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

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.

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

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.

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

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.

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.

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

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

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

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

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

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

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

0

50

100

150

200

250

300

350

400

2010Q1

2010Q2

2010Q3

2010Q4

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

2014Q1

2014Q2

2014Q3

2014Q4

Opioid Prescriptions per 1,000 State ResidentsBy State and Quarter

CA

DE

FL

ID

KY

LA

ME

OH

WA

WV

Tramadol becomes Sch. IV

0

50

100

150

200

250

2010Q1

2010Q2

2010Q3

2010Q4

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

2014Q1

2014Q2

2014Q3

2014Q4

Hydrocodone Prescriptions per 1,000 State ResidentsBy State and Quarter

CA

DE

FL

ID

KY

LA

ME

OH

WA

WV

Hydrocodone rescheduled to Sch. II

0.00

5.00

10.00

15.00

20.00

25.00

2010Q1

2010Q2

2010Q3

2010Q4

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

2014Q1

2014Q2

2014Q3

2014Q4

Percent of Opioid Patients Receiving an Average Dosage > 100 MME per Day

CA

DE

FL

ID

KY

LA

ME

OH

WA

WV

0.00

5.00

10.00

15.00

20.00

25.00

2010Q1

2010Q2

2010Q3

2010Q4

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

2014Q1

2014Q2

2014Q3

2014Q4

Percent of Opioid Prescription Days with Overlapping Benzodiazepine Prescriptions, by State and Quarter

CA

DE

FL

ID

KY

LA

ME

OH

WA

WV

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

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

2010Q1

2010Q2

2010Q3

2010Q4

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

2014Q1

2014Q2

2014Q3

2014Q4

MP

E ra

tes

pe

r 1

,00

0 s

tate

re

sid

en

tsMultiple 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

0.00

5.00

10.00

15.00

20.00

25.00

30.00

2010 2011 2012 2013 2014

MP

E ra

te p

er

10

0,0

00

sta

te r

esi

de

nts

Multiple Provider Episodes by State and YearRates per 100,000 Residents, Annual Averages

CA

DE

FL

ID

KY

LA

ME

OH

WA

WV

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

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 opioidprescriptions

Percent of total stimulantprescriptions

Percent of totalbenzodiazepineprescriptions

0

10

20

30

40

50

60

70

80

1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Ave

rage

dai

ly d

osa

ge o

f p

atie

nts

in M

MEs

Prescriber decile based on volume of opioid prescriptions

California 2012: Average Daily Dosage of Patients by Prescriber Decile

Based on Volume of Opioid Prescriptions

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 20 40 60 80 100 120 140

Pe

rce

nta

ge o

f p

resc

rib

er

pat

ien

ts w

ith

MP

E

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 patientswith MPE

Log. (Percentage ofpatients with MPE)

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

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

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

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)

Contact Information

Peter Kreiner, Ph.D.

Principal Investigator

PDMP Center of Excellence

Brandeis University

781-736-3945

pkreiner@brandeis.edu

www.pdmpexcellence.org

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

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.

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

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

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 activepatients to promote appropriate prescribing practices and prevent fraud

– Detect inappropriate prescribing (or dispensing) practices

– (Occasionally) research

• Now in all states but Missouri

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

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

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

Candidates for MetricsProviders 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

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

Example of metric distribution

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20 25 30 35

Nu

mb

er

of

Pro

vid

ers

Average daily rate that providers write opioids for >100 MMEs

Average daily rate that NC providers write opioid prescriptions for >100 MMEs

43

Example: Distribution tail

47

34

810

11

24

21

20 0

1 10

10 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

Nu

mb

er

of

Pro

vid

ers

Average daily rate that providers write opioids for >100 MMEs

Average daily rate that NC providers write opioid prescriptions for >100 MMEs

44

SO WHAT? So Nothing, until Each Metric is ValidatedInitial 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

46%77%

n=57

n=31

0

10

20

30

40

50

60

Highest 1% of this metric Highest 1% of this metric + 1% ofprescribers

Co-prescribed benzodiazepines + opioids >100MMEs

Providers who did not prescribeopioids to a decedent

Providers who prescribed opioids to adecedent

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% ofprescribers

Temporally overlapping prescriptions

Providers who did not prescribe opioidsto a decedent

Providers who prescribed opioids to adecedent

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% ofprescribers

Prescriptions for opioids >100MMEs

Providers who did not prescribe opioidsto a decedent

Providers who prescribed opioids to adecedent

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% ofprescribers

Prescriptions for any opioids

Providers who did not prescribe opioidsto a decedent

Providers who prescribed opioids to adecedent

30%

32%

n=271

n=167

0

50

100

150

200

250

300

Highest 1% of this metric Highest 1% of this metric + 1% ofprescribers

Prescriptions for any benzodiazepine

Providers who did not prescribe opioidsto a decedent

Providers who prescribed opioids to adecedent

50

Key Metrics Validated by this Mechanism

Metric labelHighest 1% of metric (Proportion, %)

Highest 1% of metric + 1% of prescribers (Proportion, %)

Co-prescribedbenzodiazepines + 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

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

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

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

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