The Association Between Opioid Prescribing and Opioid-Related Mortality Within Neighborhoods in Ontario, Canada: A Case-Control Study
Bibliographic record
Abstract
OBJECTIVE: Recent Canadian data show that the prescribing of opioids has declined while the number of opioid deaths continues to rise. This study aimed to assess the relationship between neighborhood-level opioid prescription rates and opioid-related mortality among individuals without an opioid prescription. METHOD: This was a nested case-control study using data in Ontario from 2013 to 2019. Neighborhood-level data were analyzed by using dissemination areas that consist of 400-700 people. Cases were defined as individuals who had an opioid-related death without an opioid prescription filled in the year prior. Cases and controls were matched using a disease risk score. After matching, there were 2,401 cases and 8,813 controls. The primary exposure was the total volume of opioids dispensed within the individual's dissemination area in the 90 days before the index date. Conditional logistic regression was used to examine the association between opioid prescriptions and the risk of overdose. RESULTS: There was no significant association between the total volume of opioid prescriptions dispensed in a dissemination area and opioid-related mortality. In subgroup analyses stratifying the cohort into prescription and nonprescription opioid-related mortality, the number of prescriptions dispensed was positively associated with prescription opioid-related mortality. There was also a significant inverse association between the increased total volume of opioids dispensed and nonprescription opioid mortality. CONCLUSIONS: Our results suggest that prescription opioids dispensed within a neighborhood can have both potential benefits and harms. The opioid epidemic requires a nuanced approach that ensures appropriate pain care for patients while also creating a safer environment for opioid use through harm-reduction strategies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".