Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 243

The DOI matches its Crossref record.

Do Prescription Monitoring Programs Impact State Trends in Opioid Abuse/Misuse?

Open access
Reifler, Liza M., Droz, Danna, Bailey, J. Elise, Schnoll, Sidney H., Fant, Reginald, Dart, Richard C., Bucher Bartelson, Becki·2012·Pain Medicine

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1526-4637.2012.01327.x

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 243

Metadata

Authors
Reifler, L. M., Droz, D., Bailey, J. E., Schnoll, S. H., Fant, R., Dart, R. C., & Bucher Bartelson, B.
Venue
Pain Medicine
Published
2012-03
Volume
13
Issue
3
Pages
434-442
ISSN
  • 1526-2375 (ISSN-L)
  • 1526-4637
OpenAlex
W1765108219
Cited by
243
References
0
FWCI
25.17
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Do Prescription Monitoring Programs Impact State Trends in Opioid Abuse/Misuse? https://app.cochat.ai/papers/10.1111/j.1526-4637.2012.01327.x
The paper
Reifler, L. M., Droz, D., Bailey, J. E., Schnoll, S. H., Fant, R., Dart, R. C., & Bucher Bartelson, B. (2012). Do Prescription Monitoring Programs Impact State Trends in Opioid Abuse/Misuse? Pain Medicine. https://doi.org/10.1111/j.1526-4637.2012.01327.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.