Opioid losses in terms of dosage and value, January 2012 to September 2017: a retrospective analysis of Health Canada data
Bibliographic record
Abstract
BACKGROUND: Canadian health care facilities must report losses or thefts of opioids to Health Canada. To broaden the understanding of opioid loss in Canada, we analyzed data describing these losses to estimate the amount of opioid lost, estimate the wholesale and street value, compare the distribution of loss types between facility types and compare loss trends. METHODS: We analyzed Health Canada records of losses of codeine, fentanyl, hydromorphone, morphine and oxycodone reported by Canadian facilities from January 2012 to September 2017. We conducted descriptive analyses of the opioid losses by calculating milligrams of drug lost, oral morphine equivalents, daily defined doses, approximate wholesale value and approximate street value, and compared loss trends when counted by incidents, dosage units or milligrams. RESULTS: There were 64 963 reports of loss of codeine, fentanyl, hydromorphone, morphine or oxycodone over the study period. Over 112 kg of opioids were lost, an estimated $8.7 million in wholesale cost and $136 million in street value. The dominant loss categories varied by facility type: armed robbery (30.9 kg [31.1%]) for community pharmacies, unexplained losses (6.4 kg [55.8%]) for companies and pilferage (0.8 kg [57.4%]) for hospitals. Loss trends over the study period varied by reporting metric and facility type: community pharmacy losses increased when measured by dosage units and incidents of loss, and remained stable when measured by milligrams; hospital losses increased when measured by milligrams and showed no clear trend when measured by dosage units and incidents of loss. Companies showed no clear loss trend with any reporting metric. INTERPRETATION: Large quantities of opioids were lost or stolen from community pharmacies, companies and hospitals over the study period, and these losses are valued in millions of dollars. Publishing milligrams of opioids lost annually alongside metrics such as dosage units and incidents of loss would help characterize the economic cost and the magnitude of drug losses.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".