Geographic Variations in Prescription Opioid Dispensations and Deaths Among Women and Men in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: We quantify patterns in prescription opioid dispensations to individuals who suffered a prescription opioid-related death. In addition, we examine the relationship between opioid dispensations and prescription opioid-related deaths in geographic regions of British Columbia (BC). METHODS: We used population-based administrative data on prescription drug dispensations to identify patterns in prescription opioid dispensations to individuals who suffered a prescription opioid-related death. We also computed the quantity of prescription opioids dispensed (morphine equivalents) in small geographic regions in BC from 2004 to 2013. We identified prescription opioid-related deaths in these small geographic areas using mortality data from BC Vital Statistics and investigated the relationship between rates of prescription opioid dispensing and rates of prescription opioid death in small geographic areas in BC by sex. We examined differences in our results when limiting opioid dispensations to strong opioids and weak opioids. RESULTS: Many individuals who suffered a prescription opioid-related death did not have an active opioid prescription in the 60 days before death (46% of women and 71% of men). Rates of prescription opioid dispensing and opioid-related deaths vary substantially across geographic regions in BC. The area-level relationship between rate of prescription opioid dispensing and rate of unintentional prescription opioid-related death is positive and statistically significant for both men and women (P<0.001). This relationship holds when opioid prescribing is limited to strong opioids. CONCLUSION: Targeted efforts to reduce high levels of opioid prescribing in BC, particularly dispensations of strong opioids and codeine, may substantially reduce opioid-related harms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".