Bibliographic record
Abstract
In recent issues of Addiction, a number of papers have described an apparent increase in the use of diverted pharmaceutical drugs [1–3]. In multiple high-income countries the use of some illicit drugs, particularly heroin, has plateaued or dropped [4,5]. However, data sources also suggest that use of diverted pharmaceutical opioids is increasing [3,5], particularly morphine, oxycodone, codeine and hydromorphone. Studies in the United States have reported dramatic increases in extra-medical prescription opioid use, e.g. fentanyl, morphine and oxycodone [6]; the number of opioid analgesic poisoning deaths in the United States almost doubled between 1999 and 2002, and by 2002 was identified in more deaths than either heroin or cocaine [1,7]. Australian epidemiological monitoring data suggest that the use of codeine, morphine and, to a lesser extent, oxycodone, has increased among people who inject drugs regularly, driven possibly by sustained decreases in the availability and quality of heroin in the country [5]. A longitudinal survey of illicit opioid users in the Canadian OPICAN study documented higher levels of pharmaceutical opioid use among its cohort members in 2005 compared to 2001 [8]. The causes and mechanisms of this phenomenon remain poorly understood, particularly outside the United States [1]. It is likely that lower availability of heroin explains the increase in some contexts; increased marketing and availability of the pharmaceutical products accounts for it in others. In other countries still, an increase in the production of pharmaceutical opioids, accompanied by a poor capacity to police supply chains and minimize large-scale diversion, accounts for it in others. Whatever the causes may be, the observed epidemiological developments described in high-income countries have major implications for interventions, policy and research. The processes of diversion are fundamentally different from those seen with traditional illicit drugs such as heroin and cocaine. Illicit drugs production and manufacture have inspired border protection and military-style intervention, with arguably little positive effect and significant identifiable negative consequences. Pharmaceutical drugs are developed for their medical benefits; they are produced by pharmaceutical companies; and extra-medical use can occur through the drugs being over-marketed, over-prescribed, misprescribed or stolen at either wholesale or patient levels [9]. This situation presents new and complex challenges and demands new evidence to inform responses at all levels. This issue of Addiction sees the first in a new series exploring the diversion of pharmaceutical drugs for extra-medical use; it will explore the processes and drivers of diversion of pharmaceutical drugs, rather than people misusing their own prescriptions. We are currently inviting colleagues from around the world to provide manuscripts and research reports describing different national experiences of diversion of pharmaceuticals (e.g. oxycodone in the United States or benzodiazepines in Australia); different experiences of diversion of treatment drugs (e.g. methadone across the United Kingdom); the effects of different policies and which (if any) policy changes have impacted on the issue; and what external factors have acted to increase, diminish or change the nature of the diversion of some drugs. We hope to report on very different experiences in the next few years, providing an opportunity for comparing and contrasting different types of research, different drugs, different policies and, of course, different locations. We begin the series with a paper by Paoli et al.[10] which explores India's role in the world illicit opioid market, particularly its role as a producer. This piece highlights how India, a major illicit opioid consumer, is also the sole licensed exporter of raw opium, and that this unique status may be enabling substantial and diversion to the illicit market. This paper serves as an excellent description of how the many factors involved with the production and distribution of pharmaceuticals interact to allow diversion into the illicit market. This is an emerging issue that will be covered in multiple papers. We invite you to submit letters commenting on articles in the series and, indeed, to submit appropriate ideas for future papers for this series.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".