Denial of prescription pain medication among people who use drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: People who use drugs experience pain at two to three times the rate of the general population and yet continue to face substantial barriers to accessing appropriate and adequate treatment for pain. In light of the overdose crisis and revised opioid prescribing guidelines, we sought to identify factors associated with being denied pain medication and longitudinally investigate denial rates among people who use drugs. METHODS: We used multivariable generalized estimating equations analyses to investigate factors associated with being denied pain medication among people who use drugs reporting pain in three prospective cohort studies in Vancouver, Canada. Analyses were restricted to study periods in which participants requested a prescription for pain from a healthcare provider. Descriptive statistics detail denial rates and actions taken by participants after being denied. RESULTS: Among 1168 participants who requested a prescription for pain between December 2012 and March 2020, the median age was 47 years and 63.0% were male. Among 4,179 six-month observation periods, 907 (21.7%) included a report of being denied requested pain medication. In multivariable analyses, age was negatively associated with prescription denial (adjusted odds ratio [AOR] = 0.98, 95% confidence interval [CI]:0.97-0.99), while self-managing pain (AOR = 2.48, 95%CI:2.04-3.00), experiencing a non-fatal overdose (AOR = 1.51, 95%CI:1.22-1.88), engagement in opioid agonist therapy (AOR = 1.32, 95%CI:1.09-1.61), and daily use of heroin or other unregulated opioids (AOR = 1.32, 95%CI:1.05-1.66) were positively associated with being denied. Common actions taken (n = 895) after denial were accessing the unregulated drug supply (53.5%), doing nothing (30.6%), and going to a different doctor/emergency room (6.1%). The period following the introduction of new prescribing guidelines was not associated with a change in denial rates. CONCLUSIONS: A substantial proportion of people who use drugs continue to be denied prescriptions for pain, with such denial associated with important substance use-related harms, including non-fatal overdose. Guidelines specific to the pharmaceutical management of pain among people who use drugs are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".