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

Abstract

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. Timelagged 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.

New psychoactive substances on dark web markets: From deal solicitation to forensic analysis of purchased substances

“…In contrast, the substance sold as “MPF‐47700” was of special interest to us because its structure was still unknown. Thus far, it has been only cursorily mentioned by its name in the DM‐related scientific literature 29,30 . According to the information stated in V3’s offer listing, it was supposed to be a newer analog of U‐47700, a synthetic opioid.…”

“…We ordered and processed two substances that, to the best of our knowledge, have not yet been mentioned in the scientific literature (O3.1: "Thirtylone" and O5.2: "JWH-X18"), and one that was mentioned by name only (O4.2: "MPF-47700") in two studies based on scraping DN marketplaces. 29,30 Of all the substances ordered, "MPF-47700" was the only truly novel NPS identified because its determined chemical structure seems to appear only once in the scientific literature with a different name and in an unrelated context. 40 In contrast, "Thirtylone" and "JWH-X18" have turned out to be wellknown psychoactive substances: ephylone and 5F-ADB.…”

“…To the best of our knowledge, substances JWH‐X18 (O5.2; According to V3’s product listing replacement for the now‐prohibited JWH‐018) and Thirtylone (O3.1) are not yet mentioned in the NPS‐related scientific literature. MPF‐47700 (O4.2; purportedly a newer analog of a synthetic opioid U‐47700) was cursorily mentioned by its name in DM‐related scientific literature, 29,30 yet its structure was still unknown. Another preferred category of NPS were substances of particular relevance to current reports and events.…”

“…Thus far, it has been only cursorily mentioned by its name in the DM-related scientific literature. 29,30 According to the information stated in V3's offer listing, it was supposed to be a newer analog of U-47700, a synthetic opioid.…”

Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

“…The literature shows that clear signals of medication abuse can be drawn from social media posts [ 5 ]. Furthermore, the literature used text mining to examine and compare discussion topics to discover the thematic similarity, difference, and membership in online mental health communities [ 8 ], provide timely information for epidemiologic surveillance [ 9 ], and analyze the public’s reactions to the opioid crisis [ 10 ].…”