Cannabis Grow

We also control for the effects of how ego’s use of one substance was influenced by alters’ use of two other substances. In the network equation, we include endogenous network effects and homophily selection effects for each substance use behavior as well as additional covariates such as race , gender, grade, and parental education as the results from score-type tests suggest to do so. 501 students in Sunshine High and 166 students in Jefferson High were 12th-graders at t1 and t2and graduated at t3 . These 667 students were constructed as structural zeroes in the networks during the last wave. Due to a survey implementation error in Add Health, some adolescents could only nominate one female and one male friend at t2and t3 . We account for this with a limited nomination variable in the network equation. A Method of Moments estimation is used to estimate the behavior and network parameters in each model so that the target statistics in behaviors and networks can be most accurately calculated. We assess satisfactory model convergence with criteria of t statistics for deviations from targets and the overall maximum convergence ratio . The results of a post hoc time heterogeneity test for the models found no evidence that the co-evolution of substance use behaviors and friendship networks was significantly different across the two time periods, providing no indication of estimation or specification problems. We also perform goodness-of-fit testing for key network statistics in both schools, and display the results in the S1 File. Besides the main SAB model for each school sample,indoor grow shelves we estimate ancillary models that test whether the interdependent effects are symmetric in increasing and decreasing substance use. This is accomplished by differentiating the “creation” function and the “endowment” function in RSiena.

This technique has been applied to explore the asymmetric peer influence effect on adolescent smoking initiation and cessation.methodological challenge we face is that whereas the questions about smoking and drinking behavior were asked at all three waves, questions about marijuana use were only asked at t2 and t3 . One approach would discard all the information at t1 , but this strategy will reduce the efficiency of analysis, increase standard errors, and decrease statistical power. Instead, we reconstruct adolescent marijuana use at t1 based on four questions. Fig 2 provides a flow chart of the logic, and shows that we in fact have a considerable amount of information that can help us reconstruct probable values for the vast majority of the adolescents. First, if an adolescent has never tried marijuana at t2 , s/he would not have used it at t1, so we can safely code them as a zero at t1 . Next, if an adolescent has tried marijuana at t2 but the age at which he or she tried was above his or her age at t1 , s/he would not have reported using it at t1, so we can safely code them as a zero at t1 . Finally, if an adolescent has tried marijuana at t2 and the age of usage was below his or her age at t1 , we utilize information from two questions “During your life, how many times have you used marijuana?” and “During the past 30 days, how many times did you use marijuana?” at t2. In a few instances the difference between these two variables is zero, which appears to be a reporting error as they reported all their usage in the last 30 days and yet that they started at a young age. We code them as a zero at t1under the presumption that this earlier usage was very limited, and perhaps experimental. However, if the difference is non-zero, since the In-School Survey was conducted at least six months before the wave-1 In-Home Survey, we divide this difference by 5 to average over five months [i.e., /5]. Those with values less than 1 were categorized as non-users at t1 , those with values between 1 and 10 were categorized as light users and those with values above 10 were categorized as heavy users .

Light users comprised about 16% of adolescents in Sunshine High and 17% of adolescents in Jefferson High. Likewise, heavy users comprised about 5% of the adolescents in Sunshine High and 8% of the adolescents in Jefferson High. Overall, this reconstruction strategy enabled us to estimate a three-wave SAB model for each of the two samples without discarding any data. The last step of the reconstruction procedure for the heavy marijuana users is not perfectly accurate and might mistakenly categorize a few light users as heavy users, since they could have used marijuana outside of the last five months. The proportion of cases that might have been misclassified is less than 10%. Furthermore, sensitivity tests in which the level of marijuana use for these uncertain cases was randomly assigned to “light” or “heavy” use exhibited similar results over a large number of samples .Regarding missing data, for students in Sunshine High the response rates were 76% at t1 , 82% at t2 , and 75% at t3 . In Jefferson High the response rates were 79% , 81% , and 74% across the three waves. We imputed missing network data using the technique described in Wang et al. given the evidence that failing to do so can result in in biased estimates. Other actor attributes at t1 were imputed using the multiple imputation system of chained equations implemented in Stata. For the later waves, missing data is handled within the Stochastic Actor-Based models in RSiena software as suggested by Huisman and Steglich and Ripley et al.. The 501 and 166 students who graduated at t3 and were no longer in the network are treated as structural zeros in the Stochastic Actor-Based models at the last wave.Network statistics are measured at three waves. As shown in Table 1, in both school samples the number of out-going ties decreased over time due to limited nomination restrictions, graduation, moving, dropping out, and sample attrition/non-response/missing network data. The proportion of reciprocal ties over all out-going ties was 4% to 10% higher in Jefferson High than in Sunshine High at each wave. The transitivity index is the proportion of 2-paths that were transitive , which is similar in the two schools. The Jaccard index measures the network stability between consecutive waves.

There were substantial changes in friendship ties across waves, with the Jaccard index staying at .16 in Sunshine High and ranging from .21 to .22 in Jefferson High. Due to a survey implementation error in Add Health, some adolescents could only nominate one female and one male friend at t2 and t3 . Most limited nomination restrictions happened at wave 2,indoor garden table and involved less than 5% in the two schools. With respect to smoking behavior, there were between 69% and 78% non-smokers in Sunshine High over the three waves, and between 7% and 10% heavy-smokers . In Jefferson High, there were between 42% and 53% non-smokers and between 26% and 32% heavy smokers. Sunshine High also had more non-drinkers than Jefferson High , and more non-users of marijuana . The descriptive statistics of covariates are reported in the lower part of Table 1.As shown in Table 2, our estimated SAB model includes a smoking behavior equation, a drinking behavior equation, a marijuana use equation, and a network equation. Based on the smoking behavior equation, those who were one point higher on the marijuana scale are 25% [exp = 1.25] and 15% [exp = 1.15] more likely to increase their own smoking behavior at the next time point in Sunshine High and Jefferson High, respectively. Those who drank alcohol did not smoke more over time. There is no evidence of cross substance influence, as having more friends who drank or used marijuana did not impact a respondent’s own smoking over time. In ancillary models, we measured average level of drinking or marijuana use for friends and these effects were also statistically insignificant. These results are shown in S1 Table. Regarding the other measures in the smoking behavior equation, we detect a negative smoking behavior linear shape parameter in both school samples along with a positive smoking behavior quadratic shape parameter. This suggests that adolescents were inclined to adopt lower levels of smoking behavior over time, but they also tended to stay as or become non-smokers or escalate to heavy-drinkers due to a pull towards extreme values of this scale. Turning to the peer influence effect, we find that adolescents’ own smoking levels were affected by that of their best friends in both schools. There is no evidence that parental support or monitoring reduced levels of smoking over time in either sample. African Americans and Latinos smoked less than Whites in Sunshine High. Depressive symptoms were found to increase smoking behavior in Jefferson High. In the drinking behavior equation, we find that an adolescent who was one point higher on the marijuana use measure was 22% and 16% more likely to increase their own alcohol use at the next time point in Sunshine High and Jefferson High, respectively. However, respondents’ drinking was not related to their greater cigarette use. There is no evidence that friends’ smoking behavior or marijuana use affected respondents’ drinking behavior. This was the case whether measured as the number of friends who smoked or used marijuana, or as the average level of such behaviors. A negative linear shape effect and a positive quadratic shape effect are also confirmed regarding drinking behavior.

An adolescents’ drinking level was positively predicted by that of one’s best friends. Whereas there is no evidence in these two networks that high levels of parental support impacted drinking levels of adolescents, we do see that higher levels of parental monitoring were associated with lower levels of drinking behavior over time in Jefferson High. In Sunshine High, African Americans were found to drink less than Whites, and depressive symptoms were found to increase drinking levels. The marijuana use equation suggests no evidence that increasing usage of the other two substances leads to increasing marijuana use. We once again see no evidence of cross-substance influence, as the number of friends who smoked or drank or the average smoking or drinking level of friends is not related to ego’s marijuana use levels over time. A negative linear shape effect and a positive quadratic shape effect are also detected on marijuana use behavior. Across both samples there is very strong evidence of a peer influence effect from anadolescent’s best friends’ marijuana use to an individual’s own marijuana use. Higher levels of parental support or monitoring were not found to reduce levels of marijuana use over time. For all three substance use behaviors, there was no evidence that adolescents who are more “popular” were any more likely to increase their substance use over time. In the network equation the expected patterns are detected regarding the endogenous network structural effects across samples. At the dyadic level, adolescents did not randomly nominate peers as friends, since friendship ties inherently require the investment of time and energy, as indicated by the negative out-degree parameters; instead, adolescents tended to nominate peers who had already nominated them as friends previously, as indicated by the positive reciprocity parameters. At the triadic level, adolescents tended to nominate a friend’s friend as a friend but avoided ending in 3-person cyclic relationships. The negative out-degree/in-degree popularity parameters and the out-out degree assortativity parameters suggest that adolescents were less likely to befriend peers who have already made/received many friendship nominations or have similar out-degrees. Instead, they were more likely to befriend peers with similar in-degrees, as indicated by the positive in-in degree assortativity parameters. We also find that adolescents were more likely to nominate peers as friends if they were of the same gender, race , and grade. Grade is a particularly strong effect, as adolescents were 86% and 77% more likely to nominate a friend if they were in the same grade than if they were in a different grade in Sunshine High and Jefferson High, respectively. Lastly, the limited nomination parameter shows that for adolescents who encountered the administrative error of being limited to nominate only one male or one female friend, their odds of nominating friends is re-adjusted by the SAB models to be 132% larger in Sunshine High and 297% larger in Jefferson High than those with no such problem.

Global neuropsychological impairment and dependence on basic and instrumental activities of daily living are more common among PWH who also use MA than among those who do not, with an additive effect of HIV and MA on neuronal injury and glial activation . Despite these negative effects, perceived benefits, such as sexual enhancement and relief of negative psychosocial symptoms, continue to drive MA use among PWH . MA functions by stimulating monoamine release , and facilitates hyperactivity, euphoria, feelings of increased mental and physical capacity, and riskier sexual behavior . Among the general population, prolonged MA use can have detrimental effects on alertness, mood, cognition, and activity levels . MA use also has been associated with poor sleep quality, increased sleep latency, and daytime sleepiness . Cessation of MA is often accompanied by withdrawal symptoms such as anxiety, depression, and craving that can further contribute to poor sleep quality. The adverse effects of MA also contribute to functional decline , such as unemployment , which also may exacerbate sleep disturbance. Among MA-using PWH, poorer adherence and missing ART doses after MA use have been reported, in part due to disrupted sleep-wake cycles . Taken together, acute and chronic MA use can have multiple direct and indirect effects on sleep quality.This study evaluates effects of lifetime MA use on self-reported sleep quality among participants with or without HIV infection.

The hypothesis was that lifetime MA use disorder would be associated with poorer sleep quality, particularly among PWH,drying rack cannabis and that this would relate to poor outcomes, including poorer cognition, reduced independence in activities of daily living, unemployment, and poorer life quality. Participants included 225 HIV-seropositive and 88 HIV-seronegative adults enrolled in NIH-funded research studies at the UC San Diego’s HIV Neurobehavioral Research Center . All participants completed a standard, selfreport evaluation of sleep quality as well as comprehensive neurobehavioral and neuromedical assessments. Exclusion criteria were: 1) sleep apnea or restless leg syndrome; 2) disruptions to sleep due to temporary circumstances ; 3) history of comorbid neurological illness or injury that would affect cognitive functioning ; 4) history of psychotic disorder; 5) alcohol dependence within a year; and 6) low premorbid verbal IQ as estimated by a Wide Range Achievement Test-4 score less than 80. The study protocol was approved by the UC San Diego Institutional Review Board and each participant provided written, informed consent. All participants completed the Pittsburgh Sleep Quality Index , a self-report questionnaire that assesses perceptions of average sleep quality and disturbances over the past 30 days . The PSQI is a widely used and well-validated measure of subjective sleep quality in adults . The PSQI has 19-items that assesses seven components of sleep, including quality, latency, duration, efficiency, disturbances, use of medications to aid sleeping, and daytime sleepiness. Component scores range from 0 to 3 . Items were summed to generate a continuous global sleep score ranging from 0 to 21. Global scores > 5 indicate problematic sleep . For purposes of the present study, the continuous global PSQI score and dichotomous sleep quality classification were used as outcome variables. All participants underwent a standardized medical history interview, neuromedical examination, and blood and urine collection.

HIV serological status was confirmed via ELISA and Western blot test, and HIV RNA levels were measured in plasma by rtPCR . Current CD4+ T-cell count was measured in blood by clinical flow cytometry. Additional HIV disease and treatment variables included nadir CD4+ T-cell count, AIDS diagnosis, estimated duration of HIV disease, and current ART regimen. MA use characteristics were self-reported. Comorbid medical conditions and current medication use were determined by self-report and medical chart review.All participants completed a comprehensive and validated neurocognitive assessment across seven neurocognitive domains commonly affected by HIV and MA use ; these include verbal fluency, executive functioning, speed of information processing, learning and memory , working memory/attention, and motor. Using established normative standards, test scores were adjusted for known influences on neurocognitive performance . Deficit scores were calculated for each domain and averaged across the test battery to derive a global deficit score ranging from 0 to 5 . Dependence in instrumental activities of daily living was determined using a revised version of the Lawton and Brody ADL questionnaire , in which participants rated current degree of independence as compared to prior best level of independence across 13 IADL domains. Participants were classified as IADL “dependent” if they endorsed requiring increased assistance in at least 2 IADL domains. Employment status and symptoms of cognitive difficulties in daily life were determined via the Patient’s Assessment of Own Functioning Inventory . The Karnofsky Performance Status Scale is a clinician administered assessment of disease-related functional impairment with a range from 0 to 100 with standard intervals of 10 . Self-reported physical and mental health quality of life were assessed using the Medical Outcomes Study Short-Form Survey . Physical and mental health composite scores were calculated via validated summary score formulas derived from an obliquely rotated factor solution .Group differences on demographics, neuropsychiatric and neuromedical characteristics, HIV disease and treatment parameters, MA use history, and global sleep outcomes were tested using analysis of variance , Kruskal-Wallis tests, Chi-square statistics, or Fisher’s Exact test .

Two-tailed t-tests were used to compare groups on HIV disease and methamphetamine use characteristics. Follow-up pairwise comparisons were conducted using Tukey’s Honest Significant Difference or Wilcoxon tests for continuousoutcomes,commercial greenhouse supplies or Bonferroni-corrections for categorical outcomes. Cohen’s d measured effect size for pairwise comparisons of means. Based on the pattern of univariable group differences in global sleep health and the small sample size of the HIV−/MA+ group, multiple linear regression examined global sleep scores as a function of MA status and clinical covariates specifically within PWH. Covariates included clinical variables from Table I with univariable associations with the primary independent variable [MA status ] as well as associations with the primary dependent variable with p values < 0.10. Variables sex and sexual orientation were included based on theoretical evidence . Additionally, HIV disease and treatment covariates were included to determine if HIV specific factors attenuated the effects of MA status on global sleep in PWH. Stepwise regression models used backward selection based on Akaike Information Criterion to select the optimal model. To determine potential co-occuring neurobehavioral functional impairments associated with poor sleep quality within the dual-risk HIV+/MA+ group, additional nominal logistic regression models based on AIC were run to examine the association between problematic sleep membership and neurobehavioral outcomes . Covariates were selected based on univariable associations with global PSQI and did not include HIV or methamphetamine characteristics. Rates of MA use are elevated among PWH and are associated with poorer sleep quality in the general population . The present study is the first to explore the relationships between past MA use disorder, HIV disease, and sleep quality. Our results demonstrate that PWH who have a history of prior MA use disorder had significantly poorer sleep quality and were more likely to be classified as problematic sleepers than those without a lifetime disorder. This relationship between lifetime MA use disorder among PWH is robust to MA group differences in bio-psychosocial factors and is linked to sleep quality above and beyond the effects of HIV disease severity and other established risk factors for poor sleep. Further, poorer sleep quality among PWH with comorbid lifetime MA use disorder was associated with a number of neurobehavioral functional outcomes, including decreased physical and mental life quality, IADL dependence, unemployment and clinician-rated functional disability. As expected, lifetime MA use disorder was negatively associated with sleep quality; however, this finding was isolated to PWH and independent of recent MA use. In addition, MA use characteristics did not differ by HIV serostatus, suggesting sleep among PWH may be specifically related to the effects of non-recent MA use. Prior studies have demonstrated detrimental effects of MA on neurobehavioral health specific to PWH, including neurocognitive impairment and associated everyday life consequences such as unemployment and difficulties performing activities of daily living . It is possible that disrupted sleep may mediate the link between MA and functional outcomes, although longitudinal studies are needed to determine causality.

Depressive symptoms in the HIV+/MA+ group are also consistent with prior research . While depressive symptoms were also associated with global PSQI scores, as expected, this did not attenuate the relationship between MA and global PSQI scores in PWH, suggesting additional mechanisms underlying MA-related sleep disturbance independent of mood.One explanation for our findings is the combined, long-term CNS effects of excessive MA use and HIV on brain structures and/or pathways responsible for sleep regulation. While MA’s major mechanism of action is through increased activity of the mesolimbic dopamine system , emerging evidence supports that GABA-ergic dysfunction results from abuse of amphetamines . Projection systems of GABA include the reticular nucleus of the thalamus to the rostral brainstem reticular formation, a structure critical for sleep regulation. Further, GABA also promotes sleep via hypothalamic projections that inhibit serotonergic, noradrenergic, histaminergic, and cholinergic arousal systems . Future studies linking GABA to MA use and sleep quality are necessary to establish this theoretical mechanism of action. Also, while the lack of evidence of sleep disturbance in the very small HIV−/MA+ group would not support long-term effects of MA use on CNS mechanisms important for sleep, a much larger subject sample would be needed to draw any confident conclusions about HIV−/MA+ individuals. Prior literature on the prevalence of sleep disturbance in PWH is variable and comparisons between demographically matched, HIV serostaus groups on sleep quality is lacking. In a meta-analysis of self-reported sleep disturbance in PWH, the overall prevalence was 58% . No comparisons have been made with HIV-uninfected individuals from the same population to determine whether this prevalence is higher than in this type of comparison group. The current findings suggest HIV status alone may not elicit poor perception of sleep, however, fragmented sleep has been identified in chronic health conditions even without the patient’s perception of poor sleep . Consistent with prior literature , detectable HIV RNA was associated with poorer perceived sleep quality in our multiple regression analyses, but the specific mechanism for this association could not be established. Other literature has suggested that HIV infection is linked to objective sleep measurements, including reduced slow wave sleep and reduced rapid eye movement latency . However, studies have failed to detect similar associations between HIV disease severity and objective sleep measurements , highlighting the uncertainty to which HIV infection, by itself, may contribute to reductions in sleep quality. The study has several limitations. First, the data are cross-sectional and cannot determine causality. Lifetime MA use disorder is suspected to precede self-reported poor sleep within the last 30 days, however, such self-reported sleep disturbances may be longstanding and could even have served as a precursor to problematic substance use . Thus, future longitudinal evaluations or with increased sample size, the use of structural equation modeling, would be helpful in better determining the timing, duration, and directionality of associations between MA use disorders and sleep. This goes alongside our report of neurobehavioral outcomes associated with problematic sleep within PWH with a history of MA use disorder. While theoretically, sleep should have some influence on function, it is also possible that there is some unique third variable quality within the HIV+/MA+ group that leads to both poor sleep and poor neurobehavioral outcomes. Again, a longitudinal research design or a larger sample size may help in teasing out the directionality of our findings. Second, the small sample size of the HIV−/MA+ group hinders our ability to detect statistically significant associations between MA use and other findings with the HIV− participants. For example, the difference between HIV+/MA+ and HIV−/MA+ groups on global PSQI was not statistically significant , yet the effect size suggests a nontrivial difference . While our sample did not demonstrate an interaction between HIV and MA possibily due to this limitation, this relationship may exist. Further, while lifetime MA use disorder independently contributed to sleep quality in PWH, we did not observe a recent MA use effect on sleep. We should note that this too may be due to low power, with very few participants reporting use in the last 30 days.

Seventeen percent of 12th graders reported past 2-week binge alcohol use in 2020 , defined as five or more drinks on the same occasion on at least 1 day in the past 30 days in males . Adolescents tend to drink more alcohol per occasion, yet less frequently than adults . Binge drinking may have serious consequences on adolescent health, including overdose, fatal injuries, and motor vehicle accidents, and long term impacts on families, other students, and the general community . In addition, binge drinking has potential detrimental impacts on brain development, cognition, mood, and school performance . One in five teens suffer from depression, over 30% suffer anxiety symptoms, and up to half of those endorsing internalizing symptoms also endorse comorbid substance use, including alcohol . Understanding youth binge drinking and its relation with co-morbid internalizing symptoms is therefore a critical problem that may affect adolescents throughout development. Persistent binge drinking, depression, and anxiety are associated with a variety of poor health outcomes that ultimately affect both quality and quantity of life . Rates of adolescents with depressive and/or anxiety symptoms in the United States have sharply increased since 2012 , and have coincided with decreases in alcohol consumption . As such, understanding the evolving relation between adolescent internalizing symptoms and developing binge drinking behaviors remains crucial in determining developmentally informed targets for prevention and intervention of substance-related health risks among youth. Theory has suggested bidirectional links between depression/anxiety and alcohol behaviors throughout adolescence .

For instance,cannabis grow equipment the self-medication model for negative affect and alcohol use proposes that because both anxiety and depressed mood produce aversive negative mood states, adolescents may develop coping motives for alcohol use via attempts to reduce negative affect symptoms through drinking. Over the long term, this behavior may lead to development of increasingly heavier use and delayed-onset alcohol use disorder by means of negative reinforcement. This model has been conceptually shared by several explanatory models for the development of alcoholism and has been supported by evidence including patients self-reporting drinking as a way of coping with their anxiety , especially in those suffering from phobias . On the other hand, substance-induced negative affect models propose that anxiety and depression develop because of persistent, heavy alcohol use . Alcohol misuse can lead to several work, school, and relationship-related difficulties, and internalizing symptoms can result from difficulties in each of these areas. The development of alcohol use disorder occurs over the course of many recurring episodes of excessive and frequent drinking, and withdrawal may cause neural changes that lead to and/or exacerbate negative mood states. Over time, repeated recurring episodes may result in increased neural adaptation that may make a person who drinks alcohol more vulnerable to developing internalizing symptoms . A number of clinical studies have demonstrated that people who drink alcohol heavily that have recently stopped drinking experience an increase in anxiety, panic, and/or low mood, as well as symptoms of autonomic hyperactivity during an extended withdrawal period . However, there have also been a number of recent studies that suggest that it is possible there are no significant associations between anxiety, depression, and binge drinking . Despite supporting theory, evidence demonstrating links between internalizing symptoms and alcohol use has been mixed, with several systematic reviews demonstrating modest associations between depression and alcohol use and minimal-to-no relation between anxiety and use .

For instance, Hussong and colleagues provided a systematic review of longitudinal studies testing the relation between negative affect symptoms and substance use controlling for externalizing factors. They found that while there is some consistent evidence of a link between depression and substance use, only 5 out of 61 studies reviewed found a positive unique association between anxiety and use, 6 found a negative association, and the remaining 52 found no relation. A more recent meta-analysis examined 97 associations across 51 studies testing the link between anxiety and alcohol use disorders. They found inconsistent evidence of this link for binge drinking or drinking frequency/quantity and no clear association between generalized anxiety and alcohol use disorder . In light of this mixed evidence, methodological considerations have been noted that may clarify the relation between internalizing symptoms and binge drinking in adolescence . First, because this relation may be confounded by other between-person factors characterizing risk, within-person designs better accounting for these factors that may reduce bias in estimating the link between internalizing symptoms and binge drinking . For instance, the common-factor model of anxiety and alcohol use disorders hypothesizes that no direct relations exist between these two conditions, and may instead be explained by confounding variables . Studies that have modeled such variables explicitly have shown that the internalizing-alcohol use link may be explained by environmental contexts [e.g., childhood and family factors, prior substance dependence, comorbid depression, and peer affiliations; ], trait-level sensitivity to anxiety , and genetic contributors . While these studies have controlled for common factors at the between-person level, alternative approaches can distinguish between- from within-person effects by relegating the influence of these factors to random-effects components of a statistical model . Utilizing such methods to explicate between- versus within-person effects may help partition the influence of these common factors from the link between internalizing symptoms and binge behaviors at the individual level.

Relatedly, few have assessed bidirectional links between internalizing and binge drinking factors . Because substance use may lead to increased risk of internalizing symptoms, assessing these associations bi-directionally will provide tests of self-medication and substance-induced negative affect models simultaneously . Finally, few studies to-date have addressed the concern of power in detecting these effects, which meta-analyses have suggested are relatively small in magnitude . Well-designed studies utilizing appropriate statistical methods may be better able to detect these effects if they are present, and conversely,cannabis grow racks increase confidence in null results when these effects are not found. The goal of the present study was to examine the extent to which adolescent and young adult depression and anxiety predict binge drinking using a within-person analytic approach. Aims were pursued in the large, diverse, prospectively followed National Consortium on Alcohol and Neurodevelopment in Adolescence cohort  from age 17 to age 21. Similar to controlling for between-person common factors , utilization of within person designs can account for between-person factors by allowing statistical models to partition between-person variation from person-level effects. Further, analyses focused on temporally lagged effects, allowing these factors to be appropriately sequenced in time and were supplemented by post-hoc power analyses to increase confidence in the presence or absence of effects. As such, the NCANDA cohort and analyses conducted in this sample may be ideal for clarifying competing hypotheses regarding the association between internalizing symptoms and binge drinking. Given extant evidence favors the self-medication model of binge drinking risk, we hypothesized that adolescent depression, and to a lesser degree anxiety, would predict adolescent binge drinking over a 5-year period for participants from the NCANDA study. Observing no within-person links between these constructs may suggest common factors observed at the between-person level may instead be driving these associations. Data were from the nationally representative National Consortium on Alcohol and Neurodevelopment in Adolescence cohort. Participants were recruited between 12 and 21 years of age at project entry from 5 site locations in 2013–2014. NCANDA is following these individuals through adolescence and into young adulthood . After 2548 participants were screened, 1110 were excluded based on criteria that included MRI contraindications, physical limitations, lack of parental consent, substance use disorders, medication use, prenatal exposure to substances and learning disorders at baseline . To test NCANDA’s primary aims and ensure our sample was optimized to detect changes over time that pertain to one’s alcohol intake, we excluded youth with a range of other factors or conditions that could obscure our ability to do so, including those with prenatal exposure to substances. Recruitment was designed to over sample individuals at higher risk for substance use issues based on endorsement of externalizing symptoms, internalizing symptoms, and family history of alcohol or substance use disorders . The majority of the sample at baseline had limited or no exposure to alcohol or other drugs as determined by the age and sex-based guidelines from the National Institute on Alcohol Abuse and Alcoholism , indicative of misuse based on the Center for Disease Control surveillance. The study’s cohort sequential design recruited adolescents in three groups , facilitating investigation of a wide developmental span due to between-subject variance in starting age. Before entering the study, most participants had not participated in binge drinking .

Participants at risk for increased drinking were identified based on screening for early experimentation with alcohol, positive family history for substance use disorder, and externalizing/internalizing symptoms; these participants were over-recruited and consisted of 50% of participants at study entry . An accelerated longitudinal design allows recruitment of all ages in the cohort starting during the baseline year; 15% of the cohort were selected for enrichment of alcohol and drug use based on NIAAA guidelines for normative drinking in community sample, which was possible due to later age of recruitment at baseline. Of the 831 enrolled participants, 139 were people who drink alcohol and 692 were people who do not drink alcohol at study entry. People who do not drink alcohol were defined as those with fewer than 1–4 drinks once a year or 1–2 drinks once a month . People who drink alcohol were those that exceeded these thresholds . Adult participants provided voluntary informed consent, while minors provided assent in addition to the informed consent of a parent or legal guardian.Alcohol and other substance use history was assessed annually with the Customary Drinking and Drug Use Record to follow use of alcohol, tobacco, cannabis, illicit drugs, and misuse of prescription medications. The CDDR is an interviewer-administered questionnaire, designed for use with adolescents and young adults, that probes recent and lifetime use of alcohol, tobacco, cannabis, illicit drugs, and misuse of prescription medications. It has been found to be internally consistent and reliable over time and across interviewers, in addition to being able to differentiate between abusing and non-abusing adolescents, and with excellent diagnostic specificity compared to other standard instruments. Past year binge drinking was assessed using the item “during the past year, how many times have you consumed 4+ / 5+ drinks within an occasion? ”. Endorsement of binge drinking in the sample increased from baseline to year 5 of data collection . People who drink alcohol endorsed using higher amounts of alcohol, cigarette, and cannabis use compared to people who do not drink alcohol, and using higher amounts of other drugs . Socioeconomic status was assessed with a modified version of the MacArthur Sociodemographic Questionnaire . This reflected parental family income except if the youth was living independently, in which case it reflected the youth’s own socioeconomic status. Twenty percent of parents endorsed education below a college degree, twenty-seven percent with at least one parent completing college, and fifty-three percent with at least one parent with education beyond a college degree for the full sample. Annual family income ranged from below $12,000 to greater than $200,000. A total of 18% of the sample reported income below $50,000 per year. While the median income in the United States at the time of study entry was $52,250, median incomes ranged from $50,988 to $90,786 across NCANDA data collection sites. Reliability across sites and training for assessments was ensured through the development of training manuals, developed by doctoral-level senior staff members, mock and practice sessions, observations, and annual visits to check for interviewer drift and confirmation of training of new staff members . NCANDA uses a cohort sequential design , in which participants spanned a large range of ages at baseline then were assessed annually thereafter. Because participants were enrolled across multiple ages at baseline and provided data in up to five subsequent waves, data resulted in a pattern of planned missingness that can be considered completely at random . We therefore used full information maximum likelihood as an estimator in our structural model to accommodate this design . Missingness was 21.7%, 23.8%, 25.9%, 29.1%, and 29.0% on outcome variables across waves 2 through 6, respectively.

The dynamic pain connectome model was derived from brain imaging data in healthy subjects who had mind-wandering away from a painful stimulus. This work found that most brain regions were activated by noxious stimuli whether the mind wandered away from the stimulus or not. The responses of the salience and default mode networks and connectivity with antinociceptive areas showed mind wandering brain activity that included a clear distinction between trials in which subjects attended to pain vs. mind-wandered from pain . Increased functional connectivity between the medial prefrontal cortex–nucleus accumbens at the beginning of back pain predicts that patients will go on to develop chronic back pain; while patients with decreased connectivity in this circuit went on to recover from back pain . Structural brain imaging in subacute back pain patients was followed longitudinally for 3 years as they either recovered from or transitioned to chronic pain. Furthermore, these results indicate that persistence of chronic pain is predetermined by corticolimbic neuroanatomical factors .There is substantial overlap between the circuits involved in human placebo analgesia and those that mediate multiple forms of context-based modulation of pain behavior in rodents, including forebrain-brainstem pathways and opioid and cannabinoid systems in particular. Subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of prefrontal regions, anterior cingulate, and periaqueductal gray are predictive of placebo response .

One study found that placebo and nocebo effects are generated through differential engagement of the periaqueductal gray-rostral ventromedial medulla pathway,vertical grow rack system which likely influences pain experience by modulating activity at the dorsal horn level .Individuals have a set point around which different biological attributes can fluctuate transiently into different states. However,if one remains in a different state other than their set point for a considerable period , this different state is considered a new set point. In pain research it is important to consider trait and state pains to gain an understanding of not only an individual’s current pain state but also more broadly to their trait pain, which may be more reflective of their general condition . Resilience is a trait that is highly associated with chronic pain–related health outcomes. The neural correlates of both pain and trait resilience are critical to understand the brain– behavior relationship in chronic pain; yet, neural correlates of resilience in chronic pain states are unknown . Therefore, regional BOLD variability and circuit connectivity have potential to provide predictive power for pain resilience or vulnerability to chronic pain and treatment efficacy . Two reviews on mechanisms and imaging biomarkers for diabetic neuropathic pain review that diabetic peripheral neuropathy and associated pain have structural and functional central nervous system changes in the spinal cord, subcortex, and cortex . Diabetic peripheral neuropathy has been associated with changes in the thalamus. A decreased thalamic NAA/creatinine ratio is suggestive of thalamic neuronal dysfunction , and thalamic microvascular perfusion changes have also been observed . Smaller spinal cord crosssectional area has been observed in those with diabetic peripheral neuropathy . In fact, in diabetic peripheral neuropathy, diffusion tensor imaging techniques found posterior column damage in the cervical spinal cord .

Diabetic neuropathic pain is related to decreased NAA in the thalamus , increased thalamic vascularity , and spinal cord posterior column damage . Diabetic neuropathic pain is associated with increased regional brain gray matter volume loss localized to brain regions involved in somatosensory perception ; furthermore, in diabetic neuropathic pain, increased total gray matter atrophy is associated with impaired ability to walk . Diabetic neuropathic pain has been shown to be related to aberrant default mode functional connectivity , decreased functional connectivity between the thalamus and cortex , and decreased functional connectivity in attention networks . Altered fMRI activation responses to experimental heat pain in limbic and striatal brain circuits are related to the duration of diabetic neuropathic pain . Diabetic neuropathic pain is related to a double dissociation such that neuropathic pain intensity is more associated with thalamus-insular cortex functional connectivity and nerve deficits are more related to thalamus-somatosensory cortex functional connectivity . Diabetic neuropathic pain is also associated with decreased functional connectivity between the thalamus and amygdala , decreased gray matter volumes and decreased white matter connectivity in pain processing and pain modulation brain regions , decreased somatosensory cortical thickness related to cortical function dysfunction , increased activity in the anterior cingulate cortex , as well as ventrolateral periaqueductal gray functional connectivity is altered and correlates with magnitude of spontaneous pain and allodynic pain . Structural brain imaging has revealed changes to the brain associated with HIV peripheral neuropathy. Total cortical volume is smaller with HIV distal neuropathic pain . In fact, in HIV distal neuropathic pain the posterior cingulate cortex is the cortical region that was found to be smaller . In another sample of people living with HIV, subjective symptoms of HIV peripheral neuropathy were associated with smaller precuneus volumes which overlap with the posterior cingulate cortex .

Smaller brain volumes for HIV distal neuropathic pain are consistent with a general pattern that brain volumes are reduced for a variety of chronic pain conditions . Interestingly, the midbrain, thalamus and posterior cingulate cortex volumes are all reduced in HIV distal neuropathic paresthesia . It has been suggested that brain atrophy associated with HIV distal neuropathic paresthesia may precede brain atrophy associated with HIV distal neuropathic pain . More recent multi-modal brain imaging research has revealed structural brain changes associated with HIV peripheral neuropathy . HIV peripheral neuropathy is correlated with decreased white matter integrity running from the midbrain to the somatosensory cortex. HIV peripheral neuropathy severity is also associated with decreased generalized fractional anisotropy along the tracts of the external capsule in both hemispheres, appearing to lead along the lateral thalamus to sensorimotor cortex. A similar correlation is found in the superior bilateral cingulum. These results indicate ascending deafferentation in HIV peripheral neuropathy extends further downstream from damaged peripheral nerves than seen previously, into the cortex. HIV-associated distal neuropathic pain is associated with decreased fMRI resting state functional connectivity in the default mode network and increased functional connectivity in the salience network . Decreased connectivity between the medial prefrontal cortex and posterior cingulate cortex and stronger connectivity between the ACC and thalamus is associated with HIV distal neuropathic pain. In the setting of experimental heat pain, significant interaction has been found within the right anterior insula during expectation of experimental pain offset in that a group with HIV distal neuropathic pain compared group without HIV distal neuropathic pain exhibited increased insula activation in the feet compared to the hand . These findings are consistent with abnormal processing of expectation of experimental pain offset or abnormal pain relief mechanisms potentially due to increased negative expectation regarding the experience of chronic endogenous neuropathic pain. Anterior cingulate cerebral perfusion and gray matter density correlate with chemotherapy-induced peripheral neuropathy symptoms including pain . Patients with chemotherapyinduced peripheral neuropathy symptoms demonstrated greater activation during painful stimulation in the precuneus compared to healthy controls and exhibited hypo-activation of the right superior frontal gyrus compared to healthy controls. Painful stimuli delivered chemotherapyinduced peripheral neuropathy symptoms patients evoke differential activation of distinct cortical regions,cannabis grow equipment reflecting a unique pattern of central pain processing compared with healthy controls providing a tool for monitoring cerebral changes during anti-cancer and analgesic treatment . A population of mixed small-fiber peripheral neuropathy was used to investigate how dysfunction of skin nerves led to abnormal recruitment of pain-related brain regions, suggesting that the brain may be affected in SFN. Greater volume reduction in pain-processing regions, particularly the bilateral anterior cingulate cortices was associated with greater depletion of intraepidermal nerve fibers. There was significant reduction in functional connectivity from the anterior cingulate cortex to the insula pain-processing cortex that is linearly correlated with the severity of intraepidermal nerve fiber depletion . Similarly, another population of mixed small-fiber peripheral neuropathy the degree of skin nerve degeneration was associated with the reduction of connectivity between the thalamus and pain-related areas. Despite altered white matter connectivity, there was no change in white matter integrity assessed with fractional anisotropy. These findings indicate that alterations in structural connectivity may serve as a biomarker of maladaptive brain plasticity that contributes to neuropathic pain after peripheral nerve degeneration .

A population of Charcot-Marie-Tooth patients had abnormal diffusion tensor imaging findings indicative of significant cerebral white matter abnormalities. Diffusion tensor imaging abnormalities were correlated with clinical disability, suggesting that there is comorbidity of central nervous system damage with peripheral neuropathy in Charcot-Marie-Tooth patients . A population of patients with hereditary neuropathy with liability to pressure palsies were compared to a population of normal controls and the fractional anisotropy values of the patients were significantly lower in bilateral frontal, orbitofrontal, and temporal areas of white matter . Patient populations of paresthesia-dominant and pain-dominant patient groups were compared and contralesional cortical thickness were correlated with pain severity . Acquired and hereditary peripheral neuropathies are associated with increased functional connectivity of the left precuneus/posterior cingulate cortex in the default mode network. This increased connectivity in the default mode network is correlated with duration of peripheral neuropathy and severity of clinical total neuropathy score . As discussed in the introduction, if used in combination, biomarkers related to pain mechanisms offer the possibility to develop objective pain-related indicators that may help diagnosis, treatment, and understanding of pain pathophysiology . One possible application of such an approach might be to determine if a patient who is not communicative is experiencing pain. Another example may be to help guide selection of treatment for neuropathy, such as whether transcranial magnetic stimulation may alter network activity among those with neuropathy. Modeling pain brain mechanisms can be achieved using multi-modal brain imaging including functional magnetic resonance imaging, structural magnetic resonance imaging, diffusion tensor magnetic resonance imaging, electroencephalography, EMG, and PET . As we have reviewed here, in addition to using imaging biomarkers, composite pain biomarkers can be investigated using a multitude of non-imaging biomarkers. Multiple analytic approaches have been used to investigate composite pain biomarkers: composite algorithms have been investigated , unsupervised and supervised multivariate analyses have been used to distinguish pain groups and non-pain groups , supervised pattern recognition have been used to cluster diagnostic groups for different pain conditions , mechanism-based pharmacokinetic-pharmacodynamic modeling has been used to identify biomarkers that help diagnose pain and predict pain treatment , principal component analysis has been applied to biochemical markers to create distinct pain profiles , patterns of inflammatory blood cytokines and chemokines have been used to differentiate pain and non-pain groups , multi-variable data analysis using simultaneous analysis of 92 inflammation-related proteins with pain intensity and pain thresholds were used to identify protein patterns which distinguish pain and non-pain groups , metabolomics have been applied to chronic pain .As detailed above, chronic pain and neuropathic pain impact multiple organ systems. Advancing the value of pain biomarkers depends on selection of measurements and metrics that are the most mechanistically valid and informative, and combining the selected measurements such that they mechanistically and statistically maximize accurate classification. Advancement of measurement accuracy is vital and the subsequent steps of the approach are entirely contingent upon the success of this step. This literature for the domains discussed in this manuscript is too voluminous for a single review. In the above reviewed literature, we attempted principally to focus on which biological systems and which biomarkers should be the focus of measurement. For effective application of measurements of these domains it is important to discuss approaches for measurement selection. In Figure 2, we provide a significantly abbreviated schematic of key available statistical approaches to handling multi-modal datasets in building composite biomarkers. We have highlighted four general areas of statistics/machine learning: feature reduction , classification , regression , and clustering . Feature reduction can occur during or prior to classification, regression, or clustering. Feature reduction primarily focuses on two primary approaches: integration of measurements toward creation of a composite variable to simplify and enhance model performance, and effective feature reduction through variable selection to use optimal variables. Thus, feature reduction can represent the effective combining of strong measurements to a meaningful and robust latent variable or elimination of unnecessary, or statistically weak, measurements. Some methods, such as random forest, has built in feature reduction .

As the VLF and LF are mainly driven by sympathetic activity, RMSSD, NN9, pNN9, and HF are driven by the parasympathetic, SDNN reflects the overall HRV, these results suggested that all of these products decreased the overall HRV, especially the sympathetic modulations. To determine if different smoking/vaping products may induce cardiac fibrosis, heart sections were stained for interstitial fibrosis in red and cardiomyocytes in green. As shown in Fig. 6, interstitial fibrosis in LA, RA, and LV was significantly increased in all non-air groups relative to the Air group . There was no significant difference among non-air groups. Measurement of fibrotic biomarkers including galectin-3 , matrix metalloproteinase-9 , and its endogenous tissue inhibitor-1 showed only MMP-9 was reduced comparably in all non-air groups, identifying MMP-9 potentially as the main mediator. Transverse sections stained with GS-I were analyzed to assess changes in microvessels. As shown in Fig. 7, both density and area percentage of microvessels were significantly decreased in non-air groups, suggesting adverse effects on the microcirculation. This study shows that 8 weeks of daily exposure to smoke or aerosol from tobacco cigarettes, e-cigs, HTPs, or marijuana can cause comparable pathophysiological changes, consequently leading to hypertension, cardiac dysfunction, and arrhythmias. Cardiac electrical, structural, and neural remodeling are all involved in inducible atrial fibrillation and ventricular tachycardia caused by smoking or vaping. It is notable that these adverse effects resulted from a single smoking/vaping session per day, with each session reflecting a relatively modest exposure mimicking 10 “puffs” over 5 minutes; i.e.,grow table we did not use an extreme exposure model. We have used the same conditions to study acute effects of a single session of cigarette smoking, IQOS use, and multiple types of e-cigarette vaping sessions.

In the course of these studies, we have validated the relevance of our exposure conditions to human real-world use by using nose cone pulsatile exposure to enable immediate switching between brief pulses of undiluted smoke/aerosol and interim periods of clean air, showing that circulating plasma nicotine and cotinine levels after a single session of exposure to Marlboro Red cigarette smoke were comparable to circulating levels in humans after smoking one cigarette, and confirming an approximate dose response relationship between number of exposure cycles in one session and resulting plasma nicotine levels.Exposure to tobacco and marijuana products progressively increased pre-exposure SBP. Within each individual measurement day, tobacco products and cannabinoid-depleted marijuana acutely increased SBP. In contrast, regular marijuana acutely reduced SBP but not all the way to baseline day 0 values, indicating that cannabinoids have a blood pressure lowering effect that may be not sufficient to counteract the prohypertensive effect of marijuana smoke. The increased norepinephrine levels in all non-air groups, with no change in angiotensin levels, suggests that the sympathetic neural drive may be more important in smoking-caused hypertension, rather than the renin-angiotensin system. Thus, beta-blockers may be better than angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers in the treatment of smoking-related hypertension. Autonomic nerve function is an important determinant of arrhythmogenesis.It is intriguing that chronic exposure to e-cigs, HTPs, and marijuana all caused reduced overall HRV, of which both sympathetic and parasympathetic nerve function were down regulated as indicated by the results from the time domain method and change of LF band from frequency domain method.

Previous studies have also suggested that chronic and acute tobacco smoking are associated with reduced overall HRV.A recent study suggested that exposure to vanillin-flavored e-cig aerosol for 10 weeks also influences autonomic nerve activity by increasing the predominance of sympathetic nerve function in mice.The reduced HRV is independently associated with the arrhythmogenesis of both AF and VT and is involved in the development of hypertension.Moreover, in the present study, we observed cardiac sympathetic hyperinnervation and parasympathetic withdrawal in rats exposed to tobacco and marijuana products. Cardiac neural remodeling plays an important role in arrhythmogenesis by inducing triggered activity and changing the automaticity of cardiomyocytes.The over-innervation of sympathetic nerves is associated with the arrhythmogenesis of both AF and VT.Both ECNS and ICNS are equally important in maintaining a normal cardiac physiology. Without ECNS control, pathological change of ICNS itself could be proarrhythmogenic.Our observed cardiac sympathetic hyperinnervation and parasympathetic withdrawal may be the cause of the subsequent cardiac electrical remodeling. Although we did not directly link the neural remodeling to electrical remodeling, previous studies have demonstrated that sympathetic hyperinnervation in the ICNS can promote Ca2+-initiated triggered activity.One reasonable explanation for the shortened APD80 and prolonged CaTD80 is that sympathetic nerves activate intracellular Ca2+ transients, while parasympathetic nerves activate IKAch, leading to triggered activity due to the late phase-3 early after depolarizations.The development of fibrosis and the decrease in capillary density in post-smoking/vaping hearts may be the consequence of the elevated SBP. In non-ischemic conditions, cardiac fibrosis leads to hypertension, cardiac hypertrophy, or heart failure with preserved ejection fraction, of which the latter is often accompanied by microvascular rarefaction.

This provides an important indication that smoking or vaping may cause multiple aspects of cardiovascular disease beyond arrhythmias, because the latter could be a symptom of other severe CVD; and the pathological findings including changes in fibrosis, microvessels, and nerves may cause more problems than we observed. Moreover, the increased ratio of TIMP1/MMP9 or the decreased MMP9 was known to accelerate fibrosis and microvessel remodeling. Accordingly, the increase of MMP9 may be able to inhibit fibrosis and restore vascular network.The present study therefore identified the important role of MMP9 in smoking-related cardiac fibrotic and microvessel remodeling. Smoking/vaping-related CVD affects countless people but is not named as a disease. Given the increasing use of marijuana and novel tobacco products including e-cigs and HTPs, and common perceptions that these products are relatively free of health risks, our results indicate that all of these products may still carry substantial risk of development of cardiac disease. Our findings may provide clues to treat smoking/vaping-related CVD by preventing hypertension, targeting HRV, intracellular calcium handling, and fibrosis. An improvement of calcium regulation may benefit heart function and reduce susceptibility to arrhythmia. In addition, since the neural remodeling of the heart, which here mainly refers to increased sympathetic innervation, is highly associated with CVD development, nerve ablation or stimulation may be a potential therapeutic measure applied to such pathophysiological changes. The improvement of HRV by multiple interventions may be accompanied by the improvements of sympatho-vagal rebalance and calcium handling. A limitation is that while most procedures underwent completely blinded analysis, only one investigator was able to access the lab during the COVID-19 shutdown and thus the exposures and the cardiac function and SBP data collection were performed by the same person. Those data were subsequently coded, randomized, and the investigator analyzed the data at least 2 weeks later,ebb flow table now blinded to the identity of each animal. Another limitation is that our study used entirely young, healthy rats; age or comorbidities presumably result in a more complex physiological effect. Whether smoking-related cardiac nerve activity correlates with frequency of arrhythmia episodes needs to be further investigated. This study was not designed to assess the effects of life-long product use, so we do not know whether the adverse effects that we observed were at their saturation point, or would have continued to worsen with continued chronic exposure. Together with nicotine, they represent a relevant health problem because of the clinical consequences of their abuse. Their psychotropic effects are well known and recent research has shown that there is a close link between cannabis and alcohol . The endogenous cannabinoid system [a functional set of lipid transmitters and receptors that is the target of both natural and synthetic cannabinoids ] has been shown to mediate some of the pharmacological and behavioral aspects of alcohol . Both cannabinoids and alcohol activate the same reward pathways and the cannabinoid CB1 receptor plays an important role in regulating the positive reinforcing effects of alcohol as well as alcohol relapse . Several studies have documented that endocannabinoid transmission becomes hyperactive in reward-related areas during chronic ethanol administration.

This hypothesis is based on two findings. First, the increase in the levels of both anandamide and 2-arachidonylglicerol, the two main endocannabinoids, observed in animals chronically exposed to ethanol. Second, the down-regulation of CB1 receptors induced by endocannabinoid-mediated over-stimulation . Following this rationale, cannabinoid CB1 receptor knockout mice show reduced alcohol preference and selfadministration . However, the role of cannabinoids in alcohol- and drug-induced reward modulation demands further research because of the few studies addressing the actions of cannabinoid CB1 receptors on self administration paradigms, with respect to the more extensive research performed in alcohol consumption tests. Cannabinoid CB1 receptor agonists increase ethanol consumption in normal and alcohol-preferring ⁄ alcohol-avoiding rodent strains . The involvement of cannabinoid CB1 receptors is further supported by the effects of the cannabinoid receptor antagonist SR141716A, which reduces ethanol intake and preference . This modulatory action of cannabinoid CB1 receptors in the reinforcing ⁄ rewarding effects of ethanol is further indicated by the reduction of ethanol rewarding properties in cannabinoid CB1 receptor knockout mice . Although the activation of cannabinoid CB1 receptor increases ethanol consumption, there are no clear indications that it may result in enhanced ethanol self-administration or relapse . Moreover, studies suggest that activation of CB1 receptor reduces operant responding for drugs and food in a variety of paradigms . As an example, cannabinoid CB1 receptor agonists decrease both cocaine intravenous self-administration in rats and the reinforcing actions of cocaine ; another study by Braida & Sala confirmed that the combination of the cannabinoid CB1 receptor agonist CP55 940 with methylene dioxy methylamphetamine reduced the number of drug-associated lever pressings. However, the cannabinoid CB1 receptor antagonist SR141716A has been shown to block the rewarding properties of both food and most drugs of abuse. The treatment with SR141716A reduced both the self-administration and the subjective effects of tetrahydrocannabinol in rats, monkeys and humans . It also decreased heroin self-administration as well as morphine-induced conditioned place preference , nicotine self-administration and nicotine-induced conditioned place preference . In at least one study, cannabinoid receptor antagonism produces a biphasic effect, with a transient increase in heroin self-administration followed by a profound inhibition of operant responding for the opiate . The different effects of cannabinoid receptor agonists on alcohol consumption vs. alcohol self-administration have been interpreted as being due to differences in apparatus, experimental design and subjects used. To date, although there is a consensus in the literature with regard to the ability of cannabinoids to increase ethanol consumption, we need to clarify the role of cannabinoid receptors in operant responding for ethanol. This is important in order to establish the contribution of the endogenous cannabinoid system in alcoholism and may help to design new endocannabinoid-based therapies for this common addiction. The wide distribution of cannabinoid receptors and their role as a modulator of synaptic transmission make difficult the interpretation of these actions of direct cannabinoid receptor agonists⁄ antagonists on alcoholism. They also limit the utility of direct cannabinoid CB1 receptor ligands for the treatment of drug abuse. A pharmacological alternative that might reduce these problems may be offered by anandamide reuptake inhibitors. These drugs have been used in vivo in an effort to demonstrate their ability to inhibit cellular accumulation of anandamide and thereby stimulate cannabimimetic signaling. The anandamide reuptake inhibitor N- arachidonoyl-ethanolamide produces physiological effects similar to anandamide in vivo and potentiates the receptor-mediated effects of exogenously administered anandamide . Although numerous studies have examined and compared the pharmacology of cannabinoid agonists and antagonists in reinforcing effects of ethanol, there are no studies addressing the effects of the anandamide transport blocker AM404 on alcohol-seeking behaviors . Considering the importance of the endocannabinoid system in ethanol intake and reward, as well as the dynamic changes in anandamide production during acute and chronic ethanol exposure , we decided to study both the effects of the administration of the anandamide reuptake blocker AM404 in rats trained to self-administer ethanol and its actions in rats exposed to relapse induced by contextual stimuli previously associated with ethanol.Training and testing were conducted in standard operant chambers located in sound-attenuating, ventilated environmental cubicles. Each chamber was equipped with a drinking reservoir , positioned 4 cm above the grid floor in the center of the front panel of the chamber, and a retractable lever, located 3 cm to the right of the drinking receptacle. Auditory and visual stimuli were presented via a speaker and a light located on the front panel.

The original dataset, with 7875 cases, was used for the missing value replacement method, because as mentioned previously, it is preferrable to include as many predictive variables as possible in the model so that the new replaced/imputed values are indeed best estimates. Once the dataset had the missing variables for age and alcohol screen result replaced, the dataset was then amended to only include participants greater than 16 years of age to meet the inclusion criteria. Once those cases were removed, the final dataset consisted of 4910 unique cases. The first aim of this study was to identify the prevalence of THC in a purposive sample of TBI patients. In this study, it was found that 27.7% of study participants tested negative for THC, and 6.2% of study participants had tested positive for THC on presentation to the emergency department. An overwhelmingly large percentage of the data was attributed as missing, 66% to be exact. This large percentage of missing data makes it difficult to have confidence in the 6% prevalence rate found in this study. National surveys on drug use and health have documented an increase in individual daily marijuana use over the last 5 years, with almost 22 million users each month in the United States . Federally, marijuana use remains illegal in the United States, however, in 2017, the year corresponding to the data of this study, 29 states had legalized marijuana for medical use,hydro trays and 8 states for recreational use. A recent study has found that marijuana use tends to be higher in states that have legalized its use compared to marijuana use in the United States overall .

As a result, it is difficult to have confidence in the low prevalence rate found in this study. Another important consideration to make regarding the large percentage of missing data is the scarcity of studies investigating marijuana use and prevalence in TBI patients. As noted earlier in the literature review, only one study, by Nguyen et al. , investigated the effects of THC presence on mortality in patients who had sustained a TBI, and they reported a prevalence rate of 18.4%. However, Nguyen’s et al. study involved a 3-year retrospective review of data obtained from a local hospital-based database, which can perhaps help explain their higher prevalence rate. The availability of a larger sample size because of 3 years’ worth of data may have contributed to that study’s higher prevalence rates. A recent publication has already noted areas of improvement necessary for the NTDB to improve data quality and completeness . It is important to note that the dataset used for this study reflects only one year worth of data, from 2017. At the start of this research study, the last dataset available for use was from 2017; datasets from 2018 and onward had not yet been released. Therefore, establishing previous prevalence rates for comparison, from the NTDB, could not be calculated because the presence of THC was never abstracted nor documented in earlier NTDB databases established before 2017. Finally, it is imperative to consider what happens at the bedside, or the clinical setting, when trying to understand why there is a large percentage of missing data when it comes to the presence of THC. When it comes to the care of the trauma patient, it is a common expectation amongst trauma centers, that a urine drug screen would be completed on every trauma patient presenting the emergency department. Despite this, drug screens are often either not obtained, not resulted, or not documented by the clinical team. At times, clinicians may simply forget to draw a screen and send it to the lab.

This commonly occurs in patients who do not receive a foley catheter, a practice that is now encouraged in hospitals. As a result, patients may take a while to urinate, often doing so in the absence of the trauma nurse, or later in another unit or when under the care of a non-trauma nurse who then simply forgets to collect the sample. At times, the sample may be collected, but the result was never documented in the medical record. All these clinical factors can also contribute to the missing data by simply not including it in the medical record, and ultimately not making it into the trauma registry itself. When examining the differences between the group of participants with THC and those without and the influence on TBI severity, it was noted the group of participants who tested positive for THC had worsened GCS scores compared to those who tested negative for THC on presentation to the emergency department. The findings were significant, indicating that individuals who were positive for THC had a worsened neurological status as evidenced by lower GCS scores than those who tested negative. This finding is different than findings reported in the study by Nguyen et al. , which examined the relationship between the presence of THC and mortality after TBI. Their study only focused on mortality after TBI and not TBI severity. Based on toxicology test results, participants who tested positive for THC had a significantly higher number of males. Additionally, participants in the group that tested negative for THC were significantly older than participants who tested positive. This is supported by the literature, which indicates that men are more likely than women to use marijuana, as well as almost all other types of drugs . Individuals 18-29 years of age were the largest group of marijuana uses in the US in 2019 . Marijuana use dropped among older age groups, with seniors the least likely to use marijuana . No differences were noted in Non-Hispanic versus Hispanic groups regarding marijuana use. Marijuana use was higher in the American Indian and Black participants when compared to all other race groups.

Participants who identified as ‘other’ had a greater proportion of testing negative compared to all other race groups. Marijuana use disorder was greatest among African Americans compared to other race/ethnicities . Marijuana policies are rapidly evolving in the United States, however, previous marijuana laws disproportionately targeted communities of color before legalization, and many policy makers argue that new policies are not being developed with the input of minority stakeholders. Biomedical research has also marginalized and underrepresented communities of color. There is an obligation on the part of researchers, especially in the context of trauma and marijuana use, to actively work toward improving equity in marijuana related research. The mean blood alcohol level for participants in the group that tested positive for marijuana was higher when compared to the group that tested negative. Though the difference was not statistically significant, it corroborates finding from the literature, that marijuana is the most used drug among individuals who drink. A study by Subbaraman and Kerr found that individuals who use both marijuana and alcohol tend to use them at the same time, and that the odds of drunk driving, social consequences and harms to self were doubled. Participants who had a history or presence of cancer were more likely to test positive for marijuana compared to those who did not have a history or presence of cancer. The difference was statistically significant. Studies examining the use of marijuana for the treatment and management of symptoms medical conditions such as cancer is growing rapidly. There is evidence suggesting that cannabis for medical use reduces chronic and neuropathic pain in cancer patients . These studies support the finding in this study that a larger proportion of patients who tested positive for marijuana had cancer documented as a comorbidity. Similarly,vertical grow system participants who had a substance abuse as a history or comorbid condition documented were more likely to test positive for THC when compared to those who did not have substance abuse as a comorbid condition. This finding too is supported in the literature, as marijuana use has been associated with concurrent use of other drugs . An important consideration needs to be made in the context of this finding; for the variable of presence of other drugs, 66% of the data was missing. Since there is a large percentage of missing data, results should be cautiously interpreted and not assumed to be valid at face value in the context of such a large percentage of missing data. Lastly, no differences were found between the two groups of participants who tested positive and those who tested negative for THC when looking at likelihood of being involved in a motor vehicle of motorcycle collision. This study indicated a significant relationship between GCS scores, sex, alcohol results, and history of substance abuse. There is a small positive correlation between age and GCS scores which suggest that increases in age were correlated with an increase in GCS scores. Conversely, there was an inverse relationship between alcohol screen results and GCS scores, where higher blood alcohol screen results were significantly associated with lower GCS scores, and ultimately, more serious TBIs. Lastly, age and alcohol were also correlated significantly, with higher alcohol levels in younger patients. These findings are supported by research studies that investigate the relationship between alcohol, age and TBI severity. In a recent study by Leijdesdorff et al. , it was found that TBI patients with high blood alcohol levels were predominantly male and were younger.

Furthermore, TBI patients with positive blood alcohol levels were found to have higher levels of disability and significantly poorer cognitive outcomes on discharge . While patients with a positive THC test had significantly lower GCS scores on admission when compared to patients who did not have THC, or were not known to have THC on admission to the ED. Once other variables, including age, presence of alcohol on admission, sex, presence of other drugs and comorbidities were considered, findings indicated that the presence of THC was indeed associated with lower GCS scores, hence worsened TBI severity, however, the findings were not statistically significant. Age, race, ethnicity, motor vehicle collisions, and motorcycle collisions were also not shown to be independent predictors of TBI severity. Conversely, sex, presence of alcohol on admission, presence of other drugs, and a history of substance abuse were identified as independent predictors of TBI severity. Being female was associated with higher GCS scores indicating a less severe TBI. Similar to findings in previous studies examining TBI and sex, 67% of the study sample were male, while 32.9% of the sample were female. Gender differences in TBI incidence have been well documented, with men more likely to engage in injury-prone work or high-risk dangerous behavior . Additionally, women are less likely to be involved in a physical altercation than men . Furthermore, gender differences, can influence clinical outcomes between men and women. Research studies have proposed that female steroid hormones may exert some neuroprotective effects through antiinflammatory and antioxidant processes and may therefore explain why women tend to have better cognitive and functional outcomes after a TBI when compared to men . As expected, this study showed that the presence of alcohol and drugs at the time of injury were independent predictors of lower GCS scores, or otherwise a moderate or more severe TBI. The TBI literature does provide evidence of a close relationship between substance abuse disorder and TBI . Large percentages of patients who have sustained a TBI have a history of alcohol abuse and drug use, up to 79% and 33% respectively . In another study by Andelic et al. found that 35% of TBI patients were under the influence of alcohol. In this study there was a large percentage of alcohol levels missing, therefore, data was imputed. If in the original data set values were consistently measured and recorded, then findings regarding alcohol presence at presentation would possibly be much higher. Nevertheless, with the imputed values only 23 unique cases did not have an alcohol result. This too, may bias the finding, but like other study findings, this study’s finding showed that when alcohol was present at the time of injury participants had a lower GCS score, hence a more severe TBI indicating a worsened neurological status at presentation. Likewise, patients who were positive for at least one substance/drug were also found to have lower GCS scores and worsened TBI severity.

The review showed a great variation existed across the studies in types of data collected and methods used, thus severely minimizing comparability. For example, the disparity in measurement of blood alcohol levels considerably reduce the reliability of data related to preinjury intoxication. In the reviewed studies, information on alcohol and substance use was obtained from a range of different sources, including self-reports and patient records, as well as a variety of different measures rendering results unreliable across studies. This review set out to answer a specific question: what influence, if any, does marijuana exposure at time of injury have on TBI severity and outcomes? Only one study about marijuana’s effect on TBI outcomes was available. Nguyen et al. reported that a positive marijuana screen is an independent predictor of survival, suggesting a potential neuroprotective effect of cannabinoids in TBI. The rest of the studies yielded a variety of findings, with the most common finding being that marijuana and other drug use, including alcohol, are common before TBI. To clearly understand what marijuana’s influence on TBI is, potential confounding variables must be identified and controlled for. The variability in all other demographic variables highlights the lack of certainty of the full range of relevant demographic variables. Another potentially important confounding variable is mechanism of injury. Historically, the most frequent cause of TBI related deaths in civilians was considered motor vehicle crashes. However,air racking recent data show that falls are actually the leading cause of TBI related hospitalizations, with the second leading cause is being struck by another object. 

Importantly, only six of the studies included mechanism of injury as a variable in their analysis of findings. Five of the eight included studies did not address TBI severity as a variable. The remaining three studies each operationalized TBI severity utilizing different methods. Andelic et al. used the Marshall classification to classify neurological anatomical abnormalities as seen on CT scans. Nguyen et al. utilized the Abbreviated Injury Scale score for the head and neck region to classify TBI severity. The use of the AIS score is common in general research studies as often times the GCS score is not always recorded for each individual participant. Hence the only study showing a link between marijuana exposure and TBI severity did not use the gold standard of GCS to measure TBI specific severity. Finally, severity as a variable in the TBI population is an important characteristic and is a parameter of interest when answering the research question of whether or not marijuana influences TBI severity; available studies are not able to answer that question mostly because the majority of them did not measure severity in the first place. Severity is important because it provides a level of specificity about the injury which determines management of care. Additionally, TBI severity can yield valuable insight about proximal and distal outcomes. It seems reasonable that it would be an important measure to include when examining the relationship between TBI and all included variables. Additional tools, such as the AIS scores and imaging studies, may be necessary in accurately capturing TBI severity in study participants; these studies, in addition to GCS, should be considered an essential variable that must be accounted for. All of the studies measured presence of marijuana, yet the methods by which marijuana was measured varied. For example, urine was the most common way to measure marijuana concentration in patients in reviewed studies, but urine tests results are not specific to time of injury: The detectable level of marijuana can be present in urine for approximately 4.6 to 15.4 days after last use for infrequent and chronic users respectively.

The presence of marijuana on a urine toxicology screen may not accurately reflect or correlate marijuana levels in an individual’s system at time of injury, rather, it reflects recent use. Therefore, when considered as a variable, a marijuana level should be considered as reflective of recent use at time of injury, not directly at time of injury. Finally, this review and other systemic reviews consistently identify blood alcohol concentration as an important potential confounder in TBI studies. All reviewed studies except included alcohol as one of the examined substances. Much has been studied about the relationship between alcohol and TBI. As a prominent pre-disposing factor in TBI, the implications alcohol intoxication has on TBI is important and must be accounted for when examining the effects of marijuana on TBI. The current systematic literature review has several limitations, the first of which was the inability to perform a meta-analysis with the studies acquired. There was heterogeneity across the studies addressing marijuana exposure and TBI; from different criteria used to classify TBI, to diverse populations of interest, to varied outcomes of measures, the studies varied widely preventing a meta-analysis of the 8 included studies. Additionally, the studies differed in the type of data they collected, especially individual level data, which do not provide the necessary statistical measures that would make a meta-analysis meaningful.The use of self-report methods, which can be susceptible to recall bias, a method utilized in the majority of the reviews included, poses another limitation. Future studies that integrate objective methods of measurement would be useful for confirming the presented results thereby enhancing comparability. Additionally, all 8 included studies were retrospective cross-sectional type studies; these types of study designs limit the ability of establishing causality and directionality of relationships as well as any inferred associations. As a trauma clinician, my theoretical orientation focuses on injury prevention and favorable outcomes in the context of severe trauma, especially TBI. Epidemiological observation shows that the landscape of TBI prevalence and outcome is changing, but there is not a good understanding of how, which would make visible actionable areas for beneficial intermediation. Therefore, this study orients to the phenomenon of TBI epidemiologically. The definition of epidemiology is the study of the distribution and determinants of diseases and injuries in human populations .

Epidemiological data includes data gathered via interviews, archival research, and record review as well as via direct observation. The unit of analysis in epidemiology is the individual, yet focuses on identifying factors that are deleterious to the public . For the phenomenon of marijuana use, there are many reasons why individuals choose to use marijuana, either for recreational purposes or medicinal purposes. While the social aspects and context for marijuana exposure and use are important in and of themselves, potentially adverse clinical outcomes are equally as important and valid. Findings of this epidemiological study may uncover important demographic characteristics and health effects that warrant further study to help better understand the positive and negative effects of marijuana exposure in the context of traumatic brain injury. In summary, while the individual and social characteristics of marijuana use may be diverse and in need of study,drying weed findings from the literature review in Chapter 2 identified that these individuals as a collective group are potentially at more at risk for incurring an injury that could eventually lead to a TBI. Determining this risk and identifying characteristics of the population that are potentially at greater risk than others is an appropriate focus for study.A correlate is defined in this study as a characteristic of the marijuana-positive patient. Correlates were identified through the literature review , other observational research, and clinical practice expertise. Identified correlates include age, gender, race, ethnicity, other substances, alcohol, alcohol use disorder, chemotherapy for cancer, disseminated cancer and mental/personality disorders. Age will be included because research has shown that certain age groups comprise of a larger percentage of current marijuana users . While no research has associated gender and ethnicity with marijuana use, they are both variables commonly studied in most observational research, therefore, it will be included in this study. Alcohol and other drug use at time of injury will be included in this study because research has shown that 1 in 8 individuals had both alcohol and an drug use disorder in the past year . Cancer dissemination and cancer treatment are included as correlates because studies show that smoked marijuana may be helpful in the treatment of nausea and vomiting because of chemotherapy . Other studies have found that smoked marijuana may be beneficial in the treatment of neuropathic pain related to chemotherapy treatment . Finally, mental illness will be included as a variable as Cannabis Use Disorder is much higher in individuals with schizophrenia, personality disorders, post-traumatic stress disorder, mood and anxiety disorders, and other types of mental illnesses when compared to the general population . Identified correlates were both identified and confirmed as extractable in the NTDB. There were no identified correlates that were NOT present in the NTDB. See Chapter 4 Table 6 for details about how each variable will be operationalized. A mediator variable is the variable that causes mediation in the dependent and independent variables; for this study, marijuana exposure and TBI severity. A TBI occurs via injury, and the mechanism of injury is the mediator variable for this study. The leading causes of injuries resulting in TBI prevalence are collision related, such as motor vehicle crashes, or non-traffic related, such as falls. Mechanism of injury variables for this study include ICD-9/10 medical classification code categories for external causes of injury.

See Chapter 4 Table 8 for details about each code to be used.Confounding is a type of bias where a variable is associated with both the exposure and a given outcome resulting in a misrepresentation of the true relationship . Confounding variables may conceal a true association, or they may falsely demonstrate an existent association between an intervention or exposure and an outcome when no association actually exists . For this study, a confounder is defined as a variable that is associated with both the independent and dependent variable. Confounders were identified through the literature review , other observational research, and clinical practice expertise. Identified confounders include age, gender, alcohol exposure at time of TBI, and alcohol use disorder, which is defined by the DSM-V as medical diagnosis indicating that the problem of drinking has become severe and chronic for the patient . Physicians typically diagnosis this disorder through their history and physical assessment, which is documented in the patient’s medical record and extracted into the NTDB by trained trauma program registrars. Participants’ age as well as gender may be potential confounders, with males being at higher risk of sustaining a TBI . Another potential confounder in this study is alcohol, or alcohol use disorder. There have been extensive studies conducted on the relationship between alcohol and TBI related outcomes, with alcohol identified in 35-50% of individuals who sustain a TBI . Another confounding variable that will be examined is the use of other drugs. Evidence suggests that there is an increase in the presence of other drugs, aside from alcohol, in injured and fatally injured drivers . Furthermore, findings from the literature review showed that the presence of other drugs in combination with marijuana was a common occurrence.The first phase of the data cleaning process will be data screening. When screening data, four types of abnormalities will be assessed: missing data, inconsistences and outliers, odd patterns of distributions, and unexpected analysis results, inferences or abstractions . Descriptive tools, such as Statistical Package for the Social Sciences will be utilized to facilitate the screening process and ensure the process is objective and systematic. A potential source of problem in this study that may be encountered during data collection is missing data, outliers, and inconsistencies due to the use of a database that includes existing data that was not specifically collected for the purposes of this study. Errors such as blank fields, unintentional deletions or duplications during data entry, blank data fields, or values incorrectly entered must be accounted for . Screening methods involving graphical exploration of distributions and statistical outlier detection will be utilized. The second phase in the data cleaning process is the diagnostic phase. In this phase, a diagnosis regarding the nature of concerning data points or patterns will be attempted. Potential diagnoses for each data point include the following: erroneous, true normal, true extreme, or idiopathic . The correct value or data point for certain fields can be obvious and easily noticed . For such erroneous or missing data points, processes regarding dealing with missing data will be implemented and corrected prior to analysis.

Leveraging comprehensive neuropsychological data from the large-scale cohort of the HIV Neurobehavioral Research Program at the University of California-San Diego, we used novel machine learning methods to identify differing profiles of cognitive function in PWH and to evaluate how these profiles differ between women and men in sex-stratified analyses. Rather, than using traditional cognitive domain scores, we used each of the NP test outcomes given that prior studies indicate that the correlation of NP test scores does not map to traditional domain scores in PWH. Furthermore, we determined how sociodemographic , clinical and biological factors related to cognitive profiles within women and men. Based on previous studies among PWH , we hypothesized that the machine learning approach would identify distinct subgroups of individuals with normal cognitive function, global cognitive impairment, and domain specific cognitive impairment. We further hypothesized that groups with domain-specific cognitive impairment would differ by sex, with WWH showing more consistent memory and processing speed impairment than MWH. Finally, we expected that similar sociodemographic/clinical/biological determinants would distinguish cognitive profiles among WWH and MWH; however, in line with previous research , we expected that depressive symptoms would be more strongly associated with cognitive impairment profiles among WWH than MWH. NP test performance was assessed through a comprehensive, standardized,indoor grow methods battery of tests that measure seven domains of cognition, including complex motor skills, executive function, attention/working memory, episodic learning, episodic memory , verbal fluency, and information processing speed.

Executive functioning was assessed by the Trail Making Test -Part B and the Stroop Color and Word Test interference score . Attention/working memory was assessed by the Paced Auditory Serial Addition Task . Episodic learning was assessed by the Total Learning scores of the Hopkins Verbal Learning Test-Revised  and the Brief Visuospatial Memory Test-Revised . Episodic memory was assessed by the Delayed Recall and Recognition scores of the HVLT-R and BVMT-R. Verbal Fluency was assessed by the “FAS” Letter Fluency test . Information processing speed was assessed by the WAIS-III Digit Symbol Test , the TMT-Part A, and the Stroop Color and Word Test color naming score. Raw test scores were transformed into age-, education-, sex-, and race/ethnicity-adjusted T-scores based on normative samples of HIV-uninfected persons . The use of demographically-adjusted T-scores are intended to control for these demographic effects as they occur in the general population.We examined sociodemographic, clinical, and biological factors associated with cognitive impairment in the literature and available with enough participants to be adequately powered in analyses. Sociodemographic factors included age, years of education, and race/ethnicity. Although these factors were used to create the T-scores, there can still be remaining demographic associations with cognition within clinical populations such as PWH. For example, there is considerable interest in the possibility of abnormal cognitive aging PWH; also, in general, older PWH tend to have had their infections longer, may have had longer periods without benefit of suppressive ART, and more history of worse immunosuppression.

Clinical factors included functional status as indicated by the number of daily activities with decreased independence from the Instrumental Activities of Daily Living questionnaire from the modified version of the Lawton and Brody Activities of Daily Living Questionnaire , reading level based on the Wide Range Achievement Test-4 Reading subtest , self-reported depressive symptoms on the Beck Depression Inventory versions I or II , and diagnosis of lifetime and current major depressive disorder as well as lifetime alcohol, cannabis, or other substance use disorder based on the Composite International Diagnostic Interview using DSM–IV criteria . Biological factors included HIV disease variables such as current CD4+ T-cell count, lowest CD4+ T-cell count ever recorded , plasma HIV viral load, estimated duration of HIV disease, current use of ART, current use of anticholinergic-based medications , Hepatitis C co-infection, and the cardiovascular comorbid conditions of hypertension, hyperlipidemia, and diabetes. All 13 NP tests were used to find groups of similar cognitive profiles within each participant subset and in the total sample using a pipeline that consisted of dimension reduction with Kohonen self-organizing maps followed by clustering to identify profiles based on those reduced dimensions. SOM was implemented using the Kohonen package in R . SOM is an unsupervised machine learning technique used to identify patterns in high-dimensional data by producing a two-dimensional representation consisting of multiple nodes where each node is a group of one or more individuals with similar cognitive profiles and the location of the nodes within the 2-D representation is also a metric of similarity. Unlike probabilistic models, each individual can only be assigned to one node.

The SOM grid consisted of a 10 × 10 hexagonal grid of nodes and the number of clusters for the final profiling was selected by looping over models created from 3 to 20 clusters and selecting the number that had the best fit based on entropy. Similar nodes were then clustered using the MClust package . MClust is an R Software package used for model-based clustering using finite normal mixture modeling that provides functions for parameter estimation via the Expectation-Maximization algorithm with an assortment of covariance structures which vary in distribution , volumes , shape , and orientation . This program identifies the best model based on entropy . Once the clustering of the nodes was completed, cluster profiles were assigned to the individuals associated with that node. By using SOM and MClust in sequence, we were able to achieve fine-tuned clustering based on patterns of performance in cognitive testing. Factors predicting profile membership between each impaired and unimpaired profile in the overall sample and within each group were explored by creating a predictive Random Forest model using the Caret package in R and then extracting variable importance . RF is an ensemble machine learning model based on classification trees that results in powerful prediction models based on non-linear combinations of subsets of input variables. Prior to model creation, the Synthetic Minority Over-sampling Technique with the DMwR package was used to control for bias due to any imbalance in the number of cases. RF models were created using internal validation using a 10-fold resampling method repeated 5 times. Pre-processing before RF creation involved removing variables as predictors if they had low variance or if they had >50% missing data. Any missing data in the remaining variables was imputed using the Multivariate Imputation by Chained Equations  package in R using random forest imputations. ROC confidence intervals were calculated using the pROC package in R with 2,000 stratified bootstrap replicates . Variable importance of all variables included in the RF models was used as the outcome metric of the predictive power of each variable. Variable importance is a scaled number [0–100] that indicates how important that variable is to the final predicted outcome in that model. For each tree in the RF model,cannabis dryer the out-of-bag portion of the data is recorded and repeated after permuting each predictor variable. The difference between the accuracy with and without each variable is averaged over all trees and then normalized by the standard error. For visualization, all variables were plotted by relative variable importance, and attention was given to the top 10 variables in each profile. Variable importance indicates how much that variable contributes to overall prediction accuracy, but as RF is non-linear model it does not indicate directionality. While the analysis pipeline and packages used along with the parameter inputs are stated above, we have added our code into a Supplementary Material to facilitate rigor and reproducibility. In this large-scale study using a novel pipeline combination of machine learning methods, we provide further evidence in support of heterogeneity in cognitive function among PWH. Our results do not negate the heterogeneity in cognitive function in HIV-uninfected individuals but rather highlights the heterogeneity among PWH that can often be masked by a dichotomous HAND categorization. In the total sample, we identified an unimpaired profile, a profile of relatively weak auditory attention and episodic memory, and a global weakness profile. As expected, given the relative sample sizes, the cognitive patterns in the total sample were in greater alignment with those found among MWH compared to WWH. Similar to results in the overall sample, we identified an unimpaired profile and a global weakness profile in MWH; however, unlike the overall sample and inconsistent with hypotheses of domain specific cognitive impairment profiles in both MWH and WWH, MWH demonstrated a profile with relative strengths in attention and processing speed.

Conversely, there were no unimpaired, cognitive strength or global weakness profiles among WWH. Rather, as hypothesized WWH demonstrated cognitive profiles reflecting a global weakness and domain-specific impairment including a weakness in learning and memory and motor skills. These findings suggest that sex and the sociodemographic factors associated with female sex within the HIV-infected population contribute to the heterogeneity in cognitive function among PWH. Studies examining cognitive function in combined samples of men and women may mask important sex differences in cognitive functioning among PWH, particularly in maledominant samples such as the current sample. These sex differences in cognitive profiles among PWH may result from biological sex differences and/or the psychosocial factors that tend to characterize WWH more than MWH . Biological sex differences include those seen in the general population such as sex steroid hormones , female-specific reproductive events and genetic factors or previously-reported sex differences specifically in HIV disease characteristics unmeasured herein . Regardless of the underlying mechanism, characterizing these sex differences in cognitive functioning among PWH can provide inroads to identifying mechanisms of cognitive dysfunction and optimizing risk assessments and diagnostic and therapeutic strategies for each sex. A notable sex difference in profiles was the lack of the unimpaired or cognitive strength profile among WWH that was observed among MWH. Our cognitive profile analyses are in line with prior studies that suggests that WWH are often but not always, more likely to demonstrate cognitive deficits than MWH . Our analysis suggests that the impairment manifests more often as domain-specific impairment in women than in men that may not be revealed in a more cross-domain summary measure like GDS or global Tscores. This female vulnerability to cognitive deficits is thought to reflect sociodemographic differences whereby low education and socioeconomic status and their associated psychosocial risk factors are more prevalent among WWH vs. MWH . These psychosocial risk factors can have adverse effects on the brain that lower cognitive reserve , suggesting that interventions geared toward addressing these psychosocial factors should be a priority for WWH and/or for women who are at increased risk of HIV. In support of these studies, Sundermann et al. found that the higher rates of cognitive impairment in WWH vs. MWH were eliminated after adjusting for the lower reading level that characterized WWH compared to MWH. Biological differences may also contribute to sex differences in the pattern and magnitude of cognitive impairment in PWH including disease characteristics, brain structure/function, sex steroid hormones and female-specific hormonal milieus . There is also evidence to suggest that WWH may be more cognitively susceptible than MWH to the effects of mental health factors . As mentioned, only women demonstrated more domain specific cognitive profiles including weakness in motor functioning and relative weakness in learning and memory. Similarly, previous studies report that learning, memory, and motor functioning are among the domains in which cognitive impairment is more common among WWH vs. MWH and these differences persisted after adjusting for HIV RNA and CD4 counts . These sex differences in domain-specific impairment may reflect psychosocial factors , biological factors , or interactions among them. Although women in general demonstrate relative advantages in verbal memory and fine motor function compared men likely due, at-least in part, to the effects of estrogen on the developing brain and the neuroprotective effects of circulating estradiol , the menopause transition has been associated with declines in verbal memory and motor function . The mean age of women in our study was 41 suggesting that a portion of women may be experiencing cognitive deficits associated with reproductive aging. Germane to the learning/memory impairment in WWH, women are more vulnerable to the negative effects of stress hormones on hippocampal-dependent tests compared to men . This finding may be particularly relevant to the current sample considering the high prevalence of psychosocial stressors among WWH including childhood trauma and domestic violence .

Newsom was endorsed by the California Teachers Association, and the CCSA had organized a group of donors to spend $23 million supporting the campaign of the more pro-charter candidate: former Los Angeles Mayor Antonio Villaraigosa . In 2019, Newsom signed a bill that enhances the authority of local school boards to reject new charter schools, with other restrictive charter bills in the pipeline . Charter advocates are now uncertain about the future of charter schools in California, with some predicting a turn back to vouchers in the education reform community. Massachusetts was also an early charter adopter, passing its initial charter law in 1993 as part of a “grand bargain” on education that offered greater funding to high-poverty districts in exchange for competition and accountability. Massachusetts was highly selective about authorizing charter schools and maintained stringent caps limiting the sector from growing quickly. The result has been slow charter growth. But the schools that have emerged are some of the best-performing charters in the country . The success of this formula generated a bipartisan consensus supporting limited charter school growth combined with strict caps and authorization procedures that persisted through the 90’s and early 2000’s. This began to change with the election of Deval Patrick as governor in 2006, who took the reins from pro-charter Mitt Romney. The Massachusetts state legislature was also growing more anti-charter with the departure of key figures who had driven forward the landmark 1993 education bill. Advocates were able to raise charter caps in 2010, but with help from the federal RTTT, and also with major strings attached. In particular, new charter school spots would only be available to charter networks already operating in the state. Even with pro-charter Charlie Baker, a moderate Republican, elected in 2014,rolling greenhouse tables lack of support in the State Senate limited prospects to raise caps again and open new schools. Some charter advocates determined that their best shot at raising caps was a ballot initiative.

They were able to marshal a huge amount of financial support for “Yes on 2”, particularly from Michael Bloomberg and the Walton family. But the teachers unions, who drove the campaign against the initiative, were also able to raise a huge pot of money. Ultimately, the initiative lost badly, and was widely seen as a disaster for the charter movement in Massachusetts. Since then, there has been little support for new efforts to raise caps and open new charter schools. Both the California and Massachusetts cases highlight the role of major philanthropists in funding pro-charter political advocacy in states with developed charter sectors. In California, philanthropists provided critical support for the powerful CCSA, and in Massachusetts, philanthropists funded the campaign to raise charter caps in 2016. Yet, these cases also highlight how, as the charter sector has grown, the politics has become more fraught in liberal-leaning states. With charter schools posing a greater threat, teachers unions have used their political sway in these states to make it more difficult for growth to continue. Changing public policy is much more difficult than preserving the status quo, and enacting reforms that challenge vested economic interests is particularly difficult . In these cases, even when new policy is made, changes are susceptible to erosion and rollback. New policies’ susceptibility to rollback depends to a great degree on whether they dislodge the political power of incumbent interests, and also whether they contribute to the growth of proreform interest group coalitions . Charter school policy represents a case in which some reforms challenging incumbent education interests have been achieved and sustained, even though the incumbents have for the most part held onto their political power.

What is more, this has occurred even as charter schools themselves—the organized interests most vested in the preservation and expansion of pro-charter policies—remain politically weak relative to their opponents. This paper argues that a critical factor in the successes the charter movement has seen was the emergence of a foundation-funded pro-charter advocacy network. The emergence of this network depended on the ability of policy entrepreneurs to enact charter laws during a favorable “window of opportunity.” These laws allowed charters to form, which provided a proof-of concept and demand for resources that drew attention and prioritization from certain foundations. Foundations, in turn, followed up their investments in seeding new charter schools with investments in political capacity to defend and expand pro-charter laws. This case helps inform our understanding of the role of philanthropists in driving policy reform in the American political economy. Whereas historically philanthropists were seen in the political science literature primarily as patrons to civil society organizations , philanthropists increasingly directly engage in politics —particularly when it comes to education policy. Newer foundations with living donors have been more likely to promote reforms and fund jurisdictional challengers like charter schools . Foundations, I show, provided the pro-charter advocacy coalition with critical resources to build political strength before charter schools had the resources to support themselves politically. Thus, the pro-charter coalition was able to compete with powerful incumbent interests even before the challenger pro-charter industry had developed. Yet, this case also highlights the dangers of relying on philanthropic foundations to build political strength. The political winds have shifted on charter schools. While core elements of the Democratic party were strong supporters in the past, the party has increasingly aligned more closely with teachers unions against chartering. Support from Republicans remains lukewarm, with many focusing to a greater extent on promoting school voucher programs.

With political support eroding, some foundations have distanced themselves from the charter school issue. They are able to do so, in part, because they have less at stake in the politics than charter schools and advocates themselves—not to mention teachers unions and others in the anti-charter camp. The foundations are not really vested interests. Unlike these other groups, foundations’ organizational maintenance, at the end of the day, does not depend on the trajectory of charter policies. The very feature that makes them powerful—their resources, and the fact that their resources do not necessarily depend on winning political victories—also makes them unreliable coalition-members. Due to the success of antiretroviral therapy and an increase in the incidence of HIV infection among older adults, the proportion of older persons living with HIV in the United States is rapidly growing . Therefore, it is important to evaluate physical and emotional health among the changing demographics of PLWH. One of the most prevalent psychiatric conditions among PLWH is major depressive disorder ,ebb and flow rolling benches with PLWH at a two- to seven-fold greater risk for depressive disorders compared to the general population . PLWH have a higher prevalence of both MDD and subsyndromal depression symptomatology than HIV- individuals of the same age or the general population . A multi-site cohort study of over 1500 PLWH found lifetime depressive symptom rates of 63% and across multiple studies diagnosis of lifetime MDD ranges from 22–54% in PLWH, compared to 4.9–17.1% lifetime MDD diagnosis in the general U.S. population . These high rates of depression among PLWH represent a major public health concern, as depression has been linked to worse psychological and medical outcomes in PLWH, including lower reported quality of life, increased viral load, and a higher likelihood of mortality . Untreated depression in PLWH has also been related to increased cognitive complaints and worse reported daily functioning compared to PLWH without depression . These medical and psychological factors may be exacerbated in older PLWH who are often burdened to a higher degree with HIV-related medical and psychological factors, in conjunction with aging related problems . Despite the high prevalence rates of depressive disorders among PLWH, depression is often under diagnosed and inadequately treated within this population, though . Given the prevalence of depression among PLWH, it is vital to evaluate other co-occurring factors that may be associated with elevated depressive symptoms. Multiple studies have found an association between higher depressive symptoms and worse quality of life , even after controlling for demographic factors . PLWH with elevated depressive symptoms report lower mental and physical health-related quality of life , supporting the idea that depression affects multiple aspects of quality of life . However, there is a dearth of research regarding the association between depression and positive psychological factors, e.g. resilience, grit, and self-rated successful aging among PLWH. Two studies have found an association between higher resilience and lower depressive symptoms among PLWH . Similarly, in PLWH greater grit has been negatively associated with major depression .

In older adult persons without HIV, lower levels of depressive symptoms have been associated with increased self-rated successful aging ; however, few studies have been conducted to evaluate positive psychological factors and quality of life in relation to depressive symptomatology in PLWH compared to control participants. Given there is an increase in the population of older PLWH and that depression is a highly comorbid condition among PLWH, assessing the relationship between depressive symptoms and other psychological factors across different age decades may provide insights for clinical interventions. Therefore, we hypothesized that: 1) PLWH aged 56–65 would have the highest proportion of elevated depressive symptoms compared to HIV- participants; and 2) elevated depressive symptoms would be associated with lower ratings of HRQoL and positive psychological factors across groups, with strongest associations in the oldest PLWH.One hundred twenty-two PLWH and 94 HIV- individuals from the Multi-Dimensional Successful Aging Among HIV-Infected Adults study conducted at the University of California, San Diego HIV Neurobehavioral Research Program and the UCSD Stein Institute for Research on Aging participated in this study . The study was approved by the UCSD Institutional Review Board, and all participants provided written informed consent after the study was explained to them by a trained staff member. In order to enroll a representative cohort of participants, minimal exclusion criteria were applied and included: 1) neurologic condition other than HIV known to impact cognitive functioning , 2) psychotic disorders , and 3) positive urine toxicology on the day of testing for illicit substances other than cannabis. Inclusion criteria were: 1) aged 36–65 years, 2) fluent in English, and 3) ability to provide informed consent.The present study provides unique findings on the interplay of depression, HRQoL, and positive psychological factors among middle aged and older PLWH and HIV− individuals in a multi-cohort design structure. In our sample, PLWH were significantly more likely to report elevated depressive scores compared to HIV− individuals. This finding supports prior studies that have found PLWH endorse more depressive symptoms than HIV− individuals . Contrary to our hypothesis, the youngest cohort seemed to drive this finding, with a significantly larger proportion of PLWH reporting elevated depressive symptoms compared to HIV-individuals within this age group. That is, the proportion of elevated depressive symptoms did not differ by HIV status among the middle aged and older age cohorts. This difference highlights the importance of age in relation to depressive symptoms. For example, rates of elevated depression among PLWH were similarly high in all age groups. In contrast, only the youngest HIV− group had relatively low rates of elevated depressive symptomology, with higher rates in older cohorts. This is consistent with research estimating high prevalence of sub-syndromal depression among middle-aged to older adults, especially those with greater medical burden, disability, and lower social support . Overall the H+/D+ group reported the lowest physical and mental HRQoL; however, the relationships between the four groups differed depending on age cohort. While depressive symptoms in PLWH consistently related to lower mental HRQoL across ages, elevated depressive symptoms most prominently impacted physical HRQoL in the oldest H+/D+ group. These findings are consistent with prior studies that have reported a correlation between worse HRQoL and depression among PLWH . However, our novel findings highlight that the relationship between depression, age and HRQoL differs for mental components compared to physical components. Importantly, there were no differences on HRQoL or positive psychological factors between the two non-elevated depressive symptom groups . Similar to prior research, the H+/D− group reported comparable grittiness, resilience, and successful aging to the H−/D− group, which indicates that in the absence of elevated depressive symptoms PLWH rate themselves as having favorable positive psychological factors .

Analysis using citizen initiative rules as an instrument, though, as discussed above, can provide causal leverage. The key treatment is a measure of state marijuana legalization status at the end of the 116th Congress.In the main analysis, I code the treatment variable as 0 for states with neither medical nor adult-use, 1 for states with medical marijuana, and 2 for states with adult-use legalization.For outcomes, first, I record binary measures of whether members sponsored or co-sponsored each of the SAFE Banking Act, STATES Act, and MORE Act. I also estimate a broader marijuana bill sponsorship score by computing the proportion of the 14 priority pieces of legislation promoted by the industry group National Cannabis Industry Association sponsored or co-sponsored by each member. Two-stage least squares regression is used to estimate the effect of liberalization of state level marijuana law on these outcomes. The first stage predicts adult-use marijuana legalization from the ballot initiatives variable. First stage results presented in the appendix demonstrate that citizen initiative rules are a strong instrument for legalization. The second stage estimates the relationship between predicted legalization and bill sponsorship.I estimate models both with and without state- and member-level covariates: party-identification , ideology ; and state-level covariates: 2016 Democratic presidential vote share; and social liberalism of the mass public. Estimates are presented in Figure 3. For the SAFE Banking Act and the STATES Act, I estimate that state-level marijuana legalization increased the likelihood that members sponsored liberal marijuana bills.

The non-covariate adjusted coefficient of for the SAFE Banking Act, for instance,cannabis grow supplies indicates a 1-point shift in legalization status is associated with an increase of 24 percentage points in likelihood that members sponsored the Act. I estimate effects of similar magnitude for the STATES Act. I do not estimate a statistically significant effect for the MORE Act, which may be driven by the fact that sponsorship of this bill was more partisan than the others . Turning to members’ broader bill sponsorship scores, I find evidence of a causal relationship. The coefficient of .06 suggests that a 1-point shift in legalization status is associated with an increase of .06 , which corresponds to additional NCIA supported bills sponsored on average.In developing the paper’s theoretical argument, I proposed that state policy might affect representation in Congress by, first, shaping the political economy in ways that influence the pressures faced by reelection-seeking members; and second, by sending signals that provide information about constituent preferences. While providing a precise decomposition of the role of these potential mechanisms in the empirical case is not possible with the available data, in this section I bring together quantitative and qualitative evidence to provide some insight as to how these mechanisms have contributed to the overall effect observed. As part of gathering qualitative data, I conducted 8 semi-structured elite interviews with individuals working in marijuana politics and policy. In considering how state legalization affected the pressures faced by members of Congress, it is worth considering, first, the extent to which legalization has actually affected organized economic interests. The answer is: quite a lot, especially in states adopting adult-use legalization. According to NCIA, as of 2018 the average state with medical marijuana featured sales of $21 per capita , while the average state with adult use legalization featured sales of $130 per capita .Revenue growth in legalizing states has increased the capacity of industry interests to engage politically at the national level.

To examine exercise of instrumental power, I draw on lobbying and campaign contributions data collected by the Center for Responsive Politics. The data reveal a sharp increase in lobbying from marijuana industry coinciding with recent state adoption of adult-use legalization. Annual federal lobbying from the marijuana industry has grown from just 45,000 dollars in 2012—the year that Colorado and Washington voters legalized marijuana for recreational use by ballot initiative—to nearly 6 million dollars in 2019 . Campaign contributions data also suggest that legalization has affected the political presence of marijuana industry. Firms in the industry did not donate to congressional campaigns prior to the 2018 cycle. In the 2018 cycle, however, marijuana industry interests contributed in 19 percent of House races in states with adult-use marijuana, and just 2 percent of House races elsewhere. Discrepancies for the Senate were less stark, with contributions in 7 percent of races with adult-use marijuana, and 5 percent of races elsewhere. IV analysis again using citizen initiative rules to instrument for legalization suggests this relationship is causal . In addition to leveraging its growing resources for lobbying and campaign contributions, the marijuana industry has leveraged its economic growth to engage politically by mobilizing consumers and employees. For instance, in Colorado, Governor Jared Polis collaborated with industry interests to turn out marijuana consumers and industry employees in his 2018 reelection. As part of this effort, the campaign matched the state’s database of marijuana employees to the voter file to identify potential supporters, and then sent them targeted text messages and mailers .

The sway of marijuana industry and marijuana voters in Polis’s 2018 bid was a major reason why former Senator Cory Gardner, who anticipated a tough re-election in 2020 , made marijuana such a priority in the 116th Congress.Outside of industry mobilization, another potential mechanism is that state legalization leads the broader public in legalizing states to be more liberal on marijuana, which then drives members of Congress to respond by supporting marijuana reform. To investigate the association between marijuana legalization and public attitudes, I use state-level estimates of support for marijuana legalization collected by Caughey and Warshaw .Using a difference-indifferences design, I compare changes to public opinion in legalizing states to changes in public opinion over the same time period in a set of similar non-legalizing states. More specifically, I leverage a method recently developed by Xu , which is particularly useful since it allows for implementation of synthetic controls in cases of multiple treated units and variable treatment periods. The method uses a linear interactive fixed effects model to impute counter factuals for each treated unit . I consider legalization of marijuana for medical use and for adult use separately.Figure 6 plots average support for marijuana legalization over time for both the treated states and the synthetic controls. In addition to estimates of state-level support for legalization,dry racks for weed the model also includes estimates of mass ideology . If legalization led to greater public support for marijuana, we would expect the solid series representing legalizing states to jump above the dashed series at treatment . The evidence, though, suggests no such effect. Indeed, public opinion, for both adult-use and medical, is slightly more favorable in the synthetic control group, though not statistically distinguishable from the treated group. This suggests that state-level legalization has not disproportionately improved public opinion in the states where it is adopted.However, even if it did not drive improved public opinion, it is likely that marijuana legalization—especially in the majority of cases when it was enacted via ballot initiative— provided a signal of public favorability to lawmakers in Congress. This is the mechanism that, along with growing influence of industry in legalizing states, interviewees working as lobbyists and policy advocates were most likely to raise. It is difficult to investigate quantitatively, though. One analysis that can provide insight into the importance of this mechanism is exploring the relationship between the length of time since legalization and representation. If signaling were driving the effect of legalization on representation, we would expect members representing states with more recent legalization votes to adopt more pro-marijuana positions. If industry influence were more fundamental, we would expect members representing states that legalized further in the past, and where industry had a greater amount of time to develop, to adopt more pro-marijuana positions. Figure 7 presents models with members’ bill sponsorship scores and roll calls on the votes for which a positive effect of legalization was estimated as outcomes, and the number of years of legalization for a members’ home state as the key independent variables.

The left panel presents results for adult-use states and the right panel presents results for medical-use states . Broadly speaking, results lend support for the industry influence mechanism—not signaling. For adult-use states, in both bivariate and covariate-adjusted models, years since legalization is positively and statistically significantly associated with both bill sponsorship and roll-call outcomes. I recover similar results in the bivariate models of medical use legalization, but they are not robust to covariate adjustment. This may reflect the adult-use legalization tends to have a much greater effect on industry growth than medical-use legalization. Using an IV design that leverages exogenous variation in marijuana legalization from longstanding differences in the availability of citizen initiatives, I have shown that the policy landscapes in the states they represent affect the behavior of members of Congress. I observed strongest effects for bill sponsorship, but also effects on certain lower-profile roll-call votes. The evidence suggests the strongest mechanism driving these effects is growing industry influence in legalizing states, though other mechanisms—particularly signaling of public preferences— cannot be ruled out. The set of analyses is not without its limitations. One limitation is a short temporal window. Since adult-use legalization and the emergence of the marijuana industry are relatively new phenomena, data is limited. For instance, only a few roll-call votes related to marijuana legislation have been taken since the state legalization wave began. As more data become available, researchers will be able to extend the analyses performed here. In addition, while this study indicates a causal link between state-level adult-use legalization and representation in Congress, there remains uncertainty with respect to the mechanisms. Finally, it should be noted that these analyses likely underestimate the full effects of state legalization on the politics in Congress. While this paper demonstrates the effect of legalization on members representing legalizing states relative to non-legalizing states, mechanisms like growing industry presence in Congress are likely to affect legislators across the country—they are simply more pronounced in legalizing states. Future empirical work can build on the theoretical framework developed here to explore the role of different mechanisms in different policy cases. For instance, the role of signaling is likely unusually important in this case due to the role of the ballot initiative compared to cases where state policy reform is achieved via legislation. The findings on the role of state policy in structuring state political economies and, as a result, the pressures faced by members is likely to be more broadly generalizable to other policy areas. Consider, for instance, climate change. Industries reliant on the burning of fossil fuels are enormously powerful in American politics, spending vast amounts of money on lobbying and campaign contributions in federal as well as state and local politics . Organized interests enriched by the extraction and burning of fossil fuels have also become a key organizing force within the Republican Party . The power of these groups in our politics is built atop a set of policies in place across the federal system that not only fail to adequately price the negative externalities of burning fossil fuels , but also subsidize the production of fossil fuels . But as states governments continue to adopt and strengthen policies driving the transition to renewable energy , this is likely to reshape the pressures faced by members of Congress, potentially opening new opportunities for federal policy. Or consider policing. The killing of George Floyd in May 2020 led to widespread protests calling for actions across levels of government to enact major policing reforms, including in Congress . Though these reforms are widely popular , a major impediment to their enactment is the power of police unions, which leverage financial resources and ability to mobilize members to prevent reforms . Analysis drawing on the framework proposed here might explore the degree to which the power of police unions to prevent reform in Congress is bolstered by pro-police state and local policies. One key scope condition for this mechanism is the degree to which policy areas feature strong vested interests dependent on material benefits from government policies . Considering the role of state and local policy in congressional representation, another key scope condition is the degree to which governance is shared between federal, state, and local levels—a core feature of American federalism . The scope of sub-national authority in American federalism and increasingly active role of state governments in American politics means it is crucial that we develop a better understanding of the implications of state policies for the broader polity.