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:

5. Why so Important?

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

7. Oregon Drug Related Trends Counts and rates/100,00

8. As I mentioned, we are developing SAS code to identify the drugs involved using the death certificate data. We did a quick and dirty run using Oregon data with our program to find the drugs listed on the death certificates in Oregon. For anyone familiar with ICD-10 coded data, you will notice that we cannot identify some of these drugs using the ICD-10 coded data as they are including in some of the “other specified” categories. Such as “other specified synthetic narcotics” and “other opioids”.
This graph shows the data for 2010 & You can see it is following as we expected. The heroin & cocaine up, methadone, methadmephetemine down a very little. Heroin & cocaine have their own ICD-10 code, but
Methamphetamine is with T43.6 Psychostimulants with abuse potential
It is possible to look specifically at fentanyl which will be important as there is perhaps some new non-pharmetical fentanyl. An CDC epi-aid showed 16 deaths in RI involving these drugs.

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 solicited reports only in response to a specific inquiry
Proactive: Generate unsolicited reports 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:
Reactive: made by a prescriber, dispenser, or other party with appropriate authority
Proactive: thus seen as a law enforcement tool
Majority of states monitor classes 2 through 5

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:

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 Revision Data Elements for PDMPs
Prescription
Number, Date issued by prescriber,
Date filled,
New or refill, Number of refills,
State-issued serial number (optional)
Drug
NDC code for drug,
Quantity dispensed,
Days’ supply dispensed
Comes from pmp model act 2010 revision document on Alliance website:

18. Model Act 2010 Revision Data Elements for PDMPs
Patient
Identification number
Name, Address, Date of birth, Sex
Source of payment
Name of person who receives prescription if other than patient
Prescriber
Dispenser
Comes from pmp model act 2010 revision document on Alliance website:

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
This graph shows the number of unique patients receiving a high dose of greater than 100 morphine milligram equivalents by year.
The number of people who received greater than 100 morphine milligram equivalents on average per day for a year has increased from 2007 through 2011.
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

27. Number of people/1,000 residents receiving an opioid
Oct 1, 2011 to March 31, 2012

28. Number of people/1,000 residents receiving an opioid and benzodiazepine
Oct 1, 2011 to March 31, 2012

29. Number of people/10,000 residents using 4 or more prescribers and 4 or more
pharmacies
Oct 1, 2011 to March 31, 2012

30. Rates of Unintentional Poisoning Mirrors Rates of Dispensed Prescriptions
Neighborhoods with Highest Rates of Opioid Prescriptions Also Have the Highest Rates of Overdose Deaths,
Of the five NYC neighborhoods with the highest rates of hydrocodone and/or oxycodone prescriptions filled,four were in Staten Island and overlapped with four of the five neighborhoods where the rate of unintentional opioid analgesic poisoning (overdose) deaths was highest during the years
Source:

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”
Author (year)
Drug
No. of
Prescribers
Pharmacies Rx
Overlap
Time
Period
Hall (2008)
Any CS 5+ NA
1 yr
Peirce (2012) 4+
6 mo
Ohio DOH (2010)
Opioid
Avg of 5+
Over 3 yrs
Gilson (2010, 2012)
“Same medication” 2+
30 d
Katz (2010)
Any CSII
Cepeda (2012) 3+
1+ day
18 mo
BJA criteria
CSII-IV
3 mo.
No standard. Not like 5 or more drinks per day.
Sensitivity and specificity not determined, but measures have been associated with abuse or overdose
Shorter time periods might be desirable if looking for short-term impact of an intervention.
More specific measures might be chosen in response to limited resources for followup.

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
-more prescription data from PDMP PH and PS go after this a little differently
-X axis shows prescriber on left and rx on right. Y axis percent.
-point: 15% of prescribers write 82% of opioid analgesic rx.
-we can then use data to better understand high volume prescribers.
Source: New York State Department of Health, Bureau of Narcotic Enforcement, Prescription Drug Monitoring Program,

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
Clinical experts in primary care, rheumatology, psychiatry, emergency medicine, and pain management, including Theodore Strange and Mark Jarrett from Staten Island University Hospital and Anne Marie Stilwell from Interventional Pain Management of Staten Island, helped us develop these guidelines.

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):
Forrester, M. B. (2011). "Ingestions of hydrocodone, carisoprodol, and alprazolam in combination reported to Texas poison centers." Journal of Addictive Diseases 30:
Hall, A. J., J. E. Logan, et al. (2008). "Patterns of abuse among unintentional pharmaceutical overdose fatalities." JAMA 300:
Katz, N., L. Panas, et al. (2010). "Usefulness of prescription monitoring programs for surveillance---analysis of Schedule II opioid prescription data in Massachusetts, " Pharmacoepidemiol Drug Safety 19:
Ohio Department of Health. (2010). "Epidemic of prescription drug overdoses in Ohio." Retrieved September 1, 2010, from
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):
Wilsey, B. L., S. M. Fishman, et al. (2010). "Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics." Drug Alcohol Depend 112: