Many people with SUDs believe they can solve the problem themselves

Posted on October 17, 2022 by hemp2022

CM programs can vary in terms of how the incentive is structured and can use incentives in the form of vouchers, prizes, or cash. The vouchers have monetary value and can accumulate in a clinic-managed account as the patient remains substance-free. Instead of providing money directly to patients, program or clinic staff can use the earned amount in vouchers to purchase items requested by the patient that are reasonable and consistent with positive lifestyle change . Typically, patients purchase clothing, electronic equipment, sporting/hobby items, and recreational items with their vouchers. Items can also be stored in a space that CM studies refer to as a “prize cabinet” . The prize cabinet provides patients the opportunity to identify tangible prizes that may motivate them to continue treatment. The prize cabinet can consist of items ranging in value, from jumbo value to large or medium prizes to small prizes . Typically, patients in incentive programs earn at least one voucher for each urine sample that tests negative for the target substance. The structure of the incentive typically escalates, with each subsequent behavior being rewarded at a higher value than the previous one, thus incentivizing longer-term abstinence.Provider supply including provider attitudes and geographic access can pose structural barriers to SUD treatment. Lack of available providers to treat SUDs in a specific geographic region, and more specifically a lack of providers who have been trained to administer CM as part of SUD treatment, will limit the extent to which patients can access treatment. Patients may also face supply issues or geographical and transportation barriers to accessing SUD treatment using CM . A provider office would need to have the ability to monitor abstinence through urinalysis as well as the administrative capacity to administer the program. Provider willingness to treat SUDs using CM can also be limited; not all providers are comfortable offering CM programs due to a lack of training, lack of office space and support resources, cost, time pressure, or personal beliefs against using incentives to treat SUDs .

Technological advancements in the field, such as webbased contingency management or smartphone technology, are addressing administrative barriers, including staffing and training,cannabis indoor grow system and have shown to increase patient compliance . For many patients with SUDs, attitudinal barriers are the most significant barrier to treatment initiation and persistence . The stigma of SUD and the ability to acknowledge having an SUD can affect patient desire to seek care even more so for those who have co-occurring psychiatric conditions.Rapp et al. tested a Barrier to Treatment Inventory tool to assess perceived barriers to treatment for those with SUDs. They reported significant correlation among six of the seven barrier factors: absence of a problem; negative social support; fear of treatment; privacy concerns; time conflict; poor treatment availability; and admission difficulty. Another barrier for patients participating in treatment specifically using CM is the requirement to travel to the provider’s office, sometimes up to two or three times a week. This can cause more of a burden for patients who do not have flexible schedules and those who are living in areas with a shortage of providers who treat SUDs and a lack of access to providers that are administering CM programs . However, when CM is administered as an adjunctive component of psychosocial treatments in the context of intensive outpatient programs , patients are already traveling to attend therapy, where they will also submit their urine samples, the required two to three times per week.Taken as a whole, treatment of SUDs is inextricably linked bi-directionally with many important SDoH. SDoH such as quality of a person’s local built environment, proximity to crime, educational opportunities, self-efficacy, and income levels can influence a person’s risk for SUDs . Conversely, SUDs can also alter a person’s baseline SDoH namely through the consequences of SUD, such as involvement with the criminal justice system, job loss, unstable housing or family situations, and discrimination against those with treated or untreated SUDs . Disparities for SUDs exist by gender, age, race, sexual orientation. Males tend to have higher rates of substance use disorders than females . Young adults tend to have the highest prevalence of all SUDs, with most rates peaking in the 20s across gender and racial groups .

Although whites tend to have a higher prevalence of most SUDs in young adulthood, Blacks tend to have a higher prevalence in later life . In addition, Blacks, Native Americans, and Mixed-Race adults have a higher prevalence of cannabis use disorder, regardless of age . Further, lesbian, gay, and bisexual individuals are more likely to have SUDs, oftentimes more severe, than heterosexuals . Another risk factor for SUD is related to mental illness. More than half of people with serious mental illness also have an SUD, a combination which is referred to as dual diagnosis . Patients with SMI have a particularly difficult time with addiction because substances are often used as a coping mechanism for mental health symptoms . Treating SUDs is very important because individuals with dual diagnosis are at higher risk of hospitalizations, suicide, premature death, and criminal justice issues than individuals with SMI but no SUD . There is not one standard way to conduct CM for SUDs. This means that there are a range of ways to structure the reward offered for different targeted behaviors across SUDs. The CM program can vary in terms of the duration, incentive value, and format . This lack of uniformity leads to difficulty in combining results across studies. In addition, although each substance is reviewed separately in this report, poly-substance use is common among those diagnosed with SUD, and many patients have more than one SUD. The diagnosis and treatment of multiple SUDs is complex and treatment and recovery rates for each SUD may vary for a single patient. It is possible for a patient to be in recovery from one SUD but not another. While some of the studies included in this review targeted multiple substances, CHBRP did not review each possible combination of substances separately to assess the impact of CM but rather focused on targeted outcomes for the two substances identified as relevant for this bill, stimulants and cannabis, both within the context of poly-substance use and as singular substances of misuse.Studies of CM for cannabis and stimulant use disorder have primarily examined outcomes related to abstinence, treatment adherence, and treatment retention/attrition. All the reviewed studies reported abstinence from targeted drugs as a primary outcome. For these studies, abstinence was measured as the longest duration of continued abstinence or as the number or percentage of negative samples tested during the study period. For studies in which treatment retention and/or treatment adherence were primary outcomes, retention was defined as the number of weeks in treatment , and adherence was defined as the number or percentage of scheduled appointments attended in a given time period. Secondary outcomes examined included health care utilization including emergency room visits and hospitalizations. For all of these studies, outcomes were reported during treatment, at the conclusion of treatment, and/or up to 12 months post treatment. None of the studies reported follow-up evaluations past a 12-month follow-up.

This following section summarizes CHBRP’s findings regarding the strength of evidence for the effectiveness of CM for SUDs. It begins with a broad overview description and evaluation of CM for SUDs and then focuses on the literature specific to stimulant and cannabis use disorder alone and in the context of poly-substance use disorder. It also describes the literature on CM for special populations, including pregnant women and persons with dual diagnoses, defined as having been diagnosed with both severe mental illness and SUD, due to this population’s unique struggles and susceptibility to SUDs. Some studies compared CM alone to treatment as usual while others compared CM as an adjunctive component of other psychosocial programs that are commonly used to treat SUDs . In these cases, CM was used for the duration of treatment and ended at the conclusion of the treatment period. Each section is accompanied by a corresponding figure. The title of the figure indicates the test, cannabis equipment treatment, or service for which evidence is summarized. The statement in the box above the figure presents CHBRP’s conclusion regarding the strength of evidence about the effect of a particular test, treatment, or service based on a specific relevant outcome and the number of studies on which CHBRP’s conclusion is based. Definitions of CHBRP’s grading scale terms is included in the box below, and more information is included in Appendix B.Although individuals with SUDs are typically treated in the same SUD treatment programs together and often use or abuse more than one substance, studies examining the efficacy and effectiveness of CM typically focus on each substance individually given the variable patterns of use across substances. Therefore, the majority of the analyses conducted and conclusions drawn below are broken down by substance. Research on other relevant variables such as vulnerable populations are also worthy of consideration and have been summarized in systematic reviews. Biochemical verification of abstinence is common across all SUDs and is a standard component of most treatment programs. It is nearly universally present in CM programs given that reinforcements are typically made based on biochemically verified abstinence . However, substances are verified by different types of specimen collection. Urinalysis is most commonly used as a verification method for stimulants and cannabis . Metabolites for stimulants in urine are typically detected for a time period of 48-72 hours, which aligns well with the typical time period between screens in treatment programs. Biochemical verification for cannabis presents challenges in terms of the amount of time it can be detected after use, which is highly dependent upon frequency and amount of use. Nevertheless, urinalysis is commonly used as a detection method for cannabis in treatment programs with modifications in terms of frequency and timing to account for potential longerterm storage in bodily tissue. Other types of biochemical verification, such as saliva, breath, and blood, are more common in other substances .This review identified four systematic reviews that examined the structure of the CM program on outcomes. Davis et al. reviewed 69 studies of voucher-based CM programs enrolling a total of 2,675 people. Eighty-six percent of these studies reported positive treatment effects, with an overall standardized mean difference between CM and usual care groups of d = 0.62 .

Lussier et al. also conducted a meta-analysis of 30 studies of voucher-based CM programs and found that greater effects were seen for immediate rewards compared to delayed rewards. They also found that the abstinence effect size was proportional to the size of the reward . Benishek et al. reviewed 19 studies enrolling 2,581 participants in prize-based CM studies. They found that prizebased CM was effective in increasing abstinence during treatment with an average treatment effect size of d = 0.46 . In another systematic review focused primarily on abstinence, Prendergast et al., found that among the 47 studies it reviewed, the three most frequently used types of CM were vouchers , take home methadone doses , and cash . The value of the rewards given in CM programs varies considerably in the literature. Although the amount that patients receive for the earliest negative samples are often relatively low , escalating values based on continuous abstinence enables them to receive rewards of higher value in relatively short periods of time. Maximum single rewards range from as little as $0.45 to as much as $25.89 , with maximal total earnings ranging from $180 to $1,155 over a 12-week period . In a review of studies, the average total available to earn over 12 weeks was $914.46 . Other studies that have extended longer than 12 weeks and/or used other types of reinforcement models have offered reward values up to $3,201 across 24 weeks, $2,294 in living expenses over 12 weeks, and $5,800 worth of take-home methadone doses over 52 weeks . One RCT examined the relative effect of the total value of rewards on abstinence and attendance among individuals with stimulant use disorder in a methadone patient population. They found that patients offered larger prizes whose value maxed out at $560 achieved longer durations of abstinence than those offered prizes whose value maxed out at $250.

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Muscle capillary density is closely related to the oxidative capacity of skeletal muscle

Posted on October 14, 2022 by hemp2022

Among athletes, this is high considering the WHO’s 25% worldwide estimate of smoking prevalence. It can be hypothesized that the metabolites stem mostly from smokeless tobacco due to the adverse effects of conventional cigarettes for athletes, which most severely affects athletes engaging in endurance type sports. Further, a large number of human and animal studies have found nicotine-induced improvements in several aspects of cognitive function, including learning and memory, reaction time and fine motor abilities . Studies addressing the question of a direct performance enhancing effect of nicotine are rare but will be summarized here. Sports most affected include ice hockey, skiing, biathlon, bobsleigh, skating, football, basketball, volleyball, rugby, American football, wrestling and gymnastics. These sports seem to gain performance benefits from the stimulating effect of nicotine as evident from the use of other prohibited stimulants according to the Anti-Doping Database. Muendel et al. found a 17% improvement in time to exhaustion after nicotine patch application compared to a placebo without affecting cardiovascular and respiratory parameters or substrate metabolism. In this sense, nicotine seems to exert similar effects as caffeine by delaying the development of central fatigue as impaired central drive is an important factor contributing to fatigue during exercise. To date, no improvement on anaerobic performance has been reported . It is important to note that, compared to rest, exercise can lead to increased drug absorption from transdermal nicotine patches, possibly due to exercise-induced increase in peripheral blood flow at the site of the transdermal patch. Lenz et al. report increased plasma nicotine levels and toxicity due to increased drug absorption during physical exercise. To prevent undesirable side effects, health professionals,4×8 grow table with wheels trainers and coaches should therefore be aware of proper transdermal patch use, particularly while exercising. Athletes are encouraged to inform their physician about their exercise routine before beginning transdermal patch use. Athletes should further be familiar with signs and symptoms of drug toxicity related to the medication contained in thetransdermal patch and consult their physician if signs or symptoms arise.

Reducing exercise intensity and duration for the first 1–2 weeks until potential side effects are known might also help to minimize toxicity. To reduce increased exercise-induced drug absorption, athletes are encouraged to avoid exercising in extreme environmental and temperature conditions, wear appropriate breathable sports garments and drink plenty of fluids to prevent dehydration. Additionally, although nicotine may have ergogenic potential, it is also highly addictive, reportedly as addictive as heroin and cocaine. Therefore, detrimental effects on motor performance can be altered after a short abstinence duration. Burtscher et al. noted that motor performance declines in heavy smokers after a short period of abstinence appears, this decline being similar to the motor symptoms of Parkinsonism. The abstinence symptoms are ameliorated by cigarette smoking. It is important to consider the concerning addictive potential with following deterioration of motor performance upon abstinence. Interestingly, however, it was noted that moderate and vigorous exercise led to significant reductions of the desire to smoke among abstaining smokers, possibly via reductions in cortisol. A recent meta-analysis showed that exercise has the potential to acutely reduce cigarette cravings and could therefore be a promising strategy to attenuate withdrawal symptoms in smokers. It is also important to mention that the vasoconstriction mediated by nicotine could limit exercise performance in a hot environment. As skin blood flow increases during exercise to transfer heat impaired nicotine-induced skin blood flow may be ergolytic. A recent meta-analysis conducted by Heishman and colleagues clearly suggests significant effects of nicotine on fine motor abilities, including attention and memory. Participants of the studies included in the metaanalysis were mainly nonsmokers, therefore avoiding confounding of nicotine withdrawal. Considering the importance of cognition in sport, such an optimization of neurobiological function in our view seems to be beneficial for a variety of sports such as sport games or track and field. Finally, nicotine’s effect on increased pain tolerance might be of advantage in a wide variety of sports. More research will hopefully fill the gap to further evaluate nicotine’s effects on exercise performance.Based on observations of possible extensive smokeless nicotine consumption among certain athletes, a recent report by Marclay et al. from the Swiss Laboratory for Doping Analyses in Lausanne caught the interest of the sporting community and the WADA. Thereafter, discussions within WADA took place in the List Committee which is a subcommittee of the Health, Medical and Research Committee. Dr. Arne Ljungqvist, chairman of the Health, Medical and Research Committee, reports that WADA wants to know more about the use of nicotine in sports.

Once the prevalence is known, WADA will discuss a potential ban. Ljungqvist also reports that the IOC has already monitored nicotine as far back as 30 years ago in collaboration with the antidoping laboratory in Cologne, and did not report abusea. Since this time, smokeless tobacco and other nicotine delivery systems that might be appealing to the sporting community have entered the market. As a reaction, WADA has included nicotine, categorized as a stimulant and ‘in-competition only’ in its 2013 monitoring program. For this purpose, WADA reports: “WADA, in consultation with signatories and governments, shall establish a monitoring program regarding substances which are not on the Prohibited List, but which WADA wishes to monitor in order to detect patterns of misuse in sport”.In summary, nicotine seems to have ergogenic potential. Athletes appear to benefit from activation of the sympathoadrenal system with increased catecholamine release and subsequent increases in muscle blood flow and lipolysis. One component of nicotine action seems to act via a central mechanism . There is evidence for the abuse of nicotine by athletes. Although the sale of snus is illegal within the European Union, anecdotal observations by coaches and research from Scandinavia shows a high prevalence of snus use among athletes. It might therefore be reasonable to assume that smoking cigarettes will not be an issue for athletes. Instead, as there are several nicotine alternatives many of the negative effects of cigarettes can be circumvented.Alcohol is and has been one the most commonly consumed and abused drugs for a substantial period in human history. Alcohol is a dependence-producing drug which affects a host of organ systems and one that increases the risk of morbidity and mortality from different diseases when abused. Indeed, some authors have suggested that alcohol is harmful similar to drugs such as heroin or cocaine and that excessive alcohol consumption is a serious world-wide health risk. Although the detrimental effects of alcohol on human physiology are well known, even elite athletes consume alcohol. When looking at the effects of alcohol on overall health, it is, however, important to distinguish between chronic, moderate alcohol consumption versus alcohol abuse. Alcohol consumption and sport have been inextricably linked since ancient times when alcohol was considered the elixir of life. To some extent that may be true, given that a substantial body of epidemiological evidence shows that moderate ingestion of alcohol may reduce the risk of cardiovascular disease.

The link between alcohol consumption and mortality is subject to a J-shaped curve i.e. improved longevity with moderate consumption with increasing intake resulting in greater mortality risk. Indeed, dietary guidelines from the American Heart Association recommend moderation of alcohol intake as it has been associated with a lower risk of cardiovascular events. Alcohol use is fairly widespread among the athletic population with 88% of intercollegiate American athletes reporting the use of alcohol. It is also noteworthy that many athletes consume alcohol prior to sports events. However, it is important to note that scientific evidence suggests that the consumption of alcohol has some detrimental effects on exercise performance. It is fairly obvious that it is unlikely for competitive athletes to be alcohol abusers and most performance studies have focused on the acute ergolytic effects of EtOH consumption. The chronic studies merely reinforce the point that EtOH is profoundly ergolytic in the long term setting. They also serve to reinforce that chronic EtOH use can be toxic to cardiac and skeletal muscle.Chronic alcohol abuse has significant detrimental effects on the human cardiac muscle and one of the putative mechanisms via which alcohol may induce cardiac dysfunction is through the induction of increased oxidative stress. Interestingly, exercise training blunted the oxidative damage observed in a rat model of chronic alcohol consumption. The authors suggest that exercise training results in an up-regulation of cardiac antioxidants which may in turn reduce the deleterious effects of chronic alcohol induced oxidative stress. Acute alcohol use can also have effects on cardiovascular determinants of exercise performance. Lang et al. examined the effects of acute alcohol administration on left ventricular contractility using echocardiography and found that alcohol had a significant depressant effect on the myocardium. Specifically,grow tray stand acute alcohol consumption resulted in a decreased endsystolic pressure-dimension slope and reduced velocity of myocardial fiber shortening. Alcohol has significant effects on skeletal muscle substrate utilization during exercise. Specifically, it has been demonstrated that alcohol consumption decreases glucose and amino acid utilization, which can have adverse effects on energy supply to exercising muscle. Ethanol consumption induces hypoglycemia and decreases glucose appearance in plasma by decreasing hepatic gluconeogenesis. Ethanol administration has been shown to worsen skeletal muscle determinants of exercise performance such as muscle capillary density and muscle fiber cross-sectional area . It was shown in vitro that alcohol can inhibit sarcolemmal calcium channel actions thereby potentially impair excitationcontraction coupling and diminishing muscular performance.Greater capillary density also allows for a greater surface area for gas exchange and delivery of metabolic substrates. Long term alcohol consumption is associated with the development of alcoholic myopathy which is characterized by a reduction in skeletal muscle capillarity. Exercise training, however, appears to attenuate these adverse changes. Epidemiological data suggest that moderate alcohol consumption is associated with many salutary changes in blood coagulation and fibrinolysis. However, compelling experimental evidence is lacking and often conflicting. Alcohol can also lead to significant post-exercise perturbations in levels of clotting factors. Moderate postexercise alcohol consumption resulted in significantly elevated levels of Factor VIII at 5 and 22 hours during the post-exercise milieu.

Both circulating catecholamine and vasopressin levels have been implicated in upregulation of Factor VIII. These factors in turn, have been implicated in the pathogenesis of atherosclerosis in prospective studies. Alcohol and exercise may interact with each other to induce disorders in platelet aggregation which are associated with an elevated risk of cardiovascular and cerebrovascular events. Alcohol intoxication has been shown to be linked to cerebrovascular infarctions in a few casecontrol studies. However, the exact pathological mechanisms of the same are currently unknown. Alcohol consumption following athletic participation is commonly observed and may be associated with disorders in platelet aggregation. El-sayed et al. demonstrated that alcohol ingestion following exercise was associated with a marked increase in platelet count 1-hour following exercise. Platelet aggregation induced by adenosine diphosphate was found to be reduced when exercise was followed by alcohol consumption. Thus, it appears that ingestion of a moderate quantity of alcohol is associated with delayed recovery of platelet aggregation. It is important to note however, that the performance impact of ethanol consumption mediated post-exercise coagulopathy is unknown. Acute alcohol consumption is associated with the deterioration of psychomotor skills. A significant difference exists in injury rates between drinkers and non-drinkers in athletic populations. Athletes that consume alcohol at least once a week have almost a 2-fold higher risk of injury compared to non-drinkers and this elevated injury rate holds true for the majority of sports examined. The exact mechanisms that may be responsible for the elevated risk of injury are unknown. Alcohol may also interfere with the body’s ability to recover from injury. Barnes et al. examined the effects of 1 g/kg body weight alcohol consumption on recovery from eccentric exercise-induced muscle injury. The authors measured peak and average peak isokinetic and isometric torque produced by the quadriceps. Alcohol consumption was associated with significantly greater decreases in torque production 36 hours into recovery. The authors concluded that the consumption of a moderate amount of alcohol after damaging exercise magnified the loss of muscle force production potential. Finally, there is some evidence to suggest that chronic alcohol consumption may result in a positive energy balance and a potentially obesogenic state.

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Our results suggest TasP may be attainable among PLH who use non-injection substances

Posted on October 13, 2022 by hemp2022

Seventy-one percent of the total sample reported taking ART in the past 3 months, with significantly more heterosexual men reporting taking ARTs than MSM . Only 53% of the total sample presented an undetectable VL at baseline, with significantly more heterosexual men presenting an undetectable VL than MSM . The median CD4 + count at baseline was also significantly lower among heterosexual men than MSM . In both sites, heterosexual men were significantly older and had lower education than MSM. Figure 1 illustrates the type and pattern of non-injection substance use. In Thailand, of men who reported using non-injection substances in the past 3 months at baseline , 91% used one drug and 9% used two drugs simultaneously. In Thailand, among heterosexual men who reported using non-injection substances , 89% reported using one substance and 11% reported using two substances . In Thailand, among MSM who reported using non-injection substances , 94% reported using one substance and 6% reported using two substances . In Brazil, of men who reported using non-injection substances , 67% used one drug, 22% used two drugs, 5% used three drugs, and 2% used four drugs. In Brazil, among heterosexual men who reported using non-injection substances , 63% reported using one substance , 32% reported using two substances , and 5% reported using three substances . In Brazil, among MSM who reported using non-injection substances , 69% reported using one substance , 24% reported using two substances , 4% reported using three substances , and 6% reported using four substances . For MSM in Brazil, the proportion of ecstasy users increased with the number of substances an individual reported to have taken in the past 3 months.Table 2 reports the odds ratio of ART adherence ability or taking ARTs in the past 3 months and having an undetectable VL on non-injection substance use, adjusting for covariates such as age and education in the model. ART adherence ability or taking ARTs in the past 3 months was further adjusted in the VL analyses. In Thailand, plant grow table non-injection substance use was not significantly associated with ART adherence ability or undetectable VL in each risk group.

There were no differences between risk groups demonstrated by non-significant interaction terms. Furthermore, ART adherence did not mediate the association between substance use and undetectable VL. In Brazil, drug and alcohol misuse was associated with an overall lower likelihood of ART adherence ability and a lower likelihood of an undetectable VL, although this association was only significant in a few models. There were no differences between risk groups demonstrated by non-significant interaction terms. Furthermore, ART adherence did not mediate the association between substance use and undetectable VL. Alcohol misuse, although not significantly associated with having taken ARTs in the past 3 months, was significantly associated with decreased in odds of having an undetectable VL over 12 months in Brazilian MSM . Additionally, the number of non-injection substances used was significantly associated with decreased in odds of having taken ARTs in the past 3 months over 12 months in Brazilian heterosexual men . Table 3 reports the odds ratio of non-injection substance use on depressive symptoms and an undetectable VL using generalized linear mixed effects models. Depression was further adjusted in the undetectable VL analysis. In Thailand, non-injection substance use was associated with an overall greater likelihood of reporting severe depressive symptoms and lower likelihood of having an undetectable VL. Reporting alcohol misuse was significantly associated with an increase in reporting severe depressive symptoms in MSM . There were no differences between risk groups demonstrated by non-significant interaction terms. Severe depressive symptoms did not mediate the association between substance use and undetectable VL. In Brazil, non-injection substance use was generally not significantly associated with reporting depressive symptoms. Alcohol misuse, although not significantly associated with reporting severe depressive symptoms, was significantly associated with decreased odds of having an undetectable VL over 12 months in MSM . There were no differences between risk groups demonstrated by non-significant interaction terms. Severe depressive symptoms did not mediate the association between substance use and undetectable VL.This exploratory study examined the overlap between reported non-injection substance use, severity of depressive symptoms, ART adherence, and HIV VL undetectability among men living with HIV in Rio de Janeiro and Chiang Mai over 12 months. We found varying types and patterns of non-injection substance use between countries and sub-groups.

One key finding is that alcohol misuse, although not associated with reported ART adherence ability in Thailand or with taking ARTs in Brazil, was associated with significantly lower odds of achieving undetectable VL among MSM in Brazil. Another key finding is that the number of non-injection substances used was associated with lower odds of taking ARTs in the past 3 months among heterosexual men in Brazil, but not in Thailand. Lastly, alcohol misuse was associated with significantly greater odds of having depressive symptoms among MSM in Thailand, although not significantly associated with HIV VL. Reported alcohol misuse was prevalent in this sample and was associated with significantly lower odds of achieving an undetectable VL among MSM in Brazil. Alcohol misuse was detected in 35.2% and 47.3% in our of HIV-infected men. This high prevalence of alcohol misuse is consistent with one review documenting that alcohol use disorders can be up to two to four times more prevalent among PLH than the general population in U.S. populations. Factors that might explain lack of HIV suppression in our sample could range from biological factors to the diminished cognitive function and dysfunctional behaviors caused by alcohol misuse that may lead to poor self-regulation. Alcohol misuse might directly affect HIV control by inhibiting ART metabolism, enhancing HIV disease progression by lowering CD4+T-cell count, and/or increasing HIV replication in peripheral blood mononuclear cells. These biological mechanisms deserve further research in human subjects, as the current knowledge base is largely limited to animal models. Regardless of the mechanism, our findings support the rationale for investing resources into alcohol misuse screening and prevention interventions among men with HIV/AIDS in middle-income countries, such as Brazil and Thailand. Another key finding is that each additional substance used was associated with lower odds of taking ART among heterosexual men in Brazil. Poly substance use among heterosexual men in Brazil involved reporting a combination of alcohol misuse, powder cocaine use, and/or cannabis use. Substance use, powder cocaine in particular, has been previously associated with poor ART adherence ability and faster HIV disease progressio. specifically, cocaine may increase HIV disease progression by increasing HIV replication in PBMCs and increasing circulating HIV-1 RNA. There are fewer studies on the effect of cannabis on ART adherence ability and HIV VL with mixed findings. Interestingly, non-injection substance use was associated with decreased odds of taking ART only among heterosexual men in Brazil. Previous studies that examined non-injection substance use among individuals with HIV have primarily focused on MSM. As there is limited research on non-injection substance use and ART adherence among HIV infected heterosexual men, future research should examine this relationship to elucidate the contributing factors. Blips in HIV VL exams are also more frequent among people who misuse alcohol and drugs.

Additional studies of ART adherence with biomarkers would enhance the understanding of how poly substance use, ART, and HIV VL interact physiologically. Lastly, we found that alcohol misuse was associated with significantly greater odds of having depressive symptoms among MSM in Thailand. Although our study found that depressive symptoms were not significantly associated with undetectable HIV VL, depression severity is consistently associated with inconsistent ART adherence and discontinuation. Future research is needed to evaluate the efficacy of psychological and psychiatric interventions in mitigating the deleterious effects of substance use and depression on HIV disease progression. A recent critical literature review highlights some promising cognitive and behavioral and motivational interview interventions conducted among HIV infected substance using MSM in the US. Such interventions need to be adapted and evaluated in other countries and socio-cultural contexts. The current findings should be considered in light of several limitations and strengths. First, non-injection substance use and ART adherence ability were self reported and subject to potential biases based on recall bias or social desirability, the intentional under-reporting of sensitive or socially undesirable outcomes. There was likely under-reporting of alcohol misuse, non-injection drug use, and ART non-adherence. Future studies should include more comprehensive measurements of substance use and ART adherence. For example, physiological biomarkers of substance use and ART adherence provide a more objective measure of chronicity and extent of substance use. Likewise, future studies would benefit from using instruments that assess substance misuse , as the current study assessed the number of days of non-injection drug use rather than misuse. Second, under-reporting, small sample size, and truncated variability could have decreased our statistical power to detect a significant association between key variables like stimulants, cannabis, poly substance use, and HIV outcomes. Furthermore, it is important to highlight that significant associations were found in only one of the four sub-groups. Inconsistent findings could reflect distinct substance use and HIV care characteristics across countries and sub-groups,hydroponic table but could also be due to type 1 error. Third, our findings are not generalizable to populations of HIV-infected men in Thailand and in Brazil as this study focused on men engaged in care in select clinics and cities in each country. Despite these limitations, this study contributes to evidence that achieving an undetectable VL is possible among male, non-injection substance users in low- and middle- income countries.However, among MSM in Thailand and Brazil who misuse alcohol and among heterosexual men in Brazil who use multiple non-injection substances, interventions that address substance use may aim to lift mood, boost ART adherence and reduce HIV VL.Humans increasingly use e-cigarette devices filled with cannabis extracts to administer ∆9- tetrahydrocannabinol and other constituents . This has spurred development of pre-clinical models which are capable of a similar route of drug administration in laboratory rodents. Recent studies showed that intrapulmonary delivery of THC using an e-cigarette based system results in a dose-dependent hypothermia and anti-nociception in male and female rats ; a similar system produces hypothermia in mice after inhalation of synthetic cannabinoid agonists . Effects of cannabidiol and nicotine inhalation in combination with THC appear to produce independent effects and repeated inhalation of THC produces tolerance in adult rats .

Repeated adolescent inhalation of THC induces tolerance that lasts into early adulthood, and changes in motivated behavior . Most provocatively, a recent study appears to show evidence of THC self-administration via the inhalation route in rats . If confirmed, this has the potential to revolutionize study of the reinforcing properties of THC alone and in context of other exogenous cannabinoids. Recent discussion of replication and reliability across scientific disciplines identifies the generalization of effects, beyond narrowly constrained protocols, as a key issue. It may be the case that what appear to be minor variations from a published method are in fact important and the target effect do not generalize across such variations. It is therefore of significant interest with new methods to determine where methodological choices, e.g., rat strain, do or do not affect experimental outcomes. Such differences may be qualitative or quantitative . While our prior studies of cannabinoid inhalation have shown efficacy in both Wistar and Sprague-Dawley rats , the observed effects have not been directly compared across the strains. The major goal of this study was therefore to determine any strain differences using age- and treatment-matched male rats. Previous studies have reported some strain-related differences in the effects of THC. For example, adolescent THC exposure differentially affects adult measures of learning, with WI rats shown to be less sensitive than Long-Evans rats , and repeated adolescent THC injection resulted in different effects on heroin conditioned place preference in Fischer 344 versus Lewis rats . Strain differences are not always found in THC response, for example, there were no differences between Fischer and Lewis rat strains reported in a study of THC-induced conditioned taste avoidance and hypothermia . Apart from direct comparison studies such as these, it is difficult to compare across strains as a given laboratory will typically adopt a single rat strain , and fixed methodological parameters with respect to route of administration , dose ranges, types of assay . A substantial decrease in body temperature, and a decrease in nociceptive sensitivity, are major indicators of cannabinoid-like activity in laboratory rodents and are therefore ideal in vivo readouts for determining efficacy.

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Each of the resulting datasets were used for analysis and the results are then pooled for inference

Posted on October 12, 2022 by hemp2022

Some of the cohorts are community-based, while others specifically focus on clinical populations. Furthermore, some cohorts are focused on young men who have sex with men with others focused on persons who inject drugs. In order to allow for cross-cohort analyses, we implemented a rigorous data harmonization process for a core set of data elements. Te specifics of the process have been described elsewhere, but briefly, we requested data dictionaries from each of the cohorts and identified a core set of variables including sociodemographic factors, clinical characteristics, and substance using behaviors. Common data elements were first reviewed by the consortium data team both qualitatively and quantitatively and the subsequent harmonized data sets were further reviewed with each cohort data team in order to ensure fdelity in the harmonization process. Given the consortium’s focus on substance use, we were particularly interested in maintaining as much information and specificity as possible related to substance use. While standardized measures of substance use were utilized by each cohort, the choice of measures differed across cohorts. Even when measures overlapped, most studies used variations making it challenging to harmonize data across studies. For instance, substance use was assessed with various time frames, including 30-day, 3-month, and 6-month recall periods. Combining these data to obtain substance use in the past six months could lead to misclassification bias particularly for occasional users who may not have used a given drug in the shorter recall periods. This challenge to harmonizing substance use data – a key variable for the consortium – resulted in a patchwork pattern of missing data. There are a number of strategies to deal with missing data resulting from the harmonization processes where disparate measures cannot be collapsed into one variable. One common strategy is to ignore the missingness and use only participants with complete data in the analyses, which is well known for its potential for bias and inefficiency. A strategy to overcome this issue, which is widely used when dealing with missing data is imputation then analyzing the full data set as if imputed values were observed. In recent years,vertical hydroponic garden as a result of significant advances in computing power, a wide array of techniques for producing imputations has emerged including regression based techniques that allow for specification of multi-variable models, hotdeck techniques, as well as multiple imputation methods.

Additionally, strategies to evaluate the statistical properties of imputation techniques have also been explored, though few studies have taken a more applied and translational approach. Te objective of this study was to move beyond consideration of the statistical properties of these methods and present an applied overview of the performance of different imputation strategies when used for data harmonization. We used data from one of the cohorts participating in the consortium as a validation set and created missing data in such a way as to mimic the missingness that results during the harmonization process. We then applied three imputation strategies that vary in complexity including logistic regression, single hot-deck imputation, and multiple imputation and evaluated the performance of each strategy.At baseline and subsequent follow-up visits, which occurred at least six months apart, participants completed a self-administered, computer-based questionnaire. Te questionnaire included questions on a number of domains ranging from sociodemographic characteristics, sexual risk behaviors, as well as an extensive battery of questions related to substance use. In this analysis we used substance use data collected as part of a modified version of the Alcohol, Smoking and Substance Involvement Screening Test . specifically, for each substance participants were asked how often they have used it in the past six months. Substances of interest were cocaine, crack, ecstasy, heroin, cannabis, methamphetamine, poppers, and prescription drugs. Response options included never, once, monthly, weekly, and daily/almost daily. For the purpose of this analysis, all those who reported using a given substance at least once in the past six months were categorized as having used the particular drug, with all others being categorized as non-users. We selected drugs which were reported at low, medium, and high prevalence of use including heroin , methamphetamine , and cannabis , respectively. This allowed us to evaluate the performance of the imputation strategies under various prevalence estimates of the outcome.Data collected from August 2014 through June 2019, from 528 participants and the resulting 2,389 study visits were used in this analysis.

A Monte Carlo simulation study with 500 iterations was run to assess the relative performance of each imputation method. At each iteration, first a proportion of the data was set to missing with this step intended to mimic the missingness that results when we attempt to harmonize disparate measures across studies that measure substance use. Second, using the amputated data, three strategies including logistic regression scoring, single hot-deck, and multiple imputation were used to impute the missing data. Each imputation generated an estimated prevalence and confidence interval which was stored until 500 iterations were achieved. Finally, summary statistics across the 500 iterations allowed us to compare the performance of each strategy against the prevalence from the original data. Details of each of the steps in the process are described below.Data amputation – the process of generating the missing data – involved simulations such that the original dataset was sampled with replacement and amputated giving consideration to several factors including the missing data mechanism, the amount of missing data, as well as the pattern of missingness. Te primary consideration for the missing data mechanism was whether the missingness was related to the underlying value for that variable. This is relevant given that strategies to handle missing data are largely reliant on correct assumptions of the mechanisms which caused the missingness. For the purpose of this analysis we gave consideration to three different missing data mechanisms including missing completely at random , missing at random , and missing not at random. MCAR indicates there is no relationship between the missing data and any observed or unobserved variables. In this scenario, the probability of missing is the same for all cases in a given data set. MAR indicates a missing data mechanism in which there is a systematic relationship between the probability of missing and some observed data, but not the missing data itself. More specifically, under MAR the missingness is conditionally independent of unobserved outcomes but there is dependence on observed outcomes . Te premise of MAR is that once the analyst controls for these auxiliary variables, the missingness is ignorable. Finally, MNAR suggests that there is a relationship between missingness and unobserved outcomes , which makes it the most difficult mechanism to handle properly. Te level of missingness used in the amputation was set at 10, 30, and 50% in order to assess low to high rates of missing data. Additionally, the pattern of missingness was varied by substance use in order to allow for any one of the following scenarios: missing heroin only; missing cannabis only; missing methamphetamine only; or missing all three drugs simultaneously.

Te ampute package in R was used to generate the missingness. In addition to the three drug use variables age and employment status were used to generate missingness in the substance use data. Te reason age and employment status were chosen as auxiliary variables is because in the context of this project, these variables serve as a proxy for the specific characteristics of cohorts across which we intend to harmonize data and will help in replicating the most plausible missing data pattern in the context of our work.After the missing data were generated in such a way as to simulate ‘real world’ missing data scenarios that may result during the data harmonization process, various data imputation strategies were used to impute the missing data. Te imputation methods used included two different single imputation strategies as well as multiple imputation including: logistic regression; single hot-deck imputation; and multiple imputation with five and twenty imputations. These imputation strategies were chosen since they reflect a range of strategies from simple to complex, both from the technical expertise required to implement as well as the computational resources needed to execute. specifics of each of the imputation strategies are described below. Imputation with logistic regression is a single imputation strategy that produces predicted probabilities obtained by regressing the missing variable on other variables. In this case, the specific drug was an outcome variable and age, employment status, and cannabis and/or heroin use served as predictor or auxiliary variables. This strategy is technically relatively simple, preserves relationships among variables involved in the imputation model, and may provide a more informed estimate of the missing value that moves beyond a strategy that ignores other auxiliary variables. Hot-deck imputation is a computationally simple imputation strategy that uses data from an individual in the sample who has similar values on other variables to impute the missing values. Observations imputed are labeled recipients and observations drawn from a pool of matching candidates are labeled donors. For this analysis, donors were matched based on age, employment status,plant bench indoor and other substance use information. For example, if a recipient with missing data on methamphetamine was 25 years of old, employed, and reported cannabis use , then all 25 year old, employed participants who reported cannabis use other than the recipient were considered donors and a random observation was taken from this pool and the methamphetamine use status of the selected donor was used for the recipient. Instead of using actual observed values from a donor pool, multiple imputation uses a stochastic logistic regression model to generate n-sets of data – in this analysis n was either five or twenty – given pre-specified auxiliary variables. Te auxiliary variables used were the same as those described above. For example, five predicted data sets were generated for missing cannabis data using a stochastic logistic regression model composed of age, employment status, as well as reported methamphetamine and/or heroin use. Multiple imputation is expected to result in lower bias, however, this strategy is computationally intensive and requires technical expertise that may makes its regular application less practical. Finally, in order to allow for direct comparison between the various imputation strategies, the auxiliary variables were the same in all strategies. Te Monte Carlo simulation study from amputation to imputation was conducted using R .

Te data amputation and subsequent imputation was repeated 500 times in order to generate a simulated distribution that allowed for calculations to assess the performance of each strategy. We calculated the prevalence estimate resulting from the simulations as an average estimate across the 500 simulations. First, we report prevalence estimates for each of the substances given 10%, 30%, and 50% missingness based on listwise deletion. Listwise deletion, also known as complete case analysis, is the default strategy in most analytic software and provides an estimate of the prevalence and potential magnitude of bias if imputation is not employed. Next, we estimated the magnitude of the potential bias based on the average difference between the prevalence estimate from the original data and the mean of the prevalence estimate across the 500 simulation replicates. We also provide calculations for the root mean squared error as well as coverage of the 95% confidence interval, which was calculated based on the proportion of times the 95% confidence interval of the estimated summary estimate contained the prevalence estimate from the original data. Comparable to the scenarios with low and medium prevalence outcomes, both single and multiple imputation strategies with lower levels of missingness with an MCAR missing data mechanism performed well . Additionally, none of the strategies were effective under circumstances where data were MNAR. For all levels of missingness and assuming data were MAR, multiple imputation outperformed all strategies with both five and twenty imputed data sets resulting in comparable outcomes. For instance, with 50% missingnes, MI with five and twenty data sets resulted in a prevalence estimate of 52%, minimal bias and otherwise comparable in terms of coverage and RMSE .We evaluated the performance of different imputation strategies used to address missingness in key variables that thwart efforts to harmonize data collected as part of HIV-cohort studies. Our findings suggest that while multiple imputation is an effective tool for re-creating unbiased prevalence rates of substance use under MAR, single imputation strategies may also be effective if the missing data mechanism is MCAR. Furthermore, we demonstrate that when the missing data mechanism is MAR , ignoring the missingness can result in underestimation of the prevalence estimates and that single imputation strategies are ineffectual in correcting this bias, especially in cases where the prevalence of the outcome is low. Finally, we demonstrate that none of the imputation strategies are effective if missingness is not at random .

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Cannabis withdrawal was not included in DSMIV because of a lack of evidence

Posted on October 11, 2022 by hemp2022

While severe consequences could accompany substance-induced mental disorders , remission was expected within days to weeks of abstinence . Despite these clarifications, DSM-IV substance-induced mental disorders remained diagnostically challenging because of the absence of minimum duration and symptom requirements and guidelines on when symptoms exceeded expected severity for intoxication or withdrawal. In addition, the term “primary” was confusing, implying a time sequence or diagnostic hierarchy. Research showed that DSM-IV substance-induced mental disorders could be diagnosed reliably and validly by standardizing the procedures to determine when symptoms were greater than expected and, importantly, by requiring the same duration and symptom criteria as the corresponding primary mental disorder. This evidence led to the DSM-5 Substance-Related Disorders Work Group recommendation to increase standardization of the substance-induced mental disorder criteria by requiring that diagnoses have the same duration and symptom criteria as the corresponding primary diagnosis. However, concerns from the other DSM-5 work groups led the Board of Trustees to a flexible approach that reversed the DSM-IV standardization. This flexible approach lacked specific symptom and duration requirements and included the addition of disorder-specific approaches crafted by other DSM-5 work groups. DECISIONS: 1) For a diagnosis of substance-induced mental disorder, add a criterion that the disorder “resembles” the full criteria for the relevant disorder. 2) Remove the requirement that symptoms exceed expected intoxication or withdrawal symptoms. 3) Specify that the substance must be pharmacologically capable of producing the psychiatric symptoms. 4) Change the name “primary” to “independent.” 5) Adjust “substance-induced” to “substance/medication-induced” disorders, since the latter were included in both DSM-IV and DSM-5 criteria but not noted in the DSM-IV title.Because of the DSM-5 Task Force interest in biomarkers, the Substance-Related Disorders Work Group, consulting with outside experts, considered pharmacokinetic measures of the psychoactive substances or their metabolites, genetic markers, and brain imaging indicators of brain structure and function.

Many measures of drugs and associated metabolites in blood, urine, sweat, saliva, hair, and breath have well established sensitivity and specificity characteristics. However, these only indicate whether a substance was taken within a limited recent time window and thus cannot be used to diagnose substance use disorders. Genetic variants within alcohol metabolizing genes ,hydroponic vertical farming genes related to neurotransmission such as GABRA2 , and nicotinic and opioid receptor genes including CHRNA5 and OPRM1 show replicated associations to substance use disorders. However, these associations have small effects or are rare in many populations and thus cannot be used in diagnosis. Perhaps in future editions, DSM may include markers as predictors of treatment outcome Positron emission tomography imaging of brain functioning indicates that dopamine is associated with substance use . However, measuring brain dopamine markers involves radioligands, limiting their use. Functional MRI produces structural and functional data, but few fMRI or PET studies have differentiated brain functioning predating and consequent to onset of substance use disorders . Furthermore, brain imaging findings based on group differences are not specific enough to use as diagnostic markers in individual cases. Finally, abnormalities in brain regions and functioning that are associated with substance use disorders overlap with other psychiatric disorders. In sum, biomarkers are not yet appropriate as diagnostic tests for substance use disorders. Continued research in this area is important.Since then, the reliability and validity of cannabis withdrawal has been demonstrated in preclinical, clinical, and epidemiological studies . The syndrome has a transient course after cessation of cannabis use and pharmacological specificity . Cannabis withdrawal is reported by up to one-third of regular users in the general population and by 50%–95% of heavy users in treatment or research studies . The clinical significance of cannabis withdrawal is demonstrated by use of cannabis or other substances to relieve it, its association with difficulty quitting , and worse treatment outcomes associated with greater withdrawal severity . In addition, in latent variable modeling , adding withdrawal to other substance use disorders criteria for cannabis improves model fit.In DSM-IV, caffeine withdrawal was included as a research diagnosis to encourage research . The accumulated evidence from preclinical and clinical studies since the publication of DSM-IV supports the reliability, validity, pharmacological specificity, and clinical significance of caffeine withdrawal .

Based on factor analysis studies, the work group proposed modifying the DSM-IV research criteria so that a diagnosis in DSM-5 would require three or more of the following symptoms: 1) headache; 2) fatigue or drowsiness; 3) dysphoric mood or irritability; 4) difficulty concentrating; and 5) nausea, vomiting, or muscle pain/stiffness . DSM-IV did not include caffeine dependence despite preclinical research literature because clinical data were lacking . Relatively small-sample clinical surveys published since then and the accumulating data on the clinical significance of caffeine withdrawal and dependence support further consideration for a caffeine use disorder , particularly given concerns about youth energy drink misuse and new alcohol-caffeine combination beverages . However, clinical and epidemiological studies with larger samples and more diverse populations are needed to determine prevalence, establish a consistent set of diagnostic criteria, and better evaluate the clinical significance of a caffeine use disorder. These studies should address test-retest reliability and antecedent, concurrent, and predictive validity .DSM-IV included nicotine dependence, but experts felt that abuse criteria were inapplicable to nicotine , so these were not included. Nicotine dependence has good test-retest reliability and its criteria indicate a unidimensional latent trait . Concerns about DSM-IV-defined nicotine dependence include the utility of some criteria, the ability to predict treatment outcome, and low prevalence in smokers . Many studies therefore indicate nicotine dependence with an alternative measure, the Fagerström Nicotine Dependence Scale . DSM-IV and the Fagerström scale measure somewhat different aspects of a common underlying trait . Because DSM-5 combines dependence and abuse, studies addressed whether criteria for nicotine use disorder could be aligned with other substance use disorders , potentially also addressing the concerns about DSM-IV-defined nicotine dependence. Smoking researchers widely regard craving as an indicator of dependence and relapse . Increasing disapproval of smoking and wider smoking restrictions suggest improved face validity of continued smoking despite interpersonal problems and smoking-related failure to fulfill responsibilities as tobacco use disorder criteria. Smoking is highly associated with fire-related and other mortality , suggesting the applicability of hazardous use as a criterion for tobacco use disorders, parallel with hazardous use of other substances. To examine the alignment of criteria for tobacco use disorder with those for other substance use disorders, an item response theory analysis of the seven dependence criteria, three abuse criteria, and craving was performed in a large adult sample of smokers . The 11 criteria formed a unidimensional latent trait intermixed across the severity spectrum, significantly increasing information over a model using DSM-IV nicotine dependence criteria only. Differential item functioning was found for craving and hazardous use, but differential total score functioning was not found. The proposed tobacco use disorder criteria were strongly associated with a panel of validators, including smoking quantity and smoking shortly after awakening . The tobacco use disorder criteria were more discriminating than the DSM-IV nicotine dependence criteria and produced a higher prevalence than DSM-IV criteria, addressing a DSM-IV concern . An item response theory secondary analysis of 10 of the 11 criteria from adolescent and young adult substance abuse patients also revealed unidimensionality and a higher prevalence of DSM-5 tobacco use disorder than DSM-IV nicotine dependence .In utero alcohol exposure acts as a neurobehavioral teratogen, with lifelong effects on CNS function and behavior . These effects are now known as neurobehavioral disorder associated with prenatal alcohol exposure. Key features include neurocognitive and behavioral impairments diagnosed through standardized psychological or educational testing, caregiver/teacher questionnaires, medical records, reports from the patient or a knowledgeable informant, or clinician observation. Prenatal alcohol exposure can be determined by maternal self-report, others’ reported observations of maternal drinking during the pregnancy, and documentation in medical or other records. Neurobehavioral disorder associated with prenatal alcohol exposure was not included in DSM-IV. The proposed diagnostic guidelines allow this diagnosis regardless of the facial dysmorphology required to diagnose fetal alcohol syndrome . Many clinical experts support the diagnosis , and clinical need is suggested by substantial misdiagnosis, leading to unmet treatment need . However, more information is needed on this disorder before it can be included in the main diagnosis section of the manual.In DSM-IV, pathological gambling is in the section entitled “Impulse-Control Disorders Not Elsewhere Classified.” Pathological gambling is comorbid with substance use disorders and is similar to substance use disorders in some symptom presentations ,vertical agriculture biological dysfunction , genetic liability , and treatment approaches .

The work group therefore concurred with a DSM-5 Task Force request to move pathological gambling to the substance use disorders chapter. The work group also recommended other modifications . The name will be changed to “Gambling Disorder” because the term pathological is pejorative and redundant. The criterion “illegal acts to finance gambling” was removed for the same reasons that legal problems were removed from substance use disorders . The diagnostic threshold was reduced to four or more criteria to improve classification accuracy . A further reduction in the threshold was considered, but this greatly increased prevalence without evidence for diagnostic improvement. Future research should explore whether gambling disorder can be assessed using criteria that are parallel to those for substance use disorders .Since 2007, the Substance-Related Disorders Work Group addressed many issues. The members conducted and published analyses, and they formulated new criteria and presented them widely for input. The DSM-5 Task Force requested a reduction in the number of disorders wherever possible, and the work group accomplished this. The DSM process requires balancing many competing needs, which is always the case when formulating new nomenclatures. The process also entails extensive, unpaid collaboration among a group of experts with different backgrounds and perspectives. Scientific controversies arose and received responses . Conflict of interest could undermine confidence in the work group’s recommendations , but in fact, as monitored by APA, eight of the 12 members received no pharmaceutical industry income over the 5 years since the work group was convened, two received less than $1,200 and two received less than $10,000 in any single year. Some individuals assume that financial interests advocated directly to the work group . Actually, this never happened. While such advocacy could have occurred surreptitiously through unsigned DSM-5 web site comments, few comments stood out as particularly influential since they covered such a wide range of opinions. An exception to this was the web site advocacy of nonprofit groups to include neurobehavioral disorder associated with prenatal alcohol exposure . Ultimately, the work group recommendations attracted considerable interest, and the DSM-5 process stimulated much substance use disorder research that otherwise would not have occurred. Implementing the 11 DSM-5 substance use disorders criteria in research and clinical assessment should be easier than implementing the 11 DSM-IV criteria for substance abuse and dependence, since now only one disorder is involved instead of two hierarchical disorders. A checklist can aid in covering all criteria. Eventually, reducing the number of criteria to diagnose substance use disorders will further aid implementation, which future studies should address. The statistical methodology used to examine the structure of abuse and dependence criteria was state of the art, and the data sets analyzed were large and based on standardized diagnostic procedures with good to excellent reliability and validity. However, these data sets, collected several years ago, were not designed to examine the reliability and validity of the DSM-5 substance use disorder diagnosis. Many studies showed that DSM-IV dependence was reliable and valid , suggesting that major components of the DSM-5 substance use disorders criteria are reliable as well. However, field trials using standard methodology to minimize information variance are needed to provide information on the reliability of DSM-5 substance use disorder diagnosis that can be directly compared with DSM-IV , in addition to studies on the antecedent, concurrent, and predictive validity of DSM-5 substance use disorders relative to DSM-IV dependence. The amount of data available to address the topics discussed above varied, and new studies will be needed for some of the more specific issues. However, major concerns regarding the combination of abuse and dependence criteria were conclusively addressed because an astonishing amount of data was available and the results were very consistent.

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The clinic pharmacists queried E-ISI participants about their current smoking at each medication pickup

Posted on October 10, 2022 by hemp2022

At baseline, participants also completed a tracking form with current information including telephone numbers, home and e-mail addresses, and the names and contact information of two individuals who could be contacted if we were unable to reach the participant as well as current living situation, use of housing providers and shelters, and neighborhood hangouts frequented. Participants were considered lost to follow-up if they failed to return three phone calls when a message was left; failed to attend three appointments; and refused an outreach visit. We then attempted to obtain smoking data by telephone, with verification from contacts, and as much of the questionnaire data by mail, as possible. All participants were contacted for all assessments independent of whether or not they continued in treatment. At each visit, participants received a written reminder of the next follow-up visit. Two weeks before a follow-up interview, the participant was contacted either in person at the pharmacy visit or by telephone or letter to schedule the interview. If three contacts failed and a participant was unreachable, a project assistant called or wrote to the participant’s contacts to help in finding the participant. If necessary, staff went to local hangouts to locate participants. The tracking form completed at baseline was updated at each followup interview; this included change of address and additional significant others. A participant who missed a follow-up appointment was rescheduled for another appointment the same week, if possible. Participants were paid $35 for completing assessments at baseline and at each of the follow-up assessments, with a $35 bonus for completing all assessments. For E-ISI participants who accepted treatment, pharmacists recorded numbers of patches, gum, and lozenges dispensed during the study. The counselors for these participants recorded minutes in E-CBT sessions and number of sessions.At the baseline interview,greenhouse grow tables patients were staged on their readiness to quit smoking, using the Expert System. The Expert System provided computerized motivational feedback individualized for each participant. The counselor and the participant reviewed the printed report together.

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.

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State medical marijuana laws include regulations that protect young people from illegally obtaining marijuana

Posted on October 9, 2022 by hemp2022

Of gravest concern is the relationship of SUD with COVID-19 mortality. Our unadjusted results suggested an inverse relationship between SUD and mortality; however, adjustment attenuated all of these associations. Our findings contrast with the prior study showing a positive relationship of SUD to COVID-19 mortality that did not adjust for medical conditions; insufficient detail concerning this sample, design, and measures preclude full explanation of the discrepant results. Three other studies found a positive relationship of SUD with COVID-19 mortality before but not after adjustment for relevant medical conditions. Thus, results may vary depending on the adjustment strategy and population investigated. An additional study, also in veterans, found a protective effect of a non-specific substance use variable on COVID-19 mortality that was attenuated to the null after adjustment for medical conditions . The authors speculated that their results were due to the strong VHA patient social and behavioral support programs, and called for empirical examination of this possibility. We did so, comparing COVID-19 mortality among those with no SUD, untreated SUD, and treated SUD. After adjustment, untreated SUD was unrelated to the odds of mortality, while those with treated SUD had lower odds of mortality, a finding consistent with research showing that among those with SUD, being in treatment reduces mortality risk . In the present study, we therefore speculate that SUD treatment among those with SUD may have been protective against mortality due to greater contact with providers, leading to earlier identification and treatment of COVID-19. In addition to the contact with providers of substance disorder treatment, this speculation would be consistent with other studies showing that those with substance use disorders tend to be greater users of medical/healthcare services than others . Further interrogation of these results was not possible due to small cell sizes, but is warranted as more data become available. We also explored the relationship of SUD severity to mortality, finding that odds of mortality were not elevated if only one SUD was present, but appeared elevated among those with two or more SUDs,rolling flood tables although results were imprecise due to small cell sizes. While number of SUDs is not a direct SUD severity indicator, our results suggest that reported elevations in risk of mortality among those with SUD in other studies are driven by patients with severe SUDs. Future studies should examine this point when more data become available. Study limitations are noted.

Patients diagnosed with COVID-19 after 11/01/20 were not included in order to define a 60-day window for COVID-19 outcomes that occurred before the end of 2020. This may have limited the prevalence of COVID-19+ that was found in the VA , since the last two months of the year were omitted in the numerator but the entire patient population was included in the denominator. In addition, patients who were tested and found to be positive outside the VA but whose test results were never noted in the patient charts would have been missed in the VA dataset. The rate of COVID-19+ that we found was lower than the U.S. rate overall for 2020.This lower rate in VA patients may have been due to missed cases, or, alternatively, due to the fact that VA patients are largely older and have fully-integrated healthcare, and may therefore have been more receptive to the ample messages about COVID-19 mitigation strategies that were disseminated to all VA patients in 2020, helping them to minimize their infection rates. Another limitation is that using the retrospective cohort design, covariates were from 2019; future studies could incorporate diagnoses and care utilization up to the COVID-19 index date. Our analyses of COVID-19 infection did not incorporate information on external circumstances that may have affected infection rates, e.g., state policies and COVID-19 regulations, including preventive measures such as mask mandates. In addition, the VHA SDR did not record negative COVID-19 tests conducted outside the VHA, limiting complete knowledge about those tested and leaving open the possibility of misclassification. Some patients may have had SUDs unknown to providers and not noted in the EHR, or hospitalizations or ICU treatment outside the VHA not noted in the EHR. Environmental variables not included in our study should be examined in future studies. Finally, VHA patients do not represent all veterans or all US adults, limiting generalizability. In contrast, however, the study had several considerable strengths. These included the large sample size, transparent source of patient data, and electronic health records from a nationwide integrated healthcare system that provided a unique opportunity to investigate SUD and COVID-19 in a manner not possible in other studies, and to explore possible explanations of the reasons that SUD was not related to increased mortality risk in the VHA patients. We also provide information from what can be considered an index or reference period in the COVID-19 pandemic, namely, the period in which vaccines were not yet available and the Delta variant was starting to emerge. Future studies will need to incorporate information on vaccine status and subsequent pandemic periods defined by predominant virus strain when evaluating the relationship of SUD to the COVID-19 outcomes. In conclusion, data from over 5.5 million VHA patients suggest that having a substance use disorder increased the odds of a positive COVID- 19 test, and among those infected, inpatient hospitalization. However, SUD was not associated with COVID-19 mortality, perhaps due to the high proportion of patients with SUD who received SUD treatment and hence were likely to have relatively regular contact with providers.

The VHA strongly supports providing evidence-based SUD care to patients who need it, in contrast to the fragmented SUD treatment in much of the rest of the US healthcare system. In an integrated healthcare system with adequate access to SUD treatment, an unanticipated benefit may be closer monitoring of patients’ medical status, ensuring that when patients need it, they receive medical treatment and ultimately survive serious illnesses. One of the more controversial questions in drug policy today is whether the trend toward legalizing marijuana for medicinal and adult recreational use could increase illicit marijuana use among young people . Since 1996, 28 U.S. states and the District of Columbia have legalized the production and sale of marijuana for medicinal use , and eight have legalized marijuana for adult recreational use. Medical marijuana laws could potentially increase the availability of marijuana and reduce perceptions of its harmfulness, leading more young people to try it. But if these restrictions are not carefully enforced, young people could gain increased access to marijuana through the diversion of medical marijuana into illegal markets, which could also lower its price . Marijuana use by young people is associated with lasting detrimental changes in cognitive functioning of the developing brain, and poor educational and occupational outcomes . Use increases the risk of unintentional injuries and auto fatalities, mood and psychotic disorders, and drug dependence, especially when use is initiated at a young age . Long term marijuana smokers have a disproportionate burden of upper respiratory illnesses, including chronic bronchitis and some cancers, and an increased risk of cardiovascular disease . Medical marijuana producers and retailers are promoting new, more potent products such as oils often used as inhalants with tetrahydrocannabinol concentrations ranging from 40 to 70 percent . They are also developing new products that appeal to youth, such as packaged edibles and candies, that may increase the hazard of overdose due to their relatively slow rates of absorption and perceived intoxication . These products could increase the risk of overdose in young people, who tend to be less experienced users with low tolerance levels. Studies have reported little evidence that medical marijuana laws increase marijuana use among young people , although well controlled studies consistently report increased consumption in adults . Using the Youth Risk Behavior Survey, researchers have compared high school students’ consumption before and after medical marijuana laws were enacted, finding no evidence of rising consumption on a national basis .

A national study of 12–17 year old found that medical marijuana laws had no causal impact on consumption , as did a carefully controlled national study of 13–18 year olds . Wen et al. reported a five percent increase in the likelihood of trying marijuana among 12–20 year olds who dwell in states with medical marijuana laws, but the study was limited by the need to pool such a broad range of ages. Prior studies have ignored or been unable to detect age related variation in the impact of medical marijuana laws by pooling children aged 12–17, or even 12–24, or by studying particular age groups in isolation. Age-related variation is important to capture because young peoples’ access to marijuana and their developmental susceptibility to drug related harms differs by age . Most studies have focused on high school students who are likely to have greater access to marijuana and are more susceptible to social pressures than early adolescents . Meanwhile,flood and drain tray young adults different substantially from these younger groups, both in terms of development and access to drugs, being in the peak years of engagement with psychoactive substances during the lifespan . We performed multi-level, serial cross-sectional analyses on 10 annual waves of the U.S. National Survey on Drug Use and Health , from 2004 to 2013. Unlike many prior studies, ours included the key years of 2010–2013—a period of rapid acceleration in the number of states implementing medical marijuana laws , but before state recreational marijuana laws began implementation. In addition, our analyses compared young people across developmentally distinct age groups to account for important age-related heterogeneity in access to marijuana, in the propensity to experiment with psychoactive substances, and in the potential harms of marijuana use.The primary data source was ten annual waves of the NSDUH from 2004 to 2013. Following security clearance and a data use agreement with the U.S. Substance Abuse and Mental Health Services Administration, our team obtained access to individual-level NSDUH data that included the state of residence for each respondent. Each wave of the survey represents the U.S. population in all 50 states and the District of Columbia. During the period studied, no major changes in sampling, data collection, or instruments were made, thus preserving comparability across survey years. Full details of the data collection protocols, informed consent, and the questions asked are available in U.S. Substance Abuse and Mental Health Services Administration methodology reports . This project received an ethics review and was approved by the University of California at San Francisco’s Committee on Human Research. The total sample, pooled over 10 years, includes approximately 450,300 individuals. We stratified young people into three discrete age groups: early adolescents , late adolescents , and young adults . Table 1 provides an overview of sample characteristics. All participant data was provided by the U.S. Substance Abuse and Mental Health Services Administration and is not based upon primary collection of clinical study or patient data requiring individual consent.We examined three dichotomous outcomes at the individual level: self-reports of the accessibility of marijuana, consumption of marijuana within the past month, and initiation or first-time use of marijuana during the past year. The NSDUH framing of the marijuana questions references smoking, edibles, and oils. Individual-level, age-appropriate predictors from the NSDUH dataset were included in the analysis. Across all three age groups, these included sex, race/ethnicity, family income, poor or fair health, and living in an urban area. We included an indicator of poor or fair health status to control for the possibility that participants in medical marijuana states might engage in the legal use of marijuana for health reasons. For early and late adolescents, we also controlled for parental monitoring and participation in group fights, variables that could be indicators of the protective factor of parental involvement and the risk factor of delinquent behavior, respectively. For young adults, additional controls included employment, college attendance, parental status, and marital status. These are strong protective factors mitigating against drug use in this age group . We augmented the NSDUH data with annually updated state-level data on medical marijuana laws and other relevant control variables. For state-level controls, we drew on publicly available sources such as Polidata , including per capita drug courts and whether or not marijuana possession had been decriminalized. We considered a wider range of state-level controls representing demographic, political and religious factors, and aspects of state drug control policies. For the sake of parsimony, we included controls that were most associated with outcome variables.

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One behavior of interest is sharing of prepared cannabis and cannabis-related paraphernalia

Posted on October 8, 2022 by hemp2022

Sharing of prepared cannabis and cannabis-related paraphernalia may increase the risk of COVID-19 given its mode of transmission.This observed difference may have several explanations.First, the COVID- 19 pandemic exacerbated economic and social challenges among SM populations which has increased mental and physical health issues and decreased general well-being.However, social connectedness and social support among SM groups has been shown to reduce anxiety, depression, and other mental health challenges before and during the COVID-19 pandemic.Moreover, gay, bisexual, and other men who have sex with men have reported substance use as a way of community connection.Thus, sharing of prepared cannabis and cannabis-related paraphernalia may provide an avenue for SM individuals to connect with one another especially during the pandemic.Second, other substance use and in particular methamphetamine use during the pandemic was greater for SM compared to non-SM individuals and among SM men compared to non-SM men.Methamphetamine use has been shown to be higher among SM men compared to the general population with use reported in various settings including public , private parties, work environments, and sexual interactions.Moreover, cannabis co-use with methamphetamine has been reported to help with coming off methamphetamine highs.Thus, the social use of methamphetamine with others and the co-use of cannabis and methamphetamine may partially explain the differences in sharing of cannabis between SM and non-SM groups, especially among men.Finally, sexual experiences with causal or new partners among SM men may have also increased opportunities for sharing cannabis dry rack.Multiple studies have shown that the number of sexual partners and new sex partners among SM has decreased especially during the first COVID-19 wave.Yet, the prevalence of more than 1 partner during this time frame was still high.Additionally, these studies highlighted that decreases in number of partners and sex with new partners started to level off in the latter half of the first pandemic wave which is the time frame of “during the pandemic period” for this study.

However, we are unable to conclude who sharing was occurring with and are unable to make any conclusions about COVID-19 risk.There are other additional limitations to consider while interpreting the results.First, this study used a convenience sample comprised of primarily non-Hispanic White individuals, who were highly educated, with a high prevalence of substance use.Findings from this study may have limited generalizability to those who use cannabis in the US at large.Second, our study examined one period during the pandemic and cannot make any conclusions about lasting differences in frequency or sharing of cannabis.Third, there may have been potential mis-classification of outcome measures and with reporting of sexual identity, but we expect it have non-differential impacts to this study.Moreover, our survey was anonymous and likely minimized the social desirability bias.Last, we were unable to conduct sub analyses for frequency and sharing of cannabis during the pandemic for women because of small sample size.At the end of December 2019, the novel SARS-CoV-2 virus, the virus that causes coronavirus disease 2019 , emerged.1-3 SARS-CoV-2 is a highly transmissible single-stranded RNA virus that leads to upper and lower respiratory infections, is spread primarily through droplets and airborne transmission, and causes symptoms of fever, cough, and dyspnea.Community transmission in the United States was estimated to have begun in February 2020 and by mid-March 2020, all 50 states and territories reported cases of COVID- 19.To slow the spread of COVID-19, non-pharmaceutical interventions were implemented across the US.However, it is unclear what effect, both intended and unintended, these policy implications had on populations and individual level behaviors.Non-pharmaceutical interventions were developed in response to the 2009 H1N1 pandemic and included a protocol to slow the spread of future novel respiratory influenza A virus pandemics.NPIs are strategies for disease control when no pharmaceutical alternative exists and include actions at the personal, environmental, and community level.Specifically, NPIs implemented during the COVID-19 pandemic included travel restrictions, limitations on mass gatherings and recommendations for transition to virtual events, social distancing measures, stay-at-home orders, closure of non-essential work spaces and schools, and cloth face covering guidance.Such NPI mitigation strategies aimed at decreasing or limiting person to person contact thus reducing the probability of infection.NPIs were recommended by the US Centers for Disease Control and Prevention and governing authorities but policies were ultimately made by state and local officials based on conditions relevant to that jurisdiction.For instance, geographical differences in the US contributed to differences in COVID-19 incidence because of varying distributions of epidemiological and population level factors.These factors include timing of COVID-19 introduction, population density, age distribution, prevalence of underlying medical conditions, timing and extent of community mitigation measures, diagnostic testing capacity, and public health reporting practices.Therefore, policies between states across the pandemic have varied in intensity, timing, and duration.

A study from the CDC examined differences among stay-at-home orders across states from March 1st to May 31st, 2020, on population movement.Stay-at-home orders were associated with decreased population movement; however, movement increased significantly as states began lifting restrictions.Kaufman et al.reported the initial effect of state variation in social distancing policies and non-essential business closures on COVID-19 rates.Social distancing and closure of non-essential businesses and public schools were shown to reduce daily COVID- 19 cases by 15.4% with effects varying across states.Finally, Pan et al.showed that there was heterogeneity in NPI domains across the US census region and concluded that states with the most aggressive policies had the highest mitigation of COVID-19 infection.While heterogeneity in intensity and duration of state policies on COVID-19 mitigation were demonstrated, all such studies have been restricted to the initial wave of the pandemic and have only assessed the associations of policies on COVID-19 infection spread at the population level.To date, there have been little to no studies that assessed the associations of policies on individual behaviors.Thus, the goal of this study is to understand how varying COVID-19 state policy influenced individual behaviors.Cannabis use in the US continues to grow with 46% of the population reporting past year cannabis use in 2019.12 Cannabis is predominantly a dried flower with smoked cannabis being the most popular mode in the US.Cannabis can be smoked as cigarettes or with pipes, water pipes , cannabis vaporizers , e-cigarettes for cannabis extracts, and rigs for cannabis extracts.Moreover, cannabis social practices before the COVID-19 pandemic have previously involved sharing prepared cannabis such as cannabis cigarettes, joints, and blunt with others.15 In this paper, we define sharing of cannabis as sharing of prepared cannabis and cannabis-related paraphernalia.Sharing of paraphernalia for cannabis, tobacco, and crack cocaine use has previously been demonstrated as a risk factor for other respiratory infections.Thus, sharing of prepared cannabis and cannabis-related paraphernalia, or rather non-sharing, is proposed as a proxy for COVID-19 risk mitigation behaviors.This study aimed to do the following: 1) Describe variations in US state COVID-19 policy; and 2) Quantify the magnitude of association that state’s COVID-19 policy has on individual level sharing of prepared cannabis and cannabis related paraphernalia.This study uses a semi-individual design where the exposure of interest is at the population level and the outcome is at the individual level as seen in air pollution studies and studies on motorcycle helmet policy.For example, motorcycle helmet laws differ across states and were grouped together based on type of helmet legislation.Following this categorization, injury patterns from individual health records were evaluated by controlling for both individual and population level variables.Thus, a semi-individual study design is more efficient than an ecological study designs as one may control for individual and group level confounding, it has less measurement error, and is inherently based on individuals.Data for this study were collected from an anonymous US-based web survey on cannabis and cannabidiol related behaviors from August 2020 – September 2020.Detailed methods on this survey have been previously specified in detail.Briefly, survey respondents were comprised of a non-random convenience sample, 18 years of age or older, who reported non-medical cannabis, cannabis for medical use, and/or CBD use in the last 12 months, and resided in the United States.

Respondents were recruited through Reddit, Bluelight, Craigslist, and Twitter, received 5 USD for their participation, and were prevented from “ballot stuffing” by limiting to a unique internet protocol address.Respondents were included in this study if they reported non-medical cannabis use and self-reported using cannabis in the following ways: smoking ; vaporizing plant; and/or vaping oil/concentrates.Individual level data for this study was draw from the National Cannabis Study described above.The COVID-19 Cannabis Survey was supported by US National Institute on Drug Abuse and Semel Charitable Foundation.In this single survey, participants were asked to recall their non-medical cannabis use behaviors at two 3-month time points: before the COVID-19 pandemic and during the COVID-19 pandemic.Data from this survey include non-medical cannabis frequency of use, mode of use, sharing of prepared cannabis and cannabis-related paraphernalia, planting racks and demographics.We then drew population/ecological level data from three different sources.The first data source was from the was the Kaiser Family Foundation’s State COVID-19 Data and Policy Actions accessed through GitHub repositories with policy data by US states starting June 4, 2020 through November 19, 2021.Specifically, we used information from 3 time points that overlapped with our study period during the pandemic.The data in June 2020 included the following policies: Stay-at-home order; non-essential business closures; larger gathering ban; and restaurant limits.The data from July and August 2020 included the following policies: those from June plus bar closures and face covering requirements.The second data source was from Johns Hopkins University and Medicine COVID-19 Dashboard by the Center for Systems Science and Engineering with data stored in a GitHub repository from April 4, 2020 until January 12, 2022.COVID-19 data included confirmed infections, deaths, recovered infections, active infections, testing, and hospitalizations by state.For this study, we used COVID-19 confirmed infections from May 24, 2020.The final data source was from the US Census, which included state population size in 2020 and state percent urbanicity in 2010.At the time of the analysis, the Census did not have state percent urbanicity beyond the year 2010.The outcome of interest was respondent’s self-reported sharing of prepared cannabis and cannabis-related paraphernalia during the COVID-19 pandemic.Respondents used a Likert-scale to agree with the following question, “I shared joints, blunts, bongs, pipes, vaporizers, or vape pens used for cannabis ,” with answer choices being never, sometimes, about half the time, most of the time, and always.We dichotomized sharing of cannabis paraphernalia to no sharing and any sharing.The exposure of interest was state’s COVID-19 policy actions.We scored policies by strength using a standardized coding method ranging from 0 to 5 as suggested by Lane et al., and the CDC on stay-at-home orders.

In short, a policy had a score of 5 if the mandate was very high and 0 for no recommendations or rules implemented for that policy.The KFF State COVID-19 Data and Policy Actions data source had policy information on six policies for each US state.These policies included stay-at-home orders, non-essential business closures, large gather bans, restaurant limits, bar closures, and face covering requirements.For instance, stay-at-home order included statewide orders, new stay-at-home order, high-risk groups, rolled back to high-risk groups, lifted, and no state order.We coded statewide and new stay-at-home order as 5, high-risk groups and rolled back to high-risk groups as 4, and lifted or no state order as 0.Policies for non-essential business closures included: some non-essential businesses permitted to reopen, some non-essential businesses permitted to reopen with reduced capacity, new business closures or limits, all non-essential business to permitted to reopen with reduced capacity, all non-essential businesses permitted to reopen, and no state order.Policies for large gathering bans included all gatherings prohibited, >10 people prohibited, new limit on large gatherings in place, expanded to new limit below 25, expanded to new limit of 25, expanded to new limit of 25 or fewer, expanded to new limit above 25, lifted, other, and no state order.Policies for restaurant limits included closed except for takeout/delivery, newly closed to dine-in services, new capacity limits, reopened to dine in service with limited capacity, limited dine-in service, reopened to dine-in service, and no state order.Policies for bar closures included closed, newly closed, new service limits, and reopened.And finally, policies for face covering requirement included required for general public, required for certain employees; allows local officials to require for general public, required for certain employees, allows local officials to require for general public, and no state order.Detailed coding of state policies can be found in Appendix 4.A.1.For each month, we summed the values for each of the four specified policies for June , the six specified policies for July , and the six specified policies for August.

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The cannabis product and regulatory landscape is changing in the United States

Posted on September 30, 2022 by hemp2022

The centerpiece of the CSC’s work so far is an ongoing preclinical study called Impact of Cannabinoids Across the Lifespan.Piomelli, who directs the study while a team of UC Irvine principal investigators conducts the bulk of the research, characterizes it as a broad research project with many components, from which a stream of independent discoveries and publications is expected over the next 3 or 4 years.Piomelli reports that the study’s main purpose is to study THC’s effect on adolescents — and particularly on the adolescent brain.The human brain routinely produces neurotransmitters known as endocannabinoids — molecules, similar to cannabis derivatives, that are important in learning, memory and experiencing emotion.The key questions that the study addresses are these: Does exposure to THC, in a persistent way, change the brain’s endocannabinoid system? If so, what changes at the cellular and molecular level explain the alterations? Does exposure to THC during adolescence carry lasting implications for learning and emotion? The study has received a $9 million Center of Excellence Grant from the National Institute on Drug Abuse.Another new entrant into cannabis research is the UCLA Cannabis Research Initiative, founded in 2017 with a broad remit — “to understand how cannabis affects bodies, brains and society.” The initiative, encompassing an interdisciplinary team of 40 faculty members from 15 university departments, aims to function as an education, research and service organization that leads public discussions of cannabis, policy and health.The initiative got its start in the months before Proposition 64 was approved by voters.According to Jeffrey Chen, the initiative’s director, leadership at the Semel Institute for Neuroscience and Human Behavior anticipated that legalization would soon create the world’s largest market for recreational cannabis — and that California and particularly Los Angeles would “play an outsize role in establishing normative behaviors” around cannabis.Los Angeles, in Chen’s view, has become the world’s cannabis capital overnight.He and his colleagues hypothesize that, given the city’s status as a major tourist destination and an exporter of culture, “what happens in Los Angeles is very likely to be transmitted around the world.” So far, Chen says, the initiative’s research remains mainly oriented toward health-related issues.One study — soon to start, and led by Kate Wolitzky-Taylor,greenhouse benches an assistant clinical professor in UCLA’s Department of Psychiatry and Biobehavioral Sciences — seeks to develop and evaluate a behavioral treatment for young adults who exhibit cannabis use disorder and who use cannabis to cope with anxiety, depression and the like.

Cannabis, according to the researchers, is the most commonly used drug among young adults, and it can be harmful when its use qualifies as a “maladaptive way” of contending with negative experiences.Wolitzky-Taylor reports that the research project is a randomized clinical trial focusing on participants’ reactions to the anxiety and depression that might lead them to use cannabis.The treatment, she says, will draw on strategies such as “mindfulness, cognitive reappraisal skills, problem solving and … gradual exposure to distressing but objectively safe stimuli.” The treatment was developed in an iterative manner — an early version has already been tested with a small group of patients and further refinements may be made after the clinical trial is complete.The research is funded by a 3-year, $450,000 grant from the National Institute on Drug Abuse.Individuals with cannabis use disorder, if they are 18 to 25 years old, are encouraged to email the project’s coordinator, Nick Pistolesi , regarding participation in the study.A second example of the initiative’s work is decidedly nonmedical.Brett Hollenbeck, an assistant professor of marketing at the UCLA Anderson School of Management, analyzed — along with Kosuke Uetake of Yale University — a large dataset of cannabis transactions in the state of Washington to learn about firm and consumer behavior in legal cannabis markets.Their goal was to provide policymakers, including in California, information useful for optimal development of cannabis taxation and regulation — optimal in the sense of maximizing tax revenues, safeguarding public health and discouraging a black market for cannabis.Washington created a legal framework for growing and selling cannabis in 2012.Legal sales began there in 2014.Since then, every cannabis transaction in the state has been recorded in an administrative dataset.The researchers used the data to model consumer demand for cannabis products and measure price elasticity.Their analysis, covering the period from November 2014 to September 2017, indicates that Washington’s strict cap on cannabis retailers — some 550 are allowed in the entire state — has permitted retailers to command high prices and behave like local monopolies.The researchers report that when prices for regulated cannabis rise in Washington, consumers often switch to cheaper cannabis alternatives available from regulated retailers, rather than seeking out blackmarket cannabis.

Indeed, the researchers argue that Washington’s 37% sales tax rate for cannabis, though it appears high, does not drive down tax revenue, and in fact the state could generate higher revenue by raising the tax rate to 40% or higher.Further, the researchers calculate that Washington could substantially increase its revenue if it acted as the state’s sole cannabis retailer, as it did for alcohol sales until 2012, and could do so without causing an increase in cannabis prices.Cannabis use is on the rise, among some groups of US adolescents, due to increased availability, less overall negative perceptions, and a proliferation of e-cigarettes and vaping.Recent population studies show rates of use in 8th and 10th grades at 15 % and 34 % respectively.Past-year cannabis use among justice-involved youth steadily increased between 2002–2017 and JIY report higher rates of cannabis use than their same-age non-justice-involved peers; often starting cannabis use by age 13.As part of the fourth wave of juvenile justice reform , legislation has increasingly moved toward diverting youth from detention to community supervision.System advances including implementation of specific behavioral health screening tools for youth in detention and on probation increased identification of youth with treatment needs.Research to identify feasible and acceptable substance use interventions to implement and sustain within juvenile justice settings to prevent or decrease substance use is emerging , but in tremendous need given the shortage of such services.Efficacious substance use interventions for JIY include family, are intensive, and typically address secondary or tertiary prevention of substance use ; these are not typically feasible for implementation within busy, often overburdened and under-resourced juvenile justice settings, yet research on brief substance use prevention interventions for JIY is lacking.Individual level, modifiable factors that can be incorporated into brief interventions and feasibly delivered within juvenile justice settings to prevent and/or reduce youth substance use must be identified.Brief, empirically-supported substance use interventions with adolescents/young adults focus on addressing social attitudes, beliefs, and cognitions and enhancing motivation to abstain from or reduce use.Research with JIY highlights increased likelihood of substance use secondary to psychiatric symptoms, trauma exposure and symptoms, chronic absenteeism/truancy and family factors.But, data on social cognitive influences on substance use among JIY are limited.

For example, data on cannabis use expectancies with JIY are limited to a single, small detained sample in one U.S.state.Findings suggest negative cannabis use expectancies are associated with less cannabis use, while positive expectancies are unrelated.The authors posit consequences associated with use may be more salient for youth completing these measures while detained, and different associations regarding positive expectancies may have emerged if measured outside detention.Of note, the negative expectancies sub-scale had very low internal consistency, thus replication of their findings with other JIY samples is warranted.Other adolescent studies show negative expectancies associated with cannabis use among Black females are related to less cannabis use over time and among a racially and ethnically diverse U.S.high school student sample changes in positive substance use expectancies most saliently predicted substance use onset and changes in negative expectancies was associated with onset of cannabis use only.Brief individual interventions addressing substance use motivations and expectancies have been successful in reducing adolescent cannabis use ; however, research on preventing initiation through brief intervention and among JIY is nascent.Extension of expectancies research with JIY samples is necessary, particularly using prospective data and examining the role of positive expectancies and cannabis use outside detention when there is greater opportunity for use.Studies of school-based and general adolescent samples have also demonstrated the importance of understanding reasons for and protective factors against cannabis use.Data from the Monitoring the Future Survey examining past 10-year trends demonstrates adolescents cite more coping-related reasons than any other motivations for use.Individual factors that positively influence social cognition and behaviors appear to buffer against substance use among early adolescents in public school , and higher self-esteem is associated with less substance use among Black adolescents exposed to community violence and with high family stress.Enhanced emotion regulation skills,growers equipment which are influenced by social cognitive factors , are also protective against cannabis use initiation among Black adolescents.Justice-involved youth, who experience high rates of trauma, poverty, stigma and discrimination, may cite multiple reasons to use cannabis as a coping strategy, however, research in this area is lacking.Reducing early initiation of cannabis use is key to preventing negative long-term health and associated psychosocial consequences.In this large sample of first-time JIY, rates of early onset cannabis use were high and 15 % of youth newly initiated cannabis use in the year following first justice contact.Youth’s internal distress, affect dysregulation, and positive expectancies about cannabis use drove new initiation, even after accounting for known associated factors.The justice system largely focuses on interventions to address co-occurring mental health and delinquent behavior, primarily through group or family-based intervention, but our data suggest there is a critical and unique window of opportunity to prevent cannabis use initiation among youth by addressing internalizing symptoms, teaching emotion regulation skills, and modifying expectancies.Such interventions can be brief and feasible to implement within existing individual-based court and justice-related services.Since adolescent cannabis use can be associated with future worse public health and legal outcomes, developing effective brief primary prevention interventions for JIY is critical; these are not mutually exclusive from essential development and empirical testing of structural-level public health and legal policy interventions to delay or reduce JIY substance use.Only two studies have tested brief interventions to reduce substance use among justice involved or diverted truant populations.Spirito and colleagues tested the preliminary efficacy of a combined family-based and individual adolescent based brief motivational enhancement therapy intervention ; the latter targeting adolescent substance use related attitudes, beliefs and norms and demonstrating feasibility, acceptability and reductions in youth cannabis use at 3 month follow-up.

Dembo and colleagues tested the efficacy of a brief intervention with youth and parents compared to youth-only BI and Standard Truancy Services in reducing cannabis use and sexual risk behavior over 12 months.No significant intervention effects were found; however, the authors note certain subgroups showed differential response to the intervention.Although mixed in success, both studies addressed individual level factors commonly associated with increased likelihood of substance use among JIY.Our data suggest with first-time JIY who have not initiated use, a brief individual youth intervention targeting internalizing symptoms, emotion regulation skills, and cannabis use expectancies is important for future intervention development and testing.Single session interventions are a cost-effective and feasible way to address youth internalizing symptoms and increase access to mental health interventions for under served youth.SSIs focused on motivational enhancement therapy for sexual risk reduction have been feasible and acceptable to deliver to large numbers of detained youth.The concept of SSIs has yet to be explored for substance use prevention among JIY, but our study suggests a SSI addressing internalizing symptoms, emotion regulation, and cannabis use expectancies and intentions may be efficacious in delaying or preventing cannabis use initiation, both of which have significant positive public health implications.SSIs could also be developed to shift expectancies and intentions about continued use for those with early onset, who are at greater risk for worse outcomes due to being younger upon first using and greater likelihood of continued use and consequences.Our results suggest incorporating alcohol use content might also be important for those already using cannabis at first-time justice contact.SSIs are also likely more feasible to implement within real-world settings already serving JIY and have strong potential to address a highly concerning gap in access to substance use intervention for community-supervised JIY.One possible approach for substance use SSIs is motivational interviewing , a communication technique used to reduce alcohol and cannabis use among school-mandated college students and in two studies of general substance using adolescent populations ; however, the limited data available suggest MI for universal prevention may not be as effective.

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An additional risk associated with working in the service industry involves the opportunity to earn tips

Posted on September 29, 2022 by hemp2022

Career disruption further results in loss of specific job-tenure and access to the social network provided by one’s workplace.Although it is difficult to assign a monetary value to injury , long-term wage loss resulting from workplace harassment can be explained by gaps in employment and reduced hours; both are actions taken by workers to reduce their exposure to an unsafe work environment.Furthermore, harassment in the workplaces can create a hostile work environment for all employees.Although fellow co-workers may not be the target of harassment, their exposure to it nonetheless can have a measurable impact as a workplace stressor.Co-workers can become exposed to sexual harassment by either directly witnessing the behavior or indirectly learning about it through a peer.Glomb and colleagues found that as the prevalence of harassment increased in the work environment, women who were not directly targeted were more likely to report lower job satisfaction and higher distress.Researchers have also found that as workers report greater instances of sexual harassment through personal experience or observation, these experiences are positively associated with greater team conflict among employees.Thus, the ramifications of sexual harassment in the workplace burdens not only targets of sexual harassment but their peers as well.Lastly, individual and interpersonal consequences of sexual harassment also have repercussions for organizations and companies.USMSPB’s report found that in 1994 the costs of lower employee productivity, sick leave,ebb and flow and higher turnover rates related to sexual harassment cost the federal government approximately $327 million , equivalent to $578 million in 2020 when accounting for inflation.

Organizations may also be forced to absorb the financial costs of fees and settlements resulting from legal battles that ensue because of the inappropriate behavior.The possible news coverage tied to an instance of abuse and harassment in the workplace can consequently cost the organization its reputation and particularly in industries such as retail, this can ultimately impact a company’s the bottom-line.Sexual harassment in the workplace is dependent on several factors within an organization that allow for such behavior to occur.In their review of the literature on sexual harassment, Pina and colleagues conclude that the occurrence of harassment in the workplace can be explained by power differentials between victims and perpetuators, sexual permissiveness of the work environment, gendered occupations, as well as the policies that govern the likelihood of harassment and the consequences that follow.Organizational theory of sexual harassment primarily argues that harassment is the result of hierarchical structures created within organizations.The stratification of roles in the work environment and the authority attached to these roles allow supervisors, for example, to sexually coerce their subordinates who are vulnerable to work related consequences if they resist.Vulnerable populations especially who are low-ranking employees face a greater risk of being exploited by a supervisor.Additionally, societal norms attached to power differentials within hierarchies create an expectation that an exertion of power between the powerful and powerless is normal and tolerable.An exertion of power can take many forms including but not limited to sexual harassment.Likewise, power differentials can help explain sexual harassment committed by subordinates as a means to gain power or eliminate the inequality in statuses.In a study on workplace authority, researchers found that female supervisors were more likely to experience harassing behaviors than female employees, particularly from male co-workers , suggesting that sexual harassment was motivated by a threat to traditional gendered power differences.Thus, hierarchies and differences in power are further affected by gender.Although men are more likely to hold leadership positions in their place of work and act as perpetrators of sexual harassment , the introduction of women into leadership positions does not necessarily deter harassment.

Although power differences are often gendered, it is important to acknowledge that despite research findings pointing to men as common perpetrators of harassment against female subordinates , abuse of power in the form of sexual harassment occurs regardless of gender and is bidirectional within a hierarchy.When discussing power differentials and harassment as the manifestation of abuse within organizational theory, researchers cannot ignore the intersection power, gender and race as factors influencing the experience of sexual harassment , as not all victims experience or are targeted for harassment equally.Particularly for women of color, their experiences of harassment are not only rooted in gender discrimination but racial discrimination as well as is evidenced by studies indicating women of color are more often targeted compared to their White counterparts in the workplace.Furthermore, women of color are also more likely to internalize their experiences with harassment and are more hesitant to report such instances.Organizational theory also posits that a work environment’s permissiveness serves as a predictor of workers falling victim to sexual harassment.Such a permissive environment is created through a lack of workplace policies, such as a sexual harassment training, procedures for reporting harassment, protection for workers who report and a no tolerance policy.These policies, when enforced, ideally aid in minimizing the prevalence of sexual harassment.Without them, perpetuators in the workplace are left unchecked, and likewise, victims are left more vulnerable.Permissive work environments are also characterized by a high tolerance for flirting, sexual jokes, and obscene language.Studies investigating sexual harassment through an organizational approach have found that if workers perceive their organization to be tolerant of sexual harassment in the workplace, they are more likely to experience instances of harassment.Co-workers are also less likely to recognize and intervene during an instance of sexual harassment.Studies find that workers weigh the efficaciousness of their actions against the authority of their employer as the sexual permissiveness in a workplace is usually maintained, if not promoted, by managers and higherups.Contributing to a sexually permissive environment is also the idea of working in an overly sexualized work environment.Through the lens of sexualized labor, Warhust and Nick separate sexualization that is inherent to certain workplaces from work that becomes sexualized at the organizational level.

They argue that organizations utilize the aesthetics of workers as a marketing strategy which then gives rise to sexualized labor and consequently gives perpetuators a sense of justification to enact inappropriate behaviors towards employees.Sexualized labor begins to take form when organizations specifically recruit employees who they consider to be handsome or beautiful as the archaic idea that sex sells remains prevalent.Although the sexualization of workers in no way justifies sexually abusive actions taken against them by co-workers, managers or clients, workers are nonetheless expected to endure unwelcome comments, stares and actions as inevitable consequence.Work environments that lend themselves to becoming overtly sexualized are those that rely heavily on customer interaction and satisfaction such as retail, food, hospitality and casinos.Not surprisingly, these are the same industries who historically have high rates of sexual harassment.Between 2000 and 2015, the combination of these industries made up 28% all sexual harassment charges filed to the EEOC.Such industries put employees at greater risk to experience sexual harassment, especially by customers and clients who sexualize workers and feel entitled to their services.Particularly in service sector industries, there is a prevailing belief in the mantra “the customer is always right” that both allows customers to becoming sexually forward without fear of consequences and employees to respond informally to such behavior as to not upset the customer.A study by the Restaurant Opportunities Center found that women employed in restaurants who earn a sub-minimum wage of $2.13 per hour as tipped workers were twice as likely to experience harassment from supervisors, co-workers and customers, compared to women employed in restaurants who received a sub-minimum wages greater than$2.13 per hour.The large reliance on tips creates an environment where workers, particularly women,dry racks are undervalued and forced to endure injustices for the sake of their income.Additional risk factors for sexual harassment can be identified at the interpersonal and individual level.At the interpersonal level, working in isolation is also associated with reports of harassment and general workplace violence.Environments in which workers are forced to become isolated from peers gives harassers easy access to targets and leaves workers with fewer chances to interact with others in their environment and signal to others if they are in need of assistance.

Additional interpersonal risk factors in the workplace include power differentials and the abuse of power, discussed in more detail below.Individual risk factors associated with a worker’s vulnerability include gender, sexual orientation and age.As previously mentioned, although anyone can experience sexual harassment, women are most often victimized and thus at greater of risk of experiencing harassment than men.Likewise, studies repeatedly indicate perpetuators are most likely to men.Aside from women, individuals who identify as queer, either in their sexual orientation or gender expression, including lesbian, gay, bisexual, and transgender folks also face great risks of experiencing general discrimination and sexual harassment.A meta-analysis of 386 studies on the victimization of LGBT individuals found that approximately of 50% of individuals in all samples experience sexual harassment.Although comparative studies examining rates of sexual harassment between heterosexual and LGBT samples have mixed findings determining effect sizes, they lean towards sexual minorities experiencing greater victimization than heterosexual identifying individuals.In addition to the risks posed by one’s gender and sexual orientation, young and unmarried female workers are most often targeted as victims of sexual harassment.Most service sector employees are relatively young adults between the ages of 15-25 years who face greater risks of harm in the workplace.Because of their age, workers are often unaware of their rights which include a safe work environment that is free of harassment as well as entitlement to fair pay.Consequently, they may not be equipped with the information or tools to formally handle an experience of sexual harassment.Responses and coping mechanisms to sexual harassment are just as critical to understanding the context of harassment in the workplace as are the individual and organizational risk factors that predict harassment among vulnerable workers.However, while the majority of studies focus on investigating the frequency and prevalence of harassing behaviors, many do not address how workers react to such behavior.According to the USMSP , individual based responses to behaviors can be categorized as active responses , avoidance and toleration.Among the three categories, the top three behaviors employed by federal workers in response to harassment were asking the harasser to stop, avoiding the harasser, and ignoring the behavior or simply doing nothing.The action, or lack there-of, that an employee takes to address sexual harassment is related to multiple levels of influence: the severity of the incident, the power they as an employee hold in their place of work, the social support provided by their workplace and their own cultural profile.Studies investigating coping mechanisms have found strong connections between both the severity and frequency of the harassment to response patterns.For example, engaging in detached behaviors was associated with significantly lower frequency of unwanted sexual attention than engagement in simultaneous avoidance of the behavior and negotiation with the perpetrator , however the direction of this relationship is ambiguous.

Studies have also found non-assertive actions to address sexual harassment to be more common if the sexually harassing behavior was not considered to be severe.Workers also opt for non-assertive responses when the source was someone other than a supervisor.This is consistent with previous studies which have found workers do not take action against customers to avoid crossing an ambiguous boundary between providing “good customer service” and protecting themselves.Studies have found that workplaces with few policies in place regarding sexual harassment are associated with passive responses to sexual harassment.This is not surprising given a lack of formal venues for filing complaints.Women whose workplace only employed informal policies for addressing harassment, were also less likely to engage in any form of direct response for similar reasons.Finally, cultural and social factors can influence a worker’s reaction and coping to harassment.The study by Cortina and Wasti found that White women more likely to practice detached behaviors compared to Latina women who practiced avoidant-negotiating behaviors and whose culture is historically more patriarchal and communal.Despite cultural differences, both styles of coping are ultimately non-confrontational.This general lack of combative action can also be explained by the shame women are socially taught to feel in response to harassment , as well as the responsibility they feel towards protecting the perpetrator.Understanding that sexual harassment is common in the service sector, the current study seeks to shed light on sexual harassment in the context of cannabis dispensaries, a recently legalized industry, within the context of Los Angeles County.With the passage of Proposition 64 during November 2016, the possession, use and retail of recreational marijuana was decriminalized in California through the Medicinal and Adult-Use Cannabis Regulation and Safety Act.Beginning in January 2018, California began to issue licenses for the legal operation of medical and adult use cannabis shops, and by the end of year, the California Department of Tax and Fee Administration reported cannabis shops produced $345 million in tax revenue for the state with the highest concentration of shops located in Los Angeles.

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