Impact of legislation and a prescription monitoring program on the prevalence of potentially inappropriate prescriptions for monitored drugs in Ontario: a time series analysis
Bibliographic record
Abstract
BACKGROUND: The increased use of opioid analgesics, sedative hypnotics and stimulants, coupled with the associated risks of overdose have raised concerns around the inappropriate prescribing of these monitored drugs. We assessed the impact of new legislation, the Narcotics Safety and Awareness Act, and a centralized Narcotics Monitoring System (implemented November 2011 and May 2012, respectively), on the dispensing of prescriptions suggestive of misuse. METHODS: We conducted a time series analysis of publicly funded prescriptions for opioids, benzodiazepines and stimulants dispensed monthly in Ontario from January 2007 to May 2013, based on information in the Ontario Public Drug Benefit Database. In the primary analysis, a prescription was deemed potentially inappropriate if it was dispensed within 7 days of an earlier prescription and was for at least 30 tablets of a drug in the same class as the earlier prescription, but originated from a different physician and a different pharmacy. RESULTS: After enactment of the new legislation, the prevalence of potentially inappropriate opioid prescriptions decreased by 12.5% in 6 months (from 1.6% in October 2011 to 1.4% in April 2012; p = 0.01). No further significant change was observed after the introduction of the narcotic monitoring system (p = 0.8). By May 2013, the prevalence had dropped to 1.0%. Inappropriate benzodiazepine prescribing was significantly influenced by both the legislation (p < 0.001) and the monitoring system (p = 0.05), which together reduced potentially inappropriate prescribing by 50.0% between October 2011 and May 2013 (from 0.4% to 0.2%). The prevalence of potentially inappropriate prescribing of stimulants was significantly influenced by the introduction of the monitoring system in May 2012, falling from 0.7% in April 2012 to 0.3% in May 2013 (p = 0.02). INTERPRETATION: For a select group of drugs prone to misuse and diversion, legislation and a prescription monitoring program reduced the prevalence of prescriptions suggestive of misuse. This suggests that regulatory interventions can promote appropriate prescribing which could potentially be applied to other jurisdictions and drugs of concern.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".