Co-involvement of stimulants with opioids in North America: A 'silent epidemic'
Bibliographic record
Abstract
The opioid epidemic unfolded in three distinct waves, with the latest comprising of deaths attributed to illegally manufactured synthetic opioids. We discuss evidence for a 'silent epidemic' alongside the opioid epidemic comprising co-ingestion of stimulants including methamphetamine and cocaine. Data regarding opioid- and stimulant-use trends (specifically methamphetamine and cocaine) were collected from the National Institute on Drug Abuse (NIDA) (1999-2021) in the United States for unintentional and intentional deaths. For Canada, opioid- (2016-2022) and stimulant-use (2018-2022) trends for unintentional and intentional deaths were collected from the Public Health Agency of Canada and the Alberta Substance Use Surveillance System. Joinpoint regression analysis was used to investigate trends for opioid and stimulant use in the United States and Canada. As a proxy for public interest in opioid- and stimulant-related trends, Google Trends Relative Search Interest (RSI) was used to measure the interest over time (2004-present) for opioid- and stimulant-related search terms. Spearman's non-parametric correlation was used to measure the relationship between the RSI and drug-related deaths. Although public attention is mostly directed at the opioid epidemic, stimulant-use is increasing year over year. Moreover, the use of stimulants and opioids together is associated with a higher rate of increase in drug-related deaths each year compared to stimulants or opioids alone. Despite the importance of the potential stimulant contribution to opioid-involved deaths, Google Trends RSI revealed public interest in stimulants has dropped from a peak value in 2004-2005, whereas relative interest in opioids is relatively much higher on Google in comparison to the interest in stimulants. Given the contribution of stimulants to the ongoing drug poisoning crisis there is an urgent need to develop and implement strategies that reduce the health risks associated with stimulant use. To achieve this, we must raise awareness among clinicians, policymakers and the general public regarding the potential impacts of stimulant use, including deliberate and unaware co-use of stimulants and opioids, on drug poisoning deaths, population health and the healthcare system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".