Faculty Bibliography

BACKGROUND: Novel psychoactive substances (NPS) are emerging at an unprecedented rate. Likewise, prevalence of use and poisonings has increased in recent years. OBJECTIVE: To compare characteristics of NPS exposures and non-NPS-drug-related exposures and to examine whether there are differences between exposures involving synthetic cannabinoid receptor agonists (SCRAs) and other NPS. METHODS: Poison control center data from the five counties of New York City and Long Island were examined from 2011-2014. We examined prevalence and characteristics of NPS exposures (classified as intentional abuse) and compared characteristics of cases involving SCRAs and other NPS. RESULTS: Prevalence of NPS exposures was 7.1% in 2011, rising to 12.6% in 2014. Most exposures (82.3%) involved SCRA use. The second and third most prevalent classes were phenethylamines/synthetic cathinones ("bath salts"; 10.2%) and psychedelic phenethylamines (4.3%). Compared to other drug-related exposures (i.e. involving licit and illicit drugs), those who used NPS were more likely to be younger, male, and to have not co-used other drugs (ps < 0.001). SCRA exposures increased sharply in 2014 and the mean age of users increased over time (p < 0.01). Females exposed to SCRAs were younger than males (p < 0.001), and in 2014, individuals exposed to SCRAs were more likely to report concomitant use of alcohol than users of other NPS (p = 0.010). Users of other NPS were more likely than SCRA users to report concomitant use of ecstasy/3,4-methylenedioxymethamphetamine (MDMA)/"Molly" (p < 0.001). CONCLUSION: Exposures reported to the poison center that involve NPS are increasing and the majority involve SCRAs. These findings should inform prevention and harm reduction approaches.

Background: SCRAs are popular novel drugs of abuse. Despite their banning, use has skyrocketed in the USA. Current emergency department (ED) presentations highlight the varied clinical effects associated with reported SCRA use. Hypothesis: In this Department of Health investigation, we confirm SCRA use with biological testing and hypothesize that toxicity is predictable based on SCRA classification.
Method(s): Since May 2015, ED patients reporting 'K2' use with SCRA toxicity were identified. Those in possession of suspected SCRA product(s) had blood and urine specimens obtained and clinical features reported to the Poison Center (PC). Blood, urine, and product samples were linked with clinical effects but de-identified from the patient and kept in a separate, secure database. Specimens were stored and shipped at -20 degreeC to an independent laboratory for analysis. This public health surveillance investigation was approved by our local department of health.
Result(s): In this preliminary report, six product and seven biological results from 10 patients were available for analysis. SCRAs found in products included the following: NM2201, MAB-Chiminaca, XLR11, AMB, AB-Chiminaca, and MDMB-Fubinaca, with some products containing multiple SCRAs. SCRAs found in biological specimens included the following: MAB-Chiminaca, MABChiminaca metabolites, and AB-Chiminaca metabolites. Not all SCRAs found in products could be identified in corresponding patient biological specimens. Some SCRAs found in biological specimens were not found in corresponding products. In patients with confirmed MAB-Chiminaca in biological specimens (n = 4), one had agitation and three presented with central nervous system (CNS) depression. CNS depression (n = 1), delirium (n = 1), and seizure (n=1) were reported in patients with biological confirmation of AB-Chiminaca.
Discussion(s): Preliminary data from this Department of Health investigation identified multiple SCRAs in products and biological specimens. Clinical effects varied from sedation to agitation in patients with the same SCRAs. This variability may result from dose-dependent effects, individual host factors, or co-exposures. Not all suspected SCRAs or their metabolites can easily be identified in biological specimens. It is unclear if the 'K2' products obtained from the patients were the exact products used.
Conclusion(s): Individuals can develop varied toxicity after using the same SCRA. This surveillance project is still ongoing and additional results will be available in the future

BACKGROUND: In recent years, there has been an increase in emergence and use of novel psychoactive substances (NPS) in the US and worldwide. However, there is little published epidemiological survey data estimating the prevalence of use in the US. METHOD: Data on self-reported NPS use came from the National Survey of Drug Use and Health (2009-2013), a national representative sample of non-institutionalized individuals in the US. Subjects were asked to provide names of (non-traditional) drugs they used that they were not specifically asked about. We examined lifetime prevalence and sociodemographic correlates of self-reported use of new and uncommon synthetic drugs (NPS) among subjects ages 12-34-years-old. RESULTS: 1.2% of subjects self-reported any use of the 57 NPS we examined. Use of psychedelic tryptamines (primarily DMT) was most common, followed by psychedelic phenethylamines (e.g., 2C series) and synthetic cannabinoids. Prevalence of self-reported use of NPS increased from 2009 to 2013 and use was most common among males, whites, older subjects, those of lower income, and among those residing in cities. Lifetime use of various other illicit drugs (e.g., LSD, cocaine, ecstasy/MDMA) was highly prevalent among NPS users. CONCLUSION: This the first study reporting on use of a variety of NPS in a nationally representative US sample; however, use appears to be underreported as other national data suggest higher rates of NPS (e.g., synthetic cannabinoid) use. Developing more adaptable survey tools and systematically assessing NPS use would allow researchers to ask about hundreds of NPS and improve reporting as new drugs continue to rapidly emerge.

Study Objectives: Previously, we built machine-learned models to automatically identify Tweets indicating alcohol use from 34,563 labeled Tweets collected over 24 hours during New Year's Day. The models demonstrated an estimated area under the receiver operating curve (AUROC) of 0.94 for identifying alcohol use Tweets. In this study, we validated our alcohol use model in an independently collected dataset - the Electric Zoo music festival on New York City's Randall's Island. This event attracted over 130,000 people in 2013 and resulted in two substance-associated deaths. Methods: The initial dataset contained all Tweets and Instagrams geo-tagged within 5 miles of Randall's Island, covering all event days from August 29-31, 2014. Two authors independently reviewed Tweets for drug- or alcohol-related content. 10% of the Tweets were randomly selected for dual independent review to determine agreement using a weighted Cohen's kappa. Identified Tweets were then jointly reviewed to determine those indicative of alcohol use according to previous definitions. Tweets and Instagrams were considered indicators of alcohol use if they referred to: intention to drink, the act of drinking, location at a bar or liquor store, mention of a specific brand, drinking paraphernalia (eg, flask), consequences from drinking (eg, drunk, wasted, tipsy), or alcohol-related hashtags. Our Bayesian logistic regression machine learned model, which had been derived only from Tweets, was applied to a restricted dataset excluding Instagrams. Results: The complete geo-located collection included 11,071 Tweets and Instagrams. The restricted dataset containing only Tweets consisted of 2,928 elements, of which 82 Tweets were classified as drug- or alcohol-related (weighted kappa = 0.92). Of these, 23 Tweets explicitly referenced alcohol use (eg, "Wine at Zoo is the right play. Instadrunk;" "Wow. I am not sober;" "#clskipfridays #livesummer #Ezoo #were dumb #and drunk"). The model achieved an AUROC of 0.87 when applied to this independent Tweet validation set. Conclusion: Our machine-learned model automatically identified alcohol use at Electric Zoo with high discriminatory power. Differences between the previous estimated AUROC performance and the validated AUROC performance are likely due to language variations between the two groups. An in-depth error analysis may identify approaches to improve model performance. The ability to automate social media geosurveillance of substance behavior at events could be coupled with real-time data feeds. Model automation would allow these real-time data feeds to be analyzed for potential public health interventions (including messaging, Tweet geodensity dependent medical presence, or other measures) to further reduce harm