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More generally speaking, Meyer argues that experiences of victimization in the forms of stigma, prejudice, and discrimination that GBM experience may be the cause for the higher prevalence of mental health conditions in GBM populations and refers to this as minority stress . Stigma may also help explain why HIV-positive GBM were more likely to report a substance use disorder in our study. HIV-related stigma has been linked to poorer mental health in a meta-analysis by Logie and Gadalla and a review by Smit and colleagues . Readers should be cautious when interpreting our results. Most notably our results rely on participants’ retrospective self-report of recent substance use and sexual behavior and compare these data with lifetime mental health diagnoses. As such, we are limited in determining causal direction, but instead position these findings as a more representative profile of GBM who had ever been diagnosed with a mental health condition given our use of respondent-driven sampling. We did not conduct diagnostic interviews to account for undiagnosed conditions, and thus underestimated the true burden of mental health issues. We attempted to address current symptomology through the inclusion of AUDIT and HADS scores. However, given the paucity of validation studies for AUDIT, but particularly HADS within GBM populations, we caution the interpretation of these findings and call for new research validation studies with GBM populations. Regardless, our analyses demonstrate some measure of construct validity in that higher scores on both measures were linked to reporting mental health conditions in our study. Our measure of sexual orientation “outness” was only asked for gay-identified participants, and a general measure should be included in future studies. A nurse-administered structured interview was used to assess mental health diagnoses and current treatments to ensure these questions were more accurately understood and answered. Given the potential impact of social desirability and reporting bias , we used CASI to collect data regarding illicit substance use. However,4×8 grow table with wheels we did not use drug testing to confirm or correct self-report data and likely underestimated the true prevalence of substances used .

Despite these shortcomings, one of the strengths of our study is the use of RDS to overcome previous sampling shortfalls with GBM and produce a more accurate representation of the population parameters of these variables of interest for the GBM population of Metro Vancouver. Our study also adds new data regarding the detailed prevalence of substance use and mental health conditions among GBM populations in Canada filling a gap in currently available published literature. Finally, our work goes further to examine explicitly the relationship between substance use and mental health conditions among GBM identifying important relationships that have implications for counseling and public health services, interventions, and policy. The greater burden of mental health conditions and higher prevalence of substance use in GBM populations highlight the need for a more explicit focus on these issues in research and service provision. Mental health specialists should be aware of the relationships with sexuality and substance use when working with GBM clients, particularly issues regarding identity disclosure, number of sexual partners, and higher background community prevalence of substance use . Future research should seek to validate current measures and to confirm the relationship between substance use and mental health conditions, which has been demonstrated to produce a syndemic including suicidal ideation among GBM and HIV acquisition . Our study was based in a major metropolitan area, which may limit generalizability to GBM in rural or remote regions, whom are a population with distinct needs and challenges that should be further examined. In order to evaluate generalizability, additional research is needed to explore these issues among GBM populations in other urban and non-urban centers across Canada, particularly if these studies employ RDS or other more representative sampling methods. Given the role of social factors in mental well-being, future research should directly examine experiences of homophobia or heterosexism as possible precursors to substance use and/or mental health issues, along with potential mediators and protective factors.

Examining demographic factors independent of one another may not reflect the diversity of experiences that exists among GBM. Using an intersectional approach, which looks at how multiple identities such as race, sexual orientation, and class, interact with one another to shape experiences , may also explain the distribution and experiences of mental health and substance use within diverse communities of GBM. In spite of experiences of marginalization and discrimination, many GBM do not go on to develop mental health conditions or engage in harmful substance use. Shilo, Antebi, and Mor found that factors such as support of family and friends, meaningful connections with the LGBT community, and having a steady partner, protect against developing poorer mental health in lesbian, gay, bisexual, queer, and questioning adults. Thus, more focus on factors such as these that promote resiliency in GBM would be beneficial to include in future research on mental health and substance use in these populations. Compared with the Canadian population, GBM living in Metro Vancouver have increased levels of substance use and mental health conditions. The strong link between substance use and mental health among GBM has important implications for public health promotion programming and care service provision. A number of social determinants increase the likelihood of mental health diagnosis among GBM, including disclosure of sexuality, low income, and race/ethnicity. GBM living with HIV were significantly more likely to have a lifetime doctor-substance use disorder compared with HIV negative GBM. Greater attention to these issues is needed across all health and social services given their disproportionate effect on GBM populations. Health promotion and interventions should address issues of substance use, mental health, and sexuality in unison and future research can help direct these efforts by examining possible precursors of these issues, which may be the result of discrimination, prejudice, and stigma.Tobacco use in the general population has declined substantially in the past three decades, but rates remain high in certain populations.Among homeless adults, tobacco-related chronic diseases including heart disease, cancer and chronic obstructive pulmonary disease are common and contribute significantly to the increased morbidity and mortality in this population.Among a clinic-based sample of homeless adults aged 50 and older, tobacco-attributable deaths accounted for 26% of the overall mortality and 54% of substance-related mortality.

The health consequences of smoking occur disproportionately among older individuals because of the cumulative effects of long term smoking.Among older adults, tobacco-related chronic diseases, particularly chronic obstructive pulmonary disease and coronary heart disease, are among the most common reasons for emergency health care services and preventable hospitalizations.Current tobacco use contributes significantly to all-cause mortality among older adults, suggesting that tobacco cessation at any age is likely to significantly reduce tobacco-related morbidity and mortality.In a nationally representative sample, older adults were less likely to quit smoking than younger adults because of reduced interest in quitting smoking,grow tray stand higher nicotine dependence, and lower support for smoke-free norms.This highlights the need for tobacco cessation interventions that address tobacco-related beliefs and practices among older adults. Over the past 2 decades, the median age of homeless adults increased from 37years in 1990 to almost 50years in 2010.Despite increased tobacco-related morbidity and mortality among older homeless adults, little is known about tobacco use and cessation behaviors in this population. Prior research on tobacco use in the homeless population has focused on younger adults, where the average age of study participants in previous studies was less than 44 years.The high prevalence of tobacco use and the increased burden of tobacco-related chronic diseases with aging underscore a need for studies that characterize tobacco use and cessation behaviors among older homeless adults in order to develop tobacco control interventions that address the unique needs of this population. We conducted a study of a cohort of homeless individuals aged 50 and older sampled from the community to examine rates of and factors associated with tobacco cessation. We hypothesized a priori that current smoking would be associated with symptoms of depression, substance use disorders, history of incarceration, and history of staying in shelters.We also hypothesized that persons who reported smoking heavily or having symptoms of depression at enrollment would be less likely to make a quit attempt at follow-up.The HOPE HOME Study is a longitudinal study of life course events, geriatric conditions, and their associations with health-related outcomes among older homeless adults. From July 2013 to June 2014, we enrolled a population-based sample of 350 homeless adults aged 50 years and older from homeless encampments, recycling centers, overnight homeless shelters, and free and low-cost meal centers serving at least three meals a week in Oakland, California. Participants were eligible if they were English-speaking, aged 50 years and older, defined as homeless as outlined in the Homeless Emergency Assistance and Rapid Transition to Housing Act, and able to provide informed consent, as determined by a teach-back method.The University of California, San Francisco Institutional Review Board reviewed and approved all study protocols.The study included an enrollment visit and a follow-up visit at 6 months. Study interviews took place at a community-based site. After determining eligibility, study staff administered an in-depth structured enrollment interview and collected extensive contact information from participants. We asked participants to check in with study staff in person or by telephone each month.

If participants missed two or more check-in visits, study staff reached out to participants using their contact information. From January 2014 to January 2015, we conducted 6-month follow-up visits with each of the participants who completed an enrollment interview. We gave participants gift cards to a major retailer for their participation: $5 for the screening interview, $20 for the enrollment interview, $5 for each month check-in, and $15 for the follow-up interview.We used previously validated questions on tobacco use at the enrollment and 6-month follow-up interviews. We asked participants whether they had ever smoked 100 cigarettes in their lifetime, and classified those who did as ever-smokers. We classified ever-smokers who reported smoking “every day or some days” as current smokers, and those who reported “not smoking at all” as former smokers. We asked current daily smokers to report the number of cigarettes smoked daily. For current non-daily smokers, we estimated average daily cigarette consumption based on self-reported numbers of cigarettes smoked on smoking days in the past 30 days. Participants reported how soon they had smoked their first cigarette after waking, which we dichotomized as greater or less than 30 minutes. We asked current smokers about their intentions to quit smoking . We asked current smokers to report whether they had stopped smoking for 1 day or longer in the past 6 months because they were trying to quit smoking. We asked participants who responded affirmatively to making a quit attempt to report the length of their last quit attempt. We defined reporting a quit attempt in the past 6-months at the follow-up visit as the primary outcome variable. We determined the proportion of participants who were abstinent for 30 days and 90 days at the 6-month study visit using self-reported information on the length of the last quit attempt. At the 6-month follow-up visit, we obtained additional information from participants on their quitting behaviors.If participants reported having made a quit attempt during the past 6 months, we asked them to report the medications, strategies, and support system they had used during their last quit attempt. Participants reported whether they had used nicotine replacement therapy and/ or any of the US Food and Drug Administration -approved medications for smoking cessation during their last quit attempt. Participants reported whether they had used other strategies to quit smoking including gradually cutting back on cigarettes, switching to smokeless tobacco, other combustible tobacco , or electronic cigarettes, or giving up cigarettes all at once. Participants self-reported their use of a telephone quit line, group or one-on-one smoking cessation counseling, hypnosis or acupuncture, and other internet or family-based support for smoking cessation. Participants also reported whether they had received advice to quit cigarette smoking from their health care provider in the past 6 months, and whether they had acted on the advice to quit smoking.Participants self-reported age, gender and race/ethnicity at the enrollment visit.

The past decade has seen a proliferation of cognitive training intervention trials aimed at remediating or reversing substance-related cognitive deficits . However, their implementation into clinical practice is almost non-existent, despite promising results and now having more flexible, precise, engaging and convenient modes of delivery . Gathering more data in this still-developing area is essential to facilitate translation. Even the most widely tested training interventions, such as cognitive bias modification, need more data to fully appraise their benefit for addiction treatment . This section summarizes recent advances in CT, identifies limitations in the evidence base, and highlights priorities and directions for future research to bridge the gap between science and practice. Current CT approaches can be broadly divided into: general cognitive remediation, working memory training , inhibitory control training , and cognitive bias modification .In SUD, general cognitive remediation approaches such as cognitive enhancement therapy and cognitive remediation therapy aim to reduce substance use and craving by targeting EF and self-regulation. Cognitive remediation has been shown to improve cognition in domains of working memory , verbal memory, verbal learning, attention, and processing speed . Positive outcomes have also been shown to be associated with increased neuroplasticity in emotion regulation-related fronto-limbic networks in individuals with schizophrenia and co-morbid SUD . A recent study delivered 12 two-hour group sessions of clinician-guided CRT and computerized CT over 4 weeks to a sample of female residents completing residential rehabilitation and found significant improvements in EF, response inhibition, self-control,grow table and quality of life relative to treatment as usual. Similar research has reported comparable improvements in cognitive functioning following CRT and CET , and improved cognitive functioning has been associated with reduced substance use at 3- and 6-month follow-ups .

Importantly, CET and CRT also demonstrate preliminary efficacy for SUD patients with cognitive impairments. However, their duration, intensity, and high cognitive demand—coupled with a current paucity of large-scale, methodologically rigorous clinical trials—may currently preclude their widespread implementation in clinical settings. Another manualized therapist-assisted group intervention is Goal Management Training , which trains EF and sustained attention and emphasizes the transfer of these skills to goal-related tasks and projects in everyday life. When combined with mindfulness meditation, GMT has been found to significantly improve WM, response inhibition and decision making in alcohol and stimulant outpatients relative to TAUand more recently also in poly substance users in a therapeutic community . A meta-analysis of GMT more broadly concluded that it provides small to moderate improvements in EF which are consistently maintained at 1–6 month followups . As such, GMT is likely to be an effective candidate cognitive remediation approach for SUD treatment; however, substantially more research is needed to validate this assertion, particularly regarding the translation of cognitive improvements into improved substance use outcomes.The most widely researched EF training intervention, WMT requires participants to repeatedly manipulate and recall sequences of shapes and numbers through computerized tasks that become increasingly difficult over time . WMT aims to extend WM capacity, so individuals can better integrate, manipulate, and prioritize important information, with the aim of supporting more adaptive decision making that leads to reduced substance use . Relative to many other approaches, WMT is intensive, typically requiring 19–25 days of training and as such, retention is often poor . While WMT has been shown to lead to improvements in near transfer effects , there is limited evidence supporting far-transfer effects of WMT on other measures of EF and importantly, on substance related outcomes . Reduced alcohol consumption 1 month after training was reported following WMT in heavy drinkers , but most studies have failed to demonstrate or even measure changes in substance use . For example, non-treatment seekers with alcohol use disorder who were trained with Cogmed showed improved verbal memory but no clinically significant reductions in alcohol consumption or problem severity .

While a study of treatment-seekers improved WM and capacity to plan for the future on a delay discounting task, there was no measurement of substance use outcomes . Similarly, studies of methadone maintenance and cannabis have found no evidence of far-transfer effects , although Rass et al.showed WMT-related reductions in street drug use among methadone users. Other forms of WMT have reported similar near-transfer but not substanceuse-related findings with methamphetamine patients and a mixed group of substance use patients. As such, the greatest limitation in the WMT literature is the failure to consistently examine substance use outcomes and therefore there is insufficient evidence at this time to support the utility of WMT as an effective adjunctive treatment for SUD.Since deficits in inhibitory control are associated with increased drug use , ICT aims to bolster inhibitory control through the repeated practice of tasks [e.g., go/no-go , stop-signal task]. Such tasks require individuals to repeatedly inhibit prepotent motor responses to salient stimuli . In a seminal study, a beer-GNG task which trained heavily drinking students to inhibit responses to “beer” stimuli resulted in significantly reduced weekly alcohol intake relative to students trained towards “beer” stimuli . A recent RCT of 120 heavily drinking students found that a single session of either ICT or approach bias modificationled to significant reductions in alcohol consumption relative to matched controls . Similarly, Kilwein et al.found that a single session of ICT reduced alcohol consumption and alcohol approach tendencies in a small sample of heavily drinking men . Despite these promising findings, each of the aforementioned ICT studies used community samples, and it has not yet been established whether these results will generalise to treatment seekers. Two meta-analyses recently concluded that ICT leads to small but robust reductions in alcohol consumption immediately after training . Di Lemma and Field reported reduced alcohol consumption in a bogus taste test after a single session of ICT or cue-avoidance training . Others have observed reduced alcohol consumption 1 and 2 weeks after ICT . These findings highlight the promise of ICT though there remains a paucity of research assessing long-term drinking outcomes outside of laboratory settings. Future studies of ICT with clinical populations should consider testing multi-session approaches akin to WMT. To date, few studies have trialled multi-session ICT: One found it to be ineffective for heavily drinking individuals, while another found that 2 weeks of ICT resulted in modest reductions of alcohol consumption among individuals with AUDs, compared to WMT or a control condition .CBM aims to directly interrupt and modify automatic processes in response to appetitive cues. Attentional bias modification aims to modify the preferential allocation of attentional resources to drug cues by repeatedly shifting attention to neutral or positive cues and away from drug-related cues. Despite several null findings , significant effects have included the reduction of alcohol consumption in non-treatment seeking heavy or social drinkers . Among treatment seekers, five sessions of AtBM have been shown to significantly delay time to relapserelative to controls who received sham training .

Similarly, six sessions significantly reduced alcohol relapse rates at a one-year follow-up relative to a sham training condition in a sample of treatment seekers with AUD . Among methadone maintenance patients, AtBM reduced attentional bias to heroin-related words, temptations to use, and number of lapses relative to TAU . However, among individuals with cocaine use disorder, it failed to reduce attentional bias, craving, and cocaine use . Likewise, 12 sessions of AtBM vs. sham training during residential treatment for methamphetamine use disorder failed to reduce craving and preferences for methamphetamine images . A systematic review of alcohol, nicotine,vertical rack and opioid AtBM studies concluded that despite numerous negative findings in the literature, eight out of 10 multiple-session studies resulted in reduced addiction symptoms , but without concomitant reductions in attentional bias . Approach bias modification , which uses the Approach Avoidance Task, requires an avoidance response to drug cuesand an approach response to non-drug cues. Several trials have examined alcohol ApBM, with evidence that short-term abstinence is increased by up to 30% with four consecutive training sessions during inpatient withdrawal and by 8%–13% at 12-month follow-up . Alcohol ApBM has demonstrated relatively consistent, moderate reductions in drinking behavior when delivered to clinical populations , and it was even added to the German guidelines for the treatment of AUD . Early neuroimaging evidence has examined the neuroadaptations that occur pre-to-post-cognitive bias modification training. This work has focused on two samples of abstinent alcoholics undergoing an fMRI cue-reactivity task. Participants showed higher baseline reactivity to alcohol cues within the amygdala/nucleus accumbens and the medial prefrontal cortex, respectively . The same samples, following a 3-week implicit avoidance task , showed reduced amygdala and medial prefrontal reactivity . Notably, these brain changes were associated with reduced craving and approach bias to alcohol stimuli but not abstinence 12 months later. While preliminary, these findings suggest that neuroadaptations associated with cognitive bias modification have clinical relevance and warrant replication in larger SUD samples using robust, active placebo-controlled designs. To date, only one study has been published that trialled ApBM in an illicit drug-using sample of non-treatment-seeking adults with cannabis use disorder . Relative to sham-training, four sessions resulted in blunted cannabis cue-induced craving but not less cannabis use. Overall, evidence suggests that ApBM is associated with reduced approach bias and reduced consumption behaviors for alcohol, smoking, and unhealthy foods . Recently, six sessions of ApBM delivered to 1,405 alcohol-dependent patients significantly reduced alcohol relapse rates at a 1-year follow-up relative to a sham-training condition . However, as these reductions were also observed following AtBM and a combined AtBM and ApBM condition, the authors concluded that all active CBM training conditions had a small but robust long-term effect on relapse rates. Finally, a meta-analysis of alcohol and smoking CBM studies showed a small but significant effect on clinical outcomes for alcohol , but a lack of evidence that reduced approach bias led to improved outcomes .

This assertion was challenged by Wiers et al.who noted that the review conflated proof-of-principle lab-studies and clinical RCTs and different samples . Importantly, these populations likely have differences in motivation/awareness for receiving an intervention to reduce alcohol use, which could explain inconsistencies in the reported effectiveness of CBM across populations .Currently CBM, particularly ApBM, appears one of the most promising approaches for individuals seeking treatment for AUDs; however, its effectiveness for other drugs is yet to be established. The most extensively trialled CT approach is WMT, which has shown promising results in alcohol and stimulants users. However, its high cognitive demand, training intensity, and apparent lack of far-transfer effects limit its application to clinical populations. ICT holds much promise for reducing alcohol consumption in heavy drinkers, but requires testing in treatment-seekers. Finally, more intensive group based approaches such as CRT/CET and GMT may improve EF and quality of life; however, their impact on substance use outcomes remains largely untested. Synergistic approaches now warrant exploration. Indeed, a study that combined WMT and AtBM has shown promising feasibility and improved EF, though substance use outcomes were not assessed. It may also prove fruitful to adopt staggered CT approaches, capitalizing on the brain,s capacity to repair itself during withdrawal, early and later abstinence by strengthening cognitive control and dampening cue-reactivity , prior to engaging in more intensive and cognitively demanding but ecologically valid group training for more extensive remediation .While there may be logistical challenges to the adoption of CT in clinical practice , the main impediment to implementing CT in clinical practice is the absence of robust evidence for treatment success of any one particular approach. This is largely due to the vast heterogeneity of studies, particularly regarding differences in treatment settings, samples , cognitive intervention approaches, number and duration of training sessions, targeted mechanisms, targeted drugs of concern and varying primary outcome measures. Similarly, the absence of brief, ecologically valid, easily-administered measures of cognition precludes the identification of candidates who are most likely to benefit from CT . As such, the evidence base for CT remains hampered by the marked lack of studies on clinical populations,the counter-intuitive neglect of assessing relevant substance use outcomes,the lack of adequately-powered RCTs,the limitations of research designs,lack of attention to individual-level trajectories of cognitive improvements in relation to substance use and quality of life outcomes , and a simple focus on direct relations between cognitive deficits and outcomes without considering person and environmental mediators and moderators of this relation .

VPA exhibits high plasma protein binding with therapeutic concentrations; however, saturation of plasma proteins may occur in the setting of acute intoxication and result in increased free VPA concentrations amenable to HD.While published data are limited, high-flux HD has been shown to be sufficient without the need for concomitant charcoal hemoperfusion.Furthermore, HD should be considered to remove ammonia or correct severe metabolic disturbances during VPA toxicity. Hyperammonemia caused by VPA toxicity is a complex process; it involves depletion of carnitine stores and ultimately results in inhibition of carbamoyl-phosphate synthetase, the primary enzyme responsible for ammonia incorporation into the urea cycle. The use of L-carnitine in the treatment of VPA-induced hyperammonemia is secondary to its ability to assist in the metabolism of long-chain fatty acids. We present a patient with two presentations of VPA toxicity, eight months apart, each successfully treated with HD and L-carnitine. The cases presented provide insight on the detrimental effects that VPA toxicity can cause and review current evidence-based treatment options. Additionally, the second presentation sheds light on the unfortunate repercussions that an incomplete discharge reconciliation can have, namely the lack of patient care transition from inpatient to outpatient when a significant medication event occurred in hospital and subsequent medication changes were made. A 32-year-old African-American male with a history of bipolar disorder, hypertension, and previous suicide attempts was brought to the emergency department with altered mental status .He was prescribed lisinopril 10mg by mouth daily, hydrochlorothiazide 25mg PO daily, and divalproex sodium delayed release 500mg PO every morning and 1000mg PO nightly. Upon presentation,flood and drain tray the patient was responsive only to painful stimuli with a Glasgow Coma Score of 12. Vitals included a blood pressure of 152/70 mmHg and heart rate of 110 beats per minute.

Computed tomography of the brain/head was unremarkable. However, magnetic resonance imaging revealed cerebral edema and possible laminar necrosis. On presentation, pertinent laboratory values included the following: VPA 481.9 µg/dL, lactate 6.9 mmol/L, ammonia 303 µmol/L, and platelets 135 x 103 microL and serum creatinine 2.61 mg/dL. Other chemistries and liver function tests were within normal limits . The urine drug screen, serum alcohol level, acetaminophen level, and salicylate level were unremarkable. The patient required intubation for AMS and acute respiratory failure, and a temporary dialysis catheter was emergently placed for HD. Prior to HD and three hours after initial presentation, VPA level rose to greater than 600 µg/mL . The patient was dialyzed six hours after presentation for a total of six hours; post HD he was started on intravenous L-carnitine 1,300 mg q4h based on literature recommendations and received lactulose 30 grams PO once.During his hospitalization, platelets reached a nadir of 63 x 103 microL, but other pertinent laboratory results remained WNL. The patient improved clinically to include a GCS of 15, allowing successful extubation on hospital day 2. VPA was not to be continued upon discharge and he was transferred to a psychiatric facility for further evaluation.During the second presentation, the patient was found unconscious and diaphoretic in his bedroom by his caregiver. He had AMS and was unable to communicate effectively. Home medications included venlafaxine 75 mg PO daily, benztropine 2 mg PO daily and divalproex sodium DR 500 mg PO twice daily. Although he was not discharged on divalproex sodium during his last visit, he had continued to refill this prescription at his outpatient pharmacy. Notably, the patient also had been using marijuana regularly the previous week. Baseline laboratory values on arrival include the following abnormalities: ammonia 48 µmol/L, VPA level 420 µg/mL, lactate 2.6 mmol/L, glucose 67 mg/dL, and platelets 127 x 103 microL. Other laboratory values were WNL. Although he did not immediately require HD, nephrology was emergently consulted in light of the complications of his previous admission. A repeat VPA level of 272 µg/mL was drawn five hours after the initial level before HD , and the patient was dialyzed for four hours. L-carnitine 2,640 mg PO q8h and lactulose 10 g PO four times a day were initiated. Laboratory findings one hour after HD included ammonia 84 µmol/L and VPA level 105 µg/ mL. His mental status and symptoms improved , and the patient was able to follow commands appropriately. He was discharged on hospital day 2 to a psychiatric facility with instructions to continue L-carnitine 2,640 mg PO every eight hours given continued elevation in ammonia levels. This presentation scored a 10 on the Naranjo scale indicating a definite adverse drug reaction.We present a unique patient with two separate presentations of VPA toxicity necessitating aggressive measures and treatment with HD and L-carnitine. On the first admission, cerebral edema was visualized on MRI and a peak VPA level of greater than 600 µg/mL was reduced to 199 µg/L at the end of a six-hour HD session.

During the second admission, a peak level of 420 µg/mL decreased to 105 µg/mL after a four-hour HD session. While VPA is highly protein bound, plasma proteins become saturated during VPA toxicity, causing an increase in unbound VPA that contributes to the signs and symptoms of toxicity. These small molecules become amenable to elimination via HD allowing for more rapid decline in the serum concentration and subsequent improvement in symptoms of toxicity, as evidenced by the patient’s first presentation.Historically, charcoal hemoperfusion was used for the treatment of VPA toxicity.However, previous case reports describe the effectiveness of using HD alone. What remains unclear is the threshold in VPA concentrations where HD may be useful. Based on the literature available, the EXtracorporeal TReatments in Poisoning workgroup recommends dialysis in patients with a VPA concentration greater than 1,300 mg/L, the presence of cerebral edema, or shock.Dialysis may be used when VPA concentrations are greater than 900 µg/mL, in the presence of coma, respiratory depression requiring mechanical ventilation, acute hyperammonemia, or pH less than 7.1.Similarly, a review article evaluating extracorporeal elimination of VPA advises HD in severe VPA toxicity and a plasma VPA level >850 µg/ mL.9 During our patient’s first presentation, the suggested criteria for HD were met due to the presence of cerebral edema on MRI and the need for mechanical ventilation. In the second presentation, AMS and his ingestion history drove the decision for HD. In a patient with therapeutic VPA concentrations, HD should not significantly impact VPA levels; it may be a viable option in acute toxicity by reducing free drug and improving clinical condition. Hemodialysis is a viable option for treatment of VPA induced hyperammonemia. VPA is metabolized primarily in the liver by means of glucuronic acid conjugation and oxidative pathways via the cytochrome P450 system. The major metabolites are 2-en-VPA, 4-en-VPA, and propionic acid derivatives which are active. 2-en-VPA has a long half-life and causes cerebral edema and coma, while 4-en-VPA causes reversible hepatotoxicity. Propionic acid is responsible for causing hyperammonemia by three proposed mechanisms. Its interaction with and depletion of carnitine impairs the transportation and metabolism of long-chain fatty acids. Also, it prevents glutamine production in the kidneys, which reduces ammonia levels in the brain. Lastly, it inhibits carbamoyl phosphate synthetase, a hepatic mitochondrial enzyme responsible for eliminating ammonia within the urea cycle. The cumulative result is accumulation of ammonia, causing encephalopathy. L-carnitine has the ability to transport and metabolize long-chain fatty acids; thus, it has shown to be beneficial in VPA-induced hyperammonemia, especially in patients with hepatotoxicity, hyperammonemia,hydroponic tables canada or significant CNS depression.An interesting aspect of our case was the reported increase in cannabis use during the week prior to the second VPA ingestion. To our knowledge, there are no reports of VPA toxicity caused by cannabis ingestion. However, cannabidiol, a component of marijuana, weakly inhibits the CYP2C9 pathway.

In addition, delta-9-tetrahydrocannabinoid has high plasma lipoprotein binding. The potential for weak inhibition of VPA metabolism via CYP2C9 and displacement of protein binding due to cannabis could have contributed to the toxicity, but this interaction has not been studied.Of most interest, is the demonstration of the importance of involving a patient’s outpatient pharmacy when a medication is discontinued for toxicity. This patient’s VPA was discontinued upon discharge after the first overdose; unfortunately, measures were not put in place to prevent patient access to medication. Thus, he continued to fill this medication from his outpatient pharmacy. Including a pharmacist in hospital discharge medication reconciliation has been previously shown to decrease 30-day hospital readmission.This service is commonly provided to patients with multiple comorbidities and complicated medication regimens; however, a second occurrence of toxicity in this patient demonstrates that coordinating discharge care for patients with high-risk overdoses should be performed. Methods for reliably informing outpatient pharmacies of discharge medication reconciliation after acute care episodes are expected to improve patient safety.Electronic cigarettes and vaping products are new devices for inhaling various substances such as nicotine and cannabinoids, with or without flavoring chemicals. “Vaping,” or “Juuling,” is a term used to describe the use of e-cigarettes and vaping products.These devices, also known as e-cigs, vape pens, vapes, mods, pod-mods, tanks and electronic nicotine delivery systems, are available in different shapes and sizes.All e-cigarettes and vaping products are made of three components. The first component is the cartridge that contains e-liquid and the atomizer, a coil that heats and converts e-liquid into aerosols. E-liquids can be broadly categorized into two types: regular e-liquids made of propylene glycol Loma Linda University, Department of Emergency Medicine, Loma Linda, California containing chemical flavors and vegetable glycerine used to dissolve nicotine or cannabis e-liquids containing tetrahydrocannabinol and cannabidiol. The second component is the sensor that activates the coil, and the third component is the battery.The hookah, also known as a water pipe, is an ancient method of smoking nicotine. In this method, the coal heats the tobacco and then the smoke passes through the water reservoir before it is inhaled.Contrary to public perception, hookah use is also associated with oral, lung, and esophageal cancers, similar to smoking cigarettes.In our study, we focused on e-cigarettes, and vaping, product-use associated lung injuries . According to the United States Centers for Disease Control and Prevention , in 2018 e-cigarettes were used by 3.05 million high school and 570,000 middle school students.EVALI is a diagnosis of exclusion, with a definition outlined by the CDC for confirmed and probable cases.EVALI was first identified in August 2019 after the Wisconsin Department of Health Services and the Illinois Department of Public Health received multiple reports of a pulmonary disease of unclear etiology, possibly associated with the use of e-cigarettes and related products.Since then, more than 2000 cases of EVALI have been reported, and in 80% tetrahydrocannabinol -containing products were used.Our study aimed to identify the clinical characteristics and hospital course of adolescents diagnosed with EVALI.We performed a retrospective chart review of adolescents presenting to our hospital between January– December 2019, with diagnosis of EVALI. Subjects were identified by the International Classification of Diseases, Tenth Revision diagnostic codes outlined by official ICD-10 guidelines.The following codes were used: J68.0 ; J69.19 ; J80 ; J82 ; J84.114 ; J84.89 ; J68.9 ; T65.291 ; and T40.7X1 . We used a standardized data collection sheet. Data were collected by trained personnel who were not blinded to the objectives of study. The data extracted from the medical records were age, gender, weight, and vital signs obtained in the ED. We also compiled data on duration of symptoms, history of cough, shortness of breath, chest pain, vomiting, wheezing, rales, use of accessory muscles, and presence of altered mental status. We also included data on respiratory support, duration of hospital stay, use of steroids during treatment, and laboratory tests and imaging obtained in the hospital and a negative infectious workup or the decision by the clinical care team to treat as a case of EVALI.Exclusion criteria were gastrointestinal and central nervous system manifestations without interstitial pulmonary involvement, ingestions of cannabinoids, duplicate visits, and if it was unclear whether vaping device was used or not. We used descriptive statistics to analyze the data. Median and interquartile range were calculated for continuous variables, and proportions were calculated with 95% confidence intervals for categorical variables.

Serious mental illnesses , which typically include bipolar dis orders and schizophrenia, are characterized as chronic and debilitating conditions that place significant burdens on patients, as well as their families and society. Despite the marked improvement in managing destabilizing symptoms that followed the introduction of psychotropic medications, most patients who suffer from a SMI continue to have a limited recovery and experience poor physical health. Fifty to 80% of individuals with SMI have one or more comorbid medical conditions that may worsen prognosis and contribute to high morbidity and premature mortality. More concerning is that over 60% of the medical comorbidities observed among persons with SMIs are non fatal and preventable, yet these persons have 15 to 25 years shorter expectancy relative to the general population. Unfortunately, the medical needs of those with SMI are often neglected, which may partly explain the reason for why their morbidity and mortality are elevated. Studies of modifiable risk factors suggest that risky sexual behaviors and poor hygiene, are linked with higher risk of genitourinary, infectious, and blood borne diseases among individuals with SMIs. In creased rates of alcohol and illicit drug use, smoking, poor nutrition and lack of exercise, may be associated with higher rates of cardiovascular and respiratory conditions; and genitourinary and metabolic diseases. Patients with SMI also present for treatment with a number of serious and chronic medical conditions, and these conditions can onset up to 10 years earlier in this population compared to age matched controls. In addition,greenhouse benches having medical comorbidities place SMI patients at risk of repeat hospital visits that raise health care costs and increase the burden of disease. Not surprisingly, the problem of medical comorbidities in SMI is now considered a major public health issue due to its destabilizing effects and high cost to families and society.

Patients with SMI continue to experience elevated morbidity despite the identification of several preventable and modifiable risk factors for poor health. Thus, a study that seeks to examine associations among patients with SMI and odds of having medical comorbidities in a large integrated health system is important to inform patient care. In this study, we examined associations among 25,090 patients with a SMI diagnosis of bipolar disorder or schizophrenia and odds of having medical comorbidities relative to 25,090 patients without an SMI in a large health system. Importantly, to inform patient care planning we examined acute conditions, which are more likely to require immediate medical attention as well as severe or chronic conditions necessitating ongoing monitoring and management.Kaiser Permanente of Northern California is a nonprofit, integrated health care delivery system providing health care services to > 4 million members, serving 45% of the commercially insured population in the region. KPNC consists of a health care plan, a sole medical group, and a hospital system. Specialty health services, such as psychiatry, substance use treatment, and other specialty care, are available to all members internally. To facilitate integrated health care services, providers have access to a mature electronic health record system with each member’s medical history, including primary care, emergency department, ambulatory, hospital and specialty health care encounters. In KPNC, about 88% of members are commercially insured, 28% have Medicare and 10% have Medicaid coverage. All patients were selected from the KPNC membership for this study. Institutional review board approval was obtained from the Kaiser Foundation Research Institute.We used EHR data for this secondary, database study. These data were used to identify all health system members who 1) were at least 18 years of age, 2) had a visit to a KPNC facility in 2010, and 3) had a recorded ICD-9 diagnosis of schizophrenia or bipolar disorder in 2010. The first mention of each ICD-9 diagnosis of schizophrenia or bipolar recorded from January 1, 2010 to December 2010 were included; patients in the sample could have multiple diagnoses . We also included all current or existing behavioral health diagnoses that were additionally documented for patients with schizophrenia or bipolar during health system visits in 2010 . EHR data were also used to identify control patients without current or existing behavioral health diagnoses.

Control patients were selected for all unique patients with bipolar disorder or schizophrenia, and matched one-to-one on gender, age, and medical home facility . This method ac counted for any differences in services, types of conditions, or unobservable differences by geographic location. The final analytical sample consisted of 50,180 patients: 20,308 with bipolar disorder, 4782 with schizophrenia, and 25, 090 controls. Institutional review board approval was obtained from the Kaiser Foundation Research Institute.Age, gender, race/ethnicity, patient medical home facility, census based median neighborhood household income, and ICD-9 psychiatric and medical diagnoses were extracted from the EHR. Race/ethnicity consisted of five categories: white, Black, Hispanic, Asian, and other. Psychiatric and medical diagnoses were determined based on ICD-9 diagnoses noted during visits made over the study period and included current and existing diagnoses.Frequencies and means were used to characterize the sample. We used McNemar’s test and paired sample t-tests to determine potential differences between the matched samples of patients with SMI and controls. These analyses proceeded by examining potential differences between patients with SMI compared to controls by age, gender, race/ ethnicity and income. A series of conditional logistic regressions were then computed, predicting each of nine medical condition categories from bipolar or schizophrenia , to compare the odds associated with having medical comorbidities in patients with SMI compared to controls. We then computed a series of conditional logistic regressions predicting each of fifteen chronic or severe medical conditions from having bipolar or schizophrenia , to com pare the odds of having chronic or severe medical comorbidities in SMI patients versus controls. All conditional logistic regressions adjusted for race/ethnicity and income. SMI and control samples were matched 1- to-1 on age and gender; and thus, no significant differences were anticipated or found between matched groups regarding these relation ships .The Hochberg method was used to adjust for multiple inference testing within each medical condition category. We report Hochberg adjusted p-values for the conditional logistic regressions comparing the odds of having medical comorbidities for patients with SMI and controls. Statistical significance was defined at p < 0.05; analyses were performed using R version 2.15.0.Overall, the sample was 70.0% women, 60.0% White, 15.6% Hispanic, 12.2% Asian, 7.4% Black, 4.8% other race/ethnicity. Patients were 49 years old on average.

As shown in Table 1, more patients with schizophrenia or bipolar were white compared to controls; fewer controls were Hispanic, Asian, or Black relative to patients with schizophrenia or bipolar. However, more patients with schizophrenia were Black relative controls. On average, patients with bipolar or schizophrenia lived in lower income neighborhoods compared to controls. Since patients were matched on age and gender,growers equipment no evidence of differences across these measures were found among the controls and the patients with schizophrenia or bipolar .The high prevalence of medical comorbidities among patients with SMI constitutes major clinical and public health problems that have not been adequately addressed in specialty mental health programs or by mainstream health care. This issue is further compounded in individuals who have a SMI by problems associated with substandard living conditions and lack of access to routine health care services, which increase the risk of having unidentified and untreated medical conditions. Lack of preventative health care in combination with high risk health behaviors among individuals with SMI place them at increased risk of several serious and chronic medical conditions. Patients with SMI remain at risk for elevated morbidity and mortality, despite that health care reform in the U.S. has increased health care service access for this population in recent years. Given the increased likelihood for individuals with SMI to have poor health and poor health outcomes despite policy and clinical intervention, obtaining current information on the degree to which having a SMI is associated with a range of medical comorbidities from large health systems which manage these persons is critical to tailor future disease prevention efforts, early diagnosis, and treatment to their needs. To inform patient care and service planning, we examined acute and chronic medical conditions in SMI patients in a large integrated health system, where SMI patients potentially may have better access to health services than in health care systems where services are not integrated. Prior to our primary analyses, we investigated potential socioeconomic differences between controls and SMI patients. Since patients with SMI and the controls were matched by age and gender no differences were anticipated or found regarding these characteristics. Patients with SMI tended to be white relative to controls, except that more patients with schizophrenia were Black. Higher rates of Black patients with schizophrenia are largely consistent with prior research, and may be in part due to the over-diagnosis of Black persons with this disorder. Also consistent with prior work, we found more SMI patients were located in lower-income neighborhood KPNC service areas than controls. Prior work has found poor socioeconomic status can dramatically limit access to health care and increase exposure to unhealthy behaviors and lifestyles. This phenomenon may partly explain the reason for why having a SMI was disproportionately associated with higher likelihood of having almost all medical co morbidities and serious or chronic conditions examined relative to controls. Notably, while all controls and patients with a SMI had broad access to a range of health services , lower income for patients with SMI may disproportionately affect their ability to support transportation costs for health system visits and follow-up preventive care, and impact health outcomes.

It will be important for future work to more fully examine the role of income in predicting health care access and associated health outcomes in the SMI population. Perhaps in part related to access and low SES, our findings also revealed the odds associated with having acute and chronic medical conditions may not be the same for everyone with a SMI. Even given differences in study design, types of health care systems and samples studied, our results were largely consistent with prior work that has found patients with schizophrenia are at high risk of having endocrine or immune diseases. Patients with schizophrenia were more likely to have endocrine or immunity dis eases, as well as diabetes and obesity. While we could not address causal relationships with our design, the odds associated with having schizophrenia to obesity and diabetes has been linked to the use of some second-generation antipsychotic medications. This is problematic and concerning, as the long-term use of second-generation antipsychotic medications combined with obesity and adverse lifestyle behavior have been linked with higher odds of serious cardiovascular events in patients with schizophrenia and other SMI patient groups. These phenomena may potentially explain the reason for why we found that having schizophrenia was associated with higher odds of having a serious cardiovascular event, such as stroke. Future longitudinal work in this area is warranted, and will need to focus on isolating predisposing conditions and other risk factors associated with future cardiovascular events and mortality in schizophrenia. Although far in excess of control patients, the prevalence of cardiovascular disease and predisposing conditions such as diabetes, hypertension, and obesity in our bipolar sample was slightly below prior reports. Nevertheless, having bipolar was still associated with higher odds of conditions predisposing cardiovascular disease. Notably, these findings may in part explain the reason for why we also found having bipolar was associated with higher likelihood of serious cardiovascular events, including stroke. These findings are of interest be cause cardiovascular mortality is a leading cause of elevated mortality in patients with bipolar, and is well above the risk associated with unnatural causes of death such as injury and suicide. Consequently, future work in this area is warranted, and will need to determine the risk of cardiovascular disease to future cardiovascular events and car diovascular mortality in bipolar, as well as whether the rates of cardiovascular mortality may be lower in patients with bipolar in integrated health systems than the general population and other health care systems.Overall, having bipolar and schizophrenia was associated with high odds of blood borne and infectious disease and of hepatitis C. Although we did not examine routes of transmission, injection drug use, high risk sexual behaviors, or comorbid substance use, SMI patients have been found to exhibit this behavior, raising the odds of blood borne and infectious disease and hepatitis C. While substantially higher than the control estimates, the prevalence of hepatitis C in our sample fell below previously published rates of hepatitis C in individuals with SMI.

There was mild benefit associated with initiation of psychological therapies, although her mother described difficulties with new settings, social anxiety, frequent negative perseverative thoughts, and ongoing panic attacks that would vary in frequency depending on her routine and social supports.Starting in the fourth grade, she had mild academic challenges that were supported with a 504 educational plan through the end of high school.She did not receive any other medication treatment. She graduated from college with academic supports, with continued periods of significant anxiety symptoms, social withdrawal, and panic attacks. After college, she began work as an educational aide in a public school, with an exacerbation of anxiety due to this transition into adulthood and pressures of increased independence. At 22 years of age, her parents provided her with a liquid formulation of hemp oil containing *43 mg of CBD daily , which she began taking. She described feeling calmer, with fewer perseverative worries, and a cessation of panic attacks. This led to more interactions and activities with peers and improved performance at work. Her mother also noted that she became more engaged socially, calmed more easily when frustrated, and was less likely to fixate on negative aspects of various situations. She missed about 1 week of dosing when on a family vacation out of town, and after a few days without treatment, she experienced recurrence of anxiety symptoms and reemergence of panic attacks. These resolved after she was home and able to restart her CBD+ treatment. She is currently working full time and living independently. She has continued CBD+ treatment for two and a half years with sustained therapeutic benefit.Based on its morbidity and prevalence, its association with a number of co-occurring problems, such as seizures, anxiety, and sleep disturbances,13 and the paucity of efficacious therapeutic options, FXS represents an important public health problem.The present article provides a brief review of recent research that documents the promise of CBD as a therapeutic agent for patients with FXS. Also described are the cases of one pediatric and two adult patients with FXS for whom CBD+ treatment appears to have contributed to positive changes in anxiety and/or language skills, with no observed adverse events. Before starting CBD+ treatment, Patient 1 experienced heightened symptoms of anxiety, frequent tantrums, and sleep difficulties. Over the first month of CBD+ monotherapy, and subsequent 3 months of CBD+ treatment combined with speech, language, and occupational therapy, the patient made considerable progress with feeding and weight gain,ebb and flow exhibited better oral–motor coordination, had decreased social avoidance and sensory sensitivities, and showed improvements in attention span/engagement, the frequency and severity of atypical motor movements, and general level of hyperactivity.

Upon discontinuation of CBD+ treatment, the patient’s prior symptoms reemerged. While a strong therapy program and later addition of other medications likely contributed to this patient’s overall developmental progress, the temporally related improvements in anxiety, feeding, tantrums, and sleep—evident when CBD+ treatment was initially started as monotherapy and then reinstated following cessation—are compelling support for the benefits of CBD+ treatment for this patient. By maintaining his adaptive functioning scores from 1 to 3 years of age, the patient demonstrated a significant improvement over the characteristic developmental trajectory of young male children with FXS, where a decrease in adaptive functioning scores is typically observed between 2 and 6 years of age.Patient 2 showed a similarly encouraging response to CBD+ treatment, experiencing reduced anxiety, improved use of language, and better sleep within 1 week of beginning treatment with a CBD+ solution. It is also remarkable that the patient continued to demonstrate symptom improvement over the initial 6-week treatment period, with longer-term follow-up highlighting continued use of CBD+ solution with sustained benefit. In Patient 3, the use of CBD+ solution was also associated with a positive effect in a higher functioning female with FXS and long-standing anxiety symptoms. Similar to Patient 1, treatment discontinuation was associated with a recurrence of anxiety symptoms, with reinitiation of CBD+ treatment leading to symptom improvement and resulting long-term use. The present findings, coupled with the available preclinical data, highlight the potential for CBD as an intervention for individuals with FXS. The existing literature combines to demonstrate that CBD may positively impact individuals with FXS through many mechanisms, including the endocannabinoid system, GABA, and serotonin. While a number of drugs have been developed to target specific systems , CBD has the potential to yield a multifaceted benefit to individuals with FXS due to its multiple mechanisms of action. CBD has not only been shown to be generally well tolerated relative to other treatments used in this population,but also numerous studies have documented its benefits in terms of sleep quality,anxiety ,and cognitive impairment—symptoms experienced by the individuals profiled in the present case series. These data serve as stepping stones upon which proof-of-concept open-label trials should be based. As with many patients, however, those discussed herein used orally administered botanical CBD+ solution that is not regulated by the Food and Drug Administration and, thus, inconsistencies in availability, quality, purity, and labeling make research, interpretations, and clinical recommendations challenging.

The present case series is limited by its reliance on manufacturer reported cannabinoid content as well as the lack of multi-method assessment of patient symptomatology, including clinimetric data. The observed clinical benefit of CBD+ treatment in case studies, particularly with respect to caregiver-reported behavioral outcomes, must also be interpreted with caution given the significant placebo effects that have been documented in clinical trials of CBD.Only placebo-controlled trials will be able to elucidate the true therapeutic effects of CBD/ CBD+ treatment on FXS symptomatology. Until rigorous clinical trials have demonstrated the efficacy of CBD/CBD+ treatment for FXS, current treatments for the many behavioral problems associated with FXS should be utilized before off-label use of CBD+ products. In an effort to overcome existing limitations, future studies should independently test patient samples to confirm actual constituents of each CBD+ preparation and utilize well-validated caregiver-reported assessments of anxiety and other FXS symptomatology, in addition to unstructured caregiver- and clinician based reports, in an attempt to more completely track each patient’s course while in clinical care. Due to inconsistencies observed in many oral botanical preparations, rigorous examinations of pharmaceutical-grade preparations of CBD should be explored as potential treatments for children and adolescents with FXS.Adolescence is a time of subtle, yet dynamic brain changes that occur in the context of major physiological, psychological, and social transitions. This juncture marks a gradual shift from guided to independent functioning that is analogized in the protracted development of brain structure. Growth of the prefrontal cortex, limbic system structures, and white matter association fibers during this period are linked with more sophisticated cognitive functions and emotional processing, useful for navigating an increasingly complex psychosocial environment. Despite these developmental advances, increased tendencies toward risk-taking and heightened vulnerability to psychopathology are well known within the adolescent milieu. Owing in large part to progress and innovation in neuroimaging techniques, appreciable levels of new information on adolescent neuro development are breaking ground. The potential of these methods to identify biomarkers for substance problems and targets for addiction treatment in youth are of significant value when considering the rise in adolescent alcohol and drug use and decline in perceived risk of substance exposure . What are the unique characteristics of the adolescent brain? What neural and behavioral profiles render youth at heightened risk for substance use problems, and are neurocognitive consequences to early substance use observable? Recent efforts have explored these questions and brought us to a fuller understanding of adolescent health and interventional needs. This paper will review neuro developmental processes during adolescence,dry racks discuss the influence of substance use on neuromaturation as well as probable mechanisms by which these substances influence neural development, and briefly summarize factors that may enhance risk-taking tendencies.

Finally, we will conclude with suggestions for future research directions.The developmental trajectory of grey matter follows an inverted parabolic curve, with cortical volume peaking, on average, around ages 12–14, followed by a decline in volume and thickness over adolescence . Widespread supratentorial diminutions are evident, but show temporal variance across regions . Declines begin in the striatum and sensorimotor cortices , progress rostrally to the frontal poles, then end with the dorsolateral prefrontal cortex , which is also late to myelinate . Longitudinal charting of brain volumetry from 13–22 years of age reveals specific declines in medial parietal cortex, posterior temporal and middle frontal gyri, and the cerebellum in the right hemisphere, coinciding with previous studies showing these regions to develop late into adolescence . Examination of developmental changes in cortical thickness from 8–30 years of age indicates a similar pattern of nonlinear declines, with marked thinning during adolescence. Attenuations are most notable in the parietal lobe, and followed in effect size by medial and superior frontal regions, the cingulum, and occipital lobe . The mechanisms underlying cortical volume and thickness decline are suggested to involve selective synaptic pruning of superfluous neuronal connections, reduction in glial cells, decrease in neuropil and intra-cortical myelination . Regional variations in grey matter maturation may coincide with different patterns of cortical development, with allocortex, including the piriform area, showing primarily linear growth patterns, compared to transition cortex demonstrating a combination of linear and quadratic trajectories, and isocortex demonstrating cubic growth curves . Though the functional implications of these developmental trajectories are unclear, isocortical regions undergo more protracted development and support complex behavioral functions. Their growth curves may reflect critical periods for development of cognitive skills as well as windows of vulnerability for neurotoxic exposure or other developmental perturbations.In contrast to grey matter reductions, white matter across the adolescent years shows growth and enhancement of pathways . This is reflected in white matter volume increase, particularly in fronto-parietal regions . Diffusion tensor imaging , a neuroimaging technique that has gained widespread use over the past decade, relies on the intrinsic diffusion properties of water molecules and has afforded a view into the more subtle microstructural changes that occur in white matter architecture. Two common scalar variables derived from DTI are fractional anisotropy , which describes the directional variance of diffusional motion, and mean diffusivity , an indicator of the overall magnitude of diffusional motion. These measures index relationships between signal intensity changes and underlying tissue structure, and provide descriptions of white matter quality and architecture . High FA reflects greater fiber organization and coherence, myelination and/or other structural components of the axon, and low MD values suggest greater white matter density . Studies of typically developing adolescents show increases in FA and decreases in MD. These trends continue through early adulthood in a nearly linear manner , though recent data suggest an exponential pattern of anisotropic increase that may plateau during the late-teens to early twenties . Areas with the most prominent FA change during adolescence are the superior longitudinal fasciculus, superior corona radiata, thalamic radiations, and posterior limb of the internal capsule . Other projection and association pathways including the corticospinal tract, arcuate fasciculus, cingulum, corpus callosum, superior and mid-temporal white matter, and inferior parietal white matter show anisotropic increases as well . Changes in subcortical and deep grey matter fibers are more pronounced, with less change in compact white matter tracts comprising highly parallel fibers such as the internal capsule and corpus callosum . Fiber tracts constituting the fronto-temporal pathways appear to mature relatively later , though comparison of growth rates among tracts comes largely from cross-sectional data that present developmental trends. The neurobiological mechanisms contributing to FA increases and MD decreases during adolescence are not entirely understood, but examination of underlying diffusion dynamics point to some probable processes. For example, decreases in radial diffusivity , diffusion that occurs perpendicular to white matter pathways, suggests increased myelination, axonal density, and fiber compactness , but have not been uniformly observed to occur during adolescence. Similarly, changes in axial diffusivity , diffusion parallel to the fibers’ principle axis, show discrepant trends, with some studies documenting decreases , and others increases in this index . Decreases in AD may be attributable to developing axon collaterals, whereas increases may reflect growth in axon diameter, processes which are both likely to occur during adolescence. Technical and demographic differences such as imaging parameters, inter-scan intervals, age range, and gender ratios may account for divergent findings. Both grey matter volume decreases and FA increases in frontoparietal regions occur well into adolescence, suggesting a close spatiotemporal relationship .

While estimates of heritability have often been derived from familial studies, work over the past 5–10 years has increasingly demonstrated that a substantial proportion of heritability is instantiated in common genetic variation captured by whole-genome genotyping arrays across a broad swath of anthropomorphic and neuropsychiatric traits . Thus, a comprehensive understanding of both normative brain and cognitive development and their relation to early SU and abuse requires genetically-informed approaches, including both familial studies of heritability and molecular genetic studies. The ABCD study will take both approaches. Another paper in this issue describes the twin component to ABCD.Human and animal studies document that early life exposures to environmental neurotoxicants including heavy metals , and prenatal exposure to drugs of abuse can negatively impact brain development, leading to maladaptive and persistent alterations in brain structure and function and cognitive and behavioral development. Despite numerous studies describing the neuro developmental toxicity of early life environmental exposures, documenting associations between in utero and early life exposures with adverse health effects is hindered by the absence of direct fetal biomarkers that can be used safely to measure exposure in large study populations. Most associations between prenatal chemical exposure and neuro developmental outcomes are based on the analyses of maternal samples obtained at the time of birth, and perhaps at one to two time points during pregnancy. This timing may be months after the exposure occurred and the pharmacokinetic and tissue distribution of various chemicals may be quite different at different stages of fetal and child development . Documenting prenatal exposure to drugs of abuse is further compounded by social stigma associated with reporting licit and illicit drug use during pregnancy . To address this gap in our understanding of fetal and early life exposures, Dr. Manish Arora and colleagues have recently developed a novel methodology to retrospectively and objectively quantify the dose and timing of environmental exposures throughout pregnancy and early childhood using naturally shed deciduous “baby” teeth .

In the following sections we briefly summarize the literature demonstrating associations between early life exposure to environmental toxicants and drugs of abuse on developmental outcomes,marijuana grow system describe the novel approach to detecting exposures to some of these toxicants in shed deciduous “baby” teeth, and present the protocol for baby tooth processing and archival. By collecting teeth and leveraging the novel tooth biomarker described below, the ABCD Study is building a valuable repository that provides a unique, exciting and valuable opportunity to study the individual, interactive and/or additive effects of early life environmental exposures on childhood neuro developmental outcomes.A growing population of children around the world is exposed to various neurotoxicants present in urban and rural environments, which may damage their developing brains. Within the last several decades, strong evidence suggests that infants and children are uniquely vulnerable to environmental toxicants due to disproportionally higher exposures, immature metabolic pathways, and rapid growth and development . It is now well accepted that low-level chronic exposure to environmental chemicals may contribute to the growing epidemic of childhood neuro developmental disorders worldwide . In adults, exposure to metals has been shown to induce psychotic behaviors or depressive symptoms and emotional instability in adults reviewed in . In children, epidemiologic studies demonstrate associations between early life exposure to metals with poor cognitive, emotional and behavioral functioning in children reviewed in . Notably, current knowledge of the neuro developmental health risks associated with environmental chemical exposure has been derived mainly from the study of single agents; however, no human is exposed to just one chemical at a time. Evidence suggest combined effects of multiple chemicals might occur at levels far below those observable for any one component reviewed in . Notably, an individual’s risk of exposure to neurotoxicants, as well as the risk of adverse outcomes associated with exposure, may vary based on socio-economic status reviewed in . Childhood socioeconomic status is characterized by a combination of factors, including family income, parental educational attainment and occupational status , and is known to be an influential factor for brain development and cognitive function .

Such associations could stem from ongoing disparities in postnatal experience or exposures, such as family stress, cognitive stimulation, environmental toxicants, or nutrition, or from corresponding differences in the prenatal environment. Given SES-related differences in brain development , and, observed relationships between brain structure and function and environmental toxicants , low SES youth may be at increased risk for negative outcomes from a multitude of environmental factors. Interactive and/or additive effects of various neurotoxicants and other environmental factors can be examined in the baby tooth biomarker as part of the ABCD study.Human studies of prenatal exposure to drugs of abuse such as alcohol , tobacco smoke , cocaine , and marijuana have shown brain and cognitive abnormalities among offspring of mothers who reported use during pregnancy. Most human studies on the impact of prenatal drug exposure on brain and cognitive development utilize retrospective samples and rely on mothers’ recollection of drug consumption patterns years after pregnancy , and/or select prospective samples of children with “heavy” exposure vs. low or no exposure. Validity of retrospective reports on maternal life style during pregnancy 10–12 years post-partum, including SU, has been shown to be sub-optimal . The ABCD protocol includes a developmental history where parents/guardians are asked to recall SU patterns both before pregnancy, and after pregnancy recognition. While data collection is on-going in the ABCD study, maternal self-report in a sample of over 2000 participants studied as of the end of May 2017, when questioned about SU prior to pregnancy most report no SU, but, approximately 25% report use of alcohol, 0.6% cocaine, 5% marijuana, and 13.6% tobacco . These percentages substantially decreased when parents/ guardians were asked about their post-pregnancy recognition SU, but some continued after pregnancy recognition. Of course, we do not know how many, if any, parent/guardians of ABCD participants denied SU when there actually was use, but, given social stigma in many communities, it is unlikely that individuals would report drug use during pregnancy if there was none. However, as described below, some of these substances can be measured using novel assays of baby teeth.As discussed above, determining exposure to environmental toxicants during the prenatal period has been hampered by the lack of appropriate biomarkers to measure direct fetal exposure. Further, until recently, no single biomarker could provide continuous, time-resolved documentation of exposure throughout the fetal and early childhood period.

Common biomarkers used for environmental assessment including maternal blood and urine are often not optimal matrixes for determining prenatal and early life exposure due to the timing and invasiveness of collection. Further, maternal biomarkers are not always a reliable measure for fetal exposure . For prenatal exposure to drugs of abuse, there are additional complications with parent self-report of licit and illicit drug use during pregnancy due to social stigma and length of time since pregnancy in remembering use patterns ∼9 to 10 years prior as will be the case in the ABCD cohort. Establishing timing of exposures, especially over the prenatal period, is a major challenge in environmental epidemiologic studies. Teeth have long been used to estimate long-term cumulative exposure, including prenatal exposure, to environmental and other substances . Notably, much of our knowledge of the impact of early life lead exposure on cognition was gained by examining associations between higher lead levels in children’s teeth and reduced IQ . However, previous tooth biomarker methods examined lead in the whole tooth providing a cumulative exposure of lifetime measure. Dr. Arora’s method incorporates laser ablation and micro-dissection techniques that leverages the physiology of tooth development to provide finely time-resolved assessments of exposure from the beginning of the 2nd trimester through the time the tooth is lost. Deciduous “baby” teeth growth proceeds in an incremental pattern,cannabis vertical farming forming rings and layers similar to the rings of a tree. Toxicants circulating in the fetal blood stream are captured in the layers and measuring the amount of toxicant in the layers provides information about exposures dose and timing. These newly developed high-dimensional analytical methods combine sophisticated histological and chemical analyses to precisely sample tooth layers and have the potential to reconstruct a timeline of exposures during early development . These methods have been tested extensively in prior research , and hold promise for establishing timelines of exposures to environmental toxicants and drugs of abuse in the ABCD sample. In addition to fine-grained timelines of exposure spanning the pre- to postnatal periods, advantages to using baby teeth as biomarkers of toxic exposures is that shed teeth can be stored relatively easily at room temperature, and does not require any invasive procedures such as blood draw.During development, enamel and dentine deposition occurs in a rhythmic manner, forming incremental lines akin to growth rings in both enamel and dentine . At birth, an accentuated incremental line, the neonatal line, is formed due to disturbances in the secretory cells during protein matrix deposition . This line forms a clear histological landmark that demarcates pre- and postnatally formed parts of teeth. Beyond the neonatal line teeth manifest daily growth lines, which allow chronological ages to be determined at various positions within tooth crowns and roots. The analytical approach involves sampling the growth rings of teeth using laser and other microdissection methods. Analyzing the sampled layers using mass spectrometers can yield time resolved information on organic and inorganic environmental compounds and their metabolites . Dr. Arora and colleagues have previously validated this biomarker for certain metals and validation for a range of organic targets is also underway in that laboratory. By collecting multiple baby teeth from individuals enrolled in the ABCD cohort, we are building a repository that may be leveraged in the future to measure not only the validated metals but also early life exposure to organics, maternal stress, and licit and illicit substances. Many other substances should be possible to measure on a detailed timeline during pre and post-natal development using baby teeth.

Previous research has shown that metabolites of licit substances, such as alcohol and tobacco, and illicit substances, such as cocaine and marijuana have been measured in the teeth of adults, though, most of these studies have been done with ground adult teeth using material from dental extractions, and do not allow for timeline of exposure in earlier development . To our knowledge, these biomarkers have not yet been validated using shed deciduous teeth, which would require contemporary measurements of more conventional biomarkers, such as maternal, newborn, and childhood urine/blood samples at various points during pregnancy and childhood. Nonetheless, there is potential for measurements of these substances in deciduous teeth by adapting existing assays, but using methods which allow timelines of exposure during development reviewed in .Participants’ parents are asked for 5 baby teeth shed between the ages of 6–13 years old. Parents either bring the teeth in during a lab visit, or mail shed teeth into the lab with provided kits . Teeth may become brittle in very cold orhot temperatures, thus shipping and storage of shed teeth occurs at room temperature. Data collection sheets completed by the parents include information about how each tooth was shed , and how it was stored .Tobacco use is the leading preventable cause of death in the US, killing over 480,000 people each year. Most of these deaths occur among cigarette smokers, 80% of whom begin smoking by age 18 and 99% of whom begin by age 25. The transition from experimentation to established smoking generally occurs in the late teens and early 20s. Tobacco use patterns vary within the population, with some people never smoking, some remaining occasional users, and others progressing to daily use or quitting. Existing research has identified 4–6 trajectories of smoking, typically classified as never-smokers, experimenters/occasional users, reducers or quitters, those who start smoking young and quickly become daily smokers, and those who start smoking as young adults and become daily smokers. Studies of smoking trajectories have primarily focused on identifying risk factors at the individual or family level, determining associations between trajectory type and health outcomes, and assessing links between trajectories and use of other products.Understanding the factors that influence smoking initiation and the transition to regular smoking is critical to developing tobacco control interventions that improve public health.

Among the 3.6 million students with past-month e-cigarette use, more than 80% used flavored products . Although banning sales of e-cigarettes and/or flavored tobacco may reduce adult smokers’ options, such policies may also help combat the epidemic of youth e-cigarette use . Non-tobacco flavored e-liquids are a primary driver of young people’s experimentation with e-cigarettes. In the 2019 National Youth Tobacco Survey, middle and high school students reported flavors as one of their primary reasons for use . The majority of youth e-cigarette ever-users initiated e-cigarette use with a non-tobacco flavored product . Further, initiation of e-cigarette use with non-tobacco flavored e-liquids is associated with more rapid progression in e-cigarette use frequency . Flavors appear to increase the reinforcing potential of e-cigarettes, both in conjunction with and independent of nicotine exposure . Hence, restrictions on non-tobacco flavored e-cigarette sales have gained attention in an effort to dissuade young people’s ecigarette use and the development of nicotine-containing e-cigarette dependence. In the U.S., it is against the law for retailers to sell tobacco products, including ecigarettes, to people under 21 years of age. Nonetheless, underage consumers in the U.S. frequently purchase vaping products at both brick-and-mortar stores and online stores. In a qualitative study, underage young adults in southern California reported buying vaping products from stores by using fake IDs and by frequenting retailers they knew did not have strict age verification. Age restrictions on online sales were evaded by using websites without strict verification, by using online delivery services , and by shipping products to an overage friend’s home . Online age verification is often easy to circumvent. Of the warning letters FDA sent to online retailers selling vaping products in 2018, 29% pertained to selling vaping products to minors . States must enforce the minimum sales age in order to receive full funding from the Substance Abuse and Mental Health Services Administration . However, many jurisdictions implemented the minimum sales age prior to the federal law. Policy enforcement is likely stronger in jurisdictions that had enforcement measures in place prior to implementation of the federal law. In addition, the odds of sales violations vary by neighborhood demographics . Youth who frequently visit convenience stores and other retailers that sell tobacco are at increased risk of subsequent e-cigarette use. Stricter retail restrictions, especially restrictions on the non-tobacco flavored products that young people prefer, may therefore decrease young people’s tobacco use.

Restrictions on flavored products may also discourage young people’s e-cigarette use via their potential impact on risk perceptions. Research indicates adolescents view non-tobacco flavored e-cigarettes as less harmful than tobacco e-cigarettes ,marijuana grow system and current use of non-tobacco flavored e-cigarettes is associated with lower risk perceptions . In sum, flavors increase the appeal of e-cigarettes to young people and may alter their judgment of the risks of e-cigarette use. If flavored e-cigarette sales were further restricted, youth may perceive e-cigarettes as more harmful and may be less likely to initiate use. Prior to the widespread popularity of e-cigarettes, flavored tobacco restrictions in New York City were followed by a decrease in reported flavored tobacco product use among youth . Moreover, research conducted in Massachusetts and Minnesota found that restricting the sale of flavored tobacco products to adult-only stores successfully decreased flavored tobacco product availability . Similarly, California youth and young adults who lived in a jurisdiction that restricted flavored tobacco sales were significantly less likely than their peers in other jurisdictions to obtain vaping products from brick-and-mortar retail sources.A national study estimated that tenth graders’ odds of current daily smoking declined 2% for each 1% increase in average retailer compliance with age restrictions . Recent evidence from Massachusetts also suggests that restricting the sale of flavored tobacco products reduces youth flavored tobacco product use, including e-cigarette use. Among young adults in Southern California, those living in jurisdictions with weaker tobacco retail licensing policies were more likely to endorse flavors as a reason for e-cigarette use compared to those in jurisdictions with stronger policies . Lastly, a few studies have evaluated the effects of San Francisco’s e-cigarette sales ban on young people’s tobacco use. Following the ban, young adults obtained a greater proportion of their e-cigarettes online or through the mail, or from retailers outside the city, yet flavored ecigarette use still decreased overall. Combustible cigarette smoking significantly increased, raising concerns that many young adults may have switched to cigarettes when e-cigarettes were less available . Moreover, a difference-in-difference analysis suggested increased odds of cigarette smoking among San Francisco high school students compared to other districts . Nonetheless, most prior research suggests positive effects of flavored e-cigarette sales restrictions on youth tobacco use. While informative, these studies did not assess the impact of flavored tobacco retail policy on youth e-cigarette risk perceptions. The present study assessed e-cigarette risk perceptions, attitudes toward flavored tobacco policy, and past-month nicotine vaping in a sample of young people in 30 major U.S. cities. Ten of the 30 cities prohibit flavored e-cigarette sales. We hypothesized that young people surveyed in the 10 cities with flavored e-cigarette sale restrictions versus the 20 cities without such restrictions would perceive e-cigarettes as riskier, have stronger beliefs in the effectiveness of flavored tobacco policy, and, among ever e-cigarette users, be less likely to report past-month nicotine vaping.

We also examined demographic differences to characterize young people in the sample reporting past-month nicotine vaping and past-month retail purchasing of nicotine vaping products. Young people were recruited between August 21, 2020 and November 9, 2020 from 30 U.S. cities that are part of the Advancing Science & Practice in the Retail Environment consortium. The cities1 are located in 23 states. At the origin of the ASPiRE project, 27 cities were members of the Big Cities Health Coalition , two cities were added for representation in the southeast , and Providence was added for early adoption of novel retail policies. Using Qualtrics Research Services, recruitment efforts targeted approximately 30 participants per city, roughly balanced on age group and ever e-cigarette use . Recruitment quotas were adjusted as needed to ensure enrollment of approximately 30 participants per city. After a brief online screener, eligible participants completed an online survey of their e-cigarette perceptions and use behavior. Participants were compensated in the form of e-rewards money or points that can be exchanged for gift cards or bank transfers. E-cigarette policies in each city at the time of data collection were derived from the Campaign for Tobacco-Free Kids and supplemented with media sources to determine policy effective dates. Cities were categorized as having a sales restriction on flavored e-cigarettes or no sales restriction on flavored e-cigarettes, based on state and local policies. Dates of state and local Tobacco 21 laws were obtained from the Campaign for Tobacco-Free Kids and from the Preventing Tobacco Addiction Foundation . State e-cigarette tax was obtained from the Centers for Disease Control and Prevention State Tobacco Activities Tracking and Evaluation System . Chicago has a city-level e-cigarette tax and was therefore coded as having an ecigarette tax. A variable was created to reflect cities’ state or local laws raising the minimum legal sales age for tobacco to 21 before the federal law, because such cities likely have stronger infrastructure for enforcing the federal Tobacco 21 law. Cities were coded as 0 , 1 , or 2 . For each of the three outcome variables , null models were estimated to determine whether there was significant between-city variance in each outcome. For outcomes with significant variation between cities, generalized estimating equations tested differences in the outcome as a function of citylevel e-cigarette flavor policy, with individuals clustered by city. Bivariate analyses tested associations between those outcome measures and potential covariates at both the city level and individual level . Location in California and Texas were tested as potential city-level covariates because we sampled from multiple cities in California and Texas,cannabis vertical farming where state-level tobacco policy and enforcement may be a confounder. City-level and individual-level covariates that were significantly related to an outcome in bivariate analyses were included in the adjusted GEE model examining that outcome. Among ever-vapers, GEE models, with participants clustered by city, also examined associations of participant characteristics with past-month nicotine vaping and, among past-month nicotine vapers, purchase of vaping products at a brick-and-mortar retail location . Each characteristic was tested in relation to past-month nicotine vaping and in-store purchasing in separate GEE models. Because these analyses were exploratory, we did not adjust for multiple comparisons. Risk perceptions and policy attitudes had intraclass correlation coefficients < 0.001, indicating that participants’ city of residence did not account for a meaningful portion of the variance in risk perceptions or policy attitudes. Hence, tests of city-level e-cigarette policy on risk perceptions and policy attitudes were not run. Among ever-vapers, a null model of past month nicotine vaping estimated a between-city variance of 0.27. Therefore, analyses of past month nicotine vaping accounted for clustering by city.

Adjusting for participants’ age and race/ethnicity, which were significantly associated with past-month nicotine vaping among evervapers in bivariate analyses, e-cigarette flavor policy was not associated with the likelihood of past-month nicotine vaping among ever-vapers . Full GEE model results are presented in Supplemental Table 1.Participant characteristics are presented by past-month vaping nicotine vaping among those who had ever vaped , in Table 2. Full results from all GEE models are presented in Supplemental Table 2. On average, young people who vaped nicotine in the past month were significantly older than those who had not . Likelihood of past-month nicotine vaping also differed by race/ethnicity . Specifically, compared to non-Hispanic white participants, Black participants were less likely to have vaped nicotine. Gender , sexual identity , and family finances were not associated with past-month nicotine vaping among ever-vapers. Past-month nicotine vaping was more likely among participants who had ever vaped marijuana , but not flavor-only e-liquid or CBD . Past-month nicotine vaping was also more likely among those with lower harm perceptions . Participants who vaped nicotine were significantly more likely to see vaping ads in convenience stores, supermarkets, or gas stations , but did not reach statistical significance for ad exposure in smoke/vape/head shops or online . Having more friends with past-month nicotine vaping and cannabis vaping , living with someone who vapes , and positive vaping attitudes of important others each was associated with a greater likelihood of vaping nicotine in the past month. Likelihood of past-month vaping was not associated with perceived ease of accessing flavored vaping products or flavored e-liquid . Among participants with past-month nicotine vaping, a null model of in-store purchasing estimated a between-city variance of 0.25; therefore, GEE models accounted for clustering by city. Full results from all GEE models are presented in Supplemental Table 3. A majority of young people with past-month nicotine vaping purchased vaping products in retail stores . Young people who did versus did not purchase vaping products in-store did not significantly differ in age, race/ethnicity, gender identity, sexual identity, family finances, lifetime cannabis vaping, lifetime flavor-only e-liquid vaping, e-cigarette risk perceptions, exposure to vaping ads in any venue, friends’ past-month nicotine or cannabis vaping, living with someone who vapes, positive vaping attitudes of important others, perceived ease of accessing vaping products, self-perceived addiction to e-cigarettes, or policy attitudes . Young people who purchased in-store were more likely to use fruit-flavored and menthol-flavored vaping products than those who did not purchase in-store. Likelihood of using mint/ice/frost , candy/dessert/sweets , and tobacco flavored products did not differ between those who did versus did not purchase vaping products in-store. In addition to purchasing vaping products in stores, participants reported using friends’ vaping products , giving someone money to purchase vaping products , buying vaping products online , or receiving vaping products from family . Tobacco retail policies prohibiting flavored e-cigarette sales may benefit young people by decreasing access to kid-friendly products; however, the influence of such policy on young people’s e-cigarette risk perceptions is unknown. Contrary to hypotheses, local e-cigarette flavor policy was not associated with e-cigarette risk perceptions, policy attitudes, or past-month nicotine vaping among young people in 30 U.S. cities. Young people who reported past-month nicotine vaping had lower harm perceptions and viewed flavored tobacco policy as less effective, compared to those without past-month nicotine vaping.

The analysis team met to review and finalize the coding scheme. An iterative process of coding, assessing intercoder agreement, and resolving differences in coding was repeated until a Krippendorff’s α = 0.862 was reached. Research assistants then coded all focus group data. The principal investigator created a subcode for each coded data fragment, which was reviewed by research assistants for appropriateness. The analysis team met and resolved conflicts to achieve a consensus subcoding of data fragments. Research assistants then recoded the data with subcodes within Atlas.ti. A phenomenological approach was utilized to analyze the focus group data . Data relating to EVALI were extracted for participants who reported past 30 days use of e-cigarettes and analyzed for major thematic elements which provided an initial framework to conduct the phenomenological analysis. Following the process described by Moustakas , a textural and structural description of e-cigarette users’ understanding of and behavioral response to EVALI was created for each participant from the focus group transcripts. Textural and structural commonalities among individual experiences were organized into themes to construct a composite description of how e-cigarette users understand and respond to EVALI. The composite description identifies both essential structures of experience as well as thematic variations in meaning. The experience of e-cigarette use within the context of EVALI was composed of four essential structures: awareness, information filtering, risk rationalization, and behavior. Awareness is the initial stage of becoming alerted to and learning about any element of EVALI. Information filtering is the process of comprehending, accepting, and prioritizing the information encountered about EVALI. Risk rationalization refers to the development of rationalizing a course of action in light of information accepted about EVALI, perceptions of harm associated with EVALI, and past and existing e-cigarette behaviors and experiences. Behavior is the action taken in response to risk rationalization. Each domain is composed of interrelated themes which constitute patterns of experience . The highly visible reporting of EVALI cases resulted in a considerable amount of – and sometimes conflicting – information and opinions that had to be comprehended, accepted, and prioritized as salient. The first level of filtering pertained to the very legitimacy of reported EVALI cases. While EVALI cases and reported symptoms were generally accepted as conveyed through information channels, five participants questioned whether EVALI was “fake news,” a “government conspiracy” funded by the tobacco industry, or part of a government effort to ban ecigarettes. A second level of filtering by some participants focused on causes of EVALI.

Three causes of EVALI were the most salient: “fake” cannabis vape products, any “fake” vaping product, and excessive vaping. The most accepted cause of EVALI, reported by nearly a quarter of respondents, was “more of the THC,cannabis grow equipment like fake THC carts” or identification of a particular brand: “Most [EVALI cases] were using something called a Stizzy… a weed type of vape and most of them get fake cartridges.” EVALI was also thought to be caused by any “fake” product , including nicotine and cannabis vaping together. Irreputable products and sources were described as “bad carts… you buy off the street” or “fake cartridges… [that] weren’t like Juul, they weren’t like Suorin, weren’t these name brand trusted sources.” A third accepted cause of EVALI noted by five participants was excessive vaping of nicotine products. Participants were told that “you can actually be harmed by smoking too much” or viewed cases as people who “were smoking three pods a day or something” or “were really addicted to [Juuls].” Accepted information from the filtering process was one variable in developing a risk rationalization which also incorporated previous or existing experiences and beliefs. Among those who accepted the legitimacy of EVALI as a potential source of harm without engaging a second level of filtering on the causes of EVALI, increased risk was rationalized as either a basis for modifying behavior or disregarded altogether .I’m going to do it anyway.” Dual users of cigarettes and e-cigarettes said that “if I wanted to be healthy, I wouldn’t smoke [cigarettes]” or that “I’m already dead right? Because cigarettes are worse.” Among those who engaged in a second level of information filtering, two dominant risk rationalizations were developed which minimized the perceived risk of continued use. The first risk rationalization constructs harm as tied to perceived causes of EVALI, and avoidance of those specific causes as the basis of protection from harm . Participants who concluded that EVALI was related to “fake” cannabis products, any “fake” products, or excessive vaping suggested that because they either did not use “fake” cannabis or nicotine products or vaped infrequently or “in moderation,” they were not at risk for EVALI because they did not use specific product types or vape excessively.

The second risk rationalization references not having experienced EVALI-like symptoms in the past as the basis of protection from harm . For example, one participant suggested that “at this point, I feel that if it [e-cigarette] was going to do that I feel that it would have done it already or something like that, so… I don’t think it’s going to be me that that happens to” encapsulates this point well. Participants who acknowledged but disregarded increased risk of harm from EVALI reported no change in or intention to reduce or stop ecigarette use. On the other hand, participants who perceived increased susceptibility to harm from EVALI considered changing their e-cigarette use with varying degrees of follow up . Some made no change because of chemical dependence/addiction . Though people knew they should not vape, quitting “was easier said than done.” Inability to carry through on desires to quit or cut back were also due to vaping as “like a lifestyle.” Reported behavior change due to EVALI included transitioning to exclusive cigarette use or increased hookah use to reduce vaping. Others were able to reduce use without substituting for other products. Participants “cut back a lot,” were “trying to Juul a lot less,” and not “buying my own” e-cigarettes and only using when offered by another person . Participants who developed a risk rationality which minimized perceived harm based on their understanding of the causes of EVALI or because of past experiences used those rationalities to justify continued e-cigarette use. EVALI “didn’t do anything to dissuade” people from vaping because they felt that “I don’t fall into the category of vaping” that puts them at risk for EVALI, such as cannabis vaping or frequent ecigarette use. Similarly, some participants continued vaping because they only “hit it every once in a while” or thought EVALI was “not going to happen to me because I’ve been doing this for a couple years now. I’ll be fine, you know?” In this study, three essential structures were found to mediate the relationship between exposure to EVALI information and behavior: awareness, information filtering, and risk rationalization. Awareness of EVALI was universally reported, and the legitimacy of EVALI as a health concern was generally accepted. There was suspicion, however, about whether EVALI was “fake news” or part of a government conspiracy. Accepted information about the causes of EVALI included fake cannabis vaping products, any fake vaping products, and excessive e-cigarette use. Behavior was linked to information filtering and EVALI risk rationalization. Those who acknowledged but disregarded the harms associated with EVALI reported no change in behavior. Those who accepted the harms associated with EVALI either reduced their e-cigarette use or intended to but did not follow through due to challenges of addiction.

Continued use was rationalized by not using implicated products, moderate use, or lack of previous EVALI-associated experiences. At the time data were collected, numerous reports on the harms associated with EVALI had been released and reported and the CDC recommended that people should refrain from using all e-cigarette products . Health communications research has found that harm messaging may be effective in discouraging vaping ,vertical grow system which would support the presumption that government reports of harm from e-cigarettes and warnings to refrain from e-cigarette use during the EVALI outbreak would be salient and heeded. However, the results of this study provide possible insights into why harm messages may not discourage vaping in young adult e-cigarette users. The essential structures in e-cigarette users’ experiences described above suggest a central role for cognitive processes in mediating the effect of EVALI information on behavior. Eveland’s emphasis on attention and elaboration as essential cognitive elements of learning from the news are reflected in the awareness and information filtering constructs . Participants were universally aware of EVALI-related harms and connected EVALI information to past experiences and existing knowledge. However, learning from news is also tied to motivation, and Kunda suggests that individuals motivated to reach a particular conclusion may attempt to be rational but selectively search for beliefs and rules or combine knowledge to create new beliefs to support a desired conclusion . This “motivated reasoning” would suggest that participants in this study filtered for information and developed risk rationalizations based on the underlining motivation to continue vaping. Thus, non-compliance with recommendations to refrain from e-cigarette use based on harm messaging may have less to do with the information needs of target audiences and more with their motivation to continue vaping. Importantly, this study identified EVALI specific situational rationalizations that could inform future public health efforts to address EVALI and e-cigarette-related harms that may have an impact on future quitting or cessation behavior . For participants who expressed a desire to cut back use or had begun to successfully do so, the EVALI outbreak acted as a focusing event on the dangers of e-cigarettes. The opportunity to support e-cigarette cessation was missed for some whose difficulty in cutting back or quitting led to continued use or transition to another product. Though both universities from which study participants were recruited have student health centers that offer cessation services, none of the participants who attempted or considered cutting back or quitting mentioned knowing about or seeking services from the student health center or other resources.

This underscores a potential need to make available cessation services tailored to young adult e-cigarette users and communicating the benefits of these programs from a harm reduction perspective; the results of this study provide some potential dimensions on which to tailor such programs. Outreach for cessation services for young adults through colleges and universities may be particularly important during focusing events to take advantage of heightened interest in cessation due to concerns related e-cigarette harm prompted by EVALI. The study was limited to college-going young adults, and thus additional research is needed among a community sample of young adults. Limiting participants to students at two universities may bias results to reflect the pool of knowledge or experiences available at those two universities. In addition, the sample of students in the analysis was drawn from those who have used tobacco products in violation of university policy and may have been particularly amenable to information justifying e-cigarette use, dismissive toward information discouraging ecigarette use, or doubtful of their ability to change behavior. Because this study focused on current users, we were unable to explore the ways in which adverse events may lead to quitting e-cigarettes. The results of this study are not generalizable to all college going young adults but provide descriptions of risk rationalities to be explored in future inquiry. While the risk rationalities described may not be exhaustive of e-cigarette user experiences and textural and structural descriptions could not be created for 9 focus group participants, the study findings provide a basis for guiding future research for public health actions against EVALI. Finally, this study highlights the need for greater understanding of how urgent public health events with large information gaps, such as EVALI and COVID-19, are understood by at-risk groups, and the most effective communication and intervention strategies to bring about desired change. Emotional health is an important component of overall well-being. Negative emotional functioning is closely associated with poorer physical health and everyday functioning, greater disabilities, and shorter lifespans . Poor emotional outcomes are common among persons living with HIV . Compared to HIV- individuals, PLWH are at greater risk for major depressive disorder and experience worse health-related quality-of-life, depression, and anxiety .

We used a likelihood ratio test to compare the fit of this model using an exchangeable covariance structure, which restricts the variance in pre and post intercepts to be equal, to one with an unstructured covariance structure, which allows the variance to differ pre- and post-Prop 47 implementation, as a test of the change in county variance. The latter model is the most flexible and was used to generate the policy effects on the marginal probabilities of felony conviction. For county-specific estimates of outcomes pre vs. post-Prop 47, we generated empirical Bayes estimates of county random pre- and post-Prop 47 implementation intercepts from the models with unstructured covariance and calculated the linear combinations of fixed and random effects corresponding to pre- and post-Prop 47 periods. We also used fixed effects models with county dummy variables, interacted with the pre-post Prop 47 variable to generate marginal probability estimates of within-county pre-post change. A large proportion of arrests had no disposition . We assumed these cases were either never presented to the district attorney or the district attorney did not file charges, and therefore assigned an outcome of no conviction. We compared case characteristics for those with and without dispositions and found support for this assumption. Those without dispositions were less severe cases, and therefore less likely to result in conviction. For example, a larger proportion had no concurrent arrests . They were also less likely to have concurrent felony arrests . It is possible, however, that some of these cases resulted in convictions and the disposition was never reported by the court. We therefore conducted a sensitivity analysis that assumed the most severe extreme: that all sale/transport cases with missing dispositions received a felony conviction, and that all Prop 47 cases with missing dispositions received felony convictions if they occurred in the pre-Prop 47 period or if they included a concurrent felony arrest in the post period. Prop 47 arrests without concurrent felony arrests in the post period are unlikely to have received a felony conviction,cannabis growing facility since the Prop 47 offense was at that point classified as a misdemeanor. Changes in the outcomes of Prop 47 arrest events must be considered in the context of potential changes in the composition of arrestees .

When comparing the full sample prior to propensity score matching, a greater fraction of post-Prop 47 arrest events had concurrent arrests of other types , suggesting a decline in arrests when drug possession was the sole offense. Post-Prop 47 arrestees also appeared to differ in terms of criminal histories, with more prior arrests . The propensity score matched sample had better covariate balance. For sale/transport arrests, pre and post groups were much more similar . Though they were not compared statistically, we find the population arrested for these offenses appears quite different from those arrested for Prop 47 drug offenses. Prop 47 offenders had more numerous but lower level prior arrests and convictions. Racial differences were notable as well, with larger racial disparities among sale/transport arrests .After Prop 47 was adopted, Prop 47 drug convictions declined in both those arrested for Prop 47 drug offenses and those arrested for sale/transport. For those arrested for sale/transport, the decline in convictions overall was approximately equal to the decline in Prop 47 convictions. Although for the Prop 47 group, the percentage point decline in Prop 47 convictions was slightly larger than the decline in overall convictions, suggesting that it may have become more common to convict these arrestees of other crime categories. The question then becomes, which type of convictions replaced the Prop 47 convictions, if any, and did these replacements counteract potential reductions in felony convictions for Prop 47 offenses? Looking first at the Prop 47 group, there was a 14 percentage point decline in felony convictions , from 21.2% to 6.9%. This approximates the percentage point decline in felony Prop 47 convictions , suggesting felony convictions for other concurrent offenses did not increase. This is corroborated by the break-down of convictions for other offenses, which shows increases in convictions for misdemeanor drug and misdemeanor “other” categories, but not felony offenses . For example, the most common concurrent felony offense was felony property, present for 9.9% and 9.8% of Prop 47 drug arrests in pre and post periods, respectively. Yet the proportion of arrestees who received convictions for felony property offenses did not rise: 2.5% pre and 2.1% post.Declines appear to be driven by declines in the fraction of these arrestees who were ultimately convicted of felony Prop 47 offenses.

The likelihood of any felony conviction in this group declined 7.1 percentage points, approximately the drop seen in the proportion convicted of a Prop 47 felony with no other felony drug conviction . In contrast, no significant change occurred in felony convictions for sale/transport, suggesting prosecutors did not pursue more convictions for these offenses as a means to maintain prior levels of felony drug convictions post-Prop 47.With regard to Prop 47 arrests, pre and post estimates for each county suggest counties where felony convictions were more likely pre-Prop 47 were reduced towards zero to a greater degree, such that post-Prop 47 outcomes were more similar across counties. Mixed models with random pre-Prop 47 intercepts, random coefficients for the policy effect, and an unstructured covariance structure allowing for a correlation between intercept and slope random effects, showed a significant, negative covariance between random effects . Aligning with the pattern depicted in Figure 3.2, this suggests counties where felony convictions were more likely in the pre-period also declined more towards the less punitive counties, reducing the variance across counties. The reduction in county differences is corroborated by variance estimates from models with county specific random intercepts for the pre and post periods . We find a larger variance in county random intercepts in the pre-Prop 47 period . The likelihood ratio test comparing an exchangeable covariance structure as a nested model indicated that the unstructured covariance structures which allowed pre- and post-Prop intercept variances to differ, was a better fit to the data . To put this in concrete terms, of the 56 California counties, prior to the policy the most punitive county had a conviction probability of .38 , whereas the least punitive county had a conviction probability of .04 . After Prop 47 was adopted,cannabis grow system the most punitive county had a conviction probability of .19 whereas the least punitive county had a conviction probability of .02 . Another way to conceptualize these results is in terms of how discrepant the statewide probability of felony conviction was from the least punitive county pre-Prop 47, and the extent to which that discrepancy changed post-Prop 47. Prior to Prop 47, the statewide probability of felony conviction was 17 percentage points higher than the least punitive county , meaning 81% of statewide felony convictions following Prop 47 drug arrests would not have occurred if prosecuted in the least punitive county.

Whereas after Prop 47, the statewide probability was just 3 percentage points higher than the least punitive county prior to passage.There was also significant variation across counties in the likelihood of felony conviction following a sale/transport arrest , ranging from 0.05 in Merced County to 0.51 in Calaveras County in the pre-Prop 47 period. However, mixed model results indicated the significant variance in the pre-Prop 47 period , did not decline post-Prop 47 . This suggests that, while people arrested for sale/transport were less likely to ultimately get a felony conviction after Prop 47 was adopted, this effect did not vary substantially across counties, and no county showed an increase in felony conviction probability for sale/transport arrests. In other words, it does not appear that more punitive counties altered plea bargaining practices for sale/transport arrests to retain pre-Prop 47 levels of felony convictions, as this would have resulted in an increase in variance in felony conviction probabilities for this category of arrest. Findings aligned with sensitivity analyses that assumed all cases with missing dispositions received felony convictions .In this study of the change in felony convictions in California counties after Proposition 47 reduced criminal penalties for drug possession, we found significant declines in the likelihood of a felony conviction following arrests for Prop 47 drug offenses and non-Prop 47 felony drug offenses . Prior to Prop 47, dramatic geographic inequalities in probability of felony convictions after drug possession arrests prevailed between counties, and these geographic inequalities were substantially reduced after adoption of Prop 47. The reduction in felony convictions aligns with reports from the Judicial Council of California on reductions in felony filings following Prop 47 passage , while providing new evidence that reductions led to declines in geographic disparities in felony convictions for drug arrests. By holding county-specific case characteristics constant across time, this study identified a reduction in the excess variation that was attributable to county practices. This impact likely reflects that Prop 47 eliminated prosecutorial discretion for how drug possession can be charged. While previous research has found that the county-specific interpretation and implementation of reforms tends to reinforce the preexisting prosecution and sentencing practices within the county , results from the current study do not indicate counties attempted to mitigate the effects of Prop 47 with felony filings for concurrent offenses, or reducing plea bargaining for sale offenses.

Several factors could explain why Prop 47 led to reductions in geographic disparities in case outcomes, when other reforms have not. Prop 47 was a voter initiative, and considering the influence of community priorities for law enforcement on charging policies and decisions, prosecution practices may be more responsive to these types of reforms. Secondly, Prop 47 called for reduced criminal penalties, whereas prior studies have evaluated reforms like three strikes laws which maximize punishment. Maximizing punishment is costly, whereas reducing it can assuage overburdened courts. Therefore, we may be more likely to see change resulting from reforms that call for lesser criminal penalties, and especially when that call comes from the public. Reducing variation in the likelihood of a felony conviction for two equivalent cases mitigates inequalities in criminal justice exposure due to unequal applications of the law. However, requiring that all drug possession offenses be prosecuted as misdemeanors also suggests that cases with different characteristics are now being treated more similarly. A defendant can still be convicted of a felony for concurrent felony offenses, so it is the effect of criminal history on case outcomes which we would expect to be minimized post-Prop 47. Criminal history is strongly associated with race/ethnicity, which may reflect biases and practices in drug law enforcement , while increasing the severity of punishment for subsequent drug offenses . There is evidence Prop 47 in fact reduced the effect of criminal histories in San Francisco, where prior to Prop 47, racial disparities in case dispositions and sentencing were attributable to more extensive pretrial detention and criminal histories among black defendants . When Prop 47 reclassified drug possession offenses to misdemeanors, these characteristics had lesser effects on case outcomes, and racial disparities declined. Further research could assess whether findings from San Francisco apply statewide. There are also implications for substance use disorder treatment. Prop 47 generated $103 million in savings in the first year, awarded through grants to counties to increase access to substance use disorder and mental health treatment, and education . Counties with few felony convictions pre-Prop 47 may have had greater support for and availability of drug diversion options which allow dismissal of charges for successful drug treatment completion. However,Prop 47 generated concerns that without the possibility of a felony conviction, the incentive to engage in treatment would be removed . Prior research has suggested that, as compared to volitional substance users, individuals with more severe substance use disorders tend to fail to meet the court’s conditions for diversion and ultimately receive harsher termination sentences . If this were the case, it would be logical that this group would opt out of diversion options now that the sentence for drug possession is less severe. Whether this is the case, and if so, understanding successful strategies counties have developed to increase access to needed treatment through other routes, would be valuable. CA DOJ’s Statewide Automated Criminal History System data is the most comprehensive data source available for studying criminal justice policy changes in the state, and has been used in significant studies of Prop 47, as well as other reforms such as Prop-36, which increased drug diversion following arrest .

A discussion of agricultural policy can be organized in a variety of ways. In this chapter we examine both major policy tools and major commodity-specific programs to summarize the influence of government. In order to provide a summary measure and a framework for the discussion, we have developed Producer Support Estimates by policy and by commodity for California agriculture.The Producer Support Estimate can be used as an approximate indicator of the magnitude of the net subsidy from a policy. The PSE is a widely applied summary measure of agricultural policy that attempts to measure the money value of explicit or implicit income transfers to agriculture. When calculated as a ratio of total transfer to total industry revenue, the percentage PSE is a rough guide that may be compared across commodities, time, and national or other geographic boundaries. When these comparisons are interpreted with care, they provide useful summary indicators. The PSE may also be decomposed by policy type to indicate the relative importance of different policies , 2002. The Producer Support Estimate is not a measure of production subsidy. It measures all transfers to an industry, including those that may do little to stimulate output. The PSE is not a substitute for a measure of import protection or export stimulant. Nor is the PSE a measure of producer benefit from government programs.The PSE does not offer a substitute for a full analysis of the market and non-market effects of government programs. It is simply a convenient summary measure of a variety of agricultural programs that does not require a full analysis of each industry. Changes in the PSE do not necessarily reflect changes in government programs. In particular, for a PSE that contains aspects of trade barriers, price support,mobile vertical rack or deficiency payments, the movement of market prices may dominate movements in the PSE over time. This means also that a PSE for a single year may not reflect accurately the degree of government support for a commodity in other years. Even with these limitations, we believe that it is useful to summarize government policies affecting California agriculture by using a variety of decompositions of the PSE for recent years.

The following sections discuss the PSE by program or policy category and by commodity, using recent data. The dollar value of the PSE is designed to reflect the government support provided to a commodity industry from a variety of policies and programs. We have used a large number of sources for information on budget outlays, internal and external prices, quantities, and other data that enter the calculation of the PSE. For many of the programs there is relatively little change from year to year. For these we have mainly use the most recent year available, often federal fiscal year 2000 , fiscal year 2001 or 2002. In some cases we use calendar year 2001 or calendar year 2002 data. In many cases we measure a portion of the government support as an average of recent years. For example, for commodity payments under the Farm Bill we use the average for crop years 2000 through 2002 for loan base program benefits and federal fiscal years 2001 through 2003 for payment programs under the Production Flexibility Contracts, Market Loss Assistance, Counter Cyclical Payments and Direct Payments. For discussion of the FSRI Act of 2002 see USDA publications by Westcott, Young and Price, and Sumner, 2003. For broad-based input subsidies, we use national data and allocate a share of the national total to California based on California’s share of national receipts. We then allocate the California total to commodities within California by their share of California agricultural receipts. In other categories of support, we use the California budget data for California fiscal year 2000 or 2001 as available. The California fiscal year runs from July 1 to June 30 so, that fiscal year 2001 runs from July 1 2000 to June 30 2001. Other specific measurements or data issues are dealt with below when we discuss individual programs and policies. The appendix contains a detailed description of our data and calculations. The PSE calculations and the percentage PSE results would differ somewhat if we chose different years or calculation methods, but, under any reasonable procedure, the pattern across commodities and policy instruments would differ little from the results presented here. The state average PSE would also change slightly if we used different base years. However, we do not believe that the current estimate represents any systematic bias. The crop PSE has likely been declining gradually over time as the share of relatively less subsidized crops has expanded. However, dairy, which is a high subsidy commodity, has an expanding share of California farm value. As noted in Table 1, the state PSE is about $3 billion or 10.7 percent of the total value of output and payments . The OECD calculates and reports PSEs for member countries for six major crop categories and seven livestock products. Fruits, vegetables, and other horticultural crops are not included in OECD figures.

For 2001, the OECD reports an aggregate PSE range from about 1 percent for New Zealand to over 69 percent for Switzerland . Norway, Iceland, Japan and Korea all have PSEs over 59 percent. The average PSE for all OECD member countries in 2001 was 31 percent . The OECD reports an aggregate PSE of 21 percent for the United States. For the thirteen commodities classified by the OECD, the average PSE in California is roughly equal to that of the United States as a whole. Support levels tend to be lower for fruits, vegetables and other horticultural commodities in the United States and some other countries. The crops that are less subsidized are particularly important inCalifornia and therefore the average PSE we report is well below the PSE for the United States as a whole as reported by OECD. Figure 1 illustrates substantial variation across commodities in the percent PSE. At that high end, sugar has a PSE of 63.9 percent. Rice is next at about 60.5 percent followed by cotton at about 40.5 percent. Wheat has a PSE of about 29.5 percent. Dairy, the state’s most important commodity in terms of value of production has a PSE of 33.4 percent. Feed grains, which include corn, oats and barley, have a PSE of about 24.3 percent. The PSEs for all other California commodities are in the single-figure percentage range, which is below the state average of 10.7 percent. Alfalfa and hay, for example, has a PSE of about 3.4 percent. Among the horticultural crops, PSEs range from 3 percent to 5 percent. Other livestock and poultry and the remaining crop categories have PSEs between 2 percent and 5 percent. These low PSE groups include such important California crops as nursery and flowers, grapes, lettuce, tomatoes, almonds, and strawberries. As background to further discussion, Figure 2 shows the distribution of total agricultural receipts in California by commodity category. The two broad categories of horticultural crops comprise well over half of all agricultural receipts in California. Dairy is the most important single commodity with about 17 percent of all receipts. Of the field crops, alfalfa hay is most important, followed by cotton and rice. Figure 2 is presented to provide a basis for comparison with Figure 3,vertical grow rack which shows the distribution of total support by commodity. Now the dairy industry is dominant in terms of its share of total support. Dairy is an important industry in California and also has a relatively high degree of government support. About 54 percent of all support in California agriculture is provided to the dairy industry. Notice that, because of their importance in total receipts, even the less subsidized categories of horticultural crops receive a combined total of over 19 percent of all the PSE for the state. Also, the heavily subsidized but relatively minor crops, cotton and rice, show up significantly in Figure 3. Table 2 provides an alternative categorization of the aggregate PSE. Rather than providing a distribution across commodities, Table 2 distributes the PSE by policy area and more specific policy tools. Import barriers account for the largest share of support, followed by government payments. Input assistance is ranked third. By far the most important policy tool in terms of the aggregate PSE is the dairy import barrier, valued at more than $1.15 billion per year.

Government payments are an important policy, accounting for an annual average of $210 million in Market Loss Assistance payments and $194 million in Production Flexibility Contract payments . Support from marketing loan benefits and Loan Deficiency Payments is valued at nearly $277 million. Direct payments account for about 25 percent of the total support in California agriculture.Dairy policy in California is important and unique. Policy governing the industry is highly developed and associated with a substantial share of industry revenue. It is unique in the sense that some policy instruments are unlike those used in other agricultural industries and, whereas much of California dairy policy is the same as in other parts of the United States, some is the instruments are unlike those used elsewhere. The California dairy industry participates in the U.S. federal price support program, the direct payment program and the industry benefits from U.S. import barriers and export subsidies. But California operates its own regulated milk marketing system, which has some features that differ from the federally regulated system governing most milk markets outside California and some federal programs have different effects in California . The federal price support program for milk in the United States is implemented with a government purchase program for manufactured dairy products. The USDA purchases butter, non-fat dry milk , and American cheese from processors at prices calculated to ensure that the farm price of milk used for the manufacture of those programs will generally remain above the legislated support price. From 1990 to 1995, the price support program included a small assessment on milk production to help offset the budget cost of the price support. The assessment varied from year to year and was implemented in a complex way, but was essentially a tax on milk output of approximately $0.11 per hundredweight . The FAIR Act of 1996 was to have eliminated price support program, but that was first delayed and then reversed. The dairy price support program was phased down 15 cents per hundredweight per year, from $10.35 per hundredweight, and was supposed to be completely eliminated by the year 2000 . The assessment on dairy production was eliminated immediately and this affected producers immediately . The FSRI Act continued the price support until 2007 at a rate of $9.90 per hundredweight of milk. Trade barriers are the most significant feature of U.S. dairy policy, and no serious trade policy reform was even contemplated in the discussions leading to the 1996 FAIR Act or the FSRI Act of 2002. In general, imports of dairy products in the United States have been limited to about 2 to 3 percent of U.S. consumption. The United States maintains binding tariff-rate quotas with high in-quota tariffs for imports of most major dairy products. These trade barriers have insulated U.S. dairy product markets from world market forces, with domestic prices for major agricultural products typically significantly higher than world prices. California’s dairy industry, which produces nearly half of the nation’s non-fat dry milk and approximately 20 percent of its cheese, benefits from these border measures. As part of the Uruguay Round Agreement on Agriculture that took effect in 1995, the system of absolute quotas gave way to a system of tariff-rate quotas . However, the second-tier tariffs that limit over-quota imports are prohibitively high; therefore, the effects of the TRQs remain the same as the absolute quotas that were replaced. The Uruguay Round GATT agreement also provided for a gradual increase in the quantity of dairy product imports into the United States under the TRQs. This provision allowed for a gradual increase in import access into the U.S. dairy market until 2000. The North American Free Trade Agreement , which became effective in 1994, eliminated dairy trade barriers with Mexico, but Mexico is a high-cost milk producer and so no new imports have arrived.