Vital sign abnormalities from the previous 8–12 hours were recorded during each patient assessment

Posted on October 20, 2022 by hemp2022

These secondary outcome data were used as an additional proxy measure to assess the availability of illegal drugs in specific regions, as has been carried out previously.All outcomes were systematically identified through publicly available illegal drug surveillance systems. Linear-by-linear association trend tests were carried out on annual estimates of all outcomes of interest. Price and purity estimates represent median values for each year, while estimates for seizures represent crude totals of quantity seized. All price estimates are expressed in 2011 USD and are, where possible, adjusted for purity.An online search of surveillance systems monitoring illegal drugs using two a priori defined inclusion criteria was carried out. Search terms included the following: drugs, illicit, illegal, price, purity, potency, surveillance system, government data, longitudinal, annual, estimate. Inclusion/exclusion criteria were as follows: only surveillance systems that included continuous longitudinal assessments of these outcomes of interest for at least 10 years were included because we specifically sought to assess the long-term impact of enforcement-based supply reduction strategies on illegal drug price and purity/ potency. Finally, data extraction was restricted to 1990 and onwards to focus on patterns of supply during recent decades. Data were obtained through online searches of registries of surveillance systems , governmental reports and peer-reviewed publications, through referrals from experts in the field, and through data requests to relevant organisations including the UNODC. All authors had complete access to all data and all had final responsibility to submit for publication. Ethics approval was not required given that we relied exclusively on publicly available data.We identified seven government surveillance systems that met inclusion criteria. Of these, 3 reported on international data, 3 on data from the USA and 1 on data from Australia.

One of the longest running surveillance system identified, the US-based Marijuana Potency Monitoring Project, is funded by the US National Institutes of Health and was established in 1975,cannabis cultivation technology while the most recent surveillance system was established in 2001 . With respect to international surveillance systems, the UNODC administers two separate surveillance systems that collect data from all participating UN member states: the Annual Reports Questionnaire surveillance system that collects price and purity/ potency data, and the Drug Seizures Database that collects seizure data. Finally, the European Monitoring Centre for Drugs and Drug Addiction administers the Reitox drug surveillance system network, which aggregates data from several country-level surveillance systems in Europe, as described below.24Table 1 presents surveillance systems that matched search criteria. An assessment of data provided by these surveillance systems demonstrated several broad trends. First, purity and/or potency of illegal drugs generally remained stable or increased overall during the study period. Second, the price of illegal drugs, with few exceptions, generally decreased. Third, seizures of cannabis, cocaine and opiates generally increased in major drug production regions and major domestic markets. Figure 1 presents data from the US Drug Enforcement Administration’s System To Retrieve Information from Drug Evidence . As can be seen, between 1990 and 2007 , the purity of heroin and cocaine, and the potency of cannabis herb in the US increased, while the inflation-adjusted and purity-adjusted retail street prices of these three drugs declined.25 Specifically, heroin purity increased by 60% , cocaine purity increased by 11% and cannabis herb potency increased by 161% during this time. During the same period, the prices of heroin, cocaine and cannabis decreased 81% , 80% and 86% , respectively.ICU patients frequently receive opioid and benzodiazepine medications to treat the pain, anxiety, and agitation experienced during a critical illness. Trauma ICU patients may require high and/or prolonged doses of opioids to manage pain associated with multiple open wounds, fractures, painful procedures, and/or surgery. They may also require benzodiazepines to prevent or manage anxiety and agitation and to facilitate effective mechanical ventilation . Although the effect of different pain and sedative medication regimens on TICU patients is unclear, prior evidence suggests that administration of opioid and benzodiazepine medications in the ICU setting is associated with the development of many complications including delirium and poor patient outcomes . Exposure to high or prolonged use of opioids and benzodiazepines may also contribute to both drug tolerance and drugphysical dependence .

Once drug dependence has developed, patients are then at risk for withdrawal syndrome , a group of serious physical and psychologic symptoms that occur upon the abrupt discontinuation of these medications . The effect of WS on patient recovery and prolonged ICU stay is unclear . Unlike in the PICU patient population, physical dependence during drug weaning of adult ICU patients exposed to prolonged doses of opioids and benzodiazepines has received little study. Indeed, there is a large discrepancy in the amount of literature regarding WS in the adult versus PICU populations. There are two descriptive studies with retrospective chart review designs and small samples in adult ICU surgical-trauma patients and burn ICU MV patients . Cammarano et al found that 32% of their sample developed WS after prolonged exposure to high doses of analgesics and sedatives. Brown et al found that all burn MV patients who received opioids and benzodiazepines for more than 7 days developed WS. In a prospective experimental study of major abdominal and cardiothoracic postsurgical ICU patients, 35% who received a combination of opioids and benzodiazepines developed marked withdrawal compared with 28% who received a combination of opioids and propofol . These three studies were reported more than 1 decade ago, prior to the current recommended change in sedative management . A recent prospective study of 54 TICU patients showed a lower occurrence of iatrogenic opioid WS than in previous studies . Regarding pediatric studies, two recent reviews evaluated 23 and 33 studies, respectively, of WS done in the PICU population . Of note, there is no valid and reliable WS assessment tool available for the adult ICU population, although there are two tools for pediatrics. These tools are the Withdrawal Assessment Tool-1 and the Sophia Observation withdrawal Symptoms-scale . The lack of a WS assessment tool for adult ICU patients may have contributed to the lower number of publications about WS in adults. This difficulty in the ability for clinicians to measure adult WS is particulary relevant considering the current U.S. opioid epidemic and was one of the reasons we undertook this exploratory work. Little is known about the actual occurrence of WS, risk factors, and its consequence in adult patients. Therefore, the objectives of this exploratory study were to identify risk factors associated with probable WS among adult TICU patients exposed to opioids and/or benzodiazepines; explore clinical characteristics, signs and symptoms, and outcomes among patients who developed probable WS, questionable WS, and patients who did not develop WS.Patients 21 years or older with an admission order to TICU at the Trauma Hospital of Puerto Rico and an expected exposure to opioids and/or benzodiazepines for 5 days or more were screened for study eligibility.

Patients who had head trauma with neurologic dysfunction, who were prisoners, and/or had alcohol use disorder by family or patient report were excluded . Consent was obtained in patients able to consent; for those unable to provide it, a family member provided authorization for the patient’s participation. When patients became capable of providing their own consent during the course of the study, they were asked about their desire to continue study participation and if the previously obtained data could be used.As established earlier, currently there is no validated tool for assessing WS in adult ICU patients which is a challenge in the study of WS in this population. Other challenges are that the signs and symptoms lack specificity, and there are similarities in these WS and signs and symptoms seen in other conditions like delirium, undersedation, pain, and anticholinergic toxidrome . This is particularly true for sign and symptoms related to CNS irritability and some nervous system activation . However, although not specific, WS has unique signs and symptoms related to gastrointestinal system dysfunction and some nervous system activation . Since we recognized the limitation of no validated assessment tool for adult ICU patients, we created a sign and symptom checklist to measure potential indicators of WS of opioids and/or benzodiazepines. For our checklist, we retrieved potential indicators from the Diagnostic and Statistical Manual of Mental Disorders , the International Classification of Diseases, 10th Edition Classification of Mental and Behavioral Disorders , and previous WS research in adult ICU patients to develop the checklist . Figure 2 depicts the signs and symptoms of opioid and/or benzodiazepine WS that were included on the checklist. Tachycardia and tachypnea were defined as more than 100 beats per minute and more than 30 breaths per minute, respectively, high blood pressure as a systolic pressure more than 150mm Hg, and/or diastolic pressure more than 90mm Hg. We used the Richmond Agitation-Sedation Scale score to determine level of arousal and the Confusion Assessment Method-ICU to determine delirium. The DSM-5 establishes that, to identify opioid and/or benzodiazepine withdrawal, the patient must develop three or more opioid and/or two or more benzodiazepine symptoms after cessation or a reduction in opioid or benzodiazepine doses after a prolonged use.Withdrawal signs and symptoms may begin to appear within 6–12 hours for short-acting opioids and 6–8 hours for benzodiazepine . Taking DSM-5 criteria into account and given that the checklist has not undergone a formal validation process,indoor grow cannabis we developed the following categories for our patients: “probable” WS: patients presenting with three or more sign/symptoms of opioid-WS and/or two or more sign/ symptoms of benzodiazepine-WS that were not present at baseline ; “no” WS: patients not presenting with the minimum sign/symptoms for opioid-WS and/or benzodiazepine-WS; and “questionable” WS: patients presenting with the required number of sign/symptoms, but one or more of these were present during baseline evaluation. For example, tachycardia that was present at baseline evaluation was not counted as a probable withdrawal sign during weaning.Recruitment and data collection were performed in TICU patients as well as patients with admission orders for TICU . If study patients were transferred to the intermediate unit while data collection was ongoing in the TICU, data collection continued in this unit. Baseline data were obtained from the patient’s clinical record or by family or patient interview. Daily and cumulative amounts of opioids and benzodiazepines and daily doses of other sedatives such as propofol and antipsychotics used from the arrival at Trauma Hospital and during the TICU stay were also collected.

Patient days on MV, length of TICU stay, and length of hospital stay were documented. Bedside patient assessment data using the sign and symptom checklist were collected on the fourth day of patients receiving opioids and/or benzodiazepines in order to establish baseline data. After the fourth day of receiving opioids and/or benzodiazepines, bedside patient assessment data were also collected once the start of the weaning process for up to 72 hours after the beginning of opioid and/or benzodiazepine weaning. If weaning was stopped and the patient returned to a similar previous dose, bedside measures ceased. When the weaning process was reestablished, measures began again and continued for up to 72 hours. Data on each of the signs and symptoms were collected twice a day .Due to a limited budget for this exploratory study, all data collection and assessments were performed by the first author.Patient demographic and clinical data are presented as medians for continuous variables and frequencies for categorical variables for patients as a total group and also according to WS category . To compare demographic and clinical characteristics in patients by WS category, we conducted Fisher exact test for categorical variables and Kruskal-Wallis test for continuous variables. A Bonferroni correction to adjust alpha for 13 comparisons was calculated, and a p value of less than 0.004 was necessary to determine statistical significance. A mixed-effects logistic regression was conducted to determine the contribution of demographic and clinical variables to the development of probable WS. We evaluated several candidate models for WS , in terms of their fit, using Akaike information criterion and Bayesian information criterion . AIC and BIC are the most commonly used criteria for candidate model selection in regression analysis, with lower values reflecting a better fit of the candidate model to the existing data .

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Two archeological DNAs gave a product with primer pairs rbcLF1/rbcLR3a that likely represented hemp

Posted on October 19, 2022 by hemp2022

These levels would have been helpful in determining if the primary study results were due to increased nicotine exposure in smokers with heavy caffeine or marijuana use. And third, some smokers had small measurable plasma nicotine levels at the time of scanning , which led to mathematical corrections for these levels. While overall study results did not differ with or without these corrections, an improved method of ensuring nicotine abstinence could have been helpful. Additionally, in the exploratory analysis from our previous study , lower caffeine use was associated with greater nAChR availability. Results from this prior exploratory analysis of a group with modest caffeine use would not have passed Bonferroni correction. In contrast, the finding here of greater nAChR availability in heavy caffeine users was highly significant . Thus, the present findings indicate a robust elevation of nAChR availability in heavy caffeine using smokers. In conclusion, smokers with concomitant heavy caffeine or marijuana use have greater α4β2* nAChR availability than smokers without such heavy use. These findings are consistent with prior research demonstrating more severe dependence on cigarettes in caffeine and marijuana users .DNA analysis can identify the biological source of archaeological artifacts. This is true for many plant-based artifacts. Plant cells contain plastids, such as chloroplasts in leaves-often many copies and plastids contain DNA sequences that are useful for identification. There is a great deal of information available concerning the base sequences of plastid genes in different plants, much of it gathered for use in determining evolutionary relationships. This information can be applied to objects like textiles and baskets. At first glance,hydroponics system for cannabis it may be surprising that DNA persists in manufactured objects, and some processes-e.g, mordanting-do break down DNA. However, even present-day rope is made with natural fibers that receive a minimum of treatment, and the rope contains fragments of tissue with intact organelles .

We expect that treatments of fibers in the past were less stringent and the products from which they were made more likely to retain plastids and nuclei. It may be even more surprising that DNA persists in ancient objects, since we can expect the rigors of time, with accompanying hydration, desiccation, and temperature extremes, to break down biological molecules. In fact, that does occur . But DNA may show a degree of resistance under certain conditions. Indeed its structure may have evolved in part to increase its stability . There have been many reports of ancient DNA isolated from, for example, mammoths preserved in glaciers , human mummies , wood , and rope . The degradation of DNA results in a gradual reduction in its length as the polymeric strands become fragmented, but even relatively short fragments retain useful information in their base sequences. With those considerations in mind, we resolved to identify DNA in fibers of samples of rope and cloth that have been found in an archeological site near Qumran and the Dead Sea. Microscopic observations have identified various samples of cloth from caves above Qumran as flax, cotton, and wool . Muller et al. , using X-ray micro-diffraction, identified flax and cotton in Qumran samples. But there have been indications of the early use of hemp in East Asia and in Europe and Asia Minor . Our objective was to learn whether the DNA of materials from an archeological site near the Dead Sea could confirm the presence of hemp or other fibers. We expected the use of DNA sequence information to confirm the identity of the major component , but also to indicate whether fibers from hemp or another plant species form a detectable fraction of one or more samples. As will be shown, our data do indicate that flax-linen dominates in every sample tested and that there is a small amount of hemp DNA in most samples.As noted in the Methods section, the choice of primers was a critical part of the study. Following the discovery that no rbcL DNA product 771 base pairs long could be amplified from the archeological DNA templates, we tested various rbcL primer combinations to find a successful set . Using template DNA from modern hemp, amplified DNA was obtained with all four primer pairs tested. However, templates from flax plants and from modern flax rope only gave product using primer pairs rbcLF2/rbcLR3a and rbcLF2/rbcLR4a. Three template DNAs extracted from archeological samples gave product corresponding to the smallest band obtained with flax.For subsequent tests, we concentrated on rbcLF2/rbcLR3a, which gave the smallest product and thus was least likely to discriminate against the gene from a minor species. The rbcL PCR products obtained using the template DNA extracted from all the archeological cordage and textile samples contained strong bands of approximately 184 base pairs .

To distinguish between flax and hemp templates, we noted that the band produced using authentic C. sativa DNA template was cut over 90% by BamH1, yielding fragments of 115 and 69 base pairs. The PCR product of the modern UK rope did not show a detectable amount of cutting and thus was entirely flax. Interestingly, the PCR product of the modern Japanese rope showed a small amount of cutting, indicating the presence of some C. sativa DNA. Most of the archeological samples showed only faint bands at 115 and 69 base pairs after BamH1 treatment, indicating that, like the Japanese rope, they contained little C. sativa DNA. Rope sample 931 and textile sample 786 were the most notable exceptions, with sample 931 showing 44% cutting and sample 786 showing 39% cutting. However, repetitions of the PCR reaction and restriction digestion, particularly of sample 786, did not consistently show the smaller bands produced by BamH1. The base sequences of DNA from the archeological samples confirmed their identity as primarily L. usitatissimum Fig. 6. Within the 184-base pair amplified DNA, there was a stretch of 86 base pairs in which accurate sequence determinations could be obtained from both primers. Within that region were nine sites at which the sequences of the Linum and Cannabis genes differed. These quence of rope sample 931 showed super positions of two bases at all nine sites , confirming that this sample contained a significant amount of Cannabis DNA. Chromatograms of the other samples, including textile sample 786, were not interpreted by the computer as having a significant amount of Cannabis DNA, but small peaks corresponding to Cannabis bases could be seen in the chromatogram for sample 786 . A few other base-sequence super positions, e.g. K or a present/deleted base , occurred near the ends of the 86- base pair stretch, but probably represented sequencing errors rather than the inclusion of a variant Linum or another species, since in each case the super positions were found with only one primer. However, one of the super positions in sample 931 indicated C/T , whereas the sequences of Linum and Cannabis at that position were C and A,respectively. In this position, the codons containing C, T, and A all code for glycine, so this is a “silent” substitution. It is possible that the rbcL gene of the archeological Cannabis differed from the modern species used for identification. In an attempt to confirm the data obtained from the rbcL analysis, we used the extracts of DNA as templates to amplify fragments of two other chloroplast genes, trnL and matK. Using the trnL primers, we obtained Cannabis fragments from rope sample 931 and most of the textile samples, especially 786 .

There was no indication of Linum template DNA in the ancient samples, although the modern control gave a good band. Using the matK primers, we could not obtain Linum or Cannabis fragments from any archeological DNA templates, although again the modern control templates worked well . Assuming that the primer-complementary sequences of the trnL and matK target genes have not changed drastically over the last 2000 years, these results suggest that different regions of chloroplast DNA fragment at different rates. Finally, we estimated the relative amounts of flax and hemp DNA using a semi-quantitative “kinetics” technique in which we compared the amount of rbcL PCR product as a function of the number of replication cycles. Although under appropriate conditions,indoor hydroponics cannabis the amplification of DNAs can give amounts of products that represent the relative amounts of different templates, many problems with the technique can confound the analysis. It is more accurate to compare the number of cycles that give equal band intensities upon staining, even given the uncertainty inherent in the relationship between staining intensity and DNA size. We applied this technique to three samples that appeared to show three different levels of hemp DNA, 931, 019, and 928. In fact, as shown in Fig. 9, the levels of hemp varied from ca one-fourth that of flax to 1/4000 .The PCR data confirmed the identities of the contents of all the cordage and textile samples as primarily flax-linen. The sequence data revealed the presence of hemp DNA, and by inference hemp fibers, but only in one rope sample, 931, and one textile sample, 786. The gel electrophoresis patterns, more sensitive to minor components, indicated the presence of hemp DNA in those samples and all the other samples, with the exception of the control Linum DNA and the DNA from the contemporary flax rope. The lack of linearity inherent in PCR made it impossible to estimate the relative amounts of flax and hemp accurately from the initial analyses, which were performed with a fixed number of amplification cycles, but a modification of the PCR protocol revealed that the fraction of DNA from hemp varied widely, from 25% to 0.025% that of the amount of flax DNA. The ubiquity of the hemp DNA, particularly in the small amounts found in most of the archeological samples, forces us to consider the possibilities for its origin. These include deliberate incorporation of hemp into flax rope and linen textiles in situ and the importation of hemp containing rope and textiles from other places. Samples 931 and 786 most likely acquired their substantial amounts of hemp DNA in their fabrication. Samples that had much smaller amounts of hemp DNA might have acquired it as dust, through their storage over several centuries in contact with some of hemp products. In the same way, samples might have acquired small amounts of hemp dust during their excavation and transport to museums, particularly if their bundles were bound with hemp rope or twine.

Finally, we cannot ignore the possibility of contamination in the analytical laboratory, although controls did not indicate this. The lack of contemporary hemp bands in the archeological samples in Fig. 4 strongly suggests that the hemp DNA, whether incorporated deliberately or through contamination, was ancient. What is the possibility that the results we interpret as representing hemp actually reflect another fiber? A comparison of the amplified segments of the rbcL genes from six old-world fiber plants, flax, hemp, date palm, cotton, banana palm, and ramie, shows that the base sequences of the central regions differ among all six. The BamH1 site in the hemp sequence occurs only in that species, so that the minor bands seen in Fig. 5 must represent hemp. The specific base super positions shown in Fig. 7 occur only in the flax-hemp pairing. The lack of other minor bases indicates that, if present, the amounts of other fibers must be very small. The 14C dating of the samples confirms a suggestion by Dr. Orit Shamir that they represent two distinct periods of use, one prehistoric and one from the time of the Roman conquest. Previous 14C dating of two wood samples from CC found even earlier dates, 3670 and 4830 BCE , but the authors pointed out that the dates might have represented “cultural activity 6000 years ago or the use of old wood.” Samples from the “Cave of the Warrior,” dated to approximately the same times, ca. 3800 BCE and ca. 4400 BCE , included textiles and baskets, a clear indication of prehistoric culture. Our three dated samples from the Early Bronze period, though somewhat younger, confirm the suggestion of very old cultural activity and add the information that this activity included the use of hemp. In conclusion, this work points out the value of PCR in determining the plant-fiber composition of textile and cordage, particularly when there are mixtures of fibers.

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Recent research suggests that risk may extend to other tobacco products as well

Posted on October 18, 2022 by hemp2022

Early experiences with tobacco are also thought to be indicators for the development of substance use disorders, thus representing an important area of youth and adolescent research . Therefore, if one wants to chart the course of drinking or drug use developmentally, starting with the earliest experiences of alcohol and drug use experimentation provides important information about substance related risk. Risk for the transition from initiating to the emergence of problem substance use in adolescence is influenced by a variety of factors. For example, youth who exhibit susceptibility cognitions are twice as likely than youth who do not see substance use this way to start smoking cigarettes during adolescence.In addition, parenting, home environment, neighborhood factors such as alcohol and drug availability, and peer influence have all been shown to impact substance use onset and outcomes . The Culture and Environment module of the ABCD protocol covers many of these potential influences on substance use, including parental monitoring, family environment and conflict, and neighborhood safety and crime. In addition to these domains, the ABCD substance use module captures low level substance use, acute subjective response, intentions to use, peer substance use, parent perception of the availability of substances in the neighborhood, and parent rules about substance use. Beginning at the one-year follow up, additional measures will be added to assess the youth’s perceived harm of substance use, disapproval of substance use, and substance-related expectancies. Thus, the ABCD substance use module that was developed measures important predictors of early substance use and escalation to SUDs.Another area of focus the Substance Use Work group identified is detailed measurement of substance use patterns, including detailed quantity, frequency, route of administration, and co-use patterns. This information is critical in order to complete one of the aims of the ABCD Study: to characterize the impact of substance exposure on adolescent neurocognitive development. Adolescents demonstrate a greater impact of substance use on neurocognitive outcomes . Alcohol has historically been the most commonly used substance in adolescents and converging lines of evidence reflect that repeated alcohol use during adolescence, especially binge drinking, has been associated with poor neurocognitive outcomes such as brain structural and function abnormalities and reduced memory, visuospatial skills, attention, and executive function in adolescents . Alcohol hangover symptoms not only reflect an immediate consequence of excessive consumption that causes distress in the drinker , they also uniquely predict acute cognitive impairment in adults , relate to worsened neurocognition in adolescents ,cannabis drying trays and prospectively predicts later alcohol use disorder onset in adults .

Of particular relevance to ABCD are findings that suggest that the studies examining pathophysiology of hangover in adults reveal that it likely involves a neuroinflammatory process , that may be similar to that observed in alcohol-related brain damage in rodent models . That is, hangover may represent an index of alcohol-related neurotoxicity that is associated with more persistent cognitive deficits. Cannabis is the second most commonly used drug, with 35.6% of 12th graders using it in the past year . Early adolescent cannabis use is strongly correlated with substance use and the abuse of other illicit drug use in youth . While there is still some degree of debate , converging data reflect that at least weekly cannabis use during adolescence has been associated with neurocognitive abnormalities, including abnormal brain morphometry and function, lower IQ, and poorer sustained attention, verbal memory, and executive function, especially in those with an early age of cannabis use onset see . It is notable that there have been challenges to this research in terms of the wide array of metrics, lack of measurement of potency and content of cannabinoids [e.g., tetrahydrocannabinol , cannabidiol ], lack of control of poly-substance use , and the majority are cross-sectional studies, making it difficult to resolve the temporal sequencing of substance exposure and neurocognitive deficits. Nicotine is the third most commonly used substance by adolescents and use of electronic cigarettes has become twice as popular as traditional tobacco products . Concomitantly, e-cigarettes have been found to increase the risk for transitioning to more traditional tobacco cigarettes . Although acute administration of nicotine may enhance cognition in teens and young adults, especially memory and attention , chronic use has been linked with attention and working memory deficits in teens . Acute withdrawal from nicotine in adolescent users has also been associated with abnormal reward processing , working memory , and verbal memory fMRI tasks, highlighting the necessity to measure last use of nicotine prior to neurocognitive assessment. Human and preclinical evidence demonstrates that other illicit substances are linked with neurocognitive deficits in adolescents and young adults, including cocaine , methamphetamine , MDMA or ecstasy , inhalants , heroin , cathinones , ketamine , gamma hydroxybutyrate , hallucinogens , and anabolic steroids . Given the common use of caffeinated beverages in youth as young as two years old and growing concern over health effects and addiction risk associated with excessive caffeine use , examining caffeine effects on health and neurodevelopment in youth is of increasing concern. Thus far, research has shown that acute caffeine administration is generally linked with improved cognition, although impact of chronic caffeine exposure is not well understood .

And at least one study reported that increased caffeine consumption is linked with increased risk-taking in adolescents . Finally, prescription stimulant medications have been linked with cognitive enhancement , while prescription anxiolytics/sedatives and opiates negatively impact memory, processing speed and attention. Over the counter cough medication containing dextromethorphan has been linked with cortical thickness in adolescents in one study , although this outcome has yet been replicated. It is notable that the majority of these aforementioned studies have numerous methodological weaknesses in that they are primarily cross-sectional, have relatively small sample sizes, lack female participants, have low power to disentangle poly drug effects, or have not been validated in younger adolescents. Another issue with this research to date is that poly drug use, which is common in adolescence , and co-use of substances is rarely studied. Co-use use can uniquely impact neurocognition ; indeed, preliminary evidence has shown that co-use of alcohol and nicotine , alcohol and cannabis , alcohol and cocaine , cannabis and nicotine , and cannabis and methamphetamine has been associated with unique neurocognitive abnormalities above and beyond single substance effects in adolescents and young adults. In summary, numerous substances have been linked with neurocognitive outcomes in adolescents. Studies have found that numerous factors can impact findings, including total exposure , potency and content , route of administration, outcomes such as hangover symptoms, and co-use of substances. Therefore, thorough measurement of substance use patterns and other qualifying factors across numerous substances categories from childhood through adolescence is an important component of the ABCD Substance Use module. The substance use patterns assessed by the module include alcohol, cannabis and cannabinoids , nicotine , caffeine, cocaine, cathinones, methamphetamine, 3,4-methylenedioxymethamphetamine , ketamine, gamma hydroxybutyrate, heroin, hallucinogens , psilocybin, salvia, anabolic steroids, inhalants, prescription stimulants, prescription sedatives, prescription opioids, and OTC cough or cold medicine. Further, the Substance Use work group will release the substance use patterns assessment tools to the scientific community in an attempt to improve harmonization .Another aim of the ABCD Study is to examine factors that impact the risk for and trajectory of SUD symptoms and consequences; other work groups will be measuring the numerous outcomes that may represent substance use consequences . Therefore, the ABCD Substance Use module will also obtain SUD diagnosis and symptoms for alcohol, cannabis, nicotine and other drugs. Several studies have reported that adolescent alcohol exposure is associated with increased lifetime risk for developing an alcohol use disorder . Earlier age of cannabis use has also been associated with increased risk for developing a cannabis use disorder ; 11.5% of adults who reported having tried cannabis prior to age 14 met DSM-5 criteria for CUD as compared to only 2.6% of those who tried cannabis after age 18 . The peak risk of developing a nicotine use disorder is associated with an onset of regular nicotine use at the young age of 10, and females demonstrate a particularly strong relationship between adolescent age of onset and higher rates of nicotine dependence .

Together, these findings support the hypothesis that adolescence is a vulnerable developmental period of high risk for development of a SUD following early substance use exposure. Therefore, the ABCD Study will assess DSM-5 diagnostic criteria of SUD , as well as symptom counts of AUD, CUD, NUD, and combined other illicit drug use disorder.Youth are administered the ABCD Substance Use module by a trained research assistant on an iPad and all questionnaires were converted for electronic data capture via REDCap . Parents are also administered three measures on an iPad using REDCap software. First, youth are introduced to the substance use module, rules of confidentiality are restated, and youth are asked if they have heard of certain substances. At this point, research assistants do not show the youth the iPad screen in order to reduce potential exposure of novel substances. The rest of the interview utilizes gating,what is needed to grow marijuana in that certain questions must be answered positively or negatively in order for the youth to receive a follow-up question or measure . Because youth may enter the study with some prior substance use, the baseline battery measures lifetime patterns of substance use [including whether they used a substance, age of first- and regular-use assessment, total lifetime quantity , maximum lifetime dose, and length of abstinence] of all major drug categories . Next, recent low level use and detailed 6-month quantity and frequency data for each of the aforementioned substance categories are assessed with a computerized modified Timeline Follow back interview . For the measures assessing substance use patterns among youth actually endorsing using a drug, visual aids are provided to improve accuracy of dosing, product identification, and routes of administration. In subsequent waves, the TLFB interview will be utilized to cover measurement of substance use patterns across all ten years to ensure continuous coverage. After the patterns of use are assessed, measures related to risk for substance use initiation and problematic substance use trajectories , along with substance use consequences are administered. The baseline substance use battery takes an average of approximately 9 min for 9 and 10 year olds to complete; with a range between 2 and 19 min and is administered between the first section of the neuropsychological testing and the mental health module. It is notable that in addition to self-report of substance use, youth undergo substance toxicology screening to measure recent substance exposure.

At baseline and year 1 follow-up sessions, biological breath, saliva, urine and hair samples are collected from youth. This includes a breathalyzer test to measure current blood alcohol content. In 10% of the sample or anyone reporting past year drug use, an oral saliva sample is collected to test for recent substance use [qualitative positive/negative results are obtained for amphetamine, benzodiazepines, cannabis , methamphetamine, cocaine, methadone, and 3,4-methylenedioxymethamphetamine are obtained]. If a youth demonstrates a positive breathalyzer or oral saliva drug toxicology result, then the test is repeated; both test results are recorded. Hair is being collected from all participants to provide quantitative information about recent substance exposure. Samples for at-risk youth are sent to Psychemedics for quantitative measurement of alcohol ethyl glucurolide, cannabis , methamphetamine, MDMA, amphetamine, opiates , and cocaine/benzoylecgonine utilizing gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry . Starting at the year 1 followup session, urine will be collected from 10% of the sample and in all self-reported nicotine users for semi-quantitative cotinine levels. All toxicology results are coded and maintained in the data repository. Positive results for the alcohol breath test are exclusionary. If a participant is showing signs of intoxication, whether or not the saliva toxicology test was positive, the participant is rescheduled for their research appointment and informed they cannot participate while under the influence of alcohol or drugs . The parents and youth are contacted between the baseline and year 1 follow-up assessment for a 6-month follow-up phone interview to capture new onsets of substance use . Once youth have access to private personal mobile devices , youth will be contacted directly to complete the on-line 6- month TLFB.

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Many people with SUDs believe they can solve the problem themselves

Posted on October 17, 2022 by hemp2022

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

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

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

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

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

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

Posted on October 14, 2022 by hemp2022

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

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

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

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

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

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

Posted on October 13, 2022 by hemp2022

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

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

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

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

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

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

Posted on October 12, 2022 by hemp2022

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

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

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

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

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

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

Posted on October 11, 2022 by hemp2022

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

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

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

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

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

Posted on October 10, 2022 by hemp2022

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

Sessions lasted about 15 min, and they were held at baseline and at months 3, 6, and 12. The Expert System is based on the Stages of Change model that posits five stages of change in quitting smoking. These stages are precontemplation , contemplation , preparation , action and maintenance . Participants in the precontemplation and contemplation stages were provided with relevant chapters of pathways to change, a self-help workbook based on the stages of change model. When participants reached contemplation, they were reminded of the availability of treatment. Patients who were in preparation stage were strongly encouraged to take part in the treatment intervention. At any point, participants who expressed a desire to quit could receive treatment.The E-CBT component provided individual treatment focused on a quit plan and on strategies to prevent relapse. Content was adapted from the extended treatment used in earlier work by our group.The treatment addressed six areas that are important to smoking abstinence, with the content and skills tailored to low-income smokers: information, education and preparation for quitting; poor mood, weight control, social support, increasing and maintaining motivation, and stress management.This content was provided in 10 individual counseling sessions during the 6-month treatment period. Sessions occurred during weeks 1, 2 3, 5, 8, 12, 16, 20, and 22. The first counseling session was conducted face-to-face. Subsequent sessions were conducted either in person or by telephone. The first session was approximately 45 min long and the subsequent sessions about 30 min long.A note was attached to buprenorphine prescription dose containers to identify E-ISI participants: STC participants were not identified.If the participant was abstinent from tobacco, the pharmacist congratulated them on being a nonsmoker. If the participant had relapsed, or had not stopped smoking, the pharmacist reminded them about the importance of continuing to attend the Expert System sessions or the continued availability of treatment, as appropriate. All were doctoral level. Before participating in the study, pharmacists participated in smoking cessation treatment training led by Dr. Gasper, using the Prescription for Change curriculum.Participating pharmacists were knowledgeable about smoking cessation. However, training insured current knowledge and consistent skill level across pharmacists.We first evaluated the data to determine whether there were differences between conditions in missing data at each assessment. None were found. Also, when entered into hypothesis testing models, number of assessments missed was not a significant predictor of abstinence and was therefore eliminated from further consideration.

To test the first through fourth hypotheses, we included in the model intervention condition, usual cigarettes per day in the month preceding the baseline assessment and sex of participant. We also included those variables that were found to correlate with abstinence as the dependent variable at two or more assessments. These were goal , ASI Psychiatric Score, SF-12 Physical Component Scale , SF-12 Mental Component Scale , and Profile of Mood States TMD. For hypothesis 1, that there would be significant differences between conditions in abstinence status at months 12 and 18, we evaluated the Intervention × Assessment interaction. For the remaining three hypotheses, the main effects for intervention were of primary interest. Tests of cigarette abstinence and goal were based on a logistic distribution; tests of quit attempts were based on a negative binomial distribution; and the test of stages of changes was based on a multinomial distribution. Differences between intervention conditions at each assessment were evaluated using a chi-square test. Differences between conditions on dependent variables with multiple categories were evaluated by the Jonckheere-Terpstra Test.To test the final hypothesis, that abstinence status would be predicted by usual CPD and FTCD, we estimated and tested a model that included these two variables at baseline along with treatment condition and assessment. The model failed to converge due to a poor distribution of variables, so we inspected the correlations of each variable at each assessment. Exploratory analyses of drug and alcohol use were conducted using a model including baseline drug and alcohol use, as assessed by the ASI. In addition to looking at composite drug use, we examined the item reporting self-reported marijuana use in the past 30 days. These three variables were entered into a model to predict abstinence across all assessments. We also examined differences between treatment conditions in use of NRT and counseling to determine whether interventions were used at a greater rate by E-ISI than STC. We compared reported use across the study period between intervention conditions using Pearson’s chi-square test.The first hypothesis, that E-ISI would produce higher abstinence rates than STC, was not supported. Although there were differences between E-ISI and STC at 3 months, these differences were not maintained. Three studies of interventions paralleling the intervention reported in this study have been reported, all with psychiatric patients who were cigarette smokers. The results of these studies are characterized by gradually increasing abstinence rates over an 18-month period and abstinence rates at month 18 ranging between 18% and 20%.The current results did not replicate those of the earlier studies, particularly with respect to the phenomenon of increasing abstinence rates over time.

The most parsimonious explanation for the findings of the current study is that the initially higher abstinence rate in E-ISI reflects a “placebo” effect due the receiving an intensive and novel intervention. Given the significant short-term results, it might be argued that outcomes at later assessments could be improved by modifications to the intervention. However, given the multiple modalities offered, and the duration of the treatment, it is difficult to conceptualize what such modifications might be, especially if feasibility and cost are considered. Cigarette abstinence rates in the current study are relatively high when compared to most studies reported with patients receiving MAT for opioid use disorder. In the SFDPH, buprenorphine maintenance was reserved for more stable individuals with opioid use disorder because less frequent clinic visits were required than for methadone maintenance and hence less monitoring. This may explain the relatively high abstinence rates, since most previous studies recruited participants from methadone maintenance. The current study is consistent with the extant literature in its failure to effect cigarette abstinence for patients receiving MAT for opioid use disorder. In that way, it replicates earlier findings.These investigations offered interventions that are efficacious in the general population and found some evidence of efficacy at the end of treatment between experimental and control groups but failed to find long term effects. The lack of efficacy of E-ISI observed in this study was not the result of lack of interest in abstinence or willingness to change, since 54% of E-ISI participants entered treatment. This compares favorably to the 37% observed in our earlier study of psychiatric outpatients.Also, at baseline, 26% of participants had a goal of complete abstinence and 21% were ready to quit smoking. These baseline figures are not markedly different from baseline figures reported in the earlier study. In that study, 31% of participants had a goal of complete abstinence, and 25% were ready to quit smoking.E-ISI participants were more likely to report at least one quit attempt, more likely to be in more advanced stages of change, and more likely to have a goal of “quit forever” than STC participants. These data, in addition to the treatment acceptance rate,cannabis growing system suggest that smokers in buprenorphine treatment are at least comparable to other populations in responsiveness to motivational interventions. Participants in E-ISI who accepted treatment used NRT, based on dispensing records. The mean number of patches dispensed would cover about two and a half months of use, if the patch were used daily. It is not possible to accurately judge the days of usage of gum and lozenge, since these would vary by frequency of use. There was moderately good participation in E-CBT, also. The mean number of sessions was almost half of those offered, and the mean minutes in sessions were over 160. Thus, participants received approximately half the E-CBT time available. The protocol was designed so that most of the new content was introduced in 6 of the 10 sessions, with the remaining sessions focusing on review. Thus, on the average, participants were exposed to most of the E-CBT content. Varenicline was of little interest to participants. This may have been due to the study being conducted during a period when that drug was receiving negative publicity in local media.

This study suggests that currently available treatment interventions do not produce cigarette abstinence in patients receiving MAT for opioid use disorder who smoke cigarettes. The best therapeutic strategy for this population may be to encourage them to use alternate strategies to obtain nicotine and avoid cigarette smoking and thereby reduce harm. These might include long-term multiple NRT medications at a wide range of doses and interventions integrating the suggestions of Miller and Sigmon, particularly the suggestion that use of bupropion, varenicline, and nicotine patches be observed and contingently reinforced.It is likely that the FTCD is a poor instrument for assessing dependence in this population. Two of the questions on the FTCD assume that the participant has non-restricted access to smoking areas. We found that 59.5% of the participants in this study were housed in living situations that would restrict smoking. As has been the case with the general population, CPD did predict abstinence rates, although the magnitude of the relationship was not strong. Cannabis use predicted continued smoking as has been the case in some studies in the general treatment population although not all.Given the mixed findings in the general population, it is difficult to argue that negative effect of cannabis use on abstinence is unique to this population. In exploratory analyses, we also examined the effects of buprenorphine dose and program participation on abstinence. Neither variable predicted outcome. In summary, current motivational interventions may be useful in increasing motivation for cigarette abstinence in patients receiving MAT for opioid use disorder. Exploratory analyses did little to shed light on the predictors of outcome in this population of smokers or variables that might differentiate them from the general population and would be useful in explaining the unique lack of efficacy. It is possible that interventions for tobacco dependence in opioid treatment patients should focus on harm-reduction strategies and other alternative strategies.

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

Posted on October 9, 2022 by hemp2022

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

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

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

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

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