Methadone Prescribing for Pain Management in Pennsylvania per the Prescription Drug Monitoring Program, 2016–2020
Bibliographic record
Abstract
Introduction Methadone is a schedule II opioid traditionally used to treat opioid use disorder (OUD) and chronic pain. However, following the identification of its contribution to opioid overdose deaths, methadone has become less commonly used for chronic pain indications. In Pennsylvania (PA), prescribers are required to report methadone prescriptions written for pain indications to the prescription drug monitoring program (PDMP), which is an electronic database that enhances the tracking and reporting of prescription data. The primary objective of our study was to describe the geographic methadone prescribing trends recorded by the PA PDMP in order to report methadone's current use for only pain indications. Methods State- and county-level methadone prescription data summaries recorded by the PA PDMP for each calendar quarter from August 2016 through March 2020 were collected from the PA Department of Health. The metric reported per quarter consisted of the total number of methadone prescriptions dispensed for pain indications unrelated to OUD. Results A total of 341,949 methadone prescriptions were dispensed in PA from the third quarter (Q3) of 2016 to the first quarter (Q1) of 2020 (range = 1106) with an overall 38.7% decrease in methadone prescriptions and a change in the rate of 85.97 per 100,000 population. The counties with the five highest prescription totals were Philadelphia, Allegheny, Bucks, Montgomery, and York (range = 46,969), and the counties with the five highest rates per 100,000 were Montour, Green, Columbia, Northumberland, and Forest (range = 964). Conclusions Methadone prescribing for pain management unrelated to OUD has decreased in PA from 2016 to 2020 per the PA PDMP. However, it is still prescribed in appreciable amounts for pain management. Further studies are required to understand the prescribing rationale and potential areas for harm reduction interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".