The opioid mortality epidemic in North America: do we understand the supply side dynamics of this unprecedented crisis?
Bibliographic record
Abstract
While there has been extensive attention to the 'demand side' - or use and adverse consequences, including mortality - of the 'opioid crisis' presently unfolding across North America, few considerations have focused on the supply side. This paper examines the supply side dynamics of this unprecedented public health phenomenon. We provide evidence for several interrelated supply-side elements that have contributed to the present public health crisis. We observe that initially, persistently high levels of prescription opioid availability and use exposed large proportions of the North American population to opioids, resulting in correspondingly high levels of medical and non-medical use (e.g., involving diversion). While various intervention measures to control prescription opioid availability and use have been implemented in recent years, leading to eventual reductions in opioid dispensing levels, these occurred late in the crisis's evolution. Moreover, these supply reductions have not been met by corresponding reductions in opioid use or demand levels. These growing discrepancies between opioid demand and prescription-based sources have left major gaps in opioid supplies. In response to such supply gaps, highly potent and toxic illicit opioid products have rapidly proliferated across North America, and become a core driver of the dramatic spikes in opioid overdose fatality levels in recent years. These supply-related interrelations are corroborated by a corresponding increase in illicit opioid-related fatalities, which arose just as medical opioid supplies began to decrease in many jurisdictions. Improved analyses and understanding of the supply-side dynamics of the opioid crisis are urgently needed in order to inform future intervention and policy development. Meanwhile, the high mortality toll related to illicit, highly toxic opioid exposure requires sustained solutions, including supply-oriented measures (e.g., safer opioid distribution for at-risk users) towards improved public health protection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".