Illegal Drug Alcohol Research Paper

Marisol S. Castaneto | David A. Gorelick | Nathalie A. Desrosiers | Rebecca L. Hartman | Sandrine Pirard | Marilyn A. Huestis

© 2014. Background: Synthetic cannabinoids (SC) are a heterogeneous group of compounds developed to probe the endogenous cannabinoid system or as potential therapeutics. Clandestine laboratories subsequently utilized published data to develop SC variations marketed as abusable designer drugs. In the early 2000s, SC became popular as "legal highs" under brand names such as Spice and K2, in part due to their ability to escape detection by standard cannabinoid screening tests. The majority of SC detected in herbal products have greater binding affinity to the cannabinoid CB 1 receptor than does δ 9 -tetrahydrocannabinol (THC), the primary psychoactive compound in the cannabis plant, and greater affinity at the CB 1 than the CB 2 receptor. In vitro and animal in vivo studies show SC pharmacological effects 2-100 times more potent than THC, including analgesic, anti-seizure, weight-loss, anti-inflammatory, and anti-cancer growth effects. SC produce physiological and psychoactive effects similar to THC, but with greater intensity, resulting in medical and psychiatric emergencies. Human adverse effects include nausea and vomiting, shortness of breath or depressed breathing, hypertension, tachycardia, chest pain, muscle twitches, acute renal failure, anxiety, agitation, psychosis, suicidal ideation, and cognitive impairment. Long-term or residual effects are unknown. Due to these public health consequences, many SC are classified as controlled substances. However, frequent structural modification by clandestine laboratories results in a stream of novel SC that may not be legally controlled or detectable by routine laboratory tests. Methods: We present here a comprehensive review, based on a systematic electronic literature search, of SC epidemiology and pharmacology and their clinical implications.

Christopher M. Jones

Background: Heroin use and overdose deaths have increased in recent years. Emerging information suggests this is the result of increases in nonmedical use of opioid pain relievers and nonmedical users transitioning to heroin use. Understanding this relationship is critically important for the development of public health interventions. Methods: Combined data from the 2002-2004 National Surveys on Drug Use and Health were compared to the 2008-2010 surveys to examine patterns of heroin use and risk behaviors among past year nonmedical users of opioid pain relievers. Results: Between 2002-2004 and 2008-2010, past year heroin use increased among people reporting past year nonmedical use (PYNMU) of opioid pain relievers (p < 0.01), but not among those reporting no PYNMU. Frequent nonmedical users - people reporting 100-365 days of PYNMU - had the highest rate of past year heroin use and were at increased risk for ever injecting heroin (aOR 4.3, 95% CI 2.5-7.3) and past year heroin abuse or dependence (aOR 7.8, 95% CI 4.7-12.8) compared to infrequent nonmedical users (1-29 days of PYNMU). In 2008-2010, 82.6% of frequent nonmedical users who used heroin in the past year reported nonmedical use of opioid pain relievers prior to heroin initiation compared to 64.1% in 2002-2004. Conclusions: Heroin use among nonmedical users of opioid pain relievers increased between 2002-2004 and 2008-2010, with most reporting nonmedical use of opioid pain relievers before initiating heroin. Interventions to prevent nonmedical use of these drugs are needed and should focus on high-risk groups such as frequent nonmedical users of opioids. © 2013.

Erin L. Sutfin | Thomas P. McCoy | Holly E R Morrell | Bettina B. Hoeppner | Mark Wolfson

Background: Electronic cigarettes, or e-cigarettes, are battery operated devices that deliver nicotine via inhaled vapor. There is considerable controversy about the disease risk and toxicity of e-cigarettes and empirical evidence on short- and long-term health effects is minimal. Limited data on e-cigarette use and correlates exist, and to our knowledge, no prevalence rates among U.S. college students have been reported. This study aimed to estimate the prevalence of e-cigarette use and identify correlates of use among a large, multi-institution, random sample of college students. Methods: 4444 students from 8 colleges in North Carolina completed a Web-based survey in fall 2009. Results: Ever use of e-cigarettes was reported by 4.9% of students, with 1.5% reporting past month use. Correlates of ever use included male gender, Hispanic or "Other race" (compared to non-Hispanic Whites), Greek affiliation, conventional cigarette smoking and e-cigarette harm perceptions. Although e-cigarette use was more common among conventional cigarette smokers, 12% of ever e-cigarette users had never smoked a conventional cigarette. Among current cigarette smokers, e-cigarette use was negatively associated with lack of knowledge about e-cigarette harm, but was not associated with intentions to quit. Conclusions: Although e-cigarette use was more common among conventional cigarette smokers, it was not exclusive to them. E-cigarette use was not associated with intentions to quit smoking among a sub-sample of conventional cigarette smokers. Unlike older, more established cigarette smokers, e-cigarette use by college students does not appear to be motivated by the desire to quit cigarette smoking. © 2013 Elsevier Ireland Ltd.

Leonieke C. Van Boekel | Evelien P.M. Brouwers | Jaap Van Weeghel | Henk F.L. Garretsen

Background: Healthcare professionals are crucial in the identification and accessibility to treatment for people with substance use disorders. Our objective was to assess health professionals' attitudes towards patients with substance use disorders and examine the consequences of these attitudes on healthcare delivery for these patients in Western countries. Methods: Pubmed, PsycINFO and Embase were systematically searched for articles published between 2000 and 2011. Studies evaluating health professionals' attitudes towards patients with substance use disorders and consequences of negative attitudes were included. An inclusion criterion was that studies addressed alcohol or illicit drug abuse. Reviews, commentaries and letters were excluded, as were studies originating from non-Western countries. Results: The search process yielded 1562 citations. After selection and quality assessment, 28 studies were included. Health professionals generally had a negative attitude towards patients with substance use disorders. They perceived violence, manipulation, and poor motivation as impeding factors in the healthcare delivery for these patients. Health professionals also lacked adequate education, training and support structures in working with this patient group. Negative attitudes of health professionals diminished patients' feelings of empowerment and subsequent treatment outcomes. Health professionals are less involved and have a more task-oriented approach in the delivery of healthcare, resulting in less personal engagement and diminished empathy. Conclusions: This review indicates that negative attitudes of health professionals towards patients with substance use disorders are common and contribute to suboptimal health care for these patients. However, few studies have evaluated the consequences of health professionals' negative attitudes towards patients with substance use disorders. © 2013 Elsevier Ireland Ltd.

Susan Calcaterra | Jason Glanz | Ingrid A. Binswanger

Background: Pharmaceutical opioid related deaths have increased. This study aimed to place pharmaceutical opioid overdose deaths within the context of heroin, cocaine, psychostimulants, and pharmaceutical sedative hypnotics examine demographic trends, and describe common combinations of substances involved in opioid related deaths. Methods: We reviewed deaths among 15-64 year olds in the US from 1999-2009 using death certificate data available through the CDC Wide-Ranging Online Data for Epidemiologic Research (WONDER) Database. We identified International Classification of Disease-10 codes describing accidental overdose deaths, including poisonings related to stimulants, pharmaceutical drugs, and heroin. We used crude and age adjusted death rates (deaths/100,000 person years [p-y] and 95% confidence interval [CI] and multivariable Poisson regression models, yielding incident rate ratios; IRRs), for analysis. Results: The age adjusted death rate related to pharmaceutical opioids increased almost 4-fold from 1999 to 2009 (1.54/100,000 p-y [95% CI 1.49-1.60] to 6.05/100,000 p-y [95% CI 5.95-6.16; p < 0.001). From 1999 to 2009, pharmaceutical opioids were responsible for the highest relative increase in overdose death rates (IRR 4.22, 95% CI 3.03-5.87) followed by sedative hypnotics (IRR 3.53, 95% CI 2.11-5.90). Heroin related overdose death rates increased from 2007 to 2009 (1.05/100,000 persons [95% CI 1.00-1.09] to 1.43/100,000 persons [95% CI 1.38-1.48; p < 0.001). From 2005-2009 the combination of pharmaceutical opioids and benzodiazepines was the most common cause of polysubstance overdose deaths (1.27/100,000 p-y (95% CI 1.25-1.30). Conclusion: Strategies, such as wider implementation of naloxone, expanded access to treatment, and development of new interventions are needed to curb the pharmaceutical opioid overdose epidemic. © 2012 Elsevier Ireland Ltd.

William J. Panenka | Ric M. Procyshyn | Tania Lecomte | G. William MacEwan | Sean W. Flynn | William G. Honer | Alasdair M. Barr

Methamphetamine (MA) is a highly addictive psychostimulant drug that principally affects the monoamine neurotransmitter systems of the brain and results in feelings of alertness, increased energy and euphoria. The drug is particularly popular with young adults, due to its wide availability, relatively low cost, and long duration of psychoactive effects. Extended use of MA is associated with many health problems that are not limited to the central nervous system, and contribute to increased morbidity and mortality in drug users. Numerous studies, using complementary techniques, have provided evidence that chronic MA use is associated with substantial neurotoxicity and cognitive impairment. These pathological effects of the drug, combined with the addictive properties of MA, contribute to a spectrum of psychosocial issues that include medical and legal problems, at-risk behaviors and high societal costs, such as public health consequences, loss of family support and housing instability. Treatment options include pharmacological, psychological or combination therapies. The present review summarizes the key findings in the literature spanning from molecular through to clinical effects. © 2012 Elsevier Ireland Ltd.

Adam R. Winstock | Monica J. Barratt

Background: The last decade has seen the appearance of myriad novel psychoactive substances with diverse effect profiles. Synthetic cannabinoids are among the most recently identified but least researched of these substances. Methods: An anonymous online survey was conducted in 2011 using a quantitative structured research tool. Missing data (median 2%) were treated by available-case analysis. Results: Of 14,966 participants, 2513 (17%) reported use of synthetic cannabis. Of these, 980 (41% of 2417) reported its use in the last 12 months. Almost all recent synthetic cannabis users (99% of 975) reported ever use of natural cannabis. Synthetic cannabis reportedly had both a shorter duration of action (. z=. 17.82, p < . .001) and quicker time to peak onset of effect (. z=. -9.44, p < . .001) than natural cannabis. Natural cannabis was preferred to synthetic cannabis by 93% of users, with natural cannabis rated as having greater pleasurable effects when high (. t(930). =. -37.1, p < . .001, d=. -1.22) and being more able to function after use (. t(884). =. -13.3, p < . .001, d=. -0.45). Synthetic cannabis was associated with more negative effects (. t(859). =. 18.7, p < . .001, d=. 0.64), hangover effects (. t(854). =. 6.45, p < . .001, d=. 0.22) and greater paranoia (. t(889). =. 7.91, p < . .001, d=. 0.27). Conclusions: Users report a strong preference for natural over synthetic cannabis. The latter has a less desirable effect profile. Further research is required to determine longer term consequences of use and comparative dependence potential. © 2013 .

Janette L. Smith | Richard P. Mattick | Sharna D. Jamadar | Jaimi M. Iredale

© 2014 Elsevier Ireland Ltd. Aims: Deficits in behavioural inhibitory control are attracting increasing attention as a factor behind the development and maintenance of substance dependence. However, evidence for such a deficit is varied in the literature. Here, we synthesised published results to determine whether inhibitory ability is reliably impaired in substance users compared to controls. Methods: The meta-analysis used fixed-effects models to integrate results from 97 studies that compared groups with heavy substance use or addiction-like behaviours with healthy control participants on two experimental paradigms commonly used to assess response inhibition: the Go/NoGo task, and the Stop-Signal Task (SST). The primary measures of interest were commission errors to NoGo stimuli and stop-signal reaction time in the SST. Additionally, we examined omission errors to Go stimuli, and reaction time in both tasks. Because inhibition is more difficult when inhibition is required infrequently, we considered papers with rare and equiprobable NoGo stimuli separately. Results: Inhibitory deficits were apparent for heavy use/dependence on cocaine, MDMA, methamphetamine, tobacco, and alcohol (and, to a lesser extent, non-dependent heavy drinkers), and in pathological gamblers. On the other hand, no evidence for an inhibitory deficit was observed for opioids or cannabis, and contradictory evidence was observed for internet addiction. Conclusions: The results are generally consistent with the view that substance use disorders and addiction-like behavioural disorders are associated with impairments in inhibitory control. Implications for treatment of substance use are discussed, along with suggestions for future research arising from the limitations of the extant literature.

Howard Barry Moss | Chiung M. Chen | Hsiao ye Yi

Background: Alcohol, tobacco and marijuana are the most commonly used drugs by adolescents in the U.S. However, little is known about the patterning of early adolescent substance use, and its implications for problematic involvement with substances in young adulthood. We examined patterns of substance use prior to age 16, and their associations with young adult substance use behaviors and substance use disorders in a nationally representative sample of U.S. adolescents. Method: Using data from Wave 4 of the Add Health Survey (n= 4245), we estimated the prevalence of various patterns of early adolescent use of alcohol, cigarettes, and marijuana use individually and in combination. Then we examined the effects of patterns of early use of these substances on subsequent young adult substance use behaviors and DSM-IV substance use disorders. Results: While 34.4% of individuals reported no substance use prior to age 16, 34.1% reported either early use of both alcohol and marijuana or alcohol, marijuana and cigarettes, indicating the relatively high prevalence of this type of polysubstance use behavior among U.S. adolescents. Early adolescent use of all three substances was most strongly associated with a spectrum of young adult substance use problems, as well as DSM-IV substance use disorder diagnoses. Conclusions: This research confirms the elevated prevalence and importance of polysubstance use behavior among adolescents prior to age 16, and puts early onset of alcohol, marijuana and cigarette use into the context of use patterns rather than single drug exposures. © 2013.

Joseph Schuermeyer | Stacy Salomonsen-Sautel | Rumi Kato Price | Sundari Balan | Christian Thurstone | Sung Joon Min | Joseph T. Sakai

© 2014 Elsevier Ireland Ltd. Background: In 2009, policy changes were accompanied by a rapid increase in the number of medical marijuana cardholders in Colorado. Little published epidemiological work has tracked changes in the state around this time. Methods: Using the National Survey on Drug Use and Health, we tested for temporal changes in marijuana attitudes and marijuana-use-related outcomes in Colorado (2003-11) and differences within-year between Colorado and thirty-four non-medical-marijuana states (NMMS). Using regression analyses, we further tested whether patterns seen in Colorado prior to (2006-8) and during (2009-11) marijuana commercialization differed from patterns in NMMS while controlling for demographics. Results: Within Colorado those reporting "great-risk" to using marijuana 1-2 times/week dropped significantly in all age groups studied between 2007-8 and 2010-11 (e.g. from 45% to 31% among those 26 years and older; p= 0.0006). By 2010-11 past-year marijuana abuse/dependence had become more prevalent in Colorado for 12-17 year olds (5% in Colorado, 3% in NMMS; p= 0.03) and 18-25 year olds (9% vs. 5%; p= 0.02). Regressions demonstrated significantly greater reductions in perceived risk (12-17 year olds, p= 0.005; those 26 years and older, p= 0.01), and trend for difference in changes in availability among those 26 years and older and marijuana abuse/dependence among 12-17 year olds in Colorado compared to NMMS in more recent years (2009-11 vs. 2006-8). Conclusions: Our results show that commercialization of marijuana in Colorado has been associated with lower risk perception. Evidence is suggestive for marijuana abuse/dependence. Analyses including subsequent years 2012+ once available, will help determine whether such changes represent momentary vs. sustained effects.

Tamara M. Haegerich | Leonard J. Paulozzi | Brian J. Manns | Christopher M. Jones

© 2014. Background: Drug overdose deaths have been rising since the early 1990s and is the leading cause of injury death in the United States. Overdose from prescription opioids constitutes a large proportion of this burden. State policy and systems-level interventions have the potential to impact prescription drug misuse and overdose. Methods: We searched the literature to identify evaluations of state policy or systems-level interventions using non-comparative, cross-sectional, before-after, time series, cohort, or comparison group designs or randomized/non-randomized trials. Eligible studies examined intervention effects on provider behavior, patient behavior, and health outcomes. Results: Overall study quality is low, with a limited number of time-series or experimental designs. Knowledge and prescribing practices were measured more often than health outcomes (e.g., overdoses). Limitations include lack of baseline data and comparison groups, inadequate statistical testing, small sample sizes, self-reported outcomes, and short-term follow-up. Strategies that reduce inappropriate prescribing and use of multiple providers and focus on overdose response, such as prescription drug monitoring programs, insurer strategies, pain clinic legislation, clinical guidelines, and naloxone distribution programs, are promising. Evidence of improved health outcomes, particularly from safe storage and disposal strategies and patient education, is weak. Conclusions: While important efforts are underway to affect prescriber and patient behavior, data on state policy and systems-level interventions are limited and inconsistent. Improving the evidence base is a critical need so states, regulatory agencies, and organizations can make informed choices about policies and practices that will improve prescribing and use, while protecting patient health.

Joseph Studer | Stéphanie Baggio | Meichun Mohler-Kuo | Petra Dermota | Jacques Gaume | Nicolas Bertholet | Jean Bernard Daeppen | Gerhard Gmel

Background: Non-response is a major concern among substance use epidemiologists. When differences exist between respondents and non-respondents, survey estimates may be biased. Therefore, researchers have developed time-consuming strategies to convert non-respondents to respondents. The present study examines whether late respondents (converted former non-participants) differ from early respondents, non-consenters or silent refusers (consent givers but non-participants) in a cohort study, and whether non-response bias can be reduced by converting former non-respondents. Methods: 6099 French- and 5720 German-speaking Swiss 20-year-old males (more than 94% of the source population) completed a short questionnaire on substance use outcomes and socio-demographics, independent of any further participation in a cohort study. Early respondents were those participating in the cohort study after standard recruitment procedures. Late respondents were non-respondents that were converted through individual encouraging telephone contact. Early respondents, non-consenters and silent refusers were compared to late respondents using logistic regressions. Relative non-response biases for early respondents only, for respondents only (early and late) and for consenters (respondents and silent refusers) were also computed. Results: Late respondents showed generally higher patterns of substance use than did early respondents, but lower patterns than did non-consenters and silent refusers. Converting initial non-respondents to respondents reduced the non-response bias, which might be further reduced if silent refusers were converted to respondents. Conclusion: Efforts to convert refusers are effective in reducing non-response bias. However, converted late respondents cannot be seen as proxies of non-respondents, and are at best only indicative of existing response bias due to persistent non-respondents. © 2013 Elsevier Ireland Ltd.

Nicole H. Weiss | Matthew T. Tull | Michael D. Anestis | Kim L. Gratz

Background: Despite elevated rates of posttraumatic stress disorder (PTSD) among substance use disorder (SUD) patients, as well as the clinical relevance of this co-occurrence, few studies have e xamined psychological factors associated with a PTSD-SUD diagnosis. Two factors worth investigating are emotion dysregulation and impulsivity, both of which are associated with PTSD and SUDs. Therefore, this study examined associations between PTSD and facets of emotion dysregulation and impulsivity within a sample of trauma-exposed SUD inpatients. Methods: Participants were an ethnically diverse sample of 205 SUD patients in residential substance abuse treatment. Patients were administered diagnostic interviews and completed a series of questionnaires. Results: Patients with PTSD (. n=. 58) reported significantly higher levels of negative urgency (i.e., the tendency to engage in impulsive behaviors when experiencing negative affect) and lower sensation seeking, as well as higher levels of emotion dysregulation and the specific dimensions of lack of emotional acceptance, difficulties engaging in goal-directed behavior when upset, difficulties controlling impulsive behaviors when distressed, limited access to effective emotion regulation strategies, and lack of emotional clarity. Further, overall emotion dysregulation emerged as a significant predictor of PTSD status, accounting for unique variance in PTSD status above and beyond facets of impulsivity (as well as other relevant covariates). Conclusions: Results suggest that emotion dysregulation may contribute to the development, maintenance, and/or exacerbation of PTSD and highlight the potential clinical utility of targeting emotion dysregulation among SUD patients with PTSD. © 2012 Elsevier Ireland Ltd.

Carlos Blanco | Yang Xu | Kathleen Brady | Gabriela Pérez-Fuentes | Mayumi Okuda | Shuai Wang

Background: Despite the high rates of comorbidity of post-traumatic stress disorder (PTSD) and alcohol dependence (AD) in clinical and epidemiological samples, little is known about the prevalence, clinical presentation, course, risk factors and patterns of treatment-seeking of co-occurring PTSD-AD among the general population. Methods: The sample included respondents of the Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Weighted means, frequencies and odds ratios (ORs) of sociodemographic correlates, prevalence of psychiatric disorders and rates of treatment-seeking were computed. Results: In the general population, the lifetime prevalence of PTSD only, AD only and PTSD-AD was 4.83%, 13.66% and 1.59%, respectively. Individuals with comorbid PTSD-AD were more likely than those with PTSD or AD only to have suffered childhood adversities and had higher rates of Axis I and II disorders and suicide attempts. They also met more PTSD diagnostic criteria, had earlier onset of PTSD and were more likely to use drugs and alcohol to relieve their PTSD symptoms than those with PTSD only; they also met more AD diagnostic criteria than those with AD only and had greater disability. Individuals with PTSD-AD had higher rates of treatment seeking for AD than those with AD only, but similar rates than those with PTSD only. Conclusion: PTSD-AD is associated with high levels of severity across a broad range of domains even compared with individuals with PTSD or AD only, yet treatment-seeking rates are very low. There is a need to improve treatment access and outcomes for individuals with PTSD-AD. © 2013 Elsevier Ireland Ltd.

Jonathan B. Bricker | Kristin E. Mull | Julie A. Kientz | Roger Vilardaga | Laina D. Mercer | Katrina J. Akioka | Jaimee L. Heffner

© 2014 Elsevier Ireland Ltd. Background: There is a dual need for (1) innovative theory-based smartphone applications for smoking cessation and (2) controlled trials to evaluate their efficacy. Accordingly, this study tested the feasibility, acceptability, preliminary efficacy, and mechanism of behavioral change of an innovative smartphone-delivered acceptance and commitment therapy (ACT) application for smoking cessation vs. an application following US Clinical Practice Guidelines. Method: Adult participants were recruited nationally into the double-blind randomized controlled pilot trial (n= 196) that compared smartphone-delivered ACT for smoking cessation application (SmartQuit) with the National Cancer Institute's application for smoking cessation (QuitGuide). Results: We recruited 196 participants in two months. SmartQuit participants opened their application an average of 37.2 times, as compared to 15.2 times for QuitGuide participants (p < 0001). The overall quit rates were 13% in SmartQuit vs. 8% in QuitGuide (OR = 2.7; 95% CI = 0.8-10.3). Consistent with ACT's theory of change, among those scoring low (below the median) on acceptance of cravings at baseline (n= 88), the quit rates were 15% in SmartQuit vs. 8% in QuitGuide (OR = 2.9; 95% CI = 0.6-20.7). Conclusions: ACT is feasible to deliver by smartphone application and shows higher engagement and promising quit rates compared to an application that follows US Clinical Practice Guidelines. As results were limited by the pilot design (e.g., small sample), a full-scale efficacy trial is now needed.

Karen Miotto | Joan Striebel | Arthur K. Cho | Christine Wang

Bath salts are designer drugs with stimulant properties that are a growing medical and psychiatric concern due to their widespread availability and use. Although the chemical compounds in the mixtures referred to as "bath salts" vary, many are derivatives of cathinone, a monoamine alkaloid. Cathinones have an affinity for dopamine, serotonin, and norepinephrine synapses in the brain. Because of the strong selection for these neurotransmitters, these drugs induce stimulating effects similar to those of methamphetamines, cocaine, and 3,4-methylenedioxy-N-methylamphetamine (MDMA). Much of the emerging information about bath salts is from emergency department evaluation and treatment of severe medical and neuropsychiatric adverse outcomes. This review consists of a compilation of case reports and describes the emergent literature that illustrates the chemical composition of bath salts, patterns of use, administration methods, medical and neuropsychiatric effects, and treatments of patients with bath salt toxicity. © 2013 Elsevier Ireland Ltd.

Eric P. Zorrilla | Markus Heilig | Harriet de Wit | Yavin Shaham

Comparative risk assessment of alcohol, tobacco, cannabis and other illicit drugs using the margin of exposure approach

Dirk W. Lachenmeiera,1,2 and Jürgen Rehm1,3,4,5,6,7

1Epidemiological Research Unit, Technische Universität Dresden, Klinische Psychologie & Psychotherapie, Dresden, Germany

2Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Germany

3Social and Epidemiological Research (SER) Department, Centre for Addiction and Mental Health (CAMH), Toronto, Canada

4Institute of Medical Sciences, University of Toronto (UofT), Toronto, Canada

5Dalla Lana School of Public Health, UofT, Toronto, Canada

6Dept. of Psychiatry, Faculty of Medicine, UofT, Toronto, Canada

7PAHO/WHO Collaborating Centre for Mental Health & Addiction, Toronto, Canada

aEmail: ed.bew@reiemnehcaL

Author information ►Article notes ►Copyright and License information ►

Received 2014 Sep 9; Accepted 2015 Jan 7.

Copyright © 2015, Macmillan Publishers Limited. All rights reserved

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A comparative risk assessment of drugs including alcohol and tobacco using the margin of exposure (MOE) approach was conducted. The MOE is defined as ratio between toxicological threshold (benchmark dose) and estimated human intake. Median lethal dose values from animal experiments were used to derive the benchmark dose. The human intake was calculated for individual scenarios and population-based scenarios. The MOE was calculated using probabilistic Monte Carlo simulations. The benchmark dose values ranged from 2 mg/kg bodyweight for heroin to 531 mg/kg bodyweight for alcohol (ethanol). For individual exposure the four substances alcohol, nicotine, cocaine and heroin fall into the “high risk” category with MOE < 10, the rest of the compounds except THC fall into the “risk” category with MOE < 100. On a population scale, only alcohol would fall into the “high risk” category, and cigarette smoking would fall into the “risk” category, while all other agents (opiates, cocaine, amphetamine-type stimulants, ecstasy, and benzodiazepines) had MOEs > 100, and cannabis had a MOE > 10,000. The toxicological MOE approach validates epidemiological and social science-based drug ranking approaches especially in regard to the positions of alcohol and tobacco (high risk) and cannabis (low risk).

Compared to medicinal products or other consumer products, risk assessment of drugs of abuse has been characterised as deficient, much of this is based on historical attribution and emotive reasoning1. The available data are often a matter of educated guesses supplemented by some reasonably reliable survey data from the developed nations2. Only in the past decade, have there been some approaches to qualitatively and quantitatively classify the risk of drugs of abuse. These efforts tried to overcome legislative classifications, which were often found to lack a scientific basis3. UNODC suggested the establishment of a so-called Illicit Drug Index (IDI), which contained a combination of a dose index (the ratio between the typical dose and a lethal dose) and a toxicology index (concentration levels in the blood of people who died from overdose compared with the concentration levels in persons who had been given the drug for therapeutic use)4. King and Corkery5 suggested an index of fatal toxicity for drugs of misuse that was calculated as the ratio of the number of deaths associated with a substance to its availability. Availability was determined by three separate proxy measures (number of users as determined by household surveys, number of seizures by law enforcement agencies and estimates of the market size). Gable6 provided one of the earliest toxicologically founded approaches in a comparative overview of psychoactive substances. The methodology was based on comparing the “therapeutic index” of the substances, which was defined as the ratio of the median lethal dose (LD50) to the median effective dose (ED50). The results were expressed in a qualitative score as safety margin from “very small” (e.g. heroin) to “very large” (e.g. cannabis). In a follow-up study, Gable7 refined the approach and now provided a numerical safety ratio, which allowed a rank-ordering of abused substances.

Despite these early efforts for toxicology-based risk assessments, the most common methods are still based on expert panel rankings on harm indicators such as acute and chronic toxicity, addictive potency and social harm, e.g. the approaches of Nutt et al.8,9 in the UK and of van Amsterdam et al.3 in the Netherlands. The rankings of the two countries correlated very well3,8. Similar studies were conducted by questioning drug users, resulting in a high correlation to the previous expert judgements10,11,12. The major criticism that was raised about these “panel” based approaches was the necessity of value judgements, which might depend upon subjective personal criteria and not only upon scientific facts13. The methodology was also criticized because a normalization to either the total number of users or the frequency of drug use was not conducted, which might have biased the result toward the harms of opiate use14 and may have underrepresented the harms of tobacco15. Problematic may also have been the nomenclature applied in previous studies, mixing up “hazard” and “risk” into the term “drug harm”. In chemical and toxicological risk assessment, the term “harm” is not typically used, while hazard is the “inherent property of an agent or situation having the potential to cause adverse effects when an organism, system, or (sub)population is exposed to that agent”. Risk is defined as “the probability of an adverse effect in an organism, system, or (sub)population caused under specified circumstances by exposure to an agent”16.

In the context of the European research project “Addiction and Lifestyles in Contemporary Europe – Reframing Addictions Project”, the aim of this research was to provide a comparative risk assessment of drugs using a novel risk assessment methodology, namely the “Margin of Exposure” (MOE) method. The Margin of Exposure (MOE) is a novel approach to compare the health risk of different compounds and to prioritize risk management actions. The MOE is defined as the ratio between the point on the dose response curve, which characterizes adverse effects in epidemiological or animal studies (the so-called benchmark dose (BMD)), and the estimated human intake of the same compound. Clearly, the lower the MOE, the larger the risk for humans. The BMD approach was first suggested by Crump17, and was later refined by the US EPA for quantitative risk assessment18. In Europe, the MOE was introduced in 2005 as the preferred method for risk assessment of carcinogenic and genotoxic compounds19. In the addiction field, the MOE method was never used, aside from evaluating substances in alcoholic beverages20,21 or tobacco products22,23. This study is the first to calculate and compare MOEs for other addiction-related substances.


The only toxicological threshold available in the literature for all of the compounds under study was the LD50. The LD50 values taken from the ChemIDplus database of the US National Library of Medicine and from Shulgin24 are shown in table 1. Using the method of Gold et al.25, the LD50 values were extrapolated assuming linear behaviour (as no other information on dose-response is available) to BMDL10 values. As shown in Supplementary Table S1 online, the full range of available LD50 values in different animal species is taken into account as a risk function assuming a normal distribution for BMDL10 rather than that a single value is entered into the calculation (except methamphetamine and MDMA for which only one value was available in the literature). The mean values of BMDL10 range from 2 mg/kg bodyweight (bw) for heroin and cocaine up to 531 mg/kg bw for ethanol.

Table 1

Toxicological thresholds selected for calculating the margin of exposure

To determine the typical range of individual daily dosage, various textbook and internet sources21,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41 were evaluated (Table 2). As no information about the most likely function for dosage distribution is available, a uniform probability distribution was entered into the calculation in this case (Supplementary Table S1).

Table 2

Exposure data selected for calculating the margin of exposure (see Supplementary Table S1 online for distribution functions used for calculation)

The data used for calculation of population-based exposure is shown in Table 2. Prevalence data was available for all drugs except methadone; and amphetamine and methamphetamine were grouped together. For a sub-group of drugs, exposure estimation based on sewage analysis is available (Table 2) (not all drugs are available in sewage analysis due to different stabilities/degradation rates of the compounds, for details see Ref. 26). The corresponding risk functions are shown in Supplementary Table S1 online. Except for ethanol and nicotine, for which certain distributions could be fitted to the data for the European countries, uniform probability distributions were chosen in all other cases as only minimum/maximum prevalence values for Europe in total were available. The detailed calculation formulae chosen for probabilistic risk assessment are shown in Supplementary Table S2 online.

The margin of exposure values were calculated for individual exposure (Figure 1), population-based exposure calculated from prevalence data (Figure 2) and population-based exposure calculated from sewage analysis (Figure 3). The full numerical results of the MOE distributions are presented in Supplementary Table S3 online. For both individual and population-based scenarios, alcohol consumption was found to have the lowest margin of exposure. For individual exposure, heroin has the second lowest margin of exposure. However, considering worst-case scenarios (e.g. 5th percentile), heroin may have a lower MOE than alcohol (compare standard deviation bars in Figure 1). On the other end of the scale, THC or cannabis can be consistently found to have high MOE values, as well as amphetamine-type stimulants and benzodiazepines. Cocaine and nicotine/tobacco were found to have intermediary MOE values.

Figure 1

Margin of exposure for daily drug use estimated using probabilistic analysis (left red bar: average; error bar: standard deviation; right gray bar: tolerant user; circle symbol (for alcohol): value based on human data).

Figure 2

Margin of exposure for the whole population based on prevalence data estimated using probabilistic analysis (left red bar: average; error bar: standard deviation; right gray bar: tolerant user; circle symbol (for alcohol and cannabis): value based on...

Figure 3

Margin of exposure for the whole population based on sewage analysis estimated using probabilistic analysis (left red bar: average; error bar: standard deviation; right gray bar: tolerant user; circle symbol (for THC): value based on human.

For sensitivity analysis, three different methods were applied: convergence testing during the probabilistic simulation, application of a factor to consider drug tolerance, and comparison with human toxicological thresholds for some of the agents.

Convergence was achieved for all calculated output MOE values. This means that the generated output distributions are stable and reliable. The estimated means change less than 5% as additional iterations are run during the simulation. From the model input variables, the highest influence (as expressed by rank of regression coefficients) on the results is caused by the exposure, rather than the toxicological thresholds or the bodyweights.

The sensitivity analysis data for tolerant users are additionally shown in Figure 1–3 based on the ratio between no-tolerance and high tolerance dosage as shown in Table 227,37,42,43,44,45,46,47,48,49,50,51,52,53,54. Even though the general results remain stable (i.e. especially alcohol at the top position), the ranks between opiates and cocaine change due to the high tolerance to extreme dosages that was reported for opiates. However, as the percentage of tolerant users is generally unknown, the most probable value of MOE would lie in the range between non-tolerant and tolerant users (the gray-marked area in Figures 1–3).

Finally, the sensitivity analysis results from application of human toxicity data for some of the compounds (alcohol, nicotine and THC21,55,56,57) are shown in Supplementary Table S3 online and marked in Figures 1–3. For alcohol, the human MOE results correspond closely to the ones calculated from animal LD50. For the other compounds, a discrepancy between animal and human data was detected (see discussion).


Many governments in Europe have favoured more restrictive policies with respect to illicit drugs than for alcohol or tobacco, on the grounds that they regard both illicit drug abuse and related problems as a significantly larger problem for society58. Drug rankings can therefore be useful to inform policy makers and the public about the relative importance of licit drugs (including prescription drugs) and illicit drugs for various types of harm58.

Our MOE results confirm previous drug rankings based on other approaches. Specifically, the results confirm that the risk of cannabis may have been overestimated in the past. At least for the endpoint of mortality, the MOE for THC/cannabis in both individual and population-based assessments would be above safety thresholds (e.g. 100 for data based on animal experiments). In contrast, the risk of alcohol may have been commonly underestimated.

Our results confirm the early study of Gable6 who found that the margin of safety (defined as therapeutic index) varied dramatically between substances. In contrast, our approach is not based on a therapeutic index, which is not necessarily associated with risk, but uses the most recent guidelines for risk assessment of chemical substances, which also takes the population-based exposure into account.

A major finding of our study is the result that the risk of drugs varies extremely, so that a logarithmic scale is needed in data presentation of MOE (e.g. Figures 1–3). Therefore, we think that previous expert-based approaches which often applied a linear scale of 0–3 or 0–1003,9, might have led to a form of “egalitarianism”, in which the public health impact of drugs appears more similar than it is in reality (i.e. more than 10.000-fold different as shown in our results on a population basis, e.g. Fig. 2 and ​3). As expected, for an individual the difference between the impact of different drugs is not as large as for the whole society (i.e. only up to 100 fold, Fig. 1).

According to the typical interpretation of MOEs derived from animal experiments, for individual exposure the four substances alcohol, nicotine, cocaine and heroin fall into the “high risk” category with MOE < 10, the rest of the compounds except THC fall into the “risk” category with MOE < 100. On a population scale, only alcohol would fall into the “high risk” category, and cigarette smoking would fall into the “risk” category. A difference between individual and whole population MOE was confirmed by the lack of correlation between average values (linear fit: R = 0.25, p = 0.53). This result is different to the previous expert-based surveys, for which the ranking performed at the population and individual level generally led to the same ranking (R = 0.98)3. Nevertheless, we judge our results as more plausible. For an individual heavy consumer of either heroin or alcohol, the risk of dying from a heroin overdose or from alcoholic cirrhosis increased considerably in each case. However for the society as a whole, the several ten-thousands of alcohol-related deaths considerably outnumber drug overdose deaths. Hence, it is plausible that the MOE for alcohol can be lower than the one for heroin, purely because of the high exposure to alcohol in the European society (see also Rehm et al.59).

Nevertheless, as previously stressed, our findings should not be interpreted that moderate alcohol consumption poses a higher risk to an individual and their close contacts than regular heroin use14. Much of the harm from drug use is not inherently related to consumption, but is heavily influenced by the environmental conditions of the drug use2, and this additional hazard is not included in a drug ranking based on (animal) toxicology.

The first major problem of the approach is the lack of toxicological dose-response data for all compounds except alcohol and tobacco. No human dose-response data are available; also no dose-response data in animals, only LD50 values are published. Furthermore, no chronic-toxicity data (long-term experiments) are available, which are usually used for such kinds of risk assessment. Therefore, we can assess only in regards to mortality but not carcinogenicity or other long-term effects. The absence of such data is specifically relevant for compounds with low acute toxicity (such as cannabis), the risk of which may therefore be underestimated.

Additionally, the available toxicological thresholds (i.e. LD50 values) have considerable uncertainty (for example, more than a factor of 10 for diazepam in different species). However it has been previously shown that the animal LD50 is closely related to fatal drug toxicity in humans60. The sensitivity analysis based on human data for ethanol shows that the average MOE result is similar to the result based on animal LD50. Our results for ethanol are also consistent with previous MOE studies of ethanol20,21. For cannabis and nicotine, the discrepancy in the sensitivity analysis can be explained in the chosen endpoints (no dose response data on mortality in humans were identifiable in the literature). For example, the only available human toxicological endpoint for cannabis as chosen by EFSA55 was “psychotropic effects”. The rationale for choosing this endpoint was the exclusion of risk for the inadvertent and indirect ingestion of THC when hemp products are used as animal feed55. We were unable to identify dose-response information for other endpoints of cannabis (e.g. mental health problems, chronic risk, or other cannabis-constituents besides THC). We think that while it is clear that different endpoints may yield quite different results, the human MOE for cannabis based on the endpoint “psychotropic effects” can be seen as general validation of the MOE concept, because the resulting values below 1 are expected as the psychotropic effect is the desired endpoint (and hence the psychotropic threshold dose is exceeded by drug users). Similar to cannabis, the sensitivity analysis for nicotine based on human data resulted in much lower MOE values. This again is based on a different endpoint (increase of blood pressure in this case, which is expected to be more sensitive than mortality). We nevertheless think that the risks of cigarettes could have been underestimated in our modelling, because in contrast to the other agents, tobacco contains a multicomponent mixture of toxicants. Previous risk assessment of tobacco (both financed and co-authored by the tobacco industry) have looked at various compounds but not included nicotine itself22,23. From the variety of investigated compounds in tobacco smoke, the lowest MOEs were found for hydrogen cyanide (MOE 15)22 and acrolein (MOE range 2–11)23. These values are reasonably consistent with our MOE for nicotine of 7.5 (individual exposure). However, it would be advisable for future risk assessments of tobacco smoking to include modelling of a combined MOE, which considers all toxic compounds.

The second major problem is the uncertainty in data about individual and population-wide exposure due to the illegal markets. There is a scarcity of epidemiological studies of cannabis use by comparison with epidemiological studies of alcohol and tobacco use61. If population data are available, they are usually provided as “% prevalence”, but for risk assessment we need a population-wide per-capita dosage in “mg compound/person/day”.

Due to both problems (or in other words the large uncertainty in input data of exposure), we cannot calculate with point estimates. To overcome this, we are using a probabilistic calculation methodology that takes the whole distribution of the input variables into account. For example, for the exposure a random sample of the number of days of annual drug use is combined with a random sample in the range of the usual dosages of the drug to provide an estimate for dosage.

The downside of the probabilistic approach is that the output also is not a single numerical value but rather a likelihood distribution. Nevertheless, using graphical approaches (Figs. 1–3) the results for all drugs under study can be quickly compared. On the other hand, this may be an advantage, as we did not try to establish a single value “to be written in stone”. The utility of “single figure index harm rankings” has also been questioned in general62.

Our approach contains some further limitations: Drug interactions cannot be taken into account as we just do not have any toxicological data on such effects (e.g. by co-administration in animals). However, polydrug use in humans is common, especially of illicit drugs with ethanol or benzodiazepines63. Addiction potential and risk of use (e.g. unclean syringes leading to increased infection risk) are also not considered by the model, because adequate dose-response data could not be identified for these endpoints.

Aside from the limitations in data, our results should be treated carefully particularly in regard to dissemination to lay people. For example, tabloids have reported that “alcohol is worse than hard drugs” following the publication of previous drug rankings. Such statements taken out of context may be misinterpreted, especially considering the differences of risks between individual and the whole population.

A main finding of our study is the qualitative validation of previous expert-based approaches on drug-ranking (e.g. Nutt et al.9), especially in regard to the positions of alcohol (highest) and cannabis (lowest). Currently, the MOE results must be treated as preliminary due to the high uncertainty in data. The analyses may be refined when better dose-response data and exposure estimates become available. As the problem is multidimensional15, it would also make sense to establish some form of harm or risk matrix64 that may be more suitable than a single indicator. Our MOE could be one piece in the puzzle that constitutes to the establishment of a “holistic drug risk”.

Currently, the MOE results point to risk management prioritization towards alcohol and tobacco rather than illicit drugs. The high MOE values of cannabis, which are in a low-risk range, suggest a strict legal regulatory approach rather than the current prohibition approach.


The methodology for comparative quantitative risk assessment was based on a previous study conducted for compounds in alcoholic beverages20 with the exception that probabilistic exposure estimation was conducted65,66,67. The MOE approach was used for risk assessment18,19. The MOE is defined as the ratio between the lower one-sided confidence limit of the BMD (BMDL) and estimated human intake of the same compound. If the BMD as preferred toxicological threshold for MOE assessment is unavailable, no observed effect levels (NOEL), no observed adverse effect levels (NOAEL) or lowest observed adverse effect levels (LOAEL) may be applied. As none of these thresholds (neither human data nor animal data) was available for the illicit drugs, LD50 values from animal experiments were selected instead and extrapolated to BMDL. The exposure was calculated for individual scenarios of daily drug use, as well as for population based scenarios using drug prevalence data and sewage analysis data for Europe, which is a promising complementary approach for estimating the drug use in the general population.

The MOE was calculated using the software package @Risk for Excel Version 5.5.0 (Palisade Corporation, Ithaca, NY, USA). Monte Carlo simulations were performed with 100,000 iterations using Latin Hypercube sampling and Mersenne Twister random number generator. Convergence was tested with a tolerance of 5% and a confidence level of 95%. The distribution functions and detailed calculation methodology is specified in Supplementary Tables S1–S2 online.

Author Contributions

D.W.L. conceived of the study, conceptualized the data analyses and performed the calculations. J.R. collected the data from WHO and provided additional data for sensitivity analysis. All authors have been involved in the drafting of the article and the interpretation of the data and in critical revisions of the content. All authors have given final approval of the version to be published.

Supplementary Material


The research leading to these results or outcomes has received funding from the European Community's Seventh Framework Programme (FP7/2007–2013), under Grant Agreement n° 266813 - Addictions and Lifestyle in Contemporary Europe – Reframing Addictions Project (ALICE RAP – Participant organisations in ALICE RAP can be seen at The views expressed here reflect only the author's and the European Union is not liable for any use that may be made of the information contained therein. Support to CAMH for the salaries of scientists and infrastructure has been provided by the Ontario Ministry of Health and Long Term Care. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the Ministry of Health and Long Term Care or of other funders.


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