Do Prescription Monitoring Programs Impact State Trends in Opioid Abuse/Misuse?
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Prescription monitoring programs (PMPs) are statewide databases containing prescriber and patient-level prescription data on select drugs of abuse. These databases are used by medical professionals or law enforcement officials to identify patients with prescription drug use patterns indicative of abuse or providers engaging in illegal activities. Most states have implemented PMPs in an attempt to curb prescription drug abuse and diversion. However, assessment of their impact on drug abuse is only beginning. This study aimed to evaluate the relationship between PMPs and opioid misuse over time in two drug abuse surveillance data sources. METHODS: Data from the RADARS® System Poison Center and Opioid Treatment surveillance databases were used to obtain measures of abuse and misuse of opioids. Repeated measures negative binomial regression was applied to quarterly surveillance data (from 2003 to mid-2009) to estimate and compare opioid abuse and misuse trends. PMP presence was modeled as a time varying covariate for each state. RESULTS: Results support an association between PMPs and mitigated opioid abuse and misuse trends. Without a PMP in place, Poison Center intentional exposures increased, on average, 1.9% per quarter, whereas opioid intentional exposures increase 0.2% (P = 0.036) per quarter with a PMP in place. Opioid treatment admissions increase, on average, 4.9% per quarter in states without a PMP vs 2.6% (P = 0.058) in states with a PMP. In addition to the time trend, population and a measure of drug availability were also significant predictors. A secondary analysis that classified PMP based upon ideal characteristic showed consistent though not significant results. CONCLUSIONS: Two observational data sources offer preliminary support that PMPs are effective. Future efforts should evaluate what PMP characteristics are most effective and which opioids are most impacted.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it