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Retrospective Analyses of High-risk NPS: Integrative Analyses of PubMed, Drug Fora, and the Surface Web

Ahmed Al-Imam

Global Journal of Health Science September 18, 2017 DOI: 10.5539/gjhs.v9n11p40 (opens in new tab)

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AI-extracted from the abstract
Characteristics Observational analysis Longitudinal Cross-sectional Peer reviewed
Citations 6
Key findings The authors report that nearly half of forty-seven popular novel psychoactive substances were high-risk, associated with relatively high incidences of intoxication and death, and propose an unconventional inferential method using surface-web, Google Trends, PubMed/Medline, and drug-forum data to analyze age and gender patterns among users.

Abstract

Background: Novel psychoactive substances (NPS) can be classified based on their safety for use into low-risk and high-risk. High-risk NPS can be either lethal or poisonous. Fatalities can be either pharmacological or behavioural-induced, including suicide and homicide.MATERIALS &

Methods: Observational analysis, including retrospective, were implemented across; Google Trends, PubMed/MedLine database; Drug Fora, and the surface web. The aim was to collect data in relation to incidents of intoxication and fatalities caused by forty-seven (47) of the most popular NPS and to infer the high-risk (hazardous) substances. Geo-mapping was also applicable. Inferential analyses were also carried out to deduct data on the different age grouping of (ab)users.RESULTS: Among the most popular NPS substances, nearly half of them were labelled as high-risk due to their relatively high incidence of intoxications and deaths. The substances included; DMA/DOX, MXE, Mescaline, Methylone, Crack, GHB, Benzodiazepines, NBOMe, 2C-B, DMT, Stimulants RCs, Shrooms, Ketamine, Opioids, Heroin, Meth, Speed, LSD, MDMA, and Cocaine. Many of these substances were either psychedelic or dissociative substance. Geo-mapping of use indicated that the top ten contributing countries were; Australia, Canada, United States, United Kingdom, New Zealand, Ireland, Norway, Netherlands, Switzerland, and Estonia. The contribution of the Middle East was insignificant, although data have regularly been noticed originating from Israel, Iran, and Turkey.CONCLUSION: In this study, an unconventional inferential method is suggested for analysis of high-risk NPS; it is based on cross-sectional and longitudinal analysis of data. It relies primarily on data from; the surface web, Google Trends, PubMed/Medline database, and drug fora. This method is not only descriptive but also inferential for age and gender among (ab)users of a diverse array of high-risk NPS substances.

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