Loss or Theft of Controlled Substances Declared to Health Canada From 2014 to 2018: A Retrospective Study
Bibliographic record
Abstract
Theft of prescription drugs is nothing new for Canadian pharmacists. Recently, an increasing body of literature has covered the diversion of controlled substances from Canadian hospitals. However, little has been published in the scientific literature concerning the data collected by Health Canada’s Loss or Theft Report Program regulated under the Controlled Drugs and Substances Act. Data from January 1, 2014, to December 31, 2018, were obtained from Health Canada’s Office of Controlled Substances (OCS). Reports to the OCS are mostly provided by pharmacies and hospitals, by veterinarian, dental, and physician clinics, pharmaceutical distributors and producers, and federal establishments and organizations. Entries include information related to the date, province, and location type; type of loss or theft; and generic name of the product, its strength, dosage form, quantity, and drug identification number. During the studied period, 45,379 submissions to the OCS provided information to create 213,895 entries to the database. After exclusions, 212,317 reports were retained for analysis. Opioids count for 45% of reports, benzodiazepines for 29%, and psychostimulants for 21%. Approximately, 29 million individual doses were lost or stolen of which 7.7 million were opioids (26%), totalizing approximately 178 million oral morphine milligram equivalents with 95% having been lost or stolen in community pharmacies. Moreover, approximately four out of 10 individual doses lost in community pharmacies are unexplained losses, which represent about 4.6 million individual doses. Reporting lost or stolen controlled substances and precursors is essential to tracking the diversion of Canada’s prescription drugs. Pharmacists therefore have an important role to play when it comes to minimizing their potential diversion. A better understanding of the situation across Canada may help to increase health care professionals’ awareness, improve practices, enhance the quality of collected data, and prevent further losses and thefts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".