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Centers for Disease Control and Prevention National Center for Injury Prevention and Control

Centers for Disease Control and Prevention National Center for Injury Prevention and Control

Prescription Behavior Surveillance Using PDMP Data

Prescription Behavior Surveillance Using PDMP Data

Len Paulozzi, MD, MPHNCIPC, CDC

From Epi to PolicyAtlanta, GA April 22-23, 2013

2

Outline of the Talk

• Background on Prescription Drug Monitoring Programs (PDMPs or PMPs)

• PDMPs as surveillance tools• Standard PDMP data elements• Descriptive measures to characterize

populations• Risk measures for populations or individuals

4

Information on Your State PDMP

• States page at the Alliance of States with PMPs website:– http://www.pmpalliance.org/content/state-profiles

• PMP parent agency, frequency of data collection, schedules monitored, access restrictions, and other information

5

PDMP Data Use on the Federal Level

• Support from CDC’s Injury Center and FDA, • Bureau of Justice Assistance funded the PMP

Center of Excellence at Brandeis University • COE established Prescription Behavior

Surveillance System• Independent, de-identified, longitudinal PDMP

database with data from selected states

6

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.

7

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 inadequate for most PDMPs

See: Lee et al, eds., Principles and Practice of Public Health Surveillance, 3rd edition, 2010.

8

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

9

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

10

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

11

Descriptive Measures: Denominators

• Person, e.g., rx per 100,000 people (most common)

• Patient, e.g., rx per 100,000 patients• Prescriber, e.g., mean daily dose/prescriber• Pharmacy, e.g., rx/pharmacy

Time period is specified: e.g., in 2012, in past quarter

12

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

13

Rates of Prescribed Opioids per 100 People by sex, Tennessee,2007–2011

Rate

per

10

0

pop

ula

tion

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

14

Kentucky All Schedule Prescription Electronic Reporting System (KASPER)

http://chfs.ky.gov/NR/rdonlyres/A4FA61AC-4399-40CD-9E02-13899AFB73E7/0/KASPERQuarterlyTrendReportQ12012.pdf

15

Risk Measures: Daily Dose for Opioids

• Converted to morphine milligram equivalents (MME)

• Handling of overlapping prescriptions: add?• 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 or median dose

• SAS coding to do MME conversions available from CDC

16

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

2007‒2011

Nu

mb

er

of

Pati

en

ts

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

South Beach - Tottenville

Willowbrook

Stapleton - St. George

Port Richmond

-- Staten Island --

Rockaway

Southeast Queens

Jamaica

Southwest Queens

Fresh Meadows

Ridgewood - Forest Hills

Bayside - Little Neck

Flushing - Clearview

West Queens

Long Island City - Astoria

-- Brooklyn --

Lower Manhattan

Union Square - Lower East Side

Greenwich Village - Soho

Gramercy Park - Murray Hill

Chelsea - Clinton

Upper East Side

Upper West Side

East Harlem

Central Harlem - Morningside Heights

Washington Heights - Inwood

-- Manhattan --

Williamsburg - Bushwick

Coney Island - Sheepshead Bay

Bensonhurst - Bay Ridge

Canarsie - Flatlands

East Flatbush - Flatbush

Borough Park

Sunset Park

East New York

Bedford Stuyvesant - Crown Heights

Downtown - Heights - Slope

Greenpoint

-- Queens --

Hunts Point - Mott Haven

High Bridge - Morrisania

Crotona - Tremont

Pelham - Throgs Neck

Fordham - Bronx Park

Northeast Bronx

Kingsbridge - Riverdale

-- Bronx --

Rate per 1000

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Age Adjusted Rate per 1000 residents

6 - 13 14 - 1616 - 20 21 - 3737 - 128

High Dose Oxycodone Prescriptions per Neighborhood, NYC, 2010

17

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Risk Measures: Prescription Drug Combinations

• Additive sedating effects• Opioids overlapping with benzodiazepines or

muscle relaxants or both• Regional specialties:

– Florida: oxycodone and alprazolam (a benzodiazepine)

– Texas: “Holy Trinity” or “Houston cocktail” of hydrocodone, alprazolam, and carisoprodol (a muscle relaxant)

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Risk Measures: Distance

• Large distances– Patient residence to prescriber office compared with

nearest prescriber– Patient residence to pharmacy compared with

nearest pharmacy– Out-of-state prescription filled in-state– Non state-resident using state pharmacy

• Requires availability of patient residence, linkage to data on prescriber and pharmacy address, and GIS mapping.

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Risk Measures: Multiples

• Multiple prescriptions from same class• Multiple classes of scheduled drugs• Multiple prescribers or pharmacies or both

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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.

22

Multiple prescriber and pharmacy patients by drug type by age group, 2008

<25 25-40 41-64 65+0

0.5

1

1.5

2

2.5

3

OpioidBenzodiazepineDiuretic

Age Group

Rate

per

1,0

00

pati

en

ts

Patients with 2+ overlapping rx by different prescribers dispensed in 3+ pharmacies over 18 months. IMS LRx database. Cepeda, Drug Safety 2012

23

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.

24

Effect of eliminating triplicate prescription forms in Jan. 2005 on

multiple provider episodes involving short-acting oxycodone, CA

Gilson. J Pain 2012;13:103

Senate bill 151 goes into effect

MM.YY

25

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

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

% of Prescribers

44

92

% of CS Prescriptions

6019

21

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

27

Percent of CS II-V prescriptions prescribed by prescriber decile by year, KY, 2009

20090

10

20

30

40

50

60

70

80

90

100

8.4

17.9

64.3

Top decile

9th

8th

7th

6th

5th

4th

3rd

2nd

Lowest decile

Perc

en

t of

pre

scri

pti

on

s

Blumenschein, K, et al. Independent Evaluation of the Impact and Effectiveness of the Kentucky All Schedule Prescription Electronic Reporting Program (KASPER) Institute for Pharmaceutical Outcomes and Policy , Univ of Kentucky, 2010

28

Distribution of opioid prescribers by volume of patients and multiple-

provider patients, 2008 (IMS LRx data)

1-17 18-35 36-65 66-149 150-227

228-457

458-915

916-1831

1832-2936

0

10

20

30

40

50

60

Pct Prescribers Pct MPPs

Number of patients per prescriber

Perc

en

t

5% prescribers, 42% multiple-provider patients

Source: Cepeda et al. J Opioid Manage 2012;8 (5):285-291

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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.

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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.

Len Paulozzi, MD, MPH

lpaulozzi@cdc.gov

The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry.

Thank You