Cannabis Grow

The food industry is predictably upset about these measures and will fight them tooth-and-nail, much like the tobacco companies fought against the TPPA.But the question remains whether the measures will survive long enough to be brought in front of the WTO Dispute Settlement Panel. While there are frightening health statistics that seem to favor implementation of the measures and a high economic burden of obesity,the regulations as they stand likely will not make it to the WTO. The measures are extremely restrictive and will probably be altered before any WTO dispute. However, if a request for consultations is filed against Chile, Chile would certainly have a good argument that the measures were enacted to combat a legitimate health risk. The WTO dispute settlement panel would most likely side against plain packaging regulations if they applied to other products such as unhealthy foods or alcohol. For example, a restrictive packaging law on alcohol that is similar to Australia’s on tobacco is unlikely to hold up because of how restrictive Australia’s packaging regulations are and how dangerous tobacco use is. To institute regulations as trade-restrictive as Australia did in the TPPA, the objective will likely have to be as compelling or more compelling than reducing smoking. Alcohol and unhealthy foods are similar to tobacco in many ways. All three are addictive and can lead to diseases that cause premature death.However,grow tables 4×8 tobacco use has one characteristic neither alcohol nor unhealthy foods have; the ability to immediately harm those around you.

There are certainly arguments concerning the dangers of drinking too much and getting behind the wheel of a vehicle or the rising cost of healthcare in Chile due to poor diet. But second-hand smoke implicates another level of harm. As noted earlier, second-hand smoke kills an estimated 890,000 people per year, globally.The economic cost is great as well.All of this comes simply from being near someone who is making the decision to smoke. This is a big reason why there has been so much attention on the dangers of smoking. It is not only a huge health concern for those that choose to partake, but also those who do not. Another reason smoking is a larger public health concern than alcohol and unhealthy foods is the minimum amount of use it takes to cause harm to the body. As previously noted, smoking one cigarette can cause irreparable harm to your body.Having one drink or one unhealthy meal is unlikely to harm you in this way. Another case to consider is marijuana use, which has been a growing trend in recent years. Marijuana is an interesting topic for several reasons. First, it is not legal in most places around the world. However, the legality of marijuana is trending, and there could be a robust market in the future.200 Second, it is consumed in many different ways. It can be smoked like a cigarette, but does this mean it should be regulated like tobacco use? It can also be eaten, and in many instances, is made into flavored treats that mask the taste. Does that make it more like the unhealthy foods discussed earlier? In any way it is consumed, it gives the consumer a “high”, much like drinking alcohol to the point of intoxication. Does this mean it should be regulated like alcohol? The truth is that this is uncharted territory. Of all the vices discussed, cannabis may be the one that passes the test for a legitimate health regulation.

Because of its widespread illegality, there isn’t as much data on the death toll and financial cost of marijuana use. However, the CDC does have some information about its effects.This data suggests that marijuana could possibly cause mental disorders, cancer, and heart and lung health issues.This is, of course, assuming the marijuana is being smoked by the user. It can also be compared to tobacco use because of similar concerns around the dangers of second-hand smoke. Though there is not as much data, it seems safe to assume this could raise, at the very least, concerns over health issues. The question remains, is it as bad as cigarette smoke? Only time will tell, but because it’s use is not as widespread as tobacco, it is unlikely that cannabis will garner as much attention for plain packaging purposes. If the growing trend of marijuana legalization continues, we will surely have clarity on these issues sooner rather than later.Recently, neuropsychiatric disorders have been conservatively estimated to be 14% of the global burden of disease, more than the burden of cardiovascular disease or cancer, and their conditions account for a quarter of disability adjusted life-years. The World Health Organization also estimates that 25% of the world’s population will suffer from mental, behavioural, and neurological disorders such as schizophrenia, mental retardation, alcohol and drug abuse, dementias, stress related disorders, and epilepsy during their lifetime. Mostly affecting the poor and people from developing countries, depression impinges on more than 450 million people and might become the second most important cause of disability by 2020 . Despite these new insights, as the 20th century revealed Herculean advancements in somatic healthcare worldwide, the mental aspect of healthcare has remained stagnant and in some cases, gravely depreciated. Mentally ill people are some of the most vulnerable people in society. They are often subject to discrimination, social isolation and exclusion, human rights violations, and an ancient, demeaning stigma which leads to bereavement of social support, self-reproach, or the decaying or straining of important relationships.

Consequences of poor mental health also include being predisposed to a variety of physical illnesses, having quality of life be reduced, having fewer opportunities for income, and having lower individual productivity, which affects total national output. Poor mental health can also account for violence, drug trafficking, child abuse, paedophilia, suicide, crime, and other social vices. Even though mental health is becoming a serious international health concern, many countries, specifically the more impoverished countries, struggle to address the inadequate amount of resources being funnelled into the nonphysical sector of health. Low-income countries often have insufficient implementations of policies and limited mental health services confined to short staffed institutions. Furthermore, in both developed and undeveloped countries, the poor are more vulnerable to common mental disorders due to experiences of rapid social change, risks of violence, poor physical health, insecurity, and hopelessness. Women, slum dwellers,plants rack and people living in conflict, war prone, and disaster areas of civil unrest constitute a large portion of the population in developing countries, and are specifically susceptible to the burden of mental illness. For instance, 90% of the 12 million worldwide schizophrenia sufferers who do not receive adequate psychiatric services are located in developing countries. Only 50% of countries in Africa have a mental health policy, and if they do have a law, it is usually archaic and obsolete. Ninety percent of African countries have less than one psychiatrist per 100,000 people, and 70% of the countries allocate the mental health sector with less than 1% of the total health budget. Less than 60% of African countries have community mental health care while the rest are focused on psychiatric hospitals. The World Psychiatric Association suggested that the development of mental health programmes are impeded in Africa because of the scarcity of economic and staff resources, lack of awareness on the global burden of mental illness, and the stigma associated with seeking psychiatric care. Mental health has been shunned in Africa, and several reports disclose a higher prevalence of stigma in developing countries than in first world countries. Similar to many other developing countries, treatment of mental health in Ghana, West Africa is low and continues to rely on institutional care, a vestige from colonialism. In Ghana, it is roughly estimated that at least 2,816,000 people are suffering from moderate to severe mental disorders, and only 1.17% of these people receive treatment from public hospitals because only 3.4% of the total health budget is dedicated to psychiatric hospitals.

Because there is one psychiatrist per 1.5 million people in the whole country, and the three major psychiatric hospitals are under-financed, congested, and under-staffed, many resort to more ever-present and more affordable, traditional or faith healing. Ghana has a deep-seated tradition of religious observance. Thus, 70– 80% of Ghanaians utilize unorthodox medicine from the 45,000 traditional healers, located in both urban and rural areas, for their vanguard healthcare despite recent advances in orthodox psychiatric services. Although research shows that mental-health patients who used spiritual healing usually reported an improvement in their condition, the quality of treatment is not easy to ensure. Sometimes in order to exorcise supposed demons, individuals are chained, flogged, or incarcerated into spiritual, prayer camps. In spite of these atrocious facts, policy-makers seem to have little concern for mental health, and focus more on physical health and population mortality. The Lunatic Asylum Ordinance of 1888, enacted by the Governor of the Gold Coast, Sir Griffith Edwards, marked the first official patronage to Ghana’s mental health services. This ordinance encouraged officials to arrest vagrant “insane people and place them in a special prison in the capital city of Accra. After the prison quickly filled, a Lunatic Asylum was built in 1906. In accordance with international trends, the asylum was later transformed into the Accra Psychiatric Hospital in 1951 with help from the first sub-Saharan psychiatrist, Dr. E. F. B. Foster. With high walls and barbed wire, to this day the hospital still resembles a prison, which harks back to how the mentally ill were dealt with during colonial times. Luckily, innovations such as the removal of chains from patients, abstaining from patient punishment, and use of chlorpromazine and electroconvulsive therapy arose in the fifties. During that time, the Accra Psychiatric Hospital was the only psychiatric facility in West Africa. In 1962, the Ghana Medical School started training undergraduates in psychiatry and a Mental Health Unit was formed within the Ministry of Health in the 1980s. Though Ghana’s psychiatric care has come a long way since the 1800s, there are still a lot of changes that need to occur in order to attain a standard of quality that is appropriate to recent advances. Ghana’s Mental Health Decree, which emphasizes institutional care and involuntary admission, has not changed since 1972, and treats the mentally ill as if they have no rights. Fortunately, a new Mental Health Bill, which was drafted in 2006, finally made it into the lap of Parliament in October of 2010. This legislation will promote practice of mental health care at the community level and protect the rights of people with mental illnesses. It has gained the support of traditional healers, nurses, and doctors, and will serve as a model for developing progressive mental health legislation in line with international human rights standards. Several researchers have noted a need to increase accurate and comprehensivedata collection on mental health impact and prevalence in order to help improve perceptions on the legitimacy of psychiatric services, and ultimately influence policy. Due to a shortage in personnel, there is a deficit of mental health information, hard community based data, and scientific estimates for neuropsychiatry disorders in Ghana. Because the World Health Organization’s agenda for mental health research in the developing world suggested to evaluate mental health services, this paper focuses on two of the three psychiatric hospitals, and analyzes the hospitals’ available services, resources, recent annual number of out-patients and in-patients, and most common diagnoses which have not been published since 2003. In an attempt to provide an argument for improving the resources and commitment to mental health, this paper also reports on the status of mental health care via information from interviews with key people in the mental health delivery system and non-governmental agencies involved in mental health. Ghana is a middle-income, developing, constitutionally democratic republic located in sub-Saharan West Africa along the Gulf of Guinea in between Cˆote d’Ivoire and Togo. Once a British colony of the Gold Coast, in 1957, Ghana was the first sub-Saharan country to gain its independence and is relatively politically stable. The population estimate for July 2011 is 24,791,073. The life expectancy is 61 years and high risk infectious diseases present include malaria, typhoid fever, meningococcal meningitis, hepatitis A, and diarrhoea. There are three prominent religions; 68.8% of Ghanaians are Christian, 15.9% are Muslim, and 8.5 percent follow a traditional religion.

Alcohol cue-elicited reward activation is predictive of treatment response; thus demonstrating that functional neuroimaging can provide mechanistic data for AUD pharmacotherapy development. This may be particularly relevant in the case of IBUD, where the mechanism of action as an AUD treatment is currently unknown, but can be hypothesized to involve the striatum, which is activated in the alcohol cue-reactivity paradigm. Therefore, the present study sought to investigate the efficacy of IBUD to attenuate alcohol cue-elicited VS activation in individuals with AUD. The current study was an experimental medication trial of IBUD compared to placebo in non-treatment-seeking individuals with an AUD. To advance the development of IBUD as an AUD treatment, the present study examined the efficacy of IBUD, relative to placebo, to reduce negative mood and reduce heavy drinking as ≥5 drinks/day for men and ≥4 drinks/day for women over the course of 2-weeks. We hypothesized that ibudilast would reduce negative mood and decrease heavy drinking over the course of the study. To investigate the neural substrates underlying IBUD’s action, the present study also examined the effect of IBUD on neural alcohol cue-reactivity. We hypothesized that ibudilast would attenuate alcohol cue-elicited activation in the VS relative to placebo. Finally, this study explored the relationship between neural alcohol cuereactivity in the VS and drinking outcomes.Participants completed a series of assessments for eligibility and individual differences.

These measures included the Structured Clinical Interview for DSM-5, the Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised, and the 30-day Timeline Follow back Interview for alcohol, cigarette, and cannabis. Participants also completed assessments regarding their alcohol use,vertical cannabis including: Alcohol Use Disorder Identification Test and Alcohol Dependence Scale, which measure severity of alcohol use problems, Penn Alcohol Craving Scale and Obsessive Compulsive Drinking Scale, which measure alcohol craving, and the Reasons for Heavy Drinking Questionnaire to assess withdrawal-related dysphoria, indicated by question #6: “I drink because when I stop, I feel bad ”. Participants also completed measures of smoking severity and depressive symptomology. At each in-person visit, participants were required to have a breath alcohol concentration of 0.00 g/dl and test negative on a urine toxicology screen for all drugs of abuse . Blood pressure and heart rate were assessed at screening and at each visit. Participants completed three in-person study visits occurring on Day 1 , Day 8 , and Day 15 . Randomization visits occurred on Mondays and Tuesdays to ensure that participants were at the target medication dose by the weekend. Side effects were elicited in open ended fashion and were reviewed by the study physicians . Adverse events were coded using the MedDRA v22.0 coding dictionary. Treatmentemergent adverse events were defined as adverse events that started after the first dose of the study drug or worsened in intensity after the first dose of study drug. Participants completed daily diary assessments, reporting on their past-day alcohol use, mood, assessed with a shortened form of the Profile of Mood States , and craving, assessed through a shortened form of the Alcohol Urge Questionnaire . Participants received daily text message reminders with links to these assessments.A set of generalized estimating equations with compound symmetric covariance structure were run in SAS 9.4 to account for repeated measures.

GEEs were selected as the analytical method because parameter estimates are consistent even when the covariance structure is mis-specified. As such, a compound symmetric covariance structure was chosen. Of note, due to missing data on all outcome and predictor variables, two participants were naturally excluded via list wise deletion for the GEE analysis. A GEE model was first run to assess the effect of medication on negative mood. The dependent variable, negative mood , was treated as continuous so a normal distribution with identity link function was chosen. A compound symmetric covariance structure was chosen to account for the repeated assessments. Independent variables for these analyses were medication , drinking day , and the interaction of medication by drinking day. Sex, age, depressive symptomology , and smoking status were examined as covariates; only significant covariates were retained in the final model to improve model clarity and ease of replication. A similar model was conducted to assess the effect of medication on craving, with the dependent variable being craving as measured by the AUQ. For both analyses, predicted means, standard errors, and 95% confidence intervals for negative mood and craving were calculated based on final models. The dependent variables for the drinking analyses were binary, such that 1 indicated a heavy drinking day or drinking day and a 0 indicated no heavy drinking or drinking, respectively. A binomial distribution with logit link function was chosen to model the binary dependent variable . Since participants were not on medication at baseline , this time point was excluded from the analysis. Independent variables included in the models were medication , time , and the interaction of medication by time. Baseline drinking information were also included in the model as a control.

As above, sex, age, depressive symptomology , and smoking status were examined as covariates; only significant covariates were retained in the final model to improve model clarity and ease of replication. For both analyses, predicted probabilities, standard errors, and 95% confidence intervals for heavy drinking and any drinking were calculated based on final models. A general linear model was used to evaluate the effect of medication on VS activation. The dependent variable was VS percent signal change between ALC and BEV blocks. Medication was the independent variable. Age, sex, depressive symptomology , and smoking status were examined as covariates; only significant covariates were retained in the final model. Finally, to evaluate if VS activation interacted with medication in predicting drinking in the week following the scan, a between-subject factor for VS activation was added to the model, along with a medication by VS activation split interaction. The dependent variable was drinks per drinking day in the last week of the study. Baseline drinks per drinking day were included as an additional covariate for this analysis.This was the first study to evaluate the effects of ibudilast, a neuroimmune modulator, on mood and drinking outcomes in a clinical sample with AUD. Contrary to our hypothesis, ibudilast did not have a significant effect on negative mood on drinking or non-drinking days. However, in support of our hypotheses, ibudilast significantly reduced the probability of heavy drinking compared to placebo. Ibudilast also significantly attenuatedalcohol cue-elicited activation in the bilateral VS. Furthermore, exploratory analyses indicated that ventral striatal activation to alcohol cues was predictive of drinking in the week following the neuroimaging scan. These results suggest a bio-behavioral mechanism through which ibudilast acts, namely,plant benches by reducing the rewarding response to alcohol cues in the brain leading to a reduction in heavy drinking per se. Unexpectedly, this study did not find support for an effect of ibudilast on negative mood or a moderating effect of baseline depressive symptomology on medication response. This contrasts with previous findings from our lab in which ibudilast improved mood response to stress and alcohol cues. The current study differs from the previous study in several important methodological variables including using a between-subjects instead of a crossover design and the use of a daily-diary mood reporting approach compared to tightly controlled human laboratory experimental paradigms. Furthermore, the current study did not directly evaluate the effect of drinking on mood, which would be more comparable to the findings reported previously. Additionally, this study recruited individuals with mild-to-severe AUD.

Negative mood states and negative reinforcement driven drinking may only occur at more severe presentations of AUD; therefore, the present study may have been under powered to identify medication effects on negative mood symptoms. Regarding the drinking outcomes in this study, IBUD significantly reduced the probability of heavy drinking compared to placebo. Specifically, individuals treated with IBUD were 45.3% less likely to drink heavily compared to individuals treated with placebo. This resulted in a 24% predicted probability of heavy drinking over the course of the study in the ibudilast group, compared with a 37% predicted probability in the placebo group. Of note, there were no significant differences in AE’s between groups, indicating that this reduction was not due to increased side effects, including nausea, in the IBUD group. There was not a significant effect of IBUD on the probability of overall drinking compared to placebo. While non-significant, the effect of IBUD for any drinking days was in the expected direction, such that individuals on IBUD were 16.9% less likely to engage in any drinking relative to placebo, but high variability in the prediction prevented conclusive statistical findings. This non-significant effect may not be surprising, as the study sample was comprised of non-treatment-seekers and therefore not motivated to abstain from drinking altogether. Rather, participants treated with IBUD reduced their heavy drinking, which produces a harm reduction benefit, particularly for those with a mild-tomoderate AUD. This finding is also consistent with preclinical studies, where treatment with ibudilast reduced ethanol intake by 50% under maintenance conditions. Importantly, the drinking results combined with the AE reports indicate that ibudilast is a safe medication for individuals who are still drinking and may want to reduce their drinking. IBUD also reduced craving on non-drinking days, at trend level, as compared to placebo. This effect supports our previous finding of a reduction in tonic craving under ibudilast during a week-long human laboratory study during which participants were instructed not to drink. This study also examined a potential bio-behavioral mechanism underlying IBUD’s action using an fMRI alcohol cue-reactivity paradigm. IBUD attenuated alcohol cue-elicited reward activation in the VS compared to placebo. PDE4 and PDE10 are highly expressed in the striatum and negatively regulate dopaminergic signaling. Thus, inhibition of these PDEs through IBUD may reduce striatal excitability to alcohol cues. In rats IBUD reduced morphine-induced nucleus accumbens dopamine release . Moreover, IBUD has been shown to enhance the production of neurotrophic factors, including glia-derived neurotrophic factor, which is a critical survival factor for dopamine neurons. Preclinical findings indicate that infusion of GDNF normalizes dopamine levels in the ventral tegmental area and the VS and reduces alcohol seeking and alcohol consumption. In humans with AUD, GDNF levels are reduced in blood serum samples. Furthermore, in individuals with AUD, presentation of alcohol cues reduced interleukin-10, an anti-inflammatory cytokine, and the level of reduction was correlated with increased alcohol craving. Thus, though the underlying molecular mechanism is still unknown, this finding indicates that ibudilast may normalize the dopaminergic response to alcohol cues in individuals with AUD. This study has several strengths and limitations which should be considered when interpreting the results. Study strengths include the use of daily diary reporting, which captures real world drinking and minimizes recall bias, and the combination of neurobiological with behavioral and self-report methodologies. However, this study recruited a non-treatment seeking sample; therefore, these findings may not generalize to a treatment-seeking sample with AUD . An ongoing randomized controlled trial of IBUD in treatment-seeking individuals with AUD will address this open question. Relatedly, this study recruited individuals with mild-to severe AUD, which may not be representative of clinical samples. This limitation may have impacted our ability to detect medication effects that require a pathology associated with more severe AUD, which is particularly relevant for negative mood and withdrawal states. Furthermore, participants were required to have a 0.00 g/dl breath alcohol reading for each in person visit. This requirement was to ensure participant safety; however, it may have artificially reduced drinking on in-person study visit days. Of note, in the daily diary assessment,participants reported on their past day drinking for the full day and were able to begin drinking when they returned home after the study visit. Additionally, the sample size for this experimental study was modest, particularly for the fMRI outcomes. This limited our ability to conduct additional, whole-brain analyses which are necessary to fully elucidate the neural mechanism of ibudilast. Finally, this study did not include a fixed-dose alcohol challenge to evaluate the safety and efficacy of ibudilast in combination with alcohol and to replicate our previous work. However, given that our sample did report drinking while taking ibudilast, we believe that ibudilast can be safely taken with alcohol with limited side effects. In conclusion, this is the first combined clinical and neuroimaging study of ibudilast , a neuroimmune modulator, to treat AUD.

The proportion of variance explained by genetic variants on GWAS chips ranges from 4 to 13% . It is possible that a significant portion of the heritability can be explained by SNPs not tagged by GWAS chips, including rare variants . For instance, a recent study showed that rare variants explained 1-2% of phenotypic variance and 11-18% of total SNP heritability of substance use phenotypes . Nonetheless, rare variants are often not analyzed when calculating SNP heritability, which can lead to an underestimate of polygenic effects, as well as missing biologically relevant contributions for post-GWAS analyses . Equally important is the need to include other sources of -omics data when interpreting genetic findings, and the need to increase population diversity . Therefore, a multifaceted approach targeting both rare and common variation, including functional data, and assembling much larger datasets for meta-analyses in ethnically diverse populations, is critical for identifying the key genes and pathways important in AUD.Temperament refers to early emerging, “constitutionally based individual differences in reactivity and self-regulation” . Reactivity is conceptualized in terms of affective and motivational responses to stimuli, and captures, for example, the tendency for some children to feel threatened in response to novel stimuli and others to feel intrigued. Self-regulation refers to individual differences in the top-down control of reactive processes, grow trays and goal setting and goal striving behaviors; it reflects the fact that children differ in the ability to control their appetitive impulses, as illustrated in delay of gratification tasks .

One prominent model posits that childhood temperament can be partitioned into three broad dimensions: effortful control, negative affectivity, and surgency . Effortful control reflects an individual’s ability to control their attention and impulses. This domain is conceptually similar to the adult personality dimensions of disinhibition and conscientiousness . Negative affectivity captures an individual’s tendency to experience fear, anger, and other types of psychological distress. It is conceptually similar to the adult dimensions of negative emotionality and neuroticism . Last, surgency refers to an individual’s tendency to experience positive emotions and approach potential rewards. It is conceptually similar to the adult dimensions of positive emotionality , and extraversion . Traits related to effortful control, such as impulsivity, have the strongest and most robust connections with substance use . In contrast, results for negative affectivity are more equivocal. Some studies have found that negative affectivity predicts increased substance use , whereas other studies have not . There are even hints that negative affectivity can predict decreased substance use . Some of the inconsistencies might stem from the varying ways negative affectivity is conceptualized and measured . For instance, fear, anger, and hostility are all components of negative affectivity, but fear might protect against early substance use, whereas anger and hostility might increase risk . A related but somewhat more complex dispositional characteristic – aggressiveness – has also been linked to substance use . Aggressiveness can be thought of as an emergent behavioral tendency related to low levels of effortful control and high levels of surgency and negative affectivity . Although some have posited reciprocal relations between aggressiveness and substance use, White and colleagues found support for a unidirectional relationship whereby aggressiveness was related to subsequent substance use, but not vice versa.

Therefore, aggressiveness might be an especially important dispositional predictor of early substance use. One concern with the current literature on temperament and substance use is that many of the existing studies lack ethnic diversity. Stautz and Cooper noted that the majority of studies reviewed in their meta-analysis consisted of predominantly Caucasian samples. Although ethnicity moderated the relationship between impulsivity and substance use, the authors concluded that there was not enough ethnic variation to draw firm conclusions . Although Stautz and Cooper focused exclusively on alcohol use, their findings highlight the need to evaluate the relation between temperament and substance use in diverse populations. The current study helps address this gap by evaluating connections between temperament and substance use in a sample of Mexicanorigin adolescents. Substance use is a multiply determined outcome that is influenced by contextual, as well as dispositional, factors. A large literature suggests that family dynamics contribute to adolescent substance use, and that such processes may moderate the effects of dispositional variables . One family factor consistently related to substance use is parental monitoring , or, “parenting behaviors involving attention to and tracking of the child’s whereabouts, activities, and adaptations” . Monitoring is considered a protective factor against substance use, and studies confirm that increased parental monitoring predicts less use, even in high-risk and diverse samples. Despite the well-documented association between parental monitoring and adolescent substance use, the actual direction of the effect between these variables is controversial. Although it is typically assumed that parental monitoring reduces problem behaviors in adolescence, monitoring may also reflect the outcome of a reactive process whereby parents increase or decrease monitoring efforts in response to adolescent behaviors . Indeed, parents sometimes decrease their monitoring efforts when their adolescents engage in delinquency . Moreover, parental monitoring may serve a protective role only for youth who have dispositional tendencies toward substance use. That is, monitoring might decrease risk for youth who have temperamental traits associated with substance use , but be less relevant for adolescents who do not have such characteristics.

The current study will contribute to the existing literature by testing both additive and interactive effects of temperament and parental monitoring.Temperament—Adolescent temperament was assessed using the 64-item Early Adolescent Temperament Questionnaire – Revised . The EATQ-R scales assess three broad dimensions of temperament – effortful control, negative affectivity, and surgency . Effortful control was measured using 16 items that reflect activation control and inhibitory control . Negative affectivity was measured using 13 items pertaining to fear , and frustration . Surgency was measured using 6 items that assessed the amount of pleasure one derives from novel and “high intensity” experiences. The EATQ-R also contains scales assessing depressive mood and aggression. The depressive mood scale contains six items related to sadness and the loss of enjoyment in activities,drying marijuana and the aggression scale contains six items related to hostile actions and hostile reactivity. Temperament scores were obtained from both the adolescents , and their mothers . Ratings were made on a scale ranging from 1 “not at all true of you/your child” to 4 “very true of you/your child”. Sample items include, “It is easy for you/your child to really concentrate on homework problems”, “When you/ your child is angry, you/your child throw or break things”, and “You/your child feel shy with kids of the opposite sex”. Table 1 provides basic descriptive information for the EATQ-R scales, including alpha reliabilities and mother-child agreement correlations. All alphas were acceptable except for the surgency scale in the 5th grade; therefore, correlations based on this scale are likely to be attenuated by measurement error and should be interpreted with caution. Mother and adolescent temperament ratings were averaged together to form a composite score for each dimension. Although the mother-child agreement correlations were small to moderate , the same patterns of results emerged no matter whose ratings were used. Parental monitoring—Parental monitoring was measured using a 14-item scale adapted from Small and Kerns . This scale assesses the degree to which parents are aware of their youth’s behavior and various life circumstances using a response scale ranging from 1 “Almost never or never” to 4 “Almost always or always”. Adolescents completed the scale once in reference to their mother, and once in reference to their father. Sample items include, “Your Father/Mother knew how you spent your money”, “When you went out, your Mother/Father asked you where you were going”, and “Your Mother/Father knew what you were doing after school”.

Monitoring scores were computed by summing up responses to the individual items. Adolescent reported maternal and paternal monitoring were correlated , so scores were averaged to create one composite “Parental Monitoring” score. Mother and Father reported monitoring scores were kept separate. Substance use intentions: This 9-item scale, adapted from Gibbons et al. , assesses willingness to use particular substances, as well as plans to use those substances in the next year. Three items were dedicated to alcohol use, three to cigarette use, and three to “illegal drug” use. Participants responded on either a three or four point scale ranging from 1 “Do not plan to/Definitely will not/Not at all willing” to either 3 “Very willing”, or 4 “Do plan to/ Definitely will.” Sample items include, “How likely is it that you will drink alcohol in the next year”, and “Do you plan to smoke cigarettes in the next year?” Scores for this measure were computed by summing up the individual items. Substance use expectancies: This 18-item scale assesses positive expectations regarding the use of alcohol, cigarettes, and other drugs. The scale was developed by Rand Conger for use in the Family Transitions Project. Participants responded to a variety of “pro-drug” statements on a scale ranging from 1 “Strongly Disagree” to 5 “Strongly Agree”. Sample items include, “Drinking alcohol helps people relax”, and “Smoking marijuana makes life more exciting”. A total positive expectancies composite was created by aggregating across the items. Substance use: This 9-item scale, adapted from Elliott, Huizinga, and Ageton , measures lifetime use of a wide range of substances. Participants responded “yes” or “no” to questions such as, “Have you ever used or tried cigarettes?”, and “Have you ever used or tried beer – more than just a few sips?” “Yes” responses were coded as 1s, and “no” responses were coded as 0s. Responses across the scale were summed up to generate a total use variety score. Means, standard deviations, and reliability information are presented in Table 2 for parental monitoring, substance use intentions, substance use expectancies, and substance use. Prospective correlations are reported in Table 3. Aggression assessed in fifth grade was associated with future substance intentions and expectancies, as well as reports of actual use. Effortful Control was negatively correlated with future substance use variables, but the effect sizes were roughly half that of the correlations involving Aggression. Depressive mood was related to intentions and actual use, but not expectancies. Child reports of parental monitoring were related to substance use variables more consistently than parental reports. Overall, there were consistent prospective zero-order correlations supporting an association between certain individual differences and early substance use. Regression analyses were used to control for fifth grade levels of the respective substance use variables when predicting the ninth grade variables . As seen in Table 3, although controlling for the baseline substance use variables reduced effect size estimates, all relevant predictors remained statistically significant.1 We should note that endorsements of the substance use variables in fifth grade were quite low , and floor effects may have attenuated the predictive power of the fifth grade assessments. However, these distributions might simply reflect the reality of low substance use at relatively young ages . The prospective associations were supplemented with concurrent analyses using temperamental variables, parental monitoring variables, and substance use variables measured in ninth grade . The correlations tended to increase in magnitude, but the pattern was generally consistent with the prospective correlations. Aggressive temperament and child reports of monitoring were the strongest correlates of substance use intentions, expectancies, and actual use. Effortful Control was also consistently linked with these outcomes. We found support for the idea that certain temperamental traits are related to substance use, and some evidence that parental monitoring is associated with substance use. We also tested whether temperament interacted with parental monitoring to predict substance use variables. We focused on child reports of monitoring for these analyses because parental reports of monitoring were not generally associated with substance use outcomes .Prior to analysis, the three substance use variables were log transformed to address concerns about skewness . All predictors were grand mean centered, and interaction variables were computed as the product of the two centered variables. When the interaction term was significant in a regression model, a set of simple slopes analysis was performed for “high” and “low” levels of a given dimension of temperament. We first considered prospective relations, using temperament and monitoring assessed in 5th grade to predict substance use variables in 9th grade. Selected results are presented in Table 4.

While federal law has remained unchanged throughout years of state experimentation with marijuana liberalization, federal enforcement in these states has varied widely. Before 2009, the federal government made direct threats toward MML states, stating that even users and suppliers in compliance with state policy would remain subject to federal prosecution . However, between 2009 and 2012, two federal memos dramatically altered perceived federal enforcement in medical marijuana states. The Ogden Memo, announced on October 19, 2009, formalized guidelines for federal prosecutors in MML states. The memorandum maintained the government’s commitment to prosecuting significant traffickers of marijuana, but emphasized that “prosecution of individuals with cancer or other serious illnesses who use marijuana as part of a recommended treatment regimen consistent with applicable state law, or those caregivers in clear and unambiguous compliance with existing state law who provide such individuals with marijuana, is unlikely to be an efficient use of limited federal resources” . In sum, the Ogden Memo de-prioritized the federal government’s involvement in prosecuting medical marijuana users and suppliers in states with MMLs. On June 29, 2011, the US government reversed this stance by issuing the Cole Memo as a response to the government’s perceived “increase in the scope of commercial cultivation, sale, and distribution and use of marijuana for purported medical purposes” . The Cole Memo stated that individuals involved in the business of medical cannabis growing equipment sales and distribution would be subject to federal enforcement action.

In the months leading up to and following the memo, the Drug Enforce-ment Administration stepped up raids on medical marijuana producers . If changes in the risk of federal prosecution shift production costs, then both state variation in supply restrictions and time-variation in federal policy will determine the size of the legal market. Moreover, in states where legal and illegal markets for marijuana co-exist, policy changes that shift production costs in the legal market may affect price and availability in the illicit market. To better understand the effects of costs associated with state restrictions and federal enforcement, I outline a simple model of supplier behavior. The purpose of the model is to provide theoretical predictions of how supplier responses to the changes in federal enforcement should differ depending on state MML regulations. I can then link these predictions to newly collected empirical evidence on the size of the legal market. The model also provides intuition for the regulatory variables used in the empirical analysis and clarifies the key assumptions upon which my approach relies. As the illegal market for marijuana is not directly observed, modeling the interaction between the legal and illegal markets is helpful in motivating the appropriate empirical framework to estimate how legalizing medical marijuana affects recreational consumption. The model describes the market for marijuana in an MML state as composed of consumers and suppliers who respond to changes in the probability of being prosecuted by state or federal enforcement. Suppliers operating in the legal medical marijuana market also face capacity constraints that vary by how strictly the state regulates producers. Each state’s market is assumed to operate in isolation.

Most MML states have mandatory medical marijuana registration programs: laws requiring medical marijuana users to register in order to receive protection from state arrest.An individual who wants medical marijuana must first obtain a physician’s certification that the individual has a medical condition which could benefit from the use of marijuana. The patient then must submit an application to the state authority, along with a registration fee. If the application is approved, the patient receives documentation providing access to dispensaries and protection from state prosecution. By definition, all other consumption is illegal. The number of registered medical marijuana patients provides an observable measure of the size of the legal market. A limitation faced by previous research has been that state records of registered patient counts are not readily available and are not maintained similarly across states. To overcome this limitation, I collected monthlydata from a number of sources, including contact with state officials, state department websites, news articles, and academic papers. Since my outcome variables are annual state-level prevalence measures, I linearly interpolate missing end-of-year registration rates using the nearest available months of registered patient counts. The final measure of market size is the annual registration rate, calculated as the percent of adults registered as medical marijuana patients at the end of December in a given year. I include the voluntary registration data available from California after 2005, but due to lack of data, Maine and Washington are excluded.From this data, the medical marijuana market in 2013 is estimated to consist of about 1,139,098 legal medical marijuana patients,and industry reports estimate annual retail sales of legal marijuana in 2013 at $1.43 billion .While the legal market size is dwarfed by the estimated $25-$40 billion size of the illicit market , growth in the legal marijuana market over the past decade is twice that of the illicit market.

The estimate of the number of registered patients in 2013 represents a more than 300% increase in the number of medical marijuana users from 2007 compared to only a 150% increase in the number of total adult past-month users . The legal market for marijuana is thus rapidly expanding. To study how changes in the legal market affect the illegal market, it is important to first understand the factors driving legal market growth.To isolate the supply-side drivers of growth in legal medical marijuana markets,cannabis drying trays the ideal policy variation would be such that production costs were exogenously shifted in some MML states but unchanged in others. Based on the model outlined in Section 2.2.2, the Ogden and Cole Memos approximated this ideal by shifting production costs differentially more in MML states with lax supply restrictions compared to those states with strict supply restrictions. Figure 2.1 connects the model’s propositions to the data by documenting registration rate trend breaks at the Ogden and Cole Memos for a sub-sample of states that exemplify the different state production restrictions. Hawaii and New Mexico serve as examples of more restrictive production states; Colorado and Montana are representative of MML states with looser supply restrictions. For all four states, few patients register during the first few years following MML passage. The initial slow growth in medical marijuana take-up suggests that, conditional on federal enforcement remaining high, reduced state risk following MML enactment had little effect on the size of the legal market. The empirical evidence from Figure 2.1 is also consistent with the model’s prediction that the Ogden and Cole Memos had far greater effect in states with loose production limits. In Hawaii, where suppliers had strict limits and could only serve a single patient, the Ogden Memo had very little effect on registration rate trends. Correspondingly, there is also little break in trend following the Cole Memo. In New Mexico, which allowed state licensed dispensaries that had higher production limits but faced heavy regulation, the Ogden and Cole Memos also seem to have had small effects. In contrast, the states with lax production limits saw dramatic changes in registration rates with the Ogden and Cole Memos.After the Ogden Memo, Colorado experienced the “Green Rush,” a proliferation of dispensaries that arose following decreased fears of federal intervention.Alongside the expansion of unregulated dispensaries in Colorado, patient registration rates spiked . Similarly, in Montana, where caregivers were permitted to produce for an unlimited number of patients and receive compensation for their services, the number of caregivers providing for 20 or more patients increased seven-fold within one year following the Ogden Memo. This was accompanied by a nearly six-fold increase in registered patients . In line with the propositions from the model, the Cole Memo reversed this trend.

The decline in registration rates in Montana is particularly dramatic because, concurrent with the Cole Memo, Montana’s legislature passed Senate Bill 423 which effectively dismantled the medical marijuana supply industry by establishing caregiver patient limits and preventing caregivers from receiving compensation.To examine this relationship for all states, I exploit the model’s predicted trend reversal between the Ogden period and the Cole period in the relationship between a state’s laxness of medical marijuana supply regulation and registration rates. Intuitively, in states with loose medical marijuana production limits, the Ogden Memo decreased marginal costs over a broader range of production , inducing producer entry and supply increases by existing producers. Column reports regression results that allow federal enforcement changes to also affect MML states with strict restrictions on supply. Consistent with the model predictions, both federal policies had far larger effects on registration rate trends in states where marijuana suppliers were relatively unrestricted. Between the Ogden and Cole Memos, states with strict production limits on average saw an additional 0.2% of the adult population register as medical marijuana patients , which is statistically significant but an order of magnitude smaller than the increases seen in MML states with loose supply regulations . There is no effect of the Cole Memo in strictly regulated MML states, consistent with evidence that federal enforcement related to the Cole Memo targeted large-scale production. Limiting the sample to only those states with MMLs by 2012 in columns and yields coefficients of similar magnitude but with slightly larger standard errors. Unlike initial MML enactment, the federal memos substantially altered the size of the legal market, with larger effects in states with looser supply regulations. The differential response of registration rates to the federal government’s policies in states with lax compared to strict producer restrictions suggests that patient registration rates are driven primarily by supply-side shifters. Therefore, to estimate the causal effect of changes in medical marijuana supply on recreational consumption, my empirical strategy uses the timing of the federal memos and differences in initial MML supply restrictions as instruments. The main threat to identification is that registration rates and illegal use may be jointly determined by unobservables affecting demand. State and year fixed effects control for time-invariant state characteristics and national trends. Time-varying state covariates included that potentially affect recreational marijuana use can be categorized as: demographics influencing recreational marijuana use, economic characteristics, and substance-related policies influencing marijuana consumption. A full listing of covariates is provided in Table 2.3. For all specifications, to account for heteroskedasticity and serial correlation, robust standard errors are clustered at the state level . To account for potential violation of the parallel trends assumption, specifications including state-specific linear trends are also presented as robustness checks in section 2.6. Even after controlling for state and year fixed effects and state-year covariates, identification of β is challenging due to concerns of endogeneity between recreational marijuana use and registration rates. While fixed effects will account for issues of cross sectional endogeneity, β could still be biased due to some omitted state-level time-varying variable that affects both registered users and illicit users. For instance, β will be biased upward if changes in local perceptions regarding the health risks of marijuana use led to changes in both medical and recreational use. To account for potential endogeneity, I instrument for registration rates by way of two-stage-leastsquares using equation 2.6 as the first-stage specification. The instrumental variable estimates are valid as long as the exclusion restriction is satisfied. In the context of equations 2.6 and 2.7, this occurs if E[εjtZjt|uj , vt , Xjt] = 0, where Zjt is the vector of instruments including the interaction of MML supply restrictions with trend breaks based on the exogenous timing of the Ogden and Cole Memos. As state and year fixed effects are included, the identification is not threatened by level differences between states or by national trends in marijuana consumption . However, the exclusion restriction will be violated if changes in federal enforcement following the Ogden and Cole Memos had differential effects on demand in states with loose compared to strict production restrictions through any channel other than supply. Evidence validating the exclusion restriction is presented in section 2.6. The measures of marijuana consumption come from the National Survey of Drug Use and Health . The NSDUH is an annual survey funded by the Substance Abuse and Mental Health Services Administration of the US population over twelve years of age.

Given that many smoking studies use CO as an endpoint for bio-verifying tobacco use status, researchers will need to determine how to use this information.It is unknown whether excluding marijuana use was done to isolate cigarette smoking as a source of CO for bio-verifying smoking reduction, or whether this exclusion was a strategy to exclude drug use. For example, dependence on chemicals other than nicotine is common exclusion criteria for e-cigarette switching studies. Interventionists should be aware that excluding cigarette smokers who use marijuana use will significantly decrease study generalizability. Researchers who wish to include combustible marijuana users in their study sample are advised to conduct a nuanced measurement of product use and account for combustible marijuana use when bio-verifying tobacco use status.The study includes several important limitations. The study was conducted in two geographical locations in the United States and among two different racial/ethnic groups and findings may not generalize to other populations. Additionally, the product characteristics and frequency patterns of marijuana use came from a relatively small sample that may not generalize to other populations of marijuana users. Findings must also be taken in light of a sample of smokers willing to switch to e-cigarettes and provided e-cigarettes at no cost for 6 weeks.

Self-reported marijuana use was higher at the San Diego site than the Kansas City site. Although participants were told their responses were deidentified and confidential, Kansas City participants could have felt pressure to conceal marijuana use due to the state’s medicinal-only status, compared with the San Diego site where recreational marijuana use is legal. In addition to decreased comfort of reporting due to associated legal risks,ebb and flow flood table there are also practical considerations that may contribute to less use such as accessibility to purchasing and exposure to advertising. Prevalence of marijuana use has been reported highest in states with legal recreational use.To address reporting bias in a multi-site setting, measuring a biomarker of cannabis use is recommended. It is also possible that the younger age of San Diego study participants compared with Kansas City participants contributed to differential marijuana use, as age is associated with marijuana use.Finally, study outcomes were taken at 6-week post baseline and the long-term impact of marijuana use on cigarette reduction and related health effects is an important topic for future study. While this study captured a majority of combustible marijuana use, understanding if there are reinforcing sensory components of vaping marijuana and vaping nicotine e-liquid remains to be studied.As of 2015, twenty-four U.S. jurisdictions have medical marijuana laws , which provide legal protection for individuals who use marijuana for medical purposes, physicians who recommend marijuana to patients with certain medical conditions, and growers and distributors who supply these patients. Past studies have exploited state-time variation in MML enactment to estimate the effects of liberalization on recreational marijuana use,1 traffic fatalities , obesity , suicides , and crime .

However, interventions may not attain full steady-state effectiveness immediately upon implementation , and estimation based on the timing of MML passage will likely not capture the full effects of these policies as medical marijuana markets evolve. While MML enactment alone may signal a shift in governmental acceptance of the drug, effects on marijuana availability and price will depend on the specific regulations established by MML policy and the duration of exposure to the more liberal regime. Changes in social access, perceived community approval, and spillover effects to illegal use and other public health outcomes may well vary according to the extent to which legal users and suppliers actively participate in the medical marijuana program. If recreational and medical marijuana consumption decisions are made based on similar consumer optimization problems,2 both medical marijuana participation and use in the general population will follow similar patterns. Understanding the factors that drive changes in medical marijuana participation can thus offer insight into the mechanisms by which MMLs generate spillovers to recreational use and other health outcomes. This is the first paper to investigate the determinants and dynamics of medical marijuana participation by legal users using newly collected data on medical marijuana patient registration rates. While some recent research has examined data on registered patients, these studies have either been descriptive , cross-sectional , or limited to one state . By collecting data on registered patient counts from both administrative and non-administrative sources, this paper presents the most comprehensive state-level monthly panel dataset to date on medical marijuana participation to date. Data was obtained from 1999-2014 for the sixteen states3 that required individuals to register as medical marijuana patients in order to receive the legal protections afforded by the MML.

The data show that there have been dramatic changes in medical marijuana participation over the last two decades. Registered patient counts were relatively low until 2009, when they sharply increased. The number of registered patients continued to climb until mid-2011, when they leveled before resuming an upward trend a few years later. These trend breaks in medical marijuana patient registration rates coincide with the timing of federal enforcement policy changes that have been widely ignored by past research. While federal law has strictly prohibited the use and distribution of marijuana since 1937 regardless of state policy, a federal statement of non-enforcement in MML states was released in October 2009 ; in June 2011, another federal statement was issued to clarify that this did not apply to large-scale producers . To understand the factors responsible for driving these changes in medical marijuana take-up, I first outline a conceptual framework whereby individuals apply to register with the medical marijuana program if the expected utility from registering exceeds that of not registering. Costs to patients include transaction costs associated with registration fees and finding a doctor to provide a recommendation, as well as perceived risk from state and federal enforcement. Benefits include access to legitimate sources of marijuana , which will vary depending on the production limits established by the state’s specific MML regulation. The federal memos are predicted to affect medical marijuana participation through changing the perceived risk associated with federal enforcement for both registered patients and state-legal producers. The empirical results confirm the descriptive evidence showing that the federal memos significantly affected medical marijuana take-up. Controlling for state-level demographic and economic variables or time-invariant state characteristics dampens the magnitude of these effects slightly, but they remain large and statistically significant. However,hydroponic drain table the federal memos did not affect all MML states equally. Interacting the federal memo changes with state-specific supply-side regulations shows that the magnitude of their effects was significantly larger in states that imposed relatively lax restrictions on legal producers. These findings imply that medical marijuana participation is primarily driven by the expected benefits associated with access to legal supply. Additional robustness checks support that the extent of medical marijuana participation is highly responsive to supply-side changes in the legal market. This paper builds on recent work recognizing that heterogeneity in the specifics of state MML regulations may generate heterogeneous effects , and it contributes to a broader economic literature showing that the effects of regulatory changes depend largely on the specifics of their design, implementation, and enforcement.Section 1.2 details the history of marijuana regulation in the U.S. and provides background on modern MMLs and changes in federal enforcement policy. Section 1.3 outlines a conceptual framework to suggest the factors determining medical marijuana participation.

Section 1.4 presents the data, empirical framework, and results for the determinants of medical marijuana participation. Finally, section 1.5 places these results in the context of the existing literature on MMLs, and section 1.6 concludes. The first federal regulation of marijuana was introduced with the Marijuana Tax Act of 1937. The Marijuana Tax Act did not criminalize the possession or use of marijuana, as this was a potential violation of the Tenth Amendment’s limitation on federal power, but instead made it illegal to grow or distribute marijuana unless the grower obtained a federal stamp. Since stamps were largely unavailable and there was no application process, the Act effectively served as federal prohibition . Marijuana use remained limited until the mid-1960s, when the baby boom generation reached adolescence. Thicker drug markets associated with this larger youth cohort resulted in a significant increase in illicit drug use among college and high school students . In 1970, Congress responded by passing the Controlled Substances Act , which repealed the Marijuana Tax Act5 but classified marijuana as a Schedule I substance with high potential for abuse and no accepted medical value.6 The CSA criminalized the manufacture, distribution, and possession of marijuana for both recreational and medicinal purposes, and it provided the system of federal penalties and enforcement that remains in place today. Despite federal prohibition, marijuana use continued to rise in the United States. By the early 1970s, eight million people were using marijuana regularly, at least half a million people were consuming it daily, and 421,000 people were arrested for marijuana offenses annually . In 1972, the National Commission on Marijuana and Drug Abuse, which had been created as part of the CSA, released a comprehensive report based on surveys of health experts and law enforcement officials. The report recommended the removal of criminal penalties for marijuana possession and advocated further scientific research on the substance’s potential medicinal value. President Nixon rejected the Commission’s recommendations, but the 1972 report helped trigger a push toward liberalization policies . In 1972, the National Organization for the Reform of Marijuana Laws filed the first petition to reschedule marijuana. In 1975, the federal government established the Individual Patient Investigational New Drug program, which enabled participating physicians to prescribe marijuana to enrolled patients. The federal program was designed to accept patients with serious illnesses and directly provide them with marijuana through the National Institute on Drug Abuse. While the IND ostensibly established a legal channel by which patients could obtain marijuana, the application process was complicated and burdensome, and only six patients were accepted into the program during its first decade of operation . Still, this signaled a movement toward federal acceptance of marijuana’s medicinal value, and many states began to adopt their own legislation allowing the use of cannabis for medical purposes under specified conditions . Figure 1.1 graphs the number of proposed state-level medical marijuana initiatives from 1972-1995. Statutes are classified as therapeutic research programs , rescheduling provisions, or physician prescription laws. While these laws demonstrated increasing state recognition of marijuana’s therapeutic value, they had little practical significance . The federal approval process for state TRPs was complicated and costly. In the few states that obtained the necessary federal permissions, enrollment was highly restrictive and largely dependent on receiving marijuana from the federal government. In theory, physician prescription laws and rescheduling provisions allow physicians to legally prescribe marijuana for medicinal purposes outside of a TRP.7 However, since the federal CSA classification of marijuana as Schedule I supersedes any state CSA, physicians who prescribe marijuana outside of an officially recognized state TRP risk facing federal sanctions. Additionally, even should a patient obtain a physician’s prescription, these statutes did not establish any legitimate supply channel for patients to obtain marijuana. By 1984, the wave of state medical marijuana initiatives had quickly come to an end. This shift occurred in large part due to the spread of the crack-cocaine epidemic and increased federal emphasis on drug policy enforcement, seizures, and interdictions under the Reagan and Bush Administrations . It became increasingly unlikely that NORML’s 1972 petition would result in federal rescheduling of marijuana, and the government’s IND program was suspended in 1991 and discontinued one year later. State policy mirrored the federal stance, and by 1990 a number of the existing decriminalization and medical cannabis statutes expired or were repealed. The discovery of naturally occurring cannabinoid receptors in the human brain in the early 1990s led to a resurgence of medical interest in the potential therapeutic value of marijuana .8 There was increasing evidence that smoked marijuana offered significant benefits for patients suffering from symptoms of cancer and HIV/AIDS, and in 1995 the Journal of the American Medical Association ran a commentary supporting the use of marijuana for medicinal purposes and calling for increased research. In 1996, with the passage of Proposition 215, California became the first state to establish an effective medical marijuana law that removed criminal penalties for the use, possession, and cultivation of medical marijuana by qualifying patients and their primary caregivers.

COMT, along with monoamine oxidase, modified the L-tyrosine-derived catecholamine neurotransmitter dopamine for excretion in the urine. In the years following, similar to the case of endogenous DMT biosynthesis, several studies identified enzymes in mammalian tissues that could catalyze the chemical transformations of dopamine-related metabolites 3- methoxytyramine into 3-methoxy-5-hydroxytyramine and 3,5-dimethoxytyramine into , although no endogenous could be identified from mammalian organisms.Several mechanisms for biosynthesis in peyote and related cacti have been proposed by metabolite isolation and radiolabeled feeding studies.One proposed pathway by Lundström is shown in Fig. 21.The proposed biosynthesis begins with hydroxylation of L-tyrosine to 3-hydroxy-Ltyrosine by tyrosine hydroxylase , followed by decarboxylation catalyzed by DOPA decarboxylase to yield . Alternatively, may also be converted to tyramine through a decarboxylation catalyzed by tyrosine decarboxylase , followed by aromatic hydroxylation to by an unknown enzyme. From either route, can be converted into 3-methoxytyramine, which has been isolated from mescaline producing plants, by the enzyme catechol O-methyltransferase using SAM as the methyl donor. The final intermediates towards mescaline production 3-methoxy-5-hydroxytyramine and 3,5-dimethoxytyramine have been determined to be naturally occurring in mescaline producing plants by inverse isotope dilution,hydroponic table but neither have been isolated from plants. These are likely to be on pathway intermediates since they are incorporated into mescaline to a greater extent than other possible intermediates.

While the biosynthesis of in peyote has yet to be elucidated, Ibarra-Laclette et al. recently generated two cDNA libraries of the L. williamsii transcriptome, one for button and one forroot, using RNA-seq.From this data set, the authors identified putative genes that may encode bio-synthetic enzymes for mescaline production including DOPA decarboxylases, hydroxylases, and O-methyltransferases based on phylogenetic analysis.Careful in vitro experiments will be required to finally ascertain the mescaline bio-synthetic pathwayIbotenic acid , a nonproteinogenic amino acid with a hydroxylated isoxazole ring, and its decarboxylated form, muscimol , are the main psychoactive constituents of the toadstool, Amanita muscaria, commonly known as fly agaric .Similar to Psilocybe sp., recreational consumption of Amanita sp. rose in popularity in the 1960s. However, contrary to other fungal psychoactives that target the serotonin receptor, these compounds are γ- aminobutyric acid type A receptor agonists.GABAA receptors are found in multiple regions of the brain and thus and can alter the activity of the cerebral cortex and cerebellum leading to alterations in sensory processing and motor function, respectively. A. muscaria is classified as poisonous, which can in part be attributed to the neurotoxicity of 72. Its structural similarity to L-glutamic acid allows to act as an agonist towards the N-methyl-D-aspartate receptor resulting in electrolytic lesions in the brain and naturally occur in low concentrations in the cap and stem of A. muscaria. Minimal dosage for psychedelic effects are estimated as low as 6 mg for 46 and 30–60 mg for 72. 226 Interestingly, A. muscaria and its constituents are not regulated by the United States federal government, in contrast to 1 and 42 from Psilocybe sp. While 72 was first isolated over 50 years ago, its biosynthesis remained elusive.

Recently, Obermaier and Muller identified a gene cluster encoding 72 and 73 biosynthesis in A. muscaria. The key to locating this cluster was the identification of a glutamate hydroxylase, an enzyme first implicated in 72 biosynthesis over 50 years ago, but never found. This enzyme, a nonheme, iron and α-ketoglutarate-dependent dioxygenase named IboH, hydroxylates L-glutamate 36 at the C3 position resulting in the formation of 3- hydroxy-L-glutamic acid 74. Treatment-seeking individuals with an alcohol use disorder exhibit a range of neurocognitive and inhibitory control deficits. A recent review describes deficits related to working memory, visuospatial functions, inhibition, and executive-based functions such as mental flexibility, problem solving, divided attention , and cognitive control . AUD also exhibit worse cognitive efficiency than controls . Of clinical relevance, inhibitory control deficits are greater in actively drinking alcoholics compared to controls and they predict relapse in AUD . Research has also noted deleterious effects on neurocognition from chronic cigarette smoking, the most common substance use comorbidity in AUD, with rates in treatment seeking populations estimated at 60–90% . Greater smoking severity in AUD predicted worse executive function , and smoking AUD performed worse than nonsmoking AUD on domains of auditory-verbal learning and memory, processing speed, cognitive efficiency, and working memory at one week and four weeks of abstinence . Furthermore, smoking was shown to significantly hinder recovery of visuospatial learning and processing speed in AUD . A large proportion of treatment-seeking AUD have a concurrent substance use disorder , with 1.3 million people in the United States alone in 2013 ; therefore, this group is better described as “polysubstance users” , a term used in the literature to describe AUD who meet dependence criteria for additional substances .

Given the cognitive and inhibitory control deficits observed in AUD studies, it is not surprising that recently detoxified individuals with a substance use dependence diagnosis on any combination of heroin, alcohol, methamphetamine, and/or cannabis also performed worse than controls on several measures of executive function, including working memory, response inhibition, cognitive flexibility, and on inhibitory control measures of decision making . Individuals with both alcohol and stimulant dependence performed worse than controls on cognitive efficiency ,complex attention and memory as well as delayed discounting . Furthermore, poorer executive function in abstinent abusers of several substances has been related to the amount of cocaine and cannabis consumed , suggesting clinically relevant consequences of chronic substance use. Despite extensive research into the neurocognitive correlates of substance abuse, only few studies investigated neurocognition in PSU relative to the more extensively studied AUD, and then only on specific tasks. Short-term abstinent alcohol and stimulant dependent individuals performed worse than AUD on immediate and delayed recall conditions of a verbal memory task , but they did not differ from AUD on cognitive efficiency tasks of visual perception and category sorting . Another study found treatment-seeking abusers of multiple substances to perform moderately worse than AUD on executive function tasks of verbal fluency, working memory, planning, and multi-tasking. Neurocognitive functions recover at least partially in AUD during sustained abstinence, and some evidence suggests that additional use of other substances by people with AUD impact neurocognitive recovery negatively . Very few longitudinal studies have explicitly investigated changes in neurocognition or inhibitory control in abstinent PSU. In one study,grow rack individuals with a combined alcohol and cocaine use disorder demonstrated significant improvements on measures of immediate memory over six months of abstinence , while another described improvements in verbal short-term memory over three to four months of abstinence from multiple substances .Intact neurocognition and inhibitory control are important for addiction treatment efficacy, retention , and maintenance of abstinence during treatment . Recent evidence has shown an association between better treatment response and longitudinal cognitive recovery in AUD . Identifying the specific neurocognitive and inhibitory control deficits that differentiate PSU and AUD may provide helpful insights into the specific clinical needs of this understudied , albeit highly prevalent population of PSU in substance use treatment centers today; such deficits potentially differ from those in the more extensively studied AUD population and therefore may require more tailored treatment approaches to increase treatment effectiveness. Our recent reports of different neurobiological abnormalities in AUD and a subset of the PSU cohort presented here further supports the view that neurocognition may also differ between AUD and PSU populations.

Accordingly, the main goals of this study were to determine the degree to which one-month-abstinent PSU and AUD differ on neurocognitive functioning and inhibitory control, and if cigarette smoking affects neurocognition in PSU, similar to what has been reported in AUD. A secondary goal was to explore if PSU exhibit improvements of neurocognitive function and inhibitory control between one and four months of abstinence from all substances except tobacco. Thirty-six treatment-seeking polysubstance users and 69 treatment-seeking alcohol users were recruited from substance abuse treatment programs at the San Francisco VA Medical Center and Kaiser Permanente for two different research projects on alcohol and substance use disorders. Table 1 displays group demographics and relevant substance use characteristics. At baseline, PSU and AUD were abstinent from all substances except tobacco for approximately 29 days. Seventeen PSU were restudied after 128 ± 29 days of sustained abstinence from all substances except tobacco. The 19 PSU not restudied at follow-up either self-reported relapse to any amount of substance use after baseline , were found to have relapse notes in their medical charts, or were lost to follow-up. All participants provided written informed consent according to the Declaration of Helsinki prior to participation. Study procedures were approved by the local Committee on Human Research. All 105 participants met DSM-IV-TR criteria for an alcohol use disorder. In addition, all 36 PSU met DSM-IV-TR criteria for at least one other substance use disorder: 27 with cocaine use disorder; 12 with amphetamine use disorder; 7 with cannabis use disorder; 5 with opioid use disorder; 1 with anxiolytic use disorder; and 1 with hallucinogen use disorder. Not considering cigarette smoking, nine PSU had two or more substance use disorders in addition to an alcohol use disorder. Specifically, of these nine, five participants met criteria for cocaine, amphetamine, and cannabis use disorder and one also met criteria for opioid and hallucinogen use disorders; two participants met criteria for amphetamine and cannabis use disorder ; one participant met criteria for cocaine and opioid use disorders, and another met criteria for opioid and anxiolytic use disorders. Nonsmoking participants smoked fewer than 20 cigarettes in their lifetime, with no cigarette use in the 10 years prior to study and no history of use of other tobacco products. Smoking participants were actively smoking at the time of the baseline assessment and smoked at least 10 cigarettes per day for 5 years or more, with no periods of smoking cessation greater than 1 month in the 5 years prior to enrollment. None of the PSU studied longitudinally changed their smoking status or severity between assessments. Medical exclusion criteria were a current or past history of intrinsic cerebral tumors, human immunodeficiency virus or acquired immune deficiency syndrome, cerebrovascular accident, aneurysm, insulin dependent diabetes, chronic obstructive pulmonary disease, nonalcohol related seizures, significant exposure to known neurotoxins, demylenating and neurodegenerative diseases, Wernicke-Korsakoff Syndrome, alcohol-induced persisting dementia, and traumatic brain injury resulting in loss of consciousness for more than 15 minutes. Psychiatric exclusion criteria included schizophrenia or other thought disorders, bipolar disorder, dissociative disorders, posttraumatic stress disorder, obsessive compulsive disorder, and panic disorder , Hepatitis C, type-2 diabetes, hypertension, and unipolar mood disorders, were not exclusionary given their high prevalence in substance use disorders . At baseline and follow-up , each participant completed the Structured Clinical Interview for DSM-IV Axis I Disorder Patient Edition, Version 2.0, as well as questionnaires assessing depressive and anxiety symptoms and Y-2 , STAI. Lifetime alcohol consumption was assessed at baseline with the Lifetime Drinking History semi-structured interview . We derived the average number of standard alcoholic drinks consumed per month, both one year before enrollment and over lifetime. Substance use history of PSU participants was assessed at baseline with a semi-structured interview developed in-house . For each substance for which a PSU participant met criteria for a current or past substance use diagnosis, date of last use, frequency of use, and quantity of use were gathered. Abstinence was assessed with self-report, and confirmed via medical chart review, mandating negative urine toxicology and blood alcohol concentration tests conducted weekly as part of routine clinical care. The Fagerstrom Tolerance Test for Nicotine Dependence was used to assess level of nicotine dependence, total years of cigarette smoking, and average number of daily cigarettes currently smoked. A comprehensive neurocognitive battery was administered to each participant at baseline and again to PSU participants at follow-up. The battery included measures of executive function, general intelligence, auditory-verbal learning/memory, visuospatial learning/ memory/skills, processing speed, working memory, cognitive efficiency, and fine motor skills. Neurocognitive domains and constituent measures are presented in Table 2. Alternate forms for Brief Visuospatial Memory Test – Revised and California Verbal Learning Test-II were used at follow-up assessments for PSU. Premorbid verbal intelligence was estimated with the American National Adult Reading Test at baseline only . All measures are well normed and commonly used in clinical and/or research settings .

The enzymology of these enzymes has been well-studied and the reader can refer to other reviews for more information.Here we will briefly summarize a few enzyme-catalyzed or enzyme-mediated reactions that will be found throughout the review. The aromatic amino acids L-tryptophan , L-tyrosine and to a less extent, L-phenylalanine, are commonly used precursors for alkaloid natural product biosynthesis. For example, the indole ring in L-tryptophan is preserved in compounds such as psilocybin and ibogaine; while the parahydroxybenzene side chain in L-tyrosine can be found in mescaline and morphine. The terminal amine-containing L-lysine and L-ornithine are also used as precursors. Relevant to this review, the four-carbon side chain of L-ornithine is required for the formation of pyrrolidines and tropanes. The first step in the utilization of these amino acids for alkaloid biosynthesis is decarboxylation to give the corresponding primary amines, although in lysergic acid biosynthesis L-tryptophan is used without decarboxylation. The decarboxylation products of L-tryptophan, L-tyrosine and L-ornithine are tryptamine , tyramine , and putrescine , respectively . In the case of tyramine , hydroxylation of one of the meta positions in the para-phenol ring gives the metabolite dopamine. Dopamine is a natural product building block, but also a neurotransmitter in mammals. The chemical logic for the early decarboxylation is straightforward: to facilitate intra- and intermolecular Mannich reactions with aldehydes and ketones using the nucleophilic amine .

This decarboxylation-Mannich two step rapidly sets up the -heterocyclic scaffold of many alkaloidal natural products. The decarboxylation reactions are catalyzed by dedicated amino acid decarboxylases. For example,plant bench indoor in the case of L-tryptophan, a tryptophan decarboxylase is involved. These enzymes typically use the PLP cofactor, as expected for many enzymes that perform Cα, Cβ and Cγ modifications on amino acids.The mechanism of the reaction is shown in Fig. 2B. The aldehyde of PLP modifies an active site lysine to form the resting aldimine in the decarboxylase active site. A transaldimination step takes place next in which the amine of the substrate amino acid attacks the aldimine and forms the amino acid–PLP aldimine. The PLP then serves as an electron sink in the enzyme-catalyzed cleavage of the Cα-COO− bond via a quinonoid species. Reprotonation of the Cα then generates the product aldimine, which can undergo another transaldimination with the active site lysine to release the product amine and regenerate the resting aldimine. Following decarboxylation of the amino acids to the corresponding primary amines, a common next step is the Mannich reaction involving the primary amine. The Mannich reaction is a two-step reaction that yields an alkylated amine.In the first step, the primary amine reacts with either an aldehyde or a ketone to form the Schiff base. The C=N double bond is then attacked by a carbon nucleophile, such as the acidic Cα of a carbonyl to form the β-amino-carbonyl product. Two examples of an intramolecular Mannich reaction can be found in the formation of the tropane unit in cocaine.Starting from putrescine , methylation of one of the primary amines gives the intermediate N-methylputrescine ; oxidation and hydrolysis of the other amine yields N-methylaminobutanal , which is in equilibrium with the cyclic N-methylpyrrolinium . Attack of the imine by the enolized 3-oxo-glutaric acid yields the adduct pyrrolidine tropane scaffold precursor .

A subsequent dehydrogenation generates a new pyrrolinium species that can be attacked with Cα of the 1,3-diketo unit in a second Mannich reaction . One variation of the Mannich reaction that is central to the biosynthesis of plant alkaloids is the Pictet-Spengler reaction involving β-arylethylamines such as tryptamine and dopamine. In the PS reaction, after the amine reacts with an aldehyde or ketone to form the Schiff base, a carbanion resonance structure of the indole in tryptamine or the parahydroxy phenol ring in dopamine can attack the imine to form the new C–C bond. This can be followed by rearrangements to form the stable tricyclic tetrahydro-β-carboline or bicyclic tetrahydroisoquinoline, respectively. The tryptamine-derived tetrahydro-β-carboline is found in harmala alkaloids and iboga alkaloids . To generate the harmala family of compounds, tryptamine is condensed with pyruvic acid , followed by attack of the imine by C3 from the indole ring to form a spirocycle, which collapses via single bond migration to complete the PS reaction .Similarly, the condensation between the aldehyde donor secologanin and tryptamine is catalyzed by a dedicated PictetSpenglerase, yielding strictosidine, the universal precursor to monoterpene indole alkaloids including ibogaine.In the biosynthesis of benzylisoquinoline alkaloids such as morphine , the PS reaction takes place between dopamine and 4- hydroxyphenylacetaldehyde , both oxidation products of tyramine , to form the key intermediate S-norcoclaurine , precursor to R-reticuline and morphine.Group transfer reactions are widely used by Nature in the biosynthesis of natural products. Functional groups that are frequently transferred from donor molecules to bio-synthetic intermediates include methyl, acetyl, small, medium and long alkyl-substituted acyl chains, isoprenyl, glucosyl, etc.

These reactions serve a multitude of purposes, including i) increasing the size and complexity of the molecules; ii) changing the lipophilicity of molecules; iii) altering the reactivity of functional groups; iv) serving as a transient chemical protection group for downstream modifications; v) acting as leaving groups in elimination reactions; and vi) changing the biological properties of the natural product. Hence, these reactions are indispensable to the structural diversity of natural products that have been isolated to date. The donor molecules, those that “carry” the groups to be transferred, are kinetically stable and thermodynamically activated: the molecules are high in energy and therefore releasing the groups is a highly exergonic reaction; yet the molecules are stable under cellular conditions and enzyme catalysis is required to overcome the kinetic barriers. We recently reviewed eight such molecules that power cellular metabolism, which include ATP, NADH, acetyl-CoA, SAM, carbamoyl phosphate, isoprenyl pyrophosphate, UDP-glucose and molecular oxygen.NADH and molecular oxygen are involved in the redox reactions and will be summarized in the next section. Among the remaining six, carbamoylphosphate is involved in nitrogen metabolism and is not directly involved in natural product biosynthesis. The remaining five, however, are frequently used group transfer donor molecules,greenhouse rolling racks and examples can be found throughout the review. ATP, the universal cellular energy currency, is the donor in the transferring of phosphate groups to nucleophilic oxygen in the presence of a phosphotransferase. This reaction is ubiquitous in primary metabolism but is quite rare in natural product biosynthesis . One such example can be found in the psilocybin pathway . Acetyltransferases catalyze the transfer of acetyl groups from the acetyl-CoA thioester to a variety of O and N nucleophiles . SAMdependent methyltransferases use S-adenosylmethionine to transfer a methyl group from the trivalent sulfonium group to C, O, N, and S nucleophiles in an SN2 type substitution reaction . This reaction can be found in the majority of bio-synthetic pathways described herein. For example, iterative N-methylation of tryptamine yields the psychoactive molecule N,N-dimethyltryptamine . UDP-glucose is an activated glucose donor in cells for the assembly of oligosaccharides and polysaccharides. UDP-glucose is thermodynamically activated but kinetically stable in the absence of glucosyltransferases.In the presence of glucosylating enzymes, UDP dissociates via cleavage of the C–O bond in an SN1 fashion to yield a C1 oxocarbonium ion, which can be attacked by incoming nucleophiles . A notable example of substrate glucosylation is in the bio-synthetic pathway of strictosidin, the precursor to ibogaine . The enzyme 7DLGT glucosylates the hemiacetal in 7-deoxyloganetic acid to give 7-deoxyloganic acid.The glucose moiety serves as a protecting group to prevent formation of the aldehyde, and remains in strictosidine . In order to transform strictosidineinto different scaffolds, a glucosidase removes the glucose moiety, unmasking the aldehyde and leading to subsequent rearrangements towards structurally diverse monoterpene indole alkaloids. The final group transfer reaction that is relevant to this review is the transfer of prenyl groups from isoprenyl pyrophosphate to different nucleophiles in small molecules. These reactions are catalyzed by a family of enzymes known as prenyltransferases.

The prenyl unit that is transferred from the pyrophosphorylated donor to the substrate can be as small, as in the five-carbon dimethylallyl , or the more elongated oligoprenyl groups such as the ten-carbon geranyl, fifteen-carbon farnesyl, etc. In the enzyme active site, the Δ2 – prenyl pyrophosphate donors can undergo C–O bond cleavage to yield the C1 carbocation, which is stabilized by delocalization of the positive charge. Attack of the carbocation by a nucleophile carbon forges the new bond and completes the prenyl transfer reaction . Electron rich aromatic rings, such as hydroxybenzenes and indoles can serve as nucleophiles to attack the allyl cation to perform in essence an electrophilic aromatic substitution. Two examples in this review illustrate this reaction. The first is the dimethylallyl tryptophan synthase in lysergic acid biosynthesis, which prenylates the C4 position in L-tryptophan to give 4-dimethylallyl-L-tryptophan .This modification introduces an olefin-containing five carbon unit into L-tryptophan, which can be further oxidized and cyclized into the hallucinogenic lysergic acid. The mechanism of this reaction has been thoroughly studied, and is likely a two-step reaction.The C3 position of the indole ring is the most nucleophilic due to resonance with the indole nitrogen lone pair. Attack on the allyl cation can occur at either C1 or C3; this attack is proposed to take place at the more stable C3 position of the allyl cation. This generates a “reverse”-prenylated product that is proposed to undergo a nonenzymatic sigmatropic Cope rearrangement to yield the “forward”-prenylated 4-DMAT. In addition to serving as the starting point for lysergic acid , indole prenylation of early pathway intermediates is commonly observed in the biosynthesis of other fungal indole alkaloids. The second notable pathway that involves prenyl transfer is in cannabinoid biosynthesis .Starting with the first intermediate in the pathway, olivetolic acid which is a resorcinol derivative, the aromatic prenyltransferase transfers the ten-carbon geranyl group from geranyl pyrophosphate to the C3 position in the ring to give cannabigerolic acid . As in the lysergic acid example, the introduced ten-carbon unit can undergo oxidative intramolecular cyclization, providing a variety of cannabinoids . Natural product bio-synthetic pathways employ powerful redox enzymes to modify the intermediates en route to the final product. The redox modification can directly modify the molecular scaffolds, or trigger rearrangement cascades, to introduce considerable structural complexities.On the reductive side, the NADH utilizing enzymes dominate as one would expect. These include ketoreductases, short-chain dehydrogenase/reductases , ene-reductases, and imine reductases, etc. The two-electron reduction of C=C, C=O or C=N bonds are initiated through the attack by a hydride equivalent from either the dihydropyridine ring of NADH or the hydroquinone form of flavin adenine dinucleotide . On the oxidative side, aerobic organisms use an assortment of enzymes and molecular oxygen as the oxidant to perform a dazzling array of chemical modifications.Both single electron and two electron manifolds are used by enzymes. These enzymes include the large family of hemedependent cytochrome P450 monooxygenases that are abundant in plants and fungi; nonheme, iron and α-ketoglutarate dependent oxygenases, copper-dependent oxidases , and flavin-dependent monooxygenases and oxidases. In two-electron oxidation of substrates catalyzed by oxidases, molecular oxygen is reduced to hydrogen peroxide. In monooxygenases where oxygen is reduced fully to water , the substrate undergoes a two-electron oxidation, while NADPH is oxidized to NADP+. Here, the substrate can incorporate one of the oxygen atoms via hydroxylation or epoxidation, or alternatively the substrate can be oxidized without incorporation of oxygen atoms. Hence, depending on the mechanism of the redox enzyme, the outcome of the reaction can be very different. This topic has been extensively reviewed in the literature, and will not be discussed in detail here. However, we will highlight two reactions to illustrate the enzymatic prowess of the P450s, a staple of the plant bio-synthetic pathways. P450 enzymes use heme as a coenzyme to bind molecular oxygen. Binding of molecular oxygen and electron transfer from the Fe and CPR leads to a hydroperoxy Fe–O–O–H species. Cleavage of the O–O bond and the loss of water generates the high valent Fe=O porphyrin cation radical, which is also referred to as Compound I. This is a highly oxidizing species that can abstract hydrogen from substrate C, O, and N atoms to generate substrate radicals, including “unactivated” sp 3 carbons. This generates the Fe– OH species also known as Compound II. Radical OH transfer to the substrate carbon radical produces the hydroxylated product in a process known as oxygen rebound.

Taken together, it appears that although marijuana use may stimulate appetite and increase caloric intake, it is not associated with increased BMI; on the contrary, it is dose dependently associated with lower BMI and lower hemoglobin A1C levels. Our study has some limitations. Information on marijuana use and other behaviors, including alcohol, smoking, and drug use, were collected via self-report and vulnerable to under reporting due to social desirability bias. Relatedly, there was no biological quantification of marijuana constituents or mode of marijuana use from participants. The imprecise measurement of marijuana use in our study could be a source of measurement error and may have contributed to the wide confidence intervals we observed. Potential confounders, such as physical activity, diet, and waist circumference, were not included in the analysis. Additionally, data from the WIHS were from predominantly black and Hispanic women, while the MACS comprises mostly non-Hispanic white men, which may signal disparities in reliable access to health care. Recent changes in the marijuana environment in the United States include decreased restrictions on use , decreased perception of marijuana as harmful , and increased adult prevalence . As of January 1, 2020, recreational use was legal in 11 states, and decriminalized in 15 others; medical use was legal in 33 . A primary concern about legalization is increased use among youth, but this concern has not been realized to date . However,greenhouse grow tables there is evidence of increased marijuana use and cannabis use disorders among adults that appears to be concentrated among adults aged 26+ .

While extant research suggests that legalization has not led to increased overall use among youth, some recent findings suggest possible increases among young adults . Repeated cross-sectional data suggest increased frequency of use among Oregon undergraduates after legalization in that state in 2015 . Additionally, early marijuana use may lead to cigarette smoking and to poor occupational and educational outcomes in young adulthood. It is also important to note that previous research on the impact of changing marijuana restrictions is largely limited to repeated cross-sectional data. There is a need for longitudinal cohort studies to identify not only trajectories of use but also predictors and correlates. Additionally, it is unclear whether loosening restrictions may have a differential impact on 18–20 year old young adults, for whom use remains illegal, compared with 21–24 year olds. Despite decreasing public apprehension, there are reasonable concerns about young adult marijuana use. Acutely, marijuana increases risk for accidents , emergency department visits , and psychosis . Persistent use predicts poor psychosocial outcomes and neuropsychological and cognitive decline in humans , and animal models suggest cognitive deficits and social anxiety . Persistent use is associated with addiction , including withdrawal . However, continued movement toward increased access makes it important to understand potential public health effects. A further concern is whether legalization modulates tobacco use. Marijuana users appear less likely to quit tobacco , and changes in use of either product are positively associated with changes in the other . Marijuana legalization could send the message that tobacco is also less dangerous than previously believed. In other words, legalization may undermine negative perceptions of tobacco use due to similar routes of administration, use of similar devices, and frequency of co-use . This risk is particularly important during emerging adulthood, when tobacco initiation peaks and patterns of long-term use are established .California was the first state to legalize medical marijuana in 1996.

At that time marijuana use was already high compared with other states, and the impact on prevalence was minimal . However, in 2016 California legalized recreational marijuana use, including possession of up to 1 ounce and individual cultivation beginning in November 2016, with commercial sales permitted as of January 1, 2018 . . State authorities began to plan for regulation of recreational sales in late 2016, and began issuing licenses for cultivation, manufacturing, distribution, testing, and retail sales at the beginning of January, 2018 . Local jurisdictions may still prohibit cultivation and sales. As of February 2018 there were 261 active retail licenses statewide; as of October 2020 that number had increased to 753, including 46 in San Diego County where this study was based . A recent report based on a survey from 2018–19 indicates that young adults generally utilize legal avenues to access marijuana and only rarely acquire it from strangers or dealers . To our knowledge, no research has evaluated changes in frequency of marijuana use in California following legalization of recreational sales, and there are few cohort studies from other states. The current study is one of the first to examine a sample of the same participants before and after legislation was implemented. Participants were at high risk for marijuana use given that all had smoked cigarettes recently at time of enrollment . This study was a secondary analysis of a study of non-daily cigarette smokers in California who were aged 18–24 when they enrolled in 2015–16 and who were followed quarterly for 3 years. Although possession and individual cultivation were permitted during 2017, we focused on legalization of sales in the belief this would have greater impact on availability based on both increased direct sales to young adults and on diversion from peer groups. Based on recent studies among young adults and on the fact that their tobacco use status likely increased risk of marijuana use, we sought to test the hypothesis that frequency of marijuana use would increase followinglegalization. A second goal was to test whether post-legalization changes in marijuana trajectories would be moderated by demographic or other substance use factors. We expected to see greater increases in use among male participants, and among those who used other substances more frequently. Finally, we explored whether changes in marijuana use frequency following legalization were related to cumulative frequency of use prior to 2018.

We recently described the relationship between marijuana and tobacco use in this sample and there is partial overlap in the data used in these studies. The primary differences are the previous study included data only from subjects’ first two years following enrollment,cannabis growing systems while the current study utilized data from all three years of follow-up, regardless of when subjects enrolled, and the previous study examined trends in use over time without regard to changes in the legal environment, while the current study explicitly examined whether frequency of marijuana use was associated with legalization of recreational sales.We recruited California residents aged 18–24 who had smoked cigarettes at least monthly for 6 months but never daily for 30+ days. Participants were recruited on a rolling basis during 2015–2016 and completed substance use assessments quarterly for three years. The present secondary analysis utilized an “intent-to-treat” approach, in which all enrolled participants were included. Average age at enrollment was 20.4 years , 51.9% were male, and most were full-time students. In terms of race/ethnicity, 41.7% identified as Caucasian, 21.0% Latinx, 19.1% Asian American, 13.1% multi-ethnic, and 5.1% from other backgrounds. Study participants completed assessments every three months for three years. All data were collected between March 2015 and October 2019. Assessments were completed online via SurveyMonkey. Compensation was $25 per annual assessment and up to $40 per quarterly assessment via electronic gift cards. Staff sent individualized survey links via email or SMS. All procedures were approved by the University of California, San Diego Institutional Review Board.. Demographics evaluated at baseline included sex, age, racial/ethnic background, and student status. Because the age range was narrow and our interest was in the potential impact of legalization, age was transformed into a time-varying binary variable reflecting whether or not participants were aged 21 at the time of each assessment. Student status was collapsed into a dichotomous variable comparing full-time students to all others. Marijuana and tobacco frequency were assessed at all 13 timepoints. At annual assessments, participants completed the Timeline Follow Back , on which they reported number of cigarettes, and whether they had used each of e-cigarettes, hookah, cigars, cigarillos, smokeless tobacco, snus, marijuana, and alcohol on each of the previous 14 days. At quarterly assessments, participants completed brief daily surveys, in which they indicated whether they had used each of the same products in the last 24 hours. We created time-varying variables representing constructs of interest. Time reflected the study timepoint, from baseline to year 3 . For all assessments, we calculated the number of days on which participants used marijuana , cigarettes , e-cigarettes , and alcohol . We counted the number of days at each timepoint at which assessment occurred to account for the fact that the maximum number of days differed for annual versus quarterly assessments, and that participants may not have provided data for all days during quarterly assessments. We created a binary legalization variable that indicated whether or not each assessment occurred prior to or after January 1, 2018. We also created a post-legalization slope variable that was coded as 0 for all pre-legalization timepoints, and to reflect time since legalization for post-legalizatin timepoints .

Finally, for each participant we calculated the total number of days prior to January 1, 2018 on which use was assessed , as well as the number of those days on which any marijuana use was reported . We used bivariate tests to evaluate whether demographic variables were related to predictors and outcomes; when associations were significant, we accounted for demographics in hypothesis tests. To test whether frequency of marijuana use changed following legalization we utilized a piecewise or segmented multilevel longitudinal regression model, an approach recommended for evaluating the impact of policy changes . This model included segments for the period prior to January 1, 2018, and for the period from that date onward. The model tested the temporal trend in frequency of marijuana use, the impact of legalization, and changes in the rate of marijuana use over time following legalization by incorporating the time, legalization, and post-legalization slope variables as predictors. Sex, race/ethnicity, and binary age were included as covariates. Second, we used multilevel longitudinal regression models to evaluate the associations of sex, race/ethnicity, binary age and frequency of alcohol, cigarette and e-cigarette use with frequency of marijuana use before and after legalization. We did so by testing for three-way interactions between the predictors of interest , time, and legalization. Significant three-way interactions would indicate that impact of legalization on the trajectory of marijuana use frequency was moderated by the predictor of interest. All demographic interaction terms were included in one model, and all substance use interaction terms in another. In both cases, non-significant interaction terms were removed in a backward manner and models refit. Finally, we used a similar multilevel modeling approach to test whether time-invariant cumulative marijuana days was associated with time-varying marijuana frequency over time post-legalization. All analyses were conducted using Stata 15.0, with alpha = .05; missing data were not imputed.The proportion of data missing was 0% for the first 3 assessments , and increased with each subsequent assessment, with 3.2% of participants missing outcome data at year 1, 10.5% at year 2, and 14.1% at the final year 3 timepoint. Missingness increased over time and was most common among participants identifying as White . Missingness was not significantly associated with sex or with frequency of cigarette, e-cigarette, or marijuana use at the previous assessment. The first 5 assesments occurred prior to legalization for all participants. The proportion of the sample for whom assessment occurred after legalization increased with each subsequent assessment, from 1.8% at the first quarterly timepoint following year 1, to 37.7% at year 2, 80.3% six months after year 2, and 100% at year 3. Bivariate analyses indicated women tended to use e-cigarettes less frequently , younger participants used marijuana more frequently , and non-White participants reported greater cigarette frequency . Consequently, sex, age, and race/ethnicity were included in subsequent analyses. Table 1 details frequency and likelihood of marijuana, alcohol and tobacco use at baseline and at each annual assessment. The proportion of days on which participants used marijuana remained relatively stable, while the number of participants who reported any marijuana use declined modestly from baseline to year 3. Alcohol use was stable across the three years of observation. Proportion of days using e-cigarettes exhibited a 50% increase, while the proportion of participants with any e-cigarette use was relatively stable. In contrast, use of cigarettes, and consequently overall use of tobacco products, decreased over time. The piecewise regression model is shown in Table 2.

The distribution of storage time for colostrum before feeding in hours underwent a square root transformation to achieve normality. If colostrum was stored for less than 2 h, zero hours were recorded for colostrum storage time. Storage temperature for colostrum was recorded as either room temperature , refrigerated, frozen, or first refrigerated then frozen. A variable for colostrum storage temperature had indicator variables for each of the 4 storage temperatures. The type of storage container for colostrum was dichotomized as solid or bags, as thawing frozen colostrum stored in bags may achieve a more uniform increase in colostrum temperature compared with colostrum stored in plastic bottles and may result in less heat damage to immunoglobulins. The variable for percent of colostrum fed that is from firstcalf heifers was dichotomized into the categories any or no colostrum from first calf heifers. The amount of colostrum fed in the first 12 h was dichotomized into <2.84 L or ≥2.84 L , as feeding 3 L of colostrum was considered a relevant biological cutpoint . Four dairies let calves nurse from their dams to meet the calves’ colostrum needs; hence, estimates for the amount of colostrum fed in the first 12 h on these dairies could not be made. In addition, a dichotomous variable for whether or not colostrum was tested for immunoglobulin content and a dichotomous variable for whether or not calves were assessed for failure of transfer of passive immunity based on serum total protein were explored in the model.Data to create variables concerning housing of calves were drawn from the questionnaire sections that captured herd-level data as well as from data collected at the individual calf level.

Questionnaire Data. Proportions of calves housed individually or in groups,rolling flood tables average group size of grouphoused calves, housing types, weaning age, age when moved from individual hutches to group housing, percent sick preweaned calves moved to a hospital pen, clinical signs to detect BRD, and treatment choices for BRD were used only for descriptive statistics purposes. This was done because these variables provide information about the whole cohort of preweaned calves and are not suitable for analyzing the association of management practices and BRD in individual calves. Data on the percent of calves raised on the premises from other dairies was dichotomized into any or no calves from other dairies on the premises. The variable feeding order by age was created with indicator variables for the answer choices youngest typically fed first , calves fed in no particular order, and oldest typically fed first. Only 1 dairy in the sample used a BRD scoring system; hence, the variable describing use of such systems was dropped from analysis. As some dairies gave multiple answers to the question about what surfaces, driven on daily, cover the roads adjacent to the calf-raising area, dichotomized variables for paved, gravel, dirt, or other surfaces were created and analyzed as separate variables. Similarly, dichotomized variables for dust-abatement procedures were created describing whether or not water, magnesium chloride, or other dust-abatement measures were implemented . For the question on how often dust was a problem in the area where preweaned calves were housed, a categorical variable was created with never as the reference and the indicator variables rarely , no week passes by without dust, daily during the nonrainy season, and daily all year round. Calf-Level Data.

Whether calves had calf-to-calf contact or not was recorded as a dichotomous variable. Hutch space was defined as the product of the width and depth, as measured during the visit. A categorical variable was created, categorizing the data as less than , greater than, or within 1 standard deviation around the mean space for wooden hutches in the sample population, and a separate category for group pens. Hutch elevation from the ground was dichotomized into elevated or not . Dichotomous variables for each of the reported floor materials were specified where absence of the specific floor material was the R [concrete, rubber, sand, Tenderfoot , dirt, grass, wood, and bedding]. In addition, dichotomous variables for slatted floors and floors that allow airflow were specified. Wall materials were categorized with indicator variables for only wood , only metal, only plastic, and a category for a mix of materials or group pens. Fourteen dairies had at least some hutches that were made of a combination of materials, such as wood and metal or metal and plastic. Calves housed in groups were added to the latter category to be able to assess the association of a single hutch material on an individually housed calf without losing group-housed calves or calves in hutches made from a mix of materials in the analysis. In addition, a dichotomous variable for solid walls or nonsolid walls was specified to explore an association with air flow in hutches and BRD. A categorical variable describing the presence and type of flush system under the calf hutches was specified with 3 levels: no flush , fresh water flush, or flush with lagoon water. A categorical variable describing the presence and type of extra shade structures above calf hutches was specified with levels no cover , partial roof, complete roof without side walls, complete roof with 1 to 3 side walls, and fully indoors.

The calving space in square meters per cow was calculated by dividing the average number of cows per pen by the dimensions of the maternity pen. Calving space was normalized using the natural log transformation. The percent of calving area that is pasture was dichotomized into any or no calving on pasture . The number of times the bedding is changed in the calving pens was dichotomized into less than or equal to 5 times and more than 5 times per month. Typical maternity pen bedding type was summarized into categories of plant fiber , recycled manure , gypsum or lime, dirt , and pasture. Those dairies that answered no maternity pen exists or no bedding in maternity pen were assigned to the category sand or dirt. Dichotomous variables were created for the presence or absence of each type of bedding,flood and drain tray as multiple dairies used more than 1 type of bedding. The data for the percent of calves removed in 1 h from the dam was dichotomized into ≥95 and <95% . Vaccine timing and frequency for cows and calves was dichotomized into whether a type of vaccine was given or not . The time since vaccination for each calf was estimated as the time between the reported age when calves receive a certain type of vaccine as stated by the interviewee in the questionnaire and the age of the calf at the time of visit. Dichotomous variables were created describing whether a certain type of vaccine was given at least 7 d before the visit to account for the fact that there is a variable lag time between vaccine administration and an immune reaction. Hygiene scores for up to 15 cows present in the maternity pen during the dairy visit were transformed into cumulative hygiene scores, which facilitated comparisons of percent of cows scoring 1, 1 and 2, or 1 and 2 and 3 versus the percent of cows scoring the respective remaining scores.Questions that could be answered with yes or no resulted in dichotomous variables including if milk was ever tested for bacterial content both before pasteurization and after pasteurization and if any medication was added to milk or milk replacer . The source of milk for each diet fed on the study dairies was recorded as a percentage of the total diet during the farm visit. For the analysis, these percentages were converted into dichotomous variables per milk source describing whether a calf’s diet consisted of >90% of one of the sources saleable milk, waste or hospital milk, unmedicated milk replacer, or medicated milk replacer for at least 7 d before the farm visit or not . No distinction was made between waste milk from fresh cows and milk from cows in a milk-withdrawal period due to medication. One dairy fed waste from a yogurt producer, which consisted of a water-yogurt mixture created during flushing of the production lines. A separate dichotomous variable for feeding of the yogurt waste product was created. The variable describing whether milk fed had been pasteurized contained a category for weaned calves to avoid exclusion of weaned calves from the model. For the number of times calves were fed milk per day, a categorical variable was created with twice feeding as the reference and levels of once daily as well as a level including 3 or 4 times or free choice feeding.

Two calves in the study were suckling from the dam based on their age and management of neonatal calves described by the producer and had no amount of milk fed or number of times fed assigned. The total volume of milk fed per day, which was calculated from the number of times calves were fed milk and the volume offered at each feeding, was categorized into ≤2.84 L , between 2.84 L and up to 5.68 L , and >5.68 L. Finally, dichotomous variables for whether or not antimicrobial drugs, vitamins, or electrolytes were added to the milk were explored. The type of medication used to treat BRD was used for descriptive statistics only.The current study is the first to evaluate BRD in preweaned calves housed on dairies throughout California and to associate BRD in these calves with management practices. Our results suggest that management relative to both housing and calf feeding practices may be the most important areas associated with the prevalence of BRD in young dairy calves on California dairies. Using lagoon water for flushing under hutches, the use of metal hutches, as well as calf-to-calf contact in older calves, and feeding Holstein calves ≤2.84 L of milk or replacer per day were all positively associated with BRD. Pasteurizing milk, feeding saleable milk, feedingJersey calves more than 5.68 L of milk or replacer per day, and providing extra shelter were all negatively associated with BRD. Interestingly, no associations were found between vaccinating dams or calves or the amount or quality of colostrum fed and BRD. Management factors significantly associated with BRD in our study may reflect some of the areas where the California dairy industry is less uniform and where changes may have the biggest effect on BRD prevalence. Current knowledge based on numerous studies underscore colostrum management and vaccinations as important components of BRD prevention; however, the current study’s dairies may have exhibited uniform colostrum and vaccination practices. The principal component analysis of a California survey on management practices related to bovine respiratory disease in preweaned dairy calves performed in 2013 identified calf housing as the component construct with the largest variation proportion . The component construct contained the variables hutch style, calf contact, flush used, and waste milk fed to heifers. Three of these variables were significantly associated with BRD in our study, supporting the idea that these are areas of highly variable management on California dairies. Although we did not observe an association between the fourth variable in the construct and BRD in our study, we did observe a negative association between BRD and feeding saleable milk for at least 7 d, which is one of the alternatives to feeding waste milk.The state-average dairy herd size in California in 2015 was 1,215 milking cows , which is less than the average herd size of 1,718 milking cows but close to the median herd size of 1,100 milking cows in our study. Herd sizes are expected to increase , so the calves enrolled in our study likely represent a growing proportion of dairy calves raised in California. Nationally, Holsteins make up 86.0% , Jerseys 7.8% , and other breeds 6.2% of US dairy cows , compared with 81.6% Holsteins, , 13.1% Jerseys , and 5.3% crossbred calves in our study. Jersey cows have increased in popularity over the last years in California due to their higher milk fat content and potential for higher feed-to-milk conversion ratios among other health related reasons . Although no official statistics for California dairy breeds are available, it may not be unreasonable to assume that Jersey cows are more common in California than nationally.

Since we largely utilize non-invasive sampling techniques, at least for the fish samples, performing quantitative measures is a challenge. Nonetheless, future studies should focus on developing non-invasive methods for accessing the quantitative measures of microbial quantities in both the BE and the fish mucous.Marijuana is one of the most commonly used drugs in the United States. It is now well known that individuals report self-treating with MJ for a number of medical and psychiatric symptoms, most commonly PTSD, pain, anxiety, and insomnia. The availability of MJ to treat these symptoms has been increasing, due in part to changes in laws related to MJ use. In the United States, MJ has been legalized in four states, decriminalized in 16 states, and there are now 23 states that have medical marijuana legalization. Many individuals using MJ medicinally or for recreational purposes use MJ for insomnia. This is despite the research suggesting that treatment seeking and non-treatment seeking 6 individuals report disturbed sleep when they stop using MJ and a only a small portion report a reduction of related symptoms as a primary benefit of use.Marijuana use is most prevalent in the United States among 18-25 year olds with approximately 32% of non-college and 35% of college-attending persons reporting past year use, and 19% of emerging adults reporting past month marijuana us. Young adults use MJ for recreational reasons, but some also use MJ for sleep difficulties. An estimated 7.3% of individuals aged 18-29 meet ICD-10 or DSM-IV criteria for the diagnosis of insomnia. In a community sample of over four thousand 18-25 year olds, 29.3% scored above the clinical cut-off on the Pittsburgh Sleep Quality Index . Thus, roll bench about 30% of individuals in this age group complain of sleep disturbance, although only one-fourth of these meet formal diagnostic criteria.

Insomnia has been associated with both self-reported impairments in daytime functioning and lost productivity. The biopsychosocial changes of young adulthood affecting sleep are well known and may contribute to MJ use. As individuals begin to live more independently, there may be fewer restrictions on sleep schedules, particularly parent-set bedtimes. Many choose to stay awake later at night to socialize or to meet academic demands. Individuals with an evening “chronotype,” who prefer to be awake late into the evening, have been shown to have more problems with reward functioning. Evening chronotypes have also demonstrated higher depression scores 16 , suicidal thoughts, more impaired work and other activities, higher paranoid symptoms, and higher anxiety, compared to a morningness-type group. Thus, the pattern of MJ use in young adults may be influenced by an interplay between changes in sleep patterns, chronotype, and mood. To date, literature on the effects of marijuana on sleep a young adult sample have been somewhat limited. While objective sleep indices have been studied in middle aged Veterans who are heavy MJ users, only one study has examined objective indices of nocturnal sleep and daytime sleepiness the following day in a community sample of young adults. In that study, 8 healthy volunteers participated in a double-blind and placebo-controlled study with either: 1) 15 mg D-9-tetrahydrocannabinol , 2) 5 mg THC combined with 5 mg cannabidiol , 3) 15 mg THC combined with 15 mg CBD or 4) placebo 22 via oromucosal spray one hour before bedtime. THC in addition to CBD was used because of the different effects associated with each compound, i.e. CBD is not as centrally activatinglike THC and has useful therapeutic/anticonvulsant properties. Participants underwent polysomnography and then sleep and morning functioning were evaluated. Nighttime sleep slightly worsened with 15 mg CBD and next day performance was impaired with 15 mg THC. This study highlights the effect of MJ on both nighttime sleep and daytime functioning.

If the relationship between MJ use and sleep disturbance in persons who are not seeking treatment for substance use disorders is substantial and documented to affect daytime function, it could have major public health significance. Health officials could publicize the relationship of MJ and sleep, primary care and behavioral health workers could highlight this information during office visits for insomnia, and drug treatment providers could meaningfully target sleep among MJ users who do seek treatment.Epidemiological studies have found associations between MJ use and insomnia over time. Adolescents, in particular, who used any illicit drug were 2.6 times more likely to report a sleep problem than those who remained substance free 24. In a nationally representative sample, adolescents with insomnia were 1.8 times as likely to report MJ use compared to adolescents without insomnia. Across ages, about one in five persons who use MJ report insomnia . This is significantly higher than the rate of insomnia diagnosed in persons not using MJ 25 which is approximately one in ten.While these epidemiological studies are informative and suggest co-occurrence, they do not inform us regarding the relationship of level of MJ use to sleep effect, or as to common versus reciprocal etiology. Participants were co-recruited from a larger study on individuals who use alcohol and marijuana between March 2012 and September 2013 through on-line advertisements in Craig’s List, Facebook, flyers, word-of-mouth, radio advertisements, and newspaper advertisements targeted at the Rhode Island/southeastern Massachusetts area. These methods are free services, widely known and used, and easy to access from a computer or smartphone. The advertisement for this study read, “Adults between 18 and 29, do you use marijuana?” You may be eligible to participate in a research study,” and asked interested persons to call the study telephone number listed. Exclusion criteria were: 1) past month cocaine, opioid, benzodiazepine, barbiturate, inhalant, PCP, hallucinogen, or stimulant use, 2) more than one episode of binge drinking in the past month.

We permitted inclusion of one binge episode because of the small number of participants who had no episodes of binge drinking in the last month, 3) night shift work, 4) self-reported diagnosis of schizophrenia, bipolar disorder, or attention deficit hyperactivity disorder, 5) lack of stable housing, 6) current suicidal ideation, and 7) past month use of sleep medication or antidepressants. We also recruited an age-matched control group who reported no MJ use in the last month. A total of 1307 persons aged 18-29 who reported using marijuana at least once a month were screened by phone for eligibility. Of these, 1052 were ineligible,drying rack cannabis due to more than one recent binge drinking episode. Other reasons for exclusion included use of other drugs , mental disorder diagnosis , currently being treated for depression , suicidal ideation , and unstable housing . Of the 146 eligible participants, 8 could not be reached to schedule a baseline appointment, 4 refused participation, and 35 did not come in for their baseline appointment. Ninety-nine consented to participate in the study; one person was excluded from participation after consent due to non-compliance with study protocols and limited cooperation with study staff. This study was approved by the Institutional Review Boards of Butler Hospital and the University of Michigan.All participants were administered the patient version of the Structured Clinical Interview for DSM-IV 27 at baseline to assess for current marijuana dependence only in the last year. During the baseline interview, a Time Line Follow Back 28was conducted to assess for MJ use over the past 4 weeks. On each day of MJ use, participants were asked how many minutes they smoked MJ. Participants were provided with the following instructions: “We would like you to look on this calendar and let us know how much time you spent using marijuana each day so we can write that in. We are also interested in knowing what time each day you used marijuana.” The day was divided into 4 six-hour quadrants. Daily smokers were defined as persons smoking MJ at least six days per week, and non-daily smokers were persons who smoked on at least 1 day in the past month to up to 5 days per week. Non-users were participants who had not smoked MJ in the last month.The basis for using minutes of marijuana as an outcome was because it is very difficult to ascertain in the naturalistic setting exactly how much THC the participant is ingesting. To clarify this measure further, we made frequency of use in days and meeting a diagnosis of dependence as our secondary measures. In looking at the validity of our chosen outcome measure, we did find support from concurrent measures of MJ use frequency. The spearman rank correlation between average minutes of MJ use during the two-week evaluation period and the percentage of days using MJ during that same period was r=0.88 .

The median was 25.18 and inter-quartile range was 12.90-62.91. The correlation between average minutes of MJ use during the twoweek evaluation period and a baseline DSM-IV diagnosis of Cannabis Dependence was r=0.33 . Although not significant statistically, those with a diagnosis of Cannabis Dependence reported an average of 42 as compared to an average of 24 among those without a diagnosis, a moderate effect size . Pittsburgh Sleep Quality Index —The Pittsburgh Sleep Quality Index is a self-rated questionnaire which assesses general sleep quality and sleep disturbances over a 1-month time period. Nineteen individual items generate seven “component” scores: subjective sleep quality; sleep latency; sleep duration; habitual sleep efficiency; sleep disturbances; use of sleeping medication and daytime dysfunction. The sum of scores for these seven components yields one global score. A global PSQI score greater than 5 has been used to define sleep disturbance.Insomnia Severity Index—The Insomnia Severity Index is a seven-item selfreport questionnaire assessing the nature, severity, and impact of insomnia in the past month. Dimensions evaluated are: severity of sleep onset, sleep maintenance, and early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, noticeability of sleep problems by others, and distress caused by the sleep difficulties. A 5-point Likert scale is used to rate each item , yielding a total score ranging from 0 to 28. We used a cut off ≥10 because this score has been shown to be optimal for detecting insomnia cases in a community sample . Epworth Sleepiness Scale—The Epworth Sleepiness Scale is an 8-item questionnaire assessing the level of daytime sleepiness. Respondents rate their likelihood to fall asleep or doze off on a scale of 0-3 in 8 different situations that may induce sleepiness . A score of 10 or greater is considered problematic daytime sleepiness, with a score of 18 signifying severe daytime sleepiness. The Morningness Eveningness Questionnaire —Smith’s MEQ is a 13- item questionnaire that assesses individual time of day preference for morning or evening activities, such as bed- and rise-times, and the clock time of becoming fully awake. This questionnaire measures behavioral temporal preference with high reliability, validity, and cross-cultural utility . Scores range from 13-52. Scores ≤22 indicate an eveningness preference, 23-43 indicated intermediate , and scores ≥44 indicate morningness.Patient Health Questionnaire-9—The PHQ9 is a 9- item questionnaire that inquires about how often mood symptoms were bothersome to the participant in the past two weeks. Participants respond on a Likert scale between 0-3, with 0 = not at all, 1=several days, 2=more than half the days, and 3=nearly every day. Total scores from 5-9 indicate mild symptoms of depression, 10-14 moderate symptoms, 15-19 moderately severe, and 20-27 severe. PHQ9 is a reliable and well validated scale.The Psychiatric Diagnostic Screening Questionnaire—To obtain information about anxiety, we used the brief 10-item PDSQ scale to measure generalized anxiety disorder. The PDSQ refers to the past 2 weeks. Participants responded to 10 questions about their anxiety with either no or yes . Higher scores reflect more anxiety symptoms.We conducted analyses on the enrolled participants that provided baseline interview responses. A hierarchical multiple regression analysis was performed to explore whether MJ use group predicted scores on sleep, daytime sleepiness, and chronotype questionnaires. Given that the high rate of unemployment in the sample may impact sleep behaviors and the possibility of gender differences in MJ use patterns, all models included gender and employment status as planned covariates. Because anxiety 40 and depression 19 are related to both marijuana use and sleep, we followed up our initial evaluations with models that controlled for these potential confounds in our analyses.