Cannabis dependence status was determined by the SCIDI–N/P for DSM-IV

Mixed evidence for the combined impact of alcohol use and HIV infection on memory functioning may be attributable to different stratifications of alcohol use , presence of co-occurring substance use and mental health conditions, as well as assessment method. For example, standardized laboratory-based neuropsychological testing tends to assess performance at a single time point and in a highly specific contextual setting. Indeed, the ecological validity of using neuropsychological tests to assess cognitive function having been questioned due to the nature of laboratory settings for neuropsychological testing, the potential for interactive effects of increased demands on cognition in everyday settings and cognitive deficits, and compensatory strategies that may be effective for a laboratory-based task but are not reflected in self-reported memory skills. Although neuropsychological tests provide objective data for specific cognitive deficits, it has been argued that subjective cognitive measures are more sensitive indices for everyday memory functioning [see], such as patients’ experience of everyday memory failures or milder, more variable cognitive struggles that fluctuate over time [e.g., ]. Use of self-report measures may therefore provide a complimentary lens through which to examine the effects of problematic alcohol use on memory functioning among individuals with HIV. For instance,grow rack self-reported memory functioning has been shown to explain variance in self-reported medication management over and beyond that accounted for by neuropsychological test measures. At present there is a general dearth of empirical investigation of the impact of varying levels of alcohol use on self-reported and everyday memory functioning among those with HIV.

This is unfortunate given the well-established associations between memory dysfunction and poor HIV-related outcomes and lower perceived quality of life among individuals with HIV. Further, because even moderate alcohol use may be harmful in this vulnerable population, such questions are relevant for the effective implementation of interventions that heavily tax memory, learning and attention. It is also important to examine these relations in the context of cooccurring substance use and mood disorders, which are common in this population. Accordingly, the primary objective of the current study is to examine the extent to which problematic patterns of alcohol use impact different facets of self-reported everyday memory functioning among a sample of HIV-infected individuals. We hypothesize that problematic alcohol use will be associated with greater perceived memory dysfunction among individuals with HIV, even after controlling for medication adherence, depression, and co-occurring substance use. Given that problematic alcohol use and memory functioning are known to independently influence HIV outcomes, our secondary aim is to assess the extent to which poor perceived memory functioning may serve as an indirect pathway between problematic alcohol use and HIV symptom severity. specifically, we hypothesize that self-reported memory functioning will mediate the association between problematic alcohol use and HIV symptom severity.The Alcohol Use Disorder Identification Test is a 10-item self-report measure developed by the World Health Organization to identify individuals with alcohol problems. The AUDIT assesses three domains: alcohol dependence, harmful drinking , and hazardous drinking . Most items are rated on a 5-point Likert scale ranging from never to daily or almost daily. In the present study, items were summed to generate an index of total alcohol problems. A wealth of literature attests to the strong psychometric properties of the AUDIT [see], in addition to its use for the detection of problematic drinking in HIV-infected samples. Alcohol abuse and dependence were determined by the Structured Clinical Interview-Non-Patient Version for DSM-IV .

Criteria for cannabis dependence were consistent with DSM-IV criteria with the addition of withdrawal as proposed for DSM-5. Participants were classified as non-dependent cannabis users if they reported any cannabis use in the past 30 days, but did not meet criteria for cannabis dependence. Participants with no cannabis use in the past 6 months were classified as nonusers. The Marijuana Smoking History Questionnaire is a 21-item measure that assesses the frequency, patterns, and history of cannabis use. Participants who reported any cannabis use in the past 6 months were asked to provide additional information indicating the number of times they had used cannabis during the month prior to assessment. In the present study, cannabis use status was included as a covariate in all analyses and the MSHQ was used to describe frequency of cannabis use.Participants were recruited via informational flyers posted throughout a VA Medical Center and in numerous San Francisco Bay Area outpatient HIV clinics. Upon contacting the research team, individuals were provided with a detailed description of the study. Interested individuals were screened on the phone for eligibility and, if eligible, scheduled for a study appointment where they provided written consent to participate in the research study. The SCID-I–N/P was administered by trained research assistants and all interviews were audio-recorded and diagnoses were confirmed by the last author following a review of recorded interviews. Participants then completed the above-described measures. All study procedures were approved by a university Institutional Review Board .Descriptive statistics and alpha reliability coefficients were calculated for all measures. Number of cigarettes smoked per day, problematic alcohol use and memory functioning were log-transformed to correct for positive skew. A valueof 1 was added to variables including zero prior to their transformation. Bivariate Pearson correlations were conducted to examine relations between the EMQ total score and sub-scale scores, problematic alcohol use, and HIV symptom severity. Bivariate spearman and pears on correlations were then performed to assess associations between memory functioning, HIV symptom severity and potential covariates .

Based on these analyses, level of education, cannabis dependence and current depression diagnosis were included as covariates in all regression models. Self-reported adherence, although unrelated to memory functioning or HIV symptom severity, was included in the model to ensure that we accounted for variance in outcomes that could be explained by poor medication adherence. Six hierarchical linear regressions were performed to determine the impact of problematic alcohol use on total memory functioning as well as each individual facet of memory functioning. For the five individual memory facets we used a Bonferroni correction with alpha set at 0.01 for an overall rejection level of 0.05. Self-reported medication adherence was entered as a covariate on step 1. Level of education was entered as a covariate in Step 2. In step 3, two dummy coded variables were entered for cannabis use status . In step 4, current depression diagnosis was entered. Finally in step 5, problematic alcohol use was entered to determine how much additional variance in memory functioning was explained by problematic alcohol use after controlling for all covariates.Previous research has implicated memory functioning as a potential pathway through which problematic alcohol use may be related to HIV symptom severity [e.g., ]. Given that both alcohol problems and self-reported memory functioning correlated with HIV symptom severity in the present sample, a series of regression models were performed to determine whether memory functioning mediated the relation between problematic alcohol use and HIV symptom severity. Self-reported medication adherence, highest level of education, cannabis dependence and current depression diagnosis were entered as covariates at steps 1, 2, 3 and 4 respectively, in all models to ensure any relations observed were not accounted for by these variables. First, to test for mediation, problematic alcohol use was regressed on HIV symptom severity . Second, to reduce redundancy, results from the primary analyses were used to determine pathway A, the relation between problematic alcohol use and self-reported total memory functioning . Third, total self-reported memory functioning was regressed on the outcome variable , after controlling for problematic alcohol use . Last,microgreens shelving both bootstrapping and Sobel tests [see] were used to confirm findings from the Baron and Kenny mediational tests. Finally, as the mediational analyses were conducted among cross-sectional data, an additional model was tested to comprehensively assess directionality of the observed effect whereby the proposed mediator and criterion variable were reversed. To account for potential overlap of cognitive symptoms on the HIV symptom severity measure and the EMQ, mediation analyses were also conducted using a HIV symptom severity score that excluded cognitive symptoms. No differences emerged between models using the different scoring systems and thus the results presented represent all HIV symptoms.The present study sought to determine the influence of problematic alcohol use on self-reported memory functioning, and to assess relations with HIV symptom severity among a sample of HIV-infected individuals. Consistent with hypotheses and previous research, problematic alcohol use was associated with lower ratings of overall everyday memory functioning as well as increased difficulty with retrieval and memory for activities. Importantly, this pattern of results suggests that problematic alcohol use tended to specifically impact retrieval-based over processing-based aspects of memory functioning. Also of note, problematic alcohol use exhibited a direct and potent effect on these aspects of perceived memory functioning even after accounting for co-occurring substance use and depression . Finally, perceived memory functioning mediated the relation between problematic alcohol use and HIV symptom severity, though the direction of this relation was unclear and possibly reciprocal.

Findings from the present study lend support to clinical researchers’ call for initiatives to tailor substance abuse treatment and HIV risk-reduction programs to better address impediments posed by cognitive impairment. For instance, techniques to improve multi-modal encoding of clinical information may be particularly beneficial in helping to reduce retrieval-failures observed in the current study. External cueing systems and environmental supports in treatment clinics can also be employed. In addition, although the cross-sectional nature of these data does not permit us to draw conclusions about causality, interventions to remediate cognitive functioning may also improve outcomes in this particularly vulnerable population. For instance, computerized neuroscience-based cognitive remediation programs that target attention, memory and executive functioning could help augment existing treatments by enhancing patients’ cognitive reserve and mental flexibility to adhere to complex treatment regimens and encode, retrieve and employ HIV transmission-prevention skills. As hypothesized, perceived memory functioning provided an indirect pathway for the relation between problematic alcohol use and HIV symptom severity. Thus, one possibility is that the deleterious effects of problematic alcohol use on memory functioning explain the relation between problematic alcohol use and HIV symptom severity. For instance, problematic alcohol use may negatively impact memory for events and retrieval that may increase HIV-risk behaviors and reduce adherence and general self-care and ultimately, increase HIV symptom severity. Importantly though, reversal of the mediational model indicated that HIV symptom severity explained the relation between problematic alcohol use and self-reported memory functioning. As such, HIV symptom severity and memory functioning were interrelated and the extent to which memory functioning offers a distinct mediational pathway between problematic alcohol use and HIV symptom severity is unclear. The blurred directionality observed in our mediation models is believed to highlight the complexity associated with comorbid health conditions that commonly characterize this population, and their impact on HIV health outcomes. More likely, and as articulated in previous work, the relations between memory functioning, HIV outcomes and alcohol use are reciprocal. Among HIV infected individuals, problematic alcohol use is associated with worse HIV health outcomes which ostensibly result in increased HIV symptom severity and lower overall quality of life. Adding to the clinical profile, HIV disease progression is commonly associated with neurocognitive impairment. Given the high prevalence of alcohol problems among HIV-infected individuals coupled with its observed negative impact on memory functioning, routine screening for problematic alcohol use in HIV care settings is thus imperative. In the current sample, problematic alcohol use and all domains of self-reported memory functioning were associated with higher HIV symptom severity. Accordingly, this research combines with previous work to suggest that HIV symptom severity is intricately related to both alcohol consumption and memory functioning and that they should be considered collectively in clinical settings. Results from the current study are consistent with the literature in several respects. Previous research indicates that alcohol and drug use exert greater effects on neurocognitive function in HIV-infected versus non-infected individuals and, thus, HIV is posited to potentiate and exacerbate the impact of alcohol and drug use on neurocognitive functioning. Although no comparison group was available, our findings appear to support this notion and indicate that both problematic alcohol use and HIV symptom severity are associated with lower everyday memory functioning among individuals with HIV. In addition, HIV-infected individuals with a current depression diagnosis were at increased risk for reduced memory functioning and heightened HIV symptom severity, which is also consistent with previous research. Findings here also broadly align with research employing animal models of HIV infection which have shown that HIV can cause neuroplastic changes that impair cellular learning process implicated in memory.

The analysis found that 87% of patients substituted cannabis for one or more substances

Our use of provider-assigned diagnoses restricted our sample to ICD-9 codes assigned during health plan visits. This method is vulnerable to diagnostic under-estimation; and thus, the rates of bipolar and schizophrenia may be somewhat higher than we report. Another potential limitation with the methods used to select the sample is that we required a single mention of an ICD-9 code for SMI during the study period to link the patient with that diagnosis and included all current and existing diagnoses . While this single mention methodology is well established, it could result in overestimation if diagnoses only mentioned one time in the EHR are more likely to be inaccurate. Since patients with bipolar or schizophrenia could have multiple behavioral diagnoses. Thus, our results should be interpreted with caution until confirmatory studies are conducted in mutually exclusive SMI groups. All data are cross-sectional; and thus, no directionality can be assumed in associations between conditions, and associations do not imply cause-and-effect relationships. Long-term follow-up studies will be required to capture the full impact medical comorbidities have on the course and outcome of individuals with SMI. The reasons why having SMI is associated with disproportionately high odds of having medical comorbidities are complex and multi-factorial and future studies will need to continue to monitor medical comorbidity in this population as health policies evolve. We found having a SMI was associated with higher odds of having several medical comorbidities as well as chronic and severe medical conditions,rolling grow table even in an integrated health care system where patients have insurance coverage and broad access to care.

Our results suggest that that SMI patients have high medical needs, and implementing enhanced outreach efforts focused on prevention, early diagnosis, and treatment of medical comorbidities may help reduce associated morbidity and mortality and improve overall prognosis in this population.In November 1996, California became the first state to legalize the use of medical marijuana. Since then, a total of 23 states have passed medical marijuana laws , along with 4 states and Washington D.C. legalizing the recreational use of marijuana. With marijuana legislation continuously being seen on state ballots, it is important to research and understand the effects it brings to society. To conduct our research, we will be looking specifically at the effect of medical marijuana use in California. The goal of this paper is to observe the effects that medical marijuana has on crime rates and other drug and alcohol use. A specific question to be answered is whether or not marijuana can be a substitute, rather than a complement, for other illegal drugs and alcohol, resulting in a decrease in crime and drug and alcohol-induced deaths. To discover an answer, we will look at medical marijuana, crime, arrest, unemployment, and mortality rates in California counties from 2005-2014. The arrest and mortality rates will be used specifically to examine the possibility of marijuana being a substitute drug. Today, there are approximately 572,762 medical marijuana patients in California, which is equivalent to 1.49% of California’s population.While recreational use of marijuana has not been legalized in California, it is estimated that 9% of Californians use marijuana.If recreational marijuana use is legalized in California, it is possible that the percentage of marijuana users will increase. Given that California already has numerous marijuana farms and is predicted to provide 60-70% of the United States’ crop if legalized within the state, according to the International Business Times, it is pertinent to analyze the outcomes marijuana has on California’s society today. In 2010, the number one cause of death among 25-64 year olds in California was drug overdose.Many individuals have grown up with the notion that marijuana is a gateway drug to other illicit “hard” drugs. These other substances could include cocaine, heroin, methamphetamines, and prescription drugs, all of which can be extremely addicting and fatal. Since 1999, deaths from painkiller drug overdoses have increased 400% for women and 237% for men.This causes us to think of potential solutions for fatal substance abuse. If medical marijuana can be offered as a substitute drug, will it decrease drug-poisoning deaths? According to a survey implemented by the U.S. Department of Health and Human Services from 2005 to 2011, illegal drug use percentages were much higher in unemployed individuals than individuals with some sort of employment.Specifically, it was shown that 18% of the unemployed were involved in illegal drug use, compared to 10% of part-time workers and 8% of full-time workers. This causes us to question whether or not there’s a relationship between drug use and unemployment. When California passed Proposition 215, referred to as the California Compassion Use Act, it allowed patients, along with their primary physicians, to possess and grow marijuana for medical use, once given a referral from a California-licensed doctor. In 2004, California passed SB 420 to supplement Prop 215. The SB 420 specified the amount of marijuana each patient could possess and cultivate and created a voluntary, statewide, ID database through California health departments. This database is run by the California Department of Public Health and will be used to estimate marijuana use for this report. While both Prop 215 and SB420 protect patients and physicians from arrest in California, marijuana continues to be a federal crime, where there is no differentiation between medical and recreational marijuana use. Currently, the Drug Enforcement Administration has marijuana listed as a Schedule I drug, defined as a drug with the highest potential for danger and abuse and is listed along with heroin, LSD, and ecstasy. Schedule I drugs are assumed worse in comparison to Schedule II drugs, which are recognized to be less abusive. Schedule II drugs include cocaine, methamphetamines, and other highly addictive prescriptions. According to the Office of National Drug Control Policy, the reason marijuana legalization is refused at the national level, is because marijuana use is believed to increase the use in other illicit drugs.This brings us back to the question of whether or not marijuana can act as a substitute, rather than a “gateway”, to other hard drugs. While there has been little to no research done in the area of recreational marijuana, there have been many articles published on the effects of medical marijuana legalization. In 2013, Anderson et al. published a paper that studied the effects of MMLs on traffic fatalities across the nation by using alcohol consumption as an instrument. The authors first used price data to observe the effects on the marijuana market after the MML took effect. They found that the supply of high-grade marijuana dramatically increased, while the lower quality cannabis was moderately impacted. Getting to the basis of their main goal, they used data on traffic fatalities within a 20-year period, across 14 states, to determine if marijuana was a substitute for alcohol. It was discovered that there was an 8-11% decrease in traffic fatalities within the first year of legalization with an even larger effect on traffic fatalities involving alcohol consumption. The authors then used individual behavioral data to examine the probability of consuming alcohol in the past month, binge drinking, and the number of drinks consumed after the MML took place. They found that these probabilities drastically decreased after the legalization occurred. When looking at alcohol sales, it was also discovered that there was a decline of 5% on beer consumption in the age range of 18-29. The MMLs were then used as an instrument of beer consumption to establish the amount of traffic fatalities. It was deduced that for every 10% increase in beer sales per capita, alcohol related traffic fatalities increased by 24%. The article goes on to conclude that marijuana does have a substitution effect on alcohol, especially among young adults, which inherently declined traffic fatalities.There is currently a working paper called “The Effect of Medical Marijuana Laws on Marijuana, Alcohol, and Hard Drug Use,” where Hefei Wen studied these effects using geographic identifiers and by estimating a state-specific time trend model that included two-way fixed effects. It was discovered that the relative probability of marijuana use among individuals over 21 increased by 16%, the frequency of marijuana use increased by 12-17%, and marijuana abuse and dependency increased by 15-27%. While there was an overall increase in marijuana use after MMLs went into effect,vertical grow rack there was no strong evidence that showed marijuana use increased in youth. While the authors predicted that there could be a spillover effect of marijuana on other substances, there was no significant evidence that marijuana caused increases in alcohol and other drug use. A more recent study done through the Drug and Alcohol Review examined medical marijuana as a substitute for alcohol, prescription drugs, and other illicit substances. The data was taken from a cross-sectional survey, completed online by 473 Canadian medical marijuana patients. This included an 80.3% substitution rate for prescription drugs, a 51.7% substitution rate for alcohol, and a 32.6% substitution rate for other illicit substances. These rates serve as evidence that marijuana can “play a harm reduction role in the context of use of these substances, and may have implications for abstinence-based substance use treatment approaches.”While these results show significant effects for marijuana substitution, there are an estimated 2.3 million users of cannabis in Canada alone, making it difficult to assume a survey of only medical marijuana patients represents the entire population of all marijuana users. An additional study was done through the University of Virginia in 2014 that examined how MMLs affect crime rates.The author, Catherine Alford, decided to use difference-in-differences estimations where she controlled for state specific crime trends by collecting data across states over time from 1995-2012. It was discovered that after the implementation of MMLs, overall property crime and robbery rates increased. However, if the MMLs allowed for home cultivation, robbery rates actually decreased by about 10%. While these results show a positive relationship between MMLs and the previously mentioned crime rates, there was no statistically significant effect on violent crime rates. However, a study done in 2012, by the Center on Juvenile and Criminal Justice, showed that after California passed the SB 1449 for the decriminalization of marijuana, youth crime rates were at an all-time low.The SB 1449 allowed for a small possession of marijuana to count as an infraction, instead of a misdemeanor. Within a one-year period from 2010-2011, youth arrests declined by 16% for violent crime, 26% for homicide, and 50% for drug arrests. The author, Mike Males, concluded that the only significant explanations for a dramatic decline in juvenile crime rates would be the passing of SB 1449 and the improvement of socio-economic programs in California’s poor neighborhoods. In the previous reports examined, crime rates, drug and alcohol use, and traffic fatalities were all studied after the passing of MMLs among multiple states to discover any significant effects. While my proposed project would like to examine both crime rates and drug use affected by marijuana, it will look purely at California county data across a 10-year period and will not focus on age-specific crimes. The following report will also include an analysis of how the issuance of medical marijuana identification cards affects other drug and alcohol use, controlling for unemployment. The methodology used to answer the research questions above will be a series of multiple regressions with county and year fixed effects. To begin the analysis, we will determine how MMIC issuance affects crime rates. This regression will include unemployment as a right hand side variable to control for variations in the workforce. A regression will be run for every type of crime rate, as well as for total crime, in order to discover if marijuana has individual effects on different types of crime. In addition to regressing crime rates on MMICs, drug and alcohol arrest rates will be regressed on MMICs to examine if there’s a substitution effect between marijuana and other drugs and alcohol. Because arrest and crime rates do not depend solely on MMICs, we will also include unemployment rates as a right hand variable. After analyzing the number of MMICs on crime and arrest rates, drug, alcohol, and other mortality rates will be regressed on the number of MMICs issued per county.

Cannabis legalization categories were assigned to participants based on their state of residence

The curated GIS database compiled by the ABCD Study LED Environment Working Group includes both vector and raster data of multiple built and natural environmental contextual variables. As shown in Table 1 and outlined in greater detail below, various environmental datasets have been used to map environmental factors to the state-, census-, residential-level for ABCD Study participants to date. Youth grow up in overlapping circles of cultural and socio-political contexts, from their local family and neighborhoods to the states and countries in which they live. We typically focus on the experience of stigma and bias at a relatively local level . Critically, there are also important indicators of more systemic or structural bias reflected in social norms at the community or institutionalized laws, policies and practices that may either reflect the behavior of individuals or shape the behavior of individuals in youths’ local environment. However, we rarely directly examine the relationship between objective measures of systemic/structural bias and function in youth. The ABCD Study provides a novel opportunity to address such critical questions with empirical data, given the geographic variability of the sites involved in the ABCD Study,hydroponic flood table which affords significant divergence across youth in their exposure to such systemic biases. To address such questions, colleagues at Harvard University created state-level indicators of three types of structural stigma : gender , race , and ethnicity . This information was linked to each youth in the ABCD Study as a function of their baseline site of participation and does not yet include information about whether the child moved to a different state, which may have different state level indicators, at later visits.

To create these state-level measures, they used several types of data. First, they obtained data about implicit and explicit attitudes about each of these three identity groups aggregated at the state-level, derived from large-scale projects that spanned several years: Project Implicit , the General Social Survey , and the American National Election Survey . Second, for information on gender, they obtained state-level data of women’s economic and political statuses and information about reproductive policies, such as information about availability of abortion providers. Third, for information on attitudes towards Latinx individuals, they examined state-level policies on immigration, recognizing that many Latinx individuals are not immigrants but that such state-level policies likely influence the experience of all individuals in the community with that identity. These data can be used to examine how these state level biases interact with youth’s identities to predict a range of factors, such as educational experience, mental health, brain development, and substance use/abuse. In the United States, public acceptance of cannabis use has increased alongside increased access because of broader cannabis legalization. Currently, 36 states have legalized either recreational or medical cannabis use. Early research suggests that cannabis legalization does not lead to increases in adolescent cannabis use . However, among younger adolescents , greater exposure to cannabis advertisements was associated with greater use, intention to use, and positive expectancies . The difference in results as a function of age highlights the importance of understanding how cannabis regulations affect younger cohorts of children and adolescents who may have greater exposure to cannabis advertisement after living in an environment with legal access to cannabis for a longer period. Furthermore, the ABCD Study is an ideal dataset to examine the effects of cannabis legalization because there are 21 sites located in 17 states with various state cannabis policies. In addition, the ABCD Study is collecting detailed substance use data unlike other national surveys.

The four cannabis legalization categories are: 1. Recreational – allows adults to use cannabis for recreational purposes, 2. Medical – allows adults to use cannabis for medical conditions, 3. Low THC/CBD – allows adults to use cannabis that is low in THC and high in CBD for medical conditions, and 4. Urbanicity can provide information as to the impact of living in urban areas. Urbanicity indices may reflect the presence of environmental and social conditions that are more common in urban areas, such as pollution, congestion, and increased rates of social interactions. To date, various health factors have been linked to urbanicity, such as increases in overweight/obesity, increased calorie intake, decreased physical activity, increased drug and alcohol use, and mental health disorders, among many others . In the ABCD Study, we have linked five measures of urbanicity to residential addresses, including two density measures , census-tract derived metrics classifying the locations as urban or non-urban areas, walk ability, and motor vehicle information including distance to roadway and traffic volumes. Population density refers to the number of people living in a given unit of area . Differences in population density have been linked to psychological and environmental quality of life , and has been shown to moderate relationships between the built environment and health outcomes . Thus, information about variability of population density may be important for contextualizing relationships between the build environment and health outcomes in the ABCD Study. As such, the population density from the Gridded Population of the World , provided by the Socioeconomic Data and Applications Center , has been linked to ABCD individual participant address information. National-level population estimates from 2010 used in this metric have been adjusted to the United Nations World Population estimates, which can often be corrected for over- or under-reporting and mapped to an ~1-km grid. Population density values represent persons per km2 . Similarly, gross residential density is a measure of housing units per acre on unprotected land and is an alternative measure of crowding. This measure was obtained from the Smart Location Database created by the United States Environmental Protection Agency based on the 2010 Census Data and also linked to ABCD Study individual addresses. While many studies have documented the effects of increased urbanicity on child and adolescent health outcomes, few studies have focused on differential risk associated with living in a rural area relative to an urban area . Although the number of studies devoted to this topic are few, linking this information to the ABCD Study may provide an opportunity to further investigate both positive and negative impacts of living in an rural area. To classify individuals as living in a rural or urban area, urban-rural census tract variables from 2010 were mapped to each address.

Based on this external database, the Census Bureau identifies two types of urban areas, including Urbanized Areas of 50,000 or more people and Urban Clusters of at least 2500, but less than 50,000 people. Rural areas are those that encompass all population, housing, and territory not included within an urban area . In urban places, city planning designs have limited the walk ability between work, home,hydroponic stands and recreational spaces, with distances too great to walk . Such reduction in walk ability leads to fewer opportunities for physical activity and a risk for health. Understanding potential links between the walk ability of the built environment of the child and physical and mental health outcomes is important in the context of the ABCD Study . A measure of walk ability was linked to ABCD participant addresses using the National walk ability Index from the Smart Location Database created by the United States Environmental Protection Agency based on 2010 census data. walk ability scores were calculated at the census-tract level, ranking each census tract on a range from 1 to 20 according to relative walk ability. The walk ability score is based on a weighted formula that uses ranked indicators as related to the propensity of walk trips. The ranked-indicator scores used in the weighted formula include a combination of diversity of employment types plus the number of occupied housing, pedestrian-oriented intersections, and proportion of workers who carpool. Beyond population density and walk ability, epidemiological studies have also reported associations between road proximity and brain health. Various neurodevelopment, cognitive functioning, and mental health outcomes have been linked to living near major roadways . As such, the ABCD Study may be valuable to help understand how the distance of a child’s home to major roadways as well as the daily traffic patterns on nearby roadways impacts cognitive and neurodevelopmental trajectories over time. Therefore, we have mapped road proximity and traffic volume estimates to residential addresses of the child in the ABCD Study to provide insight into both the major roadways nearby and how many cars and trucks typically utilize these roads. the geospatial coordinates of the major roads were obtained through the North American Atlas for roads, as last updated July 2012 , and the shortest distance to a major roadway in meters was linked to participant’s residential addresses. In the field of developmental cognitive neuroscience, socioeconomic status has traditionally been treated as an individual-level variable, specific to each family or person. However, socioeconomic status can also be attributed to neighborhoods and communities, which may represent an independent construct from family-level socioeconomic status with considerable effects on child development . In the ABCD Study, detailed questions are asked about socioeconomic and social factors at the family-level. Thus, the ABCD Study is an ideal dataset to examine the independent and multiplicative associations of family- and neighborhood-level socioeconomic status on adolescent health. Investigations with these ABCD data can elucidate the underlying mechanisms by which various contexts uniquely influence development and potential emerging health disparities . Accordingly, the ABCD Study has incorporated the Area Deprivation Index measure of neighborhood-level socioeconomic status in past data releases, as well as information on crime and risk of lead exposure. Moving forward, three additional metrics, including the Social Vulnerability Index, Opportunity Atlas, and the Child Opportunity Index, have been linked in the 4.0 annual data release. The ADI represents a composite multi-variable metric of neighborhood disadvantage , with higher values representing greater disadvantage. Developed and popularized by Singh , the ADI was initially constructed to determine how area deprivation was associated with mortality. However, as more pertinent to ABCD, per studies of related measures of neighborhood disadvantage, increased disadvantage is indirectly associated with children’s developmental outcomes and adult health problems through other neighborhood- and/or family-level variables. The ABCD Study includes the composite ADI metrics, including the weighted ADI score and its national percentile, along with the 17 component variables used to create the composite scores at the census-tract level for participants’ primary, secondary, and tertiary addresses at baseline, all of which were derived from the 2011–2015 American Community Survery . A description of the 17 component variables is included in Supplemental Table 2. Like the ADI, the SVI incorporates 15 variables from the ACS, which are described in Supplemental Table 3. These 15 items are grouped into 4 themes: socioeconomic status , household composition and disability , minority status and language , and housing type and transportation . SVI is calculated by deriving percentiles of each variable , summing the percentiles within the theme, and summing these totals across themes, with higher values of SVI representing greater vulnerability to disaster and disease. Here, linking SVI to ABCD data provides the opportunity to better understand not only how environmental contexts are interrelated with adolescent development, but how environmental vulnerability to external stressors may invoke downstream effects on developmental outcomes. The 4.0 annual release for the ABCD Study includes the census-tract level SVI for participants’ primary, secondary, and tertiary addresses. The neighborhoods in which children in America grow up can influence outcomes in adulthood. As such, the Opportunity Atlas estimates measures of average outcomes across 20,000 people in adulthood according to the census tracts in which they grew up . The ABCD Study includes scores from the Opportunity Atlas that indicate the predicted 2014–2015 mean income earnings of adults aged 31–37 years that grew up in that census tract as children. Scores are provided based on the childhood census tracts of the Opportunity Atlas cohort, but we also provide the adult mean earnings disaggregated by parental household income percentiles based on the national income distribution during their childhood. For example, the mean income earnings at the 25th percentile rank correspond to the mean income earnings of adults whose parents were at the 25th percentile of the national income distribution.

It is notable that the use of cannabis is associated with a higher prevalence of periodontitis

In our experiments we presented the olfactory and visual stimuliCannabis sativa contains more than 140 terpene-like compounds, called cannabinoids, that share the cannabinoid chemical scaffold. The 2 main members of this chemical family are Δ9 -tetrahydrocannabinol and cannabidiol . Animal and human studies have demonstrated that THC is responsible for the majority of the intoxicating effects of cannabis; it acts by binding to G protein-coupled cannabinoid receptors in the brain and other tissues of the body. By contrast, CBD exhibits a distinct set of pharmacological properties, including anti-epileptic and anti-inflammatory effects that are mostly independent of CB receptor activation.Data obtained from the National Health and Nutrition Examination Survey indicate that frequent use of recreational cannabis is positively associated with severe periodontitis, which was observed both in a bivariate analysis and in a multi-variable analysis adjusted for demographics , alcohol and tobacco use, diabetes mellitus, and past periodontal treatment. Research has also found that cannabis may produce adverse effects on oral tissues including gingival enlargement, nicotinic stomatitis, and uvulitis. Remarkably, a number of beneficial effects have also been reported. Considerable evidence supports that pharmacological strengthening of the endogenous cannabinoid system may exert beneficial effects on periodontal inflammation and nerve pain. CBD was shown to exert anti-inflammatory and anti-oxidative effects resulting in a faster resolution of oral mucositis in a murine model. Additionally, enhancing endocannabinoid signaling in cells that initiate local immune responses in the periodontium, the periodontal ligament cells, greenhouse grow tables significantly dampened their proinflammatory responses to lipopolysaccharide produced by Porphyromonas gingivalis.

It has been also shown that selective agonists for type 2 CB receptors exert anti-inflammatory effects in human periodontal ligament fibroblasts. Finally, pharmacological activation of the endocannabinoid system in periodontal ligament cells exhibited hostprotective effects by both dampening inflammation and preserving cellular integrity, while palmitoylethanolamide, a bio-active lipid structurally related to endocannabinoids, exacerbated inflammation. All in all, these results suggest that targeting the endocannabinoid system, in particular by boosting local CB2 receptor signaling, may lead to novel therapeutics that improve current treatments for periodontal disease and other oral inflammatory pathologies.The coronavirus disease 2019 pandemic due to the worldwide spread of severe acute respiratory syndrome coronavirus 2 infection has significantly affected the use of cannabis in 2 particular human populations, among others. First, it was shown that those who engaged in self-isolation used 20% more cannabis during the pandemic than those who did not, which was associated with self-reported isolation and loneliness. In addition, people with mental health conditions reported increased use of medicinal cannabis by 91% during the COVID-19 pandemic, compared to those with no mental health conditions. Therefore, during the pandemic, health care providers should pay particular attention to oral diseases. Importantly, communication and cooperation between physicians and dental practitioners should be encouraged in managing and treating patients. In addition, the seemingly opposite contribution of the 2 main ingredients of cannabis, THC and CBD, to periodontitis should be kept in mind when addressing the effects of cannabinoids. Certainly, further research is required to evaluate the beneficial and harmful effects of various phytocannabinoids and pharmacological modulators of the endocannabinoid system.In 2000, the Surgeon General identified oral disease as a “silent epidemic” . Despite the availability of effective prevention and treatment methods, oral health has improved little over the past two decades. Among some sub-populations , oral health disparities remain . In the United States, nearly a quarter of adults aged 20-64 have untreated dental caries and more than half have lost a permanent tooth .

Oral pain and tooth loss have a significant negative impact on quality of life and employment by affecting the ability to eat, speak, and smile . Older adults have worse oral health than younger adults due to age-related physiological changes and a higher prevalence of chronic conditions . Despite their heightened need for dental care, older adults have less access to such care . The homeless population is aging, with a growing proportion of adults experiencing homelessness at ages 50 and over . Older homeless adults have a high prevalence of chronic disease and poor dental health . In California, most adult dental services were discontinued as a Medicaid benefit in 2009 , eliminating coverage for more than 8 million people . Enactment of the Affordable Care Act in 2014 expanded Med-Cal medical insurance coverage to 3.8 million people in California, and restored basic adult dental coverage . Unlike pediatric dental care, which is considered an essential health benefit under the ACA, adult dental care coverage is not mandatory. In California, after the enactment of the ACA, adults with Medi-Cal became eligible for dental services, including basic preventive and restorative treatments, complete dentures, and complete denture reline/repair services through the Denti-Cal program . Access to dental care is important because poor oral health is associated with poor nutrition, oral pain, and impairments in oral functioning . People experiencing homelessness have inadequate resources for regular dental hygiene and a higher prevalence of risk for tooth loss, including smoking and substance use . Tooth loss, or edentulism, is a key indicator of oral health; it is affected by both access to dental care and risk factors for poor oral health . Edentulism is a risk factor for coronary artery plaque formation, diabetes, and certain cancers . Prior research in a sample of homeless adults found homeless adults had a higher prevalence of poor oral health than the general population, with high prevalence of tooth loss, or untreated dental decay . In a national study of homeless adults, approximately half of homeless adults had an unmet need for dental care as assessed by tooth or gum problems in the past year . Little is known about oral health in the growing population of homeless adults aged 50 and older. We examined the prevalence of tooth loss, oral pain, denture fit, and impairments in eating or sleeping due to oral pain as well as factors associated with poor oral health, in a population-based cohort of older homeless adults in Oakland, CA. The HOPE HOME Study, is a longitudinal study of life course events, geriatric conditions, and their associations with health-related outcomes among older homeless adults.

From July 2013 to June 2014, we enrolled a population-based sample of 350 homeless adults aged 50 years and older from all 5 overnight homeless shelters in Oakland that served single adults over age 25, all 5 low-cost meal programs that served homeless individuals at least 3 meals per week, a recycling center, and homeless encampments. Study visits took place at St Mary’s Center, a non-profit that serves indigent older adults. Participants did not have to receive services at St Mary’s to be eligible. To be eligible, participants had to be English-speaking, aged 50 years and older, defined as homeless as outlined in the Homeless Emergency Assistance and Rapid Transition to Housing Act , and able to provide informed consent. After determining eligibility, study staff administered an in-depth structured enrollment interview and collected extensive contact information from participants. We gave participants a $25 gift card to a major retailer for their participation in the screening and enrollment interview. The University of California, San Francisco Institutional Review Board reviewed and approved all study protocols. This analysis uses data from the baseline interview. Participants self-reported age, sex, race/ethnicity, and highest level of education. We categorized race/ethnicity as African American, White, or Other. We categorized highest level of education as less than high school versus high school graduate/General Educational Development or greater. Participants reported their total lifetime years of homelessness after the age of 18. To assess the prevalence of depressive symptoms, we administered the Center for Epidemiologic Studies Depression Scale. Using a shortened time frame of the previous 6 months to correspond to study time intervals, we administered the World Health Organization’s Alcohol Use Disorders Identification Test . To assess illicit drug use, we administered the WHO’s Alcohol, Smoking, and Substance Involvement Screening Test to assess for amphetamines, cocaine, opioids and cannabis growing system, using a lengthened time frame of the previous 6 months. We dichotomized substance use risk for each substance as low vs. moderate-to-high severity . We used the California Tobacco Survey to assess tobacco use. We classified smokers who had smoked at least 100 cigarettes in their lifetime as “ever smokers.”We adapted oral health questions from the Oral Health Impact Profile – 14 . We asked participants about tooth loss . For our primary dependent variable, we dichotomized responses as missing less than half versus missing half or more. We asked participants who reported having any teeth if they were able to eat with their teeth. For participants who reported missing all of their teeth, we asked if they had dentures, and if so, whether they fit . We asked participants how often they had oral pain in the last six months . If participants noted oral pain, we asked if the pain kept them from eating or sleeping . To assess access to dental care, we asked participants about how long it had been since they last visited a dentist: <6 months, 6 months to 1 year, >1 year to <5 years, or ≥5 years. To assess unmet dental need, we asked participants if, during the past 6 months, there was a time when they needed dental care but could not obtain it. We described sample characteristics and reported oral health variables using medians for continuous variables and proportions for categorical variables.

We examined oral health status by evaluating bivariate associations between independent variables and our primary dependent variable using Wilcoxon rank sum tests for continuous variables and chi-squared tests for categorical variables. Using multivariate logistic regression, we examined factors associated with participants having lost half or more of their teeth. We included all covariates in the model and through stepwise removal eliminated variables failing to achieve a significance of less than 0.2. We conducted these analyses using Stata version 14 . In a population-based sample of older homeless adults with a median age of 58, we found evidence of poor oral health and poor access to dental care. Over half of participants reported oral pain, which is over three times greater than the prevalence of oral pain in the general population over age 65 and more than twice that of the general poverty population over age 65 . Despite oral health needs, older homeless adults had poor access to dental care. Only a quarter reported visiting a dentist in the prior year, compared with 62% of adults in the general population . We found that over half of older homeless adults had been unable to get dental care in the prior year, compared with fewer than ten percent of adults aged 65 and older in the general population . Whereas approximately 10% of edentulous adults in the general population lack dentures, we found that almost half of edentulous participants either lacked dentures or had ones that couldn’t be used due to poor fit . Over a quarter of our participants reported that mouth pain prevented them from eating. Edentulism and oral pain may limit homeless individuals’ ability to eat, worsening food insecurity . Dental care is ranked as one of the leading unmet needs among the general homeless population . Many of our participants lacked health insurance; in addition, prior to Medicaid expansion , Med-Cal in California did not include access to dental care. However, even with Medicaid-supported dental coverage, access to dental care remains limited. Only about 20% of the nation’s 179,000 practicing dentists accept Medicaid payment for dental services and more than 49 million people live in areas categorized as dental health shortage areas . In California, only 1 in 4 dentists provide services to Medi-Cal beneficiaries. On average, throughout California, there are only 7.3 dentists that accept Medi-Cal per 10,000 beneficiaries. As many dentists who accept Medi-Cal limit the number of Medi-Cal patients they are willing to provide services to, the shortage is worse than it appears. . Having lost half or more of teeth was strongly associated with increased age, consistent with previous studies in both the general and homeless populations . This could reflect the increased adoption of preventive measures such as improved fluoridation and dental sealants with later birth cohorts .

Disparities exist in the rates of overdoses due to amphetamines by race/ethnicity and gender

Galve-Roperh et al. found that cannabinoids cause a bi-phasic increase in ceramide levels in C6 glioma cells. The first phase of ceramide accumulation occurred within seconds or minutes after cannabinoid administration, which was likely to be a result of stimulus-dependent hydrolysis of sphingomyelin. Two days after the addition of the drug, a second increase in ceramide levels took place, which coincided with the onset of the apoptotic response—probably reflecting an increase in de novo ceramide biosynthesis through the ceramide synthase pathway8 . How these changes in intracellular ceramide intervene in apoptosis is unknown, but the fact that they are synchronized with increases in extracellular signal-regulated kinase and Raf-1 kinase indicates that these three factors may cooperate in mediating cannabinoid-induced glioma cell death. But how likely is it that the discovery of anti-tumor effects of cannabinoids will affect malignant glioma therapy? At present, glioma patients who are subjected to an aggressive, multimodal treatment consisting of surgery, radiation therapy and chemotherapy have a median survival rate of 40–50 weeks9 . This bleak scenario alone should provide sufficient motivation to continue the studies initiated by Galve-Roperh et al. The risk of typical cannabinoid side effects—euphoria, amnesia,rolling flood tables decreased psychomotor performance and hypotension—may be outweighed by therapeutic advantages, and eventually be overcome through the development of selective CB2-selective agonists.CM is a type of behavioral therapy in which individuals are “reinforced,” or rewarded, for evidence of positive behavioral change . CM typically consists of monetary-based rewards or vouchers to reinforce abstinence from the target drug or to encourage retention in pharmacological or psychosocial treatment .

As presented in the Benefit Coverage, Utilization, and Cost Impacts section, with the amount of funding that would be available unknown, CHBRP has purposefully modeled a limited expansion — for only 1,000 beneficiaries — intending to provide two examples that could be scaled larger, depending on the amount of available funds. These two examples, stimulant use disorder and cannabis use disorder, serve as case studies on what the cost and utilization implications would be of Medi-Cal enrollees getting treatment for SUD with and without CM. As presented in the Medical Effectiveness section, evidence varies by SUD regarding the impact of CM. While there is clear and convincing evidence that CM is effective for stimulant use disorder and a preponderance of evidence that CM is effective for cannabis use disorder, these findings are related to outcomes during treatment. For both stimulant use disorder and cannabis use disorder, it is not clear how this may impact results in post treatment abstinence, but there is evidence to suggest that achieving abstinence during treatment is the greatest predictor of long-term recovery. The public health implications of these two simulations are discussed below.This simulation projected that for every 1,000 Medi-Cal enrollees engaged in treatment for stimulant use disorder, there would be 14,400 group counseling appointments and urinalysis tests without CM increasing to 16,800 with treatment including CM. As shown in Table 2, CM would lead to an additional 2,400 group counseling sessions per 1,000 enrollees attended and urinalyses performed. In absence of CM, 40% of the 14,400 urinalyses would be negative for stimulants for a total of 5,760 stimulant-free samples. With the addition of CM, it is expected that 60% of the 16,800 urine samples would be negative for a total of 10,080 negative samples.

Therefore, for every 1,000 Medi-Cal enrollees engaged in treatment for stimulant use disorder using CM, CHBRP would expect to see an increase of 4,320 additional negative urine samples. Therefore, as each negative urine sample represents roughly three days of abstinence, this translates roughly into nearly 13,000 additional stimulant-free days. SUD often involves cycles of relapse and remission, can vary in severity, and often requires ongoing professional treatment, lifestyle changes, and case management . Therefore, although abstinence may not persist post treatment, achieving periods of abstinence is still one goal of treatment, especially considering the best predictor of long-term recovery is abstinence during treatment . In addition, as there is no FDA-approved medication to treat stimulant use disorder, CM to improve treatment engagement and abstinence may be the best treatment option available. Patients addicted to stimulants such as methamphetamine are at higher risk for a range of physical and psychological issues including mental illness, cognitive issues, antisocial behaviors, cardiovascular events, sexually transmitted diseases, and blood-borne infections including HIV and hepatitis B and C, and consequently are at increased risk of death . The rate of amphetamine-related overdose deaths was 5.8/100,000 Californians in 2018 . Methamphetamine has taken over as the leading cause of overdose deaths in California, followed by the rate of all opioid overdose deaths of 5.23/100,000 . In addition, impacts of methamphetamine use are exacerbated by its association with increased violence and crime . Other downstream effects of methamphetamine use include reduced work related productivity and increased family and housing instability. It is possible that the additional 13,000 stimulant-free days among the 1,000 Medi-Cal enrollees in this simulation would lead to reductions in many of these short-term outcomes.The California Opioid Overdose Surveillance Dashboard shows that Blacks had the highest rates of hospitalizations for amphetamine overdose , which were more than double rates of whites , Latinos , Native Americans , and Asians . Yet, Native Americans had the highest amphetamine overdose mortality rates in California in 2018 , followed by Blacks and whites .

Asians had the lowest amphetamine overdose mortality rate at 1.4/100,000 . Disparities by gender existed in the rates of ER visits for amphetamine overdoses and deaths with males being more than twice as likely to have an ER visit for an overdose and more than three times as likely to die from amphetamine overdose .This simulation projected that for every 1,000 Medi-Cal enrollees engaged in treatment for cannabis use disorder in absence of CM, 30% of the 7,200 urinalyses would be negative for cannabis for a total of 2,160 cannabis-free samples. With the addition of CM, it is expected that 45% of the 7,200 urine samples would be negative for a total of 3,240 negative samples. Therefore, for every 1,000 Medi-Cal enrollees engaged in treatment for cannabis use disorder using CM, CHBRP would expect to see an increase of 1,080 additional negative urine samples. Therefore, as each negative urine sample represents roughly seven days of abstinence, this translates roughly into more than 7,500 additional cannabis-free days. The impacts of 7,500 additional cannabis-free days include reductions in risks of psychiatric disorders, impairments in learning and coordination, and lung inflammation/chronic bronchitis, and potential opportunities for improvements in cognitive function and educational and workplace outcomes . There is also a potential for a reduction in ER visits and hospitalizations due to cannabis use disorder.Some interventions in proposed mandates provide immediate measurable impacts , while other interventions may take years to make a measurable impact . When possible, CHBRP estimates the long term effects to the public’s health that would be attributable to the mandate. As presented in the Medical Effectiveness section, there is no research that examines the long term impacts of CM for SUD treatment. For this analysis, CHBRP modeled a 12-week substance use disorder treatment program using contingency management , one for stimulant use disorder and one for cannabis use disorder. It is unclear how many providers would choose to offer CM as part of SUD treatment and how many patients would participate in the long term. In addition,flood and drain tray since there is no research that examines long-term impacts of CM for SUDs treatment on health care utilization, it is not possible to quantify the long-term utilization and cost impacts of SB 110. As with other chronic conditions, effective management of SUDs will require repeated, short-term treatments or longer-term treatment over time. Current practices involve short-term episodic treatments, which have limitations when treating long-term chronic conditions. Of those that achieve long-term recovery, it is estimated that nearly half are able to enter recovery on the first try, 14% have one recurrence, 19% have 2-5 recurrences, and 15% have 6 or more recurrences prior to achieving recovery stability . It is estimated that between 2-5 recovery attempts are made by persons with stimulant use disorder and cannabis use disorder prior to successfully resolving the SUD . Therefore, to the extent that participating in CM treatment programs produce better during treatment abstinence results, this may encourage patients to try to make another recovery attempt in the future, with each attempt making it more likely they will enter long-term recovery. As discussed previously, a key barrier to abstinence for any SUD is patient interest and readiness to abstain. It is possible that the availability of CM will attract more patients to participate in treatment in the first place. In addition, CHBRP anticipates that the demand for treatment of SUDs would continue as relapsed patients attempt abstinence again and first-time initiators would join the pool of patients seeking care. This in turn could contribute to long-term positive public health impacts, as programs become more available and patients become more aware of them over time. However, limited patient readiness for SUD treatment and limited number of providers may remain significant barriers to care. To the extent that SB 110 results in an increase in SUD treatment with CM, and the extent to which this leads to additional quit attempts and long-term abstinence, it is possible SB 110 would contribute to reductions in substance use–related morbidity and mortality.

Sleep disturbance is one of the most prevalent symptoms reported by HIV-infected individuals , with up to 73% reporting significant sleep disturbances . Unlike some other symptoms associated with HIV that typically present during the initial phase of illness , sleep disturbance has been shown to be present over the course of the disease . This is particularly concerning as disturbed sleep has been associated with poorer antiretroviral medication adherence , viral load , greater HIV symptom severity , and higher rates of negative psychological symptoms . While the prevalence and consequences of sleep disturbances among individuals with HIV have been established, relatively little work has investigated malleable factors that may confer greater risk of sleep disturbances for this population. One relevant factor in this area is anxiety sensitivity , a cognitive vulnerability defined as the fear of anxiety, its relevant bodily sensations, and its potential negative social, physical, and mental consequences . AS has unique relations to sleep disturbances and, among individuals with HIV, specifically, has been linked to greater physiological distress, anxiety, and depression symptoms , suicidality , as well as self-reported HIV symptom severity . Unfortunately, there has been little work in terms of understanding whether greater AS might relate to decrements in sleep quality among individuals with HIV. Drawing from the literature more broadly, Vincent and Walker found that, in a sample of adults with chronic insomnia, AS was related to sleep-related impairment, with a trend relation between AS and frequency of medication use, after accounting for general worry and presence of Axis I psychopathology. Babson, Trainor, Bunaciu, and Feldner found that AS interacted with sleep anticipatory anxiety to predict sleep onset latency, after accounting for negative affect, gender, age, cannabis use, nicotine dependence, and alcohol use. In a similar investigation conducted among individuals with panic disorder, Hoge et al. found that after accounting for relevant covariates including age, major depression, and panic disorder severity, individuals with elevated AS reported significantly greater latency to sleep. Taken together, these studies indicated that elevations in AS confer risk for greater sleep disturbances, although these associations appear nuanced in terms of particular aspects of sleep quality, with no research having sought to elucidate these relations for individuals with HIV. Our study sought to explore the incremental association between AS and global sleep quality, as well as to determine differential associations between AS and a variety of facets that comprise global sleep quality, including perceived sleep quality, latency, duration, efficiency, disturbance, medication use, and daytime dysfunction in HIV-infected individuals receiving treatment at community clinics. We sought to explore AS in relation to global sleep impairment and specific components in order to explicate which aspects of sleep interference might be most relevant to AS.

Scoring was based on the total correct responses for congruent and in congruent trials

The 12-item Electronic Cigarette Attitudes Survey was administered to further examine attitudes toward the use of e-cigarettes versus combustible NTP. The Questionnaire of Smoking Urges and the Hooked on Nicotine Checklist were also given to NTP users to acquire information on NTP addiction severity, with total scores calculated. Neurocognition, NIH Toolbox: Participants completed the National Institutes of Health toolbox cognition battery, consisting of seven individual tasks. All tests were completed using the NIH Toolbox app on 3rd generation iPad Air devices . Research subjects were seated upright and used their dominant index finger to make each response. To prevent participants from inadvertently skipping through instructions, a one-second touch-and-hold button was required to advance to the next task. Tasks completed included the Picture Vocabulary Task, where participants identified the picture matching the meaning of a word they were read aloud; oral reading, where participants received a single word as visual stimuli written on screen and were asked to read each aloud; the Dimensional Change Card Sort Test, where participants had to respond to stimuli based on changing rules displayed on top of the screen; Flanker Inhibitory Control and Attention Test, where participants had to select a left or right arrow based on a displayed target stimulus arrow in the midst of a row of arrows; the List Sort Working Memory Test was administered to assess working memory and required participants to sort stimuli,planting racks presented both visually and auditorily, from smallest to largest in size; Picture Sequence Memory Test, a measure of episodic memory, where participants had to recall a sequence of displayed pictures; and Pattern Comparison Processing Speed Test, where participants had to quickly decide if two side-by-side images were the same or different.

Population-adjusted scores, which adjusted for age, gender, race, ethnicity, and education level, were used in the present analyses. For the Rey Auditory Verbal Learning Test, participants were read 15 words over five trials and asked to recall the list after each repetition. A new, second list was read, and then participants were asked to again recall the original list. After 30 min, they were again asked to recall the original list. Raw scores on initial recall, total score over all trials, and long-delay recall were collected. The Game of Dice Task assesses decision-making and risk-taking behaviors. Participants view a virtual single die shaking in a cup, guessing one single number or a combination of two, three, or four numbers . A correct prediction would add the specified monetary amount to their total earnings, while an incorrect guess would result in a loss of the same amount. Performance was measured by a net score, computed by subtracting the number of disadvantageous, high-risk choices from the number of advantageous, low-risk decisions. Participants also completed a variation of the Emotional Stroop Task as a measure of emotional processing and cognitive control. The presentation of an emotional word was overlaid on a picture of a face that is displaying an emotion either congruent or in congruent to the word presented. The stimuli appeared one at a time on screen, and participants worked to sort the words into two categories while ignoring the distractor image in the background.Selection of Covariates: Sociodemographic characteristics were considered for inclusion in all analyses. Inclusion of covariates was determined based on characteristics that differed by group. Models with measures that were corrected for sociodemographics did not include sociodemographic variables as covariates. For primary outcome analyses of mental health and neurocognition, ANCOVAs were run, controlling for the past six months of alcohol and cannabis use and sociodemographic factors, which differed by group . Primary analyses: SPSS 28.0 was used for all primary analyses, and and Rstudio were used for multiple comparison corrections using the “sjstats” package for false-discovery rate corrections.

Differences by group in sociodemographics, substance use attitudes, substance use history are first presented. The selection of covariates was determined by ANOVAs and chi-squares. ANCOVAs assessed differences in mental health and neurocognition by group, controlling for alcohol and cannabis use in the past 6 months, and relevant sociodemographic covariates . Benjamini and Hochberg’s false discovery rate method was used within models to correct for multiple comparisons in ANCOVA models. In models with significant differences by NTP group , Fisher’s post-hoc tests were run to identify specific group differences, and marginal means were reviewed to determine directionality. Given the age range of participants, post-hoc analyses were conducted with only participants 18–22 years old. All models were re-analyzed, with covariates again selected by sociodemographic differences between groups and including past six-month cannabis and alcohol use.Despite the sharp rise in emerging adult NTP use in recent years, little is known about mental health and neurocognitive differences of e-cigarette use relative to traditional combustible NTP use , in concert with attitude differences toward NTP use. Here, we delineate clear and significant differences in NTP users’ substance use habits and attitudes despite minimal mental health or cognitive differences by nicotine group status. There appear to be qualitative differences in motivations , and expectancies of smoking behavior among individuals who have recently used combustible versus noncombustible products. Combustible product users at this young age also reported greater dependency on nicotine and craving for nicotine products as compared to individuals who only use e-cigarettes. Of note, individuals in both NTP groups had more favorable views of e-cigarettes as compared to traditional tobacco products and largely reported using e-cigarettes for the taste. In addition, combustible product users tended to use substances more heavily overall, including more episodic NTP use. Finally, both nicotine use groups reported higher levels of depression and stress symptoms as compared to NTP naïve controls.Interestingly, when considering the moderating influence of gender, male combustible users reported more depression symptoms than the other groups, while females did not differ in depressive symptoms by group status. Though Combustible+ users were significantly older than the other groups, when restricting analyses to only those between 18 and 22 years old, results remained consistent, suggesting that it is not merely that Combustible+ users have had more time to transition into heavier substance use.

While there is a growing body of literature on susceptibility and predictors of NTP use, there is a paucity of knowledge about NTP attitudes in young adults who use combustible and non-combustible NTPs. Harm is often a focus, with e-cigarette users stating the belief that e-cigarettes are less harmful than combustible cigarettes. However, here, we did not find a difference in perceived harm that was moderated by nicotine product use type. Motivations to use e-cigarettes in our sample tended to be more about the consideration of others or alleviating dependence on combustible products, rather than about harm, cost, or other motivating factors. Combustible product users also reported combustible NTP use for its reinforcing properties, affect regulation, social facilitation, and relief of boredom. Results suggest NTP users, and combustible product users in particular, have higher levels of substance use and more severe nicotine dependence. While alcohol and cannabis use were present in each group, E-Cig reported more use than NTP Naïve, while combustible users reported the most use. The Combustible+ group also reported more ecigarette use than the E-Cig group and had higher levels of severity of nicotine dependence across measures. Here, it is not clear that the use of combustible NTPs are driving the attitude and/or behavioral differences, rather than other individual characteristics or preexisting factors which impact overall substance use. This finding is in line with other reports of increased dependence in dual users of combustible and non-combustible NTP and has similarly been found in younger adolescents, in adults, in those with higher levels of tobacco product use, and in populations with greater substance use in general. Therefore, it may be that tobacco cigarette or dual users are at increased risk of substance dependence in general and downstream negative sequelae. NTP users in the present study reported higher levels of BDI depression and DASS stress symptoms,sub irrigation cannabis including a higher prevalence of symptoms crossing the threshold of clinical depression than NTP-Naïve participants. Male Combustible+ had higher levels of depression symptoms than either E-Cig or NTP Naïve, while there was no significant difference by group for females. There was also no difference in their self-reported anxiety symptoms when considering all participants together, regardless of gender. This is a cross sectional analysis in which direction and causality cannot be determined, and therefore it is unclear if NTP use was a risk factor for depression and stress, or vice versa. Indeed, a longitudinal analysis of emerging adults found depression was an important risk factor for nicotine dependence, while another longitudinal study found NTP use was associated with later depression.

Others have found cigarette, but not e-cigarette use, linked to mental health functioning. Given preliminary evidence here of heightened risk for combustible use among males with elevated depression scores, gender and emotional functioning should continue to be monitored and examined as potential risk factors for combustible product use. Future research is necessary to disentangle modes of nicotine administration and mental health outcomes in adolescents and emerging adults. This investigation is the first known study of e-cigarette use that objectively measured neurocognitive performance, and no differences in cognition by nicotine group status were observed. Cognitive differences in adolescent and young adult NTP users have been noted previously, though not always. E-cigarettes specifically have been linked to poorer self-reported neurocognition, though subjective concerns may not relate to true performance deficits. Lower or more acute doses of nicotine are linked to cognitive enhancement, while chronic and/or high doses of nicotine are linked to desensitization of nicotine acetylcholine receptors , including alterations of the modulation of dopamine, serotonin, and other receptors, which potentially impact cognitive performance. While 12 h of abstinence from alcohol, cannabis, and all other drug use were required for the present study, participants were allowed to smoke/vape during their study session to prevent withdrawal effects and, therefore, potentially enhance neurocognitive performance. Our participants were also using NTPs at relatively low levels in the past six months, vaping three-to-four times a day on average and, in combustible users, smoking combustible products five-to-six days per month. The lack of differences may imply that relatively low level NTP use may not be as detrimental as previously thought, suggesting that there is still time for intervention before more of the negative sequelae of sustained nicotine use and dependence become apparent. Interestingly, while not a primary aim of the present study, past six-month cannabis use was related to poorer Emotional Stroop congruent emotion processing , with no difference on the non-congruent condition. Congruent processing accuracy is particularly relevant for processing speed and attention, deficits previously shown in cannabis users and in pre-adolescent youth with higher externalizing symptoms. Further, prior research indicates cannabis users may be particularly vulnerable to cognitive control and affective processing deficits, including when identifying emotional faces. Cannabis users may have to use more neural resources to achieve the same level of performance, though this may not happen as readily on a less demanding task, such as on a congruent processing task. Future research should continue to investigate socio-affective response in cannabis users. Limitations: All groups included alcohol and cannabis use which, while better generalizing to typical real-world use patterns, may limit ability to detect differences due to NTP use alone, despite attempts to statistically control for these substances in the analyses. In addition, both E-Cig and Combustible+ used e-cigarette products, which may also be a source of bias, and makes it difficult to disentangle findings that are unique to e-cig vs. combustible product use. As mentioned above, NTP users in this age range, and particularly combustible NTP users, may not yet be using at a level to meaningfully impact neurocognition or mental health. Alternatively, the lower level of use may have contributed to limited neurocognitive differences in this young adult sample. Though group differences in attitudes are noted, and data are descriptive, it is unclear what individual and environmental characteristics may contribute to the acquisition of these attitudes. Finally, the present analyses are cross sectional in nature; longitudinal studies designed for causal inference are needed to establish directionality of results. The present findings add to the field’s understanding of the unique and shared characteristics between adolescent and young adult combustible and non-combustible NTP users.

2-AG might also be synthesized by the hydrolysis of lysophospholipids or triacylglycerols

Since the discovery of the first cannabinoid receptor 12 years ago1,2, important advances have been made in several areas of cannabinoid pharmacology. Endocannabinoid compounds and their pathways of biosynthesis and inactivation have been identified, and the molecular structures and anatomical distribution of cannabinoid receptors have been investigated in detail. Pharmacological agents that interfere with various aspects of the endocannabinoid system have been developed, and pathophysiological circumstances in which this system might be active have begun to emerge. The manner in which these discoveries might impact our understanding of endocannabinoid signaling and help unlock its potential for developing novel therapeutic agents will be discussed.The two endocannabinoids isolated so far – anandamide and 2-arachidonylglycerol 3–5 – are lipid in nature but differ from amino acid, amine and peptide transmitters in ways other than just their chemical structures. Classical and peptide transmitters are synthesized in the cytosol of neurons and stored in synaptic vesicles, from where they are secreted by exocytosis following excitation of nerve terminals by action potentials. By contrast, anandamide and 2-AG can be produced upon demand by receptor-stimulated cleavage of membrane lipid precursors and released from cells immediately after their production. Anandamide can be produced from the hydrolysis of an N-acylated species of phosphatidylethanolamine N-arachidonyl PE,growers equipment a process catalysed by phospholipase D. The stimulation of neurotransmitter receptors appears to play a determinant role in initiating this reaction, as indicated by the finding that anandamide release in the striatum is strongly enhanced by activation of dopamine D2 receptors.

Once released, anandamide can act on cannabinoid receptors or accumulate back into cells via an energy- and Na1 -independent transport system. The selectivity of this system for anandamide has been documented but its molecular structure remains uncharacterized. Inside cells, anandamide can be catalytically hydrolysed by an amidohydrolase, whose gene has been cloned. The most likely route of 2-AG biosynthesis involves the same enzymatic cascade that catalyses the formation of the second messengers inositol -trisphosphate and 1,2- diacylglycerol . Phospholipase C , acting on phosphatidylinositol -bisphosphate, generates DAG, which is converted to 2-AG by DAG lipase.Regardless of the mechanism involved, 2-AG formation can be triggered by neural activity or by occupation of membrane receptors. Following its release, 2-AG can be taken up by cells via the anandamide transport system and hydrolysed by an unknown monoacylglycerol lipase activity. Thus, anandamide and 2-AG can be released from neuronal and non-neuronal cells when the need arises, utilizing analogous but distinct receptor-dependent pathways. The nonsynaptic release mechanisms and short life spans of anandamide and 2-AG suggest that these compounds might act near their site of synthesis to regulate the effects of primary messengers, such as neurotransmitters and hormones.Drugs that block the formation or inactivation of anandamide and 2-AG should help identify the physiological functions of these compounds and might be beneficial in disease states in which regulation of endocannabinoid levels might produce more selective responses than those elicited by cannabinoid receptor ligands. Although this area of pharmacology is still largely unexplored, inhibitors of the two main steps of anandamide disposition have recently become available.

Anandamide transport is inhibited by the compound AM404 . This drug potentiates various responses elicited by exogenous anandamide and interacts very poorly with cannabinoid CB1 receptors For example, AM404 enhances anandamide-induced hypotension without producing direct vasodilatory effects. Furthermore, when applied alone, AM404 decreases motor activity and elevates the levels of circulating anandamide . However, AM404 can accumulate in cells where it might reach concentrations that are sufficient to inhibit anandamide amidohydrolase. Anandamide amidohydrolase is blocked reversibly by transition state analogs such as arachidonyltrifluoromethylketone , which might act by forming a stable intermediate with a serine residue at the enzyme active site18 . Moreover, irreversible inhibition can be achieved with a variety of compounds including the fatty acid sulfonyl fluoride AM374 . AM374, one of the most potent anandamide amidohydrolase inhibitors identified thus far, potentiates anandamide responses in vitro and in vivo, but its specificity is limited by a relatively high affinity for CB1 receptors.The two cannabinoid receptor sub-types characterized so far, CB1 and CB2, belong to the super family of G-protein-coupled membrane receptors. Their molecular and pharmacological properties have recently been reviewed21. Three issues that might be relevant to the use of cannabinoid agents in medicine will be discussed: the apparently exclusive role of CB1 receptors in mediating central cannabinoid effects; the rapid tolerance that results from repeated cannabinoid administration; and the possible existence of multiple cannabinoid receptors in peripheral tissues. Although CB1 receptors are expressed throughout the body, they are particularly abundant in the CNS where, despite a great deal of effort, no other cannabinoid receptor sub-type has yet been found. This unusual situation – most neurotransmitters act on multiple CNS receptors – accords with data that indicate that a single pharmacological site accounts for all central effects of cannabimimetic drugs, whether therapeutically favorable or harmful . Consequently, although potent CB1 receptor agonists have been available for some time , the therapeutic development of these compounds has been very limited. Given this situation, how might centrally active cannabinoid agents that are more selective than those currently available be developed? One possibility is to target the mechanisms of endocannabinoid inactivation.

Blocking such mechanisms might cause an activity-dependent accumulation of anandamide and 2-AG at their sites of release, which might in turn result in a more localized activation of cannabinoid receptors than that elicited by direct receptor agonists. Another important issue that should be considered in the development of cannabinoid agonists for therapeutic use is receptor desensitization. This process, which might be mediated by the GPCR-kinase–b -arrestin pathway, causes a pharmacological tolerance that limits the prolonged use of cannabinoid receptor agonists. Partial agonists might offer a clue as to how to circumvent this obstacle. Evidence indicates that the CNS contains a large reserve of CB1 receptors; thus partial CB1 receptor agonists, which are expected to cause less receptor desensitization than full agonists, might produce adequate therapeutic responses with diminished tolerance liability. Although CB1 receptors are thought to mediate the effects of cannabinoid receptor agonists in the CNS, several peripheral effects of cannabimimetic drugs might only depend partially on CB1 receptor activation. The high expression of CB2 receptors in B cells and natural killer cells suggests that this sub-type contributes to the potential immuno suppressant and anti-inflammatory effects of cannabinoids. Additional tests of this hypothesis will be facilitated by the recent availability of selective CB2 receptor agonists and antagonists .Furthermore, cannabinoid-like receptors that are distinct from the CB1 and CB2 sub-types might participate in the vasodilatatory and analgesic effects of cannabinoids. Although the hypotensive actions of anandamide are mostly mediated by CB1 receptors, the endothelium-dependent vasorelaxation produced by this compound in mesenteric arteries appears to require a receptor that is pharmacologically distinct from CB1 and CB2 . Furthermore, the peripheral analgesic effects exerted by the endogenous anandamide analog palmitylethanolamide might also involve a novel cannabinoidlike receptor . In addition to acting on cannabinoid receptors, anandamide has been suggested to act on a variety of other targets, including capsaicin receptors. The concentrations required to attain these effects are, however, too high to be considered physiologically relevant and claims that vascular effects of anandamide might be mediated by vanilloid receptors appear unwarranted.The endocannabinoid system might serve important regulatory functions in physiological processes; thus, cannabinoid agents might prove useful in the treatment of pathological conditions that are associated with such processes. Exhaustive evaluations of the medicinal potential of cannabis and its derivatives in other therapeutic areas can be found elsewhere.Cannabinoid drugs strongly reduce pain responses by interacting with CB1 receptors in brain, spinal cord and peripheral sensory neurons . Brain sites that participate in cannabinoid-induced analgesia include the amygdala, thalamus, superior colliculus, periaqueductal gray and rostral ventromedial medulla. In the spinal cord, CB1 receptors are found in the dorsal horn and lamina X ,plant benches where they are located on intrinsic spinal neurons, nerve terminals of afferent sensory neurons and terminals of efferent supraspinal neurons. CB1 receptors are also expressed in the dorsal root ganglia by a subset of small- and large-diameter sensory neurons that contain the pain-stimulating peptides, substance P and a-calcitonin gene-related peptide. Although quantitatively small, the presence of CB1 receptors on CGRP containing neurons appears to be functionally significant because CB1 receptor agonists effectively reduce CGRP release from dorsal horn tissue. Immunohisto chemical experiments suggest that CB1 receptors are present not only on central terminals of primary sensory afferents, but also on their peripheral counterparts. In agreement with these findings, local applications of CB1 receptor agonists to skin reduce the responses to formalin and other irritants. The clinical impact of these advances is still modest but worth noting. Since a previous literature review, new studies have documented the analgesic effects of CB1 receptor agonists in humans , providing additional impetus for a re-evaluation of the endocannabinoid system as a target for analgesic drugs.Cannabinoids are potent in alleviating two hallmarks of neuropathic pain: allodynia and hyperalgesia. Indeed, in a rat model of neuropathic pain , the CB1 receptor agonist WIN552122 attenuates such responses at doses that do not cause overt side-effects. In this model, the CB1 receptor antagonist SR141716A reverses the analgesic response to WIN552122 and exacerbates pain behaviors when administered alone. One possible explanation for the pain-inducing effects of SR141716A is that nerve injury might be associated with an increase in endocannabinoid levels and/or a sensitization of CB1 receptors. Plastic modifications in endocannabinoid signaling during persistent pain can also be inferred from experiments conducted in a rat model of inflammation. In this model, SR141716A enhances the sensitivity to mechanical stimuli applied to the paw contralateral to the inflammatory focus, which suggests that inflammation can be accompanied by an increased cannabinergic activity that can be unmasked by the CB1 receptor antagonist. Furthermore, the peripheral administration of formalin stimulates anandamide release in the periaqueductal gray, a brain region involved in pain control. Whether CB1 receptor function and/or endocannabinoid levels are changed in neuropathic pain is unknown. If this syndrome is accompanied by a hypersensitivity of CB1 receptors in injured tissues, partial CB1 receptor agonists could alleviate pain at doses that might exert few undesirable effects and produce little tolerance. By contrast, if neuropathic pain is associated with elevated endocannabinoid release, drugs that interfere with the inactivation of these substances might offer an alternative to direct CB1 receptor agonists.

Elucidating the alterations in endocannabinoid function associated with neuropathic pain should be instrumental to define the value of these strategies.The finding that cannabinoid receptor agonists can alleviate pain by acting at peripheral CB1 receptors has both theoretical and practical ramifications. Theoretically, this observation emphasizes the notion that nociceptive signals can be modulated at the first stage of neural processing by a peripheral ‘gate’ mechanism in which endogenous cannabinoid lipids can act in concert with opioid peptides. Practically, it points to the possibility of achieving an effective control of peripheral pain without causing the psychotropic effects that follow the recruitment of brain CB1 receptors. The antinociceptive effects of palmitylethanolamide add a new dimension to this hypothesis. Palmitylethanolamide is produced in tissues through an enzymatic route similar to that of anandamide synthesis6. When administered as a drug, palmitylethanolamide potently reduces peripheral pain through a mechanism that is synergistic with anandamide and is blocked by the CB2 receptor antagonist SR144528 . However, palmitylethanolamide does not interact with the CB2 receptor , which suggests that the compound might produce its analgesic effects by activating an as-yet uncharacterized CB2-like receptor.CB1 receptors are densely expressed in the basal ganglia and cortex, CNS regions that are critical for movement control21. This distribution provides an anatomical substrate for functional interactions between the endocannabinoid system and ascending dopaminergic pathways. Several observations suggest that these interactions might indeed occur. First, in the striatum of freely moving rats anandamide release is stimulated by activation of dopamine D2 receptors. Second, the CB1 receptor antagonist SR141716A, which has little effect on motor activity when administered alone, potentiates the motor hyperactivity produced by the D2 receptor agonist quinpirole. Third, D2 and CB1 receptor agonists produce opposing behavioral responses after injection into the basal ganglia. These and other findings suggest that anandamide might modulate dopamine-induced facilitation of psychomotor activity. In further support of this hypothesis, disruption of the gene encoding the CB1 receptor profoundly affects motor control, decreasing locomotor activity.

Multilevel regression models were used to examine change over time for SSIS

Participants’ responses to the ASSIST during the SBIRT intervention procedure were not used to determine this outcome.Baseline demographic characteristics and SSIS were summarized with descriptive statistics.Major advantages of multilevel regression over traditional repeated measures analysis include the fact that cases are not dropped due to missing observations on the dependent variable at any assessment, numeric as well as categorical predictors can be used, and methods for non-normal outcomes are available. Estimation was carried out in Stata/SE Release 14.1 using maximum likelihood and the Expectation–Maximization algorithm. Models were estimated to examine unconditional change over time for unit-increases over the assessment months; differences in the change trajectories as a function of baseline risk of use for each substance, and differences in the change trajectories due to the intervention by initial risk interaction. Due to strong right-skewness in many of the substance use scores , estimation for the multilevel regression models was carried out with a non-parametric bootstrap with 5000 repetitions to obtain bias-corrected confidence intervals, unaffected by either extreme values or skewness. Te study sample size for the overall study was calculated based on the results of a prior clinic waiting room survey that measured current substance use in 33% of clinic patients . Te sample size estimates were large enough to detect a 15% difference in alcohol use between the Computer and Clinician groups,dry racks with 80% power and 95% confidence.A total of 225 people living with HIV were assessed for eligibility to participate in the study . Of these, seven were excluded because they did not meet eligibility criteria, and 10 were excluded because they did not complete the baseline survey.

Te remaining 208 individuals were enrolled in the study and randomized. These 208 participants were primarily male and largely African American , with a mean age of 45.4 years . Te majority had a high school education or less , were unemployed , and reported substance use in the past 3 months . Te mean time since HIV diagnosis was 12.4 years. Mean Specific Substance Involvement Scores were in the moderate risk range for all substances except inhalants, hallucinogens, and other substances. SSIS were highest for tobacco, alcohol, cannabis, cocaine, and amphetamine. Of the 208 individuals enrolled in the study, 134 individuals completed the baseline assessment visit and also received an SBIRT intervention. Of the 134, follow-up assessment rates were: 123 at 1-month, 106 at 3-month and 109 at 6-month; 92 completed all four study assessments and the intervention. Ninety-five participants with high SSIS accepted referrals to the clinic social worker, but only four met with the social worker. There were no significant baseline differences in socio-demographic characteristics or mean SSIS between those who received the intervention and those who did not . Similarly, we found no differences between SBIRT treatment modality in our outcome measures of interest over time . For all substances, mean SSIS increased over time among those initially in the lower risk groups. Te increase was statistically significant for all substances except amphetamines and sedatives . However, among those individuals with moderate–high risk at baseline, mean SSIS for all substances decreased at 6 months. Te decrease was statistically significant for all substances except tobacco and cannabis. For all substances, the decrease in mean SSIS for the moderate– high risk group differed significantly from the increase in mean SSIS for the lower risk group.We conducted a screening, brief intervention, and referral to treatment intervention in an urban safety-net HIV primary care clinic and detected a high prevalence of self-reported alcohol, tobacco, cannabis, cocaine, amphetamine, sedatives, and opioid use at enrollment. For all substances, the mean SSIS score for participants whose baseline substance use risk was moderate–high and who received the SBIRT intervention declined over the 6 months following the intervention, and this decrease was significant when compared to those at baseline lower risk.

While active substance use was not one of the inclusion criteria, 92% of study participants reported any substanceuse in the prior 3 months. This finding is consistent with the known higher prevalence of substance use for PLHIV compared to the general U.S. national population. Our results also show that, in an HIV primary care population, while mean SSISs were in the moderate range for most substances, a number of individuals were in the high risk range, as indicated by the large standard deviations for each substance. These indicators of the severity of self-reported substance use, underscore the opportunity for detection and intervention in HIV primary care settings. We measured a significant reduction over time in the mean SSIS for alcohol − 1.59 among participants who scored in the medium high risk categories. Several other studies that measured self-report of substance use before and following SBIRT implementation in clinical settings have been conducted and allow for a comparison with the findings of our analysis. One of the first studies to determine the effect of SBIRT in diverse clinic populations found SBI to be associated with a decrease in self-reported alcohol use at follow-up. Other studies evaluating measures of alcohol use severity before and after participating in SBIRT show similar results. A more recent study among PLHIV, however, found that although alcohol use declined over time, the decline was not associated with receipt of a brief intervention. We also measured moderate but statistically significant decreases in mean SSISs for illicit drugs, including reductions in cocaine − 0.82 , amphetamines − 0.69 , sedatives − 1.58 and opioids − 1.31 . Other studies have shown mixed results of the impact of SBIRT on illicit drug use following participation in SBIRT. Te ASPIRE study , a 3-group randomized controlled trial for unhealthy drug use among adults from an urban primary care setting, did not demonstrate a decrease in unhealthy drug use following receipt of a primary care based SBIRT intervention. Other studies have shown similar negative results of the effects of SBIRT on illicit substance use. In contrast, Humeniuk and colleagues found significantly reduced SSISs among participants receiving a brief intervention compared to control participants, for all substances except opioids. And Bernstein and colleagues found reductions in cocaine and heroin use among individuals receiving SBIRT. In our study, we saw a reduction in mean SSIS for tobacco use among participants at moderate–high risk at baseline.

Cropsey and colleagues also found that PLHIV who smoked at least five cigarettes per day significantly reduced their smoking over time following an SBIRT intervention that included a counseling session, nicotine replacement therapy, and follow-up visits, compared to those in usual care. In a pilot study of 30 women living with HIV, those who received a motivational interviewing session reported significant reductions in the mean number of cigarettes smoked, compared to those who did not receive the MI intervention. Surprisingly, we found that mean SSIS scores for participants who scored in the lower risk range at baseline increased over the 6 months for all substances at the same time that use dropped for those in the moderate–high risk group . It is possible that the Brief Intervention that was given to those in the moderate–high risk groups had an important effect on reducing SSIS scores, but that the minimal intervention given to those in the lower risk group was not fully effective at keeping risk levels low. This was particularly the case for tobacco and cannabis; for both of these substances, mean SSISs in the lower risk group increased more than the scores decreased in the moderate–high risk group . In some studies, ASSIST scores were reported as lower,drying racks moderate or high risk. Reporting and analyzing SSIS by risk categories is important because individuals who fall into the lower and moderate risk group may derive different benefits, and because brief interventions have previously been shown to be more effective among people with less severe substance use problems. In contrast to other studies, our outcome observation is based on a mean change score and on dichotomized risk groups, which may or may not be clinically useful distinctions. This area needs further study and exploring more effective interventions for people at lower risk for substance use related problems is an important area for future research.While the levels of substance use self-reported among this cohort of PLHIV patients is higher than in the general U.S. national population, it can be difficult to make comparisons between studies because of the variability of substance use measures. For example, in a study with a safety-net primary care population, participants received an intervention based on reports of problem drug use via the Addiction Severity Index-Lite measure.

Because of differences between the Addiction Severity Index and the ASSIST, similar participants in each study may have been assessed at different levels of risk, and therefore may have differed in whether they qualified to receive an SBIRT intervention or not. Such differences make comparisons difficult. Our study has several limitations. First, the data reported here were all self-reported, which could be biased due to participant recall or social desirability. Second, while these findings suggest that SBIRT delivery in HIV care settings may be associated with a decrease in the mean SSIS scores for moderate–high risk substance use, we do not have a good understanding of the clinical significance of these changes in mean scores. A decrease in the SSIS for any substance is a change in the right direction when our goal is to address substance use in HIV clinical settings. However, in the absence of a non-treatment control group it is possible that the decrease in SSIS scores across both arms of the study could be due to regression to the mean and not the intervention. Third, for this analysis, which examined only those participants who received the intervention, our analytic sample may have been under powered, despite the fact that we enrolled a sufficient number of participants into the study based on our a priori sample size calculations for a randomized trial. Notwithstanding the smaller analytic sample size, we did detect a statistically significant decrease on the moderate–high risk mean SSIS of those who received the SBIRT intervention when compared to those with lower risk scores. Fourth, while the ASSIST measure includes many drugs and does not solely capture the level of use for the substance of concern to the participant, the BI that was delivered by each modality was based on the substance of most concern to the patient. Nonetheless, our use of the ASSIST allowed us to gain a more expansive understanding of the number and types of substances used by this HIV primary care sample and this may be one benefit for using the ASSIST. Use of the ASSIST in clinical settings could have the advantage of giving providers screening and assessment information for multiple substances. Fifth, as part of our study procedures we did not adequately document the number of brief intervention visits either with a clinician or through the computer portal so we were unable to capture meaningful information about the dose of the exposure to the intervention to allow for dose–response analyses. Sixth, our findings may not be generalizable to PLHIV who are not engaged in primary care or to patients of HIV clinics that do not serve an urban safety-net population. Further, while we collected baseline substance use and follow up data on 208 participants, and all participants were assigned to one of two SBIRT modalities, we observed a significant drop of between assessment and participation in the intervention; only 64% of participants who completed the baseline study visit actually received the intervention by either modality, leading to our decision to present an “as treated” analysis. This was particularly the case for those assigned to the computer group, which may indicate difficulties or discomfort with accessing computers and the Internet, or concern about the privacy of data entered into a computer linked to the Internet . In addition, very few of those referred to treatment actually met with the social worker as indicated, possibly indicating that they were not ready to take the initiative to seek out treatment for themselves, and that a more immediate and supported linkage might be needed.

This appears likely due to the volatility of the aerosol from the cannabis vaping pen

Side stream smoke, which is emitted directly from the source between puffs and not exhaled by the smoker, has been shown to produce greater emissions than mainstream smoke . Based on observations of the participant, the inhalation time of the puff was about 2 s, and the exhalation time was ~2–4 s, making the total puff time about 6 s. We applied the same 3-puff protocol to the Absolute Xtracts vaping pen, which carried out an internal 15-s pre-heat mode prior to the start of puffing and produced little side stream emissions between puffs. An important advantage of the 3-puff protocol in our experiments is that it avoided the extremely high PM2.5 concentrations expected to occur in the 43 m3 room if a marijuana joint had been smoked completely in the room, thus allowing the participant to avoid exposure to unacceptably high concentrations. Based on our interviews with experienced cannabis smokers and information available on the Internet, we concluded that marijuana smoking often differed from tobacco cigarette smoking in several respects. Smoking a marijuana joint often takes place in a group setting, where more than one person smokes, following the rule, “take two puffs and pass it to the left.” We also learned that a marijuana smoker, when smoking alone, often takes just 2 or 3 puffs, then puts the joint out so it can be smoked later in the day. The 3-puff protocol in Fig. 1 includes nearly a minute between puffs for the burning joint to emit side stream smoke, thus producing both mainstream and side stream smoke in a realistic manner. This protocol also has a mathematical advantage for calculating emission source strengths,cannabis vertical farming since the 3-min emission time is much shorter than the residence time of the room, which averaged 115 min for the 60 pre-rolled joints, bongs, glass pipes, vaping pens, and cigarettes.

An objective of this study was to compare emission rates from different sources smoked in the same manner by a human participant. Although a smoking machine may reduce experimental variability, we focused on determining whether the differences between the mean emission rate of the prerolled marijuana joints and the mean emission rates of the other sources, including the tobacco cigarettes, were statistically significant. The statistical methods used in this study are designed to test whether there is a statistically significant difference between the means of two unpaired groups. The unpaired t-test is a parametric test based on estimates of the mean and standard deviation of normally distributed populations from which the samples were drawn. It tests whether the difference between two groups is greater than that caused by random sampling variation. The p value is the probability of being wrong in concluding that there is a true difference between the two groups. The smaller the p value, the greater the probability that the samples are drawn from different populations. We chose the probability p < 0.05 as our criterion for statistical significance. The statistical analyses were performed using Sigma Plot 11 , which employs the Kolmogorov-Smirnov test for a normally distributed population. This program also tests for equal variances. If these conditions are met, it performs the unpaired t-test. If either of these conditions is not met, it informs the user that the data are unsuitable for the unpaired t-test, and it recommends using the non-parametric Mann Whitney Rank Sum Test instead, which performs comparisons based on the ranks of the observations. Table 1 provides summary statistics for the 60 experiments in this study with the various cannabis and tobacco sources, based on the 3-puff protocol. The background concentrations were subtracted from the measured PM2.5 concentrations in Table 1, and the last two columns show the background PM2.5 concentrations were much smaller than the background-corrected maximum PM2.5 concentrations measured in the room for all five sources.

The background-corrected ymax concentrations of PM2.5 observed in the 24 experiments with pre-rolled joints had a mean of 540 μg/m3 and ranged from 143 to 809 μg/m3 . By comparison, the PM2.5 ymax concentrations in the 9 Marlboro tobacco cigarette experiments had a mean of 154 μg/m3 and ranged from 22 to 209 μg/m3 . Each marijuana source produced a larger mean maximum concentration ymax than the tobacco cigarettes. Table 2 shows the summary statistics for the 60 experiments with five different sources. The 24 joints had a mean PM2.5 emission rate of 7.8 mg/min, which was greater than all the other cannabis emission rates and was 3.5 times the mean PM2.5 emission rate of the Marlboro cigarettes of 2.2 mg/min. The mean emission rates of the bong and the glass pipe were 67% and 54% of the joint’s mean emission rate, respectively, and the mean emission rate of the vaping pen was 44% that of the mean emission rate of the joints. The box plots shown in Fig. 3 illustrate the frequency distributions of the PM2.5 emission rates, allowing them to be compared graphically. Only the pre-rolled cannabis joints had enough observations to show the 5th and 95th percentiles of the emission rates , while all the box plots showed the 10th and 90th percentiles . The box boundaries themselves represent the 25th and 75th percentiles, and the bong had the largest spread between these two percentiles. This result was consistent with Table 2, which shows the bong also had the greatest coefficient of variation of 0.71 for the five sources. The mean emission rate in Fig. 3 was higher for the joint than for the cigarette, which also is evident in the emission rate column of Table 2. The median in Fig. 3 showed a pattern similar to that of the mean. Table 3 shows the results of applying standard statistical tests to 10 comparisons of the different methods of smoking marijuana, vaping marijuana, and smoking tobacco cigarettes. In five of the comparisons, the t-test met the requirement that the data were normally distributed but did not meet the requirement of equal variances. In these five cases, Sigma-Plot substituted the non-parametric Mann-Whitney Rank Sum Test for the t-test. With both tests, the criterion for statistical significance was the probability p < 0.05.

The difference between the mean emission rate of the joint and the mean emission rate of the bong was statistically significant , and the differences between the mean emission rate of the joint and the mean emission rates of the glass pipe, vaping pen, and cigarette were highly statistically significant . The probabilities listed above the box plots in Fig. 3 show the statistical significance of the differences between the groups. Although there were n = 24 experiments with joints, there were only n = 9 experiments each with bongs, glass pipes, vaping pens, and Marlboro cigarettes. Comparisons of the bong vs. the glass pipe, the bong vs. vaping, the bong vs. the cigarette, the glass pipe vs. vaping, and the cigarette vs. vaping did not show a statistically significant difference in mean emission rates, which is partly due to the small sample sizes. An exception was the mean emission rate of the glass pipe compared to the mean emission rate of the cigarette, which was statistically significant . In general, groups that did not include the joint were less likely to show a statistically significant difference when compared to groups that included the joint with its high emission rate and larger sample size. The difference between the mean emission rate of the marijuana joints and the mean emission rate of the tobacco cigarettes was highly statistically significant . The largest mean decay rate in Table 2 of 0.690 h− 1 occurred with the grow cannabis in containers vaping pen, while the other four mean decay rates were fairly close together, averaging 0.509 h− 1 . When we compared the differences between the five mean decay rates, we found that only one difference was statistically significant: comparison of the mean decay rate of the 24 marijuana joints with the mean decay rate of the 9 vaping pen experiments. Based on the Mann-Whitney Rank Sum Test, the difference between the mean decay rate of the marijuana joints and the mean decay rate of the vaping pens was highly statistically significant .The measured decay rate φ for the SidePak monitor is the sum of the air exchange rate a and the deposition rate k, as well as the other possible particle losses or gains due to evaporation, condensation, and coagulation. That is, the decay rate φ = a + k + other. If we subtract the observed air exchange rate from the observed decay rate, we are left with a term called the “removal rate” due to aerosol dynamics, which is the sum of the deposition rate k and all the other gain or loss mechanisms, excluding the effect of air exchange. For the 24 cannabis joints, the mean removal rate was 0.085 h− 1 . For the bong, the glass pipe, and the cigarette, the mean removal rates were 0.111 h− 1 , 0.096 h− 1 , and 0.103 h− 1 , respectively. The average removal rate of the four marijuana combustion sources was 0.10 h− 1 , which was smaller than deposition rates listed by Thatcher et al. for a furnished room with a small fan or no fan. In contrast, the mean removal rate of the 9 vaping pen experiments was 0.321 h− 1 , which was the largest removal rate of the five sources and was 3.2 times the average removal rate of the four combustion sources . It is likely that this larger removal rate of the vaping pen was due to volatility of the vaping aerosol and its greater evaporative losses. Evaporation of particles from cannabis vaping is not expected to be as great as evaporation from e-cigarette vaping .

We believe this is an important topic for future research. Since each new marijuana joint included a factory label showing the joint’s percent THC content, we also compared the THC listed for each joint with our measurements of the joint’s PM2.5 source strength. Applying the t-test, we found the relationship between the THC percentage and the source strength was statistically significant . However, this result may occur mainly because the larger joints in our study happen to have higher THC percentages, and their larger size may cause their greater source strength. A more detailed study that controls for the size of the joint would be useful. Our measurements of ultra fine particles > 10 nm used a pair of TSI 3007 condensation particle counters that were collocated with the other monitors in the room during these experiments. The UFP results are summarized in Table S2. Of the five sources, the pre-rolled marijuana joints had the greatest average UFP source strength , while the Marlboro cigarettes had an almost equal UFP source strength . The mean UFP source strengths of the three other methods of consuming marijuana were 1.3 x 1012 particles for bongs, 6.4 x. 1011 particles for glass pipes, and 3.3 x 1011 particles for the vaping pens. Overall, the UFP source strengths of bongs, glass pipes, and vaping pens were smaller than the UFP source strengths of either the pre-rolled marijuana joints or the Marlboro cigarettes. McClure et al. studied 20 heavy users of marijuana, reporting that heavy users smoked an average of 11–12 marijuana cigarettes per day, averaging 13–14 puffs per joint. Since our study compared the PM2.5 emission rates based on 3.0 min of smoking or vaping, we also attempted to estimate the emissions produced by a fully-smoked marijuana joint. We used a precision laboratory scale to measure the weights of the 24 marijuana joints before they were smoked, which ranged from 0.56 to 1.35 g with a mean of 1.024 g . By comparison, the pre-smoking weights of the 9 Marlboro cigarettes ranged from 0.83 to 0.89 g with a mean of 0.863 g . We found that measuring the difference in the weight of a joint before and after it was smoked was challenging, because the water used to put out the joint affected its tightly rolled cannabis leaves, causing the post-smoking weight sometimes to be larger than the original weight. In addition, it was difficult to account for the smoking ashes lost in the water. Therefore, we concluded that comparing the weights before and after smoking a joint would need to use a different method of putting out the joint.

Prevalence findings from a single location may not generalize to other regions

The levels of substance use in our results, similar to those found in a primary care sample with depression and an emergency services sample , indicate that a substantial number of patients were at risk. The results suggest that providers in psychiatric settings should conduct screening and offer treatment as needed. For example, brief motivational interventions could effectively supplement other psychiatric services and prevent escalation of alcohol and drug problems. The recent Screening, Brief Intervention, Referral, and Treatment initiative launched by the Substance Abuse and Mental Health Services Administration is actively promoting early intervention with non-dependent patients in primary care, mental health care, and other settings; online resources and multiple training opportunities are available for providers. Effective January 2008, codes approved by the U.S. Centers for Medicare and Medicaid Services allow reimbursement for screening and brief intervention. These policy initiatives recognize that early alcohol and drug use identification and treatment are important medical services that can improve multiple health outcomes. As a mechanism to facilitate identification, we found that the computerized system was acceptable to most patients, consistent with a prior study of computerized depression assessment . Alcohol and drug use questions also were acceptable , although we note that patients with serious alcohol and drug problems had previously been screened out. One limitation was that older adults appeared less willing or able to use the computer,vertical grow system but this may have been due in part to disabilities of patients seeking geropsychiatric services or to assumptions of reception staff regarding disabilities. Service issues for further investigation include staff training, optimal procedures to ensure confidentiality , software options for alcohol and drug screening, and computer-based interventions .

Potential benefits of computerized systems include the ability to collect more detailed information than is easily obtained by paper forms, greater validity for sensitive questions, and more time-efficient assessment. The records obtained from patients are useful for treatment planning at a program level and as a resource for clinical and services research. These findings need to be interpreted with the limitations of the study. Because patients completing the electronic intake were younger on average than those who did not complete it, our substance use findings are less representative of older adult patients.Our results may not generalize to psychiatry clinics that do not prescreen patients with serious alcohol and drug problems before intake. It would have been preferable to use a lower heavy-drinking cutoff for women than men because women are more sensitive to alcohol. Our use of the higher cutoff indicates that our findings regarding heavy drinking by women may be conservative. Although computerized measures are considered valid, under reporting of alcohol and drug use by patients would also make our prevalence rates conservative. Thus substance use in the sample may be even greater than our results indicate.An estimated 15.3 million adults in the United States met criteria for an alcohol use disorder in the past 12 months. Of those with alcohol use disorders, 2.3 million adults also met criteria for a drug use disorder with odds ratios estimated to be 7.4 for any drug use disorder, but 3.4 to 19.2 for specific drug use disorders . Both alcohol and drug use disorders are heritable, with approximately 50% of the variance attributable to heritable factors , although this estimate varies dramatically by substance , age and other characteristics, including co-morbid psychopathology . This heritable variation can be parsed into those genetic influences that are specific to each drug and importantly, those genetic factors that confer a general predisposition to alcohol and/or substance use disorders, and even other dis-inhibited behaviors . Two large twin studies have convincingly shown that a preponderance of the genetic factors influencing illicit drug use disorders overlap . Noticeably, when these models were extended to include alcohol use disorders, there was evidence for highly correlated genetic factors that individually influenced the covariation in alcohol and nicotine dependence as well as cannabis and cocaine dependence . The extent of genetic overlap was strong for some substances—for instance, 55% and 24% of the genetic variance in alcohol dependence was due to the licit and illicit drug factors, respectively, with the remainder being substance specific. In contrast, for nicotine dependence, 63% of the genetic variance was drug specific . Similar to the individual heritability of each substance, there is growing evidence that the heritable covariation across substances changes across development .

Irrespective of development and substance-specific variation, there is broad consensus that gene discovery efforts targeting aggregate genetic variation that indexes a shared liability to a variety of substance use disorders, as well as dis-inhibition, can be c , with one study showing evidence for genome wide pleiotropic effects across substance use disorders . There are multiple approaches, both phenotypic and genetic, to capture the commonality underlying alcohol and substance use disorders and the present study utilizes two straightforward phenotypic approaches. We opted for simple dependence-based phenotypic traits as they lend themselves to replication and future meta-analysis. First, we utilized a binary phenotype, with affection status defined as meeting dependence criteria for at least one substance , termed ANYDEP. Second, we used factor analysis to combine dependence criteria across substances into a continuous quantitative trait representing vulnerability to multiple substance dependence, termed QUANTDEP. This quantitative measure is heritable and has previously been used in genomic studies , the most recent of which utilized a similar expanded factorial measure of behavioral dis-inhibition to conduct genome-wide association and rare nonsynonymous variant analyses . These studies did not identify any single common or rare variant at a genome-wide significant level; however, the authors reported that 84% of the heritability in illicit drug use was explained by both common and rare variants. While the work of McGue and colleagues included multiple measures of nicotine use and dependence, we elected to exclude nicotine from these measures of general liability based on the work by Kendler and colleagues , which showed significant drug-specific genetic influences on nicotine dependence. In this study, we utilized data from 2322 subjects from 118 families of European-American descent ascertained for alcohol dependence liability to conduct genome-wide association analysis of a binary and a continuous index of general substance dependence liability. While some prior genome-wide efforts have utilized similar phenotypes in population samples of related individuals, the ascertainment strategy and extended family-based design in our study should increase our ability to detect genetic variation in this phenotype. First, there is substantial evidence that alcohol use disorders that co-aggregate with other substance use disorders may represent a more heritable form of addiction . Secondly, by modeling the strength of the phenotypic correlation across different degrees of genetic relatedness , we utilize data on all related individuals, even those not meeting criteria for diagnoses, allowing us to better explore the extent of co-aggregation of genetic risk across alcohol, cannabis, cocaine and opioid dependence.

Six sites participating in the Collaborative Study on the Genetics of Alcoholism recruited alcohol-dependent probands from in-patient and outpatient facilities. The probands and their family members were administered a poly-diagnostic interview, the Semi-Structured Assessment for the Genetics of Alcoholism . Individuals 17 years of age or younger were administered an adolescent version of the SSAGA. Institutional review boards at all sites approved the study. A subset of the most genetically informative families was selected for a family-based GWAS. This sample has been described in detail elsewhere but salient characteristics are presented here. Families were prioritized based on the number of family members with: available DNA who were also alcohol dependent; available DNA who also had electrophysiology data; and available DNA, regardless of other phenotypes. To reduce heterogeneity,only families consisting primarily of self-reported European-American ethnicity were included in the sample. The final sample was comprised of 118 large European-American families consisting of 2322 individuals with available DNA.Phenotype data for four substances were obtained from the SSAGA. Some individuals were assessed more than once,cannabis grow equipment in which case data from the SSAGA interview at which an individual reported the maximum number of DSM-IV criteria endorsements for the particular substance was used. Two phenotypes were used in the genetic analyses: ANYDEP, a binary aggregate substance dependence phenotype, and QUANTDEP, a quantitative substance dependence phenotype developed using factor analysis. For ANYDEP, individuals were considered affected if they met DSM-IV lifetime dependence criteria for any of the four substances, and unaffected if they did not meet DSM-IV dependence criteria for all four drugs. Individuals younger than 23 years old at their most recent interview who did not meet criteria for dependence on any of the four drugs were recoded to missing/unknown because they had not passed through the primary age of risk. Selection of this age cutoff was based on the median age of onset of alcohol, cannabis, cocaine and opioid/ heroin dependence in the White sub-sample of the US population-based National Epidemiologic Survey of Alcohol and Related Conditions . The median ages ranged from 18 to 22 years, supporting a cut-off of 23 years. In addition, those individuals with insufficient SSAGA to determine whether they were or were not dependent were also coded as unknown . QUANTDEP, the quantitative factor score, was constructed by conducting a confirmatory factor analysis of the seven DSM-IV lifetime dependence criteria for each of the four substances . As we were interested in those genetic underpinnings that were common to all dependence criteria across the four substances, we elected to use a single factor confirmatory model and did not conduct exploratory analyses, in addition to limiting the factor analysis to the dependence criteria to exclude abuse. All individuals with DSM-IV criteria data were utilized, regardless of age or substance use. The factor score from the resulting confirmatory analyses was utilized as the quantitative phenotype. Heritability was estimated for the two phenotypes using the polygenic option in SOLAR .

The correlation between the total number of DSM-IV criteria endorsed and QUANTDEP was estimated using the Pearson correlation coefficient. ANOVA was used to test if QUANTDEP differed according to the number of substance dependence diagnoses met. We also tested if the average QUANTDEP value differed across alcohol, cannabis, cocaine and opioid dependence diagnoses. Post hoc pairwise comparisons employed a Tukey correction for multiple testing.Genotyping for 2105 subjects in these 118 families was performed at the Genome Technology Access Center at Washington University School of Medicine in St. Louis using the Illumina Human OmniExpress array 12.VI . In addition, genotypes previously generated on the Illumina Human 1M-Duo BeadChip by the Center for Inherited Disease Research were included for 224 subjects from these families . Further details describing data cleaning can be found in Wetherill et al. . The final analytic sample included 2322 genotyped individuals. This yielded an average of 19.6 genotyped members per family. The Genome-Wide Association Analysis with Family Data package was utilized to analyze ANYDEP, implemented as a logistic regression model. Relatedness between family members was accounted for via generalized estimating equations. QUANTDEP was analyzed using a linear mixed effects model as implemented in the kinship library in R . This model in the kinship function allows for the covariance matrix to be completely specified for the random effects. The result is that each family has a different covariance pattern based on the kinship coefficients, to model the familial genetic random effects. Gender and birth cohort defined by year of birth were included as covariates in all analyses described above, including statistical models of association, to account for secular trends . As needed, genomic control was applied to correct for inflation. To reduce the scope of multiple testing, only genotyped single-nucleotide polymorphism were included in the initial analyses. After correcting for the final number of autosomal SNPs , the genome-wide significance threshold was P = 8.45 × 10−8 . In regions with significant association results, we analyzed imputed SNPs to further evaluate the evidence for association. SNPs were imputed to 1000 genomes using BEAGLE 3.3.1 as described in Wang et al. . Secondary analyses were performed for significant SNPs to test whether the observed genetic association could be attributed to dependence on a specific substance.