rx16 pdmp tues_1230_1_small_2kreiner_3baumgartner_4traven
TRANSCRIPT
PDMPs as Prevention ToolsPresenters:• Tina Farales, Department of Justice Administrator, Prescription Drug
Monitoring Program, California Department of Justice• Peter Kreiner, PhD, Senior Scientist, Brandeis University• Chris Baumgartner, Drug Systems Director, Washington State
Department of Health• Neal D. Traven, PhD, Epidemiologist, Prescription Monitoring Program,
Washington State Department of Health
PDMP Track
Moderator: John L. Eadie, Coordinator, Public Health and Prescription Drug Monitoring Program Project, National Emerging Threat Initiative, National HIDTA Assistance Center, and Member, Rx and Heroin Summit National Advisory Board
Disclosures
Chris Baumgartner; Tina Farales; Peter Kreiner, PhD; Neal D. Traven, PhD; and John L. Eadie have disclosed no relevant, real, or apparent personal or professional financial relationships with proprietary entities that produce healthcare 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:– John J. Dreyzehner, MD, MPH, FACOEM – Ownership interest:
Starfish Health (spouse)– Robert DuPont – Employment: Bensinger, DuPont &
Associates-Prescription Drug Research Center
Learning Objectives
1. Explain how state and county public health officials use de-identified PDMP data to coordinate opioid abuse prevention and mitigation efforts.
2. Identify challenges of using PDMP data for public health purposes.
3. Describe the Washington State model for providing PDMP data to local jurisdictions to inform their resource allocation and policy decisions.
4. Provide accurate and appropriate counsel as part of the treatment team.
De-Duplicated/De-Identified Data
PDMPs as Prevention Tools
Mike Small has disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.
Peter Kreiner has disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.
PDMPs as Prevention ToolsDe-Duplicated/De-Identified Data
Learning Objectives:
Identify challenges of using PDMP data for public health purposes
Explain how state and county public health officers use de-identified PDMP data to coordinate opioid abuse prevention and mitigation efforts.
California Health and Safety Code section § 11165. (a)
To assist health care practitioners in their efforts to ensure appropriate prescribing, ordering, administering, furnishing, and dispensing of controlled substances, law enforcement and regulatory agencies in their efforts to control the diversion and resultant abuse of Schedule II, Schedule III, and Schedule IV controlled substances, and for statistical analysis, education, and research, the Department of Justice shall . . . maintain the Controlled Substance Utilization Review and Evaluation System (CURES)…
The prescription drug epidemic is predominantly a public health Problem. Public Health program design, implementation and success measurement is typically data and research driven.
PDMP data can and should assist the public health sector with the means to devise data driven mitigation strategies and the ability to measure the success of those efforts.
Support the Public Health Sector
The clinical community requires a much more informative data Presentation than CURES 1.0’s simple provisioning of a basic 12-month PAR.
Today’s technology can provide a better “eye” on prescribers’ patients; and is capable of providing both proactive and reactive reporting of patient prescription activity.
Technology is also capable of denoting treatment exclusivity compacts, and providing prescribers an ability to communicate securely across health care plans.
Enhance Informational Delivery
The Public Can and Should Know
The PDMPs store the most informative data regarding the currentpublic health crisis.
The public debate should not be deprived of the vast, telling datahoused by the PDMP.
Analytics
An analytics engine, however expensive, is essential for the delivery of optimal PDMP information.
De-Duplication
PDMP patient data lacks positive identifiers.
Name:Mike Small, Michael Small, Michael J. Small, Mikey Small, Mike Smalls
DOB:06/19/1953, 06/19/1935, 06/19/1963
Address:2101 Columbus Avenue, Sacramento, CA 958142101 Columbus Street, Sacramento, CA 958141201 Columbus Boulevard, San Diego, CA 95828
De-Duplication
Name and DOB and Zip(5) OR Name and Street Address and City
Mike Small Michael J. Small04/19/196304/19/19632101 Columbus Ave 2100 Columbia WaySacramento, CA 95814 Sacramento, CA 95814
Mikey Small04/19/1963
1201 Columbus Boulevard
San Diego, CA 92111
Michael Small Mike Smalls
04/19/193604/19/19632101 Columbus Avenue 2101 Columbus Ave.Sacramento, CA 95814 Sacramento, CA 95814
One Mike SmallEntity
De-Duplication
Every day approximately 145K new Rx records are added to the CURES 2.0 data base. With this new data, the analytics engine must re-resolve patient, prescriber and dispenser entities across the 1TB database every night in order to produce daily CURES 2.0 Patient safety messaging alerts.
The de-duplicated data also contributes to the quarterly and annual systematic production of a statewide and 58 county de-identified data sets for use by public health officers and researchers.
De-Identified Data
Anonymized Patient IDAnonymized Prescriber IDAnonymized Pharmacy IDPatient Birth YearPatient GenderPatient Zip CodePatient CountyPatient StatePrescriber Zip CodePrescriber CountyPrescriber StatePharmacy Zip CodePharmacy CountyPharmacy State
Product NameNDCDrug FormStrengthQuantityDays SupplyDate FilledRefill NumberPayment CodePrescriber SpecialtyPrescriber Board Certification Indicator
• Personally identifying information redacted.
• Anonymized patient IDs maintained to be consistent from report to report.
• Generated quarterly and annually for each county and the entire state.
De-Identified Data Normalization
With PDMPs in 49 states and all territories, it is important to normalize PDMP de-identified data sets for national level research and analysis.
Examples: CURES California County Data Shared with State and County Departments of Public Health
• Opioid prescribing rates (minus buprenorphine formulations thought to be associated with MAT)
• Average opioid dosage/Percent of residents with high (> 100 MME) average daily dosage
• Concurrent opioid and benzodiazepine prescriptions• Change in opioid prescribing rates, 2010 – 2013• Change in average opioid dosage, 2010 – 2013• Change in number of waivered physicians, 2010 -
2013
California Opioid Prescribing Rates per 1,000 Residents, by County, 2013
Opioid prescriptions per 1,000 population386.2 - 568.7568.7 - 678.3678.3 - 961.4961.4 - 1163.81163.8 - 1767.1
California: Average Opioid Dosage per 1,000 Residentsin 2013, by County
Dosage in MMEs306.2 - 599599 - 745.9745.9 - 1201.41201.4 - 1721.91721.9 - 2732.7
Resident
California: Percent of Opioid Patients Receiving > 100 MMEDuring a 30-Day Period in 2013, by County
Percent with > 100 MME3.7 - 8.18.1 - 9.89.8 - 15.215.2 - 23.223.2 - 41.1
Residents per 1,000
Number of Residents per 1,000
For at Least 30 Days During 2013, by County
California: Patients with Concurrent Opioidand Benzodiazepine Prescriptions, Per 1,000 Residents, by County, 2013
Concurrent prescription rate per 1,0003.5 - 88 - 11.711.7 - 16.616.6 - 2525 - 41
Residents per 1,000 with Bothby County, 2013
California: Change in Opioid Prescribing Rates,2010 to 2013, by County
Change in opioid prescribing rates< -3 Std. Dev.-3 - -2 Std. Dev.-2 - -1 Std. Dev.-1 - 0 Std. Dev.Mean0 - 1 Std. Dev.1 - 2 Std. Dev.
California: Change in Average Opioid Dosage Rate,2010 to 2013, by County
Dosage change 2010 to 2013< -3 Std. Dev.-3 - -2 Std. Dev.-2 - -1 Std. Dev.-1 - 0 Std. Dev.Mean0 - 1 Std. Dev.1 - 2 Std. Dev.2 - 3 Std. Dev.
California: Change in Number of Waivered Physicians,2010 to 2013, by County
Change in waivered physicians
-2 - -1 Std. Dev.-1 - 0 Std. Dev.Mean0 - 1 Std. Dev.1 - 2 Std. Dev.2 - 3 Std. Dev.
Observations
• Several northern counties with relatively small population were highest in rates of risk indicators (e.g., Del Norte, Lassen, Plumas, Tehama, Trinity), suggesting need for treatment and prevention
• Two of these (Plumas and Trinity) also had high percent increases in average MMEs per resident, 2010 – 2013, and low percent increases in number of physicians waivered to prescribe buprenorphine for medically-assisted treatment over the same period
Thank You!
PDMP Track: Linking and Mapping PDMP Data
Chris Baumgartner, WA State Dept. of HealthNeal Traven, WA State Dept. of Health
Disclosure Statement
• Chris Baumgartner and Neal Traven have disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.
Learning Objectives
1. Explain how state and county public health officials use de-identified PDMP data to coordinate opioid abuse prevention and mitigation efforts.
2. Identify challenges of using PDMP data for public health purposes.
3. Describe the Washington State model for providing PDMP data to local jurisdictions to inform their resource allocation and policy decisions.
4. Provide accurate and appropriate counsel as part of the treatment team.
Unintentional Prescription Opioid Overdose Deaths Washington 1995-2014
Source: Washington State Department of Health, Death Certificates
Unintentional Opioid Overdose Deaths Washington 1995-2014
Source: Washington State Department of Health, Death Certificates
WA State Unintentional Poisonings Workgroup (UPWG)
• Began quarterly meetings in June 2008• Representatives from public & private organizations:
• State/local health agencies, tribal authorities, insurers, law enforcement, substance abuse prevention/treatment, poison control, health professional associations, academic institutions, etc…
• Developed short-term actions• Increase provider and public education• Identify methods to reduce diversion through emergency departments• Increase surveillance• Support evaluation of practice guidelines for providers treating chronic,
non-cancer pain • Support prescription monitoring program
2016 Washington State Interagency Opioid Working Plan
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Goal 1: Prevent opioid misuse and abuse
• Improve prescribing practices
Goal 2: Treat opioid dependence
• Expand access to treatment
Goal 3: Prevent deaths from overdose
• Distribute naloxone to people who use heroin
Goal 4: Use data to monitor and evaluate
• Optimize and expand data sources
Opioid Plan - Goal 4 Strategies1. Improve PDMP functionality to document and
summarize patient and prescriber patterns to inform clinical decision making
2. Utilize the PDMP for public health surveillance and evaluation
3. Continue and enhance efforts to monitor opioid use and opioid-related morbidity and mortality
4. Monitor progress towards goals and strategies and evaluate the effectiveness of our interventions
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County Profiles Project• Provide information – counts, rates, maps, analyses – to
Local Health Jurisdictions (LHJs), for use in building their programmatic solutions
• Time trends in prescription drug useo Which drugs are commonly prescribed?o How frequently are they used?o In combination with other Controlled Substances?
• Geographic patterns of drug useo Apply online mapping toolso “Overdose and At-Risk Behaviors”o Identify “treatment deserts”
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Our InspirationOregon PDMP County Reports• Approximately 20 tables
o Age-group counts and rateso Specific drugs or drug classes
• Little analysiso No comparisons between countieso No time trendso No graphics or mapso Brief, generic discussion
• One-time effort?o County reports not published for
2013, 2014, 2015
Using this as our takeoff point…
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Question: • What kinds of information will be most valuable to Local Health
Jurisdictions in developing programs regarding Controlled Substances?
Answer:• We aren’t really sure, so let’s ask them!
Action:• Invited all LHJs to join Advisory Workgroup, to collaborate with
the PMP in designing a report framework that will contain the most useful information.
LHJ Advisory Workgroup (I)
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Seven county-level LHJs volunteered to participate in shaping the profile reports
Department of Health convened teleconferences, which discussed:• Cross-referencing LHJ wishlists to available PMP
data fields• Useful counts, groupings, summaries selected• Decision to adjust, where appropriate, by age
group and gender
LHJ Advisory Workgroup (II)
LHJ Advisory Group counties
Clallam Snohomish
Grant
Spokane
Klickitat
Thurston
Clark
Table 3. Top 10 Controlled Substances by Number of County Residents Receiving Such Medications
Table 5. Unique Recipient Count and Usage Rate for Most Common Opioid, by Age-Sex Group
Table 13. Unique Recipient Count and Usage Rate for All Benzodiazepines and for Most Common Benzodiazepine, by Source of Payment
Table 19. Unique Recipient Count and Usage Rate for Opioid and Benzodiazepine Combination, by Age-Sex Group
Figure 3. Time Trends in the Proportion of Patients Exhibiting At-risk Behavior Among Opioid Users, in County and Statewide
Proposed Profile Content: Examples
So … where are we now on the County Profiles project?
We ran into a few problems and issues in the
PMP dataset
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PMP Data Issues (I)• Database size, Security
o Highly confidential information Analysis on non-networked computer Encryption with BitLocker
o 45.0 million prescription records as of 07/20/2015 Add almost 1 million records per month
o Processing power Dedicated SQL server Analytic workstation with lots of RAM
• Fully-identified Datao Prescribers (>130K), Dispensers (~3,300) – DEA #, Addresso Recipients (>5.2M, or is it really 4.1M??) – Name, Address, DOB o Create alternate identifiers for use by external researchers
Maintain crosswalks between full and alternate identifiers
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PMP Data Issues (II)• Clustering and Linking to Individual Recipients
o Tradeoffs in Under- or Over- clustering Under- = Overestimate Number of Recipients Over- = Overestimate Number of High-Risk Recipients
o Improve accuracy of clustering Machine learning Better clustering algorithms
• Data cleaning and editingo Non-human recipients (Species Code?)o Malformed or unknown identifiers (DEA, NDC, Zip Code)o Data entry and/or upload errors
Really? 11.9 billion doses of tramadol? Correct street, city, Zip, county … but state code is blank State code defaults to WA, so we see things like:
Atlanta, 30318, Fulton, WALouisville, 40206, Jefferson, WA
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PMP Data Issues (III)• Reference Databases
o DEA Numbers Available at no charge to State Agencies Real-time snapshot, possibly retrospective views
o NDC Codes Obtain from FDA’s database, very frequently updated Linking Packaging and Product tables Morphine Equivalent Dose reference
o NPI Prescriber specialty
o Zip Code Frequent redrawing, addition of new ones Use 3-digit to identity state What to do about non-existent Zip codes?
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All Controlled Substances
Opioids Benzodiazepines Stimulants Sedatives0
400
800
1,200
1,600
2,000
CY 2012 CY 2013 CY 2014
Pres
crip
tions
per
1,0
00 R
esid
ents
Prescriptions per 1,000 Residents, 2012-2014Washington State, by Class of Controlled Substance
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Prescriptions per 1,000 Population:All Controlled Substances, 2014
2,050-2,800 1,800-2,050 1,650-1,800 1,450-1,650 700-1,450
Whatcom
Skagit
Clallam
San Juan
Island
Jefferson
Grays Harbor
Snohomish
Mason
KingKitsap
Pierce
Thurston
Pacific Lewis
Wahkiakum Cowlitz
Clark
Skamania
Douglas
Chelan
Whitman
Okanogan
Walla Walla Asotin
Spokane
Pend OreilleFerry
Stevens
Kittitas
Yakima
Grant
Klickitat
Lincoln
Adams
Benton
Garfield
Columbia
Franklin
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1,330-1,700 1,060-
1,330 925-1,060 850-925 400-850
Whatcom
Skagit
Clallam
San Juan
Island
Jefferson
Grays Harbor
Snohomish
Mason
KingKitsap
Pierce
Thurston
Pacific Lewis
Wahkiakum Cowlitz
Clark
Skamania
Douglas
Chelan
Whitman
Okanogan
Walla Walla Asotin
Spokane
Pend OreilleFerry
Stevens
Kittitas
Yakima
Grant
Klickitat
Lincoln
Adams
Benton
Garfield
Columbia
Franklin
Prescriptions per 1,000 Population:Pain Relievers (Opioids), 2014
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375-500 340-375 317-340 265-317 140-265
Whatcom
Skagit
Clallam
San Juan
Island
Jefferson
Grays Harbor
Snohomish
Mason
KingKitsap
Pierce
Thurston
Pacific Lewis
Wahkiakum Cowlitz
Clark
Skamania
Douglas
Chelan
Whitman
Okanogan
Walla Walla Asotin
Spokane
Pend OreilleFerry
Stevens
Kittitas
Yakima
Grant
Klickitat
Lincoln
Adams
Benton
Garfield
Columbia
Franklin
Prescriptions per 1,000 Population:Tranquilizers (Benzodiazepines), 2014
49
225-300 198-225 165-198 150-165
80-150
Whatcom
Skagit
Clallam
San Juan
Island
Jefferson
Grays Harbor
Snohomish
Mason
KingKitsap
Pierce
Thurston
Pacific Lewis
Wahkiakum Cowlitz
Clark
Skamania
Douglas
Chelan
Whitman
Okanogan
Walla Walla Asotin
Spokane
Pend OreilleFerry
Stevens
Kittitas
Yakima
Grant
Klickitat
Lincoln
Adams
Benton
Garfield
Columbia
Franklin
Prescriptions per 1,000 Population:Stimulants, 2014
50
165-315 145-165 132-145 119-132
65-119
Whatcom
Skagit
Clallam
San Juan
Island
Jefferson
Grays Harbor
Snohomish
Mason
KingKitsap
Pierce
Thurston
Pacific Lewis
Wahkiakum Cowlitz
Clark
Skamania
Douglas
Chelan
Whitman
Okanogan
Walla Walla Asotin
Spokane
Pend OreilleFerry
Stevens
Kittitas
Yakima
Grant
Klickitat
Lincoln
Adams
Benton
Garfield
Columbia
Franklin
Prescriptions per 1,000 Population:Sedatives, 2014
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Drug Name N of Tablets/Capsules
Prescriptions per 1,000 State Residents
Hydrocodone 2,690,470 386Oxycodone 1,779,532 255Zolpidem 737,864 106
Alprazolam 600,700 86Lorazepam 587,326 84
Dextroamphetamine/Amphetamine 547,771 79Clonazepam 494,936 71
Codeine 458,487 66Methylphenidate 440,009 63
Morphine 312,270 45
Ten Most Frequently Prescribed Drugs, 2014:Statewide, Tablets and Capsules only
Population estimate = 6,968,170WA Office of Financial Management, Population Unit
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Clallam Clark Garfield Snohomish
Hydrocodone 472 Hydrocodone 386 Hydrocodone 903 Hydrocodone 392
Oxycodone 449 Oxycodone 258 Morphine 191 Oxycodone 329
Codeine 74 Codeine 64 Oxycodone 189 Codeine 67
Methadone 74 Morphine 56 Codeine 113 Morphine 50
Morphine 67 Tramadol 31 Tramadol 84 Buprenorphine 41
Five Most Frequently Prescribed Opioids, 2014:Selected Counties, Prescriptions per 1,000 Population
Population estimates:Clallam 72,500Clark 442,800Garfield 2,240Snohomish 741,000
WA Office of Financial Management, Population Unit
Since we started the County Profiles project…
• Greatly increased attention has been paid to opioids – nationally, statewide, and locallyo Frequent reports in newspapers, TV newso Locally produced documentarieso Frontline on PBS, reported from King and Kitsap Counties
• Developing the state’s Interagency Opioid Working Plano PMP database now seen as a vital data source for public health
efforts at surveillance, monitoring, and evaluationo As part of the Working Plan, the County Profiles project will
provide information on trends in opioid prescribing and useo Dissemination of PMP reports, including the Profiles project,
beyond Local Health Jurisdictions
And as we look ahead…• We believe we are close to resolving the pitfalls and problems
we have encountered • Documentation is being written so that the scripts and
programs that emerged from our deep dive into the PMP data will be maintained and, when necessary, updated
• Going back to the raw datasets obtained from our vendor, we will build “clean” data files that will be placed on our secure SQL server
• The one-time code written thus far will be converted to scripts and macros so as to “automate” production of reports and analyses
• GIS views of the PMP data and other layers will continue to be developed and studied
• And maybe we’ll finally be able to catch our breath!
PDMPs as Prevention ToolsPresenters:• Tina Farales, Department of Justice Administrator, Prescription Drug
Monitoring Program, California Department of Justice• Peter Kreiner, PhD, Senior Scientist, Brandeis University• Chris Baumgartner, Drug Systems Director, Washington State
Department of Health• Neal D. Traven, PhD, Epidemiologist, Prescription Monitoring Program,
Washington State Department of Health
PDMP Track
Moderator: John L. Eadie, Coordinator, Public Health and Prescription Drug Monitoring Program Project, National Emerging Threat Initiative, National HIDTA Assistance Center, and Member, Rx and Heroin Summit National Advisory Board