Risk Prescription Opioid Users May be Linked to Prescribing" - - PowerPoint PPT Presentation

"Demographic Characteristics of High Risk Prescription Opioid Users May be Linked to Prescribing"

Todd Schneberk MD MA, Brian Raffetto MD MPH, David Kim MD, David L Schriger MD MPH

What can we learn from a closer look at the California Prescription Drug Monitoring Program (PDMP) Database (CURES)?

Disclosures

• None

Methods

• Prescription Drug Monitoring Program (PDMP) Controlled Substance

Utilization Review and Evaluation System (CURES) database 2008- 2015

• 193 million opioid prescriptions
• 30 million individual patients
• Prescriber, patient and pharmacy identifiers
• National Drug Classification Crosswalk to convert drugs to Morphine

Milligram Equivalents (MME)

• Combined with zip code data from American Community Survey

Census Data 2010

Who is at high risk?

• High risk use by Chronic High Daily MME
• > 20 MME consumed daily for a minimum of 90 days

Inverse Relationship of Poverty and Opioids

Are they doctor shopping?

• High risk use by Doctor Shopping
• > 5 prescribers or 5 pharmacies within 6 months of use in the database

DOCTOR SHOPPERS CHRONIC HIGH MME

Chronic High MME* Users (N=306,484) * >20 MME/day for > 90 days) Doctor Shoppers (N=42,472) All* Persons >= 1 Opioid Script (N ≈ 3,000,000) *10% of all persons receiving script 2008-15

If we take providers who prescribe:

• short acting opioids on > 95% of all of their scripts,
• <= 31 pills on 95% of all scripts
• only 1 script in the database for >90% of all patients to whom they

gave opioids

• less than 6 scripts in the database to >99% of patients given opioids
• < 540 scripts per year

Limitations

• Retrospective Cross-Sectional Dataset Analysis
• CURES database did not include majority of demographic data
• Zip Code ecological data
• Minimal covariates

Conclusions