Transcript Slide 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
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
4.0
3.3
200
3.0
2.6
Number
150
2.4
2.3
2.0
2.0
3.5
2.5
2.0
2.0
100
1.5
1.0
50
0.5
0
130
152
131
137
158
173
220
2005
2006
2007
2008
2009
2010
2011
Number of opioid analgesic overdose deaths
Age-adjusted opioid analgesic rates per 100,000 New Yorkers
Source: New York City Office of the Chief Medical Examiner &
New York City Department of Health and Mental Hygiene 2005-2011
0.0
Age-Adjusted Rate per 100,000
250
Oregon Drug Related Trends
Counts and rates/100,00
Unintentional drug poisoning deaths by year and drug type,
Oregon 2000-2011
450
12
400
10
Count
300
8
250
6
200
150
4
Unadjusted rate/100,000
350
Cocaine
Heroin
Prescription opioids
Rate of drug poisoning
100
2
50
0
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
7
Methadone Death Rates Parallel
Methadone Sales
Grams methadone sold per 100,000 persons
6000
4.5
4
Grams sold/100,000 population
5000
3.5
Methadone death rate
4000
3
2.5
3000
2
2000
1.5
1
1000
0.5
Oregon Public Health Division- Injury Prevention Program
0
0
1999
2000
2001
2002
2003
2004
2005
2006
Rate of methadone-associated poisoning deaths per
100,000 persons
Retail distribution of methadone in Oregon and poisoning mortality rate
asociated with methadone in Oregon, 1999-2006
Note: grams sold on left axis, death rate on right axis
Sources: US Dept. of Justice, Drug Enf orcement Administration, Of f ice of Diversion Control, Automation of Reports and
Consolidated Orders System (ARCOS); Oregon Center f or Health Statistics mortality data f iles. Includes unintetnional and
undetermined intent deaths.
9
More Drug Overdose Deaths than
Motor Vehicle Crash Deaths
Unintentional drug overdose and motor vehicle death rates,
Oregon 2000-2011
16.0
14.0
12.0
10.0
Unintentional drug overdose
8.0
Motor vehicle crash
6.0
4.0
2.0
0.0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Year
Source: Oregon Vital Records
10
Unadjusted rate/100,000
Oregon Hospitalization Rate/10,000 residents
20
18
16
14
12
10
8
6
4
2
0
Prescrip. opioids no methadone
Methadone
Benzodiazepines
Antiepileptic, sedative-hypnotic,
antidepressant
Psychostimulats
15-24
25-34
35-44
45-54
55-64
65-74
75-84
85+
Age Group
14.0
Unadjusted rate/100,000
12.0
Psychostimulants with abuse potential
10.0
Other,unspecified drugs
8.0
Heroin
Prescrip opioids
6.0
Benzodiazepines
4.0
Methadone
Alcohol
2.0
Antidepressants,etc,psychotropic drugs NEC
0.0
2000
2001
2002
2003
2004
2005
2006
Year
2007
2008
2009
2010
2011
11
What are General Characteristics
and Data Elements?
12
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 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
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
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
24
Number of Patients
Number of Patients Receiving Opioid Dosages > 100
MME/day, Tennessee, 2007‒2011
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
% of opioid prescriptions that are for > 100
morphine equivalent mgs of opioids
% of opioid prescriptions filled that are high dose, by borough of
residence
25%
20%
Staten Island
15%
Bronx
Manhattan
10%
Brooklyn
Queens
5%
0%
2008
2009
2010
2011
2012
Schedule II opioids + hydrocodone, New York State Prescription Drug Monitoring Program
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 benzodiazepine
Oct 1, 2011 to March 31, 2012
28
Number of people/10,000 residents using 4 or more prescribers and 4 or more
pharmacies
Oct 1, 2011 to March 31, 2012
29
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
Time
Period
Hall (2008)
Any CS
5+
NA
NA
1 yr
Peirce (2012)
Any CS
4+
NA
NA
4+
NA
NA
6 mo
6 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
Prescriptions filled by NYC residents, 2010
100%
90%
15%
1%
14%
80%
Percent
70%
36%
60%
50%
30%
49%
31%
Frequent
Prescribers
50-529 RX/year
Occasional
Prescribers
4-49 RX/year
40%
20%
Very Frequent
Prescribers
530-10,185 RX/year
82%
51%
Rare Prescribers
1-3 RX/year
10%
15%
0%
2%
Prescribers
Prescriptions
Prescribing frequency
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
% of CS Prescriptions
4 4
21
19
60
92
Oregon Health Authority. Prescription Drug Dispensing in Oregon, October 1, 2011 – March 31, 2012
Examples of Outreach and
Evaluation
36
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.
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
38
NYC Opioid Treatment Guidelines
• Avoid prescribing opioids for chronic non-cancer, nonend-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
References Cited
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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.