Trends in opioid use and dosing among socio-economically disadvantaged patients.
Bibliographic record
Abstract
BACKGROUND: Opioid therapy for patients with chronic nonmalignant pain remains controversial, primarily because of safety concerns and the potential for abuse. The objective of this study was to examine trends in opioid utilization for nonmalignant pain among recipients of social assistance and to explore the relation between dose of analgesic and mortality. METHODS: Using a cross-sectional study design, we characterized annual trends in prescriptions for and daily dose of opioid analgesics between 2003 and 2008 for beneficiaries (aged 15 to 64 years) of Ontario's public drug plan. We defined moderate, high and very high dose thresholds as daily doses of up to 200, 201 to 400, and more than 400 mg oral morphine (or equivalent), respectively. In an exploratory cohort study, we followed, over a 2-year period, patients who received at least one prescription for an opioid in 2004 to investigate the relation between opioid dose and opioid-related mortality. RESULTS: Over the study period, opioid prescribing rates rose by 16.2%, and 180 974 individuals received nearly 1.5 million opioid prescriptions in 2008. Also by 2008, the daily dose dispensed exceeded 200 mg morphine equivalent for almost a third (32.6%) of recipients of long-acting oxycodone but only 20.3% of those treated with fentanyl or other long-acting opioids. Among patients for whom high or very high doses of opioids were dispensed in 2004, 19.3% of deaths during the subsequent 2 years were opioid-related, occurring at a median age of 46 years. Two-year opioid-related mortality rates were 1.63 per 1000 population (95% confidence interval [CI] 1.42-1.85) among people with moderate-dose prescriptions, 7.92 per 1000 population (95% CI 5.25-11.49) among those with high-dose prescriptions, and 9.94 per 1000 population (95% CI 2.78-25.12) among those with very-high-dose prescriptions. INTERPRETATION: Among socio-economically disadvantaged patients in Ontario, the use and dose of opioids for nonmalignant pain has increased substantially, driven primarily by the use of long-acting oxycodone and, to a lesser extent, fentanyl. The findings of our exploratory study suggested a strong association between opioid-related mortality and the dose of opioid dispensed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".