Opioid Abuse among Injection Drug Users and Adsociation with City-level Prescription Opioid Dispensing Rates in Ontario
Bibliographic record
Abstract
Introduction: The opioid crisis is widespread and expanding in Ontario. Despite increases in opioid-related deaths in recent years, prescription opioids (POs) continue to be prescribed at high levels. The goal of this study was to better define connections between local PO dispensing levels and opioid abuse among IDU (injection drug users) in urban areas of the province.Methods: Data were obtained from the Ontario Drug Benefit claims database and the I-Track surveillance system. Using an unmatched repeated cross-sectional design, individual-level opioid abuse was nested within year and again within city-level (Kingston, London, Sudbury, Thunder Bay and Toronto) PO dispensing rates. Analyses were conducted with mixed-effects multilevel logistic regressions. Results: Total PO dispensing levels and total PO abuse among IDU were not associated. For opioid types, there was much variation in measures by city. Possible substitution effects were observed between Dilaudid dispensing levels and morphine abuse among IDU (OR=0.99, 0.98-1.00) as well as morphine dispensing and Dilaudid abuse among IDU (OR=0.93, 0.92-0.94). Dispensing of morphine (but not other POs) was related to heroin abuse among IDU (OR=1.04, 1.00-1.08). Dilaudid dispensing levels were associated with methadone abuse among IDU (OR=0.96, 0.94-0.98). Discussion: Distinctions between cities in both PO dispensing and opioid abuse among IDU were probably due to differences in distances to trafficking points and sociodemographic factors. The relationship between total PO and total opioid abuse among IDU was not reflective of relationships between opioid types. PO dispensing changes were related to changes in abuse of more than traditional PO types, including illicit (heroin) and restricted (methadone) opioids. Conclusions: Policies involving PO dispensing or opioid abuse among IDU should emphasize local evidence based on opioid types. The urban nature of the study also necessitates supplementary research in rural areas. New studies should examine PO dispensing with fentanyl and Suboxone abuse among IDU.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".