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

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

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

Why so Important?

5

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

2005 2006 2007 2008 2009 2010 20110

50

100

150

200

250

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

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130 152 131 137 158 173 220

2.02.3

2.02.0

2.42.6

3.3

Number of opioid analgesic overdose deaths

Age-adjusted opioid analgesic rates per 100,000 New Yorkers

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Oregon Drug Related TrendsCounts and rates/100,00

72000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

50

100

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200

250

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450

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2

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Unintentional drug poisoning deaths by year and drug type, Oregon 2000-2011

CocaineHeroinPrescription opioidsRate of drug poisoning

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Methadone Death Rates Parallel Methadone Sales

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1000

2000

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1999 2000 2001 2002 2003 2004 2005 2006 Rat

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Note: grams sold on left axis, death rate on right axis

Retail distribution of methadone in Oregon and poisoning mortality rate asociated with methadone in Oregon, 1999-2006

Grams sold/100,000 population

Methadone death rate

Sources: US Dept. of Justice, Drug Enforcement Administration, Of f ice of Diversion Control, Automation of Reports and Consolidated Orders System (ARCOS); Oregon Center for Health Statistics mortality data f iles. Includes unintetnional and undetermined intent deaths.

Oregon Public Health Division- Injury Prevention Program

10

More Drug Overdose Deaths than Motor Vehicle Crash Deaths

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

Unintentional drug overdose and motor vehicle death rates, Oregon 2000-2011

Unintentional drug overdoseMotor vehicle crash

Source: Oregon Vital Records

Year

Oregon Hospitalization Rate/10,000 residents

11

15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+02468

101214161820

Prescrip. opioids no methadoneMethadoneBenzodiazepinesAntiepileptic, sedative-hypnotic, an-tidepressantPsychostimulats

Age Group

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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.0

2.0

4.0

6.0

8.0

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Psychostimulants with abuse potentialOther,unspecified drugsHeroinPrescrip opioidsBenzodiazepinesMethadoneAlcoholAntidepressants,etc,psychotropic drugs NEC

Year

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What are General Characteristics and Data Elements?

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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: http://www.simeoneassociates.com/simeone3.pdf

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/

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.

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.

Model Act 2010 RevisionData 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

Model Act 2010 RevisionData 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 Identification number

Dispenser Identification number

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

Questions that can be Answered

20

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

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

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

Examples of Data

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Number of Patients Receiving Opioid Dosages > 100 MME/day, Tennessee, 2007‒2011

Num

ber o

f Pati

ents

Baumblatt J. Prescription Opioid Use and Opioid-Related Overdose Death TN, 2009–2010, CDC EIS Tuesday Morning Seminar, 1/8/2013

Opioid Prescriptions Filled by Staten Islanders Are More Frequently High Dose

2008 2009 2010 2011 20120%

5%

10%

15%

20%

25%

% of opioid prescriptions filled that are high dose, by borough of residence

Staten IslandBronxManhattanBrooklynQueens

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Schedule II opioids + hydrocodone, New York State Prescription Drug Monitoring Program

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

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Number of people/1,000 residents receiving an opioid and benzodiazepineOct 1, 2011 to March 31, 2012

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Number of people/10,000 residents using 4 or more prescribers and 4 or more pharmacies

Oct 1, 2011 to March 31, 2012

Rates of Unintentional Poisoning Mirrors Rates of Dispensed Prescriptions

Source: http://www.nyc.gov/html/doh/downloads/pdf/epi/epi-data-brief.pdf

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.

Measures of “Shopping” or “Multiple Provider Episodes”

Author (year) Drug No. of Prescribers

No. of Pharmacies

Rx Overlap

TimePeriod

Hall (2008) Any CS 5+ NA NA 1 yr

Peirce (2012) Any CS 4+NA

NA4+

NANA

6 mo6 mo

Ohio DOH (2010)

Opioid Avg of 5+ NA NA Over 3 yrs

Gilson (2010, 2012)

“Same medication”

2+ 2+ NA 30 d

Katz (2010) Any CSII 4+ 4+ NA 1 yr

Cepeda (2012) Opioid 2+ 3+ 1+ day 18 mo

BJA criteria CSII-IV 5+ 5+ NA 3 mo.

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

15% of prescribers write 82% of opioid analgesic prescriptions

Prescribers Prescriptions0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

49%

2%

36%

15%

14%

51%

1%

31%

Very Frequent Prescribers530-10,185 RX/year

Frequent Pre-scribers50-529 RX/year

Occasional Prescribers4-49 RX/year

Rare Prescribers1-3 RX/year

Prescribing frequency

Prescriptions filled by NYC residents, 2010

15%

82%

Per

cen

t

Source: New York State Department of Health, Bureau of Narcotic Enforcement, Prescription Drug Monitoring Program, 2008-2010 34

Distribution of CS II-IV prescriptions to prescribers, Oregon, 1/12 to 9/12

% of Prescribers

4 4

92

% of CS Prescriptions

6019

21

Oregon Health Authority. Prescription Drug Dispensing in Oregon, October 1, 2011 – March 31, 2012

Examples of Outreach and Evaluation

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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 pharmaciesSource: Oregon Prescription Drug Monitoring Program Evaluation

• 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

NYC Opioid Treatment Guidelines

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.