Insights from the Field on State Policy, Insurer and Health Systems Levers to

Curb Prescription Drug OverdosePresenters:• Rachel Seymour, PhD, Senior Scientist, Carolinas Medical Center• Joseph Hsu, MD, Orthopaedic Trauma Surgeon, Carolinas Medical Center• Janette Baird, PhD, Associate Professor of Emergency Medicine, Brown University• Gerald Cochran, PhD, Assistant Professor, School of Social Work, University of Pittsburgh• Daniel Hartung, PharmD, MPH, Associate Professor, Oregon State University College of

Pharmacy• G. Caleb Alexander, MD, MS, Associate Professor of Epidemiology and Medicine, Johns

Hopkins Bloomberg School of Public Health

Advocacy Track

Moderator: Jan Losby, PhD, Behavioral Scientist, Division of Unintentional Injury Prevention, CDC

Disclosure Statements• Janette Baird, PhD; Gerald Cochran, PhD; Daniel Hartung, PharmD, MPH;

Jan Losby, PhD; and Rachel Seymour, PhD, have disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.

• Joseph R. Hsu, MD, wishes to disclose that he received a consulting fee from Acumed and received a speaker fee from Smith & Nephew for a speakers bureau. He will present this content in a fair and balanced manner.

• Caleb Alexander, MD, MS, wishes to disclose that he is Chair of the FDA’s Peripheral and Central Nervous System Advisory Committee, and serves as a paid consultant to IMS Health and serves on an IMS Health scientific advisory board. He will present this content in a fair and balanced manner.

Disclosure Statements

• 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. Evaluate the impact of state Medicaid-related policies and state pill mill laws on prescribing behavior, inappropriate use of opioids and patient outcomes.

2. Explain interventions that promote judicious opioid prescribing and reduce opioid-related abuse, misuse and overdose.

3. Identify effective evidence-based practices for state health departments, insurers, pharmacy benefit managers and health care systems.

4. Provide accurate and appropriate counsel as part of the treatment team.

Prescription Reporting with Immediate Medication Utilization Mapping (PRIMUM)

Rachel Seymour, PhD and Joseph R. Hsu, MD

Carolinas Healthcare System Charlotte, NC

Funded by the Centers for Disease Control and Prevention (CE14-004 Award Number CE002520)

PRIMUM

• Needed a solution to the prescription drug epidemic that would be integrated into prescriber workflow with minimal disruption– Right information at the right time– 15 minute visits (AMA RVU standard)

• Subspecialty• Global period follow-up• Urgent care

• Multidisciplinary team of clinicians, researchers, Information Services, and other key stakeholders across the System

PRIMUM

Goals of our System-wide intervention:1) To identify patients at high risk for misuse, abuse, and

diversion of prescription opioids and benzodiazepines. 2) To provide critical information to the prescriber at the

point of care in order to inform clinical decision-making

Intervention: Alert System• Prescriber selects

controlled substance• EMR searches patient

chart for defined risk factors for abuse/misuse/diversion

• Provides prescriber with alert

• Prescriber can continue or discontinue script

Development and Testing

• Utilized our Expert Panel and peer-reviewed literature to identify objective risk factors that are searchable in the EMR

• “Silent Surveillance” phase to properly tune the alert and provide baseline data

• Timing of the “alert” chosen to maximize impact on prescribing behavior

Alert Triggers• Current prescription with >50% remaining expected• 2+ visits to ED or Urgent Care with onsite treatment

with opioids (not including visits that led to admission) within previous 30 days

• 3+ prescriptions for opioids and/or benzodiazepines within previous 30 days

• Previous presentation for opiate or benzo overdose• A positive BAC or toxicology screen for cocaine or

marijuana

Baseline Data• 81,841 prescriptions • 2,640 prescriptions/day• 1.33 prescriptions per prescribing encounter

Characteristic N % of Prescribing EncountersAge

<1818-64>65

1,55245,57114,624

2.5173.8023.68

Facility Type

ED/Urgent CareInpatient DischargeOutpatientOther

18,2674,65638,310514

29.587.5462.040.83

Class of Drug

OpiateBenzodiazepineBoth

45,16514,2682,314

73.1523.113.75

Baseline DataCharacteristic N % of Prescribing EncountersNumber of Criteria Met

012345

48,16410,5172,654369430

78.0017.034.300.600.070.00

Criteria Met

Prescription with 50% remaining2+ visits with onsite administration3+ prescriptions Positive BAC or tox screenPrevious presentation for overdose

8,3581,2082,8734,165500

13.541.964.656.750.81

Next Steps

• Evaluate the impact of PRIMUM on prescribing practices, including subgroup analysis

• Inpatient order intervention• Dissemination to other sites/EMRs• Collaboration with NCCSRS (NC PDMP)

Janette Baird, PhDAssociate Professor, Brown University/Rhode Island Hospital

Funded by the Centers for Disease Control and Prevention (CE14-004 Award Number CE002520)

Improving opioid prescription safety in trauma patients

Safer Opioid Prescribing?

• Trauma patients• Ubiquitous use of prescription opioids to

manage pain• Co-prescription of sedative medications• Substance use issues among trauma patients

Unintentional Opioid Overdose Risk Factors

• Discharge medication MME ≥ 100mg/24 hour period

• Co prescribing benzodiazepine at discharge• Home medication risk• Comorbidity• Alcohol/drug screen positive• Past opioid overdose

Risk Factor

Level 1 Trauma CenterJuly 1-Septmember 30th 2014 N = 161

Co-morbidityN (% 95%CI)

Respiratory Disease 9 (4.7%; 1.06 , 7.08)

Renal Disease 2 (1.0%; 0.00 , 2.41)

Cardiac Disease 6 (3.1%; 0.55 , 5.56)

Liver Disease 12 (6.3%; 2.85 , 9.75)

Alcohol/Substance Abuse (last 12 months) 8 (4.2%; 1.36 , 7.04)

Medication

Sedative Home Risk 41 (21.5%; 15.7 , 27.3)

Sedative Rx Risk 30 (15.7%; 10.5 , 20.9)

Both Home and Rx Risk 10 (5.2%; 2.05 , 8.35)

Benzodiazepine Use 18 (9.4%; 5.26 , 13.5)

Methadone/Suboxone Use 7 (3.7%; 1.02 , 6.38)

Opioid Dose > 100 (Mev) 61 (37.9%; 31.3 , 43.5)

Home Opioid Use 18 (9.4%; 5.26 , 13.5)

Practice Protocol Development

• Develop electronic medical record system alert of patient risk at discharge

• Provide patient educational literature• Prescribe naloxone when indicated• Train physicians, nurse practitioners, and

nurses• Best Practice Alerts- Naloxone• Education on safer use and disposal

Decision Logic for BPA

MEV Table

This is a hard stop for prescribers

Prescriber’s Notification

Nursing BPA

Nursing Education

Preliminary Data•BPA activated September 2015•Pre BPA no naloxone prescribed•Since BPA September to mid December 2015:•MEV tables has been responded to 451 times; •800 BPA educations been responded to •85 prescription for naloxone co-prescribed with an opioid

Team Members• Michael Mello MD, MPH• Charles Adams, MD• Traci Green, MS, PhD• Jonathan Howland, MPH, PhD• Kristen Bunnell, PharmD• Ann George, RN• Melinda Hodne, APRN-CNP• Craig Mallioux, RN, BSN• Jheraldines Gonzalez, BS

Claims-based Risk Factors for Opioid Medication Overdose in

Pennsylvania Medicaid

Gerald Cochran, PhDSchool of Social Work

University of Pittsburgh

28

Acknowledgements

• Thanks to project team and collaborators:– Julie Donohue– Adam Gordon– Walid Gellad– Joyce Chang– Jenny Lo-Ciganic– Ping Zheng– Carroline Lobo– Winfred Frazier

• This project is funded by a cooperative agreement from the Centers for Disease Control (Oct 2014-Sept 2016)

29

Background and PurposeBackground• Up to one-quarter of Medicaid

enrollees who use opioid medications for more than 90 days are involved in misuse– PA Medicaid program is one of the

largest in the nation, with enrollee demographics similar to national figures

– Among adult, non-dual, non-cancer treatment enrollees in PA Medicaid; 1/3 filled opioid medication in 2012

• PA is among top states for overdose in the US

Purpose• Identify claims-based

measures that health systems can employ to identify individuals at-risk of overdose who can be targeted for restrictions on opioid prescribing, dispensing, or referral to treatment

30

Methods• Data sources

– PA Medicaid claims and encounters from 2007-2012• Design

– Retrospective cohort study• Analyses

– Descriptive statistics– Tests of proportional differences– Multivariate model

31

Dependent variable

• Overdose (Unick et al. 2013) – Opioid medication poisoning codes (ICD9 965.00,

965.02, 965.09, E850.1; E.850.2)– Dichotomous (yes/no) outcome

32

Opioid use concepts and measurement

• How to define abuse and misuse in administrative data?

• Conducted systematic review

• Identified most valid measures

33

Key independent variablesOpioid indicator Measurement Categories

Categorical morphine milligram equivalents (MME; Bohnert et al. 2011)

-Total morphine equivalent within opioid treatment episode-Days supplied

<20 MME per day20-<50 MME per day50-<100 MME per day≥100 MME per day

Misuse (Sullivan et al. 2010) Code per 6 mo. and merge 1 yr.-# Prescribers (≤2=0, 3-4=1, ≥5=2)-# Pharmacies (≤ 2=0, 3-4=1, ≥5=2)-# LA/SA days supplied (≤185=0, 186-240=1, >240=2)

No misuse (0–1)Possible misuse (2–4)Probable misuse (≥5)Insufficient data*

Abuse (White et al., 2009) -Opioid use disorder (ICD9 304.0, 304.00, 304.01, 304.02, 304.03, 304.7, 304.70, 304.71, 304.72, 304.73, 305.5, 305.50, 305.51, 305.52, 305.53) -Opioid medication fill(Use disorder occurs >7 days before overdose event in analyses)

No abuseYes abuse

* Some enrollees missing prescribing provider ID in pharmacy claims

Cohort Selection: Opioid use episodes

Incident user cohort• Index event is the first opioid prescription fill • Baseline period=6 months prior to opioid fill during which:

– Enrolled in Medicaid – No opioid fills– No opioid use disorder diagnoses– No overdose

• Exclusions – Non-PA residents, dual eligible, cancer diagnosis, non-qualifying opioid,

<18 or >64 years, long term care >90 days, and/or hospice • Opioid use episodes: gap ≥6 months in opioid use ends episode

• 52.2% of enrollees with episodes have >1 episode• Average length of enrollment within episodes 481 days

34

35

Results: Baseline demographics of study sample person level

Patient-level Cohort Characteristics 2007 to 2012 (N=376,073)Characteristics n (%)

Age, years a 31.4 (11.5)Female 26,1073 (69.4)Race

White 206,922 (55.0)Black 108,927 (29.0)Hispanic 455,71 (12.1)Other 14,653 (3.9)

Type of eligibilityGeneral Assistance 44,328 (11.8)Supplemental Security Income 95,455 (25.4)Temporary Assistance to Needy Families 22,1859 (59.0)Waiver 14,431 (3.8)

Type of health planManaged care 29,0620 (77.3)Fee-for-service 85,453 (22.7)

Urban living area 32,0631 (85.3)

a Mean (standard deviation)

Results: Overdose Rate Based on Number of Opioid Treatment Episodes per Person (N=376,073) a

1 2 3 4 5 6 >=70

0.2

0.4

0.6

0.8

1

1.2

0.18

0.38

0.69 0.70

0.90

1.02

0.91

Number of episodes per person

Ove

rdos

e Ra

te (%

)

a n of patients (denominator) for each number of opioid treatment episodes: 1 episode n=179,724; 2 episodes n=109,746; 3 episodes n=53,701; 4 episodes n=23,271; 5 episodes n=7,743; 6 episodes n=1,669; >=7 episodes n=219. The numerator for the rate is the number of overdose events.

37

Results: Cross tabulation of overdose events, abuse, misuse, and MME

a Counted for all eligible episodes. ᵇ 147,057 (24.9%) records are missing in total due to provider ID missing in the claim files.

Potential Risk Factors Total, N (%)Overdose

pYes, n (%) No, n (%)Totala 589,228 1,369 (0.2) 587,859 (99.8) Abuse Yes 22,446 330 (1.5) 22,116 (98.5) <.001 No 566,782 1,039 (0.2) 565,743 (99.8)Misuse Probable misuse 2,923 53 (1.8) 2,870 (98.2)

<.001 Possible misuse 21,246 187 (0.9) 21,059 (99.1) Missing misuse b 147,057 259 (0.2) 146,798 (99.8) No misuse 418,002 870 (0.2) 417,132 (99.8)MME/day <20 92,369 215 (0.2) 92,154 (99.8)

<.001 20-49.9 395,676 878 (0.2) 394,798 (99.8) 50-99.9 85,341 200 (0.2) 85,141 (99.8) 100+ 15,842 76 (0.5) 15,766 (99.5)

38

Results: Predictors of overdose (1/2)Opioid use predictors OR (95% CI)* p

Abuse 2.80 (2.40-3.26) <.001

Misuse (reference=no misuse) Missing misuse 0.89 (0.78-1.03) .12 Possible misuse 2.03 (1.71-2.40) <.001 Probable misuse 2.70 (2.01-3.64) <.001

MME/day (reference=<20 MME/day) 20-49.9 0.92 (0.79-1.07) .30 50-99.9 1.01 (0.83-1.23) .89 100+ 1.50 (1.15-1.97) .003

* Odds ratios from multi-level mixed model adjusted for age, sex, race/ethnic, plan, eligibility category, alcohol, other drug dependence, mental health, medical comorbidities, pain diagnoses, benzo and muscle relaxant use, medication assisted treatment, and length of episode.

39

Results: Predictors of overdose (2/2)Comorbidity/healthcare predictors OR (95% CI) p

Alcohol abuse/dependence 1.47 (1.21-1.79) <.001Drug abuse/dependence 1.88 (1.60-2.21) <.001Adjustment disorders 1.04 (0.77-1.40) .80Anxiety disorders 1.30 (1.12-1.51) <.001Mood disorders 1.33 (1.15-1.52) <.001Personality disorders 1.12 (0.74-1.70) .59Miscellaneous mental health disorders 0.77 (0.58-1.03) .08Back pain 1.16 (1.01-1.34) .04Neck pain 0.95 (0.76-1.19) .66HIV/AIDS 1.10 (0.67-1.79) .71Arthritis/joint pain 0.98 (0.85-1.13) .74Headache/migraine pain 0.93 (0.70-1.23) .61ED visit 1.35 (1.21-1.52) <.001Methadone maintenance 1.82 (1.44-2.30) <.001Buprenorphine for opioid use disorders 1.76 (1.22-2.52) .002Any benzodiazepine use 1.74 (1.52-1.99) <.001Any muscle relaxant use 1.37 (1.14-1.63) <.001Elixhauser comorbidity index 1.02 (0.98-1.06) .42

*Same multivariable analysis as previous slide

*

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Conclusion and Implications• Claims-based indicators strongest

associations with overdose• Concomitant use of other

medications with abuse liability and health comorbidities also had strong associations with overdose

• Rate of overdose increased as number of episodes increased

• Health systems (e.g., payers, monitoring programs, health care organizations) could engage in active surveillance in order to:

– More closely manage prescribing and

filling– Provide wrap-around for comorbidities

– Prevent patients on and off opioids

– Target lock-in programs to opioids

Utility of Prescription Drug Monitoring Program (PDMP) Data for Policy Analysis

in a State Medicaid Program

OSU: Dan Hartung (PI), Luke Middleton, Sharia AhmedOHSU: John McConnell, Hyunjee Kim, Rick Deyo

U of OK: Shellie KeastOregon Public Health Division: Josh Van Otterloo

CDC U01CE002500-02

Pharm

acy Benefit P

olicy

HealthOutcomes

Filled

Inappropriate Rx

Appropriate Rx

Written

Prescriber

Rx Misuse

Unobserved Utilization

Inappropriate Rx

Appropriate RxPatient Patient

Oregon Medicaid Data

Oregon PDMP Data CashOr

Third Party Transaction

Or?

Opioid-related overdose and poisoning

1. Describe fill and patient characteristics associated with OPR fills without a corresponding pharmacy claim

2. Evaluate the impact of missing paid claims on OPR policy evaluations

– Oregon Medicaid high dose opioid limit policy (April – June 2012)

Using linked Oregon Medicaid PDMP data, ascertain the frequency, characteristics, and implications of Medicaid patients who obtain opioid pain relievers (OPRs) without a paid claim

Oregon Prescription Drug Monitoring Program (PDMP)

• Web-based database containing information on controlled substances II-IV– Operational 2011– Housed in Public Health Division

• Nearly all eligible pharmacies reporting by 2012– Waiver for institutional pharmacies (inpatient, LTC,

community-based facilities etc)• Probabilistic Match to Medicaid (the Link King)

– Name– DOB– Gender– Zip

Study Sample Inclusion/Exclusion

• Inclusion– Continuous non-interrupted Medicaid enrollment

from 2012-2013– One OPR fill matching a pharmacy claim in both years

• Exclusion– Dual Medicare enrollment– Residence in long-term or community based (group

home) care facility– Evidence of third party liability on file with Medicaid

Medicaid Lives Matched To PDMP >= 1 opioid

N=135,656

N=81,381

Medicaid Lives (2012-2013)>=1 Rx

N=390,701

PDMP Fills

N=555,103

N=33,592

Not matchedN=255,045

Dual Medicare, LTC, TPLNon-continuously enrolled

N=54,275

<2 matched fillsN=47,789

Patients Exclusions

Prescription Matching Process*1. Patient, NDC, Quantity, Date2. For those remaining unmatched after

step 1, -Patient, NDC, Quantity, Date (+/- 7 days)

3. For those remaining unmatched after step 2-If 2 unmatched fills occurred within 3 days for same patient, NDC, we collapsed (summed quantity) and matched

4. For those remaining unmatched after step 3-Patient, NDC, +/- 7 days, regardless of quantity

N=555,103 Addl Matches

Claims Matched

%

Step 1 474,603 85.5%Step 2 4317 478,920 86.3%

Step 3 366 479,286 86.3%

Step 4 864 480,150 86.5%

*Exclude buprenorphine fills

13.5% of OPR fills could not be matched to pharmacy claim

Unmatched Fills By DrugGeneric Name

Matched Fills Unmatched Fills Total Fills % Unmatched

HYDROCODONE/ACETAMINOPHEN 228065 33400 261465 12.8%OXYCODONE HCL 98820 13974 112794 12.4%OXYCODONE HCL/ACETAMINOPHEN 72636 9936 82572 12.0%METHADONE HCL 28134 5879 34013 17.3%MORPHINE SULFATE 27645 4700 32345 14.5%ACETAMINOPHEN WITH CODEINE 8908 1110 10018 11.1%HYDROMORPHONE HCL 5979 2579 8558 30.1%FENTANYL 4250 957 5207 18.4%Other 5713 2418 8131 29.7%Total 480150 74953 555103 13.5%

Mean MEQ 1154 mg 12953 mg

Unmatched Fills By Day of Week

13.9% 13.5% 13.1% 13.1% 13.3% 13.9%

15.2%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Sun Mon Tue Wed Thu Fri Sat

Unmatched Fills By Misuse IndicatorIndicator Unmatched Matched Total % Unmatched

Overall 74953 480150 555103 13.5 %Opioid Overlap*

Yes 32043 172464 204507 15.7 %No 42910 307686 350596 12.2 %

Benzodiazepine Overlap*Yes 11634 62844 74478 15.6 %No 63319 417306 480625 13.2 %

Muscle relaxant/Sedative Overlap*Yes 4043 21949 25992 15.6 %No 70910 458201 529111 13.4 %

Carisoprodol Overlap*Yes 2079 8119 10198 20.4 %No 72874 472031 544905 13.4 %

Pharmacy ShoppingYes 7486 28380 35866 20.9 %No 67467 451770 519237 13.0 %

Prescriber ShoppingYes 12057 62804 74861 16.1 %No 62896 417346 480242 13.1 %

* fill within 7 days

OPR Intensity By Proportion of Fills Matched

*Assuming 30 day supply** fill within 7 days

All Matched Proportion of fills matched (Quartiles)Most Matched Least Matched

N=33,592 17938 3914 3913 3914 3913Average fill count (SD) 10.2 (11.7) 33.3 (17.4) 25.0 (17.5) 18.9 (17.5) 21.1 (19.4)Median fill count (IQR) 5.0 (3.0, 13.0) 28.0 (22.0, 41.0) 22.0 (11.0, 32.0) 12.0 (6.0, 26.0) 16.0 (6.0, 29.0)Average MEQ (SD) per day* 13.2 (26.7) 39.2 (52.4) 28.8 (44.2) 23.1 (42.1) 26.2 (44.6)Median MEQ (IQR) per day* 5.0 (3.3, 10.6) 22.1 (9.7, 47.7) 13.1 (5.5, 32.2) 8.2 (4.4, 23.2) 10.3 (4.9, 27.1)Fill count

<=5 9237 (51.5%) 0 (0%) 0 (0%) 923 (23.6%) 798 (20.4%)6-10 3483 (19.4%) 0 (0%) 879 (22.5%) 807 (20.6%) 684 (17.5%)11-20 2307 (12.9%) 814 (20.8%) 864 (22.1%) 787 (20.1%) 776 (19.8%)21-30 1915 (10.7%) 1474 (37.7%) 1120 (28.6%) 661 (16.9%) 784 (20%)31-40 419 (2.3%) 597 (15.3%) 457 (11.7%) 312 (8%) 358 (9.1%)>40 577 (3.2%) 1029 (26.3%) 593 (15.2%) 424 (10.8%) 513 (13.1%)

Concurrent MedicationsBenzodiazepine overlap** 3423 (19.1%) 1812 (46.3%) 1573 (40.2%) 1236 (31.6%) 1366 (34.9%)Carisoprodolol overlap** 258 (1.4%) 195 (5%) 184 (4.7%) 169 (4.3%) 226 (5.8%)

Patient Characteristics Proportion of Fills MatchedAll Matched Proportion of fills matched (Quartiles)

Most Matched Least Matched

N=33,592 17938 3914 3913 3914 3913Average % of Rx matched 100% 95.3% 88.8% 77.7% 49.8%Females 12488 (69.6%) 2642 (67.5%) 2682 (68.5%) 2764 (70.6%) 2699 (69%)Race

White 14275 (79.6%) 3375 (86.2%) 3315 (84.7%) 3306 (84.5%) 3314 (84.7%)Black 1206 (6.7%) 176 (4.5%) 143 (3.7%) 137 (3.5%) 84 (2.1%)Other/Unknown 2457 (13.7%) 363 (9.3%) 455 (11.6%) 471 (12%) 515 (13.2%)

Disability 4753 (26.5%) 1687 (43.1%) 1316 (33.6%) 1137 (29%) 1159 (29.6%)Age

<20 1778 (9.9%) 25 (0.6%) 79 (2%) 179 (4.6%) 155 (4%)20-29 3855 (21.5%) 375 (9.6%) 614 (15.7%) 791 (20.2%) 744 (19%)30-49 7447 (41.5%) 1791 (45.8%) 1883 (48.1%) 1811 (46.3%) 1807 (46.2%)50-64 4781 (26.7%) 1703 (43.5%) 1321 (33.8%) 1114 (28.5%) 1198 (30.6%)>64 77 (0.4%) 20 (0.5%) 16 (0.4%) 19 (0.5%) 9 (0.2%)

DiagnosesPain Conditions – Spinal disorders 10445 (58.2%) 3162 (80.8%) 3025 (77.3%) 2771 (70.8%) 2790 (71.3%)Pain Conditions – Musculoskeletal 12521 (69.8%) 3379 (86.3%) 3195 (81.7%) 2994 (76.5%) 3002 (76.7%)Pain Conditions – Headache 5988 (33.4%) 1596 (40.8%) 1647 (42.1%) 1594 (40.7%) 1501 (38.4%)Cancer 864 (4.8%) 300 (7.7%) 241 (6.2%) 206 (5.3%) 246 (6.3%)Serious mental illness 1479 (8.2%) 481 (12.3%) 460 (11.8%) 422 (10.8%) 369 (9.4%)Depression 7454 (41.6%) 2206 (56.4%) 2058 (52.6%) 1914 (48.9%) 1884 (48.1%)Substance use disorder 9980 (55.6%) 2684 (68.6%) 2707 (69.2%) 2514 (64.2%) 2419 (61.8%)

Effect of Data Source on Policy Effect

3.2 MED 17.5 MED

Conclusions

• Considerable proportion of PDMP fills cannot be matched to corresponding Medicaid claim

• Discrepancy variation by– Drug – Day of fills– Involvement in common indicators of misuse

• Patients with fill/claim discrepancies have more disability and more co-morbidity– Reflective of higher intensity of use

• Exclusive use of claims data may exaggerate opioid-related policies effects

Caleb Alexander, MD, MSAssociate Professor of Epidemiology and

Medicine, Johns Hopkins Bloomberg School of Public Health

Questions and Discussion

Insights from the Field on State Policy, Insurer and Health Systems Levers to

Curb Prescription Drug OverdosePresenters:• G. Caleb Alexander, MD, MS, Associate Professor of Epidemiology and Medicine, Johns

Hopkins Bloomberg School of Public Health• Janette Baird, PhD, Associate Professor of Emergency Medicine, Brown University• Gerald Cochran, PhD, Assistant Professor, School of Social Work, University of Pittsburgh• Daniel Hartung, PharmD, MPH, Associate Professor, Oregon State University College of

Pharmacy• Joseph Hsu, MD, Orthopaedic Trauma Surgeon, Carolinas Medical Center• Rachel Seymour, PhD, Senior Scientist, Carolinas Medical Center

Advocacy Track

Moderator: Jan Losby, PhD, Behavioral Scientist, Division of Unintentional Injury Prevention, CDC