Prescription Behavior Surveillance Using PDMP Data

1. Prescription Behavior Surveillance Using PDMP Data Dagan Wright, PhD, MSPH (Oregon Health Authority) Denise Penone, PhD (New York City Department of Health) Special thanks and acknowledgement to Len Paulozzi who could not attend as all contributors

2. Outline of the PDMP Talk • What is PMP or PDMP? • Why so important? • What are general characteristics and data elements? • What are questions that can be answered? • Examples of data • Examples of outreach and evaluation

3. What is PMP or PDMP? • Tool utilized for reducing prescription drug misuse and diversion • Drug Epidemic Warning System • Drug Diversion & Fraud Investigative Tool • Public Health Surveillance tool to collect, monitor, and analyze dispensing data • Avoidance of Drug Interactions • Patient Care Tool • Identification & Prevention of “Doctor Shopping”* • Data now can used to support states’ efforts in education, research, quality assurance (better healthcare), enforcement and abuse prevention • Not meant to infringe on the legitimate prescribing of controlled substances *Doctor Shopping: Practice of obtaining multiple controlled substance prescriptions from multiple doctors Source: http://www.pmpalliance.org/content/prescription-monitoring-frequently-asked-questions-faq

5. Why so Important?

6. Opioid analgesic overdose deaths increased 65% Opioid analgesic overdose deaths, NYC, 2005-2011 Source: New York City Office of the Chief Medical Examiner & New York City Department of Health and Mental Hygiene 2005-2011

7. Oregon Drug Related TrendsCounts and rates/100,00

9. Methadone Death Rates Parallel Methadone Sales

10. More Drug Overdose Deaths than Motor Vehicle Crash Deaths Year Source: Oregon Vital Records

11. Oregon Hospitalization Rate/10,000 residents

12. What are General Characteristics and Data Elements?

13. PDMP: General Characteristics • Typically require monthly or bi-weekly reporting • Some States require weekly reporting i.e., Florida, Oregon • Oklahoma, requires reporting at time of sale • Reactive vs. Proactive • Reactive: Generate solicitedreports only in response to a specific inquiry • Proactive: Generate unsolicitedreports whenever suspicious or potentially at risk to the patient behavior is detected • Drug Schedules Monitored by states: • 24 collect Schedules II -V • 17 collect Schedules II –IV • 1 collect Schedule II only • 2 collect Schedules II & III Source: http://www.simeoneassociates.com/simeone3.pdf

14. PDMP: Information Collected • Patient identification • Name & Address • DOB & Gender • Prescriber Information &Dispenser Information • DEA number • Drug Information • National Drug Code (NDC) Info: • Name • Type • Strength • Manufacturer • Quantity & date dispensed Source: http://www.pmpalliance.org/

15. PDMP Attributes As a Surveillance System • Simplicity: single data source, few data elements, drug code (NDC) is complicated • Flexibility: limited fields • Data quality: insurance and system error checks • Acceptability: mandatory See: Lee et al, eds., Principles and Practice of Public Health Surveillance, 3rd edition, 2010.

16. PDMP Attributes As a Surveillance System • Sensitivity: high, required by law • Predictive value positive: metrics untested • Representativeness: population-based • Timeliness: days to weeks • Stability: in most cases operating for years • Cost: support for many is inadequate for most PDMPs • Other sources Oregon uses a provider licensing fee to support the PDMP See: Lee et al, eds., Principles and Practice of Public Health Surveillance, 3rd edition, 2010.

17. Model Act 2010 RevisionData Elements for PDMPs

18. Model Act 2010 RevisionData Elements for PDMPs

19. Descriptive Measures: Prescription Counts • Specific compound, formulation • Drug class • Opioids, benzodiazepines, stimulants, etc. • All extended-release formulations of opioids • Class within a schedule, e.g., Schedule II opioids • Daily dosage of an opioid prescription

20. Questions that can be Answered

21. Descriptive Measures: Denominators • Person, e.g., rx per 1,000 people (most common) • Patient, e.g., rx per 1,000 patients • Prescriber, e.g., mean daily dose/prescriber • Pharmacy, e.g., rx/pharmacy Time period is specified: e.g., in 2012, in past quarter

22. Descriptive Measures: “By” Variables • Patient sex, age group • Patient/prescriber/pharmacy by county or zip code • Month, year (prescribed or dispensed) • Prescriber specialty (requires linkage based on prescriber number) • Source of payment (where collected) • Patient type, e.g., opioid-naive

23. Risk Measures: Daily Dose for Opioids • Converted to morphine milligram equivalents (MME) • Usually categorized, e.g., • High, e.g., >100 MME/day • Going beyond specific dosing guidelines • e.g., more than 30 mg of methadone per day for an opioid-naïve person • Also quantified by measures of central tendency: mean, median , quartiles dose • SAS coding to do MME conversions available from CDC

24. Examples of Data

25. Number of Patients Receiving Opioid Dosages > 100 MME/day, Tennessee, 2007‒2011 Number of Patients Baumblatt J. Prescription Opioid Use and Opioid-Related Overdose Death TN, 2009–2010, CDC EIS Tuesday Morning Seminar, 1/8/2013

26. Opioid Prescriptions Filled by Staten Islanders Are More Frequently High Dose Schedule II opioids + hydrocodone, New York State Prescription Drug Monitoring Program

30. Rates of Unintentional Poisoning Mirrors Rates of Dispensed Prescriptions Source: http://www.nyc.gov/html/doh/downloads/pdf/epi/epi-data-brief.pdf

31. Use of PMP Data by MA Dept. of Public Health “Shopping” as a portion of all prescriptions Overdoses in ED Data Slide provided courtesy of Peter Kreiner, PMP Center of Excellence at Brandeis. Doctor shopping, the questionable activity, was defined as 4+ prescriber s and 4+ pharmacies for CSII in six months.

32. Measures of “Shopping” or “Multiple Provider Episodes”

33. Patient vs. Provider Metrics? • Top 1% of prescribers based on number of prescriptions might account for 33% of the morphine equivalents (MME) in your state.(1) • Top 1% of patients might account for 40% of MME.(2) 1. Swedlow 2011; 2. Edlund 2010

34. 15% of prescribers write 82% of opioid analgesic prescriptions Prescriptions filled by NYC residents, 2010 15% 82% Percent Source: New York State Department of Health, Bureau of Narcotic Enforcement, Prescription Drug Monitoring Program, 2008-2010

35. Distribution of CS II-IV prescriptions to prescribers, Oregon, 1/12 to 9/12 % of Prescribers % of CS Prescriptions Oregon Health Authority. Prescription Drug Dispensing in Oregon, October 1, 2011 – March 31, 2012

36. Examples of Outreach and Evaluation

37. Patient vs. Provider Metrics? • 100 patients in the PMP for every prescriber • It takes roughly 100 times more effort to address the same fraction of problematic prescriptions. • For interventions, provider case-finding is preferred based on efficiency.

38. 1st Evaluation of Oregon PDMP soon followed by NIH study – survey use • 65% say it is very helpful to monitor patients’ prescriptions for controlled substances • 64% report it is very helpful to control “doctor shopping” • 78% have spoken with patient about controlled substance use after using system • 59% reduced or eliminated prescriptions for a patient after using system • 49% contacted other providers or pharmacies Source: Oregon Prescription Drug Monitoring Program Evaluation

39. NYC Opioid Treatment Guidelines • Avoid prescribing opioids for chronic non-cancer, non-end-of-life pain • E.g. low back pain, arthritis, headache, fibromyalgia • When opioids are warranted for acute pain, 3-day supply usually sufficient • Avoid whenever possible prescribing opioids in patients taking benzodiazepines • If dosing reaches 100 MED, reassess and reconsider other approaches to pain management

40. References Cited • Cepeda, M., D. Fife, et al. (2012). "Assessing opioid shopping behavior." Drug Safety. • Edlund, M. J., B. C. Martin, et al. (2010). "Risks for opioid abuse and dependence among recipients of chronic opioid therapy: results from the TROUP study." Drug Alcohol Depend 112(1-2): 90-98. • Forrester, M. B. (2011). "Ingestions of hydrocodone, carisoprodol, and alprazolam in combination reported to Texas poison centers." Journal of Addictive Diseases 30: 110-115. • Hall, A. J., J. E. Logan, et al. (2008). "Patterns of abuse among unintentional pharmaceutical overdose fatalities." JAMA 300: 2613-2620. • Katz, N., L. Panas, et al. (2010). "Usefulness of prescription monitoring programs for surveillance---analysis of Schedule II opioid prescription data in Massachusetts, 1996--2006." Pharmacoepidemiol Drug Safety 19: 115-123. • Ohio Department of Health. (2010). "Epidemic of prescription drug overdoses in Ohio." Retrieved September 1, 2010, from http://www.healthyohioprogram.org/diseaseprevention/dpoison/drugdata.aspx. • Peirce, G., M. Smith, et al. (2012). "Doctor and pharmacy shopping for controlled substances." Med Care. • Swedlow, A., J. Ireland, et al. (2011). Prescribing patterns of schedule II opioids in California Workers' Compensation, California Workers' Compensation Institute. • White, A. G., H. G. Birnbaum, et al. (2009). "Analytic models to identify patients at risk for prescription opioid abuse." Am J Manag Care 15(12): 897-906. • Wilsey, B. L., S. M. Fishman, et al. (2010). "Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics." Drug Alcohol Depend 112: 99-106.