Linking the Narcotics Monitoring System Database to Quantify the Contribution of Prescribed and Non-Prescribed Opioids to Opioid Overdoses in Ontario, Canada
Bibliographic record
Abstract
IntroductionThe Ontario Narcotics Monitoring System (NMS) captures information on all prescriptions for controlled medications dispensed from outpatient pharmacies in Ontario, Canada, regardless of payer. This system was introduced in 2012, as a strategy to promote appropriate prescribing and dispensing practices. Objectives and ApproachWe sought to explore the degree to which prescriptions in the NMS can be linked to other health claims databases, and describe the types of medications dispensed between July 2012 and December 2016. We also linked opioid prescriptions to hospitalization and mortality data to examine the relative contributions of prescribed and non-prescribed opioids to opioid toxicity events in 2016. A recent opioid prescription was defined as a prescription with a days’ supply that overlapped the opioid toxicity event. Analyses were stratified by gender and age. ResultsWe examined 1.3 million prescriptions in the NMS during the study period: 72.8% for opioids, 21% for benzodiazepines, 4.4% for stimulants and <2% for other medications. Approximately 97% of prescriptions in the NMS could be linked because an Ontario health card was used at the time of dispensing. In 2016, we found that 52.8% of individuals with an opioid-related hospitalization (N=804/1,524) and 32.5% of those with an opioid-related death (N=278/855) had a recent opioid prescription. The proportion of opioid-related hospitalizations and deaths with a recent opioid prescription was significantly higher among females vs. males (57.2% vs. 48.0% and 45.6% vs. 26.4%, respectively; p<.001), and older (aged 45-64) vs. younger (aged 0-24) individuals (66.9% vs 9.9% and 46.4% vs 11.6% respectively; p<.001). Conclusion/ImplicationsLinkage was possible for the majority of prescriptions in the NMS. We found that a large proportion of opioid overdoses involved a non-prescribed opioid, particularly among men and younger individuals. These findings highlight an important difference in patterns of opioid use and toxicities in the population that policy-makers should consider.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".