PDMP Track: Ensuring Appropriate Prescribing: Using PDMPs to Identify and Address Problematic Prescribing

1. PDMP Track
Ensuring Appropriate Prescribing:
Using PDMPs to Identify and
Address Problematic Prescribing
Presenters:
• Peter W. Kreiner, PhD, Senior Scientist, Institute for
Behavioral Health, Brandeis University
• Christopher Ringwalt, DrPH, MSW, Senior Scientist,
Injury Prevention Center, University of North
Carolina at Chapel Hill
Moderator: John L. Eadie, Director, Prescription Drug
Monitoring Program (PDMP) Center of Excellence, and
Member, Rx Summit National Advisory Board

2. Dislosures
• Peter W. Kreiner, PhD; Chris Ringwalt, DrPH; and John L. Eadie
have disclosed no relevant, real or apparent personal or
professional financial relationships with proprietary entities
that produce health care goods and services.

3. Disclosures
• All planners/managers hereby state that they or their
spouse/life partner do not have any financial
relationships or relationships to products or devices
with any commercial interest related to the content of
this activity of any amount during the past 12 months.
• The following planners/managers have the following to
disclose:
– Kelly Clark – Employment: Publicis Touchpoint Solutions;
Consultant: Grunenthal US
– Robert DuPont – Employment: Bensinger, DuPont &
Associates-Prescription Drug Research Center
– Carla Saunders – Speaker’s bureau: Abbott Nutrition

4. Learning Objectives
1. Advocate use of PDMPs to identify and
address problematic prescribing.
2. Explain the purpose, operation and
epidemiological findings of the Prescription
Behavior Surveillance System.
3. List metrics that can be used to identify
providers manifesting unusual or
uncustomary prescribing practices.

5. Ensuring Appropriate Prescribing
Using PDMPs to Identify and
Address Problematic Prescribing:
Epidemiological Findings from the
Prescription Behavior Surveillance
System
April 8, 2015
Peter W. Kreiner, Ph.D.
Brandeis University

6. Disclosure Statement
Peter Kreiner, Ph.D., has disclosed no relevant,
real, or apparent personal or professional
financial relationships with proprietary entities
that produce health care goods and services.

7. Learning Objectives
1. Advocate use of PDMPs to identify and
address problematic prescribing.
2. Explain the purpose, operation and
epidemiological findings of the Prescription
Behavior Surveillance System.
3. List metrics that can be used to identify
prescribers manifesting unusual or
uncustomary prescribing practices.

8. Overview
• Development of the Prescription Behavior
Surveillance System (PBSS):
– Federal and state PDMP partners
– Data submitted by state partners
– Measures of prescribing behavior; and patient,
prescriber, and pharmacy risk indicators
• Applications of PDMP data: Examples of trends in
prescribing behaviors and risk indicators
• Issues in data quality and its assessment,
including record-matching procedures

9. The Prescription Behavior
Surveillance System (PBSS)
A longitudinal, multi-state database of de-identified
PDMP data, to serve as:
1. An early warning public health surveillance tool
2. An evaluation tool, in relation to state and local
laws, policies and initiatives, such as prescriber
educational initiatives
Info available at:
http://www.pdmpexcellence.org/content/
prescription-behavior-surveillance-system-0

10. 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, KY, LA, ME, OH, TX,
WA, WV
– Additional state partners in process
– Adjunct state partners (MA, OK, TN) – unable to share data
but may be willing to provide PBSS surveillance measures
– No release of data or findings without Oversight
Committee approval

11. 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 – 4 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

12. PBSS Measures
• Prescribing measures
– Rates of opioid, benzodiazepine, stimulant
prescriptions
• By quarter and year, by drug class, sex, and age group
• By quarter and year, by major opioid, benzodiazepine,
and stimulant drug category
• Patient risk indicators
– Average daily dosage of opioids (MMEs)
– Days of overlapping prescriptions
– Multiple provider episode rates
• By drug class, age group, and drug category

13. PBSS Measures Continued
• Prescriber risk indicators
– Prescriber percentile ranking, based on daily
prescribing volume
• By quarter, year, and drug class
– Average daily dosage for opioid patients (MMEs)
– Median distance in miles, patient to prescriber
– Percentage of patients with MPE
– Percentage of prescriptions by payment type
– Percentage of patients prescribed LA/ER opioids who
were opioid-naïve
• Pharmacy risk indicators
– Analogous to prescriber risk indicators

14. Some Examples
• Trends in prescribing rates, by state
– Opioids in general
– Hydrocodone in particular
• Trends in patient/prescriber risk indicators, by
state
– Overlapping opioid and benzodiazepine prescriptions
– High average daily dosage of opioids
– Multiple provider episodes (MPEs)
• Framework for validation studies of prescriber
risk indicators

15. 0
50
100
150
200
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400
2010
Q1
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Opioid Prescriptions per 1,000 State Residents
By State and Quarter
CA
DE
FL
ID
KY
LA
ME
OH
WA
WV
Tramadol becomes Sch. IV

16. 0
50
100
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250
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Hydrocodone Prescriptions per 1,000 State Residents
By State and Quarter
CA
DE
FL
ID
KY
LA
ME
OH
WA
WV
Hydrocodone rescheduled to Sch. II

17. 0.00
5.00
10.00
15.00
20.00
25.00
2010
Q1
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Q1
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2014
Q4
Percent of Opioid Patients Receiving an Average
Dosage > 100 MME per Day
CA
DE
FL
ID
KY
LA
ME
OH
WA
WV

18. 0.00
5.00
10.00
15.00
20.00
25.00
2010
Q1
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Percent of Opioid Prescription Days with Overlapping
Benzodiazepine Prescriptions, by State and Quarter
CA
DE
FL
ID
KY
LA
ME
OH
WA
WV

19. Multiple Provider Episodes
• Defined as the number of patients with CS
prescriptions from 5 or more prescribers and 5
or more pharmacies in a 3-month period, per
100,000 state residents
• Differences in how states determine which
prescription records belong to the same
patient preclude comparisons between states
• We can, however, compare state MPE trends
– Simeone reported decreasing trends nationally
2008 - 2012

20. 0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
2011
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2012
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Q4
MPEratesper1,000stateresidents
Multiple Provider Episodes by State and Quarter
Rates per 1,000 Residents
CA
DE
FL
ID
KY
LA
ME
OH
WA
WV
Kentucky mandatory PDMP use law implemented

21. 0.00
5.00
10.00
15.00
20.00
25.00
30.00
2010 2011 2012 2013 2014
MPErateper100,000stateresidents
Multiple Provider Episodes by State and Year
Rates per 100,000 Residents, Annual Averages
CA
DE
FL
ID
KY
LA
ME
OH
WA
WV

22. Prescriber Risk Indicators
• Prescribing volume by prescriber decile:
Proportion of total prescriptions accounted
for by prescriber 10% groupings
• Average daily opioid dosage (MMEs) by
prescriber decile (volume)
• Distance patients travel to prescriber and
proportion of prescriber practice who meet
MPE threshold

23. 0
10
20
30
40
50
60
70
1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th
California, 2012: Proportion of Total Opioid,
Stimulant, and Benzodiazepine Prescriptions Written
by Prescriber Deciles
Percent of total opioid
prescriptions
Percent of total stimulant
prescriptions
Percent of total
benzodiazepine
prescriptions

24. 0
10
20
30
40
50
60
70
80
1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th
AveragedailydosageofpatientsinMMEs
Prescriber decile based on volume of opioid prescriptions
California 2012: Average Daily Dosage of Patients
by Prescriber Decile
Based on Volume of Opioid Prescriptions

25. 0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 20 40 60 80 100 120 140
PercentageofprescriberpatientswithMPE
Average distance patients travel in miles
California 2012: Prescriber Deciles Based on Average
Distance Patients Travel, Compared with Percentage of
Prescriber Patients with an MPE
Percentage of patients
with MPE
Log. (Percentage of
patients with MPE)

26. Validation Studies
Purpose:
1. Examine frequency of prescribers highest on
prescriber risk indicators having actions taken
against them (Medical Board, DEA, law
enforcement)
– Vs. prescribers lower on these indicators
2. Develop predictive models of actions taken
to estimate relative effects of different
prescriber behaviors

27. Validation Studies: Analytic Strategy
• Prescriber outcomes
– Identify prescribers against whom actions have
been taken
• By the state Medical Board/Board of Osteopathic
Medicine
• By the DEA
• By other law enforcement
– Categorize types of offense and types of action
taken
– Examine/take into account prescriber license type
and physician specialty

28. Validation Studies: Analytic Strategy
• Predictor variables
– Prescriber risk indicators
• Yearly, prior to year of action(s) taken
• Trajectory analysis: identify different groups/patterns
over time
– Measure of prescribing complexity?
• Pattern of drugs prescribed, in relation to peers
• Control variables
– Prescriber age, sex, location

30. Limitations of PDMP Data for
Surveillance and Evaluation
• No unique identifier for patients: record linking
procedures vary by PDMP
– Probabilistic vs. deterministic record linking
• PDMP relies on submitting pharmacies for data
accuracy
• Practices to assess and ensure data quality vary by
PDMP
• Recording of PRN prescriptions subject to pharmacist
discretion (e.g., 30 pills may be recorded as 30 days’
supply)

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

32. Chris Ringwalt, DrPH*
Sharon Schiro, PhD**
Meghan Shanahan, PhD*
Scott Proescholdbell, MPH***
Harold Meder, MBA*
Anna Austin, MPH,***
Nidhi Sachdeva, MPH ***
*UNC Injury Prevention Research Center
**UNC Department of Surgery
***NC Division of Public Health
Using the NC Controlled Substances
Reporting System to Identify
Providers Manifesting
Unusual Prescribing Practices
32

33. Disclosure Statement
• Chris Ringwalt, DrPH, has disclosed no
relevant, real or apparent personal or
professional financial relationships with
proprietary entities that produce health care
goods and services.

34. Learning Objectives
1. Advocate use of PDMPs to identify and
address problematic prescribing.
2. Explain the purpose, operation and
epidemiological findings of the Prescription
Behavior Surveillance System.
3. List metrics that can be used to conduct an
initial screen of providers manifesting
unusual or uncustomary prescribing
practices.

35. 35
Introduction
• In 2012, the percent of the population admitting
to the misuse of prescriptions drugs in the past 12
months was:
– 5.3% of youth aged 12-17
– 10.1% of young adults aged 18-25
– 3.8% of adults >25
• In 10 years, the annual number of prescriptions
for opioid analgesics has increased from 76 to 210
million
• 1.2 million visits to EDs for the nonmedical use of
prescription drugs in 2009
• 11,700 deaths were attributed to the nonmedical
use of prescription drugs in 2011
• Total cost to society in 2007: $55.4 million

36. 36
Prescription Drug Monitoring Programs: A
Powerful Clinical and Research Tool
• Registries of all scheduled drug (controlled substances)
prescriptions filled in a given state
• Typically include:
– Date dispensed
– Type, strength, and duration of each prescription
– Identifying information relating to each patient, prescriber, and
dispenser (pharmacy)
• Designed to be used for multiple purposes:
– Querying by registered providers and pharmacies of active
patients to promote appropriate prescribing practices and
prevent fraud
– Detect inappropriate prescribing (or dispensing) practices
– (Occasionally) research
• Now in all states but Missouri

37. 37
Problems with Use of PDMPs to
Detect Inappropriate Prescribing
• Lack of clarity as to which indicators may serve as a good
screening tool
• Concerns about the potential for many false positives
• Lack of resources to investigate providers identified by
these screens
• Lack of information in PDMPs concerning provider
specialty (e.g., oncologists, end-of-life treatment
specialists)
• Concern that providers treating chronic patients may:
– Dismiss those prematurely
– Treat them sub-optimally
– Decline to accept these patients into their practices

38. 38
How do Regulatory Authorities Detect
Inappropriate Prescribing Now?
• Complaints from patients and colleagues
• Audits of medical records
• Investigations by coroners or chief
medical examiners
However, currently, there is no standardized
screening tool to apply to Prescription Drug
Monitoring Programs for this purpose

39. 39
Project Goal
To develop and validate a set of algorithms
from metrics that utilize data from North
Carolina’s PDMP to develop a screening tool
to identify prescribers who manifest
unusual and uncustomary prescribing
patterns

40. 40
Candidates for Metrics
Providers who Write the Highest:
• Rates of prescriptions for daily doses of
opioids >100 milligrams of morphine
equivalents (MMEs)
• Average daily dose of MMEs
• Total MMEs for each prescription
• Rates of prescriptions for following drug
classes, irrespective of dose:
– Benzodiazepines
– Opioids
– Stimulants
• Rates of co-prescribed benzodiazepines +
opioids >100 MMEs
• Temporally overlapping prescriptions

41. 41
Candidates for Metrics
Providers with patients who:
• Travel long distances from their homes to their:
– Providers
– Pharmacies
• Fill prescriptions received from multiple providers
(doctor shopping) for:
– Opioids
– Stimulants
– Benzodiazepines
– Any controlled substance
• Fill prescriptions at multiple pharmacies
(pharmacy hopping)

42. 42
Example of metric distribution
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 5 10 15 20 25 30 35
NumberofProviders
Average daily rate that providers write opioids for >100 MMEs
Average daily rate that NC providers write opioid
prescriptions for >100 MMEs

43. 43
Example: Distribution tail
47
34
8
10
11
2
4
2
1
2
0 0
1 1
0
1
0 0 0 0 0 0 0 0 0
1
0
5
10
15
20
25
30
35
40
45
50
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
NumberofProviders
Average daily rate that providers write opioids for >100 MMEs
Average daily rate that NC providers write opioid prescriptions
for >100 MMEs

44. 44
SO WHAT? So Nothing, until Each Metric is Validated
Initial Validation Strategy:
• Combed NC Vital Statistics records for deaths (N=465)
in 2012 related to opioid overdose – used t-codes
representing drug-related poisonings
• Recorded DEA #s of providers who had prescribed
opioids to these patients within 30 days of their
death.
• Any given decedent could have received prescriptions
from multiple providers (N=651)
• Matched these to metrics relating to:
– List 1: Top 1% of prescribers of controlled substances in
each tail
– List 2: Top 1% of prescribers in each tail + top 1% of
prescribers for all controlled substances
– Thus List 2 is a subset of List 1
• Note that because the number of providers in each
full distribution varies, the number in the top 1%
will also

45. 46%
77%
n=57
n=31
0
10
20
30
40
50
60
Highest 1% of this metric Highest 1% of this metric + 1% of
prescribers
Co-prescribed benzodiazepines + opioids >100MMEs
Providers who did not prescribe
opioids to a decedent
Providers who prescribed opioids to a
decedent

46. 10% 61%
n=165
n=18
0
20
40
60
80
100
120
140
160
180
Highest 1% of this metric Highest 1% of this metric + 1% of
prescribers
Temporally overlapping prescriptions
Providers who did not prescribe opioids
to a decedent
Providers who prescribed opioids to a
decedent

47. 34%
43%
n=157
n=96
0
20
40
60
80
100
120
140
160
180
Highest 1% of this metric Highest 1% of this metric + 1% of
prescribers
Prescriptions for opioids >100MMEs
Providers who did not prescribe opioids
to a decedent
Providers who prescribed opioids to a
decedent

48. 36%
42%
n=290
n=176
0
50
100
150
200
250
300
350
Highest 1% of this metric Highest 1% of this metric + 1% of
prescribers
Prescriptions for any opioids
Providers who did not prescribe opioids
to a decedent
Providers who prescribed opioids to a
decedent

49. 30%
32%
n=271
n=167
0
50
100
150
200
250
300
Highest 1% of this metric Highest 1% of this metric + 1% of
prescribers
Prescriptions for any benzodiazepine
Providers who did not prescribe opioids
to a decedent
Providers who prescribed opioids to a
decedent

50. 50
Key Metrics Validated by this Mechanism
Metric label
Highest 1% of metric
(Proportion, %)
Highest 1% of metric + 1% of
prescribers (Proportion, %)
Co-prescribed
benzodiazepines + opioids
>100 MMEs
26/57 (46%) 24/31 (77%)
Temporally overlapping
prescriptions
16/165 (10%) 11/18 (61%)
Prescriptions for opioids
>100 MMEs
54/157 (34%) 41/96 (43%)
Prescriptions for any opioids 105/290 (36%) 74/176 (42%)
Prescriptions for any
benzodiazepines
80/271 (30%) 54/167 (32%)

51. 51
Non-Performing Metrics*:
Providers with Patients who
• Travel long distances to their
– Providers
– Pharmacies
• Are doctor shoppers
• Are pharmacy shoppers
* With this validation effort, at least

52. 52
Discussion
• Some of these metrics performed remarkably well
• However, prescribing opioid analgesics within a month of a
patient’s death does not constitute causality
• Further, attributing deaths to opioid overdoses is not a perfect
science
• Thus we assessed concurrent, not criterion, validity
• And findings from these metrics only represent an initial screen
• Sensitivity analyses may be helpful: nothing magical about top 1%
of providers
• Greater concurrent validity related to providers in top 1% of all
prescribers of a controlled substance (2nd bar) may be a function
of greater exposure – i.e., they write the most prescriptions
• Our PDMD:
– Lacks specialty information
– Lacked (until last year) payer information
• Further validation required, ideally within the context of a
longitudinal study that examines the results of screening metrics
relative to investigative outcomes

53. Conclusions
• A few metrics show considerable promise as a screening
tool for aberrant prescribing
• Others await further validation before they should be
employed
• Appropriate regulatory bodies (law enforcement,
medical boards) can now open investigations for
proactive in addition to reactive reasons
• Potential for metric placement (rate & rank) to assist
investigations by demonstrating to providers exactly
where they lie on these distributions
• Effects of use of screening mechanisms like this should
be carefully evaluated to determine potential for
“chilling” effects on prescribing behaviors

54. PDMP Track
Ensuring Appropriate Prescribing:
Using PDMPs to Identify and
Address Problematic Prescribing
Presenters:
• Peter W. Kreiner, PhD, Senior Scientist, Institute for
Behavioral Health, Brandeis University
• Christopher Ringwalt, DrPH, MSW, Senior Scientist,
Injury Prevention Center, University of North
Carolina at Chapel Hill
Moderator: John L. Eadie, Director, Prescription Drug
Monitoring Program (PDMP) Center of Excellence, and
Member, Rx Summit National Advisory Board