Hemp Growing

The physical environment also plays a role. Varying wind direction and speed lead to the transitory nature of odors, and multiple sources in the vicinity lead to difficulty in source attribution. Even temperature and humidity play roles in the perception of odor, which is often overlooked during exposure sampling and analysis. In addition to the large number of chemical compounds present in malodorous air, their typically low concentrations challenge the limits of even the best instruments . Known as the “odor gap,” the human nose can usually detect odors well below analytical instrument detectors’ capabilities . Methods that use human panels to evaluate odors have been standardized over the years and can work well in parallel with traditional analytical instrument methods. The vision is to have analytical instruments that completely mimic the human nose and sense of smell.The measurement and evaluation of exposure to conventional air pollutants is considered more evolved than that for odors . The framework and methodology applied to conventional air pollutants – risk assessment – offers grounding principles and useful conventions that have evolved over time. Both fields evaluate human responses to chemicals in the air. Although risk assessments are often predictive of future events, they may also be conducted retrospectively as an investigative technique.Risk is, by definition, is the probability of an adverse outcome and its severity. For chemical exposures, risk is a function of hazard and exposure . The fundamental framework for risk assessment was established in the 1980s . Figure 3.1 provides an overview of the various steps. These steps begin with the generation of basic information, proceed through identifying the hazards of the chemical under evaluation, predicting how adverse effects vary with dose,greenhouse tables and end with combining that information with exposure data to determine the incidence of adverse effects in a population.

Beyond risk assessment, and beyond the scope of this paper, is subsequent regulatory, management and communication steps based on the risk assessment’s output and other factors. Given the variety of information required in a risk assessment, the field is truly multidisciplinary. The data and assumptions made along the way are evaluated for how much uncertainty they contribute to the results. Often an order of magnitude or more of uncertainty and variability are inherent in the output, which needs to be explained transparently to not “over sell” the results with a false sense of precision and accuracy.A pragmatic approach to risk assessment is to first conduct a screening-level assessment based on crude approaches likely to overestimate risk. If the risk is found to be reasonable from such an approach, no further work is necessary. If not, then a more detailed, refined assessment is conducted. For the exposure assessment , the focus of this paper, a conceptual model guides the evaluation. The conceptual model traces the origin of the chemical , indicates how it is released, allows for transport of the chemical, includes possible routes of exposure, and indicates who might be exposed . Odors are released from a variety of sources, travel through the air and then are inhaled by local populations. Risk varies across a population due to biological differences , culture, lifestyle, level of exposure and prior exposures. To protect vulnerable sub-populations, a safety factor is usually applied. Perhaps the greatest challenge for both odor assessment and risk assessment is mixtures. We are exposed to a wide variety of chemicals through food, medicine and the environment, yet risk assessment often focuses on a single chemical in isolation. Odor assessment follows suit, focusing often on only one odorant. Such an unrealistic approach is destined to produce highly skewed or biased results, probably in unknown directions . Odor assessment has the advantage of tests being performed by human panels, which can evaluate the whole mixture of the sample. Risk assessment relies on epidemiological reconstructions for human data.Risk assessment, however, has developed approaches for mixtures. A simple, screening level approach is to determine the risk-driver for the mixture. Adding up the individual effects is another crude approach. A simplifying aspect for odor exposure assessment is that human olfaction has evolved to differentiate between only a few significant stimuli.

Typically, around 3 or 4 odors are sensed at a time, which decreases the complexity of the mixture . Those odorants that trigger intense, familiar or unpleasant sensations are more likely to be noticed while the remainder are lost in the signal “noise” or sensory filters. Or this limitation may due to inability to name a substance, rather than failure to detect the difference between odors . Both risk assessment and the evaluation of odors suffer from high degrees of uncertainty and variability. The personal nature of odor perception introduces fundamental variability. The health effects evaluated in risk assessment have a similar range of variability due to the biological variability of humans, which is increased further by the extrapolation of animal studies to humans. Therefore, each health effect benchmark value, such as a toxic reference dose, is typically presented with one significant figure due to the inherent uncertainty, which typically spans an order of magnitude. Exposure results, too, are uncertain due to modeling assumptions or analytical imprecision, as well as sample collection issues. In reality, one significant figure is a misrepresentation, and a range would be more accurate. Making judgements using ranges, however, is difficult so single values are typically used. A sensitivity analysis helps show the possible range of results.Transparency each step of the way is paramount, otherwise overconfidence in shaky results may occur. Both the best practices and draft guidance include a tiered approach to odor evaluations. Such has long been used in risk assessment to streamline the work. First, a screening-level evaluation is performed using crude assumptions and approaches. If the exposure is deemed acceptably low, there is no need for further work. The same applies to odor investigations. If a straightforward evaluation by an air inspector identifies the source and resolves the issue, no complex further investigation need ensue. In both cases, if the screening-level approach identifies concerns, then a detailed analysis is undertaken.Describing an odor in detail is often difficult, so most complainants start with saying “something smells bad” and then struggle to give further details. Unlike other senses with broad vocabularies, smell is anchored in the source of the odor and the person’s history with that source. In a way, our sense of smell is learned.

Attributing words and meanings to odors occurs over a lifetime and even changes over time. The food and beverage industry has attempted to make a science out of sensory description. Beer, wine and coffee are prime examples. Perfume formulation takes this to another level. To avoid complaints,vertical farming the drinking water industry has developed taste-and-odor assessment protocols.Environmental odors are typically mixtures of chemicals . The rare exception is the release of a single odorant from a chemical industry facility. The various odorants within a mixture trigger the olfactory sense in “concert” similar to the various notes in an orchestral piece of music. The perfume and fragrance industries are built largely upon this principle. The interplay of odorants in a mixture can be complex, with both synergistic and antagonistic effects taking place. Perfume has the function of covering up other odors. In odor terminology, this is called “masking.” Landfill and bio-waste sites are known to use scents such as “cherry” at their perimeter , yet in an evaluation of commercially available masking products only 4 out of 26 were able to mask odors successfully . All 4 were neutralizing agents that reacted with odorants. Within an environmental odor sample, certain odorants may mask others. Only upon dilution to a point where the major odorants are no longer perceptible are the minor odorants noticed. This dilution effect has been termed “peeling the onion” , where one layer of odor leads to another. Further discussion of this effect is in the section on odor intensity. The odorants within a mixture are subject to the same physicochemical processes and dispersion as any conventional air pollutant. The same exposure models, such as fate and transport, apply; however, the identities and concentrations of the individual odorants are often unknown, rendering such modeling impossible. To get around this issue, a pseudo-concentration approach has been developed, which is discussed in Section 4.2. The overwhelming majority of the molecules in air are odorless. These include nitrogen, oxygen, water, hydrogen, helium and carbon monoxide. Rather uniquely, carbon dioxide is odorless until it reaches 200-fold above background levels , at which point is triggers the nasal trigeminal receptors rather than the olfactory receptors.Colors have agreed-upon descriptions, and graphic artists often use Pantone® numbers as specific identifiers. Musical notes have frequencies assigned to them and arranged into scales . Odorants, too, have descriptors, known as “notes,” the term used in ISO 5492:2008 . For example, “fishy,” “swampy,” “rotten egg,” “pungent,” or “tingly” are odor notes. An atlas of panel-derived odor notes has been published . The odor note, however, may change with the concentration . Hydrogen sulfide at levels above 20 ppm changes from its characteristic “rotten egg” odor note to a “sweet” odor note, and at even higher concentrations, which are toxic, hydrogen sulfide becomes odorless. The response to an odor is highly personal and depends on “odor memory” – previous exposure and knowledge about the odor source . Common descriptors associated with specific odorants, however, may aid in determining the source of an odor.

Odor wheels have been developed for specific odor notes associated with certain sources, such as landfills, composting and WWTPs . Odors as mixtures make assigning odor notes more complex. As with wine tasting, several dominant notes may be present, along with several subtle notes. These, too, change as the mixture is diluted or ages, or as temperature and humidity change.As with sound and color, some odor notes may be perceived as pleasant or unpleasant. This is the “odor hedonic tone,” also known as the acceptability of the odor. Dravnieks published on this topic, and a scoring system is named after him. Odor hedonic tone is a highly subjective determination, open to large variation across a population and appears to be learned rather innate . Odor hedonic tone varies as the odorants increase or decrease, sometimes progressing through flip-flops between pleasant and unpleasant .Odor intensity – the magnitude or strength of an odor – has received considerable attention. Unlike odor notes and hedonic tones, which can be fairly subjective for the untrained, odor intensity is pursued as a quantifiable, even scalable, attribute of odor perception. The belief is that odor intensity is akin to brightness or loudness, which are quantifiable through physics, yet odors are a chemical sense with accompanying complexities. Nonetheless, two approaches have been attempted: assigning words or numeric scores to intensity levels, or determining the amount of dilution required until the intensity is no longer detectable. For a single odorant, intensity appears to be linked to the odorant’s concentration. In mixtures, such a link is tenuous or absent. Although odors are typically mixtures, it is much easier to study individual odorants. The mathematical connection between odorant concentration and perceived intensity is called the Steven’s power law . Whether the concentration is used directly or divided by a reference concentration does not impact the relationship between intensity and concentration. The inclusion of an intercept, however, fundamentally changes the slopes of the lines and does fundamentally alter the relationship. The original Weber-Fechner law included no intercept . Researchers added the 0.5 intercept to account for “the definition of the odour threshold concentration which states that 50% of the panellists perceive weak odour while the others perceive no odour” and proceeded to use an intensity scale that ranged from 0 to 5 . Other researchers also used the equation with the 0.5 intercept, but the intensity scale ranged from 0 to 6 . The effect of the 0.5 intercept in both of these studies was to assign an intensity score of 0.5 to the ODTC50 concentration, which had nothing to do with the percent of the panelists perceiving or not perceiving and odor. Another researcher allowed the intercept to float uniquely for each odorant and used a 0 to 12 intensity scale , which scale been used in flavor and drinking water profiling .

Subjects were defined as having DM if they answer ‘yes’ to the question ‘Have you ever been told you have sugar/diabetes?’ or had a fasting blood glucose level $126 mg/dl . Of the 719 patients with DM, 418 answered the question about whether they take insulin and 116 reported that they do take insulin. Of those, nine reported that they began using insulin at age #20 years, the majority being likely to have type 1 DM, although a few may have had type 2 DM. Thus, we estimate that 1.5% of patients with DM had type 1 DM, and because of this low number, we analysed all subjects with DM together. There was no difference in any of our analyses if the nine patients of age #20 years were excluded. The study included 151 pregnant women . Of them, eight women had diabetes. There was no difference in the use of marijuana by DM. Because of the low number in the diabetes category, we included them in the analysis. A series of sensitivity analyses excluding the pregnant women showed no difference. Plasma glucose and whole blood haemoglobin A1c were measured at the University of Missouri Columbia School of Medicine Department of Child Health, Diabetes Reference Laboratory, Columbia, Missouri, by David Gold stein, MD, director.Subjects were classified as obese/non-obese according to the BMI level using a cut-off of 30 kg/m2 . We analysed data related to DM, age, gender, race/ ethnicity, education level, family history of DM, physical activity, BMI, cigarette smoking, cocaine use, alcohol use, total serum cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, serum 25-hydroxy vitamin D , HbA1c,vertical outdoor farming fasting plasma glucose level, C reactive protein level and the serum levels of less robust inflammatory markers count and uric acid that have been previously used in NHANES III analysis.

Physical activity was assessed using self-report to several questions . For the physical activity variable, subjects were classified as inactive if they did not report engaging in any of the following activities during the previous month: walking, jogging, bike riding, swimming, aerobics, dancing, calisthenics, gardening, lifting weights or other physical activity outside their occupation. Physical activity was classified as moderate or vigorous intensity based on metabolic equivalent intensity levels. Individuals were considered to fulfil national recommendations for physical activity if they reported five or more episodesper week of moderate-intensity physical activity or three or more episodes per week of vigorous-intensity physical activity.Descriptive statistics were used to characterise the subjects . To test the statistical difference between the groups, we used c2 test for categorical variables and two-sided t tests for continuous variables. A p value of <0.05 was considered significant. Univariate and multivariate logistic regression analyses were used to determine the relationship between DM and marijuana use. We used multivariate logistic regression to adjust for confounding variables and reported the OR and the 95% CI. Variables considered as possible confounders in the multivariate analysis were age, gender, race/ ethnicity, BMI, education level, cigarette smoking, alcohol use, physical activity, serum total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, vitamin D, CRP, ferritin, fibrinogen, WBC count and uric acid. In order to confirm that marijuana use was associated with DM and not due to confounders, we analysed how each potential confounder changed the OR of having DM. Variables that changed the OR by $10% were considered as confounders and included in the multivariate model. We performed stratified analysis to test for effect modification. For effect modifier variable, multivariate logistic regression model was constructed for each subgroup.

In addition, to help adjust for selection bias, we analysed the data using the propensity score matching and estimated the average treatment effect for the treated, bootstrap SE and t statistics. We added the propensity score to the logistic regression model as inverse weight, blocks that satisfy the balancing property and quartiles. Data were analysed using SAS and the survey module of STATA . Sample weights, provided by the National Center for Health Statistics, were used to correct for differential selection probabilities and to adjust for non-coverage and non-response.Among NHANES III participants aged 20e59 years, there were 6667 non-marijuana users, 3346 past marijuana users, 557 light current users and 326 heavy current users. As shown in table 1, current and past marijuana users tended to be <40 years old, be male, had a BMI of <30 kg/m2 , smoked cigarettes and used alcohol and cocaine more frequently compared to non-marijuana users. Compared to non-marijuana users, past users tended to be white and to have a college education, while current users included more white and black subjects and were more likely to have a high school education or less. Non-marijuana users, past and current marijuana users had a similar percentage of family history of DM but significantly different percentage of physical activity levels , with past and current marijuana users being more active than non-marijuana users. As shown in supplement table 1, marijuana users had a lower adjusted prevalence of DM, but not hypertension, stroke, myocardial infarction or heart failure compared to non-marijuana users. The unadjusted prevalence of DM for non-marijuana users, past marijuana users, current light marijuana users and current heavy marijuana users was 6.3%, 2.9%, 1.9% and 3.0%, respectively, and there was a statistically significant difference between the groups . For subjects without DM , 46.4% were marijuana users and 53.6% were non-marijuana users . For subjects with DM , 26.9% were marijuana users and 73.1% were non-marijuana users . The difference in % of marijuana users between those with and without DM was highly significant .

As shown in table 1, all marijuana users had a higher prevalence of serum HDL cholesterol >40 mg/dl, total cholesterol <240 mg/dl and triglycerides <200 mg/dl compared to non-users . Current marijuana users had a higher prevalence of LDL cholesterol <160 mg/dl . All marijuana users had a higher prevalence of CRP <0.5 mg/dl . Past users, but not current users, had a lower prevalence of vitamin D level <70 nmol/l compared to non-users . All marijuana users had a higher prevalence of plasma HbA1c <6.0% . Serum glucose levels and BMI were lower in all marijuana user groups compared to non-marijuana users . We then examined the variation of markers of inflammation with marijuana use . Serum CRP and fibrinogen were significantly lower in past marijuana users compared to current and non-marijuana users suggesting lower inflammation in past marijuana users. In contrast, serum ferritin levels were higher in past and current heavy users, and lower in light users,rolling grow table compared to non-users. Serum uric acid levels were higher in past and lower in current users compared to non-users. WBC count was higher among current users relative to non-users and past users. In order to confirm that marijuana use was associated with a decreased prevalence of DM and not due to confounders, we analysed how each potential confounder changed the OR of having DM. Variables that changed the OR by $10% were considered as confounders . Table 2 shows the unadjusted as well as the cumulative effect of the confounders, including race/ethnicity, physical activity and those variables that showed changes of $10% in the OR of having DM among all marijuana users relative to non-users in a series of regression models. Of note, race/ethnicity and physical activity did not change the OR by $10%, but we included them in the model because they are known risk factors. The interaction effect of the marijuana use and age was significant in the model indicating that age is an effect modifier . Stratified analysis by age group found an association between marijuana use and DM among subjects aged >40 years and no association among subjects aged #40 years . The association of DM and marijuana was significant in both the overall and older age group even after adjusting for social variables , laboratory variables , inflammatory marker and the comorbidity variable to the previous model. Using the propensity score matching, we found similar results showing a lower prevalence of DM among marijuana users relative to non-users. The average treatment effect for the users ¼0.024, bootstrap SE¼0.005 and t¼4.46, p<0.05 . When we added the propensity score to the logistic regression model, marijuana users still had lower odds of DM than non-users . Adding it as inverse weight, yielded an OR¼0.52 . We also added it as blocks and found an OR¼0.53 . Adding it as quartiles yielded an OR¼0.51 . All still revealed a lower odds of DM with marijuana use. For age group 41e59 years, adding the propensity score as quartiles to the model, we found an OR¼0.55 , whereas for age group 20e40 years, OR¼0.88 . We examine whether DM as diagnosed by self-report as compared to laboratory evidence of hyperglycaemia was correlated with different prevalence of marijuana use. As shown in the supplement table 2, there was no difference in marijuana use among those with DM by self-report and those with DM who were included based on an elevated fasting glucose . Patients with DM by self-report who were hyper glycaemic at the time of sampling had a statistically similar rate of marijuana use as those whose DM was well controlled at the time of sampling , although there was a trend for patients with a history of DM by self-report who were euglycaemia at the time of sampling to be associated with a lower rate of non-marijuana use.

Those with DM by self-report and those with DM who were included based on an elevated fasting glucose had similar rates of the type of marijuana use . Additionally, for subjects who did not have DM by self-report and did not have an elevated fasting glucose level but had an elevated HbA1c , their prevalence of non-marijuana use was similar to the prevalence of non-marijuana use among subjects with DM . We then examined the prevalence of all marijuana users among subjects with different fasting glucose levels. As shown in figure 1, the highest prevalence of marijuana users was found in those with the lowest glucose levels. As the glucose levels increased, the prevalence of marijuana users decreased. For subjects with DM , the prevalence of marijuana users was 23.6%. Similarly, the highest prevalence of marijuana users was found in those subjects with the lowest plasma HbA1c values . As the HbA1c levels increased, the prevalence of marijuana users decreased. Furthermore, we analysed the data using logistic regression to assess the odds of having DM, an elevated glucose value or an elevated HbA1c for the categories of marijuana use. The OR for all marijuana users to have DM was 0.42 , which was statistically significant . Relative to non-marijuana users, past marijuana users had an OR of having DM of 0.44 , current light marijuana users had an OR of 0.29 and current heavy marijuana users had an OR of 0.47 , all were statistically significant from non-marijuana users . Relative to non-marijuana users, marijuana users had significantly lower odds of having glucose level of >125 mg/dl and HbA1c level >7.0% .Our analyses of adults aged 20e59 years in the NHANES III database showed that participants who used marijuana had lower prevalence of DM and had lower odds of DM relative to non-marijuana users. We did not find an association between the use of marijuana and other chronic diseases, such as hypertension, stroke, myocardial infarction and heart failure. This could be due to the smaller prevalence of stroke, myocardial infarction and heart failure in the examined age group. We noted the lowest prevalence of DM in current light marijuana users, with current heavy marijuana users and past users also having a lower prevalence of DM than non-marijuana users. The finding that past marijuana users had lower odds of prevalent DM than non-users suggests that early exposure to marijuana may affect the development of DM and a window of time of marijuana exposure earlier in life could be a factor to study. Similarly, our findings of a significant association between marijuana use and DM was only found in those aged $40 years suggest that the possibility of some protection from marijuana use may require many years before they become manifested.

In the US, the refusal to enfranchise Blacks or Native Americans led to the development of racial categories, and these categories were in the US census from the beginning. In some of the federated states of the US, there were laws, including the “one drop of blood” rule that determined that to have any Black ancestors meant that one was de jure Black .Native Americans appeared in 1820, Chinese in 1870, Japanese in 1890, Filipino, Hindu and Korean in 1920, Mexican in 1930, Hawaiian and Eskimo in 1960. In 1977, the Office of Management and Budget , which sets the standards for racial/ethnic classification in federal data collections including the US Census data, established a minimum set of categories for race/ethnicity data that included 4 race categories and two ethnicity categories . In 1997, OMB announced revisions allowing individuals to select one or more races, but not allowing a multiracial category. Since October 1997, the OMB has recognized 5 categories of race and 2 categories of ethnicity . In considering these classifications, the extent to which dominant race/ethnic characterizations are influenced both by bureaucratic procedures as well as by political decisions is striking. For example, the adoption of the term Asian-American grew out of attempts to replace the exoticizing and marginalizing connotations of the externally imposed pan-ethnic label it replaced, i.e. “Oriental”. Asian American pan-ethnic mobilization developed in part as a response to common discrimination faced by people of many different Asian ethnic groups and to externally imposed racialization of these groups. This pan-ethnic identity has its roots in many ways in a racist homogenizing that constructs Asians as a unitary group , and which delimits the parameters of “Asian American” cultural identity as an imposed racialized ethnic category . Today,vertical rack the racial formation of Asian American is the result of a complex interplay between the federal state, diverse social movements, and lived experience.

Such developments and characterizations then determine how statistical data is collected. In fact, the OMB itself admits to the arbitrary nature of the census classifications and concedes that its own race and ethnic categories are neither anthropologically nor scientifically based . Issues of ethnic classification continue to play an important role in health research. However, some researchers working in public health have become increasingly concerned about the usefulness or applicability of racial and ethnic classifications. For example, as early as 1992, a commentary piece in the Journal of the American Medical Association, challenged the journal editors to “do no harm” in publishing studies of racial differences . Quoting the Hippocratic Oath, they urged authors to write about race in a way that did not perpetuate racism. However, while some researchers have argued against classifying people by race and ethnicity on the grounds that it reinforces racial and ethnic divisions; Kaplan & Bennett 2003; Fullilove, 1998; Bhopal, 2004, others have strongly argued for the importance of using these classifications for documenting health disparities . Because we know that substantial differences in physiological and health status between racial and ethnic groups do exist, relying on racial and ethnic classifications allows us to identify, monitor, and target health disparities . On the other hand, estimated disparities in health are entirely dependent upon who ends up in each racial/ethnic category, a process with arguably little objective basis beyond the slippery rule of social convention .If the categorization into racial groups is to be defended, we, as researchers, are obligated to employ a classification scheme that is practical, unambiguous, consistent, and reliable but also responds flexibly to evolving social conceptions . Hence, the dilemma at the core of this debate is that while researchers need to monitor the health of ethnic minority populations in order to eliminate racial/ethnic health disparities, they must also “avoid the reification of underlying racist assumptions that accompanies the use of ‘race’, ethnicity and/or culture as a descriptor of these groups. We cannot live with ‘race’, but we have not yet discovered how to live without it” .

Reinarman and Levine have argued that investigations of ethnicity in alcohol and drugs research have typically taken the form, whether intentionally or not, of linking “a scapegoated substance to a troubling subordinate group – working-class immigrants, racial or ethnic minorities, or rebellious youth” . Different minority ethnic groups have often been framed at one time or another by their perceived use of alcohol and illicit drugs, regardless of their actual substance using behaviors and regardless of their relative use in comparison with drug and alcohol use among whites . Such framing arguably has led to extensive stereotyping of minority cultures, their characters, and their behaviors. For example, in the 18th century, white settlers in the US used stereotypical portrayals of Native drinking to justify the confiscation and exploitation of Native lands . In the early part of the 19th century, Chinese immigrants were victimized and controlled for their supposed opium use, despite the fact that only 6% at the time used opium . In the early 1900s, cannabis was relatively plentiful along the Texas border brought to the US by Mexican migrants, and its popularity among ethnic minorities practically ensured that it would be classified as a narcotic and attributed with addictive qualities . By the early 1930s, cannabis had been prohibited in 30 states. In 1937 the Marijuana Tax Act was passed by Congress which banned cannabis at the Federal level . And, the most recent drug scare, which fueled the development of the War on Drugs, linked crack cocaine to impoverished African Americans and Latinos in inner city neighborhoods .These statistics lie in sharp contrast to the available empirical data on differential rates of alcohol and substance use between whites and non-whites . The evidence from Monitoring the Future – a longstanding and reliable source of data on drug use among youth in the US – suggests that crack cocaine cannot be considered a drug consumed primarily by Blacks in American nor can marijuana be considered a drug used primarily by Latino/as. Rather, white youth have higher rates of use for most drugs of abuse. For example, Terry McElrath and colleagues reviewing 30 years’ worth of data from MTF, found that for all drugs except heroin, past year prevalence rates were significantly higher among whites compared to blacks and Latinos .

In spite of the backdrop, the vast majority of alcohol and drug research has failed to mention the injustices of drug laws and high rates of imprisonment of ethnic minority youth. Instead of situating research within a context of oppression and inequality,microgreen flood table researchers have tended to ignore this situation and instead focus on risk factors associated with drug use among racial/ethnic groups, an approach that dominates alcohol and drugs research today. This trajectory in alcohol and drug research is unfortunate in light of recent debates in social epidemiology about the importance of examining health disparities within a framework that considers “social structures and social dynamics that encompass individuals” . Social epidemiologists have argued that mainstream research tends “to focus on the body, lifestyle, behaviour, sex/gender, race/ethnicity and perhaps the personality, emotional state or socioeconomic status of the single person” . Just as mainstream epidemiology has been criticized for having little regard for social structures, social dynamics, and social theory , most existing studies of ethnicity within drug and alcohol research can similarly be critiqued for failing to adopt a structural approach as well as neglecting contemporary social science theories of and debates about ethnicity. In mainstream drug and alcohol research, traditional ethnic group categories continue to be assessed in ways which suggest little critical reflection in terms of the validity of the measurement itself. This is surprising given that social scientists since the early 1990s have critiqued the propensity of researchers to essentialize identity as something ’fixed’ or ’discrete’ and to neglect to consider how social structure shapes identity formation. Recent social science literature on identity suggests that people are moving away from rootedidentities based on place and towards a more fluid, strategic, positional, and context-reliant nature of identity . This does not mean, however, that there is an unfettered ability to freely choose labels or identities, as if off of a menu . An individual’s ability to choose an identity is constrained by social structure, context, and power relations. Structural constraints on identity formation cannot be ignored, as people do not exist as free floating entities but instead are influenced and constrained in various ways by their socioeconomic and geographical environment . As such, an identity is not just claimed by an individual but is also recognized and validated by an audience, resulting in a dialectical relationship between an individual and the surrounding social structures . Similarly, a ‘new’ perspective on ethnic identity specifically has emphasized the fluidity and contextually-dependent nature of ethnicity, minimizing notions about ethnicity as a cultural possession or birthright and instead emphasizing ethnicity as a socially, historically, and politically located struggle over meaning and identity . Ethnicity or ethnic identity is not some immutable sense of one’s identity but rather something produced through the performance of socially and culturally determined boundaries . Hence, individuals are not passive recipients of acquired cultures but instead active agents who constantly construct and negotiate their ethnic identities within given social structural conditions .

In spite of these sociological contributions, which have enriched our understanding of identity generally and ethnicity specifically, the alcohol and drugs fields have not adequately integrated these perspectives, thwarting our ability to understand the relationships between ethnicity and substance use. As such, the field is ripe with correlations between ethnic group categories and substance use problems, resulting in solutions to problems that focus on reifying questionable social group categorizations and revealing little about how drugs are connected to identities and shaped by broader social and cultural structures. It is important to note that we do not intend to argue that existing categories of ethnicity be disregarded in the alcohol and drugs fields. As Krieger and colleagues have noted in another context , surveillance data documenting health disparities, in our case in substance use, are exceedingly important in terms of identifying potential inequities in health. However, without understanding the complexity of ethnic identity and its relationship to substance use, these surveillance data may perpetuate stereotypes and the victimization of specific socially-delineated ethnic groupings, obfuscate the root causes of substance use and elated problems, and reify politicized categories of ethnicity which may have little meaning for the people populating those categories. While acknowledging that socially-deliented ethnic categories are important for documenting social injustices, we must also be vigilant about questioning the appropriateness of those categories. Conceptually this type of critical approach is important for considering how substance use is related to negotiations of ethnicity over time and place and bounded by structure. Maintaining a static and homogenous approach towards ethnic categorizations in the alcohol and drugs fields presents at least two problems. First, it risks overlooking how drugs and alcohol play into a person’s negotiation of identity, particularly ethnic identity, thus revealing little about the pathways that lead to substance use. Cultural researchers have long emphasized the importance of commodity consumption in the construction of identities and lifestyles , particularly within youth cultures , and how it can be an important factor in demarcating and constituting social group boundaries . A limited body of research in the alcohol and drugs field has emphasized the role of substance use in constructing and performing identities , particularly ethnic identities , uncovering how subgroups within traditionally-defined ethnic minority categories use drugs and alcohol to distinguish themselves from ethnically similar others. For example, in a qualitative study of Asian American youth in the San Francisco Bay area in the US, narratives illustrated how youths’ drug use and drug using practices were a way of constructing an identity which differentiated them from “other Asian” youth groups, specifically allowing them to construct an alternative ethnic identity that set them apart from the “model minority” stereotype . Thus taste cultures and consumption-oriented distinctions highlight the continuing salience of and interconnections not just between substance use and changing notions of ethnicity but also between substance use, class and ethnicity. Ethnic identity gets translated into social captial which in turn has ramifications for one’s economic and social standing . Second, failing to critically appraise our use of fixed and homogenous ethnicity categories in the alcohol and drugs fields jeopardizes our ability to identify the broader social and structural determinants of alcohol and drug use and related problems—like poverty, social exclusion, and discrimination—which are crucial issues for addressing social injustices.

Conservative estimates showed that care in the first year of life for a PTB infant costs $47,100 per infant, while an alternative algorithm estimated that costs could be up to $78,000 per preterm infant . We used the most conservative lower bounds of the published cost estimates in all analyses.Two logistic regressions analogous to those published were fit with policy indicators as the exposure and odds of LBW and odds of PTB as outcomes. Each policy indicator was coded as 0 if it was not in effect for the mother’s state of residence during the month/year of conception and 1 if it was in effect during the month/year of conception; this method of linking the policy indicators to the month and year of conception improves the accuracy of exposure timing. Both regressions adjusted for all policies simultaneously, as well as individual- and state-level covariates, fixed effects for state and year, and state-specific time trends; previously published analyses found no differences when controlling for all policies together simultaneously vs. each policy individually. Individual-level covariates were maternal age, race, marital status, education, nativity, parity, and version of birth certificate. State-level covariates were state- and year- specific poverty, unemployment, per capita cigarette consumption, and per capita total ethanol consumption,microgreen rack for sale as well as indicators for whether government control of wine sales and government control of spirit sales were in effect for that state in that year. Predicted margins for each significant policy were calculated and used to compute the excess proportion of low birthweight and preterm births under each significant policy. These proportions and their 95% confidence intervals were then applied to the number of births for 2015 in all U.S. states to estimate the number of excess LBW and PTBs in 2015.

Finally, using marginal effects and the most conservative lower bounds of published cost estimates obtained from, costs associated with excess LBW and PTB under each policy were estimated.Multiple state alcohol/pregnancy policies–specifically MWS, PTPREG, LCP, and CACN–lead to thousands of babies born low birthweight or preterm each year. These increased rates of adverse birth outcomes cost hundreds of millions of dollars in health care and related costs annually. The actual prevalence and associated costs indicate that the harms related to alcohol/ pregnancy policies are not only statistically significant, but also significant from a public health and public policy perspective. As most alcohol/pregnancy policies also apply to drugs, findings from this study for all policies other than MWS can be interpreted as applying to alcohol+drug/pregnancy policies rather than specific to alcohol/ pregnancy policies. The MWS finding in particular, though, suggests that as states continue to legalize recreational cannabis, public health policy makers may want to exercise caution before expanding MWS to apply to cannabis. Findings of adverse outcomes associated with MWS and CACN are plausible given previous literature showing that the fear of being reported to CPS and fear of having already irreversibly harmed ones baby are reasons women avoid prenatal care. However, explanations for findings related to both LCP and PTPREG are not as intuitive. One possible explanation comes from the historical context in which these particular policies emerged. Neither LCP nor PTPREG emerged as public health policies developed through a public health policy-making process, but rather emerged as advocacy arguments in response to the War on Drugs-related criminalization and punishment of pregnant women who used crack cocaine. It is also worth noting that that limits on criminal prosecution focuses primarily on limiting use of medical test results in criminal prosecutions related to alcohol/drug use during pregnancy– thus, states that adopt this policy could have more criminal prosecutions related to use during pregnancy.

Priority treatment policies could be a marker of a state that does not have sufficient treatment slots either in general or for pregnant women, and thus the findings could be due to the lack of treatment availability for pregnant women, for women prior to becoming pregnant, or for women’s partners. Future research should examine these mechanisms. In the meantime, our findings strongly suggest that new policy approaches to alcohol/drug use during pregnancy are needed. Strengths of this study include rigorously coded policy data and an outcome dataset that does not rely on self-report and that encompasses the entire population of singleton births for more than 40 years. Another strength is that analyses were able to incorporate state-specific time trends in addition to multiple individual and state-level controls and fixed effects for state and year. Adjusting for state-specific time trends helps address endogeneity and provides more confidence that the results we observe are due to the policies rather than the reverse. Limitations include that Vital Statistics data are not collected for research purposes, that they may not include key individual-level control variables, and that the measurement of some key variables have changed over time. However, our adjustment for birth certificate version and inclusion of fixed effects for state and year alleviates concerns regarding changes in birth certificate data collection over time.College-going individuals in the United States may have unique attitudes toward substance use behavior and tobacco use, including shifts in attitudes and behaviors that are associated with the constantly changing product landscape of alternative tobacco products , such as electronic-cigarettes . Psychosocial behaviors and campus culture, including class attendance, peer socializing, campus policies, and residential environments, may also facilitate these unique attitudes toward favorability of smoking among college subgroups, while also introducing a unique risk environments for tobacco initiation, uptake, transition, and use .

In addition, part of the variation explaining these health behaviors may be influenced by the specific demographic and socioeconomic characteristics of a college campus population and community. Data from social media platforms are often used to self-report and publicly communicate health-related attitudes and behaviors . Young adults [ages 18–25 ] in the United States are much more likely than older populations to actively use social media, including popular platforms Twitter, Snapchat, and Instagram . Infoveillance research, which uses online information sources to detect trends about the distribution and determinants of disease, including health knowledge and behaviors, has been used to develop insights on numerous public health issues including infectious diseases, vaccination sentiment, opioid use disorder, mental health issues, and, relevant to the exploratory aims of this study, tobacco and alternative tobacco use attitudes and behavior . However, smoking-related discussions on social media tied to specific colleges with geographic specificity has not been widely investigated. Existing studies using social media to examine tobacco-related attitudes and behaviors in college aged populations have primarily focused on evaluating the impact of social media health promotion anti-tobacco campaigns, recruiting hard-to-reach college populations using social media platforms, and examining the influence of exposure to tobacco-related social media content and marketing on current and future behavior and use . Other research on college-aged populations has focused on assessing tobacco initiation and transition of use patterns,cannabis grow facility layout particularly as new alternative and emerging tobacco products become available . Accelerating research using social media to assess tobacco-related attitudes/inflfluences among youth has also been supported by U.S. Federal initiatives, including projects funded by the National Cancer Institute and U.S. Food and Drug Administration Tobacco Centers of Regulatory Science, which for have identified and characterized e-cigarette advertisements on image-focused social media sites and tobacco user experiences with little cigars and e-cigarettes as discussed on Twitter . Changes in local, state, and national health policy related to tobacco and other products smoked or used concurrently with tobacco and electronic cigarettes can also have an impact on attitudes and behaviors of these populations. For example, recent debate in the United States regarding the legalization of marijuana/cannabis may positively influence marijuana-related attitudes for college populations, who tend to skew toward more liberal policies regarding decriminalization, legalization, and increased access . Similarly, the 2019 outbreak of e-cigarette and vaping-related lung injury associated with products containing tetrahydrocannabinol may dissuade tobacco or THC use in certain young adult populations, particularly since they were most heavily impacted by the disease . Examining the changing public attitudes and behaviors of college-aged smokers is particularly salient for the State of California, USA. As of January 2014, all campuses in the statewide University of California system became tobacco free , and the California State University system followed suit in 2017 . In addition, voters in California approved Proposition 56 in late 2016, which added a $2.00 increase to the cigarette tax effective April 2017, with an equivalent increase on other tobacco products and electronic cigarettes . Voters in 2016 also approved Proposition 64, which legalized the use of recreational cannabis in November 2016 . During this time, the popularity of e-cigarettes in the United States was increasing . These changes in policy and preferences underscore the interconnected nature of the Triangulum of tobacco products , including potential for dual-use, transition between products, and challenges associated with conducting surveillance and implementing cessation programs .

This changing policy landscape supporting tobacco control measures, as reflected in the shift of California’s public university systems to become smoke-free, is a key impetus for this study. The ability of these colleges to eliminate on-campus smoking relies in large part on understanding past and existing knowledge and attitudes held by the campus smoking populations, along with their perceptions and behaviors that may be associated with compliance or non-compliance to smoke free campus policies. In response, this study conducted exploratory research on the popular micro-blogging platform Twitter. specifically, we used big data, data mining, and geospatial approaches to identify and characterize tweets originating from Twitter users specifically geolocated at California 4-year university campuses. Our primary objective was to assess types of tobacco and ATP products mentioned by users, the distribution of user sentiment toward tobacco and smoking behavior, and to assess the feasibility of detecting self-reported smoking behavior that may represent a violation of campus smoke free policies. Secondarily, we also sought to conduct a cross-campus assessment to determine how these factors vary across different university and college communities and over time. The objective of the study’s data collection approach was to obtain a highly refined subset of tweets, which were both posted from college campus’ geolocated coordinates in California and also included user discussions about smoking, in preparation for manual review to more purposefully identify tweets that specifically discussed different types of tobacco and smoking products, sentiment of users toward smoking behavior, and self reported smoking behavior on campus. Data were collected from the Twitter public streaming Application Programming Interface using the cloud-computing service Amazon Web Services . The public streaming API was set with filters to collect all tweets that included metadata containing latitude and longitude coordinates, initially with no filter for keywords. Tweets were collected continuously from 2015 to 2019. All tweets collected included the text of the tweet and associated metadata, including the date and time of tweets. The use of the public Twitter streaming API to collect data pre-filtered only for tweets including latitude and longitude coordinates represent a subset of all tweets posted during the time frame of the study. There exists the potential for sampling bias associated with different Twitter APIs that are not representative of all Twitter data , and data filtered only for geocoded data may omit many conversations from college aged populations about topics, such as smoking, which may be linked to college-related user groups . Though resulting in a much smaller volume of data, our approach nevertheless allows for detection of tweets in specific geospatial bounds at the high resolution of latitude and longitude coordinates in the state of California. Therefore, by using this data collection approach, we were able to isolate tweets originating from geospatial coordinates within the formal spatial boundaries of all 4-year universities in California. To enable this geolocation, a base map of California 4-year universities from the Stanford Prevention Research Center was obtained and cross-referenced. Tweet geolocated points were spatially joined to campus polygons using ArcGIS software.College areas were comprised of multiple polygons for different campuses and associated properties, though aggregation was conducted at the overall college level to enable comparison across different colleges. Tweets were then filtered for 37 keywords which were broadly related to tobacco-related topics, including the names and brands of different tobacco and ATPs and descriptive terms associated with smoking and vaping as expanded upon from those used in prior studies . specifically, the following keywords were used: bidis, cigarette, cigarettes, cigarillos, cigars, cigie, class, dip, e-cig, hookah, huqqa, joint, JUUl, kereteks, Marlboro, Newport, njoy, pipe, roll-up, shag, smoke, smoking, snuff, snus, tobacco, vape, vaped, vapejuice, vaper, vapes, vaping, vapor, water pipe, waxpen, and weed.

Combined NRT with patch and a more immediate acting product results in higher quit rates than single NRT [Cochrane meta-analysis: risk ratio , 1.34; 95% confidence interval , 1.18 to 1.48] . The combination of varenicline and nicotine patch has been evaluated with mixed results . The mechanism for why NRT should augment effects of varenicline is unclear, but the combination appears to be safe. The combination can be considered in a smoker who does not quit with dual NRT or varenicline. Bupropion in combination with nicotine patch or dual NRT increases quit rates compared to these drugs given alone . One trial reported promising results with the combination of varenicline and bupropion, although neuropsychiatric adverse effects were greater in the first 2 weeks compared to varenicline alone .Many smokers would like to quit but are not prepared to commit to a quit date when seen by a healthcare provider. Starting pharmaco therapy while the smoker is still smoking with the expectation that quitting will be easier at a later date has been studied with the use of nicotine patches and varenicline. The pharmacological basis for this approach is that NRT, by desensitizing nicotinic receptors and reducing withdrawal symptoms between cigarettes, and varenicline, by antagonizing effects of nicotine from cigarettes and also providing relief of withdrawal symptoms, will reduce satisfaction from smoking and decrease the number of cigarettes smoked per day. Preloading trials with nicotine patches have shown mixed benefit on quitting with a weak overall effect, although some trials showed large beneficial effects.Varenicline trials have shown benefit with a flexible quit date, and this approach is approved by the FDA .

The attraction of precessation pharmacotherapy is that the clinician can now approach every patient who smokes,trim tray pollen regardless of whether they are prepared to quit at the time of the visit, with a pharmacological intervention along with communication that this will help with quitting smoking in time, just as the clinician would advise every patient with hypertension to take medication to prevent future disease. In this regard, a small trial involving heavy smokers with COPD, who were initially unprepared to quit, prescribed varenicline for as long as they wanted, without a fixed quit date, and by 18 months, most had quit .Personalized medicine aims to use individual patient characteristics to select the most effective and/or safest medications for their medical problem. With long-term quit rates of 30% or less in most smoking cessation trials, there is interest in individualizing treatment to enhance efficacy.Rapid metabolizers of nicotine, on average, smoke more cigarettes and take in more nicotine per day compared to slower metabolizers, presumably to maintain desired levels of nicotine in the body . Rapid metabolizers also have more severe withdrawal symptoms when not smoking . The nicotine metabolite ratio is a phenotypic marker of the rate of nico tine metabolism, which can be measured in blood, saliva, or urine . In a prospective clinical trial, smokers were stratified as slow or normal metabolizers and treated with nicotine patch, varenicline, or placebo. In slow metabolizers, varenicline and nicotine patch were equally effective [odds ratio , 1.13; P = 0.56], but in rapid metabolizers, varenicline was more effective . Side effects were greater for varenicline in slow metabolizers. The results indicate that slow metabolizers can be successfully treated with nicotine patch, at lower cost and with fewer side effects, but normal metabolizers are better treated with varenicline.

More research is needed for confirmation.While not approved by the FDA, nortriptyline and clonidine have demonstrated efficacy in clinical trials for smoking cessation . These drugs are used primarily by smoking cessation specialists for patients who have not responded to other treatment. Nortriptyline is a tricyclic antidepressant that blocks neuronal reuptake of nor epinephrine, thereby increasing levels of the neurotransmitter in the brain. These actions simulate some of the actions of nicotine on brain neurotransmitters. Clonidine is a central 2 adrenergic receptor agonist that reduces sympathetic activity, resulting in sedation and anxiolysis. The benefit of clonidine is thought to be mediated by its anxiolytic and calming effects and appears to be most useful in smokers with anxiety as a major withdrawal symptom.A number of medications have been considered as possible candidates for smoking cessation . While animal and/or small studies in people show effects on nicotine reward or smoking behavior, none of these medications alone has been shown in adequately sized clinical trials to be effective in promoting cessation, including serotonin agonists , acetylcholinesterase inhibitors , drugs affecting GABA recep tors , and N-methyl-daspartate receptor modulators . A promising new medication is lorcaserin, a selective 5-hydroxytryptamine 2c receptor agonist. The drug induces food satiety by increasing pro-opiomelanocortin production in the hypo thalamus and is FDA approved for weight loss in overweight individuals. Lorcaserin has also been reported to reduce nicotine self-administration in rodents. Because weight gain after stopping smoking is common and sometimes triggers relapse, lorcaserin alone or in combination with other smoking cessation medications has been of interest. In a placebo-controlled trial combining lorcaserin with varenicline, the combination significantly increased 3-month continuous abstinence versus placebo , and weight gain was significantly less.

Medications evaluated in clinical trials and judged ineffective for quitting smoking include mecamylamine, serotonin-specific uptake inhibitors, anxiolytics , MAO inhibitors , modafenil, naltrexone, rimonabant, silver acetate, ondansetron, lobeline, nicotine vac cines, and Nicobrevin .A general discussion of e-cigarettes and other tobacco products for harm reduction, including consideration of benefits versus risks, is presented in the “Discussion: What Evidence Is Needed” section. Here, we specifically discuss evidence regarding e-cigarettes for smoking cessation. To date, no e-cigarette company has undergone FDA review and approval for use of e-cigarettes as a therapeutic aid for quitting smoking. Less than a handful of randomized controlled trials of e-cigarettes for smoking cessation have been published, and none has been conducted in the United States; hence, most of the evidence to date is observational. E-cigarettes produce an aerosol from a liquid that typically contains nicotine. The e-cigarette concept is to deliver nicotine by an inhaled route without generating products of tobacco combustion. NRT medications can aid cessation as discussed previously, but most smokers do not find NRT very satisfying, and quit rates are modest. The performance of e-cigarettes as nicotine delivery devices has evolved over time. The earliest devices looked like cigarettes but delivered very low levels of nicotine. The two clinical trials per formed with these devices were encouraging, but the quality of evidence was low . Recently, a randomized clinical trial with 886 smokers treated in the United Kingdom’s National Health Service evaluated a second-generation e-cigarette refillable tank–type device to patients’ choice of NRT provided free of cost for up to 3 months . All received standard behavioral support. At 1 year, the sustained abstinence rate in the e-cigarette group was twofold greater than the NRT group . Among participants randomized to the e-cigarette arm who quit smoking, 80% were still using e-cigarettes at 1 year; in comparison,trim bin tray among those randomized to the NRT arm, continued use of NRT was 9% for those who quit smoking. While e-cigarettes were found to significantly increase smoking cessation, some have expressed concern about the unknown health risks of long-term e-cigarette use. Adverse effects reported during the trial included greater throat or mouth irritation in the e-cigarette group and more nausea in the NRT group. Overall, adverse effects were minor in severity.Population-based observational studies report different results depending on the intention of the smokers to quit, how e-cigarettes are used, and where the study was conducted. A four-country comparison found the likelihood of quitting with e-cigarettes to differ by the regulatory environment . In Canada and Australia, which have more restrictive e-cigarette regulations, e-cigarette use was associated with a significantly lower likelihood of quitting smoking relative to unassisted quitting , whereas in the United States and United Kingdom, which have less restrictive e-cigarette regulatory environments, e-cigarette use was associated with increased quitting, consistent with other reports . The United Kingdom estimates that, annually, 22,000 to 57,000 long-term cigarette quitters are associated with e-cigarette use, more than quits attributed to NRT or other forms of pharmacotherapy .

In the United States and United Kingdom, daily use of e-cigarettes is associated with a greater likelihood of quitting smoking than nondaily use . In a study from France, e-cigarette use was associated with not only higher smoking cessation rates but also greater relapse to smoking . In conclusion, with respect to e-cigarettes, there is evidence that e-cigarettes can aid smoking cessation. This can occur both in the general population, where e-cigarette use is adopted as an acceptable and safer alternative to cigarette smoking, and in the context of a health service. The risks of long-term e-cigarette use are still unknown, and some medical professionals oppose the use of e-cigarettes for that reason. As discussed in the “Discussion: What Evidence Is Needed” section, there are also concerns about the use of e-cigarettes by children possibly creating a new epidemic of primary nicotine addiction, leading some U.S. public health professionals to conclude that the potential benefits of e-cigarettes for smoking cessation in adults are outweighed by the risks to youth.U.S. population-based and policy approaches successful for tobacco control include mass media tobacco education campaigns, expanded healthcare coverage for tobacco cessation treatment, excise taxation on tobacco products, clean air laws, and Tobacco 21 policies, which raise the minimum legal age to purchase tobacco products to age 21 . Other population-based interventions to reduce tobacco use have faced challenges in the United States at the federal level , and even state tobacco taxes and clean air laws have slowed . In contrast, interventions in the tobacco retail environment are increasing rapidly at the local level . Also gaining traction at the FDA, and discussed in the “Discussion: What Evidence Is Needed” section, is an effort to reduce the amount of nicotine in combusted tobacco products to reduce its addictive effects.An important component of comprehensive tobacco control pro grams, mass media tobacco education campaigns are composed of paid and earned media on TV, radio, community placements , magazines, newspapers, and digital/ social media platforms. Well-designed mass media campaigns implemented with sufficient reach, intensity, and duration can help counter pro-tobacco marketing, build support for tobacco control policies, increase awareness of tobacco’s harmful effects, promote quitting, and reduce smoking prevalence . Here, we describe the success of two ongoing U.S. campaigns.The CDC’s Tips national mass media tobacco education campaign has been implemented annually since 2012. Tips profiles real people living with serious long-term health consequences from smoking and secondhand smoke exposure based on evidence that messages graphically depicting the physical consequences of smoking-related diseases can encourage quit attempts . While Tips primarily targets adult smokers, secondary audiences include family members, healthcare providers, and faith communities able to reach people who smoke. Campaign goals include building public awareness of tobacco’s harms to self and others, encouraging smokers to quit, and making free help available . Tips has been effective at increasing population-level quit intentions, quit attempts, and sustained quits, with effectiveness persisting over time . In 2016, Tips ads featured Rebecca, a former smoker with depression. In a national evaluation, greater exposure to the Rebecca ads was associated with a greater likelihood of intending to quit and with making a quit attempt specifically among smokers with mental health conditions . National media campaigns are an important population-level strategy for reaching specific population groups, such as people living with mental health conditions, who are experiencing tobacco-related disparities.Healthcare reform legislation can increase receipt of tobacco cessation treatment for smokers from disparity groups. The U.S. Affordable Care Act mandated comprehensive coverage for tobacco treatment for most private health plans and newly eligible Medicaid beneficiaries in states that expanded Medicaid, including at least two tobacco cessation attempts per year and four tobacco cessation counseling sessions and prohibited cost-sharing and previous authorization restrictions for FDA-approved tobacco cessation medication. The ACA also removed coverage limits and preexisting condition exclusions. Concerning, however, was the ACA’s allowance for states to decide whether employers could charge smokers up to 50% more in premiums. Several states rejected the surcharge outright, while other states capped the maximum penalty at a lower level.

Adjusted analyses included an e-cigarette dependence propensity score covariate calculated from a prediction model of e-cigarette dependence status regressed on 25 baseline variables, as detailed in the eAppendix in the Supplement. Additional and supplemental sensitivity analyses were conducted. Analyses were tested in Mplus statistical software version 7 using full information maximum likelihood estimating to account for missing data, and participants’ high school was accounted for using complex modeling. For primary analyses, Benjamini Hochberg multiple-testing corrections20 were applied to control study wise false-discovery rate at 0.05, based on 2-tailed corrected P values. Data were analyzed from March 2019 to December 2019. Sensitivity analyses of cross-tobacco product comparisons of dependence prevalence, severity, and symptom patterns that adjusted for age at onset and past 30-day use frequency of the 2 respective products yielded results that were similar to the primary results . Additional cross-product analyses restricted to dual users who vaped nicotine found differences on the same dependence outcomes as the primary results, although differences were less robust . Youth who use multiple tobacco products may have difficulty distinguishing the source of dependence symptoms; however, baseline past-year combustible cigarette use did not significantly moderate associations of e-cigarette dependence symptoms with subsequent vaping at 6-month follow-up . For descriptive purposes to examine whether results generalized across sex and non-nicotine substance use, tests of differences in cross-sectional and prospective analyses stratified by sex and number of nonnicotine substances used are reported in Tables 7, 8, 9,drying cannabis and 10 in the Supplement. They did not show marked differences by sex and concomitant substance use.

Past-month e-cigarette and combustible cigarette use patterns by past-month nicotine vaping days are presented in eTable 11 in the Supplement for descriptive purposes. Associations with additional behavioral health outcomes were tested for exploratory purposes, and analyses found that e-cigarette dependence was significantly associated with increases in ADHD symptom level at 6-month follow-up ; this association was amplified by the number of other substances used . Additionally, reporting 1 or more e-cigarette dependence symptoms at baseline was associated with heavier combustible cigarette smoking at follow-up, including more cigarette smoking days and more cigarettes smoked per day .This cohort study provides some of the most detailed evidence to date on the prevalence and symptom presentation of e-cigarette dependence and its association with future e-cigarette use among youth, to our knowledge. Previous studies have provided less comprehensive characterizations of e-cigarette dependence, without comparisons with combustible cigarette dependence.To our knowledge, this is also the first prospective longitudinal investigation of the association of e-cigarette dependence symptoms with subsequent vaping patterns. Our results suggest that e-cigarette dependence symptoms may be associated with future vaping patterns. In this study, the prevalence of e-cigarette dependence symptoms was relatively low and, consistent with prior research,primarily characterized by cravings and a perceived need to vape. Although prevalence and severity of dependence symptoms were approximately 2-fold as large for combustible cigarettes as e-cigarettes in users of both products, the most common and least common symptoms were similar across the 2 products, as were the qualitative profiles across all 10 symptoms. Importantly, the cross-product comparisons were conducted within-persons among dual users, eliminating confounding differences between smokers and vapers. Unlike combustible cigarettes, e-cigarettes and e-liquids vary in nicotine content and delivery.Few youth in this study likely used the now-popular pod mod–style e-cigarette products that deliver large amounts of nicotine efficiently.

Considering the elevated dependence symptoms reported among youth who vaped nicotine in this study despite low probability of pod mod use, these results suggest that e-cigarette dependence may be of notable clinical and public health significance. Electronic cigarette dependence symptoms were elevated in certain subgroups expected to be at higher risk, including youth who vaped recently, used e-cigarettes that contained nicotine, or used both e-cigarettes and combustible cigarettes. A 2018 study found that dual users had higher levels of nicotine biomarkers than users of e-cigarettes only. To our knowledge, this is among the first investigations to find that e-cigarette dependence symptoms were experienced even among youth who reported using only e-cigarettes without nicotine—a sizeable proportion of youth e-cigarette users.Nicotine-containing products may have been mislabeled, or youth with histories of nicotine vaping in early adolescence who later switched to only nicotine-free vaping over the past year may have experienced cravings triggered by cues associated with the act of vaping.Youth who used e-cigarettes and reported at least 1 e-cigarette dependence symptom were more likely to continue vaping and to vape more frequently and intensely 6 months later than their peers who did not report any dependence symptoms. This finding is consistent with a 2019 study that suggested that vaping continuation, with escalation of use frequency and dependence symptoms, is common. Our study further suggests that youth with dependence symptoms are at elevated risk for continuation and escalation. Symptoms of e-cigarette dependence may directly increase motivation to use, and increased use may recapitulate a cycle of worsening dependence. Although our observational study design precludes such causal inferences,ebb flow findings suggest that e-cigarette dependence may be associated with subsequent vaping patterns even after adjusting for dependence propensity defined by numerous potentially confounding influences.

In clinical settings, e-cigarette dependence symptom screening questions may identify youth at risk for vaping progression who may benefit from intervention. In regulatory decision-making, dependence is a potential health consequence of e-cigarette use that should be considered. Adolescents are particularly vulnerable to nicotine exposure,and our findings suggest that dependence symptoms associated with nicotine exposure via e-cigarettes are associated with greater risk for escalation of vaping behavior. The development of dependence in youth is an important public health consequence that should not be overlooked.This study has some limitations. First, all participants were recruited from high schools in Los Angeles, California; therefore, extension to different regions would be informative. Second, data were collected in the 2016 to 2017 school year, before high-nicotine e-cigarettes became popular among youth.Third, all measures were self-reported and did not include clinical diagnosis of nicotine dependence. While the measure of dependence was selected for its presumed applicability to both tobacco products and its ability to capture key features of the dependence syndrome ,other measures of e-cigarette dependence that are correlated with nicotine exposure merit inclusion in future research to address varying aspects of the dependence syndrome.6 Fourth, the follow-up period was limited to 6 months, leaving unclear the long-term association of e-cigarette dependence with future use.Combusted tobacco use remains a major cause of premature disability and death around the world . Cigarette smoke contains an estimated 7000 different chemical compounds, of which at least 70 are proven or suspected human carcinogens including arsenic, benzene, formaldehyde, lead, nitrosamines, and polonium 210. Tobacco smoke also contains poisonous gasses: carbon monoxide, hydrogen cyanide, butane, toluene, and ammonia.Tobacco smoking causes about half a million U.S. deaths annually, of which 50,000 are among nonsmokers exposed to secondhand smoke . More than half of all long-term smokers die from a tobacco-caused disease, with an average loss of at least 10 years of life . Smoking causes 87% of lung cancer deaths, 61% of pulmonary disease deaths [chronic obstructive pulmonary disease and emphysema], and one in three cancer deaths. In the 50 years following the U.S. Surgeon General’s first report on tobacco , 20 million Americans died from smoking, and an estimated 1 billion people will die worldwide this century . For every person who dies from smoking, at least 30 people live with serious smoking-related illnesses costing >$300 billion annually, with nearly $170 billion in direct medical costs and $156 billion in lost worker productivity . The health harms of combusted tobacco use are now undeniable . With market and regulatory pressures to reduce the harms of nicotine delivery by combustion, the tobacco product landscape has diversified . Nicotine now comes in smokeless tobacco prepackaged pouches , in electronic devices that heat nicotine to an inhalable aerosol from a plug of tobacco or from an e-liquid , and in pharmaceutical grade nicotine replacement therapies . Cigars come in a variety of sizes down to little filtered cigars, some discernible from cigarettes only by their tobacco leaf wrapper.

Despite the diversification, conventional combusted cigarettes remain, by far, the most common nicotine product used by adults in the United States and in most places globally. Worldwide, there are approximately 1 billion smokers . While products of tobacco combustion are the main cause of smoking-induced disease, nicotine addiction sustains tobacco use . Nicotine addiction, in the form of cigarette smoking, causes more harm to public health than any other drug addiction. Reflected in the quote above, at least since the 1950s, the tobacco industry has researched and recognized, decades before it became generally understood in the scientific community, that nicotine is an addictive drug and central to their business . An understanding of the clinical features of nicotine addiction and the behavioral conditioning that occurs with frequent nicotine dosing is important for informing pharmacologic and behavioral treatment targets. We review current advances in research on nicotine addiction treatment and recovery. The “Tobacco Product Use and Nicotine Addiction” section covers the changing landscape of nicotine products with comparison of use patterns among adults and adolescents in the United States. The pharmacology of nicotine and effects on the brain are then reviewed, with consideration of particularly vulnera ble populations. The “Treating Nicotine Addiction in Adults, with a Focus on Conventional Cigarettes” section focuses on treatment of nicotine addiction with attention to counseling and behavioral approaches and cessation medications. The tobacco treatment literature is far more developed for combusted cigarettes and relatively sparse in other product areas. We focus on adults given develop mental differences in adolescents’ preferred nicotine product type, use patterns, addiction profile, and treatment efficacy. The tobacco treatment literature with adolescents largely consists of failed smoking cessation trials , and while youth nicotine vaping is drawing public health concern and policy attention, no study, to date, has evaluated an intervention to treat e-cigarette use in adolescents. The “Tobacco Control Population-Based and Policy Approaches” section gives greater attention to use in youth with review of the evidence for tobacco control policy interventions. The “Discussion: What Evidence Is Needed” section closes with discussion of emerging areas and consideration of new directions for advancing the field.Nicotine is a tertiary amine that can exist in a charged or uncharged form, depending on pH. Nicotine is a weak base with a pKa of 8.0 such that, at physiological pH , 69% is ionized and 31% is unionized. The unionized form of nicotine passes readily though membranes, such as the buccalmucosa, such that the pH of smokeless tobacco influences the rate and extent of systemic nicotine absorption. The more alkaline , the more rapidly nicotine is absorbed from smokeless tobacco. Cigarette smoke has an acidic pH of about 5.5 to 6, so little nicotine is absorbed through the mouth, while large cigars have an alkaline pH, facilitating oral absorption. The differences in pH of tobacco products depends on the strains of tobacco used and curing processes, as well as on chemicals used in processing. The pH of nicotine solutions also influences the pharmacology of e-cigarettes. The earliest forms of e-cigarette liquid contained mostly nicotine in free base form , which results in a considerable nicotine-related harshness during inhalation. Recently, e-liquids have used nicotine salts , with acidic pH , similar to that of cigarettes. This results in less irritation with inhalation and has been implicated in the current popularity of e-cigarette use in never-smoker adolescents . When cigarette smoke is inhaled, nicotine moves quickly to the lungs, arterial blood, and the brain in only 15 to 20 s , where it exerts its addiction-related effects. Rapidity of delivery to the brain is thought to be an important factor in the abuse liability of inhaled nicotine compared to other routes of nicotine administration. The importance of rapid delivery relates to higher arterial concentrations, nearly immediate psychological effects, and the ability to titrate doses to desired effects. Higher arterial levels also allow the smoker to overcome effects of tolerance to the desired psychological effects of nicotine. Inhaled nicotine from e-cigarettes potentially carries a similar abuse liability to that of tobacco cigarettes, but empirical data, to date, suggest that it is not the case.

An alternate pathway is possible, whereby 2-AG could be formed by the sequential actions of phospholipase A1 and lysophospholipase C enzymes. The primary route for 2-AG hydrolysis in neurons is afforded by the enzyme monoacylglycerol lipase. Recently, a pharmacologically distinct monoglyceride lipase activity in microglial cells has been reported. In order to understand better the role endocannabinoids might have in CB1-regulated behaviors, a number of pharmacological tools, which target events in endocannabinoid metabolism, have been developed. Anandamide deactivation is prevented by the transport inhibitors AM404, UCM707, OMDM-1 and OMDM-2, and VDM11, and the FAAH-selective anandamide hydrolysis inhibitors URB597and OL-135. 2-AG hydrolysis is blocked by the MGL inhibitor URB602. Pharmacological inhibition of endocannabinoid deactivation has been shown to produce anxiolytic, analgesic, and antidepressant-like effects. The antidepressant-like effects of anandamide deactivation inhibitors will be discussed in detail later in the present article.Limited, but compelling evidence indicates that the endocannabinoid system is altered during stress-related states in both rodents and humans. The chronic mild or chronic unpredictable stress protocol are two related models of depression that produce sequelae reminiscent of those observed in humans afflicted with the disease. These include, among others, a reduction in body weight gain and ingestion of palatable foods. In rats subjected to 3 weeks of CUS, Hill and colleagues found a significant reduction of 2-AG content, as well as levels of CB1 receptor protein in the hippocampus. Stressed animals also showed impairment of reversal learning in the Morris water maze,mobile vertical rack which was corrected by administration of the cannabinoid agonist HU 210, suggesting that this effect was due to decreased endocannabinoid signaling. Similarexperiments in our lab have shown that after 10 weeks of CMS, CB1 receptor mRNA is increased in the prefrontal cortex and decreased in the rat midbrain.

Anandamide levels in the hippocampus, prefrontal cortex, midbrain, thalamus, and striatum were not significantly altered in these studies. 2-AG was similarly unchanged in the hippocampus, prefrontal cortex, midbrain, and striatum, but was reduced in the thalamus of stress-exposed rats.In a study of 20 human subjects, Hungund et al. found an increase in both CB1 receptor mRNA and CB1 receptor-stimulated [35S]GTPγS binding in the dorsolateral prefrontal cortex of subjects with a life-time diagnosis of major depression who committed suicide, compared to normal controls who died by accident or natural causes. Miller and colleagues reported reduced serum 2-AG levels in drug-free females diagnosed with major depression compared to demographically-matched controls, with levels of 2-AG negatively correlated to the duration of the depressive episode. In the latter study, serum anandamide was not associated with major depression, but was negatively correlated with measures of anxiety. The results of these studies of both rodents and humans provide evidence that endocannabinoid signaling is changed – at least in some brain regions and, perhaps, in the periphery – during depression. The alterations observed in the hippocampus, prefrontal cortex, and thalamus are of particular interest, given the likely involvement of these neural structures in the regulation of emotion. In humans, Δ 9 -THC, the natural cannabinoid agonist that is the major psychoactive component of marijuana, produces subjective feelings of relaxation and euphoria, but also promotes anxiety and dysphoria in a context- and dose-dependent manner. Similarly, when administered to rodents, exogenous cannabinoid agonists produce mixed effects on mood related behavior. Low doses of cannabinoid agonists are usually anxiolytic, while moderate to high doses are anxiogenic, but these dose-dependent effects are also contingent on other factors, including strain, age, sex, environment and previous experience with the drug. In mice, Δ 9 -THC produced anxiolytic effects in the light/dark box at a dose of 0.3 mg-kg−1 , i.p., but at 5 mg-kg−1 , i.p., induced anxiogenic effects.

HU 210, a highly potent cannabinoid receptor agonist, at a dose of 0.1 mg-kg−1 , i.p., has also been reported to produce anxiogenic effects in the defensive-withdrawal test after acute administration, but, when this same dose was administered for 10 days it exerted antidepressant-like effects in the novelty suppressed feeding and forced swim tests. Comparable dose- and context-dependent effects on mood-related behavior in the elevated-plus maze and social interaction tests have been noted following treatment with another synthetic cannabinoid agonist, CP 55,940.Data from experiments with CB1 knockout mice suggest that prevention of cannabinoid signaling either increases or has no effect on anxiety- and depression-related behaviors, depending on the conditions of the test. Notably, in these studies, CB1 knockout mice displayed increased anxiety-like behavior compared to wild-type controls under conditions that are stressful to the animals . Additionally, CB1 receptor knockout mice have increased sensitivity to develop anhedonia in the CUS model of depression, and display several other behavioral responses that are similar to the symptoms of melancholic depression . Likewise, several researchers have reported that administration of the CB1 receptor antagonists SR141716 and AM251 produced anxiogenic-like effects. By contrast, few groups reported anxiolytic- and antidepressant-like effects of CB1 receptor antagonists. However, in clinical trials of rimonabant for the treatment of obesity, anxiety and depression are among the most frequent adverse events reported. Together, these studies suggest that CB1 receptor signaling is important for coping behavior, especially during intense or prolonged stress. As described in the previous section, changes in endocannabinoid activity might occur during depression in animal models and, possibly, in humans. Furthermore, direct activation or reduction of CB1 receptor signaling has important effects on mood and stress-related behaviors. These findings raise the intriguing possibility that modulation of endogenous cannabinoid signaling could be a useful target for depression therapy.

Indeed, enhancement of endocannabinoid signaling by pharmacological inhibitors of anandamide degradation has been shown to modulate stress-related behavior in assays for antidepressant-like drug activity – the forced swim test and tail suspension test – and in a rodent model of depression – chronic mild stress . The anandamide transport inhibitor, AM404, at a dose of 5 mg-kg−1 , was reported to decrease immobility time in the rat FST. Likewise, the fatty acid amide hydrolase inhibitor, URB597 , decreased immobility – presumably by increasing swimming behavior – in the rat FST, and also increased struggling behavior in the mouse TST. These effects of URB597 in the FST and TST were sustained after 4 days of repeated dosing. In each of these tests, the antidepressant-like activity of AM404 or URB597 was prevented by preadministration of a selective CB1 receptor antagonist. Given that symptoms of anxiety are often present during depression, it is noteworthy that anandamide deactivation inhibitors also appear to have anxiolytic-like effects. Administration of URB597 decreased isolation-induced ultrasonic vocalizations in rat pups, and increased the time spent in the open arms of the elevated zero and plus mazes. Similarly, AM404 dose-dependently reduced isolation-induced ultrasonic vocalizations in rat pups,vertical grow rack and increased the time spent in the open arms of the elevated plus maze or in the open field during the defensive withdrawal test. However, it appears that the effects of inhibition of anandamide deactivation on stress-coping behaviors are sensitive to environmental conditions. In a recent report, Naidu and colleagues failed to find a reduction of immobility in the TST or an increase in the percentage of time spent in the open arms in the elevated plus maze in FAAH−/− mice or in wild type mice treated with URB597 when conducted under normal laboratory lighting. However, when they adopted lighting conditions similar to those used by Patel and Hillard in the elevated plus maze , or Gobbi and colleagues in the TST , they did observe anxiolytic and antidepressant-like effects of FAAH deletion or inhibition. The reported sensitivity of the anxiolytic- and antidepressant-like effects of URB597 to the lighting conditions is consistent with recent findings in our lab, which show that the anxiolytic-like effect of URB597 in the elevated plus maze varies with experimental context. It is important to note that both the tail suspension and forced swim tests are only assays for antidepressant-like drug activity, not models of depression. In the reports cited above, the experiments were performed in undiseased animals, demonstrating an enhancement of active stress-coping behavior by URB597 or AM404 in a manner similar to standard antidepressant drugs during normal physiological conditions, but under specific environmental contexts. The ability of inhibitors of anandamide degradation to regulate stress-related behaviors under pathophysiological conditions should be more indicative of their efficacy in the treatment of depression. In the CMS model, administration of URB597 for 5 weeks at a dose of 0.3 mg kg−1 reversed chronic stress-induced reductions in sucrose consumption and in body weight gain. In this same study, treatment with URB597 also opposed the increases in CB1 mRNA expression in the prefrontal cortex and midbrain that were observed after 10 weeks of CMS. The magnitude and time course for the antidepressant-like effect of URB597 in this study was comparable to that seen in the treatment of depression with the known antidepressant compound, imipramine. These findings are important because they demonstrate, for the first time, the ability of an anandamide deactivation inhibitor to reverse behavioral symptoms observed in a model of depression with high construct and face validity.

It is important to note though, that alterations in 2-AG are observed both in depressed humans and in animal models of depression, and the significance of these changes are unclear. FAAH inhibitors have proven to be valuable tools for investigating the role of anandamide in mood disorders, and DGL and MGL inhibitors will no doubt further elucidate the interaction between endogenous cannabinoid signaling and stress-related behaviors. For example, the MGL inhibitor, URB602, when injected locally into the dorsolateral periaqueductal grey of the midbrain, produced an enhancement of stress-induced analgesia, demonstrating a role for 2-AG in a specific stress-coping response. Inhibition of MGL has also identified 2-AG asa mediator of synaptic plasticity in the hippocampus, a structure likely involved in the effects of chronic stress and antidepressant treatment on behavior. Unfortunately, URB602 has low potency and cannot be administered systemically to study the effects of global in vivo modulation of 2-AG on stress-coping behavior. As inhibitors of DGL and MGL are developed and tested in behavioral models of emotional reactivity, we will have a better understanding of the functions of both endocannabinoid signaling molecules, perhaps each with distinct roles in stress-coping and mood disorders. This is the first population-based study of HCV screening among US Medicaid patients with and without schizo phrenia. As hypothesized, we found that HCV screening varied over time, across states, and by patient demographic and comorbid characteristics. However, contrary to our hypothesis, screening was higher for patients with schizo phrenia compared to controls. We also found that despite 1998 CDC guidelines to target high-risk populations for annual screening,over 95% of Medicaid patients with schizophrenia who were eligible for screening were not screened for HCV within any clinical setting in 2012. Among patients with schizophrenia, states in the northeast had the highest HCV screening rates and increases in screening from 2002 to 2012. Large state level rises in HCV screening were potentially due to various integration of care initiatives implemented state wide, warranting further examination that is beyond the scope of our exploratory study. For example, by 2012, several northeastern states had active initiatives to reduce Medicaid fragmentation and were among the highest in Medicaid spending per enrollee.There were also state wide integration initiatives aimed to decrease hepatitis transmission. For instance, New York State launched the 2004 Viral Hepatitis Strategic Plan modeled after the HIV/AIDS prevention and care continuum.Similarly in 2008, the Massachusetts’ Office of HIV/AIDS was created to integrate strategies for HIV prevention with HCV programs.Also, Connecticut stakeholders partnered with the CDC to develop the Viral Hepatitis Prevention Plan that was released in 2004.Despite these efforts however, CMS reports that chronic HCV prevalence for Medicaid only enrollees in 2012 was still higher than the national average in Connecticut and New York , but not Massachusetts.It is conceivable that a nationwide comprehensive integrated prevention program could have wide ranging positive impacts on HCV screening and prevention efforts; however, the low rate of HCV screening found during the current study period suggests that national CDC guidelines were insufficient when compared to state-level efforts. In addition to new CDC recommendations for universal HCV screening in adults, expanding statewide HIV prevention policies to include HCV might mitigate traditional clinical challenges in HCV screening among this vulnerable population.

The groups did not differ on sex or ethnicity ; however, there was a relation of follow-up status with age , with post-hoc tests indicating that CHR individuals whose symptoms remitted were significantly younger than controls at baseline. Group status was significantly associated with both antipsychotic use and any other psychotropic use at baseline; in both cases, individual chi-squared tests indicated that all four CHR groups were more likely to be treated with medication relative to controls but did not differ from each other. The same pattern was observed for cannabis use, whereby a significant overall association was observed between current use and group status with individual tests showing that current cannabis use was more common among all CHR groups relative to controls but that the prevalence did not differ across CHR subgroups. Non-parametric Kruskal Wallis tests performed on the continuous time-lapse variable indicated that the groups did not differ on the lapse-of-time between cortisol collection and daily stressor assessment , cortisol collection and life event assessment , or cortisol collection and trauma assessment .Age was positively associated with life event exposure and life event distress in controls and all four CHR groups, with daily stressor distress in CHR remitted, symptomatic, and progressed groups, and with cortisol in controls, CHR remitted, and CHR converted groups . Female sex was likewise positively associated with life event exposure in controls, daily stressor exposure in the CHR remitted group, daily stressor distress in CHR symptomatic individuals,grow cannabis and with all five psychosocial stressor measures in the CHR progression of positive symptoms group. In contrast, ethnicity was not associated with any stress measure or cortisol in any group.

With regards to psychotropic medication, antipsychotic use at baseline was negatively correlated with daily stressor exposure in the CHR remitted group and with life event exposure and trauma in the CHR progressed group, but positively associated with daily stressor distress in CHR individuals who later converted to psychosis; similarly, other psychotropic medication was negatively correlated with daily stressor exposure in the remitted group but positively correlated with daily stressor and life event distress variables in the converter group. Current cannabis use was associated positively with daily stressor exposure, life event distress, and trauma in the control group and with life event exposure in remitted and symptomatic groups. The above analyses identified the following baseline factors as potential confounders in the relationship between stress and cortisol: age, sex, current antipsychotic use, current other psychotropic medication use, and current cannabis use. However, owing to multicollinearity issues , all models included antipsychotic use only as a covariate, with sensitivity analyses performed using other psychotropic medication in place of antipsychotic use. We were additionally concerned that controlling for current cannabis use might obscure important relationships between stress and cortisol , given that recent evidence indicates that stress can precipitate cannabis use in healthy and clinical samples , and therefore included cannabis use as a covariate in sensitivity analyses only.ANCOVAsand logistic regression indicated significant main effects of group status on basal cortisol and all stress measures after adjustment for age, sex, and antipsychotic use at baseline . Post-hoc comparisons indicated that only CHR convertors were characterised by elevated basal cortisol compared to controls , no other group differences were observed. With regards to continuous stress measures , all four CHR subgroups were characterised by significantly higher scores relative to controls; for daily stressor exposure only, symptomatic, progressed, and converted groups also showed significantly higher scores relative to remitted CHR youth. To confirm that the greater exposure to life events observed in CHR subgroups was not simply due to events that could be caused by illness, we additionally compared groups on exposure to independent life events and observed a significant main effect of group status .

Post-hoc tests indicated that all CHR subgroups, except for the remitted group, reported increased exposure to independent life events compared to controls. Finally, childhood trauma was more common in all CHR groups compared to controls but did not distinguish among CHR subgroups. All results were largely unchanged when cannabis use was included as an additional covariate and other psychotropic medication use was additionally included in place of antipsychotic medication, with the exception that CHR remitted youth no longer showed significantly greater life event exposure compared to controls.In line with predictions, stressor-cortisol concordance varied according to the lapse-of-time between assessments : When acquired on the same day as saliva sampling, all stress measures showed significant, positive correlations with cortisol , except for life event distress scores which were positively correlated but not significantly. In contrast, stress measures were not significantly correlated with cortisol in any other time-lapse category except for life event exposure and cortisol which were positively associated when the lapse-of time was 31 days or longer. To account for the moderating effect of time-lapse between assessments, interaction terms were additionally included in subsequent regression models.The current study aimed to further characterise the nature of HPA axis abnormalities among individuals at-risk for psychosis by examining psychosocial stressors, basal cortisol, and the concordance between these measures in a large sample of CHR youth categorised according to clinical status at the two-year follow-up. In line with hypotheses, all CHR groups were characterised by significantly greater psychosocial stressor exposure and distress relative to healthy controls; however, only those who converted to psychosis demonstrated elevated basal cortisol levels. In contrast to expectations, whilst CHR converters showed the greatest degree of stressor-cortisol concordance when pooled across stressors, confidence intervals substantially overlapped with the control group; moreover, the degree of concordance among CHR youth who remitted, remained symptomatic, or whose positive symptoms had progressed at follow-up was lower than that observed in the control group. After adjustment for potential confounders and correction for multiple testing, only CHR converters showed elevated basal cortisol relative to healthy controls.

This finding cannot be simply attributed to greater stressor exposure or distress experienced by CHR converters relative to controls, as these features characterised all CHR subgroups. This elevation might instead reflect an amplification of the normative adolescent increase in cortisol secretion , or metabolic abnormalities [more common among CHR youth ], independent of stress exposure. Consistent with a recent meta-analysis , pairwise comparisons showed that basal cortisol levels did not distinguish CHR converters from CHR non-converters. Whilst this suggests that within the CHR population, baseline cortisol levels do not signal risk for psychosis transition, repeated measurement of cortisol is needed to determine whether longitudinal increases predict poorer outcomes in this group. Moreover, it should be assumed that at 2-year follow-up there are some false negative cases in the CHR non-converted groups , and thus differences between converters and non-converters may increase with a longer follow-up period. We predicted that the degree of stressor-cortisol concordance, when pooled across stressors,indoor cannabis grow system would increase in parallel with the level of symptom expression at follow-up . Whilst the highest degree of concordance was indeed observed among the CHR converters, the control group was intermediate, and pooled beta coefficients in the three non-converted CHR subgroups were approximately zero . Moreover, confidence intervals for pooled stressor-cortisol concordance estimates for all CHR sub-groups showed a high degree of overlap with the control group ; it has been proposed that for many effect sizes, confidence intervals overlapping by greater than 50% suggests that effect sizes are not significantly different . Thus, none of the CHR subgroups showed significant hyper- or hypo-responsivity of the HPA axis in response to psychosocial stressors encountered in the natural environment when compared to controls. Stressor-cortisol concordance was, however, substantially higher among CHR converters compared to all other CHR subgroup . This finding is consistent with the only previous study to examine the relationship between stressor-cortisol concordance and outcome status in CHR individuals: Labad and colleagues similarly reported a moderate-to-strong correlation between salivary cortisol and stressful life events among those who later transitioned to psychosis, but only a weak correlation in the non-transitioned CHR group . Overall, we found few significant associations between individual stressors and basal cortisol across all groups . Whilst this could be due to the HPA axis and/or stress measures employed, previous studies of at-risk youth which have used different measures have likewise found inconsistent associations between stress and cortisol . Similarly, in healthy subjects, correlations between self-reported stress and cortisol have not been observed . Although the exact mechanisms underlying HPA responsivity to stress are unknown , it has been demonstrated that there are individual differences in responsivity that are partially determined by genetic variants that modify the effect of acute and chronic stress/trauma on cortisol levels in healthy adolescents and adults and patients with psychosis . Thus, genetic and other vulnerability factors are likely responsible for the different patterns of association between stressors and cortisol that we observed across both individual stressor types and, when pooled across stressors, CHR subgroups.Despite the large overall sample size, individual CHR subgroups were notably smaller there by reducing our ability to detect statistically significant associations between psychosocial stressors and cortisol.

Conversely, as we did not adjust for multiple comparisons in our primary analyses examining stressor-cortisol concordance, some significant associations may have arisen by chance. However, we tested specific a priori hypotheses and were largely interested in the overall pattern of stressor-cortisol concordance rather than statistical significance. Moreover, we adjusted for a range of potential confounders which, had we not accounted for these variables, would have led to spurious associations. A further limitation is that a small proportion of participants , experienced a long delay between baseline assessment visits, which led to a large lapse-of-time between completion of psychosocial stressor assessments and cortisol collection. Including these participants in the analyses increased statistical power to test the moderating effect of time-lapse on stressor-cortisol concordance.There are a range of other stressors relevant to psychosis that might conceivably impact on HPA axis function ; it is possible that examining a wider range of stressors might yield different patterns of stressor-cortisol concordance across CHR subgroups. Similarly, our findings are specific to basal salivary cortisol, other measures may have produced different results. Whilst the aim of our study was to examine the relationship between baseline features and subsequent outcome , it is important to note that there are limitations with this approach. First, we did not account psychosocial stressors and other confounding factors/events that may have occurred in the time between baseline and follow-up. Indeed, it is possible that stressor cortisol concordance at follow-up does in fact distinguish between CHR subgroups, but that our measure at baseline was too distal to outcome. Second, CHR individuals are at elevated risk for a wide range of psychiatric disorders, particularly depression and anxiety , and so worsening of prodromal symptoms/transition to psychosis is only one of several potential outcome measures, all of which will inevitably involve more false negatives the shorter the follow-up period. Indeed, a recent study suggested that well-established risk factors are better at predicting poor functioning in CHR populations than transition to psychosis . The extent to which stressor-cortisol concordance at baseline is associated with other non-psychotic disorders and functioning at follow-up is therefore warranted.We assessed HPA axis function using basal salivary cortisol collected in the laboratory, as it is more reliable and, unlike home sampling methods, unlikely to be influenced by confounding factors such as exercise . However, meta-analytic evidence indicates that the effect of chronic stress on cortisol varies across cortisol measures; whilst diurnalcortisol, afternoon/evening cortisol, and the CARi are elevated following chronic stress, basal morning levels are lower and the diurnal rhythm appears to be flatter . Employing alternative cortisol measures might therefore reveal different patterns of stressor-cortisol concordance across CHR individuals and controls. Indeed, using a home sampling procedure, Cullen and colleagues reported a negative correlation between the CARi and negative life event distress in at-risk children with a family history of schizophrenia but a positive correlation in typically-developing children , whilst a study of adults found that diurnal cortisol was associated negatively with stressful life event exposure in first-episode psychosis patients, most of whom were receiving antipsychotic medication, but positively in controls . Thus, employing multiple measures of cortisol may be more informative than basal cortisol alone and enable the identification of dissociated relationships in at-risk individuals/psychosis patients and healthy controls.

OD had significant metabolite alterations in markers of neuronal integrity , cell membrane turnover/synthesis , glutamate concentration , cellular bio-energetics , and astrocyte integrityin frontal lobe regions implicated in the development and maintenance of addictive disorders. OD had lower NAA, Glu, Cr and mI concentrations than CON in the DLPFC and lower NAA, Cr and mI in the ACC. The metabolite concentration deficits in OD were most pronounced in the DLPFC, were associated with various substance use measures, and correlated with worse performance on measures of global cognition, executive and visuospatial functioning. However, OD and CON were equivalent in regional GABA concentrations, most cognitive domains, and self-regulation measures. Relative to 3 week abstinent ALC, OD had significantly lower NAA, Cr, Cho and mI concentrations in the DLPFC, with NAA and Cho deficits having cognitive ramifications. Consistent with most previous reports, we found metabolite deficits in the ACC of OD. In addition, OD had similar deficits in NAA, Cr, and Glu concentrations in the DLPFC. This suggests reduced neuronal and astrocyte viability and cellular bioenergetics in both the ACC and DLPFC, with additional glutamatergic injury in the DLPFC. ACC Glu and also DLPFC NAA and Cho metabolite abnormalities related to poorer cognitive function, which, however, did not differ significantly from CON. Of note, GABA concentrations in ACC and DLPFC of OD were equivalent to those in smoking CON, similar to findings in 3-week abstinent ALC versus smoking CONand 1 week abstinent ALC vs. mostly nonsmoking CON. However, ACC GABA reductions were reported in abstinent individuals with cocaine- and polysubstance-dependence. The POC and occipital region have been used as control regions in MRS studies as they are typically not altered in addiction. This appears to be true also for OD, who showed the most pronounced metabolite deficits in anterior frontal brain regions.The lateral OFC sub-serves motivation, drive, reward valuation, and aspects of social executive skills,vertical grow system is affected in opiate dependence and other drug abuse, and the OFC has altered brain activity in decision making task-based fMRI studies of individuals with substance use disorders.

OFC metabolite concentrations did not differ between OD and CON, the latter including mostly non-smokers. However, and in contrast to DLPFC and ACC findings, OD showed elevated Glu and Cho concentrations in the OFC when compared to a subset of CON, the small group of smoking CON. Although the small group size warrants caution when interpreting results, our finding of lower OFC Cho concentration in smoking vs. nonsmoking CON is consistent with lower Cho measured in frontal, midbrain and vermis regions of smoking vs. non-smoking controls. In OD, lower DLPFC Glu and strong trends for lower ACC GABA correlated with greater severity and duration of opiate use. These findings are congruent with other neuroimaging studies that reported lower DLPFC GM density and poorer functional connectivity between DLPFC and parietal regions associated with greater duration of opiate use. ACC Glu and NAA were not related to opiate use, consistent with previous reports. However, greater cocaine and marijuana misuse in our OD group was associated with significantly lower metabolite concentrations, commensurate with findings in other substance using/ dependent populations. Metabolite concentrations in the DLPFC and ACC of OD related to executive function, visuospatial skills, global cognition and working memory, but not to self-regulation measures. Previous 1H MRS studies in opiate dependence did not report on such relationships, but studies in marijuana-dependent and recreational ecstasy users reported relationships between altered frontal metabolite levels and impaired cognition or higher impulsivity. Although previous research in opiate addicts reported neuropsychological deficit, our OD group performed in the average range across various cognitive domains and self-regulation measures. There is some evidence that buprenorphine maintenance is associated with better cognition compared to other maintenance drugs, and buprenorphine has been shown to improve brain perfusion in cocaine dependence; correspondingly, buprenorphine may have had an effect on cognitive performance in OD in this study. Future studies on the effects of buprenorphine on brain function and cognition in OD may be useful to inform effective treatment.

Our study showed that OD on maintenance therapy had greater anterior frontal brain metabolite abnormalities than 3 week abstinent ALC, and we found previously that even 1 week abstinent ALC did not show metabolite abnormalities in the DLPFC. The greater DLPFC metabolite abnormalities in OD may relate to the greater relapse rate in opiate than alcohol dependence, which may require differently tailored approaches for treatment of OD and ALC. Metabolite deficits in the DLPFC of OD are more reminiscent of 1H MRS results in poly-substance users, recreational cannabis users, and methamphetamine dependent individuals. The DLPFC is critically involved in executive functions, such as working memory, cognitive flexibility, planning, inhibition, and abstract reasoning. As such, DLPFC brain metabolite abnormalities, in addition to those in ACC, may be promising targets to monitor the efficacy of cognitive behaviour therapy in OD treatment, especially as they correlate with cognition and substance use behaviour. This study has limitations. Drug use histories were based on self-report and gender effects across groups could not be assessed due to the small number of females . Menstrual cycle appears to affect brain GABA levels, but data on the time since last menstrual cycle was not collected. However, excluding the female participants from statistical analyses did not alter the finding of no significant GABA differences between groups. The number of analysed spectra for some comparisons was relatively small, especially those involving smoking CON with OFC and POC VOIs; therefore, these analyses need to be considered hypothesis generating rather than definitive. Further, differences to previous metabolite and neuropsychological research in OD may relate to differences in comorbid tobacco, alcohol, marijuana and stimulant abuse as pointed out previously. Of note in this context is the relatively low lifetime and current alcohol use in our OD sample. An additional limitation is that the duration of buprenorphine maintenance therapy was not assessed, although OD had to be on therapy for at least 3 months. Furthermore, the results may not be generalizable to OD who are not on buprenorphine therapy. Finally, we cannot rule out the possible contributions of premorbid, developmental,cannabis grow tray and dietary/nutritional factors to the neurobiological group effects reported.

That genetic factors have an age-specific influence on the onset of alcohol dependence is suggested by the findings that there are strong genetic effects contributing to risk for alcohol dependence particularly connected with early onset of drinking activity . Correspondingly, the rate of adult alcohol dependence is significantly greater among those who start drinking at a relatively early age than among those who start drinking after the age of 19. Studies of adolescent brain development point to neurophysiological factors that could enhance the likelihood of substance use/abuse in those between 14 years of age and 17 years of age . Significant changes in the dopaminergic system occur during adolescence, as well as growth and refinement of prefrontal and limbic circuitry . As a result of the early enhanced activity of the mesolimbic system in contrast to the more slowly maturing prefrontal control systems and their connections to other brain regions, changes in the adolescent brain may lead to enhanced risk taking compared to earlier and later stages of maturation. specifically, these changes may lead to a reduced cognitive control of the reward system in the brain in early to middle adolescence, leading to increased risk for alcohol and other substance abuse disorders . Alcohol dependence and risk for alcoholism in both adults and adolescents is associated with reduced power in event related oscillations in a number of different experiments which elicit a P3 or P300 response. ERO power in a task that elicits a P3 response is also associated with a number of SNPs in the CHRM2 gene . Alcohol dependence in adults was found to be associated with a number of SNPs in the cholinergic M2 receptor genein two studies . A refinement of the study of Wang et al.showed that the association was present only in those subjects who had comorbid illicit drug dependence . This group of subjects and their family members form a genetically vulnerable group, that is, a group whose alcohol dependent members have a more heritable form of the disorder. The alcohol dependent members of this group had a significantly earlier age of onset of drinking compared to the alcohol dependent subjects without comorbid drug dependence. A generalized measure of externalizing psychopathology including alcohol dependence and illicit drug dependence is associated with the same group of SNPs in the CHRM2 gene . Additionally, there is variation in the genetic factors associated with alcohol dependence; multiple genetic factors were found to contribute to a DSM-IV diagnosis of alcohol dependence in adults . Some differences were found between genetic factors involved in alcohol consumption in adolescents and in young adults in twin study models. In order to investigate the age specificity of the genetic and endophenotypic factors noted above on the early onset of alcohol use and dependence, we studied adolescents and young adults drawn from the Collaborative Studies on the Genetics of Alcoholism sample . Because we wanted to understand the processes which lead from non-drinking to regular drinking to alcohol dependence we used both the onset of regular alcohol use and of alcohol dependence as dependent variables.

As we noted above, more severe cases of alcohol dependence in adults were found associated with earlier ages of onset of drinking and are more likely to be the result of genetic factors, thus we hypothesized that specific genetic and related neurophysiological endophenotypes would have a greater predictive power in those with the earliest ages of onset. Discrete time survival analysis was used to investigate the contribution of genetic variants in CHRM2, ERO power, and environmental factors to the onset of regular alcohol use and of alcohol dependence in adolescents and young adults, to deal with the first two items of investigation. DTSA provides age-specific measures for the effects associated with predictive variables. Additional statistical tests, including both genetic and endophenotypic independent variables, were used to link the onset of regular alcohol use to the onset of alcohol dependence, to deal with the third item of investigation. To deal with the fourth item, the same DTSA methodology as was used for the entire sample was applied to a behaviorally defined sub-sample, the definition of which is discussed subsequently . The results of the DTSA calculations suggested further investigation of age related changes in the genotypic distributions of those who became alcohol dependent. A further test was made to determine whether there was an effect of alcohol use on our endophentypic covariates.Data were analyzed in a cross sectional sample of subjects who were assessed at least once when they were between the ages of 12 and 25 years. They were drawn from multiplex alcoholic families and a set of community families in the Collaborative Studies on Genetics of Alcoholism . Written informed consent was obtained from all subjects, and the Institutional Review Boards of each collaborative site approved all procedures. The procedures used by COGA for diagnostic interviews and recording and analyzing EEG data have been described previously . A detailed description of population characteristics of alcohol use and dependence are given in ‘‘Population description’’ section.A substantial literature indicates that alcohol dependence and risk for alcoholism are associated with reduced levels of brain activity when subjects respond to infrequent target stimuli within a sequence of non-target stimuli . Representation of this response in terms of brain rhythms or EROs has proved fruitful . The ERO amplitudes used in this study were obtained from responses to rare target stimuli that elicited a P3 component in a visual oddball experiment at three midline leads . Three leads were chosen because of topographical variation in the significance of results in previous studies . The amplitudes were calculated using the S-transform applied to the recorded data for the delta frequency band extending from 300 to 700 ms post-stimulus. Jones et al.provides a complete description of the experiment and the calculation of the values. The values were log transformed and non-parametric age regression was performed on the variables and the standardized residuals used for further analysis.

Drinking patterns, however, fluctuate over time. Changes in college drinking can reflect periods of transition, such as moving from home to the freshman year of college . Other transitions have less often been studied, such as when students return to their home environment over the summer , or the transition from summer back to university. Drinking is also likely to escalate during celebrations, including 21st birthdays, spring-break parties, and campus-centered events such as football games . An annual celebration at our university is the 1-day Sun God Festival, which on May 16, 2014 occurred during our study of the impact of a program aimed at decreasing campus heavy drinking . That year the festival attracted more than 16,000 participants, among which 155 attendees received student-conduct violations primarily for alcohol, 70 needed help to sober up on site, 21 required more intense medically monitored treatment for intoxication on site, 21 were transported to a healthcare facility, and 11 participants were arrested . Our 2014 campus heavy drinking prevention study gathered drinking-related data at 8 time points over 55 weeks. Ninety-two percent of students who entered the study completed at least 7 assessments, which allowed us to compare drinking patterns across a range of relatively rarely studied transitions, and to evaluate predictors of drinking patterns at each time point. In light of concerns expressed about heavy episodic, or “binge,” drinking, the analyses focus on drinking quantities. These longitudinal data were used to test the following 4 hypotheses. Despite the single-day duration of the Sun God event, Hypothesis 1 predicted that drinking quantities will increase during the month of the Sun God Festival compared with earlier alcohol-related practices. reflecting recent results with students mandated for counseling for drinking infractions and potential dampening effects parents might have on drinking practices, Hypothesis 2 stated that students will decrease drinking quantities over the summer. reflecting drinking practices related to specific environments , Hypothesis 3 anticipated students will resume higher quantities upon return to school.

Hypothesis 4 stated that predictors of drinking patterns over time will encompass a wide range of characteristics that include demography,pots for cannabis plants substance use, and environment as well as attitudes toward drinking.Following approval by our university’s Human Protections Committee, in November, 2013, questionnaires were emailed to 4,000 freshmen using questions derived from the Semi-Structured Assessment for the Genetics of Alcoholism interview . Data were gathered as part of an experimental protocol in which subjects with low and high LRs were assigned either to watch 1 of 2 sets of videos aimed at decreasing heavy drinking or to a control group and followed over time . With about a 70% response rate to the mailings, we identified recent drinkers who did not meet lifetime criteria for alcohol or illicit drug dependence, bipolar disorder, antisocial personality disorder, or schizophrenia . Asian individuals who became physically ill after 1 or 2 drinks, and who were probable homozygotes for aldehyde dehydrogenase mutations, were also excluded . The drinking related questions included recent 30-day histories of the days on which alcohol was consumed, numbers of standard drinks on usual and maximum drinking days, and alcohol problems. Subjects’ LRs to alcohol early in their drinking careers, and before tolerance was likely to develop, were evaluated using the Self-Rating of the effects of Alcohol questionnaire as the average number of standard drinks required for up to 4 effects the first 5 times of drinking . These included drinks to first feeling any effect, slurring speech, unsteady gait, and unwanted falling asleep, with higher scores indicating needing more drinks for effects, or a lower LR per drink . The Cronbach alpha for the SRE in the current sample was 0.88, with repeat reliabilities in the literature >0.66 . Based on the time frame described at the bottom of Fig. 1, 90% of subjects who were invited to participate agreed to enter the experimental protocol where they were paid $20 for each of 8 Internet-based assessments. As part of the prevention study , students were randomly assigned to either a control condition with no intervention or watched 5 alcohol-related educational videos over the first 3 months. Subjects were then followed and evaluated with Internet-based assessments similar to the baseline SSAGA based questionnaire . Among the 500 students enrolled, 462completed at least 7 of the 8 assessments and were included in these analyses, with any missing data handled using SPSS multiple imputation . Potential baseline predictors representing the 3 domains had to be limited to those included in the campus heavy drinking prevention study.

For demography, for reasons stated in the Introduction, we selected age, sex, and self-reports of an EA ethnicity, with the latter representing the largest ethnic background that related to heavy drinking in a past university-based study . Regarding substance use patterns, reflecting our long-term interest in LR, we included SRE-based LR scores along with SSAGA based alcohol problems and usual and maximum quantities in the prior month. Recent cannabis use from the SSAGA was included because of the high prevalence of experience with this drug on campus as well as the relationship between alcohol and cannabis use patterns . Finally, several environment and attitude items that have related to higher drinking quantities in our prior studies were selected as baseline predictors, including a short version of the Alcohol Expectancy Questionnaire that has a Cronbach alpha of 0.88 in this population and similar retest reliabilities. As described elsewhere , this AEQ version included 3 items with the highest factor loadings from each of 4 AEQ sub-scales . The second relevant measure was the Beck Depression Inventory with Cronbach alpha of 0.91 and retest reliability of 0.93 . Using alcohol to cope with stress was measured by the 6-item Drinking to Cope scale that used a 4-point scale to measure frequencies of using alcohol to cope with specific stressors . Injunctive norms were evaluated using a modification of the scale of Lewis and colleagues as the sum of the subject’s estimate of approval on a 7-point scale regarding 14 drinking behaviors by the typical same-sex person, with higher scores indicating greater approval, and descriptive norms related to the usual number of drinks per occasion estimated for typical students . Finally, the perceptions of drinking in 4 close peers were based on the Important People and Activities Scale that included an estimate of whether each peer drank alcohol in the prior month, and, if so,cannabis flood table the frequency and maximum number of drinks per day . Statistical analyses included product–moment correlations between baseline characteristics and drinking usual and maximum quantities during the 30 days prior to each assessment. Baseline items that related significantly to a relevant outcome were simultaneously entered into multiple linear regression analyses to determine which predictors were most robust when considered in the context of other significant predictors, as well as the proportion of the variance explained . As alcohol outcomes are sometimes evaluated as count variables, the regression analyses were also run using Poisson and negative binomial approaches. Differences in drinking quantities between assessments were evaluated with repeated measure analyses of variance. reflecting non normal distributions of drinking quantities, square-root transformations were used for these variables in correlations, multiple linear regressions, and repeated measure analyses.From among the original 500 students enrolled in the campus prevention protocol, the subjects in the current analyses were 462 individuals who completed at least 7 of the 8 assessments over the 55 weeks, of which all but 12 participated in all 8 periods. As shown in the first data column of Table 1, regarding demography, at the time of the baseline assessment used in these analyses , subjects were an average of 18 years old, 63% were female, and about a third were EA.

While not shown in the table, 36% were Asian, 16% Hispanic, and 14% listed other ethnic backgrounds . Substance use patterns in the month prior to baseline included an average of 4 standard drinks on a usual occasion and a maximum of 6 drinks at any occasion in the past month, with more than 40% having used cannabis in that same time period. Early in their drinking careers, these students required an average of 4 drinks to produce up to 4 potential alcohol effects as measured by the SRE questionnaire. While at baseline no subject was alcohol dependent, 45% reported 1 or more of 19 possible alcohol problems in the prior month, including about 20% each for ARBs and/or drinking more or for longer periods than intended, with about 15% each reporting needing more drinks to get effects and/ or consuming 4 or more drinks per occasion and/or drinking heavily for at least 2 consecutive days . Table 1 also lists scores for several environment and attitude characteristics shown in our prior work to relate to heavier drinking, including alcohol expectancies , depressive symptoms, using alcohol to cope with stress , descriptive and injunctive drinking norms, and drinking among peers. The final item in Table 1 reports that in the prevention protocol, 86% viewed educational videos, while 14% were controls, and, as discussed below, the assignment to active intervention versus control groups did not relate to patterns of increases and decreases in drinking over time. Table 2 and Fig. 1 present maximum and usual drinks per occasion across Periods 5, 6, and 7 that include the spring– summer–fall periods of the study. To place these dates into perspective, the legend in Fig. 1 presents dates for the academic quarters. While the emphasis in these analyses is on the Sun God–summer–return to school time frames set off by vertical bars in Fig. 1, the previously described school year drinking patterns for Times 1 to 4, and 8 are also shown. The current results demonstrate changes from prior to subsequent periods, including 18% increases in maximum drinking quantities during the Sun God Festival Period , 29% reductions in alcohol quantities over the summer , and 31% increases when students returned to school in the fall . Note that if the time frame prior to the Sun God Festival is used as a base, the decrease from Time 4 to summer was almost 17%. During summer months, 60.0% of these students lived with their parents, 22.1% were away from campus but not with parents, and 18.1% remained in campus dorms. While not shown in the figure, the patterns of drinking across the key time periods were similar for students in the control group and those in the active educational groups during the campus prevention protocol. Returning to Table 1, the remaining 6 data columns give product–moment correlations between baseline characteristics and drinking quantities the 30 days prior to Sun God, summer, and school return assessments. Regarding demography, on a univariate level an EA ethnicity was associated with higher drinking quantities in all follow-up periods, older age related to lower drinking during Sun God and school return periods, but female sex was only related to lower maximum drinks over the summer. All baseline substance-related variables correlated with higher quantities over the year, including higher SRE scores that indicated a lower LR per drink, higher baseline alcohol quantities, and cannabis use. Among environment/attitude baseline measures, higher depression scores correlated with lower drinking during the summer and return to school periods, and higher injunctive norms only related to heavier drinking during the Sun God and school return periods. Other than for AEQ, higher scores for all remaining variables in this group of potential predictors were more consistently related to higher alcohol intake across time frames. The experimental condition in which a person was placed did not relate to whether drinking at any time period across the 55 weeks was higher or lower. Therefore, to better identify baseline characteristics that are more likely to stand alone as predictors, all significant predictors of drinking for each follow-up period were entered into a simultaneous entry multiple linear regression analysis to evaluate which items performed most robustly when evaluated in the context of others. As shown in Table 3, the most consistent predictors of higher drinking quantities across multiple periods included higher SRE scores , higher baseline maximum quantities, and descriptive norms .