Evidence map›Paper›PMID 33742304›Full record

ArticleJournal of general internal medicine2021

Using Prescription Drug Monitoring Program Data to Assess Likelihood of Incident Long-Term Opioid Use: a Statewide Cohort Study.

Stephen G Henry, Susan L Stewart, Eryn Murphy, Iraklis Erik Tseregounis, Andrew J Crawford, Aaron B Shev, James J Gasper, Daniel J Tancredi, Magdalena Cerdá, Brandon D L Marshall and 1 more

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Article in Journal of general internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Stephen G HenryDepartment of Internal Medicine and Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA. sghenry@ucdavis.edu.ORCID 0000-0002-5643-0881
Susan L StewartDepartment of Public Health Sciences, University of California, Davis, Davis, CA, USA.
Eryn MurphyCenter for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA.
Iraklis Erik TseregounisCenter for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA.
Andrew J CrawfordViolence Prevention Research Program, University of California, Davis, Sacramento, CA, USA.
Aaron B ShevViolence Prevention Research Program, University of California, Davis, Sacramento, CA, USA.
James J GasperDepartment of Family and Community Medicine, University of California, San Francisco, San Francisco, CA, USA.
Daniel J TancrediDepartment of Pediatrics and Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA.
Magdalena CerdáDepartment of Population Health and Center for Opioid Epidemiology and Policy, New York University, New York, NY, USA.
Brandon D L MarshallDepartment of Epidemiology, Brown University, Providence, RI, USA.
Garen J WintemuteViolence Prevention Research Program, University of California, Davis, Sacramento, CA, USA.

Funding

Developing Patient-level Risk Prediction Models for Prescription Opioid OverdoseR01DA044282 · NIDA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI HENRY, STEPHEN G · 2017 to 2020
$2.5M
NIDA NIH HHS R01 DA044282NIDA NIH HHS R01DA044282
6 · The paper itself

Abstract

backgroundLimiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms.

objectiveTo identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription.

designStatewide cohort study using prescription drug monitoring program data

participantsAll opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017. METHODS AND MAIN MEASURES: Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic). KEY

resultsAmong 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents.

conclusionsData routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

Indexed as

Opioid-Related DisordersPrescription Drug Monitoring ProgramsAnalgesics, OpioidChildCohort StudiesDrug PrescriptionsHumansOdds RatioPractice Patterns, Physicians'Analgesics, Opioidhealth policylong-term opioid useopioid analgesicspainprescription drug monitoring programs

Identifiers

PMID33742304
PMCPMC8642457

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.