalandi from seapens, gill, and skin microbiomes correlated with disease state suggesting these communities were either responding to overall health decline or contributing to stress . For a freshwater hatchery, the tank side and tank water were shown to significantly influence the skin and gut microbiomes of Atlantic salmon . Despite the array of studies evaluating impacts of various husbandry methods on microbiome composition of mucosal sites , there is a lack of information for how microbiomes on surfaces in the built environment directly contribute to marine fish. To evaluate how the collective hatchery microbiome influences the mucosal microbiome of a marine fish, we investigated the economically important YTK S. lalandi. This study sought to answer three primary questions: Are body sites differentially influenced by the BE or feed microbiome?, What surfaces within a hatchery environment contribute to the mucosal microbiome of the fish?, and Does the BE and feed microbiome source contribution vary across age and development of the fish? To answer these questions, we sampled the mucosal microbiomes of 92 fish across three broad development stages . Specifically, we used 16S rRNA amplicon sequencing of microbial communities from the fish together with various hatchery surfaces including tank water, tank side, inlet water pipe, air stones, and air diffusers along with feed used in all stages of production.

To our knowledge this is the first study to quantify and compare the relationship of the BE microbiome with the fish microbiome across multiple age classes of a marine fish. All sampling events occurred in June of 2018 in Port Stephens Australia at the Department of Primary Industries New South Wales. Two broad sampling regimes were carried out . A total of 92 “YTK” were sampled in Port Stephens, Australia. In the first experiment, gill and skin swabs were sampled from a total of 36 living fish across three different indoor rearing condition tanks along with corresponding BE samples including tank water, the tank side, inlet pipes, and air diffusers. These fish were all siblings and 130 days post hatch “dph.” Fish were reared in either a flow through system “FT,” a traditional moving bed bioreactor “MBBR” Recirculating Aquaculture Systems “RAS,” or a modified BioGill RAS. Fish were reared at a max of 25 kg/m3 fed at a maximum of 0.5 kg food/day/m3 and reared in 10 m3 tanks. Additional details can be found in the white paper . Fish were non-lethally sampled during routine biometric measurements where individuals were weighed and measured. Prior to taking the weight and length, the skin and gill of each fish was swabbed using a cotton swab [Puritan] and placed directly into a 2 ml PowerSoil tube. For these three tank conditions, “BE” samples were taken from the tank water, swab of tank side , swab of air diffuser, swab of air stone, and swab of inlet water pipe. For the two RAS tanks, an additional inlet water sample was taken which represents cleaned water . Comparisons were made to determine if there was a relationship between the external fish mucosal sites and the BE and if so how that varied across the water filtration or rearing system. For the second experiment, fish were sampled cross sectionally at different ages including 43 dph , 137 dph , and 430 dph . Fish at 430 dph included fish sampled from an ocean net pen along with fish which were transferred from an ocean net pen back to an indoor system. For the age comparison cohort, three body sites were sampled including the gill, skin, and digesta “allochthonous” samples along with corresponding BE samples described in experiment 1. The BE “built environment” samples included tank water, inlet pipe, airstone, air diffuser, and tank side. Specifically 12 fish were similarly non-lethally sampled from three different age classes: 43, 137, and 430 dph from indoor tanks. The 430 dph fish from the indoor tank were initially reared indoor until 245 dph following methods described by Stewart Fielder et al. and then transferred to ocean netpens where they were grown for 106 days. At 351 dph, they were then transported back to the indoor system where they were held until sampled at 430 dph. An additional 20 fish at 430 dph from the seapen were harvested for another experiment and opportunistically sampled. All fish were measured for length and mass with condition factor calculated. A total of 92 fish were sampled across the two experiments.

The relationship between the use of these two substances has a basis in the biological connection between them, as the endogenous opioid system is an underlying mechanism for several behavioral outcomes related to nicotine . Like marijuana, nicotine is involved in anti-nociception via endogenous opioid system mediation, suggesting that nicotine is used for the selfmedication of pain ; and in fact, nicotine heightens the anti-nociceptive effects of both opioids and marijuana . Several studies have documented common use patterns among tobacco, marijuana, and opioids/PPRs . For example, a prospective study of NESARC data demonstrated that early-onset of smoking cigarettes increased the odds of beginning opioid use and that frequency of both cigarette and marijuana use increased the odds of beginning opioid use, re-initiating opioid use after previously stopping, and continuing opioid use among current users . Thus, the three substances share anti-nociceptive actions mediated by the endogenous opioid system, and evidence indicates that marijuana and nicotine use predict opioid use among adults. From 2003 to 2012, NSDUH data revealed a significant increase in the co-use of marijuana and tobacco . Further, smoking tobacco is significantly associated with cannabis dependence . Given the national trend toward marijuana legalization, co-use is likely to increase. Cigarette smokers and marijuana users are a crucial population to study, as nicotine and marijuana share mechanisms of action with each other and with opioids, and use of each substance has been shown to be associated with use of opioids/PPRs . However, whether there is an association between prevalence of marijuana and PPR use among current smokers has not been determined. The present study addresses this gap by using the Tobacco Attitudes and Beliefs Survey II to investigate the relationship between marijuana use and PPR use among current cigarette smokers. This study examines 1) the likelihood of PPR use by marijuana use and 2) the frequency of marijuana use and current PPR use.

Findings may help elucidate whether marijuana use is associated with PPR use, and if so,pot for cannabis whether marijuana is used as a substitute or complement to PPR use. This is a cross sectional analysis of data from the TABS II, a web-based longitudinal survey of U.S. adult former and current cigarette smokers, aged 24 years old and older. The survey included topics such as individuals’ use of tobacco, tobacco-related products, marijuana, and other substances including PPRs. The present analysis used demographic data from Wave 1 from August 2015 . Wave 3 data were collected in August 2016 and included survey items on marijuana use and new items on PPR use . Surveys were administered by Qualtrics, which uses a combination of online panels to establish national samples from which survey participants can be randomly selected. Qualtrics invited potential participants to take the survey via an email notification and offered them a $10 incentive to complete each survey wave. For Wave 1, 2,378 individuals clicked on the survey link, and 819 went on to complete the survey, yielding a completion rate of 34.4% . Current smokers were included in the current analysis. The TABS II project was approved by the UCSF Institutional Review Board. Cigarette use.—Participants were categorized as a current cigarette smoker if they responded “yes” to the question, “Are you a current cigarette smoker?” and if they responded with any number of days greater than 0 for the question “During the last 7 days, on about how many days did you smoke cigarettes, even 1 or 2 puffs” or to the question “During the last 7 days, on about how many days did you smoke menthol cigarettes, even 1 or two puffs.” The question “On average, how many cigarettes a day do you smoke?” was used to control for cigarette consumption in the analysis of the relationship between cannabis use and PPR use. Marijuana use.—Definitions of each user type were: “never users,” never used marijuana in their lifetime; “ever” users, used marijuana at least once in their lifetime, but not in the past 30 days; and “current” users, used marijuana in the past 30 days. If participants answered the question “During the last 30 days, on about how many days did you use marijuana, even 1 or 2 puffs?” with any number above 0, they were classified as a current user. If participants responded with “I have never tried marijuana” to the question asking “Which of the following forms of marijuana have you EVER used?” then they were classified as a never user.

If they responded to this question with any other option besides “Don’t know/refused” and if they were not categorized as a current user, they were classified as an ever user. For the analysis involving frequency of marijuana use as a continuous variable, responses to the question “During the last 30 days, on about how many days did you use marijuana, even 1 or 2 puffs?” were used. Medical marijuana lawstatus in state of residence.—Participants were categorized as: 1) no legal medical marijuana in state of residence, 2) legal medical marijuana for less than 10 years in state of residence, or 3) legal medical marijuana for 10 or more years in state of residence. PPR use.—PPR users were categorized as “never” users if they reported they had never used PPRs in their lifetime, as “ever” users if they had used PPRs but not in the past 30 days, and “current” users if they had used PPRs in the past 30 days. If participants selected “Prescription pain relievers” for the following two questions, they were classified as current PPR users: 1) “Have you EVER used any of the following substances? Mark all that apply” and 2) “Have you used any of the following substances in the PAST 30 DAYS? Mark all that apply.” If participants selected “Prescription pain relievers” in the first question , but did not select them in the second question , then they were classified as ever users. If they did not select “Prescription pain relievers” in the question inquiring about ever use, they were classified as never users. Statistical Analysis—Descriptive analyses were used to test for normality. Chi-square tests were used for categorical variables , and an ANOVA was used for the continuous variables to compare sample characteristics between marijuana never users, ever users, and current users. For PPR status, a Bonferroni adjustment was made to account for multiple comparisons. A logistic regression was used to examine whether the frequency of marijuana use influenced PPR use among current marijuana users. SAS University Edition, which contains SAS Studio 3.6 and SAS 9.4, was used for all analyses.Participants ranged in age from 24 to 88 years old . The majority were female and Caucasian . On average,pot for growing marijuana participants smoked over 15 cigarettes per day . Table 1 presents demographic information and PPR status stratified by marijuana use. Chi-square tests and an ANOVA revealed no significant differences in sample characteristics across marijuana never, ever, and current users.

A significant difference was found between marijuana never, ever,and current users for PPR status . We tested three comparisons using SAS proc multest, which provides Bonferroni adjusted p values. A significant difference remained for the following PPR use groups: never vs. ever and never vs. current .As shown in Table 2, logistic regression was used to further examine the relationship between marijuana and PPR use, with PPR current use set as the reference category for the criterion variable and marijuana never use set as the reference category for the predictor variable. The model was adjusted for all sample characteristics, and compared to marijuana never users, both marijuana ever users and current users were more likely to have used PPRs in the past 30 days, with ever users having an AOR=2.58 and current users having an AOR=3.38 . A logistic regression was used to investigate the relationship between the frequency of marijuana use among current marijuana users and PPR use, and no significant results were found . Results suggest that adult current cigarette smokers have differential use of PPRs depending on their use of marijuana. Those who were current and ever marijuana users were over 2–3 times more likely to have used PPRs in the past 30 days, respectively, when compared to cigarette smokers who never used marijuana. Results support the findings of previous studies that addressed a possible complementary effect of marijuana use with PPR use. Novak, Peiper, and Zarkin analyzed NSDUH data in 2003 and 2013 and found that greater marijuana use was associated with more frequent PPR use. An analysis of NESARC data found higher levels of marijuana and cigarette use predicted initiation, re-initiation, and sustained opioid use ; and another study using NESARC data determined that marijuana use was associated with an elevated risk of using nonmedical prescription opioids three years later . Two Swedish teams found similar results. One study found a positive association between use of marijuana and unauthorized use of PPRs . In a re-analysis of a Swedish national household survey, non-medical PPR use was associated with both frequent cigarette smoking and marijuana use . Studies with adolescent and young adult samples found non-medical use of PPRs is associated with marijuana use . Though longitudinal studies are needed to make definitive conclusions about the nature of the relationship between marijuana and PPR use among cigarette smokers, the interface among biological effects of PPRs, marijuana, and nicotine could influence the strength and direction of this relationship. For one, PPRs and marijuana share anti-nociceptive effects, the two substances act on some of the same brain regions, and THC partly exerts its analgesic influence by relying on opioid receptors . Nicotine additionally interacts with the opioid system, and the systems have almost identical influences in key pleasure-sensing areas of the brain . Therefore, the behavioral responses to nicotine use and withdrawal are likely affected by the opioid system .

As with marijuana and opioids, nicotine has antinociceptive actions . Consequently, the interconnected neural activity and biological effects of nicotine, marijuana, and opioids could play a role in the relationship between PPR and marijuana use among cigarette smokers. Another explanation for the higher likelihood of current PPR use among ever and current marijuana users in cigarette smokers could be that some participants had used marijuana and/or PPRs to reduce pain symptoms. Epidemiologic and prospective cohort studies point to a relationship between smoking and chronic pain, with smokers having a greater likelihood of developing chronic pain disorders than non-smokers . And the most frequently reported reason for adult misuse of PPRs in 2015 was to alleviate physical pain . Our results do not support prior findings of a negative association between marijuana and PPR use. Boehnke, Litinas, and Clauw report that among individuals with chronic pain, use of medical marijuana was negatively associated with opioid use. Further, legalization of medical marijuana has been correlated with a drop in the number of hospitalizations attributed to opioid dependence/abuse and PPR ODs, and a decline in opioid OD mortality rates . States with medical marijuana dispensaries also report fewer PPR ODs, a reduction in PPR treatment admissions, and a decline in opioid-related deaths . However, none of these studies stratified their results by cigarette smoking status. As such, it is possible that the inclusion of only current cigarette smokers in the present study could help explain the discrepancy between the present findings and other results. Of note, studies investigating the effect of marijuana use on opioid/PPR use vary in their sample composition , use of covariates , and outcome measures . This variation in study design is likely responsible for some of the discrepant findings in the extant literature. As previous studies have not stratified their analyses by cigarette smoking status, our study provides an important and unique contribution to current evidence, and this dynamic helps to explain why our findings differ from those that found a negative relationship between marijuana and PPR use. We did not find a significant association between PPR use and frequency of marijuana use among current marijuana users. Our findings align with those of Lucas et al. , who determined that among Canadian medical marijuana users, there was no association between frequency of marijuana use and illicit drug substitution, though this finding is attenuated because their analysis was not stratified by cigarette use status. On the other hand, our findings contrast with those of Arterberry et al. , who reported that frequency of marijuana and cigarette use was predictive of opioid use among an adult sample in the NESARC. This dissimilarity may be due to differences in study design.

Several sources could be promoting misperceptions that marijuana use is safe and beneficial for pregnant and breastfeeding women. One potential source is social media, through which misinformation about maternal marijuana use receives increasing spread, attention, and engagement. Analyses of Twitter communications suggest an increasing engagement with misinformation about benefits of marijuana use during pregnancy and breastfeeding . The proliferation of online support groups advocating benefits of use while pregnant and breastfeeding suggests growing popularity in beliefs that marijuana is a safe and affordable treatment for discomfort, depression, and morning sickness ; that it is safe because it is plant-based and natural; and that it facilitates breastfeeding. Qualitative interviews with pregnant women have documented these beliefs but systematic, quantitative examinations of endorsements of these beliefs in communities remain lacking. Another potential source of misperceptions is the legalization of marijuana for recreational and medicinal use in a growing number of states and regions. This trend could foster expectations that marijuana must be a safe substance if it is legal to use and it must be healthy if it is prescribed for medicinal purposes such as pain and depression. Among pregnant women, risk perceptions about the use of marijuana during pregnancy have decreased in recent years and these decreases might partly explain the increases in marijuana use by pregnant and breastfeeding women . If maternal marijuana use is expected to rise in line with the increases in beliefs about its safety and benefits,mobile grow system then it is essential to determine which beliefs about specific risks and benefits are commonly held and which cultural and social groups are prone to misconceptions.

This information can be used to identify specific beliefs to address and target audiences for health communications aimed at promoting informed decisions about using marijuana by pregnant and breastfeeding women.Whereas it is important to understand beliefs about maternal marijuana use held by adults across societies, understanding beliefs held by vulnerable populations hold particular importance given the likely health disparities if risk-elevating beliefs common within these communities are not addressed through health communications and interventions. The San Joaquin Valley is home to vulnerable, underserved communities that struggle with significant health disparities and lack of access to health care resources . Latinos make up the majority of residents and, more generally, represent one of the fastest growing ethnic groups in the United States . Latinos face numerous adversities that heighten their risk of substance use and have seen the most growth in marijuana use across ethnicities in the U.S. , further indicating the need to understand and target misconceptions within these communities.There is a paucity of evidence, and particularly quantitative data, on the beliefs about marijuana use while pregnant and breastfeeding held by community members, and particularly those residing in vulnerable and underserved communities. The aims of the present study were to address these research gaps by surveying community residents, with an emphasis on recruiting Latino, rural, and disadvantaged residents into the sample, to gather information about their beliefs regarding marijuana use by pregnant and breastfeeding women. The survey was designed to inform two primary research questions: What beliefs about the risks and benefits of marijuana use when pregnant or breastfeeding are commonly held by adults representing these rural and vulnerable populations? And How do these beliefs vary as a function of gender, ethnicity, marijuana use, and parental status? While the study aims are primarily exploratory, we made general predictions based on prior findings of social group differences in marijuana risk perceptions, substance use beliefs, or health risk perceptions more generally.

We predicted that risk beliefs would be lower and benefits beliefs would be higher for participants who had ever used marijuana and who had used marijuana in the past six months relative to those who had not ; participants who were not of Latino ethnicity relative to Latino participants ; male relative to female participants ; and participants who were not parents relative to parents .A network of 8 promotores de salud and 20 research assistants were trained to recruit and administer the survey to residents from counties within the San Joaquin Valley, California. The promotores network enhanced opportunities to recruit “hard to reach” community members, including those who reside in isolated geographic regions. Spanish-speaking promotores and research assistants provided Spanish versions of the survey to those who preferred it. The Spanish surveys were translated and back-translated by trained translators and then validated through focus groups with Spanish-speaking promotores. Promotores and research assistants recruited residents attending local events , community organizations , organizations serving parents , and home visits by promotores to community clients. Data collection took place between April 2019 and November 2019. Promotores and research assistants distributed informational fyers inviting adults aged 18 and older to participate in a 15–20-min survey about their views on marijuana use during pregnancy and breastfeeding. Participants who could not read or write in English or Spanish were offered the opportunity to have it read to them; three participants completed the survey in this manner. Upon completion, participants received debriefng information and a $20 gift card in a Table 2 presents rates of agreement with statements about benefits and risks of marijuana use while pregnant or breastfeeding for the total sample. A slight majority were either neutral about or agreed with the statement that using marijuana while pregnant helps to reduce pain and discomfort whereas 49% disagreed with this statement. In contrast, a strong majority of participants disagreed that marijuana use during pregnancy helps to reduce depression, has no lasting harms for the baby, is safe because it is plant-based and natural, and helps to reduce morning sickness and nausea.

Over 60% of participants agreed that use during pregnancy poses risks to the baby including attention and learning difficulties, lowered IQ, THC addiction, behavioral problems, brain damage, preterm birth, low birth weight,mobile vertical rack and pregnancy complications. Proportions who were neutral or disagreed with these statements ranged from 29.0% for risk of behavioral problems to 39.0% for risks of preterm birth and low birth weight. Comparable patterns of agreement emerged for beliefs about use while breastfeeding. A slight majority were neutral about or agreed that it helps to reduce pain and discomfort whereas a majority disagreed with the other five statements about benefits. Proportions of participants who were neutral or agreed with these five statements ranged from 24.7% for use helps calm the baby to 44.2% for use helps reduce depression. Over 60% agreed that use while breastfeeding poses risks to the baby including attention and learning difficulties, lowered IQ, THC addiction, behavioral problems, and brain damage. Proportions of participants who were neutral or disagreed with these statements ranged from 29.0% for risk of attention and learning difficulties to 31.9% for risk of brain damage. In response to items assessing beliefs about the presence and effects of THC in breast milk, 65.6% of participants believed that THC passes to the baby and has negative effects on the baby whereas 21.0% believed it passes to the baby and has positive effects on the baby and 13.4% believed that none to some THC passes to the baby and has no effect on the baby. Only 20% of participants gave the most scientifically accurate response that THC lasts in breast milk for 6 days to 2 weeks; 42% believed it lasts 0 h to 2 days and 37.3% believed it stayed in breast milk permanently.We used χ2 tests to determine differences in agreement with potential risks and benefits of marijuana use during pregnancy and breastfeeding as a function of marijuana use, Latino ethnicity, gender, and parental status. Table 3 presents the proportions of agreement with benefits and risk statements by participants who had ever versus never used marijuana. As predicted, those who had ever used marijuana were significantly more likely to be neutral about or agree with all statements about potential benefits of marijuana use while pregnant or breastfeeding, and to be neutral about or disagree with all statements about potential health risks of marijuana use while pregnant or breastfeeding. Analyses of differences between participants who had used marijuana in the past 6 months and those who had not revealed similar patterns of group differences.

Those who had used marijuana in the past 6 months reported higher agreement with all benefits statements and lower agreement with all risk statements for marijuana use in pregnancy and while breastfeeding . Latino and non-Latino participants were generally comparable in their agreement about benefits and risks of marijuana use while pregnant and breastfeeding, with the following exceptions in which, as predicted, Latino participants reported relatively more health-cautious beliefs . Fewer Latino than non-Latino respondents endorsed the beliefs that marijuana use during pregnancy reduces pain and discomfort and that it reduces morning sickness and nausea, and more Latino than non-Latino respondents agreed that use could lower a child’s IQ. For statements about marijuana use while breastfeeding, fewer Latino than non-Latino respondents were neutral about or agreed that it helps calm the baby and more Latino than non-Latino respondents believed that use increases the risk of attention and learning difficulties.In terms of gender differences, predictions that benefits beliefs would be higher and risk beliefs would be lower for male than female respondents were supported for 10 of the 24 beliefs . More males than females agreed that marijuana use during pregnancy reduces pain and discomfort whereas more females than males agreed that marijuana use during pregnancy increases the risks of preterm birth and low birth weight. With respect to marijuana use during breastfeeding, more males than females agreed that it helps to reduce pain and depression, poses no lasting harms to the baby, is safe because marijuana is plant-based and natural, and increases a mother’s milk supply. In contrast, more females than males indicated agreement that use during breastfeeding can lead to infant THC addiction and increase the risk of behavior problems.The present study revealed distinctive patterns of beliefs about marijuana use while pregnant and breastfeeding in a sample of residents in primarily rural, Latino-majority, and disadvantaged communities in California, a state that has legalized the use of marijuana for recreational and medicinal purposes. Taken together, the levels of endorsement of beliefs about benefits of use and lack of endorsement of beliefs about risks to infant health and well-being highlight the need for public health education about risks of maternal marijuana use in these communities and identify specific beliefs and community groups to prioritize in these educational efforts. Overall, the most common beliefs regarding marijuana use both during pregnancy and while breastfeeding are that it helps to reduce pain and depression. These beliefs are not supported by empirical research. In contrast with modest support that cannabis can reduce pain for people with chronic pain conditions , evidence that it reduces pain sensitivity is inconsistent , and research on the effects of marijuana on pain during pregnancy and while breastfeeding remains lacking. For depression, the evidence base suggests that cannabis can worsen rather than improve depression and, to our knowledge, no studies have tested its impact on depressive symptoms for pregnant and breastfeeding women. Almost 30% of participants reported neutral or positive beliefs that marijuana reduces nausea during pregnancy, another belief that is not empirically supported. Although some evidence suggests that cannabis can aid in pain relief and nausea in cancer patients , it is also positively linked with episodes of nausea and vomiting in cancer patients and the general public . Despite perceptions that cannabis reduces nausea during pregnancy and prevalent advice from cannabis dispensaries to use cannabis to alleviate pregnancy-related nausea , evidence remains lacking. In addition to the substantial endorsement of beliefs that use provides symptom relief for maternal users, up to 25% of participants were neutral about or agreed that it is safe because it is plant-based and natural and that it poses no lasting harms to the infant. Beliefs that use while breastfeeding helps calm the baby received the least endorsement, suggesting that it might be given lower priority in health communications and guidelines. Overall, these survey results are consistent with qualitative findings of benefit beliefs held by pregnant women and demonstrate that they are shared by community members more generally.

SWM task reaction time and accuracy data were collected during scanning and composite scores were calculated to provide a single, comprehensive measure of performance and use fewer degrees of freedom in analyses, providing more statistical power . Reaction-time data and accuracy measures were converted into z-scores, and reaction-time z-scores were subtracted from accuracy z-scores to compute the performance composite score. Using this approach, high accuracy would result in a high positive z-score, while low reaction time, which is better, would result in a high negative zscore. Therefore subtraction of the negative reaction time z-score from the positive accuracy zscore would yield a positive index indicating high overall performance Imaging Data. Imaging data from each teen were processed as in our prior studies using Analysis of Functional NeuroImages . Time series data were corrected for motion. Number of removed repetitions and average movement in each direction throughout the task were examined in relation to group, task, and interactions using correlational analyses. The average percent of repetitions removed for excessive motion during the task was 8%, resulting in 92% retained for analyses. There were no significant differences between groups in bulk motion in any of the six movement directions . The average rotational movement throughout the task for MJ users was 0.04, 0.14, and 0.05 degrees for roll, pitch, and yaw, respectively. In controls the average rotational movement throughout the task was 0.07, 0.13, and 0.06 degrees for roll, pitch, and yaw, respectively. Among MJ users, the average translational movement was 0.11, 0.05, and 0.08 mm for superior, left, and posterior, respectively; the average translational movement of controls was 0.14, 0.06, and 0.07 mm for superior, left, and posterior, respectively. There was a significant group difference in the roll direction = 2.35, p = 0.03, although such movements were quite small. Next,grow cannabis fMRI data were deconvolved with a reference function that coded the hypothesized BOLD signal for each task condition .

Controlling for linear trends, spin history effects, and delays in hemodynamic response, we computed for each brain voxel a fit coefficient that represented the relationship between the observed and hypothesized signal change for contrasts between SWM and vigilance conditions . These functional datasets were warped into standard space , resampled into 3 mm3 voxels and smoothed with a 5 mm Gaussian filter. Statistical Analyses. Regression analyses determined the variability in brain response accounted for by group, task performance, and their interaction. These group level analyses were performed in each voxel of the brain and examined the BOLD response contrast between SWM and vigilance. To control for Type I error, we only interpreted significant effects in clusters of 50 contiguous significant voxels , yielding an overall clusterwise α = .05, determined by Monte Carlo simulations . Exploratory follow-up regression analyses were performed to determine the nature of the group by performance interaction. A main effect of group revealed that marijuana users showed significantly greater activation than controls in a cluster encompassing the right basal ganglia, as well as in a second cluster encompassing the right precuneus, postcentral gyrus, and superior parietal lobule 7) and in the left precuneus and superior parietal lobule . There was no region in which marijuana users demonstrated reduced activation compared to controls . Across all subjects, both users and controls, behavioral performance data positively predicted activation in seven clusters : right middle temporal gyrus, parahippocampal gyrus, and inferior temporal gyrus; right cerebellar tonsil; right inferior parietal lobule, supramarginal gyrus, angular gyrus, and middle temporal gyrus; left middle temporal gyrus and superior temporal gyrus; left middle occipital gyrus, middle temporal gyrus, and inferior temporal gyrus; right middle frontal gyrus; and left middle frontal gyrus and inferior frontal gyrus. There were no regions in which performance was negatively associated with brain response .

A group by performance interaction was found in five clusters : left superior temporal lobule, left superior temporal gyrus and left middle temporal gyrus; right temporal gyrus and right uncus; left anterior cingulate; left uncus and left parahippocampal gyrus; and right thalamus and right pulvinar. However, findings were re-examined using movement as a covariate, and all findings remained unchanged . Performance and BOLD response data were checked for outliers, and none were found. Cases appearing as possible outliers on scatter plots were removed and analyses were redone; results remained unchanged. Both groups were checked for outliers on mood measures; although neither group contained an outlier on the BDI, the marijuana group contained one outlier on the Hamilton Anxiety Rating Scale. Analyses were re-run excluding this subject and results remained unchanged.This study examined the association between behavioral performance and brain response during a SWM task among 16- to 18-year-old marijuana users and controls after 28 days of abstinence. Results suggest that, in general, marijuana-using teens performed similarly on SWM than controls, perhaps due to the low difficulty level of the task , which approached ceiling effects. This has been observed in fMRI studies of SWM in adult marijuana users . However, specific localization and intensity of response varied between the MJ users and controls, with MJ users showing more performance-related activation in certain regions and less in others. These differential patterns emerged despite similar overall task performance across groups, suggesting an alternate relationship between task performance and brain activity among marijuana users. MJ users showed significantly more activation than controls in the right basal ganglia, an area associated with skill learning . Since the subjects were only allowed to practice the task once before entering the scanner, it is possible that the MJ users were still in the skill learning process during imaging.

The other two clusters, which were significantly more activated in marijuana users than controls, were the right and left parietal lobes. Bilateral parietal regions have been implicated in attention, spatial perception, imagery, working memory, special encoding, episodic retrieval, skill learning monitoring, organization, and planning during working memory . It is possible that there is compensatory neural effort in these areas, as observed in SWM studies of adult marijuana users . The performance data positively related to activation in several areas, and did not negatively associate with brain response in any region. Performance was positively associated with activation in the left and right temporal regions, which are associated with verbal mechanisms and episodic, nonverbal working memory and retrieval, respectively . This suggests that good task performance may be related to using multiple memory modalities. High-scorers showed more activation in the bilateral prefrontal and bilateral parietal regions that have been shown to activate during SWM tasks in youths . The performance by group interactions were the focus of this study and yielded the most interesting results. In particular,indoor cannabis grow system an interaction in the left superior temporal gyrus suggested a positive association in the users and a negative association in the controls. This may imply that the MJ users used more of a verbal strategy to achieve high task performance scores than the controls. This is interesting when considering the previous findings of deficits in verbal learning and IQ in marijuana using adolescents compared to controls . Furthermore, the right superior temporal gyrus showed an interaction where users had a negative association and controls had a positive association. Previous studies have shown this area to be involved in poorer recognition of previously seen words . This would support the notion that users are relying on a verbal strategy so that better performance linked to a decrease in activation in the right superior temporal gyrus. Moreover, an interaction in the right thalamus and pulvinar showed a negative association in the users and a positive association in the controls. These sub-cortical structures have shown an association with spatial neglect when damaged . It is interesting that these areas have a negative association in users and a positive association in controls, and may suggest that marijuana users utilize less spatial strategies than controls. The nature of the interaction revealed a positive association in marijuana users and a negative association in controls in the left anterior cingulate. This region has been linked to attention, decision-making, cue response, and response monitoring . It may be that good performing marijuana users are making a more conscious decision to react to task cues than controls, who may be reacting more automatically. The left parahippocampal gyrus demonstrated an interaction of negative association in marijuana users and positive association for controls. This region is involved in working memory and is recruited when the temporal lobe is not in use . Since marijuana users are using more energy in the left temporal lobe as their performance increases, higher scoring subjects may rely less on the parahippocampal gyrus. The distinct interactions viewed in these different areas of the brain can mean that different systems are at work, and as one part of a system decreases in action, the other area of the system increases in activation.

Previous studies suggest that subjects who do not use traditional strategies for specific tasks showed an increased extent of activation and recruitment of additional areas, specifically verbal areas, to accomplish the task . More specifically, the pattern of results suggests that marijuana users may apply a verbal strategy to the task when achieving higher scores. It is possible that this alternative way of using the brain may be less efficient; this would explain the greater overall activation in users versus controls and recruitment of other brain regions as a compensation method. A recent review also found that multiple neuroimaging studies of marijuana users pointed toward recruitment of compensatory regions as well as task-related regions to achieve task demands . A possible limitation to this study is the interpretation of a difference in fMRI activation between experimental groups. It is possible that alternative neural pathway use is more dynamic and versatile. It is unclear whether the results are an adverse effect of the marijuana use or merely a benign difference. Further studies that more carefully describe the relationships between task performance and brain response will clarify this question. Another limitation of the current study is that most marijuana users were also moderately heavy alcohol drinkers. While these participants are representative of the population of adolescent marijuana users, most of whom also consume alcohol , it is nonetheless difficult to disentangle the effects that may be related to alcohol use. Alcohol use correlated with brain response in the right thalamus and pulvinar in the current study, but results remained significant even when accounting for alcohol use, and alcohol use did not correlate to activation in any other significant regions. Our previous research identified brain response abnormalities among marijuana users above and beyond those demonstrated by users of alcohol alone , supporting the hypothesis of marijuana-specific differences in brain response, even among teens who are heavy drinkers. Future studies should attempt to clarify the differential and interactive impact of concomitant alcohol and marijuana use on brain functioning on adolescents. Furthermore, lifetime marijuana use episodes were associated with activation in the right uncus and superior temporal gyrus. Future analyses could further investigate the associations of other brain regions, as well as neuropsychological performance, with lifetime use episodes. These subtle differenced among users may provide additional insight into the mechanisms involved with prolonged abstinence from marijuana. Future studies should also focus on investigating the nature of interactions in other domains of cognition to test if other types of tasks show these patterns. If a user’s neural differences are actually a compensatory tool, then a more difficult task may overcome their compensation abilities, therefore resulting in performance deficits. In addition, a parametric manipulation of working memory load could help specify degree of compensatory activation in marijuana users compared to controls, as marijuana users may reach a limit earlier than controls. Further studies could also explore which mechanisms and strategies subjects utilize during the tasks through qualitative data investigation. Recreational marijuana commercialization is gaining momentum in the US. Among the 11 states and Washington DC that have legalized recreational marijuana since 2012, retail markets have been opened or anticipated in 10 states, where over a quarter of the US population live. The presence of recreational marijuana dispensaries increased rapidly following the commercialization. Children are at a high risk of initiating marijuana use and developing adverse consequences related to marijuana. 

A number of studies have also examined secondary changes in marijuana use following receipt of an alcohol-specific intervention. A recent integrative data analysis study indicated that alcohol BMIs may not facilitate changes in marijuana use among college students ; instead, regardless of treatment condition, college students who successfully reduced their drinking at short- and long-term follow-ups were more likely to be non-users of marijuana or reduce their marijuana use at follow-up. This complementary relationship between marijuana and alcohol use is also supported by research indicating that the risk factors for initiation and maintenance of problematic use are similar across substances . Together, these studies suggest that interventions for alcohol may lead to secondary changes in marijuana use. Consistent with this hypothesis, young adults who participated in an in-person BMIs for alcohol use in an emergency department setting reported greater decreases in marijuana use at the 6-month follow-up than those who received feedback only . Similarly, weekly marijuana users who were seeking treatment for cigarette smoking and completed a brief alcohol intervention within the context of the smoking cessation intervention, demonstrated reductions not only in heavy drinking and tobacco smoking but also in marijuana use . In the college setting, BMIs that target multiple substances have also been associated with reductions in poly-drug use . One explanation for the differential influence of alcohol interventions on marijuana use across these studies may be related to the populations examined. Thus far, alcohol interventions delivered to acute-risk populations have had an impact on marijuana use outcomes, while collectively, interventions delivered to ‘college students’ have not. However, college students are a heterogeneous population, and not all require the same level of intervention . To our knowledge, no one has examined the influence of an alcohol intervention on marijuana use when alcohol interventions are provided sequentially in the context of stepped care,cannabis drying racks in which individuals who do not respond to an initial, low-intensity level of treatment are provided a more intensive treatment .

The purpose of the current study was to examine marijuana use in the context of a stepped care intervention for alcohol use.We conducted a secondary analysis of data from a randomized clinical trial implementing stepped care with mandated college students . In this study, all participants received a brief advice session administered by a peer counselor. Participants who continued to drink in a risky manner six weeks following the BA session were randomly assigned to either BMI or AO conditions . Step 2 participants who completed the BMI as opposed to AO reported greater reductions in alcohol-related consequences at all follow-up assessments . We tested three hypotheses to examine whether interventions that reduce alcohol-related outcomes may also reduce marijuana use. First, because dual marijuana and alcohol users consume higher levels of alcohol use and experience more alcohol-related consequences , we hypothesized that marijuana users would report higher HED frequency, peak blood alcohol content , and alcohol related consequences in the 6 weeks following a BA session, after controlling for their pre-BA drinking behavior. Second, we hypothesized that heavy-drinking marijuana users who did not respond to the BA session and, therefore, were randomized to a Step 2 BMI or AO would report worse alcohol-related outcomes at 3-, 6-, and 9-month follow-ups than non-users. Third, we examined whether marijuana users changed their marijuana use frequency at any of the three assessment time points following the Step 2 BMI. Examination of marijuana use in this context will improve our understanding of whether marijuana use lessens the efficacy of alcohol interventions, even when delivered sequentially in stepped care. Furthermore, it will inform future intervention efforts aimed at reducing both alcohol and marijuana use. 2. Method 2.1. Participants and procedures Participants were 530 undergraduate students age 18 years and older who violated the campus alcohol policy at a four-year, private, liberal arts university in the Northeast . Students were referred to the student health office for mandatory counseling following adjudication by campus judicial affairs staff, agreed to participate in the study and provided informed consent. All students received Step 1, a manualized, 10 to 15-min Brief Advice session that was administered by a peer counselor .

Six weeks after the BA session, participants completed an online assessment. Higher risk students were eligible to receive the next step of care and were randomly assigned to BMI or AO . Lower-risk drinkers were not randomized to Step 2 nor were provided additional intervention, but completed follow-up assessments at 3, 6 and 9 months.Participants indicated how many times they used marijuana in the past 30 days at baseline and at each follow-up assessment time point. Because marijuana use was highly zero-inflated , and due to our interest in whether being a marijuana user influenced intervention outcomes, dichotomous variables were created to group individuals into user versus non-user for use in analyses to compare these subgroups.Alcohol use was assessed using the Alcohol and Drug Use Measure at baseline and each follow-up. To determine if participants who completed Step 1 of the intervention would also complete Step 2, participants reported the number of times they engaged in heavy episodic drinking , defined as consumption of 5+ drinks for males , in the past month. The maximum number of drinks consumed during their highest drinking event in the past month and the amount of time spent drinking during this episode were used to calculate the students’ estimated peak blood alcohol concentration using the Matthews and Miller equation and an average metabolism rate of 0.017 g/dL per hour.First, distributions of outcome variables were examined, and outliers falling three standard deviations above the mean were recoded to the highest non-outlying value plus one , resolving initial non-normality in outcomes. Demographic information and descriptive statistics for the outcome variables were calculated . To examine marijuana users’ drinking behavior following BA for alcohol misuse , multiple regression models were run to predict each alcohol outcome variable at the 6- week assessment from baseline marijuana user status , controlling for gender and the corresponding alcohol outcome assessed at baseline. To test hypotheses 2 and 3, hierarchical linear models were run in the HLM 7.01 program , using full maximum likelihood estimation. HLM is ideal for data nested within participants across time, for testing between-person effects and within-person effects on outcomes. An additional advantage of HLM is its flexibility in handling missing data at the within-person level, allowing us to retain for analysis any participant that contributed at least one follow-up assessment. We interpreted models that relied on robust standard errors in the determination of effect significance.

All intercepts and slopes were specified as random in order to account for individual variation in both mean levels of the outcomes and time-varying associations. Fully unconditional HLM models were run first in order to determine intraclass correlations for each outcome. ICCs provided information on the percentage of variation in each outcome at both the between- and within-person level. Next, three dummy coded time components were created for inclusion at Level 1. The first was coded and therefore allowed examination of the impact of effects on change in the outcome variable from baseline to the first followup, the second was coded to model the impact of effects on change in the outcome variable from baseline to the second follow-up ,cannabis grow tray and the third was coded in order to estimate the impact of effects on change in the outcome variable from the first to the third follow-up . In the context of these three dummy codes, effects on the intercept represent effects when all time effects are equal to 0 . Of note, as all participants received a BA session in the interim between the true baseline and 6-week assessment, marijuana user status at the 6-week assessment was used as the baseline for these analyses .To address hypothesis 2 , Level 2 effects for marijuana user status, treatment condition, and the interaction between marijuana user status and treatment condition were regressed on the three time components. Following recommendations of Aiken and West , prior to forming interactions, marijuana user status and treatment condition were recoded using effects coding , to remove collinearity with interaction terms so that all main effects of time could be evaluated in the context of models including interactions. To control for potential baseline group differences, we also regressed marijuana user status and treatment condition on the intercept. To address hypothesis 3 [i.e., whether treatment group impacts marijuana use frequency at any of the three follow-up time points, among those who reported marijuana use at 6-week pre-BMI assessment], at Level 2, treatment condition was regressed on the Level 1 intercept and all three time effects of marijuana use frequency. In models for both hypotheses 2 and 3, at Level 2, gender also was included as a covariate.Results of the HLM models predicting three alcohol outcomes at each follow-up by marijuana user status, treatment condition, and marijuana user status by condition interactions are displayed in Table 4. In the prediction of HED frequency, marijuana user status was associated with higher baseline HED frequency; however, being a marijuana user was not associated with more or less change in HED frequency between the pre-BMI assessment and any of the three follow-ups. There were no interactions between marijuana user status and treatment condition at any follow-up, suggesting that the BMI was not more or less effective for marijuana users. In the prediction of pBAC, marijuana user status was associated with higher pre-BMI pBAC. Additionally, those in the BMI condition had significantly lower pre-BMI pBACs. Controlling for these pre-BMI differences, being a marijuana user, treatment condition, and their interaction were all non-significantly associated with change in pBAC from pre-BMI to each of the follow-ups. In the prediction of alcohol consequences, being a marijuana user was associated with higher pre-BMI levels of consequences.

There were no significant effects of marijuana user status, treatment condition, or their interaction on change in consequences between baseline and either the 3- or the 6- month follow-ups. At the 9-month follow-up, those in the BMI reported fewer alcohol consequences1 ; however, this was not moderated by marijuana user status. Overall, these findings suggest that collapsing across treatment condition, marijuana users had heavier alcohol consumption and consequences compared to non-users at the pre-BMI assessment, but they did not increase or decrease their consumption or consequences between pre-BMI and any of the follow-ups. Additionally, marijuana users responded to the BMI similarly to non-marijuana users at each time point .The purpose of the current study was to examine whether heavy drinking marijuana users demonstrate poorer response to two different alcohol-focused interventions compared to non-users and to examine the efficacy of an alcohol-focused BMI on marijuana use frequency among marijuana users receiving stepped care for alcohol use. Our findings indicated that marijuana users and nonusers evidenced equivalent treatment responses to the alcohol-focused BA session and reported similar alcohol-related outcomes following the BMI. Consistent with prior research , the alcohol-focused BMI did not significantly reduce marijuana use frequency in comparison to the assessment-only group. In our sample, marijuana users did report higher alcohol consumption and problems at baseline/pre-BMI regardless of condition, and these differences between users and nonusers persisted over time. The findings of the current study are somewhat consistent with studies indicating that marijuana use does not decrease the efficacy of alcohol interventions . Although marijuana use did not necessarily lessen the efficacy of the BA and BMI sessions on alcohol use and consequences, regardless of condition, marijuana users reported higher levels of alcohol consumption and consequences at baseline and the pre-BMI assessment. This is especially noteworthy because dual users typically report increased consequences related to their alcohol use and may have a higher likelihood of being referred to alcohol-focused treatment or mandated to receive intervention for alcohol-related sanctions. Although heavy drinking marijuana users may demonstrate reductions in alcohol consequences following an alcohol-focused intervention , their frequency of marijuana use did not change as a result of receiving a BMI. We can posit several reasons for the participants’ continued use of marijuana, despite a decrease in alcohol-related consequences. First, the parent study found a reduction in alcohol consequences following the alcohol-focused BMI, but not a decrease in alcohol consumption.

While these studies provide compelling data about the scope of problems stemming from the co-use of these substances, a fundamental limitation of such population-level, cross-sectional studies is that they are unable to answer questions about event-level patterns of use. That is, the cross-sectional nature of these studies cannot provide information about the pattern and predictive relationship of simultaneous co-use within a given day or drug-use event, which may be especially critical to understanding the co-use of marijuana with other substances. For example, individuals report both using marijuana as a substitute for tobacco or alcohol and in a sequential/simultaneous manner to produce additive or subtractive subjective effects . It is difficult to differentiate such patterns of use unless examining event-level data. The few, recent studies that have used a fine-grained approach to study simultaneous use, while important, have limitations that may affect generalizability. A study examining event-level alcohol and marijuana co-use in adolescents did not report patterns of co-use, only the context of and consequences from simultaneous co-use . Another study that examined daily patterns of marijuana and alcohol co-use, but not cigarette use, in a predominantly male , veteran population found that moderate and heavy-drinking were more likely to occur on days which marijuana was used. Further, while individuals with AUD or comorbid AUD + CUD were more likely to drink heavily on such days, individuals with CUD were less likely to drink heavily, which the authors interpreted as supporting marijuana substitution . Lastly, Gunn et al., found that daily marijuana use was associated with greater alcohol consumption in college students, and this predictive relationship strengthened over a two-year period. However,cannabis grow equipment they did not report on tobacco use nor the influence of sex on the relationship between daily marijuana and alcohol use.

In light of the high rates of marijuana co-use with alcohol and tobacco in epidemiological studies but relatively absent data on event-level patterns of use, the goal of the present study was to examine daily patterns of alcohol, tobacco, and marijuana co- and tri-use in non-treatment seeking drinkers who report regularly using tobacco and marijuana. To our knowledge, no studies have examined the daily co-use or triuse of all three substances at the individual, event-level in the same sample. Because of the strong evidence for the co-use of all three substances, we hypothesized that use of one substance would indiscriminately increase the odds of same-day use of a second substance, and the use of two substances would subsequently increase the odds of using the third. Finally, as an exploratory aim, we examined whether sex moderated any observed daily patterns of co- or tri-use. While men tend to use marijuana, alcohol, and cigarettes earlier, heavier, more frequently, and have greater dependence rates than women , women may have more severe consequences from substance abuse and enter treatment earlier than men . Given the general dearth of event-level substance use studies, sex differences in patterns of simultaneous co-use have obviously not been well characterized. However, at the population level, 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 . The characterization of sex differences in patterns of event-level co-use also may have important implications for understating the etiology and treatment of addiction. All study procedures were approved by the University of California, Los Angeles Institutional Review Board and conducted in accordance with the Declaration of Helsinki. The reported sample draws from baseline data collected as a part of four human laboratory studies. Three studies examined pharmacotherapies for alcohol use: naltrexone in an Asian American sample , ibudilast , and ivermectin .

The fourth study was an alcohol self administration study , resulting in a total sample of 551 participants. Each study recruited a sample of non-treatment seeking, regular drinkers from the Los Angeles area using identical recruitment methods of print and online advertisements. Interested participants completed an initial telephone screening to determine eligibility. During the telephone screening, all participants were asked to report their drinking over the past three months prior to enrollment. The drinking requirement for each study had the following inclusion criteria: naltrexone in Asian Americans – female requirement of > 4 drinks per week and male requirement of > 6 drinks per week, as well as have an Alcohol Use Disorder Identification Test score greater than 8; ibudilast and ivermectin – requirement of > 48 drinks per month and score > 1 on the CAGE questionnaire assessing for alcohol problems; self-administration – female requirement of > 7 drinks per week and male requirement of > 14 drinks per week. Age restrictions for the naltrexone and ibudilast study were between 21–55, whereas participants had to be between 21–65 for the ivermectin study and 21–45 for the self-administration study. Only two studies had ethnicity requirements. Participants in the naltrexone in Asian American study were of East Asian ethnicity and participants in the self-administration study were Caucasian. All studies shared the following exclusion criteria: 1) current involvement in treatment programs for alcohol use or having received treatment in the past 30 days; 2) use of nonprescription drugs or prescription medications for recreational purposes; 3) self-reported history of exclusionary psychiatric disorders assessed during telephone interview; 4) currently using antidepressants, mood stabilizers, sedatives, anti-anxiety medications, seizure medications, or prescription pain killers ; 5) self-reported history of contra-indicated medical conditions or any other medical condition that may interfere with study participation; 6) intense fear of needles or adverse reactions to needle puncture; and 7) if female, pregnancy, nursing, planning to get pregnant in the next 6 months or refusal to use reliable method of birth control.

Specific to the ivermectin study, participants were excluded if they had a Body Mass Index less than 18.5 or greater than 30. For the self-administration study, participants were excluded if they weighed over 265 pounds. If participants were eligible following the telephone screening, they completed an in-person screening visit where written informed consent was obtained. During the screening visit, participants were required to produce a breath alcohol concentration of 0.000 g/dl, and test negative for pregnancy and drug use, except for marijuana, on a urine toxicology screening.Participants completed a battery of measures at the screening visit. A demographics questionnaire assessed age, sex, and ethnicity. The Timeline Follow-Back queried daily alcohol consumption in standard drinks, number of cigarettes smoked per day,mobile grow system and marijuana use during the previous thirty days. Marijuana use was assessed in a dichotomous fashion ; route of marijuana administration was not recorded. Alcohol and cigarette use was assessed as a continuous variable. Use of other tobacco products, e.g. snus or chewing tobacco, was not assessed. The Fagerström Test of Nicotine Dependence queried extent of nicotine dependence. The AUDIT was administered to evaluate severity of drinking. The Cannabis Use Disorders Identification Test , a reliable and valid adaptation of the AUDIT, was given to assess marijuana use severityOf the 551 total subjects who were screened for the four studies from which we culled data, 541 reported using alcohol on the AUDIT and/or TLFB, 296 reported using marijuana on the CUDIT and/or TLFB, and 260 reported using cigarettes on the FTND. As this study aimed to understand patterns of co- and tri-use among all three substances, we included only participants who reported using alcohol, cigarettes, and marijuana on a monthly basis. This selection resulted in a final sample of N = 179 participants. While this represents a significant decrease in the number of subjects, statistical power is still quite high for these analyses. The proposed analyses test the association between drug use on a per-day basis . Thus, the sample size for this study is properly conceptualized in terms of both the number of subjects, 179, but also the number of Level 1 observations which is 5390 total days. To confirm that this study is well powered, GPower 3.1.9.2. was used to conduct a power analysis. Based on a simplified repeated measures approach, a small effect size and a nominal α = 0.05 threshold, power for this study was exceptionally high . To explore patterns of marijuana, alcohol, and/or cigarette co-use a series of multilevel logistic models were run on 30-day timeline follow-back drug use data. Owing to the one on-one clinical interview nature of data collection for the key variables, there was no missing data in this study. Only individuals who reported using all three substances at least once per month were analyzed.

Multilevel logistic modeling was chosen because the data structure is nested with days nested within subjects which is appropriately modeled with a multilevel modeling approach and the outcome variable of whether a given drug was used on a given day is binary necessitating the logistic modeling approach. Multilevel logistic models were run via PROC GLIMMIX in SAS version 9.4 with a binomial dependent variable distribution and a logit link function. Models were run with cigarette use and marijuana use as the dependent variable and the other drug classes treated as predictor variables with main effects and interactions to test for potentially synergistic effects of combined use on the likelihood on the third drug use , or a suppressive effect where use of both drugs is associated with the same risk as singular use . Variables tested were Drink, a binary Level 1 variable coding whether alcohol was consumed on a given day, Smoke, a binary Level 1 variable coding whether cigarettes were smoked on a given day, and Marijuana, a binary Level 1 variable coding whether marijuana was used on a given day. To disentangle within-person effects from between person effects , person-means for each predictor variable were entered into models as Level 2 variables. To further ensure that the effects reported are within-subject effects, all Level 1 variables were treated as random at Level 2, meaning the effects were allowed to vary between subjects. Where interactions were observed, analysis of simple slopes were conducted through a recentering scheme to test the lower-order effects at specific levels of the interacting variables. In accordance with the NIH policy on considering sex as a biological variable and given the sex differences in the prevalence of marijuana, tobacco, and alcohol use in the US , we also tested for sex differences in the propensity for drug co-use. To test the robustness of these results several covariates were explored including: age, ethnicity, and source study, all of which were included as Level 2 variables. Ethnicity was examined as a covariate because one of the source studies was completely composed of individuals of East Asian descent , and age was included due to findings that patterns of co-use may differ by age . In line with the recommendations of , where discrepancies between models which included vs. omitted covariates were observed, we report the results of both models.Please see supplemental information for summary tables of all results. To first test whether average drug use frequency across these three drugs of abuse were correlated at the subject level, a series of linear regressions were conducted analyzing the correlation between proportion of days using each drug from the TLFB. Both drinking frequency and marijuana use frequency were found to independently predict cigarette smoking frequency , but drinking frequency did not predict marijuana use frequency . While these results suggest that subjects who use cigarettes and drink alcohol more often also use marijuana more frequently, these analyses do not address the central question posed in this paper of whether use of one substance on a particular day increases the likelihood of co-use or tri-use on that same day.To test whether use of one drug increases the likelihood of same-day co-use, a series of multilevel models were run with daily use of each drug included as Level 1 variables and drug use frequency person means also included as covariates to disentangle the between subject effects summarized in 3.1 from same-day effects. such that the effect of combined use of alcohol and marijuana on a given day was sub-additive . This interaction was such that on non-drinking days, marijuana use was associated with relatively large increases in the likelihood to smoke cigarettes .

We placed flyers in marijuana dispensaries, vape shops, cafes, stores, on bulletin boards at community colleges, and on Craigslist and Facebook. We attempted to interview participants twice, in order to allow conversations to develop more deeply and to use the content of the first interview to inform questions in the second. Out of 32 enrolled participants, 24 completed the second interview. This study was approved by the University of California, San Francisco Committee on Human Research. Participants provided written informed consent. We used pseudonyms for this and all publications. Semi-structured qualitative interviews lasting 60-90 minutes were conducted between January and August 2015 by six trained interviewers. Discussion topics included definitions of smoking, experiences with tobacco, e-cigarettes, marijuana, marijuana vaporizers, and other products. To further generate discussion of comparative harms and benefits of products, participants were asked to arrange labeled pictures of various products from the least harmful to the most harmful and talk through their sort process.We audio recorded and professionally transcribed the interviews and coded transcripts using Dedoose software. LP and SS independently blind-coded and compared a sub-set of transcripts to develop the study’s coding guidelines. We created code definitions, developed a consistent coding scheme and discussed the coding results to ensure codes were applied consistently. SS coded the larger set of remaining transcripts. Given the emerging nature of the legalized marijuana market and the lack of existing research in this context, ebb and flow trays we adopted a thematic analysis approach that would allow us to discover emerging behaviors and meaningful categories for our participants and to generate themes iteratively during review of coded transcripts.

All authors reviewed memos with illustrative quotes summarizing each theme and discussed themes iteratively to reach consensus and theme saturation. Participants primarily evaluated products along five dimensions: whether or not the product was combusted, potency of the psychoactive agent delivered, presence of unnatural chemicals, addictiveness, and source of information about the product. Participants generally assessed harms in the context of perceived benefits, and frequently discussed alternative products delivering similar benefits, but with less harm. They gauged harms in nuanced ways, with criteria for judging harm differing between tobacco and marijuana products and comparing them with alcohol, illicit drugs, and pharmaceuticals. Participants commented on the addictive nature of tobacco compared to alcohol, opiates, and prescription drugs. In contrast, participants spoke about the habitual urges to use marijuana but rarely reported physiological withdrawal symptoms. Several participants also explicitly mentioned physiological changes in the body associated with addiction to marijuana. ‘Ben’, age 22, commented, “I feel that for a lot of people, especially in Colorado, it’s very much emotionally dependent, mentally dependent, on marijuana…Over a chronic period of [using] it, your body adjusts and lowers the blood flow to your cerebellum so that when you smoke, you have normal flow.” Some, like ‘Brad’, age 25, acknowledged that they “need [marijuana] to function.” Some participants discussed addiction to marijuana as affinity for euphoria or the positive feelings associated with getting high. Thus, it was the psychological benefits of marijuana that were viewed as addictive. When asked what the addictive component in marijuana is, ‘Owen’, age 20, said, “Euphoric feeling;” an observation confirmed among several other participants. Participants did not generally identify tolerance as a sign of addiction. Instead, some marijuana users discussed high potency products as a useful way to overcome tolerance and reclaim positive effects. Participants also discussed tolerance increasing with use; some temporarily stopped use for a few days or weeks to bring their tolerance level down in order to be able to better feel the effects of marijuana when using smaller doses or less concentrated products.

The ability to abstain from use without physical withdrawal validated for some participants that marijuana was not addictive. As ‘Erin’, age 24, commented, “I smoke [marijuana] because I like it, but, I don’t have to wake up in the morning and smoke a bowl like I have to wake up in the morning and smoke a cigarette. I’ve gone periods of time without smoking marijuana, and you can quit like that. I don’t feel like it has the addictive properties that cigarettes have.” Because of its perceived naturalness, marijuana was also believed to be superior for therapeutic functions to pharmaceutical “pills” , which were perceived as more addictive. Informational sources mentioned by participants can be broadly classified as external and internal . Participants described information from governmental agencies as exclusively concerning harms of products, and mostly related to tobacco use. Participants reported seeing warning labels on tobacco products, which was particularly salient to ‘Rashawn’, age 24, who had lost his grandmother to tobacco-related illness. As he explained, “my friends wanted to do [blunt wraps]…but I looked at them and it has this warning. … and I thought that was just for cigarettes. I wasn’t really prepared to have a warning of cancer on the blunt wrap. I was like, so you guys see this? … I’m not okay with it. I could possibly have cancer because my grandmother, she’s my favorite person in the world, she died of lung cancer.” While several participants explicitly referenced media campaigns when discussing tobacco related harms , it was less common for participants to cite health authorities as sources of knowledge on the harms of e-cigarettes or marijuana. Some participants noted the contradiction between government information on harms and their own experiences. ‘Sadie’, 24, shared, “My whole life I was taught that weed was really bad, and then when I smoked it, I was like, wow, this really makes me feel good. And I was depressed at the moment. And that was … the first time … I was okay.” Participants’ own bodies were a primary source of knowledge when discerning whether something was harmful or beneficial. ‘Timothy’, age 25, explained, “generally when things make you feel bad, they’re bad for you.” Similarly referencing bodily sensations, ‘Angela’, age 18, reflected, “Dabbing is really harsh on your lungs. It feels like someone is stabbing you in the lungs.” Participants often continued to use a product if their bodies did not communicate acute harms . ‘Timothy’, age 25, commented, “if I felt it affecting my lungs, then I would consider something else.” Few participants cited medical providers as a source of information about marijuana.

Employees of marijuana dispensaries and vape shops were an important source of guidance about benefits of particular products and strains. If a product yielded an unpleasant experience, retailers would recommend a different product or a lower dose rather than abstention. One of our participants, ‘Sadie’, age 24, worked at a marijuana dispensary and reported, “we have to find different strains out and what they do, how it affects people, and of course it affects people in a different way… if it’s an upper, a downer, if it’s a mellow one…” In sum, among our participants, retailers served as a source of information on product selection and customization, but not on potential harms. Our participants judged the harms and benefits of various tobacco and marijuana products along the dimensions of combustion, potency, presence of chemicals, potential addictiveness,4×8 flood tray and sources of knowledge. The narrative that non-combustible products are safer appears in the scientific literature, tobacco industry statements, and documents from regulatory agencies. While some participants recognized that smoking anything – tobacco or marijuana – was dangerous, more often combusted marijuana was perceived as safer because it was seen as having fewer chemicals and lower potency. Our findings complement the small literature on the comparative perceptions of harm of various tobacco and marijuana products, where perceptions of risk were lowest for marijuana flower. Our study enriches previous research by examining the reasons behind differential perceptions. Contrary to a previous hypothesis that marijuana flower was perceived as safer than marijuana concentrates because it was less addictive, few participants in our study considered the dependence risks of marijuana concentrates, but viewed them as a way to overcome increased tolerance. Perceived harm of marijuana concentrates was due in part to immediate adverse psychological effects, such as panic attacks. Chemicals were commonly perceived as harmful, particularly when used to manufacture tobacco or process marijuana into concentrates. Few participants associated harm with chemicals that naturally occur in tobacco or marijuana plants, thus perceiving “natural” tobacco products, such as Natural American Spirit cigarettes as less harmful, which may be unintentionally reinforced by national media campaigns educating youth about chemicals added to tobacco.

Participants utilized distinct sources of knowledge to evaluate the harms and benefits of tobacco and marijuana products. Anti-tobacco campaigns informed tobacco-related harm perceptions, whereas few public education campaigns informed perceptions of marijuana and e-cigarettes. Unpleasant personal experiences with marijuana intoxication were mentioned as potential harms more often than marijuana-related diseases or addiction, which might reflect a lack of information from government agencies on the health risks of marijuana and e-cigarettes. Even though the Colorado public awareness campaign ‘Good To Know’ publicized risks of highly potent marijuana edibles, our participants were experienced marijuana users, which may explain why no participants discussed how retailers had warned them to “go slow” with initial marijuana use. The findings from our study with a small convenience sample limited in age and geographically to young adults in Denver, CO, may not be generalizable. However, they point to issues worthy of future exploration and, together with other tobacco research, have implications for health education and product labeling. Educational campaigns about tobacco and marijuana products may be more relevant to young adults if they include diverse messages reflecting multiple dimensions of harms and benefits of tobacco and marijuana. Some aspects of tobacco harm might be relevant to marijuana, such as the dangers of marijuana combustion, as marijuana smoke impairs vascular function in ways similar to tobacco smoke. The harms of secondhand tobacco smoke exposure has been an effective theme in tobacco prevention and control media campaigns. However, educational messages that focus solely on dangers of combustion may be missing important nuances in how young people understand tobacco and marijuana related harms. Young adults in our study reported that they would rather smoke marijuana than use edibles, primarily because edibles were seen as more potent and more difficult to titrate dosage. While additional research is needed to ensure the generalizability of these findings, this study suggests that the availability of lower dose marijuana edibles may mitigate the tendency for users to choose combustible over edible products due to potency concerns. Our study also identified little awareness that there are harmful, naturally-occurring chemicals in tobacco in addition to chemicals added by manufacturers. This may be an opportunity for tobacco education that is also relevant to marijuana and e-cigarettes. These messages should be carefully tested to ensure they do not unintentionally encourage the belief that tobacco chemicals are harmless because they are natural. Studies are needed to determine how reduced risk perceptions of organic plant matter, whether tobacco or marijuana, impact use and prevalence among young people, and what education messages would address these misperceptions. Our findings suggest that messages using the words “addiction” or “dependence” may not resonate with young adults, particularly with respect to marijuana. Messages about tolerance or dose escalation may be the dimensions of addiction most relevant to marijuana users. Illustrating physical changes in the body or the brain as a result of long-term marijuana use may also aid understanding that marijuana dependence has physiological in addition to psychological risks. Stories from young users who have difficulty controlling their use would be worth exploring, similar to how the FDA’s Real Cost Campaign focused on “loss of control” as the relevant dimension of nicotine addiction. Future studies should evaluate these messages with larger representative samples. Additional research on the addictive qualities of marijuana and which demographics are particularly susceptible to these qualities is also needed. Medical professionals have important opportunities to educate patients about health risks of tobacco and marijuana use when screening for substance use. Recommendations will need to be updated continuously to address new products and delivery systems and provide guidance for medical professionals in this arena. Our finding that marketing describing products as “natural”, “pure”, “clean”, “additive-free”, or “organic” may increase product appeal or reduce perceived harm is in line with other studies. Taken together, these findings indicate the need for regulatory agencies to prohibit the use of such terms on tobacco and marijuana products .