Detecting signals of opioid analgesic abuse: application of a spatial mixed effect poisson regression model using data from a network of poison control centers
Bibliographic record
Abstract
PURPOSE: The recent rise in the non-medical use of opioid analgesics in the US has underscored the importance of comprehensive post-marketing surveillance of these products. To assist pharmacovigilance efforts, we developed a methodology for detecting geo-specific "signals" of potential outbreaks of prescription drug abuse by 3-digit ZIP (3DZ) code. METHODS: The number of intentional exposure calls involving nine specific opioid analgesics were obtained from eight regional poison control centers between first quarter 2003 and fourth quarter 2004. The unit of analysis was a combination of drug-quarter/year-3DZ. We fitted an empirical Bayes mixed effects Poisson-Gamma regression model that adjusted for differences across 3DZs in opioid analgesic exposure. A relative report rate (RR) >or=3 at a probability of >0.95 was the signal threshold criterion. RESULTS: A total of 15,769 valid drug-time-3DZ combinations were identified. Of these, 1.9% (n = 294) met the signal threshold criterion. The number of signals generated per drug-quarter/year-3DZ combination ranged from 0 to 13. The largest number of signals were those involving methadone (n = 71), hydrocodone (n = 57), and branded oxycodone extended-release (n = 45). Signals for methadone and branded oxycodone extended-release were predominantly clustered in Appalachia. Hydrocodone-related signals showed less geographic clustering with approximately 26% reported from California, and the remainder from other regions in the US. CONCLUSIONS: Our results show marked regional differences in reported abuse of specific opioid analgesics. Additional research is needed to determine the sensitivity and specificity of signals obtained using this spatial mixed effect Poisson regression model.
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".