For participants assigned to the clinician group, this might take the form of a warm hand-of from the clinician conducting the screening to the social worker. For those using the web-based interface, a direct link in the portal to make an appointment, or some form of chat function could be useful. In addition, the 4 h per week may have occurred at a time when the participants could not participate and may have necessitated a follow-up or additional clinic visit to meet with the social worker.Social science contributions to understanding multiple drug use have lagged behind those from the natural sciences . The regular and combined use of multiple substances is disproportionately practiced by some minority and socially marginalized groups, such as gender minority individuals . The particular risks and benefits associated with multiple drug use demand a better understanding of its unique characteristics, including its specific patterns, combinations, intentions, and contexts . We use the term multiple drug use to encompass both ‘drug use repertoires’ and ‘drug use combinations’. Drug use repertoires refer to the variety of substances a person ingests during a particular time frame . ‘Drug use combinations’ refer to the ingestion of two or more substances at the same time or in close temporal proximity so that overlapping psychoactive effects are produced . Prominent methods for researching multiple drug use include retrospective surveys that inventory participants’ drug use repertoires over the past month or year, and in-depth interviews and ethnographic field work that examine practices and experiences of drug use combinations. Increasingly,cannabis drying mobile and geo-enabled technologies are being integrated with qualitative research methods to ground drug use practices and experiences in their social and physical environments . In the spirit of creative research methods like these , we integrated geo-enabled smartphone survey data collection with a qualitative mapping interview method and piloted it to explain tobacco use disparities among bisexual young adults.

The pilot study revealed smoking patterns and situations that reflect young adult smokers, generally, but also the unique roles that smoking plays for bisexual young adults as they navigate differently sexualized spaces in everyday life . This brief report draws from preliminary data to demonstrate how the method may also provide integrated insights into the unique patterns, intentions, and socio-structural contexts of multiple drug use for different groups of people.Smartphone apps that repeatedly administer surveys to participants and record their locations over time are often used to research recurring and episodic behaviours. These approaches can ‘reach into’ the fabric of everyday life to collect data within participants’ natural environments and routines . Smartphone ownership is increasingly ubiquitous even among low income and rural groups, making this approach feasible with diverse populations. Mobile health research methods, such as these, minimize the retrospective recall bias that occurs when participants are asked to characterize their behaviours or experiences, and can be integrated into spatial frameworks and analyses when geolocation data are also collected . The value of mHealth methods for researching tobacco use is established . mHealth methods are now used to research patterns and situational predictors of use of other substances, including cannabis, opioids, cocaine, MDMA, and alcohol . Because mHealth surveys must be kept short to reduce participant burden and encourage data collection compliance, they cannot capture the richness of individuals’ experiences of use contexts and practices, nor how individuals make sense of their drug use within the context of their broader life narratives. Integrating qualitative mapping methods with mHealth momentary assessements can provide reliable and ecologically valid measures of substance use behaviours while also revealing the richness of experiences and contexts of use. Qualitative mapping, also known as qualitative Geographic Information System , integrates mapping techniques with qualitative methods to explain the processes that produce spatial patterns, relationships, and behaviours .

It has been used in research on substance use, including to understand place-based practices and norms of tobacco use , the impact of area restrictions on people who use drugs , and characteristics of drug overdose contexts . Our mixed method approach leverages the “productive complementarity” of multiple methods, acknowledging that different ways of knowing about social phenomena, like drug use, are all inherently partial and are shaped by the conditions and actors involved in the creation of knowledge . We integrate real time, smartphone-collected surveys, location tracking, and subsequent in-depth interviews that are guided by viewing maps of participants’ own mHealth data in an explanatory sequential mixed methods approach . Participants use a smartphone app for a period of time to report on the substances they used and the situations they used them in via participant-initiated real-time reports of use, prompted momentary surveys about use and non-use situations, and prompted daily diary surveys. Subsequently, real-time reports of use and location tracking data are visualized in mapping software and brought into in-depth interviews to guide and ground discussion of drug use experiences within everyday contexts and situations of use. The interviewer and/or participant toggle between map layers and zoom in and out of places in Google Earth. Together, they identify apparent spatial clusters of use of different substances and discuss what those places are, what they usually do and experience there, who they interact with, and how it is that use of particular substances fold into those experiences. This is similar to the use of travel and activity diaries to guide interviews, but further ‘grounds’ interview discussion by interacting with spatially-visualized representations of participant data. Quantitative and qualitative data are analysed separately and then integrated in a table. Visually organizing and juxtaposing the quantitative and qualitative data sets helps to identify threads of interest to explore across the data sets and to observe the convergence, complementarity, and/or dissonance between their depictions of participant’s everyday use of and experiences with substance use. We collected data in 2019-2020 with 32 young adults in California who regularly used both tobacco and cannabis with the mixed method . We draw from that study on tobacco and cannabis use to explore one participant’s data, which provided particularly informative insights into the complex patterns, intentions, and socio-structural contexts of multiple drug use repertoires and combinations.

IRB approval included consent to publish the data as a case study. The participant did not respond to our request for feedback on the manuscript. To protect the participant’s identity, we use a pseudonym and have added fictional details about the participant that are not relevant to interpreting the data presented below. mHealth data were descriptively analysed using STATA statistical analysis software. Transcript analysis followed an inductive-deductive thematic approach . Transcripts were coded with NVIVO qualitative data analysis software. The initial transcript coding scheme was informed by our previous studies and the literature and was used to sort content by substance type, location, social identity, and roles/intensions of use. Emergent themes regarding roles and intensions of use were identified in a series of group readings of transcript excerpts, as we have done in the past .‘Jason’ was a transgender man in his mid-twenties who lived in a rural community, had a history of homelessness, and reported having autism spectrum disorder . He worked part-time in constrution and lived with his partner in a small house. Jason completed 70% of all prompted surveys during his 30 days of data collection. Jason’s mHealth survey data indicated that he smoked a daily average of 6.5 cigarettes. He most often smoked alone, and frequently smoked at home, in a vehicle, or at someone else’s home. On the minority of occasions that he smoked cigarettes with others, it was usually with friends or his partner. Jason’s mHealth survey data indicated that he used cannabis almost every day ; 4.9 times per day, on average. He frequently used cannabis in his garage or backyard, someone else’s home, or in a vehicle. He was with friends or his partner during most of these sessions , and was alone for the rest. On most days of the study he reported using cannabis and cigarettes together some or most of the time. He used alcohol on only 15 days and most of those reports were for one drink. On only 5 days did he report using alcohol and cigarettes together some or most of the time. In short, Jason’s mobile data indicated that he used cigarettes and cannabis daily or almost daily, less regularly used alcohol, and that he often used cigarettes and drying cannabis at the same time.The qualitative mapping interview elicited rich descriptions of the experiences, contexts, and intentions linked to these and other substances in Jason’s everyday life. Jason’s account of his drug use repertoire and combinations converged with the quantitative sketch made by his mHealth survey data, and complemented it by providing insight into the place-based experiences and intentions behind his use of these substances. The map provides a zoomed in view of Jason’s use around his home and nearby friend’s house . He described his alcohol use as mostly related to accompanying a friend at their house while they drank. In contrast, his cannabis dab pen went everywhere with him beyond this home area to help navigate social interactions as a person with ASD.This one individual’s mHealth and map-led interview data set offered an integrated understanding of the complex use patterns, combinations, and intentions within his drug use repertoire , and linked these to his intersecting identities and the particular social and structural characteristics of his environment. Specifically, it revealed relationships between how and why he uses multiple drugs and his day-to-day experiences as a transgender person with ASD living in a rural community. A key strength of this mixed method appears to be its capacity to go beyond examination of individual substances and individual drug use ‘risk factors’, to link use patterns and intentions of multiple drugs to the intersecting characteristics and place based experiences of different people.

The perspective offered by integrating mHealth and qualitative mapping methods may help identify particular drug use patterns and combinations that increase risk of drug-related harm for priority groups, like gender minority individuals, as well as provide insight into the place embedded experiences that give rise to motives for those ‘risky’ drug use practices . Our findings suggest that participant narratives of multiple drug use patterns, intentions, and experiences can be enhanced and further grounded in context by viewing and discussing maps of participants’ own data during interviews. Maps that show where and how frequently participants use different drugs provide an avenue for the participant and interviewer to organize their discussion around the complexities and diverse factors related to multiple drug use. Moreover, similar to other creative methods that integrate images or other objects into interviews , the visual representation of drug use practices in map form may help depersonalize highly stigmatized use practices, like methamphetamine use, and reinforce the participant’s role as expert while cultivating an experience of discovery, reflection, and ownership over the interpreted ‘story’ of their data. This mixed method is limited, however, by being time-consuming and resource-intensive, especially with regards to participant and investigator time effort, as well as obtaining the smartphone data collection software and mapping software. Participant burden must be considered when designing the frequency and length of smartphone-collected surveys, the duration of data collection , and participant incentives. Moreover, great care must be taken to protect participant confidentiality when using any geo-enabled data collection method. This method could be used with a larger sample size by grouping participants for comparison rather than at the individual case level, and triangulating between the quantitative and qualitative data for each group. Future studies can build on research that has identified individual ‘risk factors’ related to multiple drug use , by gaining integrated and geographically-grounded insights into how these diverse and place-embedded factors intersect and interact with one another to shape drug use repertoires and combinations. In-depth knowledge like this can inform the resources and services directed toward and tailored to the needs of diverse groups of people who experience the unique pleasures, roles, and risks of multiple drug use.Cannabis, a substance derived from the Cannabis indica and Cannabis sativa plants, has a wide array of both beneficial and harmful properties which makes its use a controversial topic.

As an alternative approach, we explored using the average length to estimate the source strength of a fully-smoked joint. The 24 marijuana joints used in the present study consisted of 9 different name brands that ranged in length from 59 to 91 mm. The mean length was 79 mm, which was the same as the mean length of the Marlboro cigarettes. Four of the name-brand joints were shorter than the Marlboro cigarette, one was the same length, and four were longer. Like the tobacco cigarettes, each joint had a mouthpiece that acted as a filter. Before and after each joint was smoked in our 24 joint experiments, we measured the length of the portion of the joint that contains the cannabis leaf. Before smoking, the mean length of the cannabis portion was 52.1 mm. After smoking, the mean length of the cannabis portion was reduced to 38.6 mm, indicating that the 3.0-min smoking period used up 52.1–38.6 mm = 13.5 mm of the marijuana-containing portion of the joint. Since this smoking period produced a mean source strength of 23.2 mg , we estimated the average PM2.5 emission per unit smoking length as / = 1.72 mg/mm. Thus, smoking the remaining 38.6 mm was estimated to add the mass emissions of × = 66.4 mg, bringing the estimated mean source strength of the fully smoked joint to 23.2 + 66.4 mg = 89.6 mg. We estimated this large source strength would produce a maximum PM2.5 concentration in the room of 2080 μg/m3 ,pot for cannabis and we estimated the smoking time would be 11.6 min.It also is instructive to compare our tobacco cigarette results with other studies of fully smoked tobacco cigarettes. Chen et al. recruited 2 volunteers to each smoke 5 Chinese tobacco cigarettes in a stainless steel mixing chamber. Their study used mass balance equations like those in the present study to calculate emission rates for each of the 10 fully-smoked cigarettes.

Their observed mean PM2.5 emission rate for the 10 cigarettes was 2.25 mg/min , which was extremely close to our mean emission rate of 2.2 mg/min shown in Table 2. We measured the lengths of the Marlboro tobacco cigarettes used in the present study and found they have a uniform manufactured length of 79 mm, which includes a 24 mm mouthpiece that acts as a filter. As a result, the length of the tobacco-containing portion of the cigarette is 79–24 mm = 55 mm. By measuring the cigarette length before and after each cigarette was smoked, we found the 3-puff protocol used up 31.7 mm of the tobacco-containing portion of the cigarette on average, producing the 6.6 mg average source strength listed in Table 2. Therefore, the Marlboro cigarettes emitted /31.76 mm) = 0.2082 mg/mm on average as they were being smoked, and smoking the remaining 55–31.7 mm = 23.3 mm would add 4.9 mg to the total, bringing the estimated total source strength for a fully smoked tobacco cigarette to 6.6 + 4.9 mg = 11.5 mg. Repace presented a histogram of fine particle mass source strengths of 50 brands of tobacco cigarettes, representing 65.3% of the US market. The average source strength for a fully-smoked cigarette was 13.8 mg , which is close to the 11.5 mg source strength we estimated for a fully smoked Marlboro tobacco cigarette in the present study. Dacunto et al. reported a 19.9 mg source strength for a fully smoked Marlboro cigarette, and Chen et al. reported a mean source strength of 17.3 mg per cigarette for 10 Chinese cigarettes smoked by two volunteer smokers. In the 60 experiments, the mean PM2.5 decay rate for the 9 vaping pen experiments of 0.690 h− 1 was greater than the mean decay rates of the four other sources, which ranged from 0.461 h− 1 to 0.563 h− 1 , and this difference was statistically significant . In comparison, the differences between the decay rates of the joint, bong, glass pipe, and cigarette were not statistically significant. The larger decay rate for the vaping pen appears likely due to the greater volatility of its aerosol. The 24 experiments with 9 different brands of pre-rolled joints produced extremely high PM2.5 concentrations. With just 3 puffs, the maximum PM2.5 concentrations in the room ranged from 143 to 809 μg/ m3 and averaged 540 μg/m3 .

By comparison, the maximum PM2.5 concentrations for the 9 experiments with tobacco cigarettes smoked in the same manner ranged from 22 to 209 μg/m3 and averaged 154 μg/m3 . As a result, the mean secondhand smoke PM2.5 emissions from Marijuana joints was 3.5 times greater than from the tobacco cigarettes. The PM2.5 emissions from the three alternative methods of smoking or vaping marijuana – the bong, glass pipe, and vaping pen – were lower than the emissions of the joint, but all three methods produced greater PM2.5 emissions than the tobacco cigarettes. Zhao et al. conducted a similar set of experiments with an experienced smoker and the same five sources used in the present study. A car parked in a garage to reduce the effect of winds was used as a 6.5 m3 mixing chamber. Like the present study, the marijuana joints had the greatest emission rates, while the tobacco cigarettes had the lowest emission rates. The emission rates of the vaping pen, bong, and glass pipe were in between the marijuana joints and the tobacco cigarettes. Graves et al. measured several thousand different compounds present in mainstream marijuana and mainstream tobacco smoke, as well as Total Particulate Matter mass concentrations. They collected the TPM on 47 mm quartz filters that were weighed on a laboratory microbalance. They report that the average TPM concentration in marijuana mainstream smoke was 3.4 times greater than the TPM concentration in mainstream tobacco smoke. Their 95% confidence interval around this ratio was ±0.6, and thus our ratio of 3.5 for the marijuana joint emission rate relative to the tobacco cigarette emission rate was within their 95% confidence interval. However, their result was for mainstream smoke, while our result was for secondhand smoke, which is a combination of mainstream and side stream smoke. Moir et al. reported the mainstream TPM mass concentrations in marijuana smoke was about the same as in tobacco smoke. They also measured the mass concentrations of 30 PAH compounds in both marijuana and tobacco smoke. Their study indicated that 89.8% of the PAHs in secondhand marijuana smoke were from side stream emissions while 10.2% were from mainstream emissions. McClure et al. reported that the volume of the puffs from an adult smoker decreases steadily over the course of smoking a cigarette.

Wu et al. studied 15 habitual marijuana smokers and reported the puff volume was smaller for the second half than for the first half of marijuana cigarettes, while Tashkin et al. reported mainstream CO, tar, and THC emissions were greater for the second half than for the first half of a marijuana cigarette. For estimating secondhand smoke emissions from a fully-smoked tobacco or marijuana cigarette, we feel our assumed linear relationship between secondhand smoke emissions and length smoked is reasonable and would be a good topic for future research. Since the marijuana joint has a long history of use and is one of the most popular methods of consuming cannabis, we chose the largest sample size, n = 24 experiments, for the pre-rolled joint. The bong, glass pipe, vaping pen, and cigarette all had smaller sample sizes of n = 9 experiments. Except for one case, the differences in the PM2.5 emission rates between these four common methods of consuming marijuana or tobacco based on 9 experiments did not reach statistical significance at the p < 0.05 level. The 24 marijuana joints used in the present study were obtained from four state-licensed stores in three California towns, and the joints included 9 different name brands that are popular in California. Only two different kinds of marijuana buds were used in the bong and glass pipe experiments, however, and the results should show greater variation if more types of cannabis buds were included and if sample sizes were larger. The Absolute Xtracts vaping pen used in the present study is battery-powered and uses an electronic microprocessor that controls the temperature of the vaping fluid. This vaping pen has several settings that a user can select by pressing a button on the side of the pen. In our vaping pen experiments, we chose the “pre-heat” mode recommended in the ABX instructions, and we selected the highest of three power levels. This approach pre-heats the vaping liquid for 15 s, followed by the 3-puff protocol that started within 1-1/2 min after preheating ended. A user might choose different settings of this vaping pen that could result in greater or lesser emissions. Using an identical ABX vaping pen, Wallace et al. reported that two different vaping protocols produced two different temperatures, resulting in about 3 times greater source strength for the high-heat protocol than for the low-heat protocol. In the present study, the 9 pot for growing marijuana vaping experiments were limited to two different commercial vaping cartridges. Many other vaping cartridges are available with different levels of THC and CBD that could be compared in a future study with a larger sample size. To compare different source types with each other, the 60 experiments in this study used the same smoker, while future studies may choose to explore differences among smokers. Hepatitis C virus co-infection is common amongst HIV-infected persons, affecting an estimated 4 to 5 million persons worldwide, and is associated with increased morbidity and mortality. Whereas the primary route of HCV transmission remains injection drug use , over recent years there has been increasing evidence of sexual transmission among HIV-infected men who have sex with men , likely driven by mucosal risk factors, including unprotected and traumatic sexual practices in the context of multiple partners, non-injection drug use, and sexually transmitted infections. Prevalence estimates for HCV co-infection in HIV-infected MSM have ranged from 6 to 15.7 %, with limited geographic characterization. The prevalence of HCV co-infection in HIV-infected MSM in Los Angeles County in the U.S. has not been defined, despite LAC being the second largest epicenter for AIDS cases nationally, with high rates of non-injection drug use and high-risk sexual practices. Our aims were to characterize the prevalence of and risk factors for HCV co-infection and patterns of HIV and HCV co-transmission and drug resistance mutations in a cohort of newly HIV-infected or HIV-diagnosed Los Angeles MSM.

Prevalence of HCV co-infection was low and there was no evidence of HIV-HCV co-transmission in this cohort of young, predominantly minority, newly HIV-diagnosed MSM. The majority of subjects had recent HIV infection and notable behavioral and clinical risk factors for sexual HCV transmission, including high-risk sexual practices, sexually transmitted infections, and non-injection substance use, with low rates of injection drug use. The lower prevalence of HCV compared with other HIV-infected MSM cohorts may reflect the younger age of the cohort with fewer cumulative exposures to HCV, lower rates of IDU, relatively greater immune preservation with earlier HIV infection, and identification of HCV by HCV RNA instead of by serology. In our study, by measuring HCV RNA, we measured prevalence of active HCV replication and not exposure or infection with possible clearance, as would be measured by serology. Assessment by both serology and HCV RNA would provide broader characterization of HCV exposure in the cohort, but due to limited sample volume, we could not perform testing for both and elected for HCV RNA testing alone as a measure of active HCV infection and risk for HCV transmission. Demographically, our cohort differed from others in its geographic and racial/ethnic composition, wherein our cohort was predominantly of minority race and half was Hispanic, as compared to most other reported cohorts that were predominantly White. The epidemiology of HCV co-infection in HIV-infected Hispanic MSM has not been well described. As described by Kunikholm et al., utilizing National Health and Nutrition Examination Survey and Hispanic Community Health Study/Study of Latinos data, HCV prevalence appears to differ by Hispanic/Latino background and the prevalence of HCV in the West Coast Hispanic population may be lower than in others [13]. While there were too few subjects with HCV infection in the cohort to explore associations between potential behavioral risk factors and HCV infection, the subjects that did have HCV co-infection all reported methamphetamine and other non-injection drug use, as well as high-risk sexual practices, consistent with risk factors identified in larger cohorts.

Analyses for ANYDEP were performed using the Mantel–Haenszel χ2 test of association assuming an additive genetic model. Analyses for QUANTDEP were performed using analysis of covariance employing substance, genotype, and the substance × genotype interaction to test for differences in genotype by substance. Gender and birth cohort were included as covariates.Independent replication of SNPs demonstrating evidence of significant association in the COGA sample was evaluated in the Study of Addiction: Genetics and Environment sample. SAGE is a case-control sample comprised of three complementary studies: COGA, the Family Study of Cocaine Dependence and the Collaborative Genetics Study of Nicotine Dependence . There were 129 individuals from the 118 COGA families in the current study that overlapped with the SAGE sample, and were removed from the SAGE replication dataset. The remaining independent SAGE sample used for replication was limited to 2647 individuals of European-American descent. Factor analysis scores from Mplus were independently estimated for this study, as described above, based on DSM-IV dependence criteria for the four substances. Analyses were implemented in Plink and included age at interview and gender as covariates.The number of individuals utilized for the categorical phenotype ANYDEP was 1770 .Nearly half the sample met the DSM-IV criteria for at least one substance . The COGA sample was ascertained through an alcohol-dependent individual in treatment and families were selected for the highest density of alcohol dependent members; therefore,mobile grow system it was expected that there would be many individuals meeting criteria for alcohol dependence . In addition, 19% met criteria for cannabis dependence . The rates for cocaine and opioid dependence were lower .

There were 832 individuals that met criteria for at least one substance dependence diagnosis; of those, 312 endorsed at least two diagnoses. Alcohol and cannabis dependence were the most common .The number of individuals included in the analysis of the quantitative factor score QUANTDEP was 2,183 . The confirmatory one-factor model fits the data well in COGA [comparative fit index = 0.96; root mean square error of approximation = 0.07], and in the replication sample, SAGE , supporting our proposed unidimensional conceptualization of dependence criteria for alcohol, cannabis, cocaine and opioids. Factor loadings in the COGA sample ranged from 0.67 to 0.99 and were highly consistent across COGA and SAGE. In general, factor loadings for alcohol and cannabis dependence criteria were lower, and ranged between 0.67 and 0.85, while those for cocaine and opioids were uniformly high . Data across drug classes and across criteria loadings for all seven criteria for the four substances are available in Supporting Information Fig. S1.This is one of the first GWAS to test for the association of overall substance dependence phenotypes, defined both categorically and quantitatively . This approach implicitly tested the hypothesis that there are genes with pleiotropic effects contributing to dependence on alcohol, cannabis, cocaine and opioids. Using these multi-substance phenotypes, we detected genome-wide significant results with SNPs in two different genes. This finding is consistent with an extensive twin literature that provides demonstrable support for common genetic liability underlying addiction to multiple substances . Furthermore, a previous study in a slightly different COGA sample demonstrated aggregation of drug dependence in relatives of alcohol-dependent probands, even after controlling for co-morbidity in the probands . Genome-wide significant association for ANYDEP was observed with a SNP in an uncharacterized gene, LOC151121 . Further evidence of association was corroborated by surrounding SNPs, both genotyped and imputed. Nominal replication was found in the SAGE sample with the same phenotype. This SNP was moderately associated with QUANTDEP and also with the number of DSM-IV alcohol dependence criteria endorsed in another related study with data from the same sample . Similar to the replication results here, this SNP was nominally associated with the alcohol dependence symptom count in the SAGE sample as well . Significant association was also detected with QUANTDEP for the SNP rs2567261 in ARHGAP28 . Further evidence of association was observed with both genotyped and imputed SNPs within the gene. ARHGAP28 is also known as Rho GTPase activating protein 28.

GTPase-activating proteins target GTPases, and are mediated by exposure to alcohol, cannabis, cocaine and opioids. For example, Rho1 and Rac moderate the stimulating and sedative effects of acute ethanol intoxication in Drosophila . Thus, there is strong biological rationale for this gene as a potential candidate for substance dependence. Of note, this SNP was modestly associated with ANYDEP in this sample and with previously published alcohol symptom count in the COGA family sample . However, rs2567261 was not significantly associated with alcohol symptom count in the SAGE sample, although there was a trend in that direction . Although the association did not replicate in the SAGE sample for this phenotype, there was a trend toward association with the other phenotype, ANYDEP . This weak replication for a different phenotype may be due to the fact that the majority of the SAGE sample was ascertained on nicotine and cocaine dependence, whereas the COGA sample was recruited based on an alcohol-dependent proband and expanded to include the maximum number of alcohol-dependent family members. The exclusion of nicotine dependence criteria may have attenuated the likelihood of replication, given ascertainment for nicotine dependence in SAGE. Since the SNP association was not primarily due to alcohol dependence, it is possible that high rates of co-morbidity in the COGA sample as compared with the SAGE sample contributed to the finding. In addition, the family-based test in the large COGA families with co-morbidity may have had greater power to detect the association. QUANTDEP seems to represent an underlying severity of addiction. As seen in Fig. 3a, the higher the number of co-morbid diagnoses, the higher the QUANTDEP scores. Overall, for the QUANTDEP measure, the loadings for the alcohol and cannabis criteria appear generalizable to general population studies ; in contrast, those for cocaine and opioid dependence are higher . In COGA, cocaine and opioid dependence criteria were less commonly endorsed than those for alcohol and cannabis , with the former also showing less range in endorsement rates . In SAGE, cocaine dependence criteria were somewhat more commonly endorsed than cannabis dependence criteria, yet the cocaine criteria had higher loadings than the cannabis criteria, identical to COGA. For both cocaine and opioids, the range of prevalence of individual criteria was highly restricted . Thus, in both COGA and SAGE, the likelihood of endorsement of each of the seven dependence criteria for cocaine and opioids was similar while certain alcohol and cannabis criteria were endorsed more often than others . This may be related to the ascertainment strategy and over-representation of family history for alcoholism in both samples. Nonetheless, all factor loadings were high, indicating that QUANTDEP reflects a general liability to dependence across multiple substances. In particular, QUANTDEP captures the liability to cocaine and opioid dependence criteria in these two studies. Therefore, in addition to being an index of severity and a measure of general liability to addiction across alcohol and drugs, QUANTDEP likely also reflects variation in prevalence and the expected pattern of co-morbid relationships and co-aggregation across alcohol and drug dependence criteria in these subjects ascertained on specific substance dependence. While the heritability of the binary phenotype of dependence on any substance was similar in this sample to the most common heritability estimate reported for any of the four substances from twin studies , the heritability for the quantitative phenotype was much higher .

This is consistent with one prior twin study of a latent genetic factor underlying alcohol and drug problems as well as measures of impulsivity and conduct problems but significantly higher than some others . Although this high heritability should not be over-interpreted , it is possible that the use of a multi-variable quantitative phenotype, utilizing the pattern of endorsement of the seven DSM-IV criteria across all four substances, captured valuable genetic information across the vulnerability spectrum. The two phenotypes used in this study were both aggregate measures of overall dependence. Although their top genetic signals did not overlap , there was evidence of association for the other phenotype for the two SNPs that attained genome-wide significance . The difference in magnitude of P-value is not surprising given the arguable validity of the diagnostic cutoff implemented in DSM-IV, which likely excluded from affected status ,mobile vertial rack a number of individuals who may have met criteria for abuse or endorsed 1–2 dependence symptoms across one or even multiple drugs, and thus did not qualify for dependence. Viewed alternatively, the unaffected individuals for ANYDEP represent a heterogeneous group varying in severity. Such variability was better captured by QUANTDEP, which while not taking abuse criteria into account, was a better approximation of the range of vulnerability to substance-related problems. Thus, it is likely that ANYDEP reflects the more severe of the QUANTDEP scores. Finally, the possibility that our findings reflect false positives cannot be excluded. There have been multiple prior GWAS that have utilized symptom counts and factor scores of alcohol dependence criteria but only one attempted to combine indices of alcohol , nicotine and drug misuse using hierarchical factorial analyses for GWAS. In that study, McGue and colleagues reported on four SNPs associated with multiple first-order and higher order externalizing factors. One of these SNPs, rs10037670 in GALNT10, with the highest association for illicit drug dependence factor was modestly associated in this study with both ANYDEP and QUANTDEP . There are several strengths of this study design, the first being the use of families densely affected with alcohol-dependent individuals. Family and twin studies suggest familial co-aggregation and heritable overlap across alcohol, cannabis, cocaine and opioids. Thus, this family-based COGA sample, enriched for dependence on multiple substances and shared genetic risk, allowed us to test for the association of common variants with risk for dependence across multiple substances. A second strength of this study was the use of a family-based association design. This allowed us to examine association within a family consisting of members who endorsed criteria for dependence on different substances. Third, family-based analysis is robust to population substructures such as nuanced differences in ethnicity, which might occur with marry-in individuals of a different race, and in turn, affects the genetic diversity of the offspring. Three caveats are worth considering.

First, only a small subset of individuals met DSM-IV criteria for opioid and cocaine dependence. Thus, it is possible that results pertain more closely to lower liabilities to these substances. Second, we did not include nicotine dependence criteria in this analysis. As we were interested in a confirmatory model of unidimensional genetic risk, we elected to exclude nicotine symptoms based on published evidence for a preponderance of non-overlapping genetic influences on these criteria. Finally, we elected not to utilize abuse criteria , despite DSM-5-related changes. Previous findings in COGA families demonstrated that abuse did not aggregate in relatives of alcohol-dependent probands . In addition, the extant psychometric literature suggests that with the exception of hazardous use, which is frequently endorsed to the exclusion of other abuse or dependence criteria, the remaining abuse criteria and craving are infrequently endorsed in the absence of co-occurring dependence criteria. This is particularly true in samples ascertained for substance use disorders, such as SAGE and COGA. For instance, in SAGE, of those who reported no alcohol dependence criteria, only 12–26 individuals endorsed at least one abuse criterion other than hazardous use . Hence, it is unlikely that the exclusion of abuse criteria resulted in our inability to capture a relevant portion of the liability continuum. In summary, this study provides evidence that there are common variants that contribute to the risk for a general liability to substance dependence, defined qualitatively and quantitatively. The results of this study require replication in independent samples to further explore whether overall dependence on multiple or individual substances is associated with the SNPs in these regions.This national Collaborative Study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism and the National Institute on Drug Abuse . Funding support for GWAS genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI and the NIH contract ‘High throughput genotyping for studying the genetic contributions to human disease’ . A.A. is supported by K02DA32573 and AAR21235 and J.E.S. by F32AA22269. Funding support for the Study of Addiction: Genetics and Environment was provided through the NIH Genes, Environment and Health Initiative [GEI] .

There is a national trend toward statewide legalization of medical marijuana despite federal classification of marijuana as a Schedule I illicit drug. There are compelling arguments for and against medical marijuana legalization and its potential impact on an array of complex social issues . Residents in states where medical marijuana is legal are more likely to have tried marijuana, report current marijuana use, and be diagnosed with marijuana abuse or dependence . Additionally, there is preliminary evidence to suggest that there is likely a dose-response relationship between the number of years since legalization and marijuana prevalence rates . A key question regarding more liberal marijuana policies is whether and how they affect use of other drugs including addictive and harmful substances like tobacco. Previous studies have found a strong positive association between cigarette and marijuana use . Epidemiologic data indicate that the prevalence of tobacco and marijuana co-use has increased from 2003 to 2012 . Moreover, the increase in co-use occurred specifically among those ages 26–34 years, and the greatest percent increase, in those ages 50 years and older . It is unknown, however, if this national increase in co-use is directly associated with statewide legalization of medical marijuana. If marijuana policies are indeed associated with co-use, the current trend toward legalization of medical and/or recreational marijuana, without any regulatory action, has the potential to influence patterns of cigarette and marijuana use/co-use over time. An increase in cigarette and marijuana co-use has the potential to create challenges for cigarette smokers who want to quit. There is evidence to suggest that cigarette and marijuana co-use is associated with greater nicotine dependence . Possible explanations for this link include the role of the endocannoboid system in nicotine metabolism , genetic predisposition for co-use grow lights for cannabis, and various environmental and cultural influences . The relationship between co-use and nicotine dependence, however, is understudied in adults, particularly among those ages 50 years and older.

Since nicotine dependence is influenced by both nicotinic receptors and nicotine associated metablism that change with age , we can expect nicotine dependence among cigarette and marijuana co-users will also vary over the lifespan. Few studies have examined cigarette and marijuana co-use and nicotine dependence from adolescence through adulthood. As the nation is well-past the tipping point on medical marijuana legalization, studies are needed to take a closer look into whether marijuana policies have the potential to influence tobacco control efforts at the population level. For example, over time, it is likely that greater access to legal marijuana will increase the absolute number of co-users who have greater nicotine dependence and difficulty quitting cigarettes. Such data can help to identify subset populations at higher risk of nicotine dependence and could have both policy and treatment implications in tobacco control. In this study, we sought to examine relationships between medical marijuana laws and cigarette and marijuana co-use. Additionally, we examined the likelihood of nicotine dependence in co-users. We analyzed data from the 2013 National Survey on Drug Use and Health and stratified the analysis by age categories. Results from this study can inform the direction of future medical marijuana policies that may inadvertently affect tobacco control efforts.We analyzed cross-sectional data from the 2013 NSDUH conducted by the Substance Abuse Mental Health Services Administration . The primary purpose of NSDUH is to measure prevalence and correlates of drug use in the civilian, non-institutionalized U.S. population aged 12 years and older. Since 1991, NSDUH has consisted of an independent multistage area probability sampling design for each state and the District of Columbia and uses a combination of the Computer-Assisted Interviewing and Automated Computer Assisted Interviewing instruments in selected individuals and households . The survey offered $30 in cash to participants and was conducted in 2013 by Research Triangle Institute . The final survey consisted of 67,838 CAI interviews with a weighted screening response rate of 84% and an interview response rate of 72%. The public use file consisted of 55,160 records due to a sub-sampling step which included a minimum item response requirement for weighting and further analysis.

A detailed description of the questionnaire items, sampling methodology, data collection/ response rates, and sample weights is published elsewhere . The present study was exempt from the University of California San Francisco’s Human Research Protections Program approval since data were publically available and subjects cannot be identified. In this analysis, only those with complete responses for all measures were included. Additionally, while the analysis included participants aged 50–64 years, those 65 years of age and over were excluded due to a small sample size . The final sample included 51,993 participants.Descriptive statistics are reported for demographics, cigarette and marijuana use, and lifetime depression as well as chi-square tests of differences by statewide medical marijuana legalization status . One-way ANCOVA models tested for differences in marijuana use and cigarette and marijuana co-use in the overall sample, and separately for each age category, between states where medical marijuana was legal vs. illegal, adjusting for age , gender, race/ethnicity, education, age at first cigarette initiation, age at first marijuana initiation, and lifetime depression. Additionally, we calculated mean NDSS and frequency of TTFC scores by statewide legalization categories across age groups. In the overall sample and within each age category, two logistic regression models examined nicotine dependence, as measured by NDSS and TTFC scores, in cigarette and marijuana co-users . Models were adjusted for age , gender, race/ethnicity, education, lifetime depression, and statewide medical marijuana legalization status. Bonferroni adjustments were applied to all models with over five independent variables . In this analysis, we used the Taylor series method for replication methods to estimate sampling errors of estimators based on complex sample designs. The regression coefficient estimators were computed by generalized least squares estimation using element-wise regression. The procedure assumes that the regression coefficients are the same across strata and primarily sampling units . All models were run in SAS 9.4 using the SURVEY procedures to obtain weighted estimates to increase the generalizability of the findings . Findings indicate an association between statewide legalization of medical marijuana and cigarette and marijuana co-use despite lower cigarette prevalence in states where medical marijuana was legal. Co-use was particularly robust among 18–34 year olds. Overall, cousers were more likely to be nicotine dependent compared to those who did not use marijuana, and 12–17 year old adolescent and 50–64 year old adult co-users were 3-times more likely to have nicotine dependence . These data suggest that medical marijuana legalization could inadvertently affect prevalence of co-use, which is linked to greater nicotine dependence, and the potential to create more barriers to smoking cessation . As more states pass marijuana laws, and the legal marijuana industry is poised to cultivate a landscape of greater access and exposure to marijuana , it is recommended that stakeholders in tobacco control prepare for any unintended effects on tobacco use including the possibility of tobacco initiation/ reinitiation among former smokers and greater nicotine dependence in current smokers . Longitudinal research is needed to evaluate the effect of state marijuana policy on tobacco use and marijuana and tobacco co-use.

Given the nationwide increase in co-use , there may be uptake of marijuana use among cigarette users as states, change their marijuana policies and cigarettes smokers gain greater exposure and access to legal marijuana. It is possible that medical marijuana may be providing cigarette smokers with an alternative to tobacco especially as the stigma associated with tobacco continues to rise and the perceived harmfulness of marijuana decreases with legalization . Further, it might be perceived that the effects of marijuana can curb nicotine cravings and withdrawal symptoms to aid in smoking cessation . Finally, alternative tobacco products such as electronic nicotine delivery systems, which are commonly promoted as cessation aids and “safe” alternatives to smoking cigarettes , might also promote use of marijuana and THC oil with vaporizers . Co-use should therefore be monitored over time and examined in response to changes in marijuana policies that will further propel industry promotion of co-use and vaping. As expected, the prevalence of cigarette and marijuana co-use differed according to age. The positive association between medical marijuana legalization and co-use was greatest among 18–34 year olds. Previous studies with adolescents have reported greater prevalence but no increase in marijuana use or changes in permissive attitudes in states where medical marijuana was legal ,grow cannabis suggesting that greater marijuana use, and therefore greater co-use, preceded medical marijuana legalization. However, most published studies have focused only on adolescents under the age of 18 years and do not reflect the adult population to which medical marijuana policies apply . Therefore, long-term longitudinal studies are needed to monitor the effects of marijuana legalization, marijuana initiation/ re-initiation, cigarette initiation/ reinitiation, and patterns of co-use across all age categories. Additionally, it is recommended that such studies take into account statewide variables including number of years since the policy went into effect to adequately capture any measurable changes. These data are needed to explore the growing evidence and public health concerns about the potential “gateway” effect of marijuana on cigarette initiation and nicotine dependence in adolescents and young adults in addition to the potential for re-initiation of cigarettes among former tobacco users. As more states pass marijuana policies, potential increases in co-use could have important treatment implications. Cigarette smokers who also reported current marijuana use were more likely to have nicotine dependence, which is a known predictor of smoking and quitting behavior . The positive link between co-use and nicotine dependence was observed across age categories but these associations differed across measures of dependence . We analyzed both NDSS and TTFC. NDSS scores might have been a better measure of nicotine dependence in our comparison across age groups since the scale addresses five aspects of dependence . In comparison, the TTFC single-item scores might not have captured dependency, particularly in adolescent and young adult populations, who have yet to become regular and established smokers. Other studies have shown problems in using TTFC as a measure of dependence in young adults . Since our analysis included both adolescents and adults, we report both NDSS and TTFC measures of nicotine dependence. In addition, in the present study, cigarette smokers who reported ever but not current marijuana use were at greater risk of having nicotine dependence compared to never marijuana users. This finding supports that the effect of THC exposure on nicotine receptors may be irreversible . Studies are needed to further examine both short term and possibly even the long-term effects of THC and nicotine exposure on nicotine dependence and tobacco cessation. In this analysis, 12–17 year old adolescent and 50–64 year old cigarette and marijuana cousers had the highest odds of having nicotine dependence.

These findings support previous studies linking co-use and nicotine dependence in adolescents and young adults and add to preliminary data that this association was also stable in adults and, surprisingly, particularly robust in 50–64 year old adults. These findings reflect evidence of a U-shaped effect between age and nicotine dependence which peaks at age 50 years due to changes in nicotinic receptors and nicotine-associated metabolism with age , and suggest that this relationship was stable among co-users. Studies are needed to determine the extent to which THC exposure and/or current marijuana use add to this effect . Additionally, 50–64 year olds may represent a unique birth cohort who spent their formative years during the 1960’s and 1970’s with minimal tobacco regulations coupled with a counterculture that promoted marijuana use among a large population . More studies on the Baby Boomer generation, specifically, their perceptions about marijuana, current marijuana use including purpose of use , modality, cigarette co-use, and health outcomes could provide a glimpse into the future as continued legalization will likely influence social norms across the general population . As more states adopt liberal marijuana policies, more studies are needed to understand co-use including the relationship between THC and nicotine in addition to other individual-level factors such as genetics and personality traits that might influence dependence and cessation . We found higher percentages of non-Hispanic Whites and Blacks/ African-Americans in states where medical marijuana was illegal. In this study, these results may be attenuated since our analysis comparing nicotine dependence depended on exclusion of blunt use.

Teams sometimes hike into sites for covert operations, but more often, they rappel down from a helicopter into the nearby area. Officers face major disadvantages when raiding sites because cultivators have been living at the location for months. Covert operations involve the most risk because hiking conditions and landscape characteristics can subject officers to ambush and provide cultivators with vantage points for armed engagement. While no officers have been fatally wounded during remote operations, there have been various cases involving gunshot wounds. During helicopter raids, cultivators generally flee from the scene while law enforcement officers are lowered into the area. While living on-site for months, cultivators develop elaborate escape routes and hiding spots. Hiding places can be as close as one hundred feet from a grow site, and are rarely found without K-9 assistance. The cultivators that are obtained are generally low-level employees with minimal knowledge about the larger organization that employs them. To complement tactical operations, government agencies have developed another significant long-term goal to develop an understanding of commercial scale, remote marijuana cultivation, within the broader public. Regional leadership conducts public education programs by presenting PowerPoint demonstrations about DTOs at meetings, forums, and presentations for politicians, government employees, and the general public. Law enforcement organizations facilitate information sharing with the media and local contacts, and have developed “bi-lingual material to be distributed in high risk areas seeking information and offering rewards.”These programs aim to increase the awareness in an effort to increase reports of suspicious activities. When marijuana related activities are reported early, enforcement agencies gain a strategic advantage in combating individual sites. In addition, vertical grow systems for sale early detection allows more sites to be discovered and raided throughout the year because enforcement efforts are spread over a longer period of time. Public education creates an understanding of the consequences of marijuana production on various scales.

This can provide political support for the prevention of DTO related activities in California, as well as alter patterns of marijuana acquisition and consumption within the general public.This means that carefully planned and executed cultivation systems are crucial to developing quality marijuana harvests, and that cultivators manipulate the environment to optimize conditions for Cannabis plants. The widespread influence of Mexican cartels on outdoor cultivation in California causes similar processes to be performed at separate sites dispersed across large geographic distances. DTO operated grow sites have developed systematic patterns of behavior that occur with regularity and make their efforts distinct. Cultivators inhabit remote sites over long periods of time to develop plantations, and create a multitude of adverse effects in the process. Site selection is a crucial aspect of the cultivation process. DTOs often choose prospective locations long before they enter into a site. Some key elements that they look for on maps and aerial photographs are isolated water sources, slight canopy cover and adequate sunlight exposure. Sites are created in areas such as logged landscapes, conservation reserves, remote areas of national parks, and other places with difficult access and visually indistinct features from a birds-eye view. These are often areas where people rarely go because entry is made difficult by physical barriers such as cliff faces, steep talus slopes, dense clusters of vegetation such as poison oak, and even man-made berms. Due to the rugged and highly vegetated condition of most prospective sites, preparing land for marijuana planting is both labor intensive and time-consuming. Laborers work long hours to provide Cannabis plants a monopolistic domination of the landscape. The dynamics of landscape alteration depend on site-specific characteristics, but many similar practices occur throughout DTO operations. During the site supply process, cultivators cut or wear trails into the landscape that weave back and forth making site access for material transport easier. In order to avoid detection, laborers try to avoid leaving evidence of their presence up to a certain point, such as a major physical barrier, after which distinct paths are worn into the ground. The sheer weight of laborers’ equipment loads combined with regular use of the trails is enough to trample and kill small vegetation. Dense stands of brush and trees are removed with saws and machetes.

The paths connect site entry routes to the food preparation area, sleeping area, latrine, and various marijuana plantations. One site may contain 30,000 plants, but within that site the plants are often divided up between multiple smaller plots. Laborers’ movement along the paths is responsible for the introduction and distribution of non-native plant species to new areas. Laborers accumulate and transport seeds or spores on their bodies, clothing, shoes and equipment. In the California central coast region, cultivator movement along self-created paths is cited for the spread of Sudden Oak Death syndrome in Tan Oak, Black Oak, and Coastal Live Oak trees.Studies conducted by the Santa Lucia Conservancy show that the occurrence of SOD is facilitated by remote inhabitance through transmission of the plant pathogen responsible for SOD, Phytophora ramorum. Marijuana cultivators contribute to the spread of Phytophora ramorum to uninfected oak trees and exacerbate the effects of Sudden Oak Death syndrome by moving throughout affected landscapes that are part of their widespread system of sites. Movement by any person or animal can effectively transmit this pathogen to uninfected oak trees, but cultivators navigate through these areas more frequently than other people who may pass through. Their movements are also responsible for the spread of a variety of harmful invasive species including thistles, Vinca, Periwinkle, English Ivy Yard, and others.Invasive organisms often out-compete native species because they possess adaptive characteristics and lack natural competitors when introduced in new areas, which results in widespread alterations to the food-web, nutrient cycling, fire regimes, and hydrology of otherwise well preserved ecosystems. Many attributes of remote ecosystems are not ideal for agriculture, so laborers invest much time and energy in altering land to make it suitable for Cannabis cultivation. Workers clear under story vegetation to eliminate potential competition and prepare the soil for Cannabis plantations. The cleared vegetation, referred to as “slash piles,” are discarded in stream beds, causing impediments to hydrologic flows, or used to create berms up to 8 feet tall in order to bar site access.Throughout the growing season, cultivators use chemical techniques to maximize THC content and bud production. These intensive methods change soil dynamics, nutrient levels and chemical makeup, thus creating the opportunity for a new composition of vegetation to emerge. Landscape alteration may awaken seed banks in the soil that have sat dormant for up to hundreds of years, alter the ability for some plants to re-grow because of changes in soil-chemistry, destroy habitat for a variety of organisms, and have many other adverse affects on otherwise preserved ecosystems. In short, remote Cannabis cultivation forever changes the ecosystems in which it takes place.

In highly mountainous areas, growers dig out terraces on hill slopes to create planting beds. In the process, soil is displaced leading to accelerated rates of hill-slope erosion. Some terrace beds are stabilized by falling trees, trimming them into logs, and inserting the logs into the terrace walls to hold the dirt in place. This is an important step to provide somewhat stable access to individual plants on steep slopes, and to prevent landslides that could destroy entire plantations. However, when these are removed, the stock of topsoil is greatly diminished. On slight grades or flat surfaces, cultivators mound soil around Cannabis stems to optimize nutrient uptake. For plantations with high percentages of gravel or sand,vertical grow system growers will bring in loamy soil to provide proper soil composition and nutrients. The affects of these changes on the natural environment can vary. For instance, fall entrees naturally promote the growth of under story species; however, the cutting of trees can disturb soil and impact the ecosystem services that they once provided such as habitat, nutrient cycling and moisture retention. Many land alterations remove perennial root structures that stabilize sediment causing the hillsides to lose stability and become more susceptible to small landslides and sedimentation of water sources during precipitation. Sedimentation alters water flow, reduces the capacity of water stocks, degrades the habitats of various species, and makes waters turbid – reducing the capacity for organisms to photosynthesize. Further, chemical toxins and metals bind to clay particles in fluvial sediment, are consumed by bottom feeding organisms, and bio-accumulate in higher order predators throughout the food chain. Cultivators approach land alterations with utter disregard; falling old growth trees, discarding of brush in stream beds, and littering the ground indiscriminately with waste. In sites intended for continued cultivation, laborers dig deep holes that are used to dispose of trash at the end of the harvest season in order to reduce the chances of detection between one season and the next. While their grow operations are usually restricted to between 5 and 10 acres, according to the National Park service, “for every acre of forest planted with marijuana, 10 acres are damaged.” In other words, the adverse effects of remote Cannabis cultivation reach far beyond the borders of the plots in which the plants are grown. An isolated water source is essential for the success of the marijuana plant to produce market grade buds. Mendocino County Sheriff, Tom Allman, claims that “one marijuana plant requires approximately one gallon of water per large plant per day,” meaning that a typical remote grow site can consume approximately 7,000 gallons of water each day over a period of three to four months. This makes water diversion no simple task. Finding a reliable water source that is available year round is especially crucial because the growing season occurs during the summer months. Ideal water sources include springs, creeks, and small bodies of water that do not dry up even during the hot California summers.

Cultivators enact a variety of methods to exploit water sources high in the watershed, some of which include makeshift dams, cisterns, storage tanks, on-site reservoirs, and gravity based PVC pipe flow systems. These systems are built to utilize gravity-based pressure to extract water from natural or man-made pools. The water is then transported through PVC pipes to cultivation sites. These water diversion systems connect water sources to marijuana plants up to four miles away. The resources that cultivators possess to build these extensive systems include shovels, pumps, sheets of plastic, tarps, string and large quantities of PVC piping. Other necessities are extracted from the nearby environment and include logs, rocks, clay, brush, and moss. One site in Carmel contained a makeshift cistern that was dug out, lined with black plastic, and held in place with rocks. Water flowed from the cistern through the 1.5 miles of piping and dropped 700 feet in elevation en route to the site. Once water reached the grow site, the large PVC fed into progressively smaller tubing that connected drip irrigation lines to each plant. This system utilized control valves to prevent over watering and to regulate watering schedules. In the case of small operations, the water is sometimes stored at the site in large plastic lined reservoirs or large storage tanks. The water is then pumped from the reservoir on regular schedules through drip irrigation lines in quantities that optimize growth. Water diversion practices create adverse effects for humans and the environment alike. When the natural flow of water from springs or ephemeral creeks is modified, the preexisting flora and fauna that rely on it are deprived. As surface level water disappears, riparian vegetation and animals have limited access to the water that they depend on. More seriously, keystone fish species die from degradation and loss of habitat. The death or removal of keystone species from ecosystems creates a void that affects the entire food chain. As one species cannot sustain its diet, it dies off, leading to the death of other species that predate upon it. Water diversion practices significantly impact human society as well. The state of California has abundant water resources that are necessary to sustain its vast population, economy, and natural environments. Though the overall fresh water supply from precipitation is immense, the public demand for fresh water far exceeds the natural supply.

In addition, commercialization may increase the Version Accepted for Publication availability of marijuana through diversion, increase exposure to aggressive marketing tactics by the emerging cannabis industry, or increase exposure to others who use or illicitly sell marijuana. Legalization of cultivation for personal use raises additional concerns about access and exposure. Although some studies have found positive associations between densities of medical marijuana dispensaries and marijuana use among adults, very little is known about the potential influence of adolescents’ exposure to marijuana dispensaries, recreational outlets, and marketing or the mechanisms through which such exposure may affect their marijuana use. Studies showing associations between adolescents’ exposure to alcohol and tobacco outlets and use of those substances, suggest the importance of investigating exposure to retail access and marketing of marijuana. The article by Shi et al., makes a timely contribution to this field of research by investigating associations of proximity and density of medical marijuana dispensaries, price of medical marijuana products, and variety of products sold in school neighborhoods with adolescents’ marijuana use and susceptibility. Results showed no associations between adolescents’ current use or susceptibility to use marijuana and proximity or density of medical marijuana dispensaries around schools, price, and product variety. Focusing on exposure around school neighborhoods, this study used traditional measures of proximity and density of outlets around schools. Such measures are often used in studies to assess influences of exposure to alcohol and tobacco outlets on use of those substances. However, research shows that the locations in which young people spend their time are varied and geographically dispersed,vertical grow rack and not captured by geographical boundaries such as school or home neighborhoods.

Activity spaces include all locations and the routes the individuals experience as a result of their Recent studies have found that adolescents’ activity spaces provide a more accurate measure of alcohol and tobacco outlet exposures than do traditional measures. Future research should consider marijuana retail availability in the broader environments where adolescents spend their time. Moreover, the cannabis market is evolving in ways that make it different than the tobacco and alcohol markets. In addition to marijuana, myriad cannabis products are available and heavily marketed. These products can be smoked, eaten, vaped, or used topically. Many of these products are easily transportable and readily concealed or disguised. Many of them can be used covertly , possibly making use by adolescents less risky than is the case for most alcohol or tobacco products. As noted by Shi et al.,future research should consider the range of cannabis products to more accurately assess the effects of marijuana commercialization on adolescents’ marijuana beliefs and use. In addition, unlike alcohol and tobacco, there remains a substantial illegal market. Given tax policies and the resulting price differentials, the underground market may remain a preferred source of marijuana for adolescents. The situation is further complicated by provisions allowing individuals to grow marijuana for personal use, possibly providing access for adolescents directly from family members, friends, and acquaintances who grow it or by providing increased opportunities to steal it. Although the legal market may not be a primary source of marijuana for adolescents, it nonetheless may have an influence by increasing open consumption in public and the home, by normalizing marijuana use, and by increasing exposure to marketing. Importantly, some adolescents may be more susceptible to exposure to marijuana outlets in their daily lives, and therefore at greater risk for marijuana use, susceptibility and problems. The lack of As the national landscape regarding marijuana legalization changes in the US, more research is needed to understand adolescents’ exposures to marijuana commercialization and the mechanisms by which exposures to marijuana dispensaries, recreational outlets, and marketing may affect marijuana use and beliefs.

Such research is important to guide policies and prevention efforts to reduce the potential negative effects of marijuana commercialization. Psychiatric disorders and substance abuse commonly cooccur. Population-based studies have provided documentation that, of all patients with major psychiatric disorders, those with bipolar disorder show the highest prevalence of comorbid substance abuse and dependence. The cause of this high comorbidity rate has not been clearly established, and the relationship is probably bidirectional. One explanation for this co-occurrence is the ‘self-medication hypothesis’, which states that some patients experience improvement in psychiatric symptoms as a result of substance use. It has been found that about 50% of individuals with bipolar disorder have a lifetime history of substance abuse or dependence. Furthermore, bipolar I subjects appear to have higher rates of these comorbid conditions than bipolar II subjects. Research has consistently shown that substance abuse in bipolar patients may have negative consequences both on clinical characteristics and long-term course: drug addiction is associated with medication non-compliance, a higher frequency of mixed or dysphoric mania and, possibly, an earlier onset of affective symptoms, more severe impairment of social functioning, greater subjective distress and less resourcefulness in coping, more hospitalizations and poorer prognoses, together with a higher frequency of suicide attempts. Alcohol and cannabis are the substances most often abused, followed by cocaine and then opioids. In terms of specificity, a link seems to exist between cocaine use, as evaluated among poly-abusers of different categories, and bipolar disorders. When abusing cocaine, bipolar patients showed significantly higher rates of post-traumatic stress disorder and antisocial personality disorder, and were more likely to present in a mixed mood state. Alcohol abuse and dependence show a lifetime prevalence 3–4 times higher in patients with bipolar disorders than in the general population, while the lifetime prevalence of mood disorders in alcohol-dependent subjects is approximately 10 times higher than in the general population.

Most bipolar patients run the risk of developing lifetime drug or alcohol-related problems, which may in their turn contribute to more varied and complex clinical presentations, so increasing the risk of a depressive episode in the near term, poorer lithium response, functional disability and elevated suicide risk, as well as high rates of suicide attempts. Moreover, alcohol addiction may exacerbate impulsive behaviours and risk-taking propensities in bipolar patients. With respect to cannabis use, some papers have pointed out that marijuana is not only often abused by patients suffering from bipolar disorder, but also induces manic symptoms. Additionally, cannabis-using bipolar patients experienced less satisfaction with life, had a lower probability of having a romantic relationship compared with non-users, and also had more severe alcohol and other drug use. Regarding heroin use, very few studies have been conducted on the specific effects of its abuse in the clinical course of bipolar patients. Studies on the self-medication hypothesis have focused on the use of heroin and cocaine dependence as an attempt to alleviate emotional suffering. Most addicts do not choose drugs randomly to alleviate painful affective states and their underlying psychiatric disorders. Rather, drugs are chosen because an individual discovers a specific psychopharmacological action that helps to alleviate an individual’s suffering. Recently, greater emphasis has been placed on understanding addiction as a form of ‘self-medication’ to alleviate suffering, with less emphasis on its severe psychopathology. As the ‘self-medication’ theory suggests, patients to modulate their mood by decreasing their dysphoria may use heroin. In other words, patients appear to select substances that they expect to have a ‘healing’ effect. In bipolar patients cocaine appears to exercise an appeal because of its ability to relieve distress associated with depression. Recently Khantzian revised his theory and expanded the number of affective states to be examined, including alexithymia, to better operationalize SMH, but some authors indicated that affective measures did not have the expected relationship with reported substance use. In this study, to further test the validity of Khantzian’s hypothesis, we compared concomitant substances of abuse in bipolar heroin addicts according to their clinical presentation . Bipolar patients have been chosen because,cannabis grow racks as compared with patients suffering for other mental illnesses, they are more likely to clearly show various different identifiable affective states. We considered heroin-dependent bipolar patients as Khantzian developed his hypothesis treating heroin addicts. Moreover, bipolar patients are generally multi-drug abusers. The choice of these patients is also interesting for the fact that heroin use in bipolar disorder is perhaps the least understood and researched groups of patients with bipolar disorder and substance use. Khantzian’s hypothesis would be supported if the use of CNS stimulants were prominent in the depressive phase and CNS depressants in hypomanic or manic phases, at least when patients complain of altered mood or insufficient balance of affective symptoms despite putative self-medication by substance use.This is a retrospective, observational, case–control study. The research study was implemented using a dataset from previous studies on MMTP carried out in Italy and used in previous published articles . The study included patients treated at Santa Chiara University Hospital, Department of Psychiatry, University of Pisa, Italy during the period 1994–2010. All patients gave their informed consent to the anonymous use of their personal data records for research purposes.Addiction-related information was collected by means of DAH-Q administered by a psychiatrist. The DAHQ is a multi-scale questionnaire that comprises the following categories: demographic data, physical health , mental health , substance abuse , social adjustment and environmental factors , clinical characteristics as frequency of drug use, patterns of use, phase, nosology, treatment history .

Items are set up so as to elicit dichotomous answers .Regarding toxicological urinalyses, we utilized the routine analyses as used for all hospitalized patients. The enzyme-multiplied immuno techniques for opiates, methadone, benzodiazepines, hypnotics, cocaine, amphetamines, hallucinogens, cannabinoids and inhalants were used. Problematic alcohol use was defined according to a lifetime history of frequent intoxication and/or negative consequences of habitual use on their social adjustment .Table 1 shows the demographic and clinical characteristics of our patients according to their present episode polarity. No statistically significant differences among the four groups were observed as regards age, sex, educational level, marital status, job and financial need. Nor were any statistically significant differences observed either among the majority of DAH-RS factors . Table 2 shows differences regarding concomitant substance abuse between the four groups of patients. No statistically significant differences were observed regarding the abuse of heroin. Patients with a depressive episode at clinical presentations showed more frequent use of unprescribed anxiolytic-hypnotics. During a hypomanic episode, patients more frequently used cocaine-amphetamines, while, during a manic episode, patients more frequently used cannabis and cocaine-amphetamines. The associated use of alcohol, cocaine-amphetamines and cannabinoids was more frequently encountered during a mixed episode.As we observed in our sample, patients take anxiolytichypnotics, which belong to the class of central nervous system depressants, with greater frequency during a depressive episode. They take CNS stimulants at a greater frequency during a hypomanic episode, whereas they tend to take both CNS stimulants and cannabinoids with a greater frequency during a manic episode; lastly, during a mixed episode they take CNS depressants , stimulants, and hallucinogens together. In the case of depressed patients, the use of CNS depressants is consistent with their toxicological status. It should be noted that benzodiazepine use in heroin addicts could be correlated with a condition of opiate dependence improperly compensated by street heroin. From a psychopathological standpoint, depressants may aggravate the slowing of cognitive and physical functions caused by depression, but it remains true that these medications are effective in treating insomnia and anxiety, which are often symptoms of depression. Also, patients may not be seeking an actual ‘lift’ of their depression but be searching for a state of ‘oblivion’ in which the pain of depression is cancelled. In depression, what is seen is not a higher use of stimulant substances, but the use of CNS Depressants that may sometimes relieve some aspects of depression – a situation that fails to provide support to Khantzian’s hypothesis. More clearly, Khantzian’s hypothesis does not seem to be supported by the other three kinds of clinical presentations. Patients during a hypomanic, manic or mixed episode, despite experiencing a state of excitement, tend to continue their abuse of psychostimulants, further reinforcing and elevating their mental state. This is consistent with a proposed bipolar-stimulant spectrum where sub-threshold bipolar traits are aggravated by stimulant abuse. If we focus on heroin-dependent subjects, the concomitant use of cocaine is reported to be a relevant phenomenon that will determine negative consequences on social adjustment and outcome.

Participants had to have consumed alcohol the prior year but not ever fit criteria for alcohol dependence in DSMIII or DSM-III-R , with individuals excluded for histories of bipolar disorder, schizophrenia, or physical problems that precluded alcohol challenges. Recruitment began with a participant who reported a father with alcohol dependence, with subsequent selection of a family-history–negative comparison individual with similar demography, substance use history, and past-year drinking pattern. Potential participants were evaluated using in-person interviews based on a precursor of the follow-up instrument described below . Laboratory-based challenges with 0.75 ml/kg of absolute alcohol established their intensity of response to alcohol using the Subjective High Assessment Scale , changes in body sway, and changes in hormones and electrophysiological measures, depending on the specific protocol . Data from different measures and across years were combined using z scores into one overall alcohol-challenge LR value in which lower scores reflected lower LRs per drink.Assessments began in 1988 and then were conducted every 5 years using a modification of the Semi-Structured Assessment for the Genetics of Alcoholism interview, with validities and reliabilities greater than .Age 30, 35, and 40 evaluations were face-to-face, with a parallel interview about the proband performed with a spouse or close friend. Reflecting fifinancial restrictions, age 50 and 55 follow-ups were limited to phone interviews of probands. At age 35, probands completed the then recently developed Self-Report of the Effects of Alcohol retrospective questionnaire regarding the average drinks required for up to four effects actually experienced during three life epochs: the first five times drinking , most recent 3 months, and their heaviest drinking period. Total scores reflected average drinks for effects across all three epochs,vertical grow rack as the sum of the number drinks required for up to four effects divided by the number of the effects reported, and SRE-5 scores reflected the average drinks required during the first 5 times of drinking only .

Higher SRE scores indicate more drinks needed for effects, or lower LRs per drink. The SRE Cronbach’s + is greater than .Age 35 follow-ups also evaluated novelty seeking , sensation seeking , and impulsivity . By age 50, 11 of the original 453 probands had died, leaving 442 eligible for follow-up . Among these, 397 participated in all follow-ups from ages 30 through 50 , 165 of whom had developed a DSMIV AUD at any assessment . With the emphasis on predictors of the course of AUDs and their prevalence during the sixth life decade, these men with prior AUDs were the focus of the current analyses.Comparisons across the four outcome groups were first evaluated using chi-square for categorical data and analysis of variance for continuous variables. We next evaluated which variables from the age 30–50 follow-ups significantly differentiated across the four age 50–55 outcome groups when considered along with other significant variables. There are three types of data used in the relevant analyses in Table 3: single assessment items ; drinking variables in which the number represents the maximum value reported across age 30–50 interviews , or a simple count of occurrences ; and dichotomous variables indicating that a subject fulfilled that item at any age 30–50 interview. For the regression analyses in Table 4, we considered a simultaneous-entry multi-nomial logistic regression analysis but rejected this approach because of our modest sample size and our desire to identify variables that predicted each outcome rather than evaluating predictors of only three groups with the fourth used as a reference group. Therefore, this final analytic step used four binary logistic regression analyses predicting each outcome independently.The characteristics reported at age 30–50 follow-ups and their relationships to age 50–55 outcome groups are presented in Table 3. For most alcohol-related variables, men who reported low-risk drinking needed the lowest number of drinks for effects , as well as lowest alcohol quantities, frequencies, problems, and treatment exposure at age 30–50 follow-ups. The pattern for alcohol-related variables generally increased across Groups 1–4, with the highest number of drinks for effects , quantities, frequencies, problems, and treatment exposure for the abstinent Group 4, followed by men in Group 3 who fulfilled criteria for DSM-5 AUDs at ages 50–55. Patterns across Groups 3 and 4 included an earlier AUD onset and greater experience with alcohol-related treatment and/or self-help groups for the abstinent Group 4.

High-risk drinking was associated with LR values and alcohol histories between Group 1 and Groups 3 and 4. The lower portion of Table 3 lists the age 20–50 patterns across groups regarding drug-related items. Most men had experience with illicit drugs during those follow-ups, but the only significant group difference was for the prevalence of cannabis use disorders, with the lowest values for Group 1 and the highest for Group 4. The analyses next turned to an evaluation of how the nine variables from the age 30–50 follow-ups that significantly differentiated across the groups in Table 3 related to age 50–55 outcome groups when considered in the same analysis. Because the two SRE measures correlated at .66, and the number of DSM-IV AUD criterion items endorsed and DSM-IV dependence diagnoses correlated at .69, to minimize multi-collinearity among the four variables only SRE-T and numbers of AUD items were used in the regression analyses. Five of the remaining seven variables from Table 3 contributed significantly to any regression analysis related to age 50–55 outcomes. Low-risk drinking was related to prior lower drinking frequencies; high-risk drinking was related to needing fewer drinks for effects and an older AUD onset; DSM-5 AUDs were related to higher prior drinking frequencies and the absence of prior treatment and/or self-help group participation; and abstinence was related to needing the most drinks for effects , higher odds ratios for prior treatment or self-help participation, and prior cannabis use disorders. Thus, the most consistent predictors of age 50–55 outcomes were LR, prior drinking frequencies, and having received prior help for drinking problems, each of which contributed significantly to two of the four regression analyses in Table 4. It is worth noting that although the odds ratio for the numbers of DSM items endorsed for Group 4 was not significant, the value is actually greater than 1 but appears lower in the table because of a suppressor effect in the regression analysis.This article presents the age 50–55 outcomes for 156 men who developed an AUD during the initial 30 years of the SDPS, as well as predictors of those outcomes. Contrary to Hypothesis 1, only 10% of these men were abstinent from alcohol during ages 50–55, and 16% reported low-risk drinking. The remaining 74% either fulfilled DSM-5 AUD criteria or reported risky drinking. Although these probands had impressive educations and incomes, the data document the tenacity of alcohol-related problems when individuals enter their sixth life decade. These results and the study by Vaillant demonstrate that many individuals with AUDs do not fifit the erroneous stereotype that they are likely to be unemployed and live on the street or in public housing.

The SDPS began with students and nonacademic staff at UCSD and their earlier high functioning predicted impressive achievements despite their AUDs. Yet, as shown in Table 3, between ages 20 and 50 these men had clearly filled AUD criteria, reporting 13–16 maximum drinks per occasion and experiencing 4–6 of the 11 DSM-IV AUD items. The inaccurate AUD stereotype is often shared by health care deliverers who might be reluctant to gather alcohol or drug problem histories from affluent and well-educated patients and to intervene when appropriate. These data support the contention that—regardless of social status, income, and age—all patients should be screened for substance intake patterns and related problems. Regarding Hypothesis 2, only 16% demonstrated sustained “controlled drinking” over the 5 years with alcohol quantities in the low-risk range . Although short-term, low-risk drinking is common , our findings are consistent with other studies that found less than 20% of individuals with alcohol dependence maintained controlled drinking over extended periods . As predicted, such non-problematic outcomes are most likely in men who were more sensitive to alcohol and had lower past drinking quantities, frequencies, and alcohol problems . This profile might help identify individuals for whom long-term controlled drinking might be an appropriate option. As predicted in Hypothesis 3, high-risk drinkers reflected earlier alcohol-related characteristics that were between those that predicted low-risk drinking and the higher quantities and problems that related to continued AUDs in Group 3 and abstinence in Group 4. The Nagelkerke’s Pseudo R2s in Table 4 indicate that the age 20–50 independent variables predicted Group 2 less well than the other outcomes,cannabis grow racks raising the question of whether some alcohol problems went unreported by this group. Even in the absence of multiple alcohol problems, heavier drinking is not optimal because it carries elevated risks for cardiovascular disease, stroke, cancers, and other adverse health outcomes that are likely to contribute to a shortened life span . Persistent abstinence was not only relatively rare but was also consistent with Hypothesis 4 and several other studies . These men had the lowest LR to alcohol and reported the highest alcohol quantities, problems, and rates of alcohol dependence. More research is needed, but one possible explanation for this finding is that experiencing greater alcohol-related problems, perhaps as a consequence of a lower LR to alcohol, may have contributed to men in Group 4 seeking help, and their exposure to treatments may have made abstinence especially acceptable to members of Group 4. Their cannabis-related problems may have also increased the likelihood of entering substance-related programs. Table 4 used a binary logistic regression analysis to evaluate how the predictors of group membership operated when considered in the same analyses. Focusing primarily on statistically significant findings in Table 4, a lower number of drinks needed for effects on the SRE was associated with a lower likelihood of being categorized as a high-risk drinker at age 50–55, and the need for more drinks for effects was associated with a greater likelihood of being abstinent during the age 50–55 follow- up. Although not statistically significant, the pattern in Table 4 indicated the possibility that the need for more drinks for effects might have increased the chances of meeting criteria for an AUD on follow up but decreased the chances of falling into the low-risk drinking category.

Drinking frequency also contributed significantly to two regression analyses, with prior lower frequencies robustly predicting low-risk drinking and prior higher frequencies indicating a higher risk for meeting DSM-5 AUD criteria after age 50. Prior frequencies might be a measure of the importance, or salience, of alcohol to probands in these outcome groups. Past experience with formal treatment or self-help groups was significantly associated with later abstinence and was less likely to be seen in participants with active DSM-5 AUDs during the age 50–55 follow-up. Later onset AUDs predicted only high-risk drinking in Table 4 and may have related to the development of an AUD at a time with greater maturity and life experiences, which, in turn, might be associated with less likelihood of alcohol-related life problems even in the context of continued risky drinking. Cannabis use disorder histories also significantly predicted one outcome, abstinence, perhaps reflecting cannabis related interference with cognitive functioning in AUDs that might contribute to more severe alcohol problems, as proposed in a recent article , and/or may have contributed to the probability of seeking help. More research will be needed with additional populations of older individuals with AUDs to determine whether these findings are replicable and apply to other samples of older individuals with histories of AUDs. The current data also generate some thoughts regarding DSM AUD approaches over the years. Regarding criteria for Group 3, we are aware that since 1987 , remission could only be diagnosed in the absence of endorsement of any DSM criterion items. Thus, earlier analyses attempted to define continued AUDs as endorsement of more than one DSM criterion item, with the result that Group 3 constituted 56% of the outcomes. Although those analyses identified the same predictors of outcome groups reported here, we were concerned that such a severe restriction regarding what was called remission might not fit the preferences of many current clinicians , and we decided to use the DSM-5–based less demanding definition requiring two criterion problems for an active diagnosis . There are no perfect and universally accepted definitions for remission versus active AUDs, but we feel the current approach is a reasonable compromise among possibilities.

These in vivo studies were an extension of much prior research, including human postmortem brain tissue studies, demonstrating that chronic smokers have increased nAChR density compared to non-smokers and former smokers. Additionally, many studies of laboratory animals have demonstrated upregulation of markers of nAChR density in response to chronic nicotine administration . In a previous study by our group comparing nAChR availability between smokers and nonsmokers , we explored the effect of many variables, including caffeine and marijuana use. Both heavy caffeine and marijuana use were exclusionary, such that participants drank an average of 1.3 coffee cup equivalents per day and only 12 % of the study sample reported occasional marijuana use. PET results indicated that caffeine and marijuana use had significant relationships with α4β2* nAChR availability in this group with low levels of usage. Based on these preliminary findings, we undertook a study of the effect of heavy caffeine or marijuana usage on α4β2* nAChR density in cigarette smokers.One hundred and one otherwise healthy male adults completed the study and had usable data. Participants were recruited and screened using the same methodology as in our prior reports , with the exception that this study only included Veterans. For smokers, the central inclusion criteria were current nicotine dependence and smoking 10 to 40 cigarettes per day, while for non-smokers, the central inclusion criterion was no cigarette usage within the past year. Heavy caffeine use was defined as the equivalent of ≥3 cups of coffee per day, and heavy marijuana use was defined as ≥4 uses of at least 1 marijuana cigarette per week. Exclusion criteria for all participants were as follows: use of a medication or history of a medical condition that might affect the central nervous system at the time of scanning, any history of mental illness, or any substance abuse/dependence diagnosis within the past year other than caffeine or marijuana diagnoses.There was no overlap between this study and prior research by our group. During an initial visit,cannabis drying racks screening data were obtained to verify participant reports and characterize smoking history. Rating scales obtained were as follows: the Smoker’s Profile Form , Fagerström Test for Nicotine Dependence , Beck Depression Inventory , Hamilton Depression Rating Scale , and Hamilton Anxiety Rating Scale .

An exhaled carbon monoxide level was determined using a MicroSmokerlyzer to verify smoking status. A breathalyzer test and urine toxicology screen were obtained at the screening visit to support the participant’s report of no current alcohol abuse or other drug dependencies. This study was approved by the local institutional review board , and participants provided written informed consent.Roughly 1 week after the initial screening session, participants underwent PET scanning following the same general procedure as in our prior reports . Participants from the smoker groups began smoking/ nicotine abstinence two nights prior to each PET session and were monitored as described previously , so that nicotine from smoking would not compete with the radiotracer for receptor binding during PET scanning. Caffeine/marijuana abstinence was initiated 12 h prior to PET scanning, so that acute ingestion/ intoxication would not affect study results. At 11 AM on the scanning day, participants arrived at the VA Greater Los Angeles Healthcare System PET Center, and smoking abstinence was verified by participant report and having an exhaled CO ≤ 4 ppm. Each participant had an intravenous line placed at 11:45 AM in a room adjacent to the PETs canner. At 12 PM, bolus-plus-continuous-infusion of 2-[ 1 8F]fluoro-3-azetidinylmethoxy pyridine was initiated, with 2-FA administered as an intravenous bolus in 5-ml saline over 10 s . Roughly, the same amount of 2-FA was also diluted in 60-ml saline, and 51.1 ml was infused over the next 420 min by a computer-controlled pump . 2- FA-specific activities were similar for the study groups . Groups did not significantly differ for injected or infused doses of 2-FA, or for specific activity . Thus, the amount of 2-FA administered as a bolus was equal to the amount that would be infused over 500 min . This Kbolus was effective for reaching an approximate steady state in recent studies by our group and collaborators . After initiation of the bolus-plus-continuous-infusion, participants remained seated in the room adjacent to the PET scanner for the next 4 h to allow the radiotracer to reach a relatively steady state in the brain. At 4 PM, PET scanning commenced and continued for 3 h, with a 10-min break after 90 min of scanning. Scans were acquired as series of 10-min frames. PET scans were obtained using the Philips Gemini TruFlight , a fully three-dimensional PET-CT scanner, which was operated in non-TOF mode. Reconstruction was done using Fourier rebinning and filtered back projection, and scatter and random corrections were applied. The mean spatial resolution for brain scanning is 5.0 mm by 4.8 mm . 2-FAwas prepared using a published method ; this radiotracer was developed as a ligand specific for β2*-containing nAChRs . A magnetic resonance imaging scan of the brain was obtained for each participant within a week of PET scanning on a 1.5-T Magnetom Symphony System scanner , in order to aid in localization of regions on the PET scans.

The MRI had the following specifications: three-dimensional Fourier-transform spoiled-gradient-recalled acquisition with TR = 30 ms, TE = 7 ms, 30 degree angle, 2 acquisitions, 256 × 192 view matrix. The MRI scanning procedure typically lasted ∼30 min. The acquired volume was reconstructed as roughly 90 contiguous 1.5-mm-thick transaxial slices. Blood samples were drawn during PET scanning for determinations of free, unmetabolized 2-FA and nicotine levels in plasma. For 2-FA levels, four samples were drawn as standards prior to 2-FA administration, and nine samples were drawn at predetermined intervals during PET scanning. 2-FA levels were determined using previously published methods . For plasma nicotine levels, blood samples were drawn prior to and following PET scanning. These samples were centrifuged, and venous plasma nicotine concentrations were determined in Dr. Peyton Jacob’s laboratory at UCSF, using a modified version of a published GC-MS method . The lower limit of quantification for this method was 0.2 ng/ ml. In addition to the participants described in this paper, 11 smokers completed study procedures but were excluded from the data analysis because their plasma nicotine levels were unacceptably high . This issue of smokers using nicotine/tobacco during the abstinence period of a brain-imaging study has been reported in prior studies by our group and others , presumably related to difficulty in having tobacco-dependent smokers remain abstinent for a prolonged period.To determine if the four study groups differed on demographic, rating scale, or substance-use variables, analyses of variance were performed with the variables as dependent measures and group as a between-subject factor. ANOVAs were also performed for the three groups of smokers for smoking-related variables . These analyses were performed to verify that groups differed on caffeine and marijuana use and to determine if groups had potentially confounding variables that would need to be considered when evaluating the PET data. For evaluating group differences in α4β2* nAChR availability, overall analyses of covariance were performed using Vt/fP values for each of the three ROIs as dependent measures, group as a between-subject factor, and education level as a covariate based on results of the above analysis demonstrating group differences for this variable. To clarify results of these overall tests, post hoc Student s t tests were performed to determine which between-group differences accounted for significant findings. Bonferroni corrections for multiple comparisons were applied to all statistical tests, with the ANCOVA results being corrected for the three regions tested and post hoc Student s t tests being corrected for the six group comparisons performed for each region. Results were considered significant if corrected results passed a threshold of P < 0.05. To maximize power, the means of left and right Vt/fP values for prefrontal cortex and thalamus were used in statistical analyses, along with values for the whole brainstem. For descriptive purposes,hydroponic cannabis system percent group differences in Vt/fP values were determined between the smoker groups and the group of non-smokers, and between smokers with and without heavy caffeine or marijuana use. Statistical tests were performed using SPSS Statistics version 23 .

The central study finding was that smokers with concomitant heavy caffeine or marijuana use have higher Vt/fp values in the brainstem and prefrontal cortex than smokers without such use. The study also replicated earlier work demonstrating higher Vt/fp values in the prefrontal cortex and brainstem of smokers than nonsmokers. Taken together, these findings indicate that smokers with concomitant heavy caffeine or marijuana use have greater nAChR up regulation than smokers without concomitant heavy use. The most straightforward and likely explanation for the central study finding is that smokers who use caffeine or marijuana heavily have more nicotine exposure than smokers without such use. This explanation is supported by study data demonstrating that smokers with concomitant heavy caffeine or marijuana use had higher exhaled CO and greater depth of inhalation levels at baseline than smokers without such use . Other data supporting this theory include research demonstrating that caffeine increase nicotine intake in laboratory animals and a study of smokers with heavy marijuana use who had altered lung permeability , which resulted in greater cigarette smoke exposure. Thus, smokers with concomitant heavy caffeine or marijuana use may have increased brain nicotine exposure due to altered smoking topography, effects of caffeine or marijuana on other aspects of nicotine absorption/intake, or both. While the smoker groups with heavy caffeine or marijuana use did have higher exhaled CO levels and greater depth of smoking inhalation than the smoker group without concomitant use, the absence of group differences in cigarettes per day and FTND scores indicate that explanations for increased nAChR availability other than greater nicotine exposure are possible. We are not aware of studies that would fully explain direct effects of caffeine or marijuana on nAChR availability; however, recent research has begun to elucidate interactions between nicotine and caffeine or marijuana on a cellular level, and future research could determine a mechanism by which caffeine or marijuana exposure directly affects nAChR availability. The specific regional findings here may have functional significance, given the roles of the brainstem and PFC in the mediation of addiction. For the brainstem, many studies demonstrate that addictive drugs acutely stimulate neurons originating in the brainstem leading to the ventral striatum to produce reward and for reviews. For the PFC, this region is known to mediate executive functions, such as attention, working memory, and decision-making , which are associated with drug use. Extensive prior research has examined associations between smoking-related symptoms and nAChR availability in the regions studied here without finding strong evidence for associations between these variables. Future research could utilize specific testing for functions of the brainstem or PFC to further evaluate the functional significance of increased nAChR availability in these regions in smokers. Study results also have clinical implications regarding the co-use of cigarettes and other drugs. Prior research examining smokers trying to quit has demonstrated that concomitant use of caffeine or marijuana predicts less likelihood of smoking cessation. Recent research by our group showed that greater nAChR availability was associated with less likelihood of smoking cessation during a quit attempt with nicotine or placebo patch administration. Taken together, our findings imply that smokers with heavy caffeine or marijuana use have greater exposure to nicotine, more upregulation of nAChRs, and more trouble quitting in smoking cessation programs than smokers without concomitant heavy drug use. Future brain imaging research in smokers with concomitant heavy drug use who undergo smoking cessation treatment could confirm this implication of the current study. This study had several limitations. First, we did not examine non-smokers with heavy caffeine or marijuana use to determine if study findings were independent of cigarette smoking. Future research with such non-smokers could determine if caffeine and marijuana use affect nAChR density directly or if the effect on nAChR density is mediated through greater nicotine exposure in smokers with heavy caffeine or marijuana usage. Second, while we did determine exhaled CO levels, depth of inhalation, reported cigarettes per day, FTND scores, and plasma nicotine levels at the time of scanning, we did not collect blood for plasma nicotine levels at baseline during normal cigarette smoking.

Only 32% of the businesses listed on Yelp were verified to be active brick-and-mortar dispensaries. This is not surprising because Yelp, which provides a general business listing service not specifically designed for marijuana industry, had more records irrelevant to marijuana dispensary than Weed maps and Leafly. Taken together, no single secondary data source could provide a reasonably complete and accurate list of active brick-and-mortar dispensaries in a large state like California. We recommend surveillance and research to consider their unique strengths and weaknesses when a single data source is used to minimize required resources. When resources are available, we recommend the integration of multiple secondary data sources, preferably including a licensing directory and multiple online crowd sourcing platforms, as well as verification through phone calls such as what has been done in this study or through even better approaches such as a field census. The verification could considerably improve the accuracy of the data compiled from secondary data sources. Our findings were overall consistent with the two smaller-scale studies conducted in California, both in Los Angeles County. One was conducted in 2016-2017, before recreational marijuana dispensaries were allowed to open . This study obtained medical marijuana dispensary information from five online crowd sourcing platforms. Weed maps was suggested to be the most accurate and up-to-date platform, contributing to 95% of the final records. Call verification was conducted in 10% of the dispensaries and found to generally align with the information posted on online crowd sourcing platforms. The other study was conducted in 2018-2019, after recreational marijuana dispensaries were allowed to open . It extracted data from Weed maps and Yelp and verified dispensary information through site visits. About 80% dispensaries that were determined to be active through online data cleaning were confirmed to be active in site visits,cannabis grow equipment and licensed dispensaries accounted for roughly 40% of the active dispensaries.

Neither study reported validity statistics for each specific data source. Our study expanded on the prior research by covering a much larger geographic region, computing detailed validity statistics for each data source by dispensary category and county population size, and by using two gold standards and two tests to demonstrate validities in different scenarios and for different purposes. This study has limitations. First, due to the lack of feasibility of conducting a field census in such a large geographic region, phone calls were made to verify information obtained from secondary data sources. While this approach was cost effective, businesses not listed in these secondary data sources were excluded from the analysis, potentially the smaller, unlicensed dispensaries that did not intend to promote themselves on online crowd sourcing platforms because of cost and law enforcement concerns. Future research using field census approach is warranted to assess to what extent unlicensed dispensaries were underrepresented in our study. We could also have mis-classified dispensaries as inactive if they provided incorrect contacts or could not be reached after multiple call attempts. Search terms in Yelp may not successfully capture all marijuana-related businesses. As a result of these caveats, our call-verified, combined database would be an underestimation instead of the true “universe” of the active dispensaries in California. Second, validity measures were not all applicable in some scenarios where “true negative” or “false positive” could not be identified with the current study design. Third, regulations on online crowd sourcing platforms have been rapidly evolving. Before our data collection, Weed maps served as the major platform to advertise and promote dispensaries including the unlicensed ones in California. Right after our data collection, California regulators required Weed maps to remove unlicensed businesses from its website. By January 2020, Weed maps had removed over 2,000 businesses . Weed maps may no longer be a good data resource for identifying unlicensed dispensaries, particularly in California, even though it had satisfactory validity statistics in our study. Future studies should consider alternative crowd sourcing platforms that post unlicensed dispensary information. Fourth, we evaluated the three most commonly used online crowd sourcing platforms.The findings were not applicable to commercial providers of business listings, either, such as Info USA and Dun & Bradstreet that recently incorporated marijuana businesses into their databases. Finally, findings may not be generalizable to the identification of delivery-only services or dispensaries in other states.

Notwithstanding the limitations, the findings of this study provide empirical evidence regarding the validity of using secondary data sources to identify brick-and-mortar marijuana dispensaries in a large geographic region. The data collection and verification protocol and validity statistics could be used by local governments and communities to best strategize regular surveillance on the availability and accessibility of marijuana dispensaries and their compliance to laws. Future research could also use these findings to replicate dispensary identification in other states where marijuana has been commercialized. We hope a comprehensive and accurate enumeration of marijuana dispensaries could facilitate future research evaluating marijuana dispensaries and their impacts on public health. One of the objectives of the NIH-initiative, the Adolescent Brain Cognitive Development Study, is to establish a national, multi-site, longitudinal cohort study to prospectively examine the youth from childhood through adolescence to examine the risk and protective factors influencing the trajectories of substance use and its consequences, examine the impact of detailed patterns of substance use on neurocognitive development, health and psychosocial outcomes, and to study the interactive relationship between substance use and psychopathology in youth . The goal of this article is to provide an overview of the ABCD Study Substance Use Work group goals, rationale for the substance use battery, and detailed methods of the battery in order for the scientific community to achieve improved harmonization in substance use assessment, which have varied widely, especially in measurement of frequency/quantity patterns of use . The Substance Use module was developed for the ABCD Study by the Substance Use Work group, comprised of experts on assessment of substance use quantity and frequency patterns, SUD diagnostic interviews, influences on substance use risk, and dimensional assessment of substance use problems and consequences in adolescents. The Substance Use Work group Co-Chairs are Drs. Mary Heitzeg and Krista Lisdahl . The Substance Use Work group members include Drs. Kevin Conway , Sarah Feld stein Ewing , Raul Gonzalez , Sara Jo Nixon , Devin Prouty , Kenneth Sher, , Susan Tapert , and Gordon Willis . In determining methods and constructs to measure, the work group considered the ABCD Study aims and requested methodology outlined by the ABCD Study NIH funding opportunity announcement .

The work group met weekly or biweekly and identified three primary areas to be measured: 1) factors impacting risk of substance use; 2) assessment of detailed substance use patterns; and 3) consequences of substance use. Constructs within these domains were identified by the work group utilizing member input, literature review, and consultation with instrument authors and external experts. During the process of finalizing the battery the work group prioritized instruments that demonstrated sound psychometric properties, fit the longitudinal design, were developmentally appropriate, reduced participant burden, were open-access, and could be administered by computer. In order to improve cross-study harmonization, if an instrument fit these criteria, priority was given to instruments provided by PhenX Patterns of Substance Use module for adolescents , the Monitoring the Future Study , the Population Assessment of Tobacco and Health Study , and the National Consortium on Alcohol and Neuro development in Adolescence Study . Further, great care and consideration was put into organizing a gating structure to avoid exposing non- or low-using children to novel substances . After developing the draft protocol, the work group received and integrated feedback from the NIH advisors and the ABCD Study Coordinating Center, and piloted the protocol at multiple sites with 9–10 year olds to ensure youth comprehension, confirm data quality and timing. Consistent with the goals of the ABCD Study, curated data and detailed data dictionaries, including all the substance use measures, will be released yearly to the NIMH Data Archive .One of the goals of the ABCD Study is to characterize youth prior to the initiation of significant substance use. Adolescence is a period of ongoing neuro development that is linked with an increase in risk-taking behaviors, including the onset of substance use . Initiation of drinking alcohol and use of most illicit substances typically begins in the early teen years, although high-risk demographic communities report initiating use during the elementary and early middle school years . In the U.S., among 8th graders,cannabis grow table lifetime use of alcohol , electronic cigarettes , cannabis , tobacco cigarettes inhalant , prescription amphetamines and prescription tranquilizers are the most commonly used substances . Data is unavailable for 8th graders, but an alarming 18% of 12th graders have used any prescription drug and 7.8% of 12th graders report non-medical use of prescription pain relievers . The latter is a particularly important area, given increase risk of developing an opiate use disorder associated with adolescent exposure, significant barriers to treatment, and alarming rate of overdose deaths in adolescents . Caffeine use is very common in youth, with 73.9% of 6–11 year olds consuming caffeinated food or beverage on any given day within the past week and adolescents consuming an average of 50 mg per day . It is notable that detailed data on substance use patterns in 9- and 10- year olds is less frequently reported, as the youngest age US national surveys assess is 12 or 13 years old [e.g., the MTF begins the assessment in 8th grade while the National Survey on Drug Use and Health begins at age 12]. Data that are available for youth younger than 12 comes from state assessments , such as the Texas School Survey of Drug and Alcohol Use, which measures substance use in youth attending grades 4–6 .

This survey reports lifetime use for the following drug categories in 4th graders: alcohol , nicotine , cannabis , and inhalants − other drug categories were not assessed. This survey also revealed that a significant portion of 4th graders report that they never heard of cannabis , inhalants , nicotine , and alcohol . Taken together, data suggests that youth may initiate first sipping or trying substances in late childhood , and incidents of substance use initiation increase from late childhood into early adolescence. Notably, although some youth may be sipping alcohol or trying tobacco, the vast majority of 9 and 10 year olds are substance-naïve and indeed may not have heard of several drug categories. Thus, studies assessing this age group need to avoid exposing substance-naïve youth to new substance use concepts.As stated above, one area identified by the Substance Use Work group to measure is factors that influence risk of substance use initiation, substance use trajectories, and substance use consequences, such as early sipping alcohol or puffing tobacco, acute initial subjective response, drug curiosity and intentions to use, peer substance use, parental rules, and availability of substances. Community samples have shown that up to a third of 8- and 9-year olds report sipping alcohol , demonstrating that very early substance experimentation begins in late childhood. Studies have found that early sipping predicted drinking onset by age 14 . Similarly, Jackson and colleagues found that sipping alcohol prior to 6th grade predicted drinking a full drink, getting drunk, and drinking heavily by 9th grade, even after controlling for a range of etiologically relevant environmental and individual difference covariates. In contrast to the literature on alcohol, which sometimes operationalizes determinants of a sip and having a full drink as distinct, the field of nicotine has not tended to make this distinction. With few exceptions , studies rarely distinguish between having had a puff and having had one or more cigarettes and data are generally missing on interim levels of progression from a puffs to first cigarette or to regular smoking. Even less is known about the progression of trying a puff or taste of cannabis to more regular experimentation. Closely assessing initial tobacco or cannabis use could help to both characterize the progression of substance use across substances as well as help to determine variables key to such progression. Another important factor to measure is individual acute subjective response to early substance experimentation, such as level of response to alcohol, which has been found to predict risk of developing alcohol related consequences in teens and alcohol use disorders in adulthood .