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

Autor: Magdalena Cerdá, Daniel J. Tancredi, Stephen G. Henry, Garen J. Wintemute, Andrew Crawford, Susan L. Stewart, Iraklis Erik Tseregounis, Aaron B. Shev, Eryn Murphy, Brandon D.L. Marshall, James J Gasper
Rok vydání: 2021
Předmět:
medicine.medical_specialty
Clinical Sciences
Opioid
Practice Patterns
Logistic regression
Drug Prescriptions
01 natural sciences
Cohort Studies
opioid analgesics
Substance Misuse
03 medical and health sciences
0302 clinical medicine
Clinical Research
General & Internal Medicine
long-term opioid use
Odds Ratio
Internal Medicine
medicine
Humans
pain
Prescription Drug Abuse
030212 general & internal medicine
Practice Patterns
Physicians'
0101 mathematics
Medical prescription
Child
Original Research
Analgesics
Physicians'
Receiver operating characteristic
business.industry
Incidence (epidemiology)
010102 general mathematics
health policy
Odds ratio
Opioid-Related Disorders
Analgesics
Opioid
Good Health and Well Being
Pill
Emergency medicine
Prescription Drug Monitoring Programs
Patient Safety
Drug Abuse (NIDA only)
business
medicine.drug
Cohort study
Zdroj: J Gen Intern Med
Journal of general internal medicine, vol 36, iss 12
ISSN: 1525-1497
0884-8734
DOI: 10.1007/s11606-020-06555-x
Popis: BACKGROUND: Limiting 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. OBJECTIVE: To 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. DESIGN: Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All 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 RESULTS: Among 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. CONCLUSIONS: Data 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-020-06555-x.
Databáze: OpenAIRE
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