Cannabis Grow

According to the U.S. Energy Information Administration , capacity increased from just under 1700 megawatts in 2010 to nearly 25000 MW in 2019. While this is still just a small fraction of generation capacity overall, in 2016 it accounted for fully 12 percent of new capacity additions, and analysts expect the industry to continue to grow rapidly over the next several decades . Rooftop solar now is seen by many climate advocates as a key piece of the energy transition.9 While the role of technological advance should not be understated—the price of solar panels has fallen exponentially, from 100 dollars per watt in 1975 to 10 dollars per watt in 1990 to under 1 dollar per watt in 2015 —policy has played a key role at each step. Government R&D policy drove advances in technology, and deployment policy has driven cost declines through economies of scale and learning-by-doing . At the federal level, the most important market-stimulating policy for rooftop solar has been the Solar Investment Tax Credit . The ITC, enacted in 2006 and extended multiple times , provides a tax credit for the installation of both utility-scale and distributed solar systems. Solar advocates view the ITC as a critical component of solar energy’s growth 2019. But the growth of rooftop solar depends perhaps even more fundamentally on favorable state-level policies. Historically, states have taken the lead in shaping electricity generation, transmission, and distribution systems through policy and regulation . States have promoted rooftop solar through pricing policy, interconnection rules, rebates and tax credits,vertical grow systems as well as mandates that utilities draw a determined amount of power from distributed sources.

The importance of state policy to rooftop solar growth has led to enormous variation across the states, as demonstrated by Figure 1. Notably, many of the leading states like Vermont and New Jersey are not particularly sunny—but have pro-solar policies. What is the relationship between state policies and rooftop solar growth? To address this question, I match solar capacity data from the U.S. Energy Information Administration to data measuring the favorability towards distributed solar of each state’s policy. Data on solar policy comes from the website Freeing the Grid, which is managed by two pro-distributed solar interest groups: VoteSolar and Interstate Renewable Energy Council. From 2007 to 2017, these groups graded state-level NEM and interconnection policies from F to A . I average grades across these two dimensions to produce a measure from 1-5 of distributed solar policy favorability.Variation in this measure across the states and over time is presented visually in the appendix. To measure solar capacity, I use EIA data available starting in 2010. Two-way fixed effects and multilevel models are used to investigate the association between policies and rooftop solar growth for the 50 states from 2011 to 2017. These panel regression models account for potential confounders within years and states, and also account for concerns of reverse causation. Specifically, I model logged increase to rooftop solar capacity in state s and year t as a function of policy in state s at the start of year t. Results are presented in Table 1. Estimates from the two-way fixed effects model presented in column suggest that, within states, a one-level change in policy is associated with a 17 percent increase in solar growth. Column presents results from a multilevel model that allows for incorporation of state-level, time-invariant variables. Specifically, I model a state’s solar resource,GDP per capita, and electricity prices. The multilevel model yields similar estimates as the fixed-effects model. Altogether, the empirical analysis supports the view—widely shared by those in the field—that state policy is a crucially important factor driving distributed solar growth.

The rise of rooftop solar has created new business opportunities and interests along an entire supply chain from manufacturing to installation, but large installers have been particularly politically active. Installers’ key role is driven by two main factors. First, unlike manufacturers, the largest installers are domestic. Second, installation is highly labor intensive , which gives installers greater political leverage. In the early 2000’s, with the industry still undeveloped, installations were generally carried out by small, regional firms. Starting around 2007, several VC-funded firms entered the market offering “solar as a service” . In this model, customers could lease third-party owned systems instead of purchasing large systems outright, paying TPO installers, not utilities, for electricity. Notably, the major TPO installer firms emerging in this period—Sunrun and SolarCity—came out of California, where their business was bolstered by the California Solar Initiative, a large incentive program that ran through 2016. The industry’s fast growth in the early 2010’s was driven by the expansion of these large installers. For instance, Sunrun was active selling systems in just 7 states as of 2010, but by 2015 was operating in 15 states, and by 2019 was selling systems in 22 states. As Figure 2 demonstrates, the period of economic expansion for large solar installers has also coincided with greater political engagement. From 2010 to 2016, the number of state-level lobbying registrations from installers that lobbied independently over the period grew from under 50 to over 300.Rooftop solar has grown despite opposition from incumbent electric utilities. Although models vary across the states, electric utilities generally profit by delivering power through transmission and distribution systems to customers. If customers are able to procure power more cheaply from solar panels on their roofs , utilities’ investments in grid infrastructure become less valuable. Utilities for the most part acquiesced to the diffusion of NEM across the country in the late 1990’s and early 2000’s since, even with favorable pricing policies, the high cost of solar panels ensured rooftop solar would not threaten their business .

However, with solar panel costs dropping rapidly and the emergence of TPO installer firms in the early 2010’s, electric utilities began leveraging their long-standing political sway to push back. In efforts to retrench NEM and block the expansion of pro-rooftop solar policies,utilities have in some cases partnered with fossil fuel, manufacturing, and conservative interests . Despite its vast resources and connections, this coalition has met mixed success. Growth slowed, but was not halted, in states like Arizona where utilities successfully rolled back NEM—and utility victories were soon reversed in Nevada and Maine. One reason for utilities’ mixed success is the feedback effects of prior policies that spurred the rooftop solar industry’s growth. As the industry has grown, large installers have developed political operations capable of challenging utilities . Moreover, I argue that, in addition to “feeding back” into the politics where they are adopted,vertical grow rack state policies have also “fed into” the politics in other states due to the horizontal mobilization of installers. Since the causal process is somewhat complicated, I first present a short illustrative case before presenting quantitative data indicating that the dynamics observed in the case are systematic.South Carolina was a distributed solar laggard up until recent years. In 2014, South Carolina’s NEM policy was given an “F” by the pro-solar website Freeing the Grid, and its interconnection policy was given a “D”. That year, local environmental and clean energy groups worked with major utilities to draft new distributed solar legislation. Early versions of the bill, which featured a buy-all sell-all provision and allowed utilities to use their monopoly status to dominate the solar market, were not seen as particularly favorable to distributed solar. Sunrun intervened late in the process, mounting a lobbying and social media campaign advocating for several distributed solar-friendly revisions to the bill. Notably, Sunrun was not, at the time, selling systems in South Carolina. Rather, Sunrun’s business depended on strong growth in states with favorable policy environments. At the time Sunrun was only active in states rated by Freeing the Grid as “A” or “B” for both NEM and interconnection policy, for an average overall score on a 1-5 scale of 4.6 . Sunrun’s intervention, while criticized by the utilities, likely had an effect. Favorable provisions like dedication of incentives to distributed solar were added to the bill, while options for utilities to meet targets through direct procurement were struck. To be sure, at the same time, the bill’s success also depended fundamentally on prior work and negotiations with utilities from South Carolina environmental groups like Coastal Conservation League and Conservation Voters of South Carolina.With the new legal environment in place, Sunrun prepared to enter the market. In early 2015, Sunrun hired a lobbyist with a strong background in conservative Southeast politics to represent them in South Carolina and other states in the Southeast—their first Southeast-based policy hire. Sunrun’s summer of 2015 entry, coupled with the new policy regime, spurred rooftop solar growth from just 6 MW at the end of 2015 to 127 MW by the end of 2017. As rooftop solar’s economic presence grew, so did its lobbying presence. The number of dollars spent by the industry grew from 0 in 2013 to over 150,000 in 2017.

From 2014 to 2017, South Carolina’s Freeing the Grid NEM score went from “D” to “B”; its interconnection score went from “F” to “A”. Moreover, rooftop solar growth precipitated an expansion of the coalition beyond long-standing environmental groups and emergent rooftop solar companies, with groups like VoteSolar and Solar Energy Industries Association developing a greater political presence. The coalition of emergent clean energy interests and existing environmental groups has been crucial to defending and expanding the new policy regime. Utilities started hitting NEM caps in 2018,17 far earlier than lawmakers and advocates had predicted. Solar advocates promoted a bill in the 2018 legislative session that would extend the cap indefinitely, but the utilities mounted an aggressive campaign against the bill. The bipartisan bill received majority support, but ultimately was not able to pass a procedural vote that required a 2/3 majority. The coalition of solar and environmental advocates regrouped in the 2019 session. While they were badly outspent by utilities in the 2018 cycle, in 2019, according to one solar advocate, the number of lobbyists representing each side was approaching parity—with Sunrun playing a major role particularly in highlighting the job-creation benefits of distributed solar. The Republican Speaker of the House, perhaps not wanting to preside over another tough legislative battle that would divide his caucus, encouraged members to reach a deal. The Energy Freedom Act, which would eliminate the NEM cap in addition to promoting solar energy through other provisions like raising the maximum size of leased systems, passed unanimously in 2019. To evaluate these questions, I match lobbying data from National Institute on Money in State Politics to solar installation data from the U.S. Energy Information Administration , which I use to measure economic activity. According to NIMSP, only 5 installer firms lobbied state governments independently between 2015 and 2017: Sunrun, SolarCity, Vivint Solar, SunEdison, and SunPower. These were also the top 5 firms by installed TPO capacity as of 2015. The analysis focuses on the relationship between installations and lobbying activity for these firms.I should note that some smaller firms were politically active via membership in Solar Energy Industry Association and local industry groups, but this activity cannot be systematically documented. NIMSP collects two types of lobbying data at the state-year level: total firm-level lobbying expenditures and firm-level lobbying registrations. While lobbying expenditures is a preferable measure, it is only available for 15 states from 2015 through 2017 . Lobbying registration data, on the other hand, is available over the full set of states. The measures of lobbying expenditures and registrations are highly correlated . To measure installations, I use data available starting in 2015 recording the total third-party owned capacity for each of the major installers.Although it introduces some measurement error, TPO capacity provides a useful measure of a firm’s economic activity since 1) over this period TPO capacity comprised a significant portion of total distributed solar installs , and 2) TPO development was an important piece of each of the firms’ business models over this period. I model lobbying activity for firm i in year y and state s as a function of 1) installed generation capacity for firm i in year y and state s; and 2) installed generation capacity outside of state s for firm i in year y.

While debated , components of social capital include reciprocity, trust, safety, social agency, social networks, value of life, and employment connections . Research on social capital and HIV has predominantly focused on preventing HIV transmission with increased social capital being associated with decreased HIV transmission . High social capital was the strongest predictor of HIV self-management in WLHIV and specifically on HIV medication adherence . Social capital has also been negatively associated with substance use among youth and African-Americans . Discrimination was associated with illicit drug use in African American women and aspects of social capital protected women against its effects . However, in rural Appalachia illicit drug use was associated with greater social capital , highlighting the need to carefully assess social capital and its relationship to substance use behaviors. Given that most examinations of social capital among PLHIV are quantitative and cross-sectional, there is a need to gather more qualitative and mixed methods data to better understand its relationship to health outcomes . We conducted a 6-month, mixed methods longitudinal study with 29 WLHIV who were either current or previous illicit substance users. The use of a concurrent mixed methods longitudional design was necessary because while numerous studies document a relationship between social capital and HIV health outcomes , little is known about how the dimensions of social capital are developed and used to improve those outcomes over time. Integrating concurrent qualitative and quantitative data to examine social capital in this population will allow a more in-depth understanding of how social capital influences HIV self-management and substance use patterns among WLHIV.

Social capital cannot be fully captured by psychometric scales,vertical growing system but by integrating qualitative data with quantitive measures we can more comprehensively describe its dimensions. Potential subjects were from an existing HIV-research registry of approximately 300 adults living with HIV in Northeast Ohio. All registry participants had contacted the study team about previous research opportunities and had given written consent to be included in the registry. All women in the registry were sent an IRB-approved letter explaining the study and asking them to contact the study team via telephone if they were interested in participating. Those who responded were screened via telephone for illicit drug use using the Drug Abuse Screening Test-10 . Subjects scoring > 1 were included in the current illicit drug use strata and those scoring a 0 were included in the not using illicit drugs strata. If we found out during the qualitative interview that the participant has used illicit substances within the past 12 months, she was given a DAST score > 1 regardless of the screening form or the surveys. If a woman met eligibility criteria, she was scheduled for a research visit. At the first research visit, a research assistant explained the study and obtained written informed consent. Next, she completed a battery of surveys in REDCap and an open-ended social capital interview. Interviews were guided by a semi-structured interview guide and audio-recorded. Quantitative surveys were completed prior to interviews to introduce the concepts that were to be discussed and to standardize the data collection. At the conclusion of the visit, participants were compensated with $20 cash for their time and travel. Data were collected between July, 2015 and June, 2016. All procedures were approved by the Institutional Review Board at the Medical Center.

To quantitatively describe how social capital influences HIV self-management and substance use patterns over time among WLHIV we assessed the following variables, based on the literature described above: social capital, HIV self-management, substance use, and potential confounding variables. Participants completed study assessments at baseline and then approximately three and six months later. Social capital was measured using the 36-item Social Capital Scale. This widely-used and psychometrically validated instrument generates a total social capital score and measures eight sub-scales including: participation in the local community, social agency, feelings of trust and safety, neighborhood connections, friends and family connections, tolerance of diversity, value of life, and workplace connections . Participants rated each item on a 1–4 Likert-type scale. Higher mean scores indicate more social capital. Cronbach’s alpha reliability for the social capital scale in adults living with HIV is 0.88 . We examined two aspects of HIV self-management, HIV medication adherence and a global measure of HIV self-management. HIV antiretroviral medication adherence was assessed with a 30-day adherence visual analog scale . To measure HIV self-management more globally, participants also completed the 20- item HIV Self-Management Scale, which generates a total score from items measured on a 0–3 scale and measures three domains of HIV self-management . This scale has been previously examined and was found to be psychometrically valid for use among WLHIV . Substance use was assessed with the valid and reliable 11-item self-report Drug Use Disorders Identification Testdeveloped to screen individuals for drug problems . Total scores range from 0 to 44, with higher scores being suggestive of a more severe drug use problem.

Descriptive and potential confounding variables included demographic and medical characteristics, traumatic events and experiences of discrimination and were selected based on previous literature reviewed above. Demographic characteristics were self-reported and included, race, education level, family composition, employment, sexuality and housing status. Medical characteristics, abstracted from the participant’s electronic medical record, included year diagnosed with HIV, current CD4+ T cell count, HIV Viral Load, HIV medication history, and retention in HIV primary care. Recent traumatic events were assessed with the 20-item List of Threatening Experiences Scale, which lists the experience of traumatic events in the past month. Each of these 20-items are summed and higher scores indicate more traumatic experiences . Experiences with discrimination were assessed with the widely-used, and valid 9-item Everyday Discrimination Scale . Participants noted how often they experienced acts of discrimination. All items are summed and higher scores indicate more discrimination. To qualitatively describe how social capital influenced HIV self-management and substance use patterns, we developed a semi-structured qualitative interview guide based on existing literature to guide in-person interviews. Prior to using it with participants, experts in substance use and women and in WLHIV assessed the interview guide for clarity, relevance, and appropriateness. The baseline interview guide initially focused on early substance use, current health practices, past and current social networks, and social capital dimensions . After several interviews, based on new themes spontaneously emerging from the interviews, it was revised to include more probes related to how trust influences health behaviors and the specific role of faith and its influence on health behaviors. In other words,plant growing rack in the first few interviews WLHIV discussed the role of trust and faith on their health behaviors and we deemed it so important that we wanted to give all respondents a chance to discuss these topics. The threeand six-month interview guides were shorter and focused on changes in life situations, health practices, social capital, substance use and resilience that were observed in their quantitative measures. These guides were designed to help us understand any changes in our variables of interest and how they influenced self-management behaviors. Data analysis of the qualitative and quantitative data occurred at the same time, but were not integrated until both types of data were analyzed. In analyzing the quantitative data, we first assessed the distribution of all quantitative variables . We summarized baseline characteristics by using means, standard deviations, medians, interquartile ranges, counts and percentages of women by substance use group , depending on the variable’s distribution. We used generalized estimating equations with an identity link function and an unstructured correlation structure to describe how social capital and substance use influences HIV self-management across the three time points. Separate models were fit for each HIV self-management outcome. In addition to the effect of social capital and substance use, we examined independent effects of age, discrimination and traumatic events by adding these covariates to GEE models.

All statistical analyses were conducted using Stata 14.0 with p-values < 0.05 considered statistically significant. Qualitative data were managed using the qualitative data analysis program Dedoose and was analyzed by the research team using qualitative description methodology . Data were transcribed and examined by two research team members who coded the data using the constant comparative method, identifying patterns and themes . These team members met regularly during coding to discuss consistencies and inconsistencies in the data. A priori codes related to social capital, substance use, and self-management based on our literature review, were initially applied then inductive codes were applied. Transcripts were revisited in a series of iterative steps to confirm coding classification and that theoretical saturation was reached. Variations on the themes and negative cases were identified to help understand the full range of data within codes. A final code book of themes, definitions, and exemplar codes was created to aid analysis. Data were coded, and analyzed using Dedoose version 8.0.42 . Study procedures are presented consistent with the Good Reporting of a Mixed Methods Study standards .State governments, in particular, play prominent roles governing critical sectors of the economy like energy, healthcare, and education. This, along with the dysfunction, polarization, and gridlock that has plagued federal politics in recent years, has made state governments attractive targets for reformers. Polarization and gridlock, of course, extend to the sub-national level, but the sheer variety in the political landscapes across the states means there are more opportunities to shift public policy. Some states also have institutions like the ballot initiative that make it easier bypass legislatures and break the status quo. While federalism and “states’ rights” has traditionally been a clarion call for conservatives seeking to preserve the status quo, there is now a movement on the Left to leverage the states to drive forward progressive reforms . Yet, relying too heavily on the states presents its own pitfalls. State policy can be an impotent tool for addressing complex political economy issues of broad scope like climate change. For one, taking on these issues often requires coordination across the federal system to address spillovers—meaning federal action is critical. Second, states also tend to be budget constrained, which restricts their ability to enact major policy change. They, unlike the federal government, do not control their own currencies, and are required to, for the most part, run balanced budgets. Attempts to raise more revenue through higher taxes risks chasing businesses and wealthier residents to lower-tax jurisdictions. Third, and finally, reduced legislative professionalism and a lower density of countervailing interests means that state politics can be a more favorable venue than national politics for incumbent economic interests—which often work to block reforms that challenge their position. The potential for policy reform in the states, combined with the limitations of state policy for addressing major issues, motivates examining political and policy inter dependencies across the federal system. A reform achieved in one state alone might make just a small dent in a problem of broad scope, but if it leads other states and the federal government to follow suit, the overall effects could be significant. Thus, this dissertation addresses the following general question: how does adoption of policy reforms in the states affect the political prospects for those reforms in the broader federal system? It is, of course, not the first to examine questions of policy interdependence in American federalism. The innovation is in the theoretical and empirical approaches, and the particular focus on political economy—interactions between elements of the political and economic systems. The conventional framework for studying policy interdependence in American federalism is policy diffusion, which refers generally to how adoption of a policy in one unit increases the likelihood of its subsequent adoption elsewhere . This general idea can be traced at least as far back as Louis Brandeis, who famously, in a 1932 judicial opinion, suggested that states could function as “laboratories of democracy” —state experimentation could be leveraged to identify promising policy designs, which might then be propagated. As the policy diffusion literature has developed, scholars have productively moved from simply tracing the spread of policies to trying to identify the mechanisms driving that spread. Two mechanisms have emerged in the federalism literature as central to the diffusion process. The first, which Brandeis alluded to, is policy learning . Because lawmakers generally aim to pursue policies that work well for constituents, they might, in considering which policies to adopt, choose policies that have already been shown to work well in other units .

The sample is derived from 7,728 patients treated in an academic health center in Los Angeles, consisting of 2,576 patients with OUD diagnosis in the EHR aged 18 to 64 years at their first OUD diagnosis and 5,152 control patients, matched by sex, date of birth , first encounter , and the Elixhauser Comorbidity Index. Data from 2006 to 2014 were collected from an electronic health record system utilizing Epic software. Among the 7,728 individuals, 5,202 individuals during 2010–2014 were matched in PDMP and were included in the study. This study was approved by the Institutional Review Boards at UCLA and the State of California.The primary outcome was all-cause mortality status at the end of follow-up, which was December 31, 2014, for the alive patients or date of death for the deceased patients. Mortality records, available through December 31, 2014, were obtained from the Centers for Disease Control and Prevention National Death Index . The California state Prescription Drug Monitoring Program , also known as CURES is a database of prescription records of Schedule II, III, and IV controlled substance prescriptions dispensed in California. The description of PDMP data and conversion of opioid medications to morphine milligram equivalent have been described previously. We defined most recent opioid use as at least one opioid prescription in the last 30 days of observation . The daily opioid dose was calculated by averaging MME of all opioid prescriptions in the last month of observation over 30 days. BDZ medications were converted to diazepam milligram equivalents for comparison purposes.

We defined most recent BDZ use as at least one BDZ prescription in the last 30 days of observation. The daily BDZ dose was calculated by averaging DME of all BDZ prescriptions in the last month of observation over 30 days. Covariates were obtained from the medical records. Sociodemographic variables included age, sex, race, and insurance status. Clinical variables were defined from the ICD-9 codes. Included comorbidities were physical conditions , cancer, heart disease, respiratory disease, liver disease, sleep disorder, chronic pain, human immunodeficiency virus , weed growing systems and Hepatitis C ; psychiatric conditions ; and substance use disorders .We conducted t-tests for continuous variables and chi-square tests for categorical variables to examine differences in demographics, comorbidities, prescription patterns, and mortality status between OUD and non-OUD patients. For multivariate analyses, a logistic regression model was used to examine the association between the prescription of BDZ together with opioids and mortality. In addition to the primary independent variable , other significant covariates from univariate analyses were included to build additional models. Covariates considered as potential confounders were identified based on a combination of clinical significance and significant p value. Akaike Information Criterion and deviance were calculated. Also, after stratifying by OUD diagnosis, an interaction term of BDZ and opioid use in the last month of observation was included in the models with same covariates as the third model to examine the moderating effect of co-prescription on mortality. All statistical tests were based on a significance level of α ≤ .05. Analyses were conducted using SAS 9.4 . Using the state’s PDMP records linked with EHR and CDC NDI data, this study explored the co-prescription of BDZ and opioids and its association with all-cause mortality among patients in a large healthcare system.

We found that 1) OUD patients had higher rates of other substance use disorders, mental and physical comorbidities relative to non-OUD; 2) OUD patients were prescribed both BDZ and opioids in significantly higher doses compared to non-OUD patients; 3) most recent average daily BDZ or opioid prescription dose was significantly associated with increased all-cause mortality after adjusting for covariates, and 4) there is a significant interaction between BDZ and opioid co-prescription on mortality among non-OUD patients. Though this study analyzed records prior to 2015, national efforts and guidelines have been implemented since that time to help combat problems associated with co-prescription of BDZ and opioids. Findings from this study support recommendations to minimize co-prescription of BDZ and opioids whenever possible, and to use lowest effective doses when prescribing BDZ or opioids. Our study extends prior literature by examining a general patient population from a large health care system with a larger proportion of privately insured individuals and focusing on a population at risk given OUD diagnosis history. Compared to non-OUD patients, we found higher rates of all-cause mortality and higher average doses of BDZ prescribed among OUD patients. Prior research has demonstrated an increased likelihood of BDZ prescription in persons with chronic noncancer pain prescribed higher doses of opioids. We also observed elevated rates of physical and mental health comorbidities in OUD patients relative to non-OUD patients. Accumulating evidence has supported the positive association between opioid use and physical or psychiatric comorbidities, which presents considerable management challenges in clinical practice and contributes to mortality risk . The findings in this study suggest that use of either opioids or BDZ is significantly associated with all-cause mortality, which is consistent with our previous findings and prior studies. Furthermore, our finding of the association between opioid dosage and mortality was in accordance with prior studies demonstrating mortality risk associated with opioid prescribing in a dose-dependent manner.

The CDC guideline recommends risk review and mitigation for opioid dosages greater than 90 MME per day; dosages as low as 50 MME per day can increase the risk for drug-related adverse events for patients with chronic pain. For BDZ, we found 36.3% of deaths among OUD patients and 34.7% among non-OUD patients were BDZ involved. We also found that increased daily BDZ dosage was associated with mortality risk after adjusting for potential confounders, which is consistent with prior literature focused on overdose risk as well as all-cause mortality associated with BDZ. Prior studies have reported BDZ dose relationships with mortality as quantified by cumulative prescribed doses over observation periods; this study extends prior work to include average prescribed dose in the final month of observation. This finding of increased mortality associated with BDZ dose may be related to management of underlying medical conditions closer to time of death, medication interaction effects, or other factors. The association between co-prescription of opioids and BDZ and overdose risk or mortality has been reported among Medicare and Medicaid enrollees and veterans. Findings from this study extend to a primarily privately insured population. In our study, it is worth noting that there was a significant interaction between BDZ and opioid co-prescription on all-cause mortality among patients without OUD diagnosis, but not in those with a diagnosis of OUD, despite elevated rates of physical and psychiatric comorbidities in those with OUD. Although our observational study cannot determine the etiology of this differential finding, a contributing factor might be higher opioid tolerance to the effect of respiratory suppression among OUD patients compared with non-OUD patients. As reported,indoor farming systems higher doses of opioids and BDZ were prescribed in patients with OUD. Clinicians should be cautious about prescribing BDZ to patients using opioids, whether or not they have a diagnosis of OUD, and should be aware of the high rates of comorbidity in this population. More studies are warranted to help elucidate the potential medication interaction of opioid and BDZ. The results of this study revealed several covariates associated with increased risk for all-cause mortality in addition to opioid and BDZ use. We found that self-pay for health care was a strong risk factor associated with mortality compared with public and private insurance; this could be related to health care access or utilization in this population or other factors and warrants further study. Not surprisingly, older age, alcohol use disorder comorbidity, and some physical health comorbidities, such as heart, liver, cancer, and HCV, were significantly associated with all-cause mortality. This study has several limitations. First, findings based on EHR data are limited by provider diagnostic coding and documentation choices, which are influenced by clinical experience and billing requirements. This may affect the accuracy of the data as it was not collected specifically for research purposes; diagnoses may be over- or under-captured. Like other records-based research, diagnoses and prescriptions are limited to this healthcare system and PDMP records; those obtained outside of the system were not captured, and thus dosages may not accurately reflect the total amount consumed.

It is possible that patients used remaining BDZ or opioids from prescriptions prior to the last 30 days of the follow-up or from other sources such as friends or illicit purchases. Therefore, our findings are likely a conservative estimate. Second, the number of overdose deaths is relatively low in this study, and overdose deaths may be misclassified, so we decided to use all-cause mortality instead. Participants were predominantly white, insured individuals living in the Los Angeles area, limiting the generalizability of findings. Lastly, residual confounding is still possible, even after adjusting for many demographic and comorbidity covariates. Advent of the coronavirus 2019 disease pandemic was associated with changes in drinking and drug use among young adults. For alcohol, most studies found increases in the number of days drinking and decreases in the number of drinks consumed per occasion , though other studies have found no significant change or a decrease in the number of days drinking. Studies found no change in the number of days using nicotine and no change or increases in the number of days using cannabis during the pandemic. The emergent literature has three key limitations. First, it is unclear whether the initial effects of the pandemic on drinking and nicotine use in the Spring-Summer of 2020 persisted over time. Most published work has focused on the immediate impact of public health policies to reduce the impact of the pandemic in March 2020 . Second, those studies with more extended follow-up have not been designed to distinguish pandemic effects from maturation effects , leaving it unclear to what extent the observed changes in drinking or nicotine use are specifically due to the COVID-19 pandemic. Developmental increases in drinking and drug use are expected as young adults mature, even absent a pandemic . Thus, characterizing the effects of the pandemic in the medium- and long-term requires a design that can subtract out the developmental change that would be expected outside the pandemic context. Third, initial evidence regarding an important potential moderator of the pandemic’s impact—its impact on financial security—has been mixed. One study found financial strain was linked to greater pandemic-related increases in nicotine use during March and April 2020 while another study found loss of income did not moderate pandemic-related changes in drinking during June 2020. The financial impact of the pandemic on U.S. adults has been heterogenous and time-varying , so both replication and extension of these findings with a longer period followup is warranted. Procedures were approved by Institutional Review Boards at each study site. The NCANDA Study was designed to investigate the impact of heavy alcohol use on neurodevelopment. 831 participants ages 12–21 years old were recruited into NCANDA in 2012–2014 and have been followed prospectively at five study sites across the U.S: Duke University, University of Pittsburgh Medical Center , Oregon Health & Science University , University of California San Diego , and SRI International . Exclusion criteria were intentionally minimized: participants lived within 50 miles of the study site, had no MRI contraindications, had no reported prenatal or perinatal exposures or complications, had no pervasive developmental disorder, had no current or persistent major psychiatric disorder that would interfere with the protocol, and were not taking medications known to affect brain function or blood flow . Each site aimed to recruit a community sample representative of the racial/ethnic distributions of their county. Participants were recruited through announcements at local schools and colleges, public notices, and targeted catchment-area calling. The current study draws data from 348 participants ages 12–15 years old at study entry—older participants were excluded to minimize the potential for cohort effects on drinking and nicotine use . 49% of participants were female. 13% identified as Hispanic; 68% as White, 12% as Black, 7% as Asian, and 8% as Alaskan Native or Pacific Islander. 84% of participants had 1 + parent who completed a Bachelor’s degree. After completing their baseline assessment at study entry, participants were assessed every six months going forward with a combination of in-person assessments and phone interviews .

To date these traits have only been studied in smaller samples but this approach will be invaluable as sample sizes increase. Another challenge for AUD genetics is that AUD is a dynamic phenotype, even more so than other psychiatric conditions, and therefore may necessitate yet larger sample sizes. Ever-larger studies, particularly those extending mere alcohol consumption phenotypes, are required to find the genetic variants that contribute towards the transition from normative alcohol use to misuse, and development of AUD. Furthermore, genetic risk unfolds across development, particularly during adolescence, when drug experimentation is more prominent and when the brain is most vulnerable to the deleterious effects of alcohol . The Adolescent Brain Cognitive Development , with neuroimaging, genotyping and extensive longitudinal phenotypic information including alcohol use behaviors , offers new avenues for research, namely to understand how genetic risk interacts with the environment across critical developmental windows. Population bio–banks aligning genotype data from thousands of individuals to electronic health records are also promising emerging platforms to accelerate AUD genetic research . Despite these caveats, the GWAS described in Table 1 have already vastly expanded our understanding of the genetic architecture of alcohol use behaviors. It is evident that alcohol use behaviors, like all complex traits, are highly polygenic . 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, indoor grow trays 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. In recent years, there has been an explosion in the availability of consumer wearables which are able, through accelerometry, heart rate, and “real time” physiologic signal, to provide insights on lifestyle, including sleep. The ubiquity of these devices means that a wealth of data is now available for understanding sleep in diverse populations, including those with serious mental illness such as bipolar disorder . Sleep disturbance is highly prevalent in BD and associated with psychopathology . Using objective forms of sleep measurement in BD may be more accurate than clinical interviews, as self-report of sleep may be influenced not only by recall, but by current affective states inherent in BD . While polysomnography is the most validated means of measuring sleep, wearables can generate important data about sleep quality that has been validated against polysomnography . Indeed, past studies show altered sleep patterns as measured by actigraphy in BD vs. healthy subjects as example. Accelerometry can estimate a number of sleep parameters, including total sleep time , wake after sleep onset , percent sleep , number of awakenings amongst others . While these individual parameters, by themselves, are clinically relevant and provide important data to identify mechanisms for outcomes, there are some statistical challenges, including Type 1 error which can be problematic when looking at multiple outcomes as are commonly measured in studies of BD . Distilling sleep parameters into one joint construct may help identify those with poor sleep and in need of sleep intervention. Approaches such as machine learning can cluster people based upon patterns of sleep, and past studies in sleep using these methods have shown promising results .

However, these approaches have limitations in their applicability and generalizability to individual patients , and are often hard to communicate to those unfamiliar with these methods, limiting their clinical utility. Other investigators have sought to identify actigraphic measures most important in distinguishing sleep quality across subjects. For example, Natale et al. used linear discriminant function analysis to find that TST, sleep onset latency, and NA were the best combination of actigraphy statistics differentiating those with insomnia from those with healthy sleep patterns. In neuropsychological assessment, cognitive test scores are commonly standardized as compared to a normative population, and can then be merged into a composite. Using a similar approach, but with actigraphic sleep measurements, may yield a clinically meaningful way to summarize sleep quality across measures among those with BD. Sleep quality is associated with a number of demographic and clinical characteristics. For example, in community-based samples, poor sleep is associated with lower overall health quality , increased risk for depression , higher body mass index , lower cognition , higher levels of inflammation , among other negative health outcomes. In BD, poor sleep is associated with these same characteristics but at a more pronounced level; and poor sleep is also associated with worsening BD symptoms , poorer overall mood , impaired cognition , and greater inflammation , among others. To have construct validity and be clinically meaningful, a composite sleep measure in BD should be correlated with demographic and clinical characteristics consistent with past studies of sleep quality. In this study we created a composite score for sleep across accelerometer-derived sleep variables in a sample of those with BD. The main aims of this study were to identify the clinical utility of this composite measure by examining demographic, clinical, and biological correlates within subjects, and to identify the sleep variables most contributing to associations between the composite measure and these associated variables. We hypothesized that better sleep quality as seen in our composite measure would be associated with fewer depressive and mania symptoms,vertical grow racks for sale greater medication load, and lower cognition and inflammation.

We hypothesized that any observed correlations would be driven by multiple sleep indices included in the overall composite score.Data came from a longitudinal study of cognition and inflammation in BD. We recruited those with BD and HCs from outpatient clinics, community settings, and other research studies at UC San Diego. BD was defined as a diagnosis of Bipolar I or II DSM-IV Disorder receiving outpatient care. Exclusion criteria included: acute illness or pregnancy, a recent vaccination, history of various health conditions , cancer treatment in past, diabetes/hypertension that is not controlled, among others. Among HCs, we also excluded individuals who had a history of DSM-IV Axis I disorders, previous use of psychotropic medications, as well as having a first degree relative with history of depression, BD, or schizophrenia. Each year of the study, participants were asked to complete a 2-week burst of assessments consisting of three in-person visits and completion of up to 14 24-hour periods of wrist-worn actigraphy. We focused on a subset of 51 persons with BD and a comparable group of healthy controls [HCs] who had valid wrist actigraphy data which we defined as having actigraphy data recorded for at least five nights. Of note, some individuals had more than one period of actigraphy assessment or did not have valid actigraphy data for year 1 but valid data for year 2. Consequently, we analyzed the first valid actigraphy assessment period available for each participant. This study was approved by the UCSD Institutional Review Board and was carried out in accordance with the Declaration of Helsinki. All participants completed informed consent prior to study involvement. Data were gathered from wrist-worn Actisleep-BT device which measures raw acceleration data in gravitational units using a tri-axial accelerometer sampling at 30Hz. Over continuous 24-hour wear periods, this device can be used to monitor movement allowing for estimation of sleep/wake patterns similar to methods described by Ancoli-Israel et al. . In-bed and out-of-bed times were set based upon both actigraphy data and information from morning surveys on a cell phone survey. If participant sleep records were missing entirely, sleep onset and awake time were manually determined by a specially trained research assistant using the detection methods outlined by Full, et al. . TST, WASO, PS, and NA were computed based upon these in/out bed intervals. To construct our composite score, in the absence of publicly-available norms, we chose to normalize BD subjects’ sleep measures based upon the HCs as a normative sample. We first computed means and standard deviations of actigraphy sleep measures of TST, WASO, PS, and NA in the HC group. Based upon this, we created standardized z-scores in the BD group by subtracting each BD individual’s sleep measure from the means of measures in HCs, and dividing by the SDs in the HC group. We multiplied WASO and NA by −1 to keep variables in the same direction . We then computed the mean of z-scores across all measures to create our composite.After computing the individual sleep statistics and composite z-scores, we sought to determine whether the sleep was of overall worse quality in BDs as compared to HCs by testing if the z-scores were different from 0 .

We then assessed the correlation of the composite with demographic and clinical variables using Pearson correlations and t-tests . Finally, for variables significantly correlated with the composite sleep z-score, we conducted LASSO regression to identify the individual sleep statistics most contributing to this correlation. The demographic and clinical variables served as outcomes and predictors were the z-scores for TST, WASO , PS, and NA . All analyses were completed in Stata SE version 15 . Past research on sleep in BD has focused on individual actigraphy measures , and there is a need for a global measure of sleep derived from actigraphy. In this study, we explored whether a composite score would relate to expected clinical, cognitive, and biological factors and whether including multiple sleep variables was important to the observed relationships. While the composite score was related to variables in BD including gender, employment, medication load, and mania symptoms, these associations were for the most part driven by only one of the individual sleep metrics with the exception of medication load for which TST and NA jointly contributed. Our results suggest a composite score does not yield gains in predictive power over individual sleep metrics. In studies of BD, it may be more appropriate to choose metrics to examine based on theory rather than summarizing multiple sleep metrics together. Our finding that a composite score is not as informative as examining individual sleep metrics alone may be explained by a number of factors. We focused on the averaged selected sleep indices measured over a two-week period, and it may be that examining changes in patterns of sleep over time, rather than means of sleep indices, could better capture more variability and thus compute a more nuanced composite score. Our goal, however, was to identify an intuitive approach for summarizing poor sleep in clinical practice, and thus we only focused on commonly used and often readily available sleep metrics. It is possible, however, that incorporating measures of circadian patterns would better support a composite measure approach. Additionally, population-based norms of actigraphic sleep indices do not currently exist, and thus we chose to use our healthy control sample as the norms for construction of z-scores. It is important that more research be conducted to generate norms for the purpose of computing composite measures as we sought to do in this study. While a sleep composite may not provide additional utility beyond that of individual measures themselves, there was substantial variability in z-scores indicating there are persons with BD who may have worse or better sleep as compared to HCs by as much as one standard deviation. This highlights the need for future research to identify cut-points based on z-scores identifying the worse sleepers. Similarly, it may be important to examine how daily fluctuations in sleep could be incorporated into a composite score. In BD, sleep often varies night-to-night and it could be that these nightly fluctuations are representative of poor sleep rather than simply average sleep measures across nights. While clinical relationships to the composite z-score were generally driven by only one sleep measure, we found correlations with clinical variables pertinent to BD confirming previous literature.

Also consistent with past work , ART side-effect severity was significantly negatively associated with ART adherence, as measured by pill counts. In addition to the severity of distress , the degree to which one is able and/or willing to tolerate such distress appears to be further influencing ART medication adherence and viral load status in our sample. Contrary to expectation, no significant relations emerged between distress tolerance measures and CD4 cell counts. The most likely explanation for this null finding is that CD4 cell count is a measure of immune function, sensitive to a host of factors affecting the immune system beyond adherence and response to ART . In concert with this explanation, Ironson et al. found that ART medication adherence was significantly related to viral load changes over time, but not to CD4 cell count changes. Indeed, once patients have started ART, clinical guidelines recommend using viral load as the key biomarker for detecting timely changes in HIV disease progression as viral load may be more immediately responsive to ART than CD4 cell count . In light of these measurement limitations, it is not surprising that psychological measures were not significantly associated with CD4 cell counts. We also found that different measures/aspects of distress tolerance predicted different measures of ART adherence. Perceived capacity or willingness to tolerate affective distress and greater task persistence in the face of distress were related to better ART adherence and viral load, respectively.

Although only replication in future work would determine whether the observed differential associations remain,indoor farming equipment one potential explanation for the observed associations could relate to measurement type/error. Indeed, one could argue that task persistence and viral load are both objective measures with little room for individual error, while self-reported distress tolerance and pill count adherence are more likely subject to individual-level factors and influence . The association between task persistence and viral load also makes conceptual sense as viral load is a reflection of ART response and adherence over a longer duration , which is functionally similar to the MTPT-C, which involves persistence in an effortful task while distressed/frustrated. The lack of relation between MTPT-C and pill count adherence indicates that pill count adherence does not serve as an indirect pathway between MTPT-C persistence and viral load. However, research indicates other potential pathways that might help explain the association between MTPT-C task persistence and viral load, such as problematic substance use. Indeed, substance dependence has been closely linked to both persistence on the MTPT-C , as well as higher viral load . Though the present cross-sectional data do not allow for the examination of these hypothesized temporal associations, future work would benefit from the prospective examination of substance dependence as a potential mediator of the association between distress intolerance, as indexed by the MTPT-C, and heightened viral load. It is important to also note that, consistent with prior studies , we observed self-report distress tolerance not to be significantly associated with the employed behavioral measure of distress tolerance . Here, the contexts in which the two forms of distress tolerance were assessed differ. The behavioral persistence measure was administered in the presence of induced distress; whereas perceived distress tolerance was not.

Moreover, the perceived inability to handle distress is defined as a cognitive factor, broadly, whereas task persistence in the face of distress is defined behaviorally . The absence of significant associations between perceived and objective distress tolerance may be in part due to the different types of distress and measurement contexts . In addition, the measurement of perceived distress tolerance relies on self-report, which presents a challenge because of the difficulty participants have in accurately reflecting upon and discriminating their sensitivity to distress from their tolerance of distress . Indeed, a strength of this study is that the multi-method approach precludes limitations of shared method variance and difficulty in accurately self-reporting by employing a behavioral and self-report measure of distress tolerance. Our findings are largely consistent with past work in the areas of ART adherence and distress tolerance, and underscore the clinically relevant role of distress tolerance in models of adherence and disease status among HIV+ patients. As has been initially shown to be effective among substance-using populations and early-lapse smokers , future interventions for individuals with HIV may benefit from specifically targeting ability/ willingness to tolerate distress through cognitive behavioral treatment approaches so that individuals may remain adherent in the face of treatment related burdens. This intervention development approach is in line with recent endeavors to enhance the impact of CBT for improving ART adherence by treating comorbid depression , and with Blashill and colleagues’ suggestion to develop combination interventions for other psychosocial problems among individuals living with HIV. Yet, our conceptualization of transdiagnostic psychological vulnerability factors, such as distress tolerance, may offer a more parsimonious approach for addressing psychosocial comorbidities.

Although there has been much work developing and testing CBT interventions for promoting ART adherence, there is still room for improvement because traditional multi-component CBT interventions for ART adherence result in small to medium effect sizes . Overall, the current study extends the literature on distress intolerance as a psychological vulnerability factor among people living with HIV. However, there are some limitations that provide opportunities for future research. First, the present study was cross-sectional, limiting inferences that can be made about directionality. Indeed, it is just as likely that low levels of distress tolerance lead to poor adherence as it is that poor adherence is prospectively associated with low levels of distress tolerance. This may be particularly relevant among immuno compromised individuals living with HIV. For instance, if poor adherence leads to an increasing viral load, then one’s immune system is mobilized to contend with the growing viral load. This is a physiological stressor and stress increases one’s drive to escape from unpleasant situations . Thus, it is feasible that stress due to immunological reactivity from an increasing viral load further limits one’s capacity to exercise tolerance of distress. It is plausible that poor adherence resulting in an increasing viral load may subsequently increase one’s vulnerability to distress intolerance. Following, as adherence was measured using pill count at only one time point, we were unable to establish a baseline level of adherence and MEMS caps would have provided a more precise indicator of adherence. Third, as mentioned earlier, though a strength of the study was the multi-method measurement of distress tolerance,hemp drying racks future work would benefit from employing additional objectives and newly refined subjective measures to better understand differential relations between multiple facets of distress tolerance and HIV adherence. Future work would also benefit from assessing tolerance of HIV symptom-related distress, specifically, and the impact of distress tolerance on other clinically relevant HIV outcomes . Finally, though the present study was quite ethnically diverse, a majority of the sample was male. Future work would benefit from recruiting a more gender-diverse sample from different geographic areas. Promoting tolerance of affective distress and distressing tasks associated with the high-adherence demands of ART for HIV management are worthwhile to consider in future research. Future investigations are needed to examine relations prospectively to identify the role of distress intolerance in the development and maintenance of poor HIV management, and then verification of clinical implications through intervention process and outcome studies.Cannabinoid receptors, the molecular targets of the active principle of cannabis 9 -tetrahydrocannabinol, are activated by a small family of naturally occurring lipids that include anandamide and 2-arachidonylglycerol . As in the case of other lipid mediators, these endogenous cannabis-like compounds may be released from cells upon demand by stimulus-dependent cleavage of membrane phospholipid precursors . After release, anandamide and 2-AG may be eliminated by a two-step mechanism consisting of carrier-mediated transport into cells followed by enzymatic hydrolysis . Because of this rapid deactivation process, the endocannabinoids may primarily act near their sites of synthesis by binding to and activating cannabinoid receptors on the surface of neighboring cells .

The development of methods for endocannabinoid analysis and the availability of selective pharmacological probes for cannabinoid receptors have allowed the exploration of the physiopathological functions served by the endocannabinoid system. Although still at their beginnings, these studies indicate that the endocannabinoids may significantly contrib-ute to the regulation of pain processing , motor activity , blood pressure , and tumor cell growth . Furthermore, these investigations point to the endocannabinoid system—with its network of endogenous ligands, receptors, and inactivating mechanisms—as a potentially important arena for drug discovery. In this context, emphasis has been especially placed on the possible roles that CB1 and CB2 receptors may play as drug targets . Here, we focus our attention on another facet of endocannabinoid pharmacology: the mechanisms by which anandamide and 2-AG are deactivated. We summarize current knowledge on how these mechanisms may function, describe pharmacological agents that interfere with their actions, and highlight the potential applications of these agents to medicine.Extracellular anandamide is rapidly recaptured by neuronal and non-neuronal cells through a mechanism that meets four key criteria of carrier mediated transport: fast rate, temperature dependence, saturability, and substrate selectivity . Importantly, and in contrast with transport systems for classical neurotransmitters, [3 H]anandamide reuptake is neither dependent on external Na ions nor affected by metabolic inhibitors, suggesting that it may be mediated by a process of carrier-facilitated diffusion . How selective is anandamide reuptake? Cis-inhibition studies in a human astrocytoma cell line have shown that [ 3 H]anandamide accumulation is not affected by a variety of amino acid transmitters or biogenic amines . Furthermore, [3 H]anandamide reuptake is not prevented by fatty acids , neutral lipids , saturated fatty acyl ethanolamides , prostaglandins, leukotrienes, hydroxyeicosatetraenoic acids, and epoxyeicosatetraenoic acids. Even further, [ 3 H]anandamide accumulation is insensitive to substrates or inhibitors of fatty acid transport , organic anion transport , and P-glycoproteins . By contrast, in the same cells, [3 H]anandamide reuptake is competitively blocked by either of the two endogenous cannabinoids, anandamide or 2-AG . Similar selectivity profiles are observed in primary cultures of rat cortical neurons or astrocytes and rat brain slices . The fact that both anandamide and 2-AG prevent [ 3 H]anandamide transport in cis-inhibition studies suggests that the two compounds compete for the same transport system. This possibility is further supported by three observations: 1) anandamide and 2-AG can mutually displace each other’s transport ; 2) [3 H]anandamide and [3 H]2-AG are accumulated with similar kineticproperties ; and 3) the transports of both compounds are prevented by the endocannabinoid transport inhibitor, N–arachidonylamide. Together, these findings indicate that anandamide and 2-AG may be internalized via a common carrier-mediated process, which displays a substantial degree of substrate and inhibitor selectivity. The molecular structure of this hypothetical transporter remains, however, unknown. Structure-Activity Relationship Studies. The structures of anandamide and 2-AG contain three potential pharmacophores: 1) the hydrophobic carbon chain; 2) the carboxamido/carboxyester group; and 3) the polar head group . Systematic modifications in the carbon chain suggest that the structural requisites for substrate recognition by the putative endocannabinoid transporter may be different from those of substrate translocation. Substrate recognition appears to require the presence of at least one cis double bond in the middle of the fatty acid chain, indicating a preference for substrates whose hydrophobic tail can adopt an extended U-shaped conformation. By contrast, a minimum of four cis non-conjugated double bonds may be required for translocation, suggesting that substrates need to adopt a closed “hairpin” conformation to be transported across the membrane . In agreement with this hypothesis, molecular modeling studies show that transport substrates have both extended and hairpin low-energy conformers . By contrast, extended, but not hairpin, conformations may be thermodynamically favored in pseudosubstrates such as oleylethanolamide , that displace [3 H]anandamide from transport without being themselves internalized . The impact that modifications of the polar head group exert on endocannabinoid transport has also been investigated . The available data suggest that ligand recognition may be favored 1) by a head group of defined stereochemical configuration containing a hydroxyl moiety at its distal end; and 2) by replacing the ethanolamine group with a 4-hydroxyphenyl or 2-hydroxyphenyl moiety. The latter modification leads to compounds, such as AM404 , that are competitive transport inhibitors of reasonable potency and efficacy .Distribution of Endocannabinoid Transport in the CNS.

Though mental illnesses do have a low morbidity, mentally ill people experience many years living with pain, stigma, lifestyle changes, complicated therapeutic regimes, the long-term threat of decline, and shortened life expectancy. With a lack of general funding for healthcare, more money is given to high fatality, international attention-grabbing physical diseases like malaria, AIDS, TB, cancer, etc. Now, after the advocacy from doctors and NGOs bombarded the media, the Ministry of Health is finally being forced to change their stance on mental health care. The Parliament is currently conducting consultations and is reviewing the bill to guarantee that fragmentation of the mental system is what is best for Ghana. Mind Freedom and Dr. Osei hope that the bill will be passed by June 2011, and if it is not, then Dr. Osei flippantly said he will personally march all of his patients at the Accra Psychiatric Hospital down to the Parliament building to fight for their rights. Dr. Osei hopes that the long struggling advocacy for mental health improvement will not lose steam and keep pushing until the bill is passed and even after to ensure the implementation of the law. Immediately after the bill is passed, he advises that a mental health board needs to be established with the purpose of overseeing the implementation of the bills requirements and the training of judiciaries, policemen, mental health personnel, nurses, and traditional faith healers in the law’s policies. He wants Ghana to have state-of-the-art mental health care which delivers care to the doorsteps of every Ghanaian, provides a wide range of medicine, is part of the national health insurance scheme, employs mental health personnel of various categories,marijuana drying rack and is adequately funded and operated by motivated leaders and supported by research and evidence based data.

This could be achieved by having one of the best mental health laws in the world and by removing the emphasis from hospital based care to community care. Similar to Dr. Osei, MindFreedom thinks that Ghana’s mental health system should change from institutional care to community care. The hospitals should be decongested, CPNs should be given transportation to move between communities, newer medication should be used, and mental health workers should be given more incentives and should be covered by insurance. Most importantly, psychiatrists need to more frequently go into the community, human resource needs to increase, and medication needs to be more available. Also, the perception of mental illness needs to be worked on. Stigma makes the situation drastically worse and makes people less likely to seek treatment even when it is important to seek early treatment so the problem does not aggravate. Despite the Accra Psychiatric Hospital’s disturbing conditions and appalling lack of resources, Dr. Osei’s undaunted and resolute passion for mental health is leading the country towards progress. In the beginning of 2011, Dr. Osei launched a repatriation of 600 recovered patients, whose families could be tracked down, to be discharged and returned home. Dr. Osei oversees each case to make sure that each discharged patient is well enough to go home and that they have a family or home to return to. So far the repatriation has been successful in decongesting the hospital, as 200 patients have been discharged by March 2011 and the total of 600 is expected to be achieved by June 2011. If the hospital does reduce its capacity to 600 inpatients by the summer, then it is well on the way to reaching the ultimate downsizing goal of 300 inpatients, what Dr. Osei wants most to happen for his hospital. Upon hearing news of the repatriation, more families are inquiring about the possibility of picking up their once abandoned relatives. Social welfare workers and CPNs are in charge of bringing the patients back home safely. MindFreedom Ghana fully supports Dr. Osei’s repatriation of patients.

They trust that sending patients back into society will help lower the stigma of mental illness by making their families reaccept them and by showing the public that survivors can become productive members of the community. Right now MindFreedom is searching for someone to fund a project that would help teach, empower, and rehabilitate the patients who are being sent home. The repatriation is noted as a sign of improvement in the mental health care system and MindFreedom thinks that the decongestion act should be replicated by the Pantang and Ankaful hospitals. MindFreedom believes that Dr. Osei and Dr. Dzadey have a lot of energy and passion for the mentally ill and are doing the best they can with the resources available. Despite challenges, many achievements have been accomplished by Dr. Dzadey and the Pantang Hospital. In 2007, a revenue-producing Rehabilitation Vegetable Garden opened and is now tended to by patients and national service personnel who have a background in agricultural science. In 2009, a Drug Treatment and Rehabilitation Unit was created and in 2010 it was recognized by the World Federation of Therapeutic Communities. It is totally inappropriate to group addicts with other mentally ill patients, seeing that drug abusers who do not consider themselves mentally ill will steer clear from entering psychiatric hospitals. Because of the initial lack of focused addiction counselling, users would often return to the hospital shortly after they were discharged. Henceforth, this Drug Rehabilitation unit was the first step taken to take care of addictions separate from the mainstream patients in the wards. Addicts pay to reside at the hospital and partake in this structured 6 am–10 pm program at the Drug Rehabilitation Centre, where they receive therapy from a well trained staff for a minimum of six months.

Occupational therapy assistants involved in VSO now help assess the therapeutic needs of the patients, teach bead making, and collaborate with the wards. A nursing assistant even started an initiative for engaging the patients in physical activities and successfully organized a week long inter-wards sports competition a year ago that was well received by both staff and patients. Also, the general supply of new generation anti-psychotics improved and two boreholes were built by the National Security office to help create an independent water supply in 2010. Awareness, stemming from mental health workers and the birth of mental health NGOs, is undeniably increasing. Before the year 2000 there were no NGOs in Ghana that directly focused on issues in mental health, and now there are at least six very active ones. BasicNeeds, MindFreedom, PsychoMental Health Foundation, the Mental Health Society of Ghana, and the Ghana Mental Health Association are the most prominent. Still,vertical grow rack system the number of NGOs for mental health is miniscule compared to the number of NGOs for malaria and AIDS. The media also began getting engaged with the movement towards improvement of mental health when journalists became more radical rather than being obsequious to the government. With the help of NGOs, the media, and certain mental health professionals, the National Development Planning Commission of Ghana finally adopted mental health as a developmental agenda for 2010–2013. MindFreedom, one of the Mental Health NGOs, began in the home of Director Janet Amegatcher in 2004 with Nii Lartey Adico as Co-Director and Dan Taylor as Executive Secretary with the mission to advocate for the rights and dignity of persons with mental disabilities in Ghana. All of the three were so dismayed by the callous popular opinion and the condition of the psychiatric hospitals that they searched for a way to educate and sensitize the Ghanaian public. Originally the NGO was funded by the World Health Organization but is now funded by America’s international Disability Rights Fund. In July of 2008, the company moved into a permanent building in Osu, Accra and is open for counselling during normal working hours. The director is trained in counselling and MindFreedom has both a clinical psychologist and a psychiatrist as board members who are utilized for referrals. The NGO also puts on advocation and awareness events in Accra about twice a year. For three years, MindFreedom has organized annual street marches through popular roads in downtown Accra, the most recent being in 2010 with 700 participants, in order to fight mental health stigma and to bring attention to the Mental Health Bill and the UN Convention on the Rights of Persons with Disabilities. They try to change perception through education by discussing mental health issues on the air, radio, and newspapers and by posting small posters and stickers around the city. The NGO also puts on training workshops for journalists, judges, lawyers, the police, prison service, and other workers who have direct or indirect contact with the mentally ill, to teach them about how to appropriately deal with the mentally ill and to educate them on current policies.

Currently MindFreedom Ghana has 154 members, either mentally ill or survivors of mental illness, most of whom have now luckily stabilized enough toreturn to work. Because of funding and the price of transportation, the members can only meet every three months to discuss their issues and their progress. MindFreedom is now submitting a proposal for a three year reintegration and rehabilitation program to help those discharged from the psychiatric hospitals. The mission of BasicNeeds, a worldwide mental health NGO, is “to initiate programmes in developing countries which actively involve mentally ill people and their carers/families that enable them to satisfy their basic needs and exercise their basic rights. Under the management of Badimak Peter Yaro, BasicNeeds Ghana was established in 2002 with the purpose “to enable people with mental illness and epilepsy to live and work successfully within their community. Over the past nine years, BasicNeeds Ghana has affected the lives of 18,838 sufferers of mental illness or epilepsy, and 17,603 of them are still receiving regular treatment and counselling thanks to the NGO. BasicNeeds has helped create 182 community self-help groups for the mentally ill and their primary carers. The NGO has trained or is currently training 4,681 beneficiaries in some form of vocational training while also hosting several public awareness events a year. In the winter of 2010, a march took place in the Upper East Region of Ghana for the celebration of the World Mental Health Day, a community durbar was held in Accra in order to increase awareness of Self-Help Groups for people with mental illness and epilepsy, and a photo project took place in 12 different districts to visually capture the conditions the mentally ill people live in. Most importantly, BasicNeeds puts on quarterly community outreach clinics in the north of Ghana, specifically in poor communities in the three northern regions where there is no permanent psychiatrist. The most recent clinic, in the last quarter of 2010, reached 155 mentally ill people from five districts in the Upper West Region. BasicNeeds strongly believes in community care and has helped many mentally ill people gain access to professional treatment. Through funding from the European Union’s project “Ensuring Secure Livelihoods for Poor Mentally ill People and their Primary Carers in Ghana, BasicNeeds organized a secure livelihoods module and assists CPNs and self-help groups in the assessment of skills priority and livelihood options of stabilized members who are then subsidized by the specific group they belong to [6]. Common livelihood options and skills priorities users pursue include farming, animal rearing, grain storage and sale, petty trading, food processing, tailoring, hairdressing, weaving, and bicycle repairs. This sustainable project encourages social, human, and economic development, while positively changing the attitude regarding the mentally ill and their carers by showing the society that they can be productive. Over the past two years, BasicNeeds has conducted one to two day workshops on procedural and financial training for self-help groups, epilepsy training for medical practitioners, mental health training for master craftsmen arranged to teach skills to the mentally ill, mental health training for Agric Extension Workers who have contact with mentally ill workers on farms, and policy and human rights training for security officers in the Ghana Armed Forces, Ghana Police Service, Ghana Immigration Service, Ghana Prisons Service, Customs Excise and Preventive Services, Ghana National Fire Service, City Guards Unit of the Tamale Metropolitan Assembly Task Force, and Bilchinsi Task force.

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.