Trajectories of non-prescribed buprenorphine and other opioid use: A multi-trajectory latent class growth analysis.

INTRODUCTION: With the increasing use of non-prescribed buprenorphine (NPB), we need more data to identify the longitudinal patterns of NPB use. The goal of this natural history study is to characterize heterogeneity in trajectories of NPB, other opioid use, and participation in medication for opioid disorder (MOUD) treatment among a community-recruited sample of individuals with current opioid use disorder (OUD). METHODS: The study recruited a community-based sample of 357 individuals with OUD who used NPB in the past 6 months in Ohio, United States, for baseline and follow-up assessments (every 6 months for 2 years) of drug use, treatment participation, and other health and psychosocial characteristics. The study used multiple imputation to handle missing data. We used a multi-trajectory latent class growth analysis (MT-LCGA) to find salient groupings of participants based on the trajectories of NPB, other opioid use, and treatment participation. RESULTS: Over time, NPB use frequency declined from a mean of 14.6 % of days at baseline to 3.6 % of days at 24-month follow-up along with declines in heroin/fentanyl (56.4 % to 23.6 % of days) and non-prescribed pharmaceutical opioid (NPPO) use (11.6 % to 1.5 % of days). Participation in MOUD treatment increased from a mean of 17.0 % of days at baseline to 52.4 % of days at 24 months. MT-LCGA identified a 6-class model. All six classes showed declines in NPB use. Class 1 (28 %) was characterized by high and increasing MOUD treatment utilization. Class 2 (21 %) showed sustained high levels of heroin/fentanyl use and had the lowest levels of NPB use (2.2 % of days) at baseline. Class 3 (3 %) was characterized as the primary NPPO use group. Class 4 (5 %) transitioned from high levels of NPB use to increased MOUD treatment utilization. It had the highest levels of NPB use at baseline (average of 80.7 % of days) that decreased to an average of 12.9 % of days at 24 months. Class 5 (16 %) showed transition from high levels of heroin/fentanyl use to increased MOUD treatment utilization. Class 6 (27 %) showed decreased heroin/fentanyl use over time and low MOUD treatment utilization. Classes showed varying levels of improvement in psychosocial functioning, polydrug use, and overdose risks. CONCLUSION: Overall, our findings suggest that NPB use was generally self-limiting with individuals reducing their use over time as some engage in greater utilization of MOUD treatment. A need exists for continuing improvements in MOUD treatment access and retention.

"Etazene, safer than heroin and fentanyl": Non-fentanyl novel synthetic opioid listings on one darknet market.

BACKGROUND: Novel synthetic opioids are fueling the overdose deaths epidemic in North America.Recently, non-fentanyl novel synthetic opioids have emerged in forensic toxicological results. Cryptomarkets have become important platforms of distribution for illicit substances. This article presents the data concerning the availability trends of novel non-fentanyl synthetic opioids listed on one cryptomarket. METHODS: Listings from the EmpireMarket cryptomarket "Opiates" section were collected between June 2020 and August 2020. Collected data were processed using eDarkTrends Named Entity Recognition algorithm to identify novel synthetic opioids, and to analyze their availability trends in terms of frequency of listings, available average weights, average prices, quantity sold, and geographic indicators of shipment origin and destination information. RESULTS: 35,196 opioid-related listings were collected through 12 crawling sessions. 17 nonfentanyl novel synthetic opioids were identified in 2.9 % of the collected listings for an average of 9.2 kg of substance available at each data point. 587 items advertised as non-fentanyl novel synthetic opioids were sold on EmpireMarket for a total weight of between 858 g and 2.7 kg during the study period. 45.5 % of these listings were advertised as shipped from China. CONCLUSIONS: Fourteen of the 17 non-fentanyl novel synthetic opioids were identified for the first time on one large cryptomarket suggesting a shift in terms of novel non-fentanyl synthetic opioids availability. This increased heterogeneity of available novel synthetic opioids could reduce the efficiency of existing overdose prevention strategies. Identification of new opioids underpins the value of cryptomarket data for early warning systems of emerging substance use trends.

Listed for sale: Analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket.

BACKGROUND: The United States is facing a "triple wave" epidemic fueled by novel synthetic opioids. Cryptomarkets, anonymous marketplaces located on the deep web, play an increasingly important role in the distribution of illicit substances. This article presents the data collected and processed by the eDarkTrends platform concerning the availability trends of novel synthetic opioids listed on one cryptomarket. METHODS: Listings from the DreamMarket cryptomarket "Opioids" and "Research Chemicals" sections were collected between March 2018 and January 2019. Collected data were processed using eDarkTrends Named Entity Recognition algorithm to identify opioid drugs, and to analyze their availability trends in terms of frequency of listings, available average weights, average prices, and geographic indicators of shipment origin and destination information. RESULTS: 95,011 opioid-related listings were collected through 26 crawling sessions. 33 novel synthetic opioids were identified in 3.3 % of the collected listings. 44.7 % of these listings advertised fentanyl (pharmaceutical and non-pharmaceutical) or fentanyl analogs for an average of 2.8 kgs per crawl. "Synthetic heroin" accounted for 33.2 % of novel synthetic opioid listings for an average 1.1 kgs per crawl with 97.7 % of listings advertised as shipped from Canada. Other novel synthetic opioids (e.g., U-47,700, AP-237) represented 22 % of these listings for an average of 6.1 kgs per crawl with 97.2 % of listings advertised as shipped from China. CONCLUSIONS: Our data indicate consistent availability of a wide variety of novel synthetic opioids both in retail and wholesale-level amounts. Identification of new substances highlights the value of cryptomarket data for early warning systems of emerging substance use trends.

Methamphetamine Use and Its Correlates among Individuals with Opioid Use Disorder in a Midwestern U.S. City.

Background: U.S. is experiencing a surging trend of methamphetamine use among individuals who use opioids. More research is needed to characterize this emerging "twin epidemic." Objectives: The study aims to identify social and behavioral characteristics associated with methamphetamine use among individuals with opioid use disorder (OUD) in the Dayton, Ohio, area, an epicenter of the opioid crisis and an emerging frontier of methamphetamine epidemic. Methods: 357 adult individuals with current OUD were recruited using targeted and respondent-driven sampling. Structured interviews collected information on social and drug use characteristics. Multivariable Logistic Regression was used to identify characteristics associated with the past 6-month use of methamphetamine. Results: 49.7% were female, and 88.8% were non-Hispanic whites. 55.6% used methamphetamine in the past 6-months, and 84.9% reported first use of methamphetamine after initiation of illicit opioids. Methamphetamine use was associated with homelessness (aOR = 2.46, p = .0001), lifetime history of diverted pharmaceutical stimulant use (aOR = 2.97, p < .001), injection route of heroin/fentanyl use (aOR = 1.89, p = .03), preference for fentanyl over heroin (aOR = 1.82, p = .048), lifetime history of extended-release injectable naltrexone (Vivitrol)-based treatment (aOR = 2.89, p = .003), and more frequent marijuana use (aOR = 1.26, p = .04). Discussion: The findings point to the complexity of motivational and behavioral pathways associated with methamphetamine and opioid co-use, ranging from self-treatment and substitution behaviors, attempts to endure homelessness, and greater risk taking to experience euphoria. More research is needed to understand the causal relationships and the association between methamphetamine and Vivitrol use. Public health responses to the opioid crisis need to be urgently expanded to address the growing epidemic of methamphetamine use.

Unintentional drug overdose: Is more frequent use of non-prescribed buprenorphine associated with lower risk of overdose?

BACKGROUND: Unintentional drug overdoses have reached epidemic levels in the U.S. This study tests the hypothesis that people who have used non-prescribed buprenorphine more frequently in the past six months were less likely to experience a drug overdose during that same time period. METHODS: Participants age 18 years or older with opioid use disorder who reported use of non-prescribed buprenorphine in the last six months were recruited from the Dayton, Ohio, area using a combination of targeted and modified respondent-driven sampling. Participants completed a structured interview, including six-month timeline follow-back, after informed consent. Logistic regression was used to test the association between (log-transformed) frequency of non-prescribed buprenorphine use and overdose in the previous six months, adjusted for confounding due to sex, homelessness, incarceration, substance use treatment, previous overdose, heroin/fentanyl injection, psychiatric comorbidity, and (log-transformed) frequencies of other (non-opioid) drug use. RESULTS: Almost 89% of 356 participants were white, 50.3% were male, and 78.1% had high school or greater education. Over 27% (n = 98) reported experiencing an overdose in the past six months. After adjusting for confounding, greater frequency of non-prescribed buprenorphine use was significantly associated with lower risk of overdose (AOR = 0.81, 95% CI = 0.66, 0.98; p = .0286). Experiencing an overdose more than six months ago (AOR = 2.19, 95% CI = 1.24, 3.97); injection as the most common route of administration of heroin/fentanyl (AOR = 2.49, 95% CI = 1.36, 4.71); and frequency of methamphetamine use (AOR = 1.13, 95% CI = 1.02, 1.27) were strongly associated with increased risk of recent overdose in multivariable analysis. DISCUSSION: The findings support our hypothesis that higher frequency of non-prescribed buprenorphine use is associated with lower risk of drug overdose, a potential harm reduction consequence of diversion. Improving the availability of buprenorphine though standard substance use disorder treatment, primary care, and other innovative methods is urgently needed.

On my own terms: Motivations for self-treating opioid-use disorder with non-prescribed buprenorphine.

INTRODUCTION: The opioid overdose crisis in the United States has prompted an expansion of treatment services, including pharmacotherapy with buprenorphine. However, many people who use illicit opioids (PWUIO) self-treat their opioid-use disorder (OUD) with non-prescribed buprenorphine (NPB) in lieu of attending formal treatment. The present study aims to qualitatively understand motivations of people who are self-treating their OUD with NPB. METHODS: Qualitative study designed to supplement and contextualize quantitative findings from natural history study of buprenorphine diversion, self-treatment, and use of substance use disorder treatment services. Interviews were audio-recorded, transcribed, systematically coded and analyzed via Iterative Categorization. STUDY SETTING: The Dayton, Ohio metropolitan area in the midwestern United States; a site previously characterized as high impact in the national opioid overdose crisis. PARTICIPANTS: Sixty-five individuals (35 men and 30 women) who met the DSM-5 criteria for OUD (moderate or severe) and had used NPB at least one time in the six months prior to their intake interview. RESULTS: Participants described four key motivators for self-treating with NPB: perceived demands of formal treatment, the desire to utilize non-prescribed buprenorphine in combination with a geographic relocation, to self-initiate treatment while preparing for formal services, and to bolster a sense of self-determination and agency in their recovery trajectory. CONCLUSIONS: Use of NPB is a recognized self-treatment modality among PWUIO, with some PWUIO transitioning into sustained recovery episodes or enrollment in formal treatment. Understanding the motivations for opting out of treatment is crucial for improving forms of care for people with OUD.

Patterns of non-prescribed buprenorphine and other opioid use among individuals with opioid use disorder: A latent class analysis.

AIM: Non-prescribed buprenorphine (NPB) use increased in the US. This study aims to characterize heterogeneity in patterns of NPB and other opioid use among individuals with current opioid use disorder. METHODS: The study recruited 356 participants in Dayton (Montgomery County), Ohio, area in 2017-2018 using targeted and Respondent Driven Sampling. Participants met the following criteria: 1) 18 years or older, 2) current moderate/severe opioid use disorder (DSM-5), 3) past 6-month NPB use. Latent class analysis (LCA) was conducted to identify subgroups based on past 6-month (days of NPB and heroin/fentanyl use; use of NPB to get high; use of non-prescribed and prescribed pharmaceutical opioids; participation in formal treatment) and lifetime (years since first NPB and other illicit opioid use) characteristics. Selected auxiliary variables were compared across classes using Asparouhov and Muthén's 3-step approach. RESULTS: 49.7% were female, and 88.8% were non-Hispanic whites. 89% used NPB to self-treat withdrawal. LCA resulted in three classes: "Heavy Heroin/Fentanyl Use" (61%), "More Formal Treatment Use" (29%) and "Intense NPB Use" (10%). After adjusting for multiple testing, the following past 6-month variables differed significantly between classes: injection as a primary route of heroin/fentanyl administration (p < 0.001), cocaine use (p = 0.044), unintentional drug overdose (p = 0.023), and homelessness (p = 0.044), with the "Intense NPB Use" class having the lowest prevalences. CONCLUSION: Predominance of self-treatment goals and the association between more intense NPB use and lower risks of adverse consequences suggest potential harm minimization benefits of NPB use. More research is needed to understand consequences of NPB use over time.

"Everything is not right anymore": Buprenorphine experiences in an era of illicit fentanyl.

BACKGROUND: Conducted in the Dayton Metropolitan area of Southwestern Ohio, this qualitative study explores the self-treatment practices of people who use illicit opioids (PWUIO) amidst the new risk environment produced by illicit, non-pharmaceutical fentanyl (NPF). We explore local perceptions of the presence of NPF in the Dayton area, and how this has both positively and negatively impacted practices of non-prescribed buprenorphine use among PWUIO. METHODS: This study analyzes qualitative data from 63 interviews conducted between October 2018 and June 2019. Participants were selected from a larger longitudinal study on non-prescribed buprenorphine use among individuals with opioid use disorder. Qualitative interviews were transcribed in their entirety, and their transcriptions were analyzed using NVivo software, drawing on a mix of thematic and inductive coding. RESULTS: Interview respondents ranged from 19 to 70 years old, with a mean age of 38.9 years. 54% of them were male, and 85.7% identified as non-Hispanic White. 98.4% of the sample had used heroin, and 93.7% of the sample reported use of NPF. Participants agreed NPF dominated the illicit opioids market in the area, and was perceived as both dangerous and desirable. The domination of NPF and associated overdose experiences prompted some to seek positive change and initiate self-treatment with non-prescribed buprenorphine. For others, NPF sabotaged established practices of harm reduction, as unanticipated experiences of precipitated withdrawals prompted some participants to give up non-prescribed buprenorphine use as a tactic of self-treatment. DISCUSSION: The changing nature of heroin/NPF necessarily gives rise to new beliefs surrounding self-treatment attempts, treatment seeking behaviors, and harm reduction practices. While buprenorphine treatment continues to offer promising results for treating opioid use disorders, it is urgent to reconsider how the unpredictable biochemical mixture of NPFs circulating on the streets today may impact the initiation and success of treatment.

Trends in fentanyl and fentanyl analogue-related overdose deaths - Montgomery County, Ohio, 2015-2017.

INTRODUCTION: There is a lack of information on illicitly manufactured fentanyl and fentanyl analogue-related (IMF) unintentional overdose death trends over time. The study analyzes IMF-related unintentional overdose fatalities that occurred between July 2015 and June 2017 in Montgomery County, Ohio, an area with the highest rates of unintentional overdose mortality in Ohio. METHODS: LC-MS/MS-based method was used to identify fentanyl analogs and metabolites in 724 unintentional overdose death cases. The Chi-square statistic was used to assess differences over time in demographic and drug-related characteristics. RESULTS: The number of unintentional overdose death cases testing positive for IMFs increased by 377% between second half of 2015 and first half of 2017. The majority of decedents were white (82.5%) and male (67.8%). The proportion of fentanyl-only (no other analogs) cases declined from 89.2%-24.6% (p < 0.001), while proportion of fentanyl analogue-containing cases increased from 9.8%-70.3% (p < 0.001) between the second half of 2015 and first half of 2017. The most commonly identified fentanyl analogs were carfentanil (29.7%), furanyl fentanyl (14.1%) and acryl fentanyl (10.2%). Proportion of IMF cases also testing positive for heroin declined from 21.6% to 5.4% (p < 0.001), while methamphetamine positive cases increased from 1.4%-17.8% (p < 0.001) over the same time period. DISCUSSION: Emergence of fentanyl analogs contributed to substantial increases in unintentional overdose deaths. The data indicate a growing overlap between the IMF and methamphetamine outbreaks. Continuous monitoring of local IMF trends and rapid information dissemination to active users are needed to reduce the risks associated with IMF use.

Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio.

As America's opioid crisis has become an "epidemic of epidemics," Ohio has been identified as one of the high burden states regarding fentanyl-related overdose mortality. This study aims to examine changes in the availability of fentanyl, fentanyl analogs, and other non-pharmaceutical opioids on cryptomarkets and assess relationship with the trends in unintentional overdoses in Ohio to provide timely information for epidemiologic surveillance. Cryptomarket data were collected at two distinct periods of time: (1) Agora data covered June 2014-September 2015 and were obtained from Grams archive; (2) Dream Market data from March-April 2018 were extracted using a dedicated crawler. A Named Entity Recognition algorithm was developed to identify and categorize the type of fentanyl and other synthetic opioids advertised on cryptomarkets. Time-lagged correlations were used to assess the relationship between the fentanyl, fentanyl analog and other synthetic opioid-related ads from cryptomarkets and overdose data from the Cincinnati Fire Department Emergency Responses and Montgomery County Coroner's Office. Analysis from the cryptomarket data reveals increases in fentanyl-like drugs and changes in the types of fentanyl analogues and other synthetic opioids advertised in 2015 and 2018 with potent substances like carfentanil available during the second period. The time-lagged correlation was the largest when comparing Agora data to Cincinnati Emergency Responses 1 month later 0.84 (95% CI 0.45, 0.96). The time-lagged correlation between Agora data and Montgomery County drug overdoses was the largest when comparing synthetic opioid-related Agora ads to Montgomery County overdose deaths 7 months later 0.78 (95% CI 0.47, 0.92). Further investigations are required to establish the relationship between cryptomarket availability and unintentional overdose trends related to specific fentanyl analogs and/or other illicit synthetic opioids.

A Twitter-based survey on marijuana concentrate use.

AIMS: The purpose of this paper is to analyze characteristics of marijuana concentrate users, describe patterns and reasons of use, and identify factors associated with daily use of concentrates among U.S.-based cannabis users recruited via a Twitter-based online survey. METHODS: An anonymous Web-based survey was conducted in June 2017 with 687 U.S.-based cannabis users recruited via Twitter-based ads. The survey included questions about state of residence, socio-demographic characteristics, and cannabis use including marijuana concentrates. Multiple logistic regression analyses were conducted to identify characteristics associated with lifetime and daily use of marijuana concentrates. RESULTS: Almost 60% of respondents were male, 86% were white, and the mean age was 43.0 years. About 48% reported marijuana concentrate use. After adjusting for multiple testing, significant predictors of concentrate use included: living in "recreational" (AOR = 2.04; adj. p = .042) or "medical, less restrictive" (AOR = 1.74; adj. p = .030) states, being younger (AOR = 0.97, adj. p = .008), and daily herbal cannabis use (AOR = 2.57, adj. p = .008). Out of 329 marijuana concentrate users, about 13% (n = 44) reported daily/near daily use. Significant predictors of daily concentrate use included: living in recreational states (AOR = 3.59, adj. p = .020) and using concentrates for therapeutic purposes (AOR = 4.34, adj. p = .020). CONCLUSIONS: Living in states with more liberal marijuana policies is associated with greater likelihood of marijuana concentrate use and with more frequent use. Characteristics of daily users, in particular, patterns of therapeutic use warrant further research with community-recruited samples.

"You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates.

BACKGROUND: "Rosin tech" is an emerging solventless method consisting in applying moderate heat and constant pressure on marijuana flowers to prepare marijuana concentrates referred to as "rosin." This paper explores rosin concentrate-related Twitter data to describe tweet content and analyze differences in rosin-related tweeting across states with varying cannabis legal statuses. METHOD: English language tweets were collected between March 15, 2015 and April 17, 2017, using Twitter API. U.S. geolocated unique (no retweets) tweets were manually coded to evaluate the content of rosin-related tweets. Adjusted proportions of Twitter users and personal communication tweets per state related to rosin concentrates were calculated. A permutation test was used to analyze differences in normalized proportions between U.S. states with different cannabis legal statuses. RESULTS: eDrugTrends collected 8389 tweets mentioning rosin concentrates/technique. 4164 tweets (49.6% of total sample) posted by 1264 unique users had identifiable state-level geolocation. Content analysis of 2010 non-retweeted tweets revealed a high proportion of media-related tweets (44.2%) promoting rosin as a safer and solventless production method. Tweet-volume-adjusted percentages of geolocated Twitter users and personal communication tweets about rosin were respectively up to seven and sixteen times higher between states allowing recreational use of cannabis and states where cannabis is illegal. CONCLUSION: Our results indicate that there are higher proportions of personal communication tweets and Twitter users tweeting about rosin in U.S. states where cannabis is legalized. Rosin concentrates are advertised as a safer, more natural form of concentrates, but more research on this emerging form of marijuana concentrate is needed.

"Retweet to Pass the Blunt": Analyzing Geographic and Content Features of Cannabis-Related Tweeting Across the United States.

OBJECTIVE: Twitter data offer new possibilities for tracking health-related communications. This study is among the first to apply advanced information processing to identify geographic and content features of cannabis-related tweeting in the United States. METHOD: Tweets were collected using streaming Application Programming Interface (March-May 2016) and were processed by eDrugTrends to identify geolocation and classify content by source (personal communication, media, retail) and sentiment (positive, negative, neutral). States were grouped by cannabis legalization policies into "recreational," "medical, less restrictive," "medical, more restrictive," and "illegal." Permutation tests were performed to analyze differences among four groups in adjusted percentages of all tweets, unique users, personal communications only, and positive-to-negative sentiment ratios. RESULTS: About 30% of all 13,233,837 cannabis-related tweets had identifiable state-level geo-information. Among geolocated tweets, 76.2% were personal communications, 21.1% media, and 2.7% retail. About 71% of personal communication tweets expressed positive sentiment toward cannabis; 16% expressed negative sentiment. States in the recreational group had significantly greater average adjusted percentage of cannabis tweets (3.01%) compared with other groups. For personal communication tweets only, the recreational group (2.47%) was significantly greater than the medical, more restrictive (1.84%) and illegal (1.85%) groups. Similarly, the recreational group had significantly greater average positive-to-negative sentiment ratio (4.64) compared with the medical, more restrictive (4.15) and illegal (4.19) groups. Average adjusted percentages of unique users showed similar differences between recreational and other groups. CONCLUSIONS: States with less restrictive policies displayed greater cannabis-related tweeting and conveyed more positive sentiment. The study demonstrates the potential of Twitter data to become a valuable indicator of drug-related communications in the context of varying policy environments.

Characterizing marijuana concentrate users: A web-based survey.

AIMS: The study seeks to characterize marijuana concentrate users, describe reasons and patterns of use, perceived risk, and identify predictors of daily/near daily use. METHODS: An anonymous web-based survey was conducted (April-June 2016) with 673 US-based cannabis users recruited via the Bluelight.org web-forum and included questions about marijuana concentrate use, other drugs, and socio-demographics. Multivariable logistic regression analyses were conducted to identify characteristics associated with greater odds of lifetime and daily use of marijuana concentrates. RESULTS: About 66% of respondents reported marijuana concentrate use. The sample was 76% male, and 87% white. Marijuana concentrate use was viewed as riskier than flower cannabis. Greater odds of marijuana concentrate use was associated with living in states with "recreational" (AOR=4.91; p=0.001) or "medical, less restrictive" marijuana policies (AOR=1.87; p=0.014), being male (AOR=2.21, p=0.002), younger (AOR=0.95, p<0.001), number of other drugs used (AOR=1.23, p<0.001), daily herbal cannabis use (AOR=4.28, p<0.001), and lower perceived risk of cannabis use (AOR=0.96, p=0.043). About 13% of marijuana concentrate users reported daily/near daily use. Greater odds of daily concentrate use was associated with being male (AOR=9.29, p=0.033), using concentrates for therapeutic purposes (AOR=7.61, p=0.001), using vape pens for marijuana concentrate administration (AOR=4.58, p=0.007), and lower perceived risk of marijuana concentrate use (AOR=0.92, p=0.017). CONCLUSIONS: Marijuana concentrate use was more common among male, younger and more experienced users, and those living in states with more liberal marijuana policies. Characteristics of daily users, in particular patterns of therapeutic use and utilization of different vaporization devices, warrant further research with community-recruited samples.

Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis.

BACKGROUND: Synthetic Cannabinoid Receptor Agonists (SCRA), also known as "K2" or "Spice," have drawn considerable attention due to their potential of abuse and harmful consequences. More research is needed to understand user experiences of SCRA-related effects. We use semi-automated information processing techniques through eDrugTrends platform to examine SCRA-related effects and their variations through a longitudinal content analysis of web-forum data. METHOD: English language posts from three drug-focused web-forums were extracted and analyzed between January 1st 2008 and September 30th 2015. Search terms are based on the Drug Use Ontology (DAO) created for this study (189 SCRA-related and 501 effect-related terms). EDrugTrends NLP-based text processing tools were used to extract posts mentioning SCRA and their effects. Generalized linear regression was used to fit restricted cubic spline functions of time to test whether the proportion of drug-related posts that mention SCRA (and no other drug) and the proportion of these "SCRA-only" posts that mention SCRA effects have changed over time, with an adjustment for multiple testing. RESULTS: 19,052 SCRA-related posts (Bluelight (n=2782), Forum A (n=3882), and Forum B (n=12,388)) posted by 2543 international users were extracted. The most frequently mentioned effects were "getting high" (44.0%), "hallucinations" (10.8%), and "anxiety" (10.2%). The frequency of SCRA-only posts declined steadily over the study period. The proportions of SCRA-only posts mentioning positive effects (e.g., "High" and "Euphoria") steadily decreased, while the proportions of SCRA-only posts mentioning negative effects (e.g., "Anxiety," 'Nausea," "Overdose") increased over the same period. CONCLUSION: This study's findings indicate that the proportion of negative effects mentioned in web forum posts and linked to SCRA has increased over time, suggesting that recent generations of SCRA generate more harms. This is also one of the first studies to conduct automated content analysis of web forum data related to illicit drug use.

Characteristics and Treatment of Patients with Clinical Illness Due to Synthetic Cannabinoid Inhalation Reported by Medical Toxicologists: A ToxIC Database Study.

INTRODUCTION: Synthetic cannabinoid (SC) abuse has resulted in numerous outbreaks of severe clinical illness across the United States over the past decade. The primary objective of this study was to determine the clinical characteristics of patients abusing SC requiring bedside consultation by medical toxicologists. METHODS: This was a multicenter analysis from a prospectively collected cohort of patients presenting to medical care after synthetic cannabinoid exposure, utilizing the ToxIC Registry. Management of cases by medical toxicologists in this cohort occurred in emergency departments, inpatient medical floors, and intensive care units. Cases were identified from January 5, 2010 - July 31, 2015. We characterized the clinical presentations, treatments, outcomes, and sociologic factors associated with SC use in these patients. RESULTS: Medical toxicologists participating in the ToxIC Registry cared for 39,925 cases between 2010 and 2015. Three hundred fifty three of these cases were determined to be SC toxicity. The median age of patients was 25 (IQR: 18, 36) and the majority were males (84%). The most common symptoms were agitation, delirium and toxic psychosis, n=146 (41%). Forty-four (12.5%) had heart rates above 140 beats per minute. Bradycardia was the second most commonly reported severe vital sign abnormality with 20 (5.7%) having heart rates of less than 50 beats per minute. Fifteen (4.2%) patients had hypotension. Fifty-nine (17%) had seizures. The most common pharmacologic treatment provided was benzodiazepines (n=131, 37%) followed by antipsychotics (n=36, 10%).Disposition was available for 276; of these 167 (61%) were managed in the emergency department, 42 (15%) were admitted to the hospital floor, and 67 (24%) were admitted to the ICU. CONCLUSIONS: Synthetic cannabinoids are associated with severe central nervous system and cardiovascular effects.

"When 'Bad' is 'Good'": Identifying Personal Communication and Sentiment in Drug-Related Tweets.

BACKGROUND: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. OBJECTIVES: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. METHODS: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant. RESULTS: In multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045). CONCLUSIONS: The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid-related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions.

Acute Poisonings from Synthetic Cannabinoids - 50 U.S. Toxicology Investigators Consortium Registry Sites, 2010-2015.

Recent reports suggest that acute intoxications by synthetic cannabinoids are increasing in the United States (1,2). Synthetic cannabinoids, which were research compounds in the 1980s, are now produced overseas; the first shipment recognized to contain synthetic cannabinoids was seized at a U.S. border in 2008 (3). Fifteen synthetic cannabinoids are Schedule I controlled substances (3), but enforcement is hampered by the continual introduction of new chemical compounds (1,3). Studies of synthetic cannabinoids indicate higher cannabinoid receptor binding affinities, effects two to 100 times more potent than Δ(9)-tetrahydrocannabinol (the principal psychoactive constituent of cannabis), noncannabinoid receptor binding, and genotoxicity (4,5). Acute synthetic cannabinoid exposure reportedly causes a range of mild to severe neuropsychiatric, cardiovascular, renal, and other effects (4,6,7); chronic use might lead to psychosis (6,8). During 2010-2015, physicians in the Toxicology Investigators Consortium (ToxIC) treated 456 patients for synthetic cannabinoid intoxications; 277 of the 456 patients reported synthetic cannabinoids as the sole toxicologic agent. Among these 277 patients, the most common clinical signs of intoxication were neurologic (agitation, central nervous system depression/coma, and delirium/toxic psychosis). Relative to all cases logged by 50 different sites in the ToxIC Case Registry, there was a statistically significant association between reporting year and the annual proportion of synthetic cannabinoid cases. In 2015, reported cases of synthetic cannabinoid intoxication increased at several ToxIC sites, corroborating reported upward trends in the numbers of such cases (1,2) and underscoring the need for prevention.

"Those edibles hit hard": Exploration of Twitter data on cannabis edibles in the U.S.

AIMS: Several states in the U.S. have legalized cannabis for recreational or medical uses. In this context, cannabis edibles have drawn considerable attention after adverse effects were reported. This paper investigates Twitter users' perceptions concerning edibles and evaluates the association edibles-related tweeting activity and local cannabis legislation. METHODS: Tweets were collected between May 1 and July 31, 2015, using Twitter API and filtered through the eDrugTrends/Twitris platform. A random sample of geolocated tweets was manually coded to evaluate Twitter users' perceptions regarding edibles. Raw state proportions of Twitter users mentioning edibles were ajusted relative to the total number of Twitter users per state. Differences in adjusted proportions of Twitter users mentioning edibles between states with different cannabis legislation status were assesed via a permutation test. RESULTS: We collected 100,182 tweets mentioning cannabis edibles with 26.9% (n=26,975) containing state-level geolocation. Adjusted percentages of geolocated Twitter users posting about edibles were significantly greater in states that allow recreational and/or medical use of cannabis. The differences were statistically significant. Overall, cannabis edibles were generally positively perceived among Twitter users despite some negative tweets expressing the unreliability of edible consumption linked to variability in effect intensity and duration. CONCLUSION: Our findings suggest that Twitter data analysis is an important tool for epidemiological monitoring of emerging drug use practices and trends. Results tend to indicate greater tweeting activity about cannabis edibles in states where medical THC and/or recreational use are legal. Although the majority of tweets conveyed positive attitudes about cannabis edibles, analysis of experiences expressed in negative tweets confirms the potential adverse effects of edibles and calls for educating edibles-naïve users, improving edibles labeling, and testing their THC content.

Predictors of transition to heroin use among initially non-opioid dependent illicit pharmaceutical opioid users: A natural history study.

BACKGROUND: Increases in illicit pharmaceutical opioid (PO) use have been associated with risk for transition to heroin use. We identify predictors of transition to heroin use among young, illicit PO users with no history of opioid dependence or heroin use at baseline. METHODS: Respondent-driven sampling recruited 383 participants; 362 returned for at least one biannual structured interview over 36 months. Cox regression was used to test for associations between lagged predictors and hazard of transition to heroin use. Potential predictors were based on those suggested in the literature. We also computed population attributable risk (PAR) and the rate of heroin transition. RESULTS: Over 36 months, 27 (7.5%) participants initiated heroin use; all were white, and the rate of heroin initiation was 2.8% per year (95% CI=1.9%-4.1%). Mean length of PO at first reported heroin use was 6.2 years (SD=1.9). Lifetime PO dependence (AHR=2.39, 95% CI=1.07-5.48; PAR=32%, 95% CI=-2% to 64%), early age of PO initiation (AHR=3.08, 95%; CI=1.26-7.47; PAR=30%, 95% CI=2%-59%), using illicit POs to get high but not to self-medicate a health problem (AHR=4.83, 95% CI=2.11-11.0; PAR=38%, 95% CI=12%-65%), and ever using PO non-orally most often (AHR=6.57, 95% CI=2.81-17.2; PAR=63%, 95% CI=31%-86%) were significant predictors. CONCLUSION: This is one of the first prospective studies to test observations from previous cross-sectional and retrospective research on the relationship between illicit PO use and heroin initiation among young, initially non-opioid dependent PO users. The results provide insights into targets for the design of urgently needed prevention interventions.