“Hooked on” Prescription-Type Opiates Prior to Using Heroin: Results from a Survey of Syringe Exchange Clients

Alcohol and Drug Abuse Institute, University of Washington, 1107 NE 45th St, Suite 120, Seattle, WA 98105, USA.
Journal of psychoactive drugs (Impact Factor: 1.1). 07/2012; 44(3):259-65. DOI: 10.1080/02791072.2012.704591
Source: PubMed


The availability and diversion of prescription-type opioids increased dramatically in the first decade of the twenty-first century. One possible consequence of increased prescription opioid use and accessibility is the associated rise in opioid dependence, potentially resulting in heroin addiction. This study aimed to determine how common initial dependence on prescription-type opioids is among heroin injectors; associations with demographic and drug-using characteristics were also examined. Interview data were collected at syringe exchanges in King County, Washington in 2009. Among the respondents who had used heroin in the prior four months, 39% reported being "hooked on" prescription-type opioids first. Regression analysis indicated that younger age, sedative use and no recent crack use were independently associated with self-report of being hooked on prescription-type opioids prior to using heroin. These data quantify the phenomenon of being hooked on prescription-type opioids prior to initiating heroin use. Further research is needed to characterize the epidemiology, etiology and trajectory of prescription-type opioid and heroin use in the context of continuing widespread availability of prescription-type opioids.

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    • "Over the past decade, the illicit use of pharmaceutical opioids has emerged as a major public health problem in the United States [18], [24], [40], [50], [51], [52], [61], [72]. In 2010, the lifetime, non-medical use of pharmaceutical opioids was reported by nearly 14% of the US population aged 12 years and older [58]. "
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    ABSTRACT: Objectives: The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel semantic web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO--pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC), through combination of lexical, pattern-based and semantics-based techniques. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. Methods: Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, and routes of administration. The DAO is also used to help recognize three types of data, namely: (1) entities, (2) relationships and (3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information, which facilitate search, trend analysis and overall content analysis using social media on prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. Results: A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. Conclusion: A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future.
    Journal of Biomedical Informatics 07/2013; 46(6). DOI:10.1016/j.jbi.2013.07.007 · 2.19 Impact Factor
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    Journal of medical toxicology: official journal of the American College of Medical Toxicology 10/2012; 8(4). DOI:10.1007/s13181-012-0267-6
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