Considering prescription opioid‐related harms in the population: Response to commentaries
Bibliographic record
Abstract
The commentaries to our recent paper 1 make some important and insightful points to which we briefly respond. Rintoul et al. 2 correctly point out that little is known from a population health—or social determinants—perspective about medical prescription opioid (PO) use and PO-related harms. Specifically, there are few data on how basic social variables such as socio-demographics, education, socio-economic status, etc. influence the above phenomena. Such gradients are fairly well explored for other legal and illegal drug use 3, 4; a recent examination of non-medical PO use among Ontario adults and students found a broad distribution across socio-economic classes 5. The empirical situation is quite different with regard to another population level-effect (as raised by Samet & Tsui 6); namely, the correlation between levels of PO dispensing and corresponding levels of harms in a population. Several studies from different North American jurisdictions have documented that the levels of non-medical PO use, as well as PO-related morbidity and mortality outcomes, covary notably with PO dispensing levels 7, 8; this may imply that meaningfully lowered PO dispensing levels result in lower levels of harm outcomes; a key question is, however, on how to achieve such reductions without undermining pain care access and quality. An important issue, here—also in light of the limited evidence on the effectiveness of PO therapy for chronic pain—is a focus upon high-dose PO prescribing, accounting for large shares of overall POs consumed yet also documented as a key determinant of PO harms (e.g. mortality) 9, 10. Clinically sensible reductions, especially in high PO dosing, may bring ways to substantively reduce overall PO consumption, and harms, in populations, and relevant mechanisms have been proposed (e.g. registration for high doses) 11. Another key area, especially in PO-rich North America, is the widespread and generous use of POs for problems where alternative interventions may be equally beneficial yet coming with fewer risks. Griffiths et al. 12 point out correctly that both in terms of PO use environments and problems, the European Union (EU) ought to be considered a heterogeneous entity, featuring substantive interjurisdictional differences 13. This compares to North America, where extensive differences exist in PO use or PO-related problems between provinces (Canada) or states (United States 14, 15). A key question for the EU region is whether PO-related problems there are (i) delayed, (ii) insufficiently noticed or (iii) simply not occurring at levels they are in North America, and what potential determinants for these realities are, as has been observed for other drug issues (e.g. cocaine/crack 16). This question is of particular interest, given that numerous EU jurisdictions have also recently seen substantive increases in overall PO dispensing levels. Finally, the question of effective policy measures is raised. There is still only limited evidence regarding the impacts of prescription monitoring programmes (PMPs), especially with regard to reducing PO-related harms 17, 18; in addition, clinical PO use guidelines are increasingly present, yet little is known about their impact on PO-related harms 19. These areas urgently require attention from rigorous research. None. B.F. and J.R. acknowledge several Canadian Institutes of Health Research (CIHR) grants (specifically grant numbers 1234 and 5678) which supported the present work. B.F. also acknowledges salary and research support from a CIHR/PHAC Applied Public Health Research Chair.
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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 | 0.001 |
| 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".