1. The Prescription Behavior Surveillance System: Applications of De-identified PDMP Data in Public Health Surveillance Rx Abuse Summit April 23, 2014 Peter Kreiner, Ph.D. PDMP Center of Excellence at Brandeis University

2. The Prescription Behavior Surveillance System (PBSS) A longitudinal, multi-state database of de-identified PDMP data, to serve as: 1.An early warning surveillance tool 2.An evaluation tool, in relation to state and local policies and initiatives, such as prescriber educational initiatives

3. PBSS Continued Began in FY2012 with support from CDC and FDA, administered through BJA Guided by Oversight Committee: – Federal partners: CDC, FDA, BJA, SAMHSA – State partners to date: CA, DE, FL, ID, ME, OH – Additional state partners in process (IN, KY, WA) – Adjunct state partners (MA, OK, TN) – unable to share data but willing to provide PBSS surveillance measures – No release of data or findings without Oversight Committee approval

4. PBSS Continued De-identified data from each participating state – Data use agreements tailored to each state’s laws and requirements – Beginning with 2010 or 2011, initial 2 – 3 years of data – Data updated quarterly (including prior 12 months) – Project-specific ID #’s for patients, prescribers, pharmacies Maintained for the duration of the data – Data housed in secure IT environment at Brandeis University

5. PBSS Continued Purpose (1): early warning public health surveillance tool – Periodic surveillance report with 40+ patient, prescriber, and pharmacy measures – Help identify emerging trends, changes in prescription patterns, indicators of risk associated with new drugs/new drug formulations – Data dictionary and technical documentation – Development of standards in relation to data quality (error rates, missing fields) and patient linking (determining which prescription records belong to the same patient) – Online access for authorized federal researchers – Specialized analyses as agreed upon with each state partner

6. PBSS Continued Purpose (2): evaluate selected initiatives to influence prescriber behavior – Inventory of state and local prescriber initiatives and assessment of their evidence base E.g., state mandates for prescribers to register with and use the PDMP – Development and validation of (population-level) measures of (in)appropriate and aberrant prescribing – Evaluation of selected REMS trainings

7. Surveillance Data: Selected Examples Opioid prescription rates by age group, comparison across states Number of Schedule II – V opioids per 1,000 state residents (in each age group) Multiple provider episode rates by age group, comparison across states Number of patients with prescriptions from 5 or more prescribers and 5 or more pharmacies in a 3-month period, per 100,000 state residents Multiple provider episode rates by quarter, 2010 – 2013 (Ohio) Multiple provider episode rates by community: Massachusetts

12. Further Data Examples Proportion of total prescriptions accounted for by prescriber deciles: prescriber 10% groupings based on prescription volume Average daily opioid dosage (morphine milligram equivalents) Average daily opioid dosage by prescriber decile (based on volume of opioid prescriptions

17. Further Data Applications Community profiles based on PDMP measures Inform local prevention and treatment needs assessment Help evaluate local interventions/track community progress in addressing prescription drug issues Mass. PMP/Brandeis are developing community profiles for: BJA Data Sharing Pilot grantee (Norfolk County District Attorney’s Office) CSAP/Bureau of Substance Abuse Services grantees addressing non-medical use of prescription drugs BSAS block grant-funded grantees addressing opioid abuse

18. Further Data Applications, Continued Versions of PBSS surveillance reports/measures for PDMP constituencies: Governor, legislators, other state agencies, consumer groups, etc. Epidemiological analyses tailed to state needs Examination of patient, prescriber, and dispenser behavior over time: what earlier patterns are associated with (eventual) risk indications? E.g., patterns of risk indicators suggestive of collective activity, drug rings More detailed examination of prescribing patterns for children or elderly persons

19. A Concluding Note Increasing use of PDMP data highlights a need for data quality and data consistency States vary in measures to ensure and improve data quality States vary in procedures to determine which prescription records belong to the same patient An important part of PBSS is the development of methods to assess data quality and to assess the adequacy of prescription linking procedures And to assist PBSS states in implementing best practices for data quality and linking procedures, and processes for ongoing quality improvement

20. Contact Information Peter Kreiner, Ph.D. Principal Investigator PDMP Center of Excellence Brandeis University 781-736-3945 pkreiner@brandeis.edu www.pdmpexcellence.org