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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

47

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

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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!

Contacts

Chris Baumgartner, PMP [email protected]

Neal Traven, PMP [email protected]

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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