Sessions lasted about 15 min, and they were held at baseline and at months 3, 6, and 12. The Expert System is based on the Stages of Change model that posits five stages of change in quitting smoking. These stages are precontemplation , contemplation , preparation , action and maintenance . Participants in the precontemplation and contemplation stages were provided with relevant chapters of pathways to change, a self-help workbook based on the stages of change model. When participants reached contemplation, they were reminded of the availability of treatment. Patients who were in preparation stage were strongly encouraged to take part in the treatment intervention. At any point, participants who expressed a desire to quit could receive treatment.The E-CBT component provided individual treatment focused on a quit plan and on strategies to prevent relapse. Content was adapted from the extended treatment used in earlier work by our group.The treatment addressed six areas that are important to smoking abstinence, with the content and skills tailored to low-income smokers: information, education and preparation for quitting; poor mood, weight control, social support, increasing and maintaining motivation, and stress management.This content was provided in 10 individual counseling sessions during the 6-month treatment period. Sessions occurred during weeks 1, 2 3, 5, 8, 12, 16, 20, and 22. The first counseling session was conducted face-to-face. Subsequent sessions were conducted either in person or by telephone. The first session was approximately 45 min long and the subsequent sessions about 30 min long.A note was attached to buprenorphine prescription dose containers to identify E-ISI participants: STC participants were not identified.If the participant was abstinent from tobacco, the pharmacist congratulated them on being a nonsmoker. If the participant had relapsed, or had not stopped smoking, the pharmacist reminded them about the importance of continuing to attend the Expert System sessions or the continued availability of treatment, as appropriate. All were doctoral level. Before participating in the study, pharmacists participated in smoking cessation treatment training led by Dr. Gasper, using the Prescription for Change curriculum.Participating pharmacists were knowledgeable about smoking cessation. However, training insured current knowledge and consistent skill level across pharmacists.We first evaluated the data to determine whether there were differences between conditions in missing data at each assessment. None were found. Also, when entered into hypothesis testing models, number of assessments missed was not a significant predictor of abstinence and was therefore eliminated from further consideration.

To test the first through fourth hypotheses, we included in the model intervention condition, usual cigarettes per day in the month preceding the baseline assessment and sex of participant. We also included those variables that were found to correlate with abstinence as the dependent variable at two or more assessments. These were goal , ASI Psychiatric Score, SF-12 Physical Component Scale , SF-12 Mental Component Scale , and Profile of Mood States TMD. For hypothesis 1, that there would be significant differences between conditions in abstinence status at months 12 and 18, we evaluated the Intervention × Assessment interaction. For the remaining three hypotheses, the main effects for intervention were of primary interest. Tests of cigarette abstinence and goal were based on a logistic distribution; tests of quit attempts were based on a negative binomial distribution; and the test of stages of changes was based on a multinomial distribution. Differences between intervention conditions at each assessment were evaluated using a chi-square test. Differences between conditions on dependent variables with multiple categories were evaluated by the Jonckheere-Terpstra Test.To test the final hypothesis, that abstinence status would be predicted by usual CPD and FTCD, we estimated and tested a model that included these two variables at baseline along with treatment condition and assessment. The model failed to converge due to a poor distribution of variables, so we inspected the correlations of each variable at each assessment. Exploratory analyses of drug and alcohol use were conducted using a model including baseline drug and alcohol use, as assessed by the ASI. In addition to looking at composite drug use, we examined the item reporting self-reported marijuana use in the past 30 days. These three variables were entered into a model to predict abstinence across all assessments. We also examined differences between treatment conditions in use of NRT and counseling to determine whether interventions were used at a greater rate by E-ISI than STC. We compared reported use across the study period between intervention conditions using Pearson’s chi-square test.The first hypothesis, that E-ISI would produce higher abstinence rates than STC, was not supported. Although there were differences between E-ISI and STC at 3 months, these differences were not maintained. Three studies of interventions paralleling the intervention reported in this study have been reported, all with psychiatric patients who were cigarette smokers. The results of these studies are characterized by gradually increasing abstinence rates over an 18-month period and abstinence rates at month 18 ranging between 18% and 20%.The current results did not replicate those of the earlier studies, particularly with respect to the phenomenon of increasing abstinence rates over time.

The most parsimonious explanation for the findings of the current study is that the initially higher abstinence rate in E-ISI reflects a “placebo” effect due the receiving an intensive and novel intervention. Given the significant short-term results, it might be argued that outcomes at later assessments could be improved by modifications to the intervention. However, given the multiple modalities offered, and the duration of the treatment, it is difficult to conceptualize what such modifications might be, especially if feasibility and cost are considered. Cigarette abstinence rates in the current study are relatively high when compared to most studies reported with patients receiving MAT for opioid use disorder. In the SFDPH, buprenorphine maintenance was reserved for more stable individuals with opioid use disorder because less frequent clinic visits were required than for methadone maintenance and hence less monitoring. This may explain the relatively high abstinence rates, since most previous studies recruited participants from methadone maintenance. The current study is consistent with the extant literature in its failure to effect cigarette abstinence for patients receiving MAT for opioid use disorder. In that way, it replicates earlier findings.These investigations offered interventions that are efficacious in the general population and found some evidence of efficacy at the end of treatment between experimental and control groups but failed to find long term effects. The lack of efficacy of E-ISI observed in this study was not the result of lack of interest in abstinence or willingness to change, since 54% of E-ISI participants entered treatment. This compares favorably to the 37% observed in our earlier study of psychiatric outpatients.Also, at baseline, 26% of participants had a goal of complete abstinence and 21% were ready to quit smoking. These baseline figures are not markedly different from baseline figures reported in the earlier study. In that study, 31% of participants had a goal of complete abstinence, and 25% were ready to quit smoking.E-ISI participants were more likely to report at least one quit attempt, more likely to be in more advanced stages of change, and more likely to have a goal of “quit forever” than STC participants. These data, in addition to the treatment acceptance rate,cannabis growing system suggest that smokers in buprenorphine treatment are at least comparable to other populations in responsiveness to motivational interventions. Participants in E-ISI who accepted treatment used NRT, based on dispensing records. The mean number of patches dispensed would cover about two and a half months of use, if the patch were used daily. It is not possible to accurately judge the days of usage of gum and lozenge, since these would vary by frequency of use. There was moderately good participation in E-CBT, also. The mean number of sessions was almost half of those offered, and the mean minutes in sessions were over 160. Thus, participants received approximately half the E-CBT time available. The protocol was designed so that most of the new content was introduced in 6 of the 10 sessions, with the remaining sessions focusing on review. Thus, on the average, participants were exposed to most of the E-CBT content. Varenicline was of little interest to participants. This may have been due to the study being conducted during a period when that drug was receiving negative publicity in local media.

This study suggests that currently available treatment interventions do not produce cigarette abstinence in patients receiving MAT for opioid use disorder who smoke cigarettes. The best therapeutic strategy for this population may be to encourage them to use alternate strategies to obtain nicotine and avoid cigarette smoking and thereby reduce harm. These might include long-term multiple NRT medications at a wide range of doses and interventions integrating the suggestions of Miller and Sigmon, particularly the suggestion that use of bupropion, varenicline, and nicotine patches be observed and contingently reinforced.It is likely that the FTCD is a poor instrument for assessing dependence in this population. Two of the questions on the FTCD assume that the participant has non-restricted access to smoking areas. We found that 59.5% of the participants in this study were housed in living situations that would restrict smoking. As has been the case with the general population, CPD did predict abstinence rates, although the magnitude of the relationship was not strong. Cannabis use predicted continued smoking as has been the case in some studies in the general treatment population although not all.Given the mixed findings in the general population, it is difficult to argue that negative effect of cannabis use on abstinence is unique to this population. In exploratory analyses, we also examined the effects of buprenorphine dose and program participation on abstinence. Neither variable predicted outcome. In summary, current motivational interventions may be useful in increasing motivation for cigarette abstinence in patients receiving MAT for opioid use disorder. Exploratory analyses did little to shed light on the predictors of outcome in this population of smokers or variables that might differentiate them from the general population and would be useful in explaining the unique lack of efficacy. It is possible that interventions for tobacco dependence in opioid treatment patients should focus on harm-reduction strategies and other alternative strategies.

As a result, older adults have reported using recreational dispensaries to obtain medical cannabis. Although cannabis may be used for medical purposes, there are also associated health risks. A 2018 systematic review found that older adults that used cannabis only were significantly more likely to report major depression and serious suicidal thoughts, more likely to report other substance use and subsequent health risks attributable to substance use, and more likely to report engaging in risky behaviors, including driving under the influence. Cannabis use is associated with and may interact with physical and cognitive efects associated with aging, including fall risk, respiratory disease, cardiovascular disease, stroke, and mental health disorders such as dementia. In addition, some research suggests people aged 65years and older favor edibles, which can contain variable and sometimes extremely high levels of THC that may lead to psychosis and could exacerbate or negatively affect the trajectory of preexisting mental illnesses such as schizophrenia. Public health research suggests that cannabis legalization, whether recreational or medical or applicable to personal use or retail sales, has led to increased consumption, yet more data is needed to assess the magnitude, timing, and predictors of these effects. Substance use has historically declined with aging , but substance use is also driven by generational trends. Since 1999 there have been calls for research on the prevalence of substance use among Baby Boomers as a cohort given their historically higher rates of use, the possibility of reduction in use over time due to age effects, and potential interactions with age-related health conditions. Although existing research suggests that Baby Boomer cohort effects will result in increased prevalence of cannabis use, models of prevalence have not previously considered the potential effects of recreational legalization in this cohort, focusing instead on medical cannabis. Past research has noted that identifying predictors of cannabis use, which can include policy changes, is critical to developing interventions for vulnerable populations.California was the first state to legalize medical cannabis use in 1996 and the effects of medical legalization were well established when the state permitted recreational use in 2016, although there was no change to the retail market until 2018. In 2018, 164 recreational retail dispensaries began selling cannabis to adults in California, and most of these dispensaries were licensed and began selling cannabis on January 1st of that year. After January 2018, few additional dispensaries were licensed to sell cannabis before mid-2019,providing a clear demarcation of the change in access to cannabis.

In this study we assessed the prevalence of cannabis use among Baby Boomers in California before and after the implementation of recreational retail cannabis sales, a policy change we anticipated would be associated with increased use due to cohort effects. We also assessed factors associated with cannabis use in this cohort.Te California Health Interview Survey is the nation’s largest state-level health survey and is conducted using computer-assisted telephone interviews in six languages: English, Spanish, Chinese , Vietnamese, Korean, and Tagalog. Data collection relies on a random-digit-dialing with the aim of contacting participants by 50% landline and 50% mobile phone numbers. CHIS explicitly seeks a sample that is representative of the state’s total population, estimated to be over 39 million in 2019. Te survey includes all 58 California counties, and geographic stratification accounts for population size and demographics, making it possible to obtain valid estimates for smaller ethnic and racial groups. CHIS data fles include population weights based on the State of California Department of Finance estimates, adjusted to remove those living in group quarters, who are excluded from data collection. Each annual wave of data collection includes approximately 20,000 Californian residents. Detailed documentation on study methodology is available from the UCLA Center for Health Policy Research. Te survey includes questions on a range of health topics.All participants studied were adults ; we specifcially considered Baby Boomers, defined as those born between 1946 and 1964, and compared them to adults in other generations. Our three primary outcomes of interest were cannabis use, and included whether respondents had ever used cannabis, had used cannabis in the past 30days, or had formerly used cannabis but did not currently use it. Use variables were identified from the following questions: “Te next questions are about marijuana also called cannabis or weed, hashish, and other products containing THC. There are many methods for consuming these products, such as smoking, vaporizing, dabbing, eating, or drinking. Have you ever, even once, tried marijuana or hashish in any form? How long has it been since you last used marijuana or hashish in any form? During the past 30 days, on how many days did you use marijuana, hashish, or another THC product?” We coded these variables as binary indicating that a respondent had ever used cannabis if the answer to was yes and currently used cannabis if the answer to was greater than zero. We defined former cannabis use to exclude “infrequent users” identified in previous research as those who might consume cannabis less often than once per year ; as a result, respondents were classified as having formerly used if their reported prior use of cannabis was at least 15years ago. We used reported year of birth to assign participants to generations .

To assess potential predictors of cannabis use we included variables associated with cannabis use in prior research. These were self-reported sex , race/ethnicity , education , household income , asthma diagnosis , retired , unemployed status , disabled , smoking history , overweight status , felt nervous most or all of the past 30days , felt depressed most or all of the past 30days , and experienced psychological distress in the past 30days . Te exact questions and answer categories underlying these variables are provided in the Supplement.We used code provided by CHIS to pool multiple cycles of data and create population weights accounting for the multi-year flews; the concatenation for our analysis only involved data of the same jackknife coefficient. CHIS only included questions in the 2017 and 2018 fles that were asked in identical format. Although item missing rates during data collection range from 0.5 to 5.6%,4×8 grow table with wheels variables do not contain missing values as CHIS imputes values when respondents do not provide a valid response. We used population-weighted logistic regression to test the hypothesis that the population prevalence of Baby Boomers using cannabis in California would increase after implementation of recreational retail cannabis sales in 2018, relative to non-Baby Boomers. We compared differences in the prevalence of cannabis use before and after this policy change; our primary outcomes were ever use of cannabis, use in the past 30days, and former use. We also used population-weighted multivariate logistic regression to identify whether known factors associated with cannabis use were predictive for Baby Boomers, non-Baby Boomers, and all adults sampled in both years. For the multivariate regressions we conducted sensitivity analyses by conducting analyses for each year separately as well as both years together. All statistical analyses were completed using Stata 17.Although previous research has noted the overall increase in prevalence of cannabis use after legalization, it has been less clear how this change will afect different parts of the population, including older adults who face different health risks relative to younger adults due to a higher prevalence of comorbid conditions that could be either exacerbated or addressed by cannabis use. Our findings compared prevalence of cannabis use and risk factors associated with use among Baby Boomers before and after legalization of recreational commercial cannabis sales in California. Although individuals may use cannabis for medical purposes, cannabis use in older adults is also associated with health risks and it is possible that increased awareness of these risks reduced the likelihood that Baby Boomers would transition to recreational cannabis.

However, previous research conducted in Colorado and the San Francisco Bay Area found that Baby Boomers may preferentially purchase cannabis in recreational dispensaries for medical use, a result that is inconsistent with this interpretation.We also found that although many of the predictors identified in past research as associated with cannabis use were significant when considering adults overall, few predicted reported cannabis use among Baby Boomers. Despite past research identifying potential associations between cannabis use and gender, race and ethnicity, education, employment status, and existing health conditions,among Baby Boomers, for the measures we considered, only a history of smoking was associated with cannabis use in the past 30days or with former use of cannabis. It is unclear what drives these differences. Individuals categorized as Asian American in previous studies, for example, reported lower rates of cannabis use than other groups in the population, which we did not observe in our sample. This finding might reflect differences among populations aggregated into the category “Asian American” that could be more apparent in California, where the share of the population represented by people typically categorized this way is relatively large.Although this research relied on a large, representative sample, the survey relied on self-report by those choosing voluntarily to participate and who are accessible by telephone, and the results were not externally validated, raising the possibility that responses were inaccurate due to sampling, recall, or social desirability bias. CHIS surveys were conducted continuously throughout 2017 and 2018; as a result, some respondents had only experienced legal recreational retail cannabis sales for a brief period. Te fact that almost all recreational dispensaries active in 2018 opened on January 1st mitigates this concern to some extent, nonetheless, these findings may change over time as the market becomes more established. In addition, the prevalence of cannabis use increased for non-Baby Boomers, indicating that our failure to identify an association between increased prevalence of cannabis use and recreational retail sales was specific to Baby Boomers. CHIS data consists of repeated cross-sectional surveys, meaning that we could only observe changes at the population level, rather than for specific individuals. Data limitations also meant that we could not account for every known potential predictor; this includes measures of alcohol use, which were not asked in these survey years. In addition, measures of cannabis use did not indicate mode of consumption , dosages, or whether any or all cannabis use was prescribed by a health care provider.

Since reaction time variability is particularly characteristic of ADHD , at least in children, we were surprised no effect was observed. Some literature suggests reaction time variability is less evident as individuals with ADHD develop so the non-significant finding may be due to maturation. We did not have information to investigate whether participants in the current study still met diagnostic criteria for ADHD. However, at the 8-year follow-up, the original ADHD group in the larger MTA sample demonstrated greater impairment even though only 30% met current ADHD diagnostic criteria suggesting a childhood diagnosis of ADHD is risk factor for continued EF deficits, which is consistent with other studies . We did not observe significant effects of cannabis use except for a small significant effect of cannabis use on decision-making, which should be interpreted with caution given the overall MANCOVA did not indicate a significant main effect for cannabis use. However, the direction of the finding is consistent with the literature and provides modest support suggesting that cannabis use is associated with poorer performance on decision making tasks. Cannabis users may have deficits in the ability to balance rewards and punishments that contribute to drug-taking behavior. This could be cause or effect. Interestingly, this task assesses a ‘hot’ executive function, i.e., one that involves incentives and motivation , which may play a more critical role in the process of addiction than ‘cool’ or more abstract executive functions . It should be noted that studies suggest that dose, persistence, and chronicity of use may impact the effect of cannabis on EF . Cannabis use in our study ranged from monthly to daily over the past year and all were abstinent on the day of testing, which may have affected our ability to detect effects of cannabis use on EF due to recovery of function. Our exploratory analyses investigating age of onset of cannabis use were not significant, potentially because of the much smaller sample size for these analyses. However, review of effect sizes revealed that earlier use of cannabis was associated with poorer performance on cognitive tasks assessing decision-making, working memory, impulsive errors, and response variability than late onset of use. These tasks involve visual attention, which is negatively influenced by early-onset cannabis use . Individuals who initiate use of cannabis before age 16 may be at higher risk for developing persistent neuropsychological deficits because their brain is still developing , especially the prefrontal cortex which is associated with several executive functions including planning, verbal fluency, complex problem-solving, and impulse control, each with its own developmental trajectory .

Thus, adolescence is a particularly vulnerable time for neurocognitive effects of substance use . Still, we clearly found that ADHD diagnosis had a much larger impact on EF than cannabis use. Because ADHD is associated with developmental delays, particularly in the prefrontal cortex , it is possible that the cognitive consequences of ADHD were sufficient that additional impact on EF from cannabis use was difficult to detect. It should be noted that a higher proportion of individuals with ADHD initiated cannabis use early, which may make it difficult to disentangle the independent impact of cannabis on cognition, given larger effect sizes of ADHD. Furthermore, there may be an interaction whereby early onset cannabis use exacerbates ADHD symptomatology through negatively impacting EF. Further investigation is clearly warranted. Our findings must be interpreted in light of several limitations. Sample sizes were small, particularly for the exploratory age of onset analyses. The cross-sectional design makes it difficult to determine causality although the ADHD diagnosis did precede cannabis use for all participants . The measure of cannabis use was based on self-report, which is not the most objective method compared to biological measures. Our results may not generalize to more persistent chronic cannabis users. Excluding regular binge drinkers may also limit generalizability given the high co-occurrence of alcohol and cannabis use . Although we requested participants abstain from prescribed medication and illicit drug and alcohol use prior to the assessment, we did not verify their compliance with this directive. The concern about participants not complying with this directive for cannabis use is somewhat mitigated by the fact that we did not observe an effect of cannabis; if participants indeed did not comply with the requested washout period, we may have observed a false-positive finding based on the negative effects of cannabis on cognitive functioning . It is also possible that discontinuation of stimulant medication may have impaired performance on the cognitive tasks ; however, with such a small proportion of our ADHD sample taking stimulant medication “sometimes” or “always”, it is unlikely that such discontinuation effects would have led to the ADHD group differences.. There are a number of issues needing further investigation. It will be imperative to investigate the effects of regular cannabis use in young adults who continue to meet diagnostic criteria for ADHD, particularly because some studies suggest persistent ADHD is associated with poorer EF and higher rates of comorbid SUD .

It will also be important to investigate whether having a diagnosed cannabis SUD results in more dramatic impact on EF than the regular use defining this sample of users. Another issue that may impact EF outcomes is the age of onset of cannabis use. Future research will need to examine whether there is a critical developmental window when cannabis use more severely affects neuropsychological functioning. Other areas of investigation might include an analysis of whether EF deficits in childhood predict poorer cognitive outcomes, and whether early deficits interact with cannabis use with and without ADHD. Our results should not be taken to indicate that cannabis use carries no risk for cognitive function, only that further investigation is needed. As of November 2021, recreational or “adult-use” cannabis is legal in 18 states and the District of Columbia.1 Cannabis policies regulate the availability of cannabis by legally permitting outlets offering cannabis products for retail sale. Alcohol availability research indicates that higher residential outlet densities make it easier to find, purchase, and use legal intoxicants.Analogously, greater availability of medical cannabis dispensaries has been linked to cannabis use and frequency.4,5 Similar effects are expected for recreational cannabis outlets.Increases in cannabis access and use may have both positive and negative health consequences. Cannabis consumption has been linked to motor vehicle crashes, psychotic disorders, respiratory disease, low birth weight, and cannabis use disorder, but substitution of opioids, tobacco, or alcohol for cannabis may prove beneficial.Outlets may also attract crime, although research on this topic is mixed.State cannabis legalization policies typically defer authority to regulate the density and locations of outlets to local governments. Local governments can limit the number of outlets permitted, establish minimum distances between outlets, and bar their location near sensitive locations such as schools. Local governments also share responsibility with state agencies for abating illegal outlets which are prevalent in California.The impacts of local cannabis policies on outlet densities may have implications for public health by limiting availability. Recreational cannabis outlets are disproportionately located in neighborhoods with high proportions of low-income and racial–ethnic minority residents.Policies that encourage greater reductions in outlets in vulnerable neighborhoods therefore have the potential to promote health equity. Little is known about the impacts of local cannabis policies. Three studies assessed local policies in Colorado, Washington, and California following recreational cannabis legalization.All identified broad variation in local regulatory approaches,vertical outdoor farming ranging from all-out bans to unlimited outlets, with a few jurisdictions allowing outlets while limiting their densities. To our knowledge, no prior study has evaluated how local policies influence outlet densities or socioeconomic and racial–ethnic equity in the distribution of outlet densities within jurisdictions. We addressed these gaps with a spatiotemporal analysis of city and county cannabis policies and cannabis outlets in California.

We evaluated whether specific local policies such as density limits cannabis outlets led to lower outlet densities. We also assessed whether the associations of local policies with outlet densities varied across neighborhoods depending on median income or racial–ethnic composition. We hypothesized that stricter local policies would be associated with lower outlet densities and less disproportionate placement of outlets in less advantaged communities. Cannabis legalization research suggests that provisions enabling outlets are influential for cannabis consumption and related health outcomes.We focus on the local-level policies that determine how many outlets can open and in which communities. Understanding which local policies effectively limit and equalize outlet densities is critical for state and local policymakers seeking to make more informed decisions about which cannabis policies to pursue to protect public health and health equity from potential harms related to legal cannabis.We classified local cannabis policies for 12 of California’s 58 counties representing 59% of the state population. The 12 counties were selected to capture a range of sizes, sociodemographic compositions, political orientations, and approaches to cannabis regulation,20 and included 230 cities and 11 unincorporated county areas . Using a legal epidemiological approach,between November 2020 and January 20021 we systematically identified and coded the characteristics of currently applicable cannabis policies in all 241 jurisdictions. We used a structured data collection instrument to capture the presence or absence and content of pre-specified provisions. Two analysts coded all jurisdictions separately until they achieved >95% agreement. Complete protocols, data collection instruments, and further detail are provided in eAppendices 1-3. However, localities retain considerable discretion. The policy measures we collected were guided by an established taxonomy of all possible cannabis policies.We coded all policies that: were regulated at the local level, varied across jurisdictions, were more restrictive than state law, and were plausibly related to public health given prior evidence, public health best practices, and expert opinion.The outcome was the count of storefront recreational cannabis outlets in each Census block group and year. We web-scraped data on outlets annually between 2018 and 2020 from Weed maps, a high-traffic online promotional cannabis business finder widely used in cannabis research.A prior validation study found that, compared to official license listings or other finders, Weedmaps was the most up-to-date and comprehensive source for capturing cannabis outlets.14 We focused on recreational rather than medical outlets because: following recreational legalization, few medical-only outlets remained; the applicable state laws for medical outlets are distinct; and Weedmaps measures of medical outlets were less valid over the study period. Recreational outlets included both newly opened outlets and outlets that converted from medical to recreational. We focused on storefront outlets, as opposed to home delivery retailers, because this study builds on conceptual models based on physical proximity to outlets offering in-person purchases.3 See eAppendix 3 for detail .

Cannabis affiliates may pay individual lobbyists or pay lobbying agencies that funnel those payments to salaried lobbyists or subcontractors. Lobbying agencies sometimes list themselves as funders even though this practice was made illegal by the Lobbyist Transparency Act Lobbyist Transparency Act, 2019. We excluded reported self-funding because it was impossible to identify the underlying funder. To prevent double counting, we only included direct payments from cannabis affiliates and excluded payments to subcontractors and employees salaried by lobbying agencies. We reviewed cannabis lobbying expenditures in Colorado over time using Stata 16 and then qualitatively reviewed lobbying positions on proposed legislation. Our analyses assessed total cannabis lobbying expenditures and the share drawn from national sources, the extent to which expenditures were clearly identified as associated with cannabis, and alliances with other industries. We conclude with a case study of cannabis industry efforts to create cannabis consumption establishments. We selected this issue because legislation on the topic was introduced multiple times over the course of three years and under two gubernatorial administrations, allowing insight into changes in lobbying practices over time. We collected data from audio recordings of legislative testimony and floor debate, legislative histories, fiscal notes, and lobbying reports for all legislation dealing with cannabis consumption establishments available through the Colorado General Assembly and Secretary of State websites. We present a narrative description of each bill’s legislative history, including information from lobbying reports and demonstrative quotations made in public testimony that indicate cannabis industry influence in the policy making process. Many cannabis affiliates that appeared independent shared professional or personal ties. In 2019, 14 different funders lobbied in support of HB1090, a bill that allowed publicly traded corporations to own or invest in cannabis businesses and removed residency requirements.

These 14 funders were exclusively cannabis affiliates or lobbying agencies with known cannabis industry connections: LivWell, Buddy Boy, Dixie Brands, Gobi Labs, Gold Dome Access, Lightshade, Medicine Man, MedPharm Holdings, Native Roots, Natural Selections, TEQ Analytic Solutions, The Green Solution, Vicente Sederberg, and Wolf Public Affairs. All but Gobi Labs shared professional ties: John Fritzel was an owner of both Lightshade and Buddy Boy, and Andy Williams was the president of both Medicine Man and MedPharm Holdings . Representatives from Lightshade, LivWell, Native Roots, Vicente Sederberg, Medicine Man, MedPharm Buddy Boy, Dixie Brands, and Columbia Care were board members or donors for the Cannabis Trade Federation. Leadership from Medicine Man, MedPharm Holdings, Native Roots, Dixie Brands, TEQ Analytical Solutions, Vicente Sederberg and the chairman of the Marijuana Industry Group all sat on the Board of Directors for Colorado Leads, an alliance of cannabis businesses. Cannabis industry affiliates with an out-of-state address spent $802,983 between fiscal years 2010–2021 . Given that some cannabis businesses are multi-state operations with locations in Colorado and others use in-state PO boxes, this proportion is likely an underestimate. Immediately following adult-use legalization in November 2012 and prior to the creation of the recreational sales market in January 2014, the Washington D.C. based nonprofit Marijuana Policy Project dramatically increased its expenditures in Colorado. The proportion of out-of-state lobbying expenditures increased from 5.5% of lobbying expenditures in fiscal years 2010–2015 to 12.6% in fiscal years 2016–2021 . California-based cannabis organizations lobbying in Colorado increased from one business spending $14,492 in 2017 to five spending $153,220 in 2020. One cannabis affiliated organization each from Ontario , New York , and Oregon lobbied in Colorado, as well as two from Washington D.C. .The bill survived less than three months before indefinite postponement by the Senate Committee on Business, Labor, and Technology in March 2017. On the same day, SB184, which would allow local governments to permit private membership cannabis clubs and clarify the constitutional definition of consumption that is conducted “openly and publicly” was heard in the same committee.

Kevin Bommer of the Colorado Municipal League testified that the CML brought the bill to the legislative sponsors after it was initiated by the city of Trinidad. Renaissance Solutions, the Drug Policy Alliance, Terrapin Care Station, Denver relief Consulting, Schultz Public Affairs, and Pueblo County supported the bill while health groups including ACS CAN and the American Heart Association, hospital systems, and other local governments opposed. The House and Senate could not agree on amendments and the bill died in May. Onsite cannabis consumption establishments were considered again in the 2018 session through HB1258. This bill proposed “Marijuana Accessory Consumption Establishments” for existing licensees and was supported by Dixie Brands, LivWell, Good Chemistry, Renaissance Solutions, Medicine Man, Native Roots, Gold Dome Access, and the Colorado Hotel and Lodging Association. It was opposed by ACS CAN, local governments, consultants, Colorado Association of Police Chiefs, and Colorado Christian University due to indoor air quality concerns related to indoor use of electronic smoking devices, which were excluded from the definition of “smoking” at the time. However, the Southern Colorado Cannabis Council and My420 tours opposed the bill because it could eliminate party bus cannabis tours and did not create true social consumption establishments. After passing the House and Senate, the bill was vetoed by Governor Hickenlooper amid concerns that it violated the Colorado Constitutional prohibition on “consumption that is conducted openly and publicly” . A parallel bill, SB211, was introduced in March 2018 by Senator Marble and would have allowed smoking in “consumption clubs” through an exemption to the Colorado Clean Indoor Air Act. The bill was again supported by Renaissance Solutions, Inc. and opposed by the City of Colorado Springs, Denver Health, Healthier Colorado, the American Heart Association, Smart Strategies, the Colorado Association of Chiefs of Police, ACS CAN, and the Colorado Association of Local Public Health Officials. It died in the Senate Committee on Business Labor and Technology in April. Our findings suggest that after recreational legalization the cannabis industry expanded its lobbying activities and used tactics comparable to those used by similar industries seeking to promote consumption. The dramatic increase in cannabis industry lobbying expenditures over time mirrored growth of the cannabis industry following recreational legalization in November 2012, which also coincided with an increase in cannabis consumption. Funding originating from out-of-state sources also increased over time, suggesting the development of a national network of cannabis drainage system affiliates with similar interests. Legislators, public health advocates, and community organizers should therefore expect industry resistance to cannabis control measures from local and national sources as well as proactive industry efforts to promote consumption and profits through policy making channels.Colorado lobbyists characterized their clients ambiguously almost half of the time, meaning that cannabis affiliates could only be identified through lengthy investigation. These characterizations resulted in the appearance that many funders supported some proposed legislation, which may have created a false impression of a broad coalition. In reality these interests shared common owners, represented the same professional associations, and used the same lobbyists.

We also found some evidence suggesting that public relations agencies may have hidden cannabis industry funding by paying salaried lobbyists on the behalf of funders without identifying them. To improve transparency, the Colorado Sunshine Law could be strengthened by a requirement in C.R.S. 24–6–301 §1.9 that lobbyists disclose their client’s identity as a cannabis business or any cannabis affiliation they hold under the “industry type” field . To accomplish this, a revision of section 1 of the same statute may also be needed to eliminate the provision protecting clients from disclosure of “the names of any of its shareholders, investors, business partners, coalition partners, members, donors, or supporters, as applicable.” These changes would easily allow researchers and members of the public to identify cannabis clients as such using the CDOS website and facilitate improved legislative accountability. Cannabis affiliates used lobbyists focused solely on cannabis as well as sharing lobbyists with other industries including tobacco, alcohol, pharmaceutical, and gaming. Like other industries, the cannabis industry is likely to work with these business interests to further their own profits. Using the same tactics employed by these industries, cannabis industry representatives self-reported lobbying positions opposing clean indoor air laws, health warnings for pregnant women, and potency restrictions, while supporting investment, onsite consumption, and access to medical cannabis in schools. Cannabis industry funding peaked in 2019, which may be related to the change in state governor: Governor Hickenlooper was moderate on cannabis, vetoing several pro-cannabis bills, while successor Governor Polis had voiced support for the cannabis industry and was publicly supported by cannabis affiliates. The industry may have viewed his first year in office as an opportunity to pass pro-cannabis industry bills, including cannabis hospitality businesses, that had failed in previous years . In light of the sophisticated and well-financed influence campaign conducted by the cannabis industry, policymakers should push for stricter separation between the industry and the policy making process. Frameworks designed to prevent undue influence from other commercial determinants of health including the alcohol, food, and tobacco industries can dampen industry influence by creating firewalls between corporations and policymakers.

Example policies, including the guidelines for implementation of Article 5.3 of the Framework Convention on Tobacco Control , the World Health Organization’s Framework for Engagement with Non-State Actors , and the Office of Economic Co-operation and Development’s recommendations for preventing policy capture , could serve as starting points. These frameworks stand in opposition to the system of private interest institutionalism in Colorado which encourages the inclusion of all stakeholders and prompts regulators to make policies that synthesize stakeholder input. If formal mechanisms preventing cannabis industry influence in policy are not established, legislators should at least guarantee an equal voice to health advocates through balanced and accessible stake holding processes. Our research has limitations. For public relations and law firms who represented multiple interests, expenditures that were not explicitly delineated as being from cannabis companies were not included in our analysis as the origin of funds could not be identified. For this reason, lobbying expenditures are likely undercounted. Second, the exactpositions or intentions of cannabis industry affiliates on proposed bills could not necessarily be determined from the lobbying record; instead, where possible, we relied on legislative testimony. Next, the exclusion of salaries from lobbying agencies with ties to the cannabis industry to their employees may lead to an underestimation of the total influence exerted by cannabis interests. Finally, our description of lobbying expenditures did not include pro-bono industry lobbying activities conducted on behalf of cannabis affiliates.

Despite this showing, supervisors passed the restrictive measures, prompting cannabis advocates to collect 4,000 signatures in 17 days to place the approved ordinances on the June 2016 ballot. Meanwhile, the Sheriff’s Office enforced the new stricter regulations .The Sheriff’s Office assumption of code enforcement blurred the line between noncompliance with civil codes and criminal acts. Stricter ordinances, still in effect in Siskiyou, created a broad, nearly universal category of “noncompliance.” No one we interviewed, including officials at the Planning Division and Sheriff’s Office, knew of a single cultivator officially in compliance. One interviewee estimated that growing 12 indoor plants would cost $40,000 in physical infrastructure, in addition to numerous licensing and inspections requirements, effectively prohibiting self-provisioning. The Sheriff’s Office notified the public that it would initiate criminal charges against “non-compliant” cultivators, specifically those suspected of cultivation for sale , child endangerment or suspected drug trafficking . Since the county regulations produced a situation where no one could comply, law enforcement could effectively criminally pursue any cultivator.Investigations were “complaint driven,” meaning not only that warrants could be issued in response to disgruntled neighbors upset about a barking dog on a cultivation site, as one person reported, but that police officers could serve as a kind of permanent, general complainant and take “proactive action” when they spotted code violations . Administrative warrants allowed deputies to enter properties with a lower evidentiary bar than they would have needed for criminal warrants, leading one patients rights group — Siskiyou Alternative Medicine — to file a lawsuit alleging county violations of Fourth Amendment protections against unreasonable search and seizure . In effect, cannabis’s criminal valences in the county endured through California’s shift of cannabis from criminal to civil provenance. Formerly illegal activities continued to be formally or informally treated as criminal matters, as researchers have noted with other stigmatized activities and groups, for example, after the decriminalization of sex workers in Mexico . Also, enforcement of civil matters can lead to substantive criminalization when those matters are stigmatized, as in the regulation of homelessness . While it is not unique for police officers to enforce civil codes, what is unique in Siskiyou County is the assumption of the entire civil process under the sheriff’s authority. To understand how this civil process became criminally inflected, in a county that voted for statewide cannabis legalization in 2016, one must first understand significant contextual shifts in who was growing cannabis where — and the challenge this posed to dominant ideas of land use, agriculture and culture.

Since 2014, cannabis gardens have emerged on many of the county’s undeveloped rural subdivisions in unincorporated areas of Siskiyou. Subdivided into over 1,000 lots each in the 1960s, these subdivisions contain many parcels that are just a few acres in size and relatively inexpensive. Previously populated mostly by white retirees, squatters and a few methamphetamine users and makers, the parcels were often bought sight-unseen as investments or potential retirement properties, with most remaining unsold and undeveloped until the mid-2010s. In 2014, these subdivisions became destinations for Hmong Americans from several places, including Minneapolis, Milwaukee and Fresno; many of them cultivated cannabis. The inexpensive, sparsely populated, rural subdivisions enabled Hmong-Americans to live in close proximity to ethnic and kin networks, which multiple interviewees expressed was especially important for elders who had migrated to the United States as refugees after the Vietnam War. The county sheriff estimated that since the mid-2010s around 6,000 Hmong-Americans had moved to Siskiyou, purchasing approximately 1,500 parcels . In an 86.5% white county with just 745 non-cannabis farms and fewer than 44,000 people , this constituted a major demographic shift. Hmong-American residents found themselves susceptible to scrutiny by white neighbors and officials.The subdivisions are often sparsely vegetated, dry and hilly, making them not only unproductive as agricultural lands but also highly visible from public roads, horseback, neighboring plots, helicopter and Google Earth. Green screen fencing, wooden stakes, portable toilets, generators, campers, plywood houses, or water tanks and trucks often signal cannabis cultivation but would be necessary for many land uses, especially since many lots are sold without infrastructure like water, sewer or electrical access. If detection of code violations depends upon visibility, Hmong Americans on subdivisions have been made especially visible and vulnerable to detection. One lawyer, for instance, reported that 90% of the defendants present at administrative county hearings for code violations in fall 2015, when the first complaint-driven ordinance was put in place, were Hmong-American. One Hmong-American resident reported being stopped by police six times in 3 months and subjected to unfriendly white neighbors patrolling on horseback for cannabis — one of whom made a complaint for a crowing rooster, a questionable nuisance in this “right to farm” county. Numerous Hmong-Americans and sympathetic whites echoed these experiences. County residents confirmed their antagonism toward Hmong-Americans by characterizing them in interviews and public records as dishonest, thieves, polluters, negligent parents and unable to assimilate, and making other racializing and racist characterizations. While written regulations and enforcement profess race neutrality, in a nuisance enforcement regime based on visibility, Hmong Americans were more visible than others, leading many to argue that they were being racially profiled. Rhetoric emerging from the county government amplified racial tensions and visibilities.

Numerous Sheriff’s Office press releases located the “problem” in subdivisions and attributed it to “an influx of people temporarily moving to Siskiyou” who were “lawbreakers” from “crime families” with “big money” and who threatened “our way of life, quality of life, and the health and safety of our children and grandchildren” . Just 2 days before the June 2016 ballot on the strict cannabis ordinances, state investigators responded to county reports that newly registered Hmong-American voters might be fraudulent or coerced by criminal actors and visited Hmong-American residences to investigate, accompanied by sheriff’s deputies . The voter fraud charges were later countered by a lawsuit alleging racially motivated voter intimidation; the suit was eventually dismissed for failing to meet the notoriously difficult criteria of racist intent. The raids may have discouraged some Hmong-Americans from voting, charges of fraud may have boosted anticannabis sentiment, and, one government official explained, “creative balloting” measures enabled some municipal voters in conservative localities to vote while others in more liberal places could not. The voter fraud charges, raids and legal contestation drew widespread media attention that further linked Hmong-Americans and cannabis. Amidst these now-overt racial tensions, the restrictive June 2016 ballot measure passed, allowing the Sheriff’s Office to gain full enforcement power over the “#1 public enemy to Siskiyou citizens … criminal marijuana cultivation” . Shortly after the June 2016 ballot measure affirmed stricter regulations, the Sheriff’s Office formed the Siskiyou Interagency Marijuana Investigation Team with the district attorney to “attack illegal marijuana grows” “mostly” around rural subdivisions . Within a month, SIMIT had issued 25 abatement notices and filed 20 criminal charges,flood table in addition to confiscating numerous plants. Meanwhile, the Planning Division’s role had diminished — code enforcement officers were relegated to addressing violations not directly related to cannabis . The November 2016 state legalization of recreational cannabis prompted Siskiyou to examine a possible licensure and taxation system for local growers .

Amidst sustained, vocal opposition, the proposal stalled for several reasons that further aggravated cultural and racial tensions: A key proponent of licensure was discovered to be running an unauthorized grow, three Hmong Americans died of carbon monoxide poisoning due to heaters in substandard housing, and a cannabis cultivation enterprise run by two Hmong-Americans attempted to bribe the sheriff. These developments were interpreted not as outcomes of restrictive regulations and criminalizing strategies, but as proof that, in the words of one supervisor, regulation was impossible until the county could “get a handle on the illegal side of things.” The sheriff encouraged this interpretation, arguing in an interview that statewide legalization was “just a shield that protects illegal marijuana” and efforts to regulate it would always be subverted by criminals. This antiregulatory logic prevailed in August 2017 when the county placed a moratorium on cannabis commerce. Still, the sheriff argued for stronger powers, citing an “overwhelming number of cannabis cultivation sites,” which, according to the Sheriff’s Office, continued to “wreak … havoc [with] potentially catastrophic impacts” across the region . Just 1 month later, at the sheriff’s urging, the Siskiyou Board of Supervisors declared a “state of emergency” aimed at garnering new resources and alliances to address the cannabis cultivation problem. Soon, the Sheriff’s Office enlisted the National Guard, Cal Fire and the California Highway Patrol in enforcement efforts, and, by 2018, numerous other agencies joined, including the Siskiyou County Animal Control Department, California Department of Toxic Substances Control, State Water Resources Control Board, California Department of Fish and Wildlife and a CDFA inspection station. These alliances multiplied the civil and criminal charges cultivators might face . Ironically, California’s cannabis legalization has enabled a kind of multi-agency neoprohibitionism at the county level, one that reinforces older criminal responses with new civil-administrative strategies and authorities. The need to “get a handle” might be regarded as a temporary emergency measure, but it may also propagate new criminalizing methods and institutional configurations. The more enforcement occurs, the bigger the problem appears, requiring more resources and leading to a logic of escalation symmetrical to the much-critiqued War on Drugs . And the more cannabis cultivators are viewed as criminal, the less likely they are to be addressed as citizens, residents and farmers.Given concerns about biased county policy and enforcement, the Sheriff’s Office held the first Hmong American and Siskiyou County Leader Town Hall in May 2018 to “foster a closer, collaborative relationship with members of the Hmong-American community,” exchange information about Hmong and Siskiyou culture and educate attendees on county policies . According to public records, racial tensions surfaced at this meeting when some white participants expressed that “our county” had been “invaded” and that Hmong-Americans were not fitting into local cultural norms . Meeting leaders — both government officials and Hmong-Americans — however, identified cultural misunderstanding, rather than criminalization and racialized claims by whites on what constitutes local culture, as the core problem to be addressed. “Misunderstanding” was an inadequate framing, given that Hmong-Americans had attempted to make themselves understood by attending public meetings, forming advocacy groups, signing petitions, demanding interpreters and administrative hearings, and registering to vote since their arrival in Siskiyou.

Variation between counties in well extraction patterns demonstrates that, although subsurface water may be the most common source of water in North Coast cannabis cultivation, the availability of alternative sources may play an important role. Humboldt County watersheds included in this study consistently receive more average annual precipitation than do those in Trinity , Mendocino and Sonoma counties . This difference translates into more available surface and spring water in Humboldt County over the course of the growing season. The observation that fewer sites in Humboldt County report well use, microgreen rack for sale compared to other counties in the study, suggests that if surface or spring water is available, cultivators are likely to use it. Conversely, the potential necessity of groundwater use in counties that receive less rainfall holds particular importance in consideration of emerging areas of industry growth throughout California. Further analysis is needed to understand how likely cultivators are to rely on wells if other sources of water are available to them. The winter preceding the 2017 growing season was the wettest on record. It is important to understand how cultivators may source their water during years in which summer water availability is not as abundant. These findings suggest that cultivators may utilize wells both as insurance against surface water scarcity in the summer drought months and as a means of achieving regulatory compliance. The observation that nearly one-third of non-compliant sites reported well extraction indicates that use of subsurface water may be a common means to avoid water scarcity in the late growing season. While Northern California receives considerable seasonal rainfall, there is also significant spatial variability in rainfall totals and in corresponding summer flow persistence of small streams . Considering the ephemeral nature of surface water in many areas , the increasing frequency of drought due to climate change and cannabis cultivation’s consistent demand for irrigation water as crops near harvest , cultivators are strongly motivated to secure reliable water sources for the entirety of the growing season. Therefore, it is likely that water extraction from wells is a common practice for cultivators, beyond those seeking participation in the regulated industry .

Although cannabis regulations place no explicit restrictions on where water is sourced, those currently within or seeking to join the regulated cannabis industry will be subject to a restriction on diversions of spring and surface water during the growing season . This requirement is already in place for permits issued by the California Department of Fish and Wildlife and will also be enforced by the State Water Resources Control Board beginning in 2019. The data provided in this study indicates that, in order to meet the forbearance period requirement, cultivators may be more inclined to drill a well to achieve compliance than to develop water storage for spring and surface water. Determining cultivators’ capability to store the water they need for the growing season may shed further light on the likelihood that growers will seek subsurface water. If compliance necessitates drilling a well, it will be important to account for the impacts of this potential shift in cultivation practices. Successful protection of freshwater resources in Northern California will require a more complete accounting of where cannabis cultivators source their water and the amount and timing of water extracted . Study of cannabis as an agricultural crop has been notoriously inadequate, but data provided by the water quality control board’s cannabis program offers critical new insights into the water use practices of cultivators entering the regulated industry. In this initial analysis, we found that subsurface water may be much more commonly used in cannabis cultivation than previously supposed. Further analyses of cannabis cultivation’s water extraction demand, as well as of geospatial variation in water demand, may help elaborate the ramifications of this finding. Ultimately, a better understanding of cannabis cultivation’s water demand will be useful for placing the cannabis industry in the greater context of all water allocation needs in the North Coast and throughout California.People living with HIV and those at increased risk for HIV are at high risk for other health problems, including substance use, mental health issues, and socioeconomic vulnerabilities.The COVID-19 pandemic and resulting efforts to curb the spread of infection—such as stay-at-home orders and physical distancing mandates—and the resulting social isolation are likely to exacerbate these issues.

A survey of young adults in the USA found that immediately following the declaration of a state of emergency due to COVID-19, levels of depression and anxiety increased with high levels of loneliness and COVID- 19-specific worry being associated with higher levels of depression and anxiety. Factors found to be associated with pandemic-related depression and anxiety include being younger, being a racial/ethnic minority, or being diagnosed with a chronic disease.The pandemic-related increases in mental health issues may also extend to substance misuse. Prior studies of disasters, including the aftermath of the 2003 severe acute respiratory syndrome epidemic, found increased rates of substance use. Emerging data related to the COVID-19 pandemic seem to corroborate these findings, with one study noting a “surge of addictive behaviors” including food, shopping, and increased reported use of cannabis, methamphetamine, and opioids. Studies that specifically focused on men who have sex with men —including both those who were HIVpositive and at increased risk for HIV—found that changes in substance use and mental health were also associated with behaviors that not only increased the risk for SARS-CoV-2 infection—the virus causing COVID-19—but also had implications in terms of STI/HIV transmission.For instance, one study found that those who had sex with casual partners during pandemic restrictions were more likely to report using substances including alcohol as compared to those who avoided interactions with casual sex partners. Engagement in ongoing health care and prevention is especially critical to the health of vulnerable populations living at the intersection of multiple colliding epidemics of COVID-19, substance use, mental health, and HIV. In order to reduce the potential for SARS-CoV-2 transmission, many clinics stopped in person clinical encounters and switched to telehealth visits starting in March 2020. While telehealth outcomes in general — including among those who live with HIV — have been largely positive, telehealth has the potential to miss the most high needs and socioeconomically vulnerable patients.Beyond limited access to technology requirements for telehealth visits including a cell phone or computer and Internet connectivity , privacy concerns and the absence of confidential surroundings may also be an issue. In fact, one study found that even with an intervention to improve telehealth attendance, access to virtual medical care was still challenging among people living with HIV who were experiencing homelessness.Therefore, cannabis grow facility layout the objective of this study was to assess the impact of the COVID-19 pandemic on substance use, changes in mental health, and interruptions in mental health care among HIV-positive and high-risk HIVnegative men. Specifcally, we focus on cannabis use given the high prevalence of use, ease of access, and its use as the substance of choice to manage negative affect including anxiety and depression.

We describe the prevalence and correlates of interruptions in mental healthcare as well as factors associated with cannabis use for COVID-19 related anxiety and depression. We compare those who report interruptions in mental health care to those who did not experience these interruptions and hypothesize that those experiencing interruptions in mental health care will experience increases in depression and anxiety and will also report other negative health behaviors, including increased substance use and increases in sexual risk behaviors.Data for this study were based on those collected from participants in the mSTUDY — a National Institutes of Health /National Institute on Drug Abuse funded cohort of racial/ethnically diverse, HIV-positive, and high-risk HIV-negative MSM. Details of the mSTUDY have been previously described,but briefy, participants were recruited from two study sites in Los Angeles, CA, including a community-based organization providing services for the lesbian, gay, bisexual, and transgender community and a community-based university research clinic. Study enrollment started in August 2014, and cohort participation is ongoing. Eligible participants were between 18 and 45 years of age at the time of enrollment, identifed as male at birth, if HIV-negative, reported condomless anal intercourse with a male partner in the past 6 months, were capable of providing informed consent, and were willing and able to return to the study site every 6 months to complete study-related activities. By design, half of the participants were living with HIV, and half were substance using. Following the COVID-19 pandemic “stay-at-home order” in California, all in-person research activities were stopped on March 13, 2020, and remote study visits were launched starting March 31, 2020. For this analysis, we included all participant remote study visit data collected from March 31, 2020, through August 30, 2021 .The Institutional Review Board at the University of California Los Angeles approved the study, and all participants provided written informed consent prior to study participation. During in-person study visits, which occur every 6 months, participants complete a self-administered, computer-assisted survey and provide biological specimen for HIV testing . During the remote visits included as part of this analysis, no biological samples were collected. HIV status was based on testing done at the previous follow-up visit, which occurred 6 to 8 months prior for most respondents. Participants were sent an electronic link to the study questionnaire for each remote study visit, which was comparable to the survey used as part of the in-person study visits and included additional questions related to the COVID-19 pandemic. In addition to sociodemographic characteristics, the questionnaire collected information on current substance use, mental health, sexual risk behaviors, and COVID-19 experiences and the impact of the pandemic on overall health and well-being.The impact of the COVID-19 pandemic has been vast in terms of who has been impacted and broad in terms of how people have been affected. Our findings highlight the experiences of those living at the intersection of multiple colliding epidemics and vulnerabilities, including COVID-19, substance use, mental health, and HIV. In particular, our results indicate increases in symptoms of depression and anxiety with the highest levels noted in the most immediate time frame following the COVID-19 pandemic and a reversion to pre-pandemic levels within 17 months of follow-up. Our results also indicate changes in substance use linked directly to experiences resulting from the COVID-19 pandemic, with a high proportion of participants reporting cannabis use to cope with their heightened anxiety and depression. Furthermore, we find that interruptions in care due to the COVID-19 pandemic, particularly interruptions in mental health care, can link with negative outcomes along substance use and STI/HIV risk and underscore the intersectional vulnerabilities experienced by these individuals.

We used MPlus to conduct exploratory and confirmatory factor analyses of the 12 DSM-IV/DSM-5 criteria in the same sample. Exploratory analyses were conducted in the full sample, while subsequent confirmatory factor analyses were conducted in African-American and European-Americans ,trim tray for weed separately by sex, using a multi-group framework. Initially, factor loadings and thresholds were constrained across the ethnic groups and across sexes. Individual sub-models were tested to determine whether allowing the factor loading and threshold for each criterion to vary across the groups resulted in a significant improvement in model fit. The model that accommodated all statistically significant differences was used to generate factor scores that were subsequently used for genome-wide association analysis.The sample used for analyses was restricted to those who reported at least one lifetime use of cannabis . These individuals are characterized with respect to the 12 individual DSM-IV/DSM- 5 criteria in Table 1. Prevalence of each criterion was higher in males than females for both ethnic groups, and males, regardless of ethnicity, were more likely than females to meet criteria for DSM-IV and DSM-5 diagnoses. However, several intriguing ethnic differences emerged. For both sexes, hazardous use, use of larger amounts or for a longer period of time and desire to quit or multiple failed quit attempts were differentially endorsed by EA and AA. EA men and women were more likely to endorse hazardous use and less likely to endorse using larger amounts or for longer than intended and failed quit attempts than their AA counterparts. In addition, tolerance, time spent using cannabis and the DSM-5 criteria of withdrawal and craving were more commonly reported by AA women than their EA counterparts—similar differences were not noted for men. The prevalence of DSM-IV cannabis abuse/dependence was higher in men compared with women, but no within-sex ethnic differences were noted. For DSM-5, cannabis use disorder was again more common in men than women, and there were no ethnic differences in men. However, AA women were more likely to meet criteria compared with their EA counterparts . Comparing the prevalence of DSM-IV vs. DSM-5 cannabis use disorders—within each group, very modest changes were observed. Decrease in overall prevalence was noted for EA, while AA women showed a slight increase and AA men remained unchanged. Examining the [95%] confidence limits for the point estimates, only the decrease in prevalence in the EA was statistically significant while the estimates in AA subjects could be equated across diagnostic classification scheme .An exploratory factor analysis of the full sample revealed that a single factor solution provided a reasonable fit to the data : 0.996, root mean square error of approximation : 0.054. While a 2-factor exploratory solution modestly improved these fit indices,the inter-factor correlation was 0.90. Hence, we proceeded with the more parsimonious single factor confirmatory analysis, which readily approximates item response parameters.

Confirmatory factor analysis of the 4 DSM-IV abuse, 6 DSM-IV dependence and the DSM-5 withdrawal and craving criteria revealed high factor loadings for all criteria except legal problems , which was excluded from further analyses comparing factor loadings and thresholds for each individual criterion across EA and AA males and females. The factor loadings and thresholds from the model allowing for statistically significant differences across individual items are shown in Table 2. Factor loadings and thresholds could not be constrained across the groups for hazardous use,interpersonal problems, withdrawal,using more than intended , repeated/failed quit attempts,time spent and physical/psychological problems . Factor scores that accommodated these differing thresholds and factor loadings were created for each of the four subgroups and used for genomic analyses.Individual signals did not surpass the Bonferroni corrected genome-wide significance threshold of p < 5 × 10−8. The results for the top 20 SNPs are presented in Table 3 . For the EA sub-sample, 11 SNPs on 17q23-24 appeared to be associated at nominal levels of significance although none surpassed the genome-wide threshold of 5 × 10−8.The top SNP, rs6504555, was an intronic variant in the bromodomain PHD finger transcription factor gene—a regional association plot for this region of chromosome 17 is shown inFig. 1, indicating a high degree of linkage disequilibrium across the associated SNPs. With the exception of rs11870068, the remaining chromosome 17 SNPs were in moderate to high linkage disequilibrium . In the AA sub-sample, results did not aggregate in any particular chromosomal region. The most significant SNP, rs4364205, on chromosome 3, was intergenic. Meta-analysis of the results from the EA and AA sub-samples did not yield a boost in statistical significance . This was evident from a comparison of results in the EA and AA sub-samples. Of all SNPs with p-values < 0.05 in EA sub-sample, only 5% had corresponding p-values < 0.05 in AA sub-sample. However, particularly for the SNPs for the EA sub-sample shown in Table 2, the direction of effect in the AA sub-sample predominantly concurred with the EA sub-sample.We sought to examine the phenotypic and genomic architecture of a continuously distributed cannabis use disorders factor, psychometrically derived from DSM-5 criteria, in samples ascertained for alcohol, nicotine and cocaine dependence.Analysis of ethnic differences indicated a modest reduction in the prevalence of DSM-5 cannabis use disorders, relative to DSM-IV, in EA. Genomic analyses, using a genome-wide scan, failed to identify SNPs that satisfied statistical thresholds for significance; however, gene-based association implicated genes on the q-arm of chromosome 17. A genome wide variance calculation revealed that 21% of the phenotypic variance in cannabis use disorders was captured by the available common variation on the genome-wide array, but this estimate had a large standard error and was not significant. We used the factor score as our phenotype for genomic analyses.

Incorporating withdrawal and craving, excluding legal problems and combining across DSM-IV abuse and dependence criteria, this factor embodies the ‘spirit’ of the new DSM-5 diagnostic scheme while not being encumbered by concerns that the threshold of 2 or more criteria for diagnosis of disorder is too lax . From a psychometric perspective, our results are consistent with the extant literature . For instance, despite our sample being ascertained for alcohol, nicotine and cocaine dependence, which inflated endorsement rates of individual criteria , our high rates of hazardous use were comparable with those reported for lifetime cannabis users from the general population as reflected in data from the National Epidemiological Survey of Alcohol and Related Conditions . Likewise, broadly consistent with numerous other studies, the DSM-IV abuse criterion of legal problems was infrequently endorsed and had a weak factor loading, affirming its proposed exclusion from DSM- 5. The overall prevalence of the remaining criteria, although much higher than in general population cohorts, supports the presence of a unidimensional construct across sexes and ethnicities. Craving and withdrawal, both of which have been added to DSM-5, performed well, with high factor loadings supporting their inclusion. Overall, trimming tray weed rates of diagnostic DSM-5 cannabis use disorders appear to be modestly lower than those for DSM-IV abuse/dependence, but only in EA, particularly men. This finding is highly comparable with epidemiological analyses of alcohol symptomatology in U.S. and with results from the 2007 Australian National Survey of Mental Health and Well being, which reported a decrease in the lifetime rate of cannabis use disorder from 6.2% to 5.4% when transitioning from DSM-IV to DSM-5 . In our sample, this decrease was uniformly attributable to individuals who endorsed hazardous use alone, which results in a DSM-IV diagnosis of cannabis abuse but not a DSM-5 diagnosis of cannabis use disorder, because it falls below the latter’s minimum two-symptom threshold. No differences were noted in AA men , and this is also not surprising. Individuals endorsing this criterion alone tend to be of higher socio-economic standing and tend to, overwhelmingly, endorse this criterion due to a history of drinking and driving . That socio-economic status may correlate with ethnicity is expected—in our data, 45.9% of AA participants reported a gross annual income of less than $20,000, vs. 15.4% of their EA counterparts. Upon examining gender and ethnic differences within classification version , the only significant variation was noted for DSM-5 diagnoses in AA women who were more likely to receive a diagnosis of DSM-5, but not DSM-IV cannabis use disorder, relative to their EA female counterparts. Intriguingly, also relative to their EA counterparts, they were less likely to endorse hazardous use but more likely to endorse numerous other criteria, with the exception of giving up important activities and use despite physical/psychological problems. This finding may be attributable to the larger number of AA women that were ascertained from the cocaine dependence study vs. other studies. Although this observation holds true for the men as well, and the prevalence did not vary across AA and EA women, it is possible that AA women from the FSCD study represent a high-risk group. For instance, when compared to the alcohol and nicotine dependence studies, AA women from the cocaine study were more likely to report lower household income and a greater likelihood of less than a high school education . Thus, this vulnerability might reflect environmental adversity rather than increased genetic susceptibility, and in any case, is accounted for in the genomic analyses by incorporating study sample and gender as covariates. From a genetic perspective, the single SNP analyses did not reveal any genome wide significant signals. This is likely because our sample is under powered, even with a quantitative trait, to detect single variants of modest effect size. Using GWA Power , we estimated power available in our dataset to identify SNPs of varying effect size. Power was 80% when an effect size of 0.01 was anticipated . Increasing efforts to amass larger samples with comparable cannabis-related data would afford greater power to detect variants of more modest effect size via meta- and mega-analyses. However, few current studies have DSM-5 criteria data. In this regard, factor scores such as ours may prove to be useful phenotypes as they can accommodate DSM-IV and DSM-5 based assessments of vulnerability to cannabis use disorders. In contrast,the gene-based analyses conducted with the EA sub-sample identified a cluster of genes, of varied function, on the q-arm of chromosome 17 that appeared to contain an aggregation of variants associated with DSM-5 cannabis use disorders.

Follow-up assessments were then conducted annually from Years 2 through 8 and then again in Years 10 and 12 through face-to-face interviews, self-administered surveys, and web based surveys. Follow-up rates were high,grow tent for sale ranging from 91% in Y2 and 73% in Y12. The university’s Institutional Review Board approved the study, and informed consent was obtained. Additional detail on recruitment methods and follow-up procedures can be found elsewhere . From the original sample of 1253 young adults, 541 participants enrolled in a degree-seeking graduate program at some point by Y10 of the study. Of these, 21 participants were excluded from analyses. Five of these participants were excluded because upon further examination of other assessment responses, they had listed graduate school enrollment by mistake, and one participant was excluded because information on their specific graduate degree type could not be identified. In addition, to ensure participants had adequate time to complete their degree, 15 participants who first enrolled in a doctorate or professional degree program in Y10 were excluded, giving a final analytic sample of 520 participants.Alcohol use was measured annually in Y1-Y12. To assess frequency of alcohol use, participants were asked, “In the past 12 months, on how many days have you drank any drink with alcohol in it?”. To assess quantity of alcohol use, participants were asked the number of drinks they had on a typical drinking day . Data on days used during the past year were used to estimate average alcohol use frequency for descriptive purposes. Marijuana use frequency was assessed annually in Y1-Y12 with the question “In the past 12 months, on how many days have you used any type of marijuana?” . Data on days used during the past year were used to estimate average marijuana use frequency for descriptive purposes. Past-month frequency of both alcohol and marijuana use were also assessed, but because of the high degree of correlation with past-year measures , only past-year variables were used in the analyses. For each participant, alcohol use frequency, alcohol use quantity, and marijuana use frequency were averaged separately for each of two time periods: before and after the first year they indicated enrollment in a graduate degree program. The mean for each of the six separate variables was used to capture variation in substance use during the pre- and post-enrollment periods, particularly because the before enrollment period included the undergraduate college years as well as the interim years after college graduation but before graduate school enrollment.Gender was coded by the interviewer in Y1 as either male or female. Race/ethnicity was measured in Y3, and response options included white; Black/African-American; American Indian or Alaskan Native; Native Hawaiian; Other Pacific Islander; Asian; and Hispanic, Latino, or Spanish. Participants could also write in a response or choose “Don’t Know/Refuse to Answer”. Given that the majority of the sample was non-Hispanic white, race was dichotomized into white and nonwhite. Marital status was measured in Y4-Y8, Y10, and Y12.

Participants indicated whether they were married, divorced, widowed, separated, in a civil union or domestic partnership, or never married. A dichotomous variable was created to represent whether or not participants were married at any point during Y4-Y12. The number of children participants had was measured in Y4-Y8, Y10, and Y12. A dichotomous variable was created to represent whether or not participants ever had children by Y12.Descriptive statistics were used to analyze the distributions of all study variables. Pearson correlation coefficients were used to analyze the relationships between all six alcohol and marijuana use predictor variables. A series of logistic regression models were fit to assess the relationships between alcohol and marijuana use and graduate degree completion. First, in Stage 1, separate logistic regression models were fit to analyze the relationships between each alcohol and marijuana use predictor variable and graduate degree completion while controlling for demographic and program characteristics. Second, in Stage 2, a best- fitting model was obtained by entering each of the six alcohol and marijuana use predictor variables into the model one at a time, retaining any predictor variable that was statistically significant and dropping those that were not significant. All demographic and program characteristic variables were retained in the final model regardless of significance. The Nagelkerke R2 value was used to examine the variance in graduate degree completion explained by the Stage 2 variables. A similar method has been used in prior work by the research team . SPSS Version 24.0 was used for all analyses, and the alpha level was set at 0.05.The majority of the sample was female and non-Hispanic white , with 42% of participants getting married and 14% having children by Y12 . About two-thirds had enrolled in master’s degree programs and 31% had enrolled in doctorate or professional degree programs, with Y5 as the most common year to begin graduate school. The majority of the sample completed their graduate degree by Y12.The majority of participants drank alcohol during at least one year before graduate school enrollment and after graduate school enrollment . Among drinkers, the average alcohol use frequency was about 75 days during the past year before enrollment in graduate school and 88 days during the past year after enrollment . Among drinkers, mean alcohol use quantity decreased from a mean of 3.9 drinks per drinking day before graduate school enrollment to 2.6 drinks per drinking day after enrollment. The typical quantity consumed for male drinkers was greater than female drinkers both before and after graduate school enrollment . Based on past-year data, it was estimated that about 35% of drinkers drank less than weekly and about 24% drank twice a week or more before graduate school enrollment.

After graduate school enrollment, 32% of drinkers drank less than weekly and about 31% drank twice a week or more.As seen in Table 2, marijuana use frequency among users was about the same prior to and after graduate school enrollment with a mean of about 40 days during the past year. Among those who used marijuana prior to graduate school enrollment, 56% used once a month or less and about a quarter used at least weekly . Among those who used marijuana after graduate school enrollment, 64% used once a month or less and about 18% used at least weekly . The correlations between the six alcohol and marijuana use predictor variables are presented in Table 3. There were moderate to strong correlations between the before enrollment estimates and the after enrollment estimates. Despite this statistical overlap, both before and after enrollment variables were retained due to their importance to the research question of interest. Alcohol use frequency before graduate school enrollment was strongly correlated with alcohol use quantity before graduate school enrollment and moderately correlated with alcohol use quantity after graduate school enrollment . To avoid the potential for multicollinearity effects on the statistical models, only the alcohol use frequency variables were retained for further analyses. There is prior evidence that frequency of alcohol use increases during the post-college period while quantity of alcohol use decreases ,indoor grow tent and alcohol use frequency has higher sensitivity and specificity in identifying alcohol-related problems than alcohol use quantity .Stage 1 results showed that, even after controlling for demographic and program characteristics, marijuana use frequency after enrollment was negatively associated with graduate degree completion . The best-fitting model included alcohol use frequency before graduate school enrollment and marijuana use frequency after graduate school enrollment, which were both significantly associated with graduate degree completion after being entered into a model together and with the demographic and program characteristics. Alcohol use frequency before enrollment was positively associated with the odds of graduate degree completion . In contrast, as marijuana use frequency after enrollment increased, the odds of graduate degree completion decreased . In the best-fitting model, gender, marital status, and first year of graduate school enrollment were associated with graduate degree completion.

Female students had almost two times higher odds of graduate degree completion when compared with male students, and married students had more than two times higher odds of graduate degree completion when compared with those who had never been married. In comparison with students who began their graduate degree in Y10 , students entering graduate school in Y5 , Y6 , and Y7 had significantly higher odds of graduate degree completion. 4. Discussion This study examined whether or not alcohol and marijuana use before and after graduate school enrollment were associated with graduate degree completion. Alcohol and marijuana use were moderate among participants in this sample. Results showed that more frequent marijuana use after graduate school enrollment was associated with decreased odds of graduate degree completion after adjustment for potentially confounding variables. This finding is consistent with prior research that has shown a relationship between frequent marijuana use and degree non-completion among high school and undergraduate college students . Marijuana use was less prevalent after graduate school enrollment as compared with before, which is consistent with research showing that marijuana use declines as young adults age . However, while past-year marijuana use frequency among marijuana users who completed their graduate degree declined from 40 days before enrollment to 35 days after enrollment, frequency among users who did not complete their graduate degree increased from 45 days before enrollment to 85 days after enrollment. There are several mechanisms through which marijuana use might affect degree completion. The first is through decreased academic performance, with underachievement cited as the most well-supported correlate of marijuana use . While little research has been done on the relationship between marijuana use and decreased academic performance among graduate students, existing evidence among high school and college students shows that frequent marijuana use is associated with academic unpreparedness , lower grades , and lower academic achievement . The relationship between marijuana use and degree non-completion might also be explained by the effects of marijuana use on cognition .

Curcumin, cinnamon, hemp seed and Polygonum cuspidatum are all naturally occurring products that have been used in traditional Chinese medicine for many years.We found that pre-treatment of our SH-SY5Y cells with compounds isolated from these products did not have an effect on rotenone toxicity. This is in contrast to previous studies that have demonstrated the protective effect of curcumin against rotenone in SH-SY5Y cells.It should be noted that while both studies have utilised SH-SY5Y cells, our cells contain human TH1. Therefore, it is possible that the transfected TH1 and the increase in TH protein expression seen with rotenone treatment could be potentiating rotenone induced cell death and playing a role in our inability to protect against rotenone toxicity. As TH is the rate limiting enzyme in DA synthesis, it is possible that the increased TH protein expression in response to rotenone could have induced an increase in DA production. The increased DA could possibly accumulate within the cytoplasm of the cells and lead to increased oxidative stress, proteasomal inhibition and mitochondrial dysfunction that could be contributing to rotenone induced cell death. This hypothesis is similar to a popular hypothesis that suggests an involvement of hyper-activation of TH and DA production in early PD pathogenesis.Interestingly, the same compounds did display the ability to protect against H2O2 induced toxicity. In addition, all tested compounds also prevented the increase in PARP-1 cleavage seen with H2O2 treatment indicating a reduction in apoptosis and supporting the findings of the viability assay as well as the findings of previous studies that suggest the novel compounds possess antioxidant activity.Moreover a previous study has demonstrated the ability of cinnamaldehyde to modulate the release of catecholamines from a rat pheochromocytoma cell line,indicating this compound has the potential to not only be neuroprotective but may also further ease the symptoms of PD by promoting catecholamine release. While the in vitro antioxidant capabilities of these compounds have been demonstrated previously, this is the first study to demonstrate the neuroprotective properties of cinnamaldehyde, caffeoyltyramide and piceatannol glucoside in a dopaminergic cell line in response to H2O2. In summary, we demonstrated that the effect of rotenone on these cells is more complicated than just the induction of oxidative stress and suggest that perhaps TH may be involved. Curcumin, cinnamaldehyde, caffeoyltyramide and piceatannol glucoside successfully prevented H2O2 induced cell death, making this the first study to demonstrate the neuroprotective potential of these natural compounds in a SH-SY5Y cellular model of oxidative stress.

The combination of traditional and man-made fiber-reinforced composites provides many advantages in different areas of engineering and technology. The commonly used natural fibers in engineering fields are sugar palm, flax, sisal hemp, kenaf, and Abaca fibers. Incorporating natural fibers in high-strength synthetic fibers like carbon and Kevlar improves mechanical properties, namely, stiffness, toughness, moisture resistance. The properties of composite specimens have been enhanced due to addition of nanomaterials and lignocellulose fibers as reinforcement in composites.The properties of composites depend on the individual element characterization like matrix and fibers type, chemical properties of matrix and fibers. The hybrid reinforced polymer composite consists of natural and synthetic fibers with short fiber and random orientation and long fiber in mat form. In short and randomly oriented fiber-based composite, the sliced fibers are mixed in a fixed quantity of thermoplastic resin, melt mixer is used for homogeneous mixing. The product taken from the mixing chamber is a chunk form of fiber-based polymer composites. The small pieces of fiber/ matrix composites are obtained from different techniques: the pultrusion process and injection molding. The different research articles have been available based on the characteristic study of fiber-based composite by varying fibers and resins, fibers length. This type of chopped fiber-based composite fabrication can be used to reduce wastage because of small size of fibers, and also it is economical. The hybrid composite consists of two or more different fibers. Sid ika et al. found the experimental results such as impact, flexural, and tensile strengths of natural fibers, namely, Jute and Coir reinforced polypropylene resin composites. It has also been found from experimental results that variation of fiber content by weight fraction such as 25:75, 50:50, 75:25 in the overall loading fibers and the matrix content remains constant. Likewise, Ranjan et al. carried out research on hybrid fibers reinforced polylactic acid resin composite and found the results, namely, tensile, flexural, and impact strength. Additionally, the investigators observed that the strength of the Sisal/Banana fibers was reduced due to the presence of weak adhesive bonding between the fibers and polylactic acid. It creates lower-strength materials. Per z et al. reported the optimum strength and modulus obtained by varying the weight fraction of banana and coir fiber content in fiber-based composite. In addition, 15% of the coir fiber-reinforced polymer composite gives good impact properties, 10% of coir fiber reinforced polymer composites provide the best modulus and strength under the flexural and impact forces. Per z et al. reported a similar enhancement of results in impact strength.

Especially the coir fiber content contributed more to the improvement of impact properties in composite due to the following reason at higher fiber loading, it requires high energy to break the fibers or fiber pullouts high lingocellulosic content. The composite strength and modulus may be reduced at higher fiber loading due to the non-maintaining homogenous distribution of fiber content, which causes agglomeration in the region. The agglomeration region acts as a stress concentrated region, and it is responsible for the initial crack to the failures. The following are the disadvantages of the natural fibers, lower strength, weaker inter facial bonding, hydrophilic and hydrophobic nature of fiber and matrix, respectively. To enhance the mechanical properties of natural fiber reinforced polymer composites following ways can be adopted, i.e., fiber treatments, the addition of synthetic reinforcements, and coupling agents. The long fiber reinforced polymer matrixes have been made from prepregs or woven mats. The prepregs have formed by the melting and pressing fiber fabrics at high temperatures. The final form of long fiber reinforced polymer composite is formed by stacking the prepregs layer by layer. Kar dumen et al. reported the influence of stacking sequence on the mechanical properties by using woven flax and non-woven jute fiber reinforced on the polypropylene resin. They said additionally that the hybrid composite consists of non-woven jute covered by woven flax, which exhibits good strength. In contrast, the hybrid materials consist of woven flax covered by the non-woven jute, displaying good impact strength. Addition of the 10% glass fiber in jute fiber reinforced polypropylene composite has improved tensile and flexural strengths. Dan Mallam et al. fabricated the hybrid composite from the kenaf fiber and polypropylene terephthalate matrix; the two types of hybrid composite, namely woven interplay and interwoven. Amo g the two kinds of hybrid composite, the interwoven ply exhibit good tensile and flexural properties,grow tent complete kit while the woven interplay displays good impact properties. It is summarized from kinds of literature that the highest mechanical properties of the synthetic and natural fibers in Kevlar and Abaca fibers respectively, also very few works have been done in hybrid composites, however, no work has been carried out in the combinations of the Kevlar and Abaca.

Similarly, Glass and Hemp fibers also have specific advantages compared to other fibers. In this work, an attempt has been made to bring benefits of both synthetic and natural fibers in hybrid composites. The mechanical properties namely tensile, flexural, and impact testing, have been performed numerically and experimentally. Aba a, Hemp, Glass, and Kevlar fibers have been used to prepare the composite specimens. The hybrid composite materials consist of Glass/Abaca, Glass/Hemp, and Kevlar/Abaca.In this work, all four fibers, namely, Abaca, Hemp, Glass, and Kevlar fibers, were purchased in the form of long fiber. The thickness of the Abaca fiber is 0.35 mm, length is 200e300 mm, which is obtained from the stem of pseudostem of Musa sepientum. Yellowish Kevlar 49 grade with bidirectional woven fabric has been used, Hemp fibers, with the thickness of 0.08 mm and length of 15e35 mm have been used, Glass fiber in mat form, length of 6e12 mm and 0.01 mm diameter. All fibers were purchased from Vruksha composite, Chennai, Tamilnadu. The hybrid composites were fabricated by alternating natural and synthetic fibers into the matrix. In the present work, epoxy resin was used as a matrix material. The liquid resin is a colorless, highly viscous liquid, at 25 C the density and viscosity are 1.16 gm cm 3 and 900 cps, respectively. Harer is used to reduce the curing time, namely, Amine purchased from Bangalore-based company, namely, Naptha resin and chemicals. The natural fibers are purchased from Maruthi Peach Company, Tirupur district, Tamil Nadu, India. The synthetic fibers, namely Kevlar and Glass fibers, are purchased as yarns from Go Green products, Coimbatore, Tamilnadu, India, and weaved of fibers alternatively with 6.7 6.7 yarns per cm. The composite specimen is bidirectional, and the bulk and linear densities were used to calculate the cross-section of yarns. The cross sectional area of yarn is 0.015 cm2 . The area is calculated by dividing linear density by its bulk modulus. The density of the different fibers is listed in Table 1.The three different combinations of natural and synthetic fibers-based composites were prepared. The following loads were applied: tensile, flexural, and impact; then, results were reported. In each experiment, three times repeated and took the average value for the graph. A similar work has been carried out by Mohanavel et al., who characterize the mechanical properties of the hybrid composite, which consists of Glass, Jute, and Madar fibers. Also, they fabricated the hybrid composite with a similar type of stacking sequence, that is, the first and last layer was synthetic fibers, due to the lesser water-absorbing characteristics of the fibers.

A high-resolution structural scan was acquired to position the voxel during data acquisition and to determine the tissue composition of the voxel through segmentation.The T1-weighted scan was processed using the standard Free Surfer pipeline for tissue segmentation and anatomical parcellation.Further details related to the Free Surfer processing can be found online,and in one of our previous publications.In-house software was used to compute the transformation matrix from the scanner coordinates to the FreeSurfer-processed T1-weighted image.A mask representing the spectroscopy voxel in the anatomical image space was then created using tools from the FMRIB Software Library,planting racks which was subsequently segmented and parcellated using the Free Surfer anatomical information.Thus,each T1-weighted voxel within the spectroscopy volume,was classified as either white matter,gray matter,cerebrospinal fluid,or non-brain,and was further parcellated into subcortical and cortical structures.This was done to confirm a consistent voxel placement across all subjects and to determine the basic tissue composition within the voxel.Further details of the voxel composition can be found in the results section below.Data were analyzed with the Statistical Package for the Social Sciences,version 19.Data were examined for normality in order to ensure appropriateness of parametric statistics.Univariate analyses of covariance were used to test group effects between the MJU individuals and the controls,with age and alcohol use entered as co-variates.Group and sex were both entered as between-subjects variables.Alcohol use frequency over the past 12-month period summarized by the PEI was used in the above model as the alcohol use co-variate.Two-way interaction effects between group and sex,when present,were examined further by running the model separately in males and females,or by examining sex effects within MJU individuals and controls.Finally,significant effects were re-evaluated by matching the MJU and control samples by age to verify that patterns remained significant with more stringent control over developmental differences that might otherwise impact the findings.There was no group by sex interaction for age or for IQ.

Marijuana users were college students of middle to high-middle socioeconomic backgrounds and most were free of a non-substance DSM-IV Axis I diagnosis.None were psychotic.Nearly all met DSM-IV diagnostic criteria for marijuana abuse or dependence.Use of other recreational drugs within the MJU group was limited,with no participants meeting DSM-IV criteria for abuse or dependence.One subject met diagnostic criteria for current alcohol dependence,and a small proportion met criteria for alcohol abuse.Compared to controls,alcohol use over the past twelve months use was found to be significantly higher in the MJU group,F = 43.93,p b 0.001.Marijuana users on average had a PEI score of 3.7,which corresponds to endorsing use of alcohol between 21 and 100 times in the previous 12 months.Controls on average had a PEI score of 1.5,which corresponds to endorsing use of alcohol between 1 and 20 times in the previous 12 months.When the sample is restricted to include only individuals aged 17 and higher,the difference in alcohol use remains significant but the mean value for control participants is slightly higher at 1.9.Thus,the amount of alcohol use endorsed over the past twelve months was entered as a covariate in analyses comparing metabolite concentrations between groups.The marijuana users reported that their age of first use of marijuana was 15.2 ± 1.2 years,and also reported smoking 9.8 hits per day during the past year.In addition,supplemental analyses were conducted to verify that female users did not differ from male users in their self-reported patterns of use,age of use onset,use of alcohol,or symptoms of psychopathology.Findings are presented in Table 3.The only group difference to emerge was that female users reported fewer symptoms overall of alcohol abuse/ dependence than did males.Otherwise,they did not significantly differ in variables that would suggest an increased frequency or duration of marijuana use,use of other substances,or presence of concomitant psychopathology.The spectroscopy voxel was consistently placed in the same anatomical location,centered in striatum,in both marijuana users and controls.

The voxel was primarily composed of gray matter,as determined by the Free Surfer parcellation procedure.The majority of the voxel composition was statistically similar between groups,with the exception of the pars opercularis,which accounted for less than 1% of the total voxel composition.The remaining 2% of the voxel composition was relatively variable.Moreover,these additional regions always represented very small amounts of tissue,and were not represented in all subjects.This study examined a cohort of college-aged heavy marijuana users and a control group of non-using young-adults.Using MR-spectroscopy,it was shown that females,but not males,who used marijuana heavily starting in mid-adolescence and persisting for several years have lower levels of glutamate and glutamine in the dorsal striatum when compared to controls,even after accounting for age and alcohol use.Similarly,female but not male users differ from controls in their estimated concentrations of myo-inositol,demonstrating higher levels than controls.These patterns are interpreted as pathological in the female users given that male users had comparable levels to controls of both sexes.Female users did not differ from male users in their overall rates of self-reported marijuana use,in their concomitant level of alcohol use,in their numbers of symptoms of marijuana dependence,or presence of other conditions that might impact brain metabolism.These findings have broad parallels in the extant literature,both in relation to the overall patterns observed but also in relation to sex differences.Decreased glutamate/glutamine concentrations have been reported in two other MRS studies of marijuana users,one that focused on the basal ganglia and one that targeted the anterior cingulate cortex.First,in an older cohort of marijuana users than is described in the current study,Chang et al.reported lower glutamate levels in the basal ganglia,suggesting that heavy marijuana use during young adulthood as well as later in life is associated with disruptions in glutamate signaling as has been shown for other drugs of abuse.Recently,Prescot et al.reported lower glutamate concentrations in the anterior cingulate cortex,which was nonetheless strongest when females were eliminated from the analysis.Interpretation of the current findings is complicated by poor resolution of the glutamate versus glutamine signal.Glutamate is present in all cell types with the largest pools evident in glutamatergic neurons; smaller pools are evident in GABA-ergic neurons and astroglia.

Upon release,astroglia convert glutamate to glutamine,which in turn is transferred back to the neuron for conversion once again to glutamate.Glutamine is primarily located in astroglia.Thus,low glutamate levels would be difficult to ascribe to a particular neuronal process.In contrast,if glutamine levels are low,then glial dysfunction may be present,a finding that would be consistent with white matter aberrations in marijuana users.Others have not reported specific metabolic disruptions in female marijuana users; indeed,within young samples,marijuana is more commonly used in males.Although it has been recognized that females are at an increased risk for some behavioral consequences of drug use such as sexual risk-taking and an increased risk of depression and anxiety following a pattern of daily marijuana use,sub irrigation cannabis there are relatively few human studies of brain-based sex differences associated with marijuana.McQueeny et al.showed adolescent girls had larger amygdalae and increased internalizing symptoms when compared to both control and marijuana using boys.Moreover,certain behavioral problems have also been linked to prenatal marijuana exposure in girls,but not in boys.Recent neuroimaging work suggests that young female users may be vulnerable to marijuana-induced alterations in brain volume,given suggestions of greater prefrontal cortex volumes and relatively poorer levels of executive function.Alcohol is similarly disruptive to females’ cognitive function and regional brain morphology,and it has long been recognized that females are more vulnerable to psychomotor sensitization with psychostimulant exposure.Preclinical data are somewhat stronger and indicate that female adolescents are particularly vulnerable to the effects of long-term THC administration on the CB1 receptor system in multiple brain regions,including the prefrontal cortex,striatum,and periaqueductal gray.A recent study of THC in mid-adolescent rats during the period of drug administration and following abstinence indicated greater sensitization of THC-induced locomotor depression in females versus males.Moreover,high doses resulted in increased anxiety-like behaviors during THC administration,particularly in females,although a general tendency is for females to experience greater anxiolytic effects of the drug.Glutamate is critically important in the neuroplasticity that accompanies the transition from drug use to abuse.Under conditions of extreme trauma or stress,its release is associated with neurotoxicity and cell death.Endocannabinoids block glutamate release under such conditions,which could lead to neuroprotection.However,the concomitant observation of high mIns levels argues against this interpretation.Given that mIns is considered to be a glial marker,high levels would be associated with gliosis as well as white matter injury as occurs in the context of neural injury.High mIns concentrations have been observed in early dementia,in frank Alzheimer’s disease,as well as in abstinent methamphetamine users,although this latter observation was in the frontal lobes.This pattern is intriguing given that deficits in learning and memory represent one of the robust areas of reported cognitive dysfunction in marijuana users.Although our data analyses do not suggest that female marijuana users in this sample are more vulnerable to cognitive impairments,this is a relatively young and high functioning sample.It may be that frank behavioral deficits will emerge more strongly in females over time as chronicity of use progresses.We hypothesize,too,that we may have observed altered NAA levels had we also measured frontal concentrations of each metabolite.Even though our statistical analyses do not show any significant effect of alcohol,it is important to consider the possibility of an underlying biological interaction between the two substances.Male marijuana users in this study had the highest levels of alcohol use,but did not show significant neurochemical alterations relative to controls.

Females showed the greatest apparent impact of marijuana use on Glx and mIns,but in the context of lower levels of alcohol use.These findings could suggest a neuroprotective effect in individuals who use both marijuana and alochol,as described by others.Alternatively,previous work has shown greater levels of Glx in the anterior cingulate of chronic alcohol users relative to controls.Considering this,taken together with the findings of the present study,it is possible use of the two substances together may drive metabolite concentrations to “normal” levels via opposing processes,as has also been suggested by others in the context of brain morphology.Differences in metabolic function in heavier versus lighter alcohol users can also impact the conversion of acetate into glutamate.It is possible,then,that the male marijuana users in this study who were heavier alcohol users as compared to females,demonstrated differences in glutamate metabolism,contributing to the observed sex difference.However this assertion is only speculative.While our data do not fully support these conclusions,the issue of alcohol use in the context of marijuana use requires careful examination in future studies.Sex but not group-related effects were also observed in total choline estimated concentrations.Independent of marijuana use,males showed higher estimated concentrations of tCho compared to females.