Approaches to Drug Overdose Prevention Analytical Tool (ADOPT ... Opioid... · patient review and...

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Final Contract Report

Approaches to Drug Overdose Prevention

Analytical Tool (ADOPT):

Evaluating Cost and Health Impacts of a

Medicaid Patient Review & Restriction

Program

Prepared for

Office of the Associate Director for Policy

Policy Research, Analysis, and Development Office

Centers for Disease Control and Prevention

Grant No: 1U58CD001370-01

Prepared by

Joy Melnikow, MD, MPH

Zhuo Yang, MS

Meghan Soulsby, MPH

Dominique Ritley, MPH

Kenneth Kizer, MD, MPH

University of California, Davis

Center for Healthcare Policy and Research

December 2012

The views in this report are those of the authors. No official endorsement by the Center for Disease Control and Prevention is

intended or should be inferred.

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Acknowledgments

The authors of this report greatly appreciate the contributions of our content expert, Dr. Barth

Wilsey, UC Davis, as well as CDC staff who gave of their time and expertise to assure correct

assumptions and clean data informed the micro-simulation model.

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Introduction

Many assert that effective policies aimed at preventing the increasing abuse of prescription

opioids among Medicaid beneficiaries could save tens of millions of dollars by substantially

reducing associated mortality, morbidity and associated healthcare costs. However, the lack of

cost-effectiveness data gives rise to the need for evidence-based investigations. This report

presents the design and findings of a micro-simulation model that evaluates how certain policies

might reduce the misuse/abuse of prescription opioids in the Medicaid population, thus reducing

associated, preventable health care costs and outcomes. Use of illegal opioids (e.g., heroin),

prescriber fraud, and opioid diversion fall outside the scope of this project.

We developed this model in response to a CDC request to examine the effectiveness of Medicaid

patient review and restriction programs (PRR), sometimes referred to as patient “lock-in”

programs. The Approaches to Drug Overdose Prevention Analytical Tool (ADOPT) is an

evidence-based tool to help inform policy decisions regarding prescription drug overdose

prevention policies. This micro-simulation model simulates the prescription opioid behavior of

an adult Medicaid enrollee cohort to explore the impact and the cost-effectiveness of such

programs. By applying various PRR policies to the simulated prescription opioid behavior, users

can assess the cost and health impacts of the policies. ADOPT supports interactive features that

allow users to customize the population demographics and policy details, and performs a "what-

if" analysis to project the outcomes of a specified policy within that population. Although

ADOPT has the potential to analyze and compare different approaches to opioid overdose/abuse

prevention (such as prescriber/patient education or monitoring strategies), the current version

focuses on the Medicaid patient review and restriction (PRR) program. The model was informed

by an analysis of a MarketScan® Medicaid dataset and a literature review.

Report Summary

This report is divided into three primary sections: Parts One and Two, the literature review and

MarketScan® data analysis; and Part Three, which contains information from the two previous

sections that are used to inform the design of the model. Each section is written to stand alone;

however, readers are encouraged to read the report in its entirety to understand the context

surrounding the model.

Part One: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and

Prevention Policies – A Review of the Literature

This literature review provides supporting material for the investigation into the health and

economic burden of prescription opioid misuse and abuse and state-level policies that may

reduce or eliminate these burdens, especially within the Medicaid population. This review

provides the necessary context to evaluate two state-level policies -- patient review and

restriction programs and prescription drug monitoring programs -- recommended by the White

House and CDC. Some data from this literature review are used to inform the cost-effectiveness

model developed for the CDC to examine the effectiveness of patient review and restriction

programs. The review includes a summary of the prevalence of prescription opioid misuse and

abuse, sources of opioids, and prescribing patterns at the state level and in the Medicaid

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population. It also includes studies about the health outcomes related to prescription opioid

misuse and abuse, the health care and societal costs attributable to misuse and abuse, and policy

options that may eliminate or curtail such misuse and abuse.

Part Two: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and

Prevention Policies – MarketScan® Data Analysis

Part Two presents an analysis of MarketScan® Medicaid data using the 90-day exposure window

and the episode-based regression models to estimate the relationship between the risk of

overdose and daily opioid dose. The results from this analysis describe the characteristics of the

patient population, their prescription opioid use, and their risk of overdose events. It also

characterizes the pharmacy shopping behavior among the long term opioid users and their rates

of overdose. These data points were used to calibrate the micro-simulation model presented in

Part Three.

Part Three: Evidence-Based Tools for Promoting Health Policy and Disease Prevention -

Prescription Opioid Overdose

Part Three introduces The Approaches to Drug Overdose Prevention Analytical Tool (ADOPT)

This is an Excel-based micro-simulation model that simulates the patterns of Medicaid enrollees’

prescription opioid use in order to evaluate associated health outcomes and costs under different

restriction policies.* It compares the counterfactual scenarios of implementing a prescription

drug overdose/abuse prevention policy versus the absence of such a policy, and evaluates the

cost and health impact of the policy. The model’s interactive features allow users to customize

the population demographics and policy details, and perform a "what-if" analysis to project the

outcomes of the specified policy within that population. Although ADOPT has the potential to

analyze and compare different approaches to drug overdose/abuse prevention (such as

prescriber/patient education or monitoring strategies), the current version focuses on the

Medicaid patient review and restriction (PRR) program.

Key Findings

Key findings from the MarketScan® data analysis:

Higher doses of opioids are associated with an increased risk of overdose in the

Medicaid population, whereas the type of opioid drug, after adjusting for dose and

other risk factors, shows little effect on overdose risk.

Medicaid opioid users who exhibited pharmacy shopping (>=4 pharmacies in any 90

days) have higher (1.8-fold) risk of overdose than those who did not, even after

adjusting for dose and other risk factors.

Medicaid opioid users who had overlapping prescriptions (same drug type with more

than 25% overlapping supply days) have about 3-fold increase in overdose risk.

Overlapping prescriptions could be a meaningful indicator for the PRR program to

identify high risk patients.

Key findings from the ADOPT model:

* The model was informed by an analysis of the MarketScan® Medicaid dataset and literature review; these analyses

are presented in Sections 1 and 2, respectively, of this report.

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ADOPT can resemble the individual patterns of opioid use in the Medicaid

population to a satisfactory extent, though some details, such as modeling of drug

type may need further revision and calibration.

Based on our exploratory analysis, the less selective PRR program criteria, show a

greater overall reduction in prescription opioid use and overdose prevention, however

they have a small effect on the average prescription reduction and overdose

prevention per program enrollee. Conversely, more selective criteria are less

effective but more efficient in targeting the high-risk users. These programs are more

likely to identify those actually misusing or abusing prescription opioids, but they

require a large population pool to justify the investment on this program.

More state-specific input values are needed to conduct more relevant analysis.

Report Assumptions and Limitations

This model relies on a number of assumptions including:

Outcome probabilities, patterns of prescription opioid use, and drug pricing derived from

MarketScan® data are generalizable to individual state Medicaid programs

The fixed PRR program cost is $300,000 annually, and the variable cost is $200 per

program enrollee (representing the additional labor and material expenditures that

increase as the program caseload increases)†

PRR enrollees remain enrolled in Medicaid and the PRR program for the duration of the

policy period

Enrollees consumed prescriptions “as prescribed” – does not consider opioid diversion

Overdose risk is based on acquisition of prescription opioids, not use of illegal opioids

Characteristics of a subsequent episode of opioid use are correlated with those of a

previous episode

All PRR program enrollees’ overlapping prescriptions (i.e., two prescriptions of the same

drug type, one of which had a supply for 5 days or longer, overlapped by 25% or more of

the days prescribed) are eliminated in the scenario of having the PRR program

All PRR program enrollees’ prescriptions that contribute to an aggregate daily dose more

than 80mg morphine equivalent will be reduced to an aggregate daily dose of 80mg

morphine equivalent in the scenario of having the PRR program

A complete list of assumptions can be found in Part 3 of this report.

Additionally, this model has several limitations including:

1. Geographic variation: Although MarketScan® data comes from multiple states (12

states in 2012), it may not be representative of the national data. It is possible that in

certain states, the Medicaid opioid users behave differently than the MarketScan®

population – in which case the analysis may not be accurate.

2. Baseline scenario: Under-estimated prevalence of opioid abuse/misuse: ADOPT uses

the MarketScan® Medicaid dataset to simulate the scenario of not having a PRR

† Cost estimations are based on estimates from Oklahoma and Washington state PRR programs, per content expert

discussions with the CDC; Jones, C.M., Roy, K. Email correspondence, August 2012

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program, then identifies the subjects who meet the program enrollment criteria and

calculates the health and financial impact if the PRR was established. However, it is

possible that some states already had a PRR program when the MarketScan® data were

collected, in which case the prevalence of opioid abuse or misuse (including drug

shopping) would be under-estimated. This may cause an undervaluation of a PRR

program in the analysis.

3. Prescriber information is imputed: Many PRR programs use the number of opioid

prescribers as an eligibility criterion; however, the MarketScan® data do not contain

prescriber information. Therefore the model uses the reported correlation between

numbers of pharmacies and prescribers from the Massachusetts’ PRR program database.

It is possible that this correlation may not reflect the experience of the MarketScan®

population.

4. Incomplete representation of PRR criteria: The current version of ADOPT can only

analyze some of the criteria that may be used in a PRR program, but in practice, PRR

programs often use other criteria such as emergency department use, number of office

visits, or history of substance abuse. If data become available, they could be incorporated

into the model.

5. Uncertainty in estimation of overdose risk: ADOPT uses the hazard ratios for opioid

overdose that are derived from the MarketScan® inpatient and outpatient datasets.

However, overdose rates may be higher than observed because patients may have expired

before entering the hospital. In addition, overdose events were identified by using the

diagnostic codes. Misclassification of diagnostic codes may cause under-estimation or

over-estimation of the overdose risk.

6. Uncertainty of PRR program costs: The model uses a fixed program cost of $300,000

annually and a variable cost of $200/program enrollee, however, these costs may not

reflect the actual cost incurred by states operating PRR programs and they do not include

the start-up costs for states newly implementing programs.

7. Assumptions about effects of PRR programs may be inaccurate: For example,

assumptions that these programs reduce dosage or overlapping prescriptions.

Despite these limitations, the ADOPT model demonstrates the potential to simulate individual

prescription consumption behavior with satisfactory similarity to real prescription consumption

behavior based on calibration with MarketScan® data. Using the current model structure and

interface, it is possible to add new functions if and when future data becomes available.

Ultimately, the strength of the ADOPT is its ability to be customized with state-specific data,

which will produce more timely, accurate, and relevant conclusions than those reached using the

MarketScan® data. Policy makers now have the opportunity to introduce valid, evidence-based

information into their decision making process about state-specific patient review and restriction

programs to ensure that the most cost-effective policies target those enrollees who will benefit

the most.





Program

Part 1

Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and


Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and


1.2

Table of Contents

INTRODUCTION ..................................................................................................................................... 1.5 Rationale ................................................................................................................................................ 1.5

METHODS ................................................................................................................................................ 1.7 Definitions.............................................................................................................................................. 1.8

BACKGROUND: PRESCRIPTION OPIOID MISUSE & ABUSE ....................................................... 1.11 Prevalence of Prescription Opioid Misuse, Abuse and Dependence ................................................... 1.11

State-Specific Prevalence Rates ....................................................................................................... 1.12 Medicaid-Specific Prevalence Rates ................................................................................................ 1.12

Sources of Misused and Abused Opioids ............................................................................................ 1.12 Doctor and Pharmacy Shopping .......................................................................................................... 1.13

Opioid Prescribing Patterns ..................................................................................................................... 1.19 State-Specific Opioid Prescribing Patterns ...................................................................................... 1.20 Medicaid-Specific Prescribing Patterns ........................................................................................... 1.21

Risk Factors Associated with Prescription Opioid Misuse and Abuse .................................................... 1.22 Sociodemographic Risk Factors ....................................................................................................... 1.22 Behavioral Risk Factors ................................................................................................................... 1.23 Pain Level & Comorbidities ............................................................................................................ 1.24 Mental Health ................................................................................................................................... 1.24 Opioid Dose and Supply .................................................................................................................. 1.25

OUTCOMES RELATED TO OPIOID MISUSE AND ABUSE ............................................................ 1.26 Health Care Utilization ........................................................................................................................ 1.26

Characteristics Associated with Prescription Opioid-Related Healthcare Utilization ..................... 1.27 Health Outcomes: Opioid-Related Comorbidities ............................................................................... 1.28 Health Outcomes: Opioid-Related Mortality ....................................................................................... 1.31

Characteristics Associated with Opioid-Related Mortality .............................................................. 1.31 State-Specific Opioid-Related Mortality ......................................................................................... 1.33 Opioid-Related Mortality: Opioid Type and Dosing Patterns ......................................................... 1.33 Opioid-Related Mortality and Doctor/Pharmacy Shopping ............................................................. 1.36

Outcomes: Health Care Costs .............................................................................................................. 1.39 Health Care Costs and Doctor Shopping ......................................................................................... 1.42 State-Specific Health Care Costs ..................................................................................................... 1.43

Outcomes: Societal Costs .................................................................................................................... 1.44 POLICY OPTIONS TO ELIMINATE OPIOID MISUSE & ABUSE .................................................... 1.46

Patient Review and Restriction Programs ............................................................................................ 1.47 Policy Effectiveness and Outcomes ................................................................................................. 1.48

Prescription Drug Monitoring Programs .............................................................................................. 1.51 Policy Effectiveness and Outcomes ................................................................................................. 1.53

SUMMARY ............................................................................................................................................. 1.57 APPENDIX .............................................................................................................................................. 1.58

Literature Review Sources ................................................................................................................... 1.58 Databases of Peer-Reviewed Literature ........................................................................................... 1.58 National Data Sources ...................................................................................................................... 1.58 Federally Maintained Sources of Grey Literature ............................................................................ 1.58 State Maintained Sources of Grey Literature ................................................................................... 1.58 Nonprofit Organizations .................................................................................................................. 1.58

Literature Review Search Terms.......................................................................................................... 1.59 BIBLIOGRAPHY .................................................................................................................................... 1.60



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List of Tables

Table 1-1. Selected Policies and Programs to Reduce Prescription Drug Misuse and Abuse ................... 1.6

Table 1-2: Defining Prescription Drug Use Patterns ................................................................................. 1.8

Table 1-3. Morphine Equivalent Dose Conversion Table ....................................................................... 1.10

Table 1-4. Prevalence of Nonmedical Prescription Opioid Use, 2002-2010 ........................................... 1.11

Table 1-5. Thresholds for Defining Doctor and Pharmacy Shopping ..................................................... 1.13

Table 1-6. Medicaid Beneficiaries Receiving Prescription Opioids from Multiple Prescribers, FY 2006

and 2007 ................................................................................................................................................... 1.14

Table 1-7. Medicare Beneficiaries Obtaining Prescription Opioids from Multiple Prescribers, 2008 .... 1.15

Table 1-8. Distribution of Patients by Number of Prescribers and Pharmacies, 2006 ............................. 1.17

Table 1-9. Distribution of Patients Using Multiple Prescribers and Pharmacies, 2006 ........................... 1.17

Table 1-10. Distribution of Patients Using Multiple Prescribers and Pharmacies, 2008 ......................... 1.18

Table 1-11. Prevalence of Nonmedical Prescription Opioid Use by Gender, 2002 & 2010.................... 1.22

Table 1-12. Prevalence of Nonmedical Opioid Use by Race/Ethnicity, 2010 ......................................... 1.23

Table 1-13. Prevalence of Nonmedical Opioid Use by Geographic Location, 2010 ............................... 1.23

Table 1-14. Odds of Opioid Misuse and Abuse by Average Daily Dose and Days Supply .................... 1.25

Table 1-15. ED Visits for Prescription Opioids Compared to Illicit Drugs, 2010 ................................... 1.26

Table 1-16. Opioid-Related ED Visits by Gender and Race/Ethnicity, 2010 .......................................... 1.27

Table 1-17. Prevalence of Select Comorbidities among Opioid Abusers compared to Nonabusers ....... 1.30

Table 1-18. Deaths Involving Prescription Opioids, 2000-2008 ............................................................. 1.31

Table 1-19. Prescription Opioid Overdose Death Rates by Selected Demographics, 2000-2008 ........... 1.32

Table 1-20. Opioid-Related Mortality by State ....................................................................................... 1.34

Table 1-21. Prescription Opioid Doctor Shopping among Medicaid Beneficiaries and Associated Costs,

2006-2007 ................................................................................................................................................ 1.43

Table 1-22. Prescription Opioid Doctor Shopping among Medicare Beneficiaries and Associated Costs,

2008 ......................................................................................................................................................... 1.43

Table 1-23. Annual Societal Burden of Prescription Opioid Abuse, 2001 & 2007 ................................. 1.44

Table 1-24. Societal Burden of Nonmedical Prescription Opioid use, 2006 ........................................... 1.45

Table 1-25. Policy Interventions to Reduce the Burden of Prescription Opioid Misuse ......................... 1.47

Table 1-26. The Presence of Prescription Drug Monitoring Programs, Overdose Mortality and Opioid

Consumption Rates .................................................................................................................................. 1.54

List of Figures

Figure 1-1. Relationship between Nonmedical Opioid Use and Misuse ................................................... 1.9

Figure 1-2. Schedule I-V Controlled Substances ....................................................................................... 1.9

Figure 1-3. Sources of Prescription Opioids, 2010 .................................................................................. 1.13



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Figure 1-4. Dual Doctor and Pharmacy Shoppers in West Virginia, 2005-2007 ..................................... 1.16

Figure 1-5. Type and Formulation of Prescription Opioids Dispensed to Doctor Shoppers Compared to

Non-Doctor Shoppers .............................................................................................................................. 1.18

Figure 1-6. Relationship between Opioid Dosage Level and Fatal/Non-Fatal Overdose Risk ................ 1.37

Figure 1-7. Percentage of Opioid Users and Overdoses, by Risk Group ................................................. 1.39

Figure 1-8. Average Annual Direct Health Care Costs* per Opioid Abuse Patient ................................ 1.41

Figure 1-9. State Lock-In Programs by Client Size, 2007 ....................................................................... 1.48



1.5

INTRODUCTION

This literature review provides supporting material for the CDC’s investigation into the health

and economic burden of prescription opioid misuse and abuse and state-level policies that may

reduce or eliminate these burdens, especially within the Medicaid population. The review

provides context necessary to reviewing two state-level policies- patient review and restriction

programs and prescription drug monitoring programs- recommended by the White House and

CDC by first summarizing the prevalence of prescription opioid misuse and abuse and its related

health outcomes, and second, by examining the health care and societal costs attributable to

prescription opioid misuse and abuse. Additionally, data from this literature review are used to

inform a cost-effectiveness model developed to examine the effectiveness of patient review and

restriction programs.

Rationale

Prescription drug misuse and abuse have been characterized as an epidemic in this country.1-5

From 2004-2009, emergency department (ED) visits attributable to prescription drug misuse and

abuse have been steadily increasing, whereas deaths due to illicit drugs have remained relatively

stable.6 In 2009, deaths due to prescription drug overdose accounted for 56% of the 37,004 total

drug overdose deaths. Deaths attributable to prescription opioids account for a considerable

proportion of both deaths due to prescription drugs (approximately 75%) and of total drug

overdose deaths (42%).7

Prescription opioids are designed to alleviate moderate to severe acute pain, chronic non-cancer

pain (such as chronic back pain, osteoarthritis, etc), chronic pain related to cancer, and pain at the

end of life.8 However, peer-reviewed literature has shown that commonly prescribed opioids,

including oxycodone, hydrocodone and methadone, are frequently misused. These medications

are contributing to increases in healthcare utilization related to prescription opioids,9-12

increased

death rates,1,2,13

and increased healthcare14-18

and societal19-21

costs.

In 2006 testimony before the Congressional Subcommittee on Criminal Justice, Drug Policy, and

Human Resources, Dr. Nora Volkow, the Director of the National Institute of Drug Abuse,

named five contributing factors to the increase in prescription drug abuse:22

1. Significant increases in the number of prescriptions

2. Significant increases in drug availability

3. Aggressive marketing by the pharmaceutical industry

4. The proliferation of illegal internet pharmacies that dispense these medications without

proper prescriptions and surveillance*

5. Greater social acceptability of medicating a growing number of conditions

In order to combat the increase in prescription drug misuse and abuse, particularly with

prescription opioids, the White House Administration’s National Drug Control Strategy and the

Center for Disease Control (CDC) have recommended the implementation of state-level policies

* This testimony came out before the Ryan Haight Online Pharmacy Consumer Protection Act went into effect in

April 2009. This Act amended the 1970 Controlled Substances Act to prohibit the delivery, distribution, and/or

dispensing of controlled substances via the Internet without a prescription from a physician who examined the

patient in person.23,24



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focusing on the contributing factors outlined by Dr. Volkow (see Table 1-1). The

Administration’s recommendations include action in four major areas to reduce prescription drug

abuse: education, monitoring, proper disposal, and enforcement.25

The CDC similarly

recommends monitoring and enforcement, as well as access to effective substance abuse

treatment programs.26

Table 1-1. Selected Policies and Programs to Reduce Prescription Drug Misuse and Abuse White House CDC

Education

Educate Healthcare Providers

o Responsible prescribing & disposal

Educate Parents, Youths & Patients

o Conduct public education/media campaign on

appropriate use, storage, and disposal

Require drug manufactures to develop educational

materials through the Opioid Risk Evaluation and

Mitigation Strategy (REMS)

Encourage research on patterns of abuse,

development of abuse-deterrent drug formulations ,

treatments for pain without potential for abuse

Tracking and Monitoring

Develop Prescription Drug Monitoring Programs

(PDMP’s)

o Work with states to continue developing and

enhancing PDMPs

o Develop incentives for healthcare programs and

providers to use PDMPs when prescribing

Evaluate Patient Review and Restriction

Policies/Programs (PRR)

o Evaluate programs requiring high utilizers to use

only one doctor/pharmacy (patient review and

restriction)

Evaluate usefulness of Drug Abuse Warning

Network (DAWN) data

Implement Prescription Drug Monitoring

Databases (PDMPs)

o Focus on patients at high risk (painkiller dosage;

number of prescriptions for controlled

substances; number of prescribers) and

prescribers with inappropriate prescribing

patterns (large doses/numbers of controlled

substances; large proportion of doctor shoppers

among their patients)

o Integrate PDMP information into health care by

linking PDMPs with electronic health record

systems

Implement Patient Review and Restriction (PRR)

o State Medicaid and workers’ compensation

programs should implement PRR programs to

monitor inappropriate use of controlled

prescription drugs

o Require patients using multiple prescribers

and/or pharmacies (without medical justification)

to use a single prescriber and/or pharmacy for

their controlled prescription drugs

Enforcement/Regulation

Increase training and education for law

enforcement and prosecutors

Enforce action against clinics and physicians not

following safe prescribing practices (i.e., pain

clinics, etc)

Write and disseminate a Model Pain Clinic

Regulation Law

Increase investigations of prescription drug

trafficking at the Federal, state, and local levels

Enforce regulatory action against prescribers who

do not follow accepted medical guidelines for safe

prescribing of controlled substances

Enact, enforce and evaluate state laws to prevent

doctor & pharmacy shopping, “pill mill” operation,

and other methods of misuse, abuse and diversion.

Other Policies/Programs



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White House CDC

Proper Medication Disposal

o Increase public awareness and provide education

on safe and effective drug return and disposal

o Engage private sector to support community-

based medication disposal programs

Increase access to effective substance abuse

treatment programs

Source: Executive Office of the President of the United States, 2011; CDC, November 2011

This review contains four main sections: (1) Methods, (2) Background on Prescription Opioid

Misuse and Abuse, (3) Outcomes Related to Prescription Opioid Misuse and Abuse, and (4)

Policy Options to Eliminate Prescription Opioid Misuse and Abuse. The Background section

contains sub-sections on the prevalence of misuse and abuse, sources of misused prescription

opioids, prescribing patterns, and risk factors. The Outcomes section contains sub-sections

discussing the impact of prescription opioid misuse and abuse on healthcare utilization, co-

morbidities, mortality, healthcare costs, and societal costs. The Policy section provides an

overview of policy options related to eliminating misuse and abuse, and focuses on two specific

policies – patient review and restriction programs and prescription drug monitoring programs –

and their effectiveness. All three sections include discussions specific to states and the Medicaid

populations when available. Data from this literature review were used to inform a cost-

effectiveness model developed to examine the effectiveness of patient review and restriction

programs.

METHODS

We conducted a literature review for the prevalence of prescription opioid misuse and abuse in

the United States, its impact on health and economic outcomes, and policies and programs that

reduce the burden of misuse and abuse. The literature search was limited to studies published in

English from January 2000 to present, with the exception of studies of state prescription drug

monitoring programs and patient review and restriction programs, which date back to 1985. We

reviewed literature from peer-reviewed journals, national data sources and surveys, grey

literature including state- and federally-maintained websites, and nonprofit organizations that

collect data and publish information about prescription opioid misuse and abuse. We also

reviewed literature suggested by two nationally recognized pain management experts, as well as

a content expert from the CDC’s National Center for Injury Prevention and Control.

The search criteria used by the project analyst and medical librarian included prescription opioid

misuse, abuse, and related outcomes; where available, we limited the scope to persons ages 12

years and older living in the United States. Only studies focused on patients with chronic, non-

cancer pain were included. When possible, our review focused on individuals misusing

prescription opioids for which they had a prescription (see definition of “misuse” below).

However, we included some relevant studies that examined the more broad definition of

“nonmedical use” (see definition below) because the largest nationally representative survey

examining drug use patterns, the National Survey on Drug Use and Health (NSDUH) uses this

definition, and many studies included in this literature review are based on data from the

NSDUH. Data on outcomes specifically related to opioid misuse and nonmedical use are scarce,

so the report also includes studies of prescription opioid abuse and dependence. Since the data

from this literature review are used to inform a cost-effectiveness model developed to examine

the effectiveness of patient review and restriction programs, which can be created by states under



1.8

Medicaid federal regulations, we focused on studies that included state or federal Medicaid

populations.

The literature search included randomized controlled trials (RCTs), non-randomized studies

(cohort studies, case-control studies, etc), narrative reviews, and reports. We reviewed more than

300 studies and reports and our three content experts reviewed and supplemented the initial

bibliography. For additional details about the methods used in this literature review, please refer

to Appendix I.

Definitions

Prescription Drug Use Patterns. As displayed below in Table 1-2, researchers and clinicians use

numerous definitions to explain patterns of prescription opioid use.27-30

Table 1-2: Defining Prescription Drug Use Patterns Definition Source

Misuse Incorrect use of medication by patients for which they had a prescription,

who may use a drug for a purpose other than that for which it was

prescribed, take too little or too much of a drug, take it too often, or take it

for too long

Center for Substance

Abuse Treatment

(CSAT), 2006

Nonmedical

Use

Use without a prescription belonging to the respondent or use that occurred

only for the experience or feeling the drug caused

National Survey on

Drug Use and

Health (NSDUH)

Use of prescription drugs that were not prescribed by a medical professional

(i.e., obtained illegally) or use for the experience or feeling a drug causes

CSAT, 2006

Patients who took a higher dose than prescribed or recommended dose of

their own medication, patients who took a pharmaceutical prescribed for

another person, malicious poisoning of the patient by another individual, and

documented substance abuse involving pharmaceuticals.

Drug Abuse

Warning Network

(DAWN)

Methodology

Report, 2009

Abuse A maladaptive pattern of substance use, leading to clinically significant

impairment or distress as manifested by one or more behaviorally based

criteria

CSAT, 2006

A pattern of maladaptive substance use that is associated with recurrent and

significant adverse consequences. A diagnosis of substance abuse requires

meeting at least one of the following criteria: 1) failure to fulfill obligations

at school/home/work; 2) use in situations that are physically hazardous; 3)

legal problems; and/or 4) social or interpersonal problems.

DSM-IV-TR

Dependence Physiological dependence is increasing tolerance for a drug, withdrawal

signs and symptoms when a drug is discontinued, or the continued use of a

substance to avoid withdrawal.

CSAT, 2006

A compulsive pattern of substance use characterized by a loss of control

over substance use and continued use despite the significant substance-

related problems. A diagnosis of dependence requires meeting three or more

of the following: 1) tolerance; 2) withdrawal; 3) taking the substance in

greater amounts of over a longer period of time than intended; 4)

unsuccessful attempts to cut back use; 5) spending excessive time procuring,

using, or recovering from the effects of the drug; 5) forgoing important

activities in order to use the drug; and 6) continued use of the drug despite

evidence that it is causing serious physical and/or psychological problems.

DSM-IV-TR

As previously mentioned, when possible, our review will focus on prescription opioid “misuse,”

which occurs when individuals misuse prescription opioids for which they had a prescription. In



1.9

contrast, “nonmedical use” is a more general description that encompasses “misuse,” but also

accounts for misuse of prescription opioids without a prescription belonging to the individual

(Error! Reference source not found.).

Figure 1-1. Relationship between Nonmedical Opioid Use and Misuse

Controlled Substances and Scheduled Drugs. As part of the Controlled Substances Act, the Drug

Enforcement Agency (DEA) classifies certain drugs as “controlled substances” and places these

drugs into five “schedules” based on (1) whether the substance has a currently accepted medical

use in the United States and (2) the drug’s potential for abuse and dependence. Currently,

Schedule I drugs do not have any accepted medical use and are what we commonly refer to as

illicit drugs, whereas Schedule II-V include mainly prescription drugs, which have the potential

for abuse and/or dependence.31

This report focuses primarily on Schedule II and III prescription

opioids (Table 1-3).

Figure 1-2. Schedule I-V Controlled Substances

Source: United States Department of Justice, Drug Enforcement Administration, Office of Diversion Control.

Controlled Substance Schedules http://www.deadiversion.usdoj.gov/21cfr/cfr/2108cfrt.htm

Morphine Equivalent Dose. Milligrams morphine equivalent (MME), morphine equivalent dose

(MED) and morphine equivalent dose per day (MED/d) are used repeatedly in studies32-38

to

Nonmedical Use: Patients incorrectly using a

medication for which they had a prescription AND/OR use without a prescription belonging to the user

Misuse: Patients incorrectly

using a medication for which they had a

prescription

•High potential for abuse, no currently accepted medical use in the US. Includes illicit drugs such as heroin and marijuana.

Schedule I

•High potential for abuse leading to severe psychological or physical dependence. Includes prescription drugs such as oxycodone, as well as drugs such as cocaine.

Schedule II

•Less potential for abuse relative to Schedule II, leading to moderate or low physical dependence or high psychological dependence. Includes prescription drugs such as hydrocodone

Schedule III

•Low potential for abuse relative to Schedule III drugs. Includes prescription drugs such as diazepam.

Schedule IV

•Low potential for abuse relative to Schedule IV drugs. Includes limited quantities of narcotics (ex: containing no more than 200 milligrams of codeine per 100 milliliters or per 100 grams).

Schedule V

http://www.deadiversion.usdoj.gov/21cfr/cfr/2108cfrt.htm



1.10

equate the strength of one milligram and/or one dose of a prescription opioid relative to

morphine. This commonly accepted conversion permits comparability among a variety of

prescription opioid types and doses. Table 1-3 lists commonly prescribed opioids and their

milligram morphine equivalent conversion. The morphine equivalent dose (MED) is calculated

by multiplying the strength of the opioid prescription by the quantity and by a drug-specific

conversion factor (milligrams morphine equivalent, or MME).

Table 1-3. Morphine Equivalent Dose Conversion Table Opioid Type Milligrams Morphine

Equivalent

Schedule III and IV

Propoxyphene (with or without aspirin/acetaminophen/ibuprofen) 0.23

Codeine + aspirin/acetaminophen/ibuprofen 0.15

Hydrocodone + aspirin/acetaminophen/ibuprofen 1.00

Tramadol with or without aspirin 0.10

Butalbital + codeine (with or without aspirin/acetaminophen/ ibuprofen) 0.15

Dihydrocodeine (with or without aspirin/acetaminophen/ibuprofen) 0.25

Pentazocine (with or without aspirin/acetaminophen/ibuprofen) 0.37

Buprenorphinea

25.0-40.0

Butorphanol 7.00

Schedule II Short-Acting*

Morphine sulfate 1.00

Codeine sulfate 0.15

Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 1.50

Hydromorphone 4.00

Meperidine hydrochloride 0.10

Oxymorphone 3.00

Fentanyl citrate transmucosalb 0.125

Tapendatol short actingc

not established

Schedule II Long-Acting*

Morphine sulfate sustained release 1.00

Fentanyl transdermald 2.40

Levorphanol tartrate 11.0

Oxycodone HCL control release 1.50

Methadone 3.00

Oxymorphone extended releasec

3.00

Hydromorphone extended releasec

5.00

Tapentadol extended releasec

not established

Source: Von Korff et al (2008); FDA Blueprint for Prescriber Education for Extended-Release and Long-Acting

Opioid Analgesics (2012)

Note: The majority of these conversation factors are based on Von Korff’s CONSORT (CONsortium to Study

Opioid Risks and Therapeutics) study. Opioids delivered by pill, capsule, liquid, transdermal patch, and

transmucosal administration were included in the data, but opioids formulated for administration by injection or

suppository were not included.

*Prescription opioids are classified as short- or long-acting based on their duration. Short-acting opoids result in a

more rapid increase and decrease in blood serum levels, where as long-acting opioids release gradually into the

bloodstream or have a long half-life for prolonged activity.39

aBuprenorphine is typically used for opioid detoxification and maintenance

40

bTransmucosal fentanyl conversion to morphine equivalents assumes 50% bioavailability of transmucosal fentanyl

and 100 micrograms transmucosal fentanyl is equivalent to 12.5 to 15 mg of oral morphine. cData for oxymorphone, hydromorphone and tapentadol obtained from FDA Blueprint for Prescriber Education for

Extended-Release and Long-Acting Opioid Analgesics



1.11

dTransdermal fentanyl conversion to morphine equivalents is based on the assumption that one patch delivers the

dispensed micrograms per hour over a 24 hour day and remains in place for 3 days.

Diversion. Diversion, as defined by the Uniform Controlled Substances Act, is the “transfer of a

controlled substance from a lawful to an unlawful channel of distribution of use.”41

Diversion

can occur in multiple forms, including theft, forgery, and illegal purchase, either from drug

dealers or illegal Internet pharmacies. An increasingly common form of diversion in some areas

of the United States are “pill mills,” which is a term used to describe a provider (physician, clinic

or pharmacy) that is inappropriately prescribing and/or dispensing prescription drugs.42

This

report focuses on two additional forms of diversion – doctor shopping and pharmacy shopping –

defined as using multiple physicians and/or multiple pharmacies to obtain prescription drugs

inappropriately.

BACKGROUND: PRESCRIPTION OPIOID MISUSE & ABUSE

Prevalence of Prescription Opioid Misuse, Abuse and Dependence

Over the past two decades, the prevalence of self-reported, nonmedical use and misuse of

prescription opioids has increased in the United States.43-47

According to the 2010 National

Survey on Drug Use and Health (NSDUH)†, the prevalence of past-year nonmedical prescription

opioid use was 4.8% (approximately 12.2 million individuals) and 2.0% (5.1 million individuals)

reported past-month nonmedical use.27

In 2010, nearly as many individuals admitted initiating

nonmedical opioid use within the past 12 months (2.0 million) as those initiating use of

recreational marijuana (2.4 million), the most commonly abused illicit drug.11

Since 2002, the

NSDUH has found that lifetime, past-year, and past-month nonmedical opioid use has remained

relatively stable (Table 1-4).27

Table 1-4. Prevalence of Nonmedical Prescription Opioid Use, 2002-2010

Year Lifetime

N (%)

Past Year

N (%)

Past Month

N (%)

2002 29, 611 (12.6) 10,992 (4.7) 4,377 (1.9)

2003 31,207 (13.1) 11,671 (4.9) 4,693 (2.0)

2004 31,768 (13.2) 11,256 (4.7) 4,404 (1.8)

2005 32,692 (13.4) 11,815 (4.9) 4,658 (1.9)

2006 33,472 (13.6) 12,649 (5.1) 5,220 (2.1)

2007 33,060 (13.3) 12,466 (5.0) 5,174 (2.1)

2008 34,861 (14.0) 11,885 (4.8) 4,747 (1.9)

2009 35,046 (13.9) 12,405 (4.9) 5,257 (2.1)

2010 34,776 (13.7) 12,213 (4.8) 5,100 (2.0)

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 1999-2009

Note: Prevalence among individuals ages 12 and older. Numbers are in thousands.

Multiple iterations of the NSDUH,46

as well as surveys of patients hospitalized for opioid

withdrawal48

and enrolled into methadone maintenance programs49

have found that the most

commonly used opioids were compounds containing hydrocodone and oxycodone. Of those

reporting past-year nonmedical opioid use in the 2002-2004 NSDUH, Becker et al found that the

† Administered by the Substance Abuse and Mental Health Services Administration, this survey is based on a target

of 67,500 face-to-face interviews with a representative sample of civilian, non-institutionalized individuals ages 12

and older.



1.12

majority reported use of only one opioid (39%) or 2-4 different opioids (45%), while 16%

reported use of more than four different opioids.50

Results from a 1998-2006 telephone survey‡

found higher rates of concomitant non-opioid medications among “regular” opioid users, defined

as individuals “using at least 5 days per week for at least four continuous weeks.” Approximately

21% of regular opioid users reported taking 10 or more medications, compared to 16% of non-

regular users and 4.5% of non-opioid users. Among regular opioid users, 18% had used opioids

for more than 5 years, 47% used opioids for more than 2 years, and 21% had used opioids for

less than six months.51

Jones analyzed the 2010 NSDUH and found that approximately one

million individuals reported chronic nonmedical use (defined as use for 200 days or more),

which is a 75% increase from 2002-2003.52

As previously mentioned, the NSDUH has found

that the prevalence of nonmedical opioid use has remained relatively stable for the past decade,27

yet opioid-related deaths have increased significantly.13

Jones hypothesizes that the increase in

chronic nonmedical use may be one contributing factor to the increased mortality.52

State-Specific Prevalence Rates

From 2008-2009, the prevalence of nonmedical opioid use ranged from 3.6% (Nebraska) to 8.1%

(Oklahoma), compared to a national prevalence of 4.8%.1 In 2008, nearly 21% of respondents to

Utah’s BRFSS§ survey reported using at least one prescription opioid within the past year; of

those respondents, 3.2% reported nonmedical use of that prescription, either more frequently or

in higher doses than directed.53

Medicaid-Specific Prevalence Rates

Using 2002-2003 data from the Medicaid Analytic eXtract (MAX) database, McAdam-Marx et

al estimated the prevalence of prescription opioid abuse in the Medicaid population at 87 per

10,000 population and found the majority of Medicaid abusers (59%) lived in the Eastern region

of the United States.18,54

Sources of Misused and Abused Opioids

Individuals misusing and abusing prescription opioids acquire their drugs from a variety of

sources.11,55

The 2010 NSDUH found that 17% of users obtained their opioids through a

prescription from one doctor, whereas 83% obtained their prescription opioids from other

sources (Figure 1-3).11,26,56

In comparison, the 2006 NSDUH found that 11% of respondents

obtained their opioids through a prescription from one doctor.46

Rosenblum et al surveyed nearly

5,700 individuals entering into methadone treatment facilities across the country and found

similar results – 28% of individuals reported their most frequent source of opioids was through a

doctor’s prescription.49

Studies have found that the source of abused opioids varies by gender and age. Back et al

analyzed results from the 2006 NSDUH and found that men were more likely to obtain

prescription opioids from family/friends (either purchased or for free) or from a drug dealer or

‡ Administered by the Sloane Epidemiology Center of Boston University, this is a telephone survey administered

from February 1998 through September 2006 on prescription and non-prescription medication use during the

previous 7 days . The sample consists of 19,150 randomly identified civilian, non-institutionalized individuals ages

18 and older. § The Behavioral Risk Factor and Surveillance System (BRFSS) survey is a cross-sectional telephone survey of

adults ages 18 years and older conducted by state health departments, with support from the CDC.



1.13

stranger, while women were more likely to take prescription opioids from family/friends without

asking.46

Green et al found that women abusing hydrocodone/ acetaminophen primarily obtained the drug

from within their social network, such as friends or family members (44.6%), followed by a

dealer (37.5%), or their own

prescription (28.8%), whereas

males abusing this opioid

obtained it primary from a

dealer (45.2%), followed by

within their social network

(40.6%), or their own

prescriptions (25.4%).57

Cicero et al surveyed opioid

users entering substance abuse

treatment facilities and found

that approximately 90% of

males and females under age

20 acquired their prescription

opioids from a dealer, while

less than half of individuals

over age 51 did so. Cicero et

al also found that males were

more likely than females to

acquire their opioids from a

dealer, (OR=1.64), whereas

females were more likely than males to use a doctor’s prescription to obtain opioids

(OR=1.71).58

Doctor and Pharmacy Shopping

“Doctor shopping” and “pharmacy shopping” are two methods of diverting prescription opioids

that contribute to nonmedical opioid use, misuse and abuse. These terms refer to visiting multiple

providers (“doctor shopping”) or pharmacies (“pharmacy shopping”) to obtain medically

unnecessary prescription opioids. As shown in Table 1-5, doctor and pharmacy shopping have

been defined using a variety of cut-off points for classifying a patient as having potential

controlled substance misuse or mismanagement that would warrant further evaluation.16,17,59-65

Published thresholds vary by number of providers or pharmacies seen by a single patient to

obtain any opioid over a given time period. However, as the numbers of providers or pharmacies

are not direct measures of misuse alone, such information should be used in conjunction with

prescription patterns to identify potential misuse and determine if intervention is needed.

Table 1-5. Thresholds for Defining Doctor and Pharmacy Shopping

Citation Number of Opioid Prescribers And/

Or

Number of Pharmacies

Dispensing Opioids

Parente et al (2004) >6 in one year OR >3 in a year

Hall et al (2008) >5 during the year before death

Katz, Panas et al (2010) >1 - >10 over a 12-month period AND >1 - >10 over a 12-month period

White et al (2009) > 2 over a 3-month period OR

Obtained

free from a

friend or

relative

55%

Prescribed

by one

doctor

17%

Bought from

a friend or

relative

12%

Took from a

friend or

relative

without

asking

5%

Got from

drug dealer

or stranger

4%

Other source

7%

Source: Centers for Disease Control and Prevention. Policy Impact:

Prescription Painkiller Overdose. November 2011

Figure 1-3. Sources of Prescription Opioids, 2010



1.14

Citation Number of Opioid Prescribers And/

Or

Number of Pharmacies

Dispensing Opioids

> 3 over a 12-month period

U.S. GAO (2009) >6 over two year period

U.S. GAO (2011) > 5 in one year

Wilsey et al (2010) >2 within one month AND >2 within one month

Wilsey et al (2011) 2-5 in one year

Peirce et al (2012) >4 in the 6 months before death OR >4 in the 6 months before death

There may be justifiable reasons that patients use multiple providers. Wilsey et al recognized the

potential for patients to either (1) substitute clinicians, (2) obtain medications from a practitioner

covering for the patient's customary provider, or (3) receive treatment from another practitioner

that could be entirely appropriate (dentist, emergency room doctor, etc.). Assuming this is

accurate, they defined the occurrence of a multiple provider episode as occurring when an

individual received a prescription for the same medication from two or more practitioners filled

by two or more pharmacies within a 30-day period. This practice negates the traditional

“gatekeeper” role of a single pharmacist who would know if the patient was obtaining a

medication in a justifiable manner (e.g., seeing a physician on-call for the patient’s customary

doctor). Using these criteria, they found that opioid prescriptions (12.8%) were most frequently

involved in multiple provider episodes, followed by benzodiazepines (4.2%), stimulants (1.4%),

and anorectics (0.9%), respectively. The greatest associations with multiple provider episodes

were simultaneously receiving prescriptions for different controlled substances (polypharmacy of

controlled substances).64

A second study by this group attempted to find a threshold for

identifying patients who used multiple providers. Using data from the California prescription

drug monitoring program, this study found that patients who used two to five providers to obtain

opioids did not differ consequentially in terms of their demographics and prescription utilization

characteristics from patients who used only one provider during a one-year period. This was

consistent with the proposition that many patients who use up to five prescribers in a one-year

period might have justifiable reasons for doing so.63

A 2009 United States Government Accountability Office (GAO) report on Medicaid fraud and

abuse of controlled substances reviewed claims during Fiscal Years (FY) 2006 and 2007 in five

states - California, Illinois, New York, North Carolina, and Texas. The GAO defined doctor

shopping as seeing six or more different prescribers to obtain prescriptions for the same type of

controlled substance during FY 2006 and 2007, and found that nearly 65,000 Medicaid recipients

met these criteria. Table 1-6 shows the number of beneficiaries receiving prescription opioids

from multiple prescribers.16

Table 1-6. Medicaid Beneficiaries Receiving Prescription Opioids from Multiple Prescribers,

FY 2006 and 2007

Prescription Opioid Number of Prescribers in Selected States

6-10 11-15 16-20 21-50 51+ Total

Fentanyl 777 41 6 1 0 825

Hydrocodone 31,364 3,518 723 340 9 35,954

Hydromorphone 590 67 14 11 0 682

Methadone 824 76 9 2 0 911

Morphine 810 50 8 1 0 869

Oxycodone 5,349 435 73 18 0 5,875



1.15


6-10 11-15 16-20 21-50 51+ Total

Total Prescription Opioids 39,714 4,187 833 373 9 45,146

Total Controlled Substances* 64,239 5,066 926 396 9 70,636

Source: United States Government Accountability Office (2009)

Note: The numbers in the columns do not represent unique beneficiaries. There are 64,920 total unique beneficiaries

*Additional substances included amphetamine derivatives, benzodiazepine, methylphenidate, and non-

benzodiazepine sleep aids

In 2011, the GAO subsequently released a report examining the prevalence of doctor shopping in

the Medicare population. The GAO reviewed 2008 Medicare Part D claims for controlled

substances from five states - California, Georgia, Maryland, Massachusetts, and Texas (only

California and Texas were also included in the Medicaid analysis). This analysis defined doctor

shopping as seeing five or more different prescribers from January through December 2008 to

obtain prescriptions for the same type of controlled substance. Using this definition, the GAO

found that approximately 170,000 Medicare recipients met this criterion. Table 1-7 shows the

number of beneficiaries receiving prescription opioids from multiple prescribers. In both GAO

analyses, hydrocodone and oxycodone were the prescription opioids most often received from

multiple prescribers; in the Medicare analysis, these two drugs were involved in more than 80%

of the doctor shopping events.17

Table 1-7. Medicare Beneficiaries Obtaining Prescription Opioids from Multiple Prescribers,

2008


5-10 11-15 16-20 21-50 51+ Total

Codeine with Acetaminophen 1,500 21 4 0 0 1,525

Fentanyl 5,043 24 8 2 0 5,077

Hydrocodone 92,801 3,553 700 335 5 97,394

Hydromorphone 2,453 77 13 8 0 2,551

Meperidine 149 8 0 0 0 157

Methadone 3,414 9 0 0 0 3,423

Morphine 6,354 33 4 0 0 6,391

Oxycodone 54,183 1,974 440 235 5 56,837

Tramadol 4,364 134 33 14 0 4,527

Total Prescription Opioids 170,261 5,833 1,202 594 10 177,882

Total Controlled Substances* 181,823 5,927 1,214 600 10 189,574

Source: United States Government Accountability Office (2011)

Note: The numbers in the columns do not represent unique beneficiaries. There are 170,029 unique beneficiaries

*Additional substances included amphetamine derivatives, benzodiazepine, carisoprodol, methylphenidate, and

non-benzodiazepine sleep aids

Epidemiologists have also explored the relationship between shopping behavior and drug-related

death. Using 2005-2007 data from West Virginia’s prescription drug monitoring program and

drug-related death data compiled in their state forensic database, Peirce et al analyzed trends in

doctor and pharmacy shopping among living and deceased individuals. They found a

significantly greater proportion of deceased subjects were doctor shoppers and pharmacy

shoppers (25% and 17%) than living subjects (4% and 1%). As depicted in Figure 1-4, these

researchers reported that 55% of pharmacy shoppers also met criteria for doctor shoppers,

whereas only 20% of doctor shoppers met criteria for pharmacy shopping.65

Thus, as the authors



1.16

point out, there is clearly a relationship between doctor and pharmacy shopping, although one

can occur without the other.

Figure 1-4. Dual Doctor and Pharmacy Shoppers in West Virginia, 2005-2007

Based on data from Massachusetts’ prescription drug monitoring database, Katz et al analyzed

individual’s use of multiple prescribers and pharmacies for Schedule II opioids in 2006. As

displayed in Table 1-8, the majority of patients (76.9%) used one prescriber and one pharmacy

for their opioid prescriptions, nearly 10% used two prescribers and one pharmacy, and 3% used

two prescribers and two pharmacies. The authors found that, relative to the overall sample size

(562,591), the number of patients using high numbers of prescribers or pharmacies was small –

only 1.5% of the sample used 5 or more prescribers and less than 0.5% used 5 or more

pharmacies. However, the authors did find that individuals with more prescribers were also more

likely to use more pharmacies (see Table 1-9). For example, fewer than 1% of individuals with

one prescriber used four or more pharmacies, whereas nearly 70% of individuals with ten or

more prescribers used four or more pharmacies.61

To define shopping behavior, the above studies relied on counting the number of prescribers or

number of pharmacies a subject uses during a specified period, but they did not distinguish

successive prescribers from concomitant prescribers. Cepeda et al examined overlapping

prescriptions, defined as at least 1 day of overlapping prescriptions written by two or more

different prescribers at any time during an 18-month period. Overlapping prescriptions are not

unique to opioids and, thus, provide a useful comparison to medications not likely to be misused.

Cepeda et al determined that having two or more overlapping prescriptions written by different

prescribers and filled at three or more pharmacies differentiated opioids from non-controlled

substances (i.e., diuretics) and constituted shopping behavior.66

In a subsequent study, Cepeda et

al estimated the prevalence of doctor shopping by analyzing prescription drug claims from a

large database that includes 65% of all retail prescriptions in the country. They found that only a

very small proportion of individuals met this criterion (Table 1-10). Of the 25,161,024

individuals in the dataset who received one opioid prescription during the study period, only

0.30% (75,215) met criteria for doctor shopping. Even among those identified as doctor

shoppers, very few had significantly high utilization; only 11% used >5 prescribers and 6.7%

used > 6 pharmacies. Cepeda et al also found variation in the schedule and formulation most

often dispensed to doctor shoppers compared to non-doctor shoppers (Figure 1-5).

20%

80%

Doctor Shopping Both Doctor & Pharmacy

Shopper

55% 45%

Pharmacy Shopping Both Pharmacy &

Doctor Shopper

Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies – A Review of the Literature

1.17

Table 1-8. Distribution of Patients by Number of Prescribers and Pharmacies, 2006 #

Prescribers

Used

# Pharmacies Used

1 2 3 4 5 6 7 8 9 10+ Total % (n)

1 76.9% 1.65% 0.25% 0.06% 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% 78.91 % (443,956)

2 9.75% 3.05% 0.41% 0.11% 0.03% 0.01% 0.00% 0.00% 0.00% 0.00% 13.37% (75,191)

3 2.61% 1.25% 0.41% 0.11% 0.04% 0.01% 0.00% 0.00% 0.00% 0.00% 4.43% (24,919)

4 0.86% 0.54% 0.24% 0.09% 0.03% 0.01% 0.00% 0.00% 0.00% 0.00% 1.77% (9,980)

5 0.30% 0.23% 0.12% 0.06% 0.03% 0.01% 0.01% 0.00% 0.00% 0.00% 0.76% (4,274)

6 0.11% 0.10% 0.06% 0.04% 0.02% 0.01% 0.00% 0.00% 0.00% 0.00% 0.34% (1,887)

7 0.04% 0.05% 0.04% 0.02% 0.02% 0.01% 0.01% 0.00% 0.00% 0.00% 0.18% (1,025)

8 0.02% 0.03% 0.02% 0.01% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% 0.10% (543)

9 0.01% 0.01% 0.01% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.05% (296)

10+ 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.09% (520)

Total %

(n)

90.62%

(509,818)

6.91%

(38,865)

1.58%

(8,870)

0.52%

(2,917)

0.20%

(1,138)

0.08%

(464)

0.04%

(248)

0.02%

(108)

0.01%

(76)

0.02%

(87)

100.00%

(562,591)

Taken From: Katz et al (2010). Usefulness of prescription monitoring programs for surveillance – analysis of Schedule II opioid prescription data in

Massachusetts, 1996-2006. Figure 2A.

Table 1-9. Distribution of Patients Using Multiple Prescribers and Pharmacies, 2006 # Pharmacies Used

# Providers Used n 1+ 2+ 4+ 6+ 8+ 10+

1 443,956 100.00% 2.5% 0.12% 0.01% 0.00% 0.00%

2 75,191 100.0% 27.0% 1.12% 0.10% 0.01% 0.00%

3 24,919 100.0% 41.2% 3.74% 0.44% 0.07% 0.02%

4 9,980 100.0% 51.2% 7.57% 0.98% 0.13% 0.03%

5 4,274 100.0% 61.2% 14.09% 2.32% 0.28% 0.05%

6 1,887 100.0% 68.0% 20.83% 5.14% 1.17% 0.21%

7 1,025 100.0% 78.0% 30.34% 9.85% 1.27% 0.10%

8 543 100.0% 81.6% 34.44% 13.08% 3.87% 0.37%

9 296 100.0% 84.5% 47.30% 19.26% 7.09% 1.35%

10+ 520 100.0% 91.2% 69.23% 42.88% 24.62% 11.92%

Total Population 562,591 100.00% 9.38% 0.90% 0.17% 0.05% 0.02%

Source: Katz et al (2010)


1.18

Table 1-10. Distribution of Patients Using Multiple Prescribers and Pharmacies, 2008

# of Prescribers Number of Pharmacies

3 4 5 6 7+ Total (n)

2 43.74% 3.32% 0.75% 0.19% 0.11% 48.10% (36,178)

3 24.46% 4.67% 1.70% 0.54% 0.26% 31.63% (23,790)

4 3.29% 3.21% 1.67% 0.69% 0.40% 9.26% (6,967)

5 0.77% 1.48% 1.18% 0.63% 0.41% 4.46% (3,357)

6+ 0.41% 1.23% 1.47% 1.19% 2.25% 6.55% (4,923)

Total

(n)

72.67%

(54,658)

13.91%

(10,460)

6.75%

(5,080)

3.24%

(2,439)

.34%

(2,578)

100%

(75,215)

Taken from: Cepeda et al (2012). Opioid Shopping Behavior: How Often, How Soon, Which Drugs, and What Payment Method? Table 2.

Note: Total population was 25,161,024; of that, 72,215 (0.30% met criteria for doctor shopping

Figure 1-5. Type and Formulation of Prescription Opioids Dispensed to Doctor Shoppers Compared to Non-Doctor Shoppers

Source: Cepeda et al (2012). ). Opioid Shopping Behavior: How Often, How Soon, Which Drugs, and What Payment Method?

Note: Other category includes Schedule IV opioids and unscheduled opioids. IR=Immediate Release; ER=Extended Release; Combo=Combination products (i.e., those containing

acetaminophen, or NSAIDs)

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

II Only III Only Other II & III II &

Other

II, III, &

Other

III &

Other

Per

cen

t D

isp

ense

d

Schedule Type

Non-Doctor Shopper Doctor Shopper

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

IR ER Combo IR & ER IR &

Combo

ER &

Combo

IR, ER,

&

Combo

Per

cen

t D

istr

iub

tio

n

Formulation Type

Non-Doctor Shopper Doctor Shopper



1.19

Doctor shoppers were more likely to be written prescriptions containing Schedule II opioids and

extended release formulations, whereas non-doctors shoppers were more likely to be written

prescriptions containing Schedule III opioids and immediate release or combination formulations

(i.e., opioid and acetaminophen, etc).67

The variable definitions for thresholds (i.e., cut-off points) for doctor or pharmacy shopping

await corroboration via interviews with patients who use multiple physicians and/or pharmacies

to obtain prescription opioids. If investigators could differentiate justifiable from illicit shopping

behavior, the sensitivity and specificity of the various cut-off points could be determined. Similar

studies have been devised involving the unlawful channeling of regulated pharmaceuticals from

legal sources to the illicit marketplace by interviewing club drug users, street-based illicit drug

users, methadone maintenance patients, and HIV-positive individuals who abuse and/or divert

drugs.49,68-70

As in the aforementioned instance, these studies would require anonymity and

confidentiality.68

Opioid Prescribing Patterns

Over the past three decades, physicians began more aggressive management of chronic non-

cancer pain (CNCP), which contributed to the increase in duration of opioids use 51,71-75

despite

limited evidence of the efficacy of opioids for the treatment of this type of pain.8,76-81

A

comparison of National Ambulatory Medical Care Survey (NAMCS) results from 1980 and 2000

revealed that although the number of visits for musculoskeletal pain remained constant during

that time, the prevalence of opioids prescribed at both acute pain visits and chronic pain visits

increased (8% to 11% and 8% to 16%, respectively). Additionally, the use of stronger opioids

(such as morphine) during chronic pain visits more than tripled (from 2% to 9%), which

translates to an additional 4.6 million visits in which strong opioids were prescribed.82

Dorn,

Meek and Shah saw a similar trend analyzing NAMCS and National Hospital Ambulatory

Medical Care Survey (NHAMCS) data for chronic abdominal pain-related outpatient visits. They

found that the number of visits for this condition decreased by approximately 18% from 14.8

million in 1997-1999 to 12.2 million in 2006-2008, yet the prevalence of visits in which an

opioid was prescribed increased nearly 107% over the same period (5.9% to 12.2%).83

In 2001,

NAMCS data showed that opioids were prescribed in 63 primary care visits per 1,000 total visits,

compared to 41 per 1,000 total visits in 1992. Physicians reported that a pain-related diagnosis

(back pain, acute musculoskeletal pain, and headache) was the primary diagnosis in nearly two-

thirds of visits resulting in an opioid prescription.84

A review of 2009 pharmacy dispensing data

(representing pharmacies dispensing over half of all prescriptions in the U.S) found that a large

proportion of opioid prescriptions were prescribed for patients between the ages of 40-59 years

old (45.7%, or 36.4 million). Additionally, over half of all opioid prescriptions in this dataset

(56.4%) were dispensed to patients who had previously filled a prescription for an opioid in the

past 30 days.85

From 2000-2009, Kenan et al found that the number of opioid prescriptions per 100 individuals

increased 35.3%, from 61.9 to 83.7. Additionally, the average prescription size (expressed as

morphine milligram equivalent [MME] per day multiplied by the prescription duration) of both

hydrocodone and oxycodone prescriptions increased nearly 70%, from 170MME to 288MEE and

923MME to 1566MME, respectively.86

In 2005, nearly 100 million prescriptions were written

for hydrocodone, making it the most commonly prescribed drug in the United States. In



1.20

comparison, the second and third most common prescriptions, atorvastatin (a cholesterol

lowering medication) and amoxicillin, had approximately 63 million and 52 million prescriptions

written, respectively.22,87

In 2009, the number of opioid prescriptions dispensed rose to 257

million (a 48% increase since 2000).25

The CDC estimated that in 2010, enough opioids were

sold to provide a typical dose of hydrocodone (5mg every 4 hours) to every American adult for

one month.1

State-Specific Opioid Prescribing Patterns

Over the past two decades, state data show increases in the number patients receiving a

prescription for opioids and the number of opioid prescriptions written and dispensed, especially

for Schedule II opioids. According to a nationwide analysis of 9 million prescription drug claims

from 2000, 64.2 per 1,000 total claims were for opioids. Although some states had a prescription

opioid claim rate below 20 per 1,000 total claims (California, Texas, Illinois, Michigan, and New

York), some states had rates over 100 per 1,000 total claims (Alaska, Arizona, Delaware,

Maryland, Massachusetts, New Hampshire, South Carolina, and Tennessee). States with long-

standing prescription drug monitoring programs (PDMP’s) had among the lowest rates.47

From 1996-2002, Franklin et al found that the number of Washington State employees with

prescriptions for Schedule II opioids increased 2.5 times. Additionally, as a proportion of all

opioid prescriptions, Schedule II opioid prescriptions increased from 19.3% to 27.2% during that

period. There was also a 55% increase in the average daily morphine equivalent dose of long-

acting opioids (from 88mgMED/day to 132mgMED/day) during that period. Since the current

CDC43

and Washington State opioid dosing guidelines recommend that physicians refer patients

for a pain management consultation for prescriptions over 120mg morphine equivalent dose per

day, Franklin et al opined that it is conceivable that the average daily dose would not have

reached or exceeded this threshold if the guideline had been enacted during this time.33

Dembe

et al reviewed Ohio workers compensation data from 2008-2009 and found that nearly 10% of

claimants had prescriptions for opioids exceeding 120mg.88

Studies of state prescribing data also found that a small number of prescribers are responsible for

prescribing the majority of opioids. Using California workers compensation claims data for

prescription drugs filled from 2005-2009, Swedlow et al analyzed physician prescribing patterns

for Schedule II opioids among this population. They found that the top 1% of prescribers

(approximately 93 physicians) accounted for one-third of the total Schedule II opioid

prescriptions and slightly more than 40% of the total milligrams morphine equivalent (MME)

prescribed. The top 10% of prescribers (approximately 917 physicians) accounted for almost

21% of the total Schedule II opioid prescriptions and nearly 87% of the total MME prescribed.

From 2005-2009, the top 10% of prescribers had an average of 17.5 claims in which they

prescribed Schedule II opioids (compared to 3.5 claims in the group overall), totaling nearly

750,000 MME (compared to slightly over 87,000 MME in the group overall).89

Blumenschein et

al found a similar trend among users of the Kentucky All Schedule Prescription Electronic

Reporting Program (KASPER). Analyzing 2005-2009 KASPER data revealed that the top 10%

of prescribers were responsible for the vast majority of all prescriptions for controlled

substances.90



1.21

Medicaid-Specific Prescribing Patterns

Analyses of Medicaid prescription drug claims have revealed increases in the number of these

beneficiaries receiving a prescription for opioids and the number of opioid prescriptions written

and dispensed. From 1996-2002, the Medical Expenditures Panel Survey (MEPS) found a 22.5%

increase in the number of Medicaid enrollees using prescription opioids.14

Compared to the

MEPS data analysis, Brixner et al found a larger increase in the proportion of Medicaid

recipients receiving prescription opioids using CMS (Centers for Medicare and Medicaid

Services) data. From 1998-2003, they found an 83% increase in Medicaid recipients with these

prescriptions, from 15 million to more than 27.5 million.15

Zerzan and colleagues found an even

larger increase among fee-for-service Medicaid enrollees from 1996-2002. They analyzed CMS

prescription drug claims data for 49 state fee-for-service Medicaid programs and found a 309%

increase in the number of opioid prescriptions dispensed between 1996 and 2002, compared to

170% for non-opioid prescription drugs. During that same period, oxycodone and methadone

prescriptions increased 1,615% and 790%, respectively.91

Zerzan et al also measured variation in

opioid dispensing rates by state; they used “defined daily dose”**

(DDD) per 1,000 Medicaid

recipients per day (DDD/1000/d). In 1996, dispensing rates varied from 6.9 - 44.1 DDD/1000/d

and increased to 7.1-164.97 DDD/1000/d in 2002. From 1996-1997, 8% of state Medicaid

programs (four states) were able to maintain or decrease their dispensing rates, while two-thirds

of states at least doubled their rates. In 2002, there was a 23-fold difference between the states

with the highest and lowest overall opioid dispensing rates.91

Using data from the Trends and Risks of Opioid Use for Pain, (TROUP††

) study, Edlund et al

found the 4% of users in the 95th

– 99th

percentile in the Medicaid population consumed

approximately 26% of total opioids and the top 1% (99th

– 100th

percentile) consumed 20% of

total opioids by milligrams morphine equivalents (MME). In comparison, the 4% and 1% of the

commercially insured population consumed 27% and 43% of total opioids, respectively.93

Braden et al found different results when comparing any chronic opioid use in these two

populations. In their analysis, TROUP data revealed that Medicaid recipients were twice as

likely to have any opioid use compared to commercial enrollees, and four times as likely to have

greater than a 90 day-supply. The authors speculate that this may be partially attributable to a

greater comorbidity and disability burden in this population.94

In another analysis of TROUP data, Sullivan et al found that the proportion of the Arkansas

Medicaid population with greater than 180-days supply of prescription opioids grew from 9.5%

to 16.0% from 2000-2005 (a 68.5% increase), compared with an increase from 2.1% to 3.2% in

the commercially insured group (a 49.9% increase). Surprisingly, the prevalence of individuals

with prescriptions for doses greater than 120mg morphine equivalent dose (MED) per day did

not vary by insurance type, nor did the mean cumulative opioid dose received within a calendar

year. During this period, the Medicaid group had a larger increase in the cumulative yearly dose

per user for short-acting Schedule II opioids than the commercially insured group (191.2% vs.

**

“Defined Daily Dose” is a conversion factor established by the World Health Organization’s Collaborating Centre

(WHOCC) for Drug Statistics Methodology and is another method of standardizing drug dose. The WHOCC defines

DDD as “the assumed average maintenance dose per day for a drug used for its main indication in adults.91,92

††

The Trends and Risks of Opioid use for Pain (TROUP) study was conducted from 2000-2005. The study

compared trends and risks of opioid use, misuse and abuse in two populations – a national commercially insured

population (HealthCore Blue Cross and Blue Shield) and the Arkansas Medicaid population.



1.22

95.5%, respectively), while the commercially insured group had a larger increase in the

cumulative yearly dose per uses of long-acting Schedule II opioids (54.0% vs. 38.3% in the

Medicaid group).34

An analysis of Michigan Medicaid recipients receiving care from a large,

rural family medicine group found that the average number of opioid prescribers per patient

within a six-month period was 3.7 (ranging from 2-10 per patient). Patients using non-opioid

analgesics had 3.2 fewer prescriptions per 6 months and were less likely to have 6 or more

prescriptions (OR=0.24, 95% CI=0.08-0.73) than those on opioids alone. This analysis also

found that the average number of opioid prescriptions per patient within the six-month period

averaged 8.4 (ranging from 3-28 per patient), with 64% of patients having more than six

prescriptions.95

Risk Factors Associated with Prescription Opioid Misuse and Abuse

Studies have identified a variety of factors associated with an individual’s risk for opioid misuse,

abuse or dependence 46,47,96-99

including (but not limited to) male gender, 37,46,60

simultaneous use

of another illicit substance or prescription drug abuse,37,50,100-104

individuals reporting severe

pain,37,73

comorbid conditions,9,18,105

and daily opioid dose.96,99,106

Sociodemographic Risk Factors

National surveys and studies report that numerous sociodemographic factors are associated with

increased risk for prescription opioid misuse and abuse, including gender,46,107,108

age, 46,50,99,101,106

race/ethnicity,107

employment status,50

income,50,107

and geographic location.107,109

Gender

Multiple iterations of the NSDUH found that lifetime, past year and past month utilization

among both males and females increased from 2002-2010, however, prevalence rates for females

remain below those of males (see Table 1-11).27

Table 1-11. Prevalence of Nonmedical Prescription Opioid Use by Gender, 2002 & 2010 Lifetime (%) Past Year (%) Past Month (%)

Males Females Males Females Males Females

2002 14.3 11.0 5.2 4.2 2.0 1.7

2010 15.7 11.8 5.6 4.0 2.3 1.7

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002-2009 Note: Prevalence among individuals ages 12 and older

Based on TROUP‡‡

data, Thielke et al found the prevalence of long-term opioid use (defined as

90 days or more of prescribed opioids within a calendar year) increased among males and

females in both the commercially insured population and the Arkansas Medicaid population. The

largest increase among males in both groups occurred in the 45-64 year old group. Females ages

45 and older saw the largest increase in the commercial population, compared to females ages 65

and older in the Medicaid group.108

In analyses of data from the TROUP§§

study, Edlund et al

found that heavy utilization (defined as “individuals in the top 5% of total opioid use”) was

associated with male gender in both the commercially insured and Medicaid populations.93

‡‡

The Trends and Risks of Opioid Use for Pain (TROUP) study was conducted from 2000-2005. The study





1.23

Race/Ethnicity

According to the 2010 NSDUH, Native Americans/Alaska Natives had the highest prevalence of

nonmedical opioid use, followed by Whites. Native Hawaiians/Pacific Islanders had the second

lowest lifetime and past-year prevalence rates (behind Asians), but their past-month prevalence

was higher, second only Native American/Alaska Natives (see Table 1-12).27

Table 1-12. Prevalence of Nonmedical Opioid Use by Race/Ethnicity, 2010 Lifetime

(n=34,776,000)

Past Year

(n=12,213,000)

Past Month

(n=5,100,000)

Overall 13.7 4.8 2.0

White 15.2 5.1 2.2

Black or African American 10.6 3.6 1.6

Hispanic or Latino 11.3 4.8 2.0

Asian 6.3 3.2 0.7

Native American or Alaska Native 19.3 8.8 4.0

Native Hawaiian or Other Pacific Islander 6.4 3.4 2.4

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2010

Geographic Location

The 2010 NSDUH found a higher prevalence of nonmedical opioid use among respondents

residing in the western region of the country, and among those from small metro counties (Table

1-13).27

Cicero et al used data from the Researched Abuse, Diversion and Addiction Related

Surveillance (RADARS) System and found that the majority of prescription opioid abuse occurs

outside of large metropolitan areas, and that certain regions have significantly higher rates of

abuse, such as the rural North East, upper Northwest, and Appalachia.110

White et al estimated

that the majority of privately insured abusers resided in the Southern region of the United States

(52%).9,54

Table 1-13. Prevalence of Nonmedical Opioid Use by Geographic Location, 2010 Lifetime

(n=34,776,000)

Past Year

(n=12,213,000)

Past Month

(n=5,100,000)

Overall 13.7 4.8 2.0

Region

Northeast 12.2 4.3 1.9

Midwest 13.1 4.7 2.0

South 13.3 4.4 1.9

West 16.4 6.1 2.3

County Type

Large Metro (>1 million population) 13.8 4.8 1.9

Small Metro (20,000 -999,999 population) 14.3 5.0 2.1

Nonmetro (0-19,999 population) 12.5 4.4 2.1

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2010

Behavioral Risk Factors

Multiple studies and surveys have identified non-opioid substance abuse as a risk factor for

prescription opioid misuse and abuse.46,50,93,99,103

The NSDUH repeatedly identifies illicit

substance and alcohol abuse as a risk factor for past-year nonmedical opioid use and abuse.50,103

Among males, other illicit drug abuse or dependence (such as cocaine, heroin, hallucinogens, or

inhalants) and alcohol abuse or dependence are significantly associated with past-year opioid



1.24

misuse.46,103

Men are more likely to misuse opioids as the result of legal and behavioral

issues.111

Significant predictors of opioid misuse among females include serious psychological

distress and cigarette use, as well as emotional issues and affective distress.46,103,111

The 2006

NSDUH found that individuals who reported past-year non-medical use of tranquilizers or

sedatives were 16 times more likely to misuse prescription opioids.46

Pain Level & Comorbidities§§

Research by Toblin et al and Bohnert et al found that individuals reporting severe pain were at

higher risk for opioid misuse and abuse.37,73

Edlund et al found that heavy utilization (defined as

“individuals in the top 5% of total opioid use”) in the TROUP***

study was associated with

headache (commercially insured population only), back pain (both populations), and arthritis

(Medicaid population only).93

Edlund et al also found that in the commercially insured and

Medicaid populations, the likelihood heavy utilization increased with the number of chronic non-

cancer pain diagnoses.93,99

Mental Health

Studies consistently report that mental health diagnoses are associated with increased

nonmedical use of prescription opioids, as well as increased risk for abuse.46,50,99,108,112

A review

of 2002-2004 NSDUH identified mental health diagnoses, including depressive symptoms, panic

symptoms, and social phobic/agoraphobic symptoms as factors associated with past-year

nonmedical prescription opioid use and abuse.50

The 2006 NSDUH found that one in four

prescription opioid abusers reported a history of serious psychological distress, and that

compared with men, rates of distress were higher among women (14.5% vs. 11.2%,

respectively).46

One study found that mental health disorders are 50-100% more common among Medicaid

recipients, compared to the overall population and another found that 29% of Medicaid

beneficiaries across six states had both a mental health condition and a history of substance

abuse.18

Using data from the TROUP study, Thielke et al found the prevalence of long-term

opioid use was higher among individuals with a mood disorder, regardless of age, gender, or

insurance group. Although the prevalence of long-term users without a mood disorder was

similar among both populations, the prevalence of long-term users with a mood disorder was

significantly higher in the Medicaid population. Similarly, the prevalence of long-term use was

higher among individuals in each population with an anxiety disorder compared to those without

the disorder, but the prevalence of long-term use with an anxiety disorder was higher in the

Medicaid population compared to commercially insured population.108

Also using TROUP data,

Edlund et al found that persons with mental health or mood disorders were more likely to be

heavy utilizers as well (defined as “individuals in the top 5% of total opioid use”). The likelihood

of being a heavy user in the commercially insured population increased with the number of

substance abuse diagnoses.93,99

§§

As comorbidities are both a risk factor for, and outcome of, nonmedical prescription opioid use, misuse and abuse,

relevant research will be discussed in both the risk factor and outcomes sections. ***






1.25

Opioid Dose and Supply

The literature identifies daily dose and days’ supply of prescription opioids as additional risk

factors for misuse and abuse. Compared to patients receiving low daily opioid doses, Dunn et al

found that patients prescribed high daily opioid doses (defined as >100mg morphine equivalent

dose per day, or MED/d) were more likely to be male (48% vs. 40%) and current smokers (40%

vs. 28%). Patients prescribed high daily doses were also more likely to have been previously

treated for depression (32% vs. 26%) and/or substance abuse (14% vs. 5%).36

An analysis of

opioid abuse within the South Central Veterans’ Affair Health Care Network found that patients

prescribed opioids continuously for 211 days were more likely to develop abuse or dependence

compared with patients prescribed opioids continuously for 91-120 days.96

Analyses of the

TROUP†††

study have also found that risk for prescription opioid misuse and abuse is associated

with an individual’s daily opioid dose and supply (see Table 1-14). In both the commercially

insured and Medicaid sample, Sullivan et al found that patients with a daily dose greater than

120mg MED/d were at a significantly increased risk of opioid misuse and abuse/dependence,

compared with patients with daily doses below 120mg MED/d.106

Edlund et al found that higher

average daily dose and greater number of day’s supply of prescribed opioids were associated

with opioid abuse in the commercially insured sample, but only higher average dose was

associated with abuse in the Medicaid sample.99

Table 1-14. Odds of Opioid Misuse and Abuse by Average Daily Dose and Days Supply Possible Misuse

a Probable Misuse

a Abuse/Dependence

b

Commercially

Insured (OR)

Arkansas

Medicaid

(OR)

Commercially

Insured (OR)

Arkansas

Medicaid

(OR)

Commerically

Insured (OR)

Arkansas

Medicaid

(OR)

Daily Dose

<Median mg/day 1.00 1.00 1.00 1.00 1.00 1.00

Median-120 mg/day 1.65 1.21 2.68 1.80 1.48 1.11

>120 mg/day 2.37 2.02 6.70 4.69 2.19 1.70

Days Supply

91-160 days NR NR NR NR 1.00 1.00

161-185 days NR NR NR NR 1.48 1.01

>185 days NR NR NR NR 1.79 1.18

Source: aSullivan et al (2010);

bEdlund et al (Nov 2010)

Note: Median daily dose was 32mg and 35 mg morphine equivalent in the commercially insured and Arkansas

Medicaid samples, respectively. NR=not reported.

Summary

Over the past two decades, as physicians managed pain more aggressively and prescribed

stronger opioids more frequently and at higher doses, studies and surveys at the state and

national level have documented an increase in the prevalence of nonmedical use, misuse, and

abuse of these drugs, particularly products containing hydrocodone, oxycodone and methadone.

Risk factors identified for prescription opioid misuse and abuse include (but are not limited to)

demographic factors (gender, race/ethnicity, etc), non-opioid substance use, and comorbid

mental health disorders. State- and national-level surveys show increased numbers of patients

who receive high doses of prescription opioids (in excess of 100mgMED/d), and who are chronic

users (continuous use for longer than 90 days). Analyses of state- and Medicaid/Medicare data



1.26

found that relatively few patients obtain their prescription opioids through doctor or pharmacy

shopping; however, the literature suggests that this minority of patients may be at higher risk of

overdose and incur increased healthcare costs and we will address this literature in the next

section.

OUTCOMES RELATED TO OPIOID MISUSE AND ABUSE

As extensively detailed in the previous section, the past two decades have seen significant

increases in the number of opioids prescribed and dispensed, as well as the prevalence of

prescription opioid nonmedical use, misuse, and abuse. These increases correlate with increases

in negative outcomes, including prescription opioid-related healthcare utilization and death, as

well as health care and societal costs.

Health Care Utilization

The increase in opioid prescriptions and the prevalence of nonmedical opioid use and abuse has

been associated with increased health care utilization (such as ED visits, hospitalizations and

physician visits) related to these drugs. Using nationwide claims data for approximately 2 million

employer-insured individuals from 1998-2002, White et al compared opioid abusers†††

to

nonabusers had significantly higher prevalence rates for a number of specific comorbidities.

These comorbidities included non-opioid poisoning, hepatitis (A, B, or C), psychiatric illnesses,

and pancreatitis, which were approximately 78-, 36-, 9-, and 21-times higher (P<0.01) among

opioid abusers, respectively. The authors concluded that the high costs associated with care of

opioid abusers were driven primarily by high prevalence rates of these costly comorbidites and

high utilization rates of medical services and prescription drugs. With regard to medical services, 97% of prescription opioid abusers had at least one outpatient physician visit, 67.8% had at least

one hospital inpatient stay, 45.5% had at least one outpatient mental health visit, and 12.6% had

at least one inpatient mental health stay (versus 71.5%, 5.5%, 4.1% and 0.2% of nonabusers,

respectively)..9

In 2010, the Drug Abuse Warning Network (DAWN) found that ED visits related to nonmedical

prescription opioid use occurred at a rate of 137.4 visits per 100,000 population, accounting for

nearly one-third of the 1,345,645 total ED visits involving misuse or abuse of prescription

drugs.12

As seen in Table 1-15, ED visits related to opioids such as oxycodone, hydrocodone

and methadone have increased substantially from 2004-2010.10

A review of the literature by

Webster et al found that while methadone was associated with 30% of all overdose-related ED

visits, when adjusted for the number of outpatient prescriptions, methadone-related ED visits

were 23 times higher than visits for hydrocodone and six times higher than visits for

oxycodone.113

Table 1-15. ED Visits for Prescription Opioids Compared to Illicit Drugs, 2010

Type of Drug-Related ED Visit Number of

Visits Rate

a Percent Change

2004-2010

†††

Patients were identified as abusers if they had at least one claim with an ICD-9 code related to prescription

opioid abuse from 1998-2002 (304.0, 304.7, 305.5, and 965, but excluding 965.01). A group of matched controls without an opioid abuse diagnosis served as the comparison group.



1.27

Type of Drug-Related ED Visit Number of

Visits Rate

a Percent Change

2004-2010

Total Drug-Related ED Visitsb 4,916,328 1589.0 94%

Prescription Opioids 425,247 137.4 156%

Oxycodone 182,748 59.1 255%

Hydrocodone 115,739 37.4 149%

Methadone 76,237 24.6 86%

Illicit Drugs 1,171.024 378.5 NR

Cocaine 488,101 157.8 NR

Heroin 224,706 72.6 NR

Marijuana 461,028 149.0 22%

Methamphetamine 94,929 30.7 48%

Source: Drug Abuse Warning Network (2010)

NR = Not Reported aRate is per 100,000 population

bIncludes all drugs (illicit drugs, alcohol and prescription drugs) and all causes (suicide attempts, abuse, adverse

drug reactions, etc)

Characteristics Associated with Prescription Opioid-Related Healthcare Utilization

As seen in Table 1-16, opioid-related ED visits vary by numerous demographic factors. In 2010,

ED visits related to all prescription opioids were higher among males and individuals ages 45-54.

In addition to having the highest number of opioid-related visits, the rate of increase from 2004-

2010 was highest among individuals ages 45-54. Oxycodone-related ED visits among females

were increasing at a similar rate as males, but hydrocodone-related ED visits among males are

increasing at a much higher rate than among females.10

Table 1-16. Opioid-Related ED Visits by Gender and Race/Ethnicity, 2010 Opiates (Total) Oxycodone Hydrocodone

# of visits Rate

a %

Changeb

# of

visits Rate

a %

Changeb

# of

visits Rate

a %

Changeb

Gender

Male 229,107 150.6 171% 104,028 68.4 257% 55,846 36.7 180%

Female 196,020 124.6 140% 78,651 50.0 253% 59,872 38.1 125%

Race/Ethnicity

White 343,620 NR 186% 155,566 NR 301% 89,330 NR 156%

Black 38,400 NR 188% 13,305 NR 406% 12,966 NR 309%

Hispanic 18,692 NR 197% 4,194 NR 308% 6,612 NR 349%

Other 3,279 NR 471% 1,776 NR NR 659 NR 663%

Age Group

>21 31,890 36.3 103% 17,420 19.8 204% 8,327 9.5 NR

21-24 51,147 297.7 231% 23,561 137.1 264% 16,066 93.5 276%

25-29 58,825 269.0 244% 23,710 112.2 279% 13,761 65.1 185%

30-34 45,524 231.7 126% 18,994 94.6 193% 14,498 72.2 119%

35-44 82,223 200.8 92% 36,100 88.2 211% 21,744 53.1 68%

45-54 89,328 198.3 153% 36,283 80.6 303% 24,048 53.4 180%

55-64 42,290 114.9 278.8 18,111 49.2 425% 10,168 27.6 385%

65< 24,782 61.3 182% 8,453 20.9 264% 7,118 17.6 241%

Source: Drug Abuse Warning Network (2010)

Note: The DAWN database does not calculate rates for race/ethnicity because this information gathered in

Emergency Departments is often missing or very limited. NR = Not Reported aRate is per 100,000 population

bPercent change is from 2004 – 2010



1.28

Small differences in the prevalence of ED visits were evident between the commercially insured

and Medicaid populations. Braden et al examined data from the TROUP‡‡‡

study and found that

28.2% of Arkansas Medicaid recipients who used prescription opioids continuously for at least

90 days had one or more ED visit within the past year, compared to 24.2% of individuals in the

commercially insured population. Approximately 0.4% of the Medicaid group had an ED visit

associated with an opioid overdose, compared to 0.2% of the commercially insured group. On

the other hand, the type and amount of the prescription opioid was more influential. Braden et al

examined the relationship between opioid dosing levels and ED visits in these two populations

and found that these variables were more influential. They found that opioid doses between the

median (32-35 MED/day) and 120 MED/day were associated with increased ED visits among

commercially insured population, but not among the Medicaid population. The data for doses

greater than 120 mg/day is noteworthy and clinically important; although not associated with

increased ED visits in either population, there was a two-fold increase in the risk for adverse

drug events in both the commercially insured and Medicaid populations. Additionally, Braden et

al found that comorbidities and substance use and abuse (opioid and non-opioid) were all

associated with increased ED visits among chronic opioid users.98

Hartung and colleagues also

found that the type of prescription opioid was an influential factor among prescription opioid-

related ED visits. They reviewed Oregon Medicaid claims from 2000-2004 and found that

patients prescribed methadone were more likely to have an ED visit compared to those

prescribed oxycodone or morphine. However, patients prescribed methadone or oxycodone were

18% and 23% less likely (respectively) to be hospitalized, compared to individuals prescribed

morphine.114

Health Outcomes: Opioid-Related Comorbidities§§§

Presented in Table 1-17 are the prevalence rates of selected comorbidities among both privately

insured and Medicaid populations, compared with matched controls. As previously mentioned,

White et al reviewed employer claims data from 1998-2002 and found that opioid abusers were

more likely to suffer multiple co-morbidities.9 In a similar vein, using data from 2002-2003,

McAdam-Marx and colleagues compared the prevalence of comorbidities among a group of

Medicaid recipients with an opioid abuse-related diagnosis (abuse, dependence or poisoning) and

matched controls and found that 84% of abuse/dependent patients and 52% of controls had at

least one of the selected comorbidities. The most prevalent comorbidities among those with an

opoid-abuse related diagnosis were psychiatric disorders, non-opoid substance abuse disorders,

trauma and hepatitis A, B, or C.18

Corroborative evidence comes from another study by White et al. From 2003-2007, this group

reviewed claims data from both a privately insured sample and Florida Medicaid recipients and

found that opioid abusers suffered from psychiatric disorders, non-opioid substance abuse

disorders, and other chronic conditions more frequently than non-abusers, regardless of

‡‡‡



population (HealthCore Blue Cross and Blue Shield) and the Arkansas Medicaid population §§§

As comorbidities are both a risk factor for, and outcome of, nonmedical prescription opioid use, misuse and

abuse, relevant research will be discussed in both the risk factor and outcomes sections.



1.29

insurance type.105

Taken together, these studies provide evidence of the disparate and copious

disease burden of opioid abusers compared to controls. The interaction of opioid abuse with

Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies – A Review of the

Literature

1.30

Table 1-17. Prevalence of Select Comorbidities among Opioid Abusers compared to Nonabusers Privately Insured

1998-2002a

National Medicaid

2002-2003b

Privately Insured

2003-2007c

Florida Medicaid

2003-2007c

Abusers

n=740

Controls

n=1,266

Abusers

N=50,162

Controls

N=150,485

Abusers

n=4,474

Controls

n=4,474

Abusers

n=4,467

Controls

n=4,467

Psychiatric Disorders 71.1% 8.4% 49.3% 26.1% 74.5% 12.0% 68.5% 23.2%

Trauma 36.5% 15.0% 31.2% 19.8% 45.5% 18.2% 45.5% 12.4%

Non-Opioid Substance

Abuse 50.4% 1.2% 45.1% 8.2% 46.6% 1.5% 59.7% 6.6%

Non-Opioid Poisoning 17.6% 0.2% NR NR 17.1% 0.6% 23.1% 0.8%

Gastrointestinal

Bleeding 8.0% 2.6% 8.6% 6.3% 13.1% 4.5% 16.9% 4.6%

Skin Infections/

Abscesses 10.1% 2.5% 12.7% 5.4% 12.4% 4.0% 17.8% 4.1%

Sexually Transmitted

Disease 8.0% 4.3% 8.6% 7.6% 8.1% 4.0% 9.6% 5.6%

Hepatitis A, B or C 6.5% 0.2% 17.1% 2.4% 4.1% 0.2% 12.4% 1.1%

Pancreatitis 0.9% 0.05% 1.7% 0.6% 2.5% 0.2% 4.8% 0.4%

Chronic Low Back

Pain NR NR NR NR 21.7% 3.2% 24.8% 3.3%

Arthritis NR NR 27.3% 19.5% 17.7% 5.1% 18.0% 3.9%

Other Back/Neck

Disorders NR NR 27.9% 18.1% 14.5% 2.3% 9.8% 1.4%

Fibromyalgia NR NR NR NR 3.8% 0.5% 2.8% 0.2%

Neuropathic Pain NR NR 9.8% 7.6% 3.2% 0.8% 2.8% 0.7%

Source: aWhite et al (2005). Privately insured population based on administrative claims data for approximately 2 million insured members from 16 large

employers. bMcAdam-Marx et al (2010). Medicaid population based on data from the Medicaid Analytic eXtract (MAX) from the Centers for Medicare and Medicaid

Services (CMS). cWhite et al (2011). Privately insured population based on administrative claims from 40 self-insured Fortune 500 companies. Florida Medicaid population based

on administrative claims for all Medicaid-eligible beneficiaries in the state.

Note: Controls were randomly selected, demographically matched individuals. Abusers were patients with least one claim with an ICD-9 code related to prescription opioid abuse during the study period (304.0, 304.7, 305.5, and 965.0 (excluding 965.01) NR = Not Reported



1.31

psychiatric disorders, substance abuse, and medical comorbidities will require combined research

and policy-making efforts to establish a knowledge base to inform risk-reduction and effective

use of evidence based treatment.

Health Outcomes: Opioid-Related Mortality

As seen in Table 1-18, prescription opioid overdose deaths have increased over 250% over the

past decade.13

Longitudinal studies have found that nearly 100 opioid-related overdose deaths

occur each day in the United States, which is greater than deaths attributable to heroin and

cocaine combined.26

In 2008, national mortality data shows that prescription opioid overdose

deaths account for over 40% of all drug overdose deaths. Among prescription opioid overdose

deaths, methadone-related deaths account for one-third.13

In 2008, the CDC reported that

prescription opioid overdose deaths occurred at a rate of nearly 4.8 deaths per 100,000

population;115

in comparison, the methadone-related overdose death rate was approximately 1.5

overdose deaths per 100,000 in the same year.116

Prescription opioid overdose has now surpassed

firearms and motor vehicle accidents as the leading cause of unintentional injury or death among

35-54 year olds, and, behind motor vehicle accidents, the second leading cause overall.1,43,105,117

Table 1-18. Deaths Involving Prescription Opioids, 2000-2008 2000 2001 2002 2003 2004 2005 2006 2007 2008

All Drug

Overdose Deaths 17,415 19,394 23,518 25,785 27,424 29,813 34,425 36,010 36,450

Opioid-Related

Overdose Deaths

4,030

(23.1%)

5,528

(28.5%)

7,456

(31.7%)

8,517

(33.0%)

9,817

(35.8%)

10,928

(36.7%)

13,723

(39.9%)

14,408

(40.0%)

14,800

(40.6%)

Methadone-

Related Opioid

Overdose Deaths

986

(5.6%)

1,456

(7.5%)

2,358

(10.0%)

2,972

(11.5%)

3,845

(14.0%)

4,460

(14.0%)

5,406

(15.0%)

5,518

(15.3%)

4,924

(13.5%)

Source: Warner et al (2011)

Note: Data based on death certificate data from the United States National Center for Health Statistics, National

Vital Statistics System. Percentages use all drug overdose deaths as the denominator.

From 1999-2002, the number of opioid-related deaths increased by 91.2%, whereas deaths due to

heroin increased 12.4% and deaths due to cocaine increased 22.8%.71,118

During that time,

opioid-related deaths without the presence of heroin or cocaine on post-mortem toxicology

screens increased by nearly 130%.71

The CDC Office of Analysis and Epidemiology found that

from 1999-2004, methadone-related deaths increased 390%, compared to a 54% increase in all

poisoning deaths.118

Webster et al reports that while methadone prescriptions represent less than

5% of all opioid prescriptions, it is associated with approximately 33% of opioid-related deaths

in the U.S.113

Characteristics Associated with Opioid-Related Mortality

Analyses of National Vital Statistics data have found increases in the rate of prescription opioid

overdose deaths since 1999, with the highest rates among males, American Indians/Alaska

Natives, non-Hispanic Whites, and individuals ages 45-54 (see Table 1-19). This analysis also

found a positive correlation between the percentage of non-Hispanic Whites living below the

poverty line and the increase in prescription opioid overdose among those individuals.1,119



1.32

Table 1-19. Prescription Opioid Overdose Death Rates by Selected Demographics, 2000-2008 1999

a 2006

a 2008

b

Overall 1.6 4.6 4.8

Gender

Male 2.1 5.8 5.9

Female 1.1 3.3 3.7

Race/Ethnicity

Non-Hispanic White 1.6 5.8 6.3

Hispanic White 1.7 2.0 2.1

Non-Hispanic Black 0.9 2.7 1.9

Asian/Native Hawaiian/Pacific Islander NR NR 0.5

Native American/Alaska Native NR NR 6.2

Age Group

0-14 0.0 0.1 0.1

15-24 0.8 3.8 3.7

25-34 1.9 6.7 7.1

35-44 3.7 8.3 8.3

45-54 3.3 9.7 10.4

55-64 1.1 4.0 5.0

65 and older 0.3 0.9 1.0

Source: aWarner et al (2009);

bCDC (November 2011)

Note: Rates are deaths per 100,000 population.; NR=Not Reported

Using 2006 data from the West Virginia prescription drug monitoring program as well as

medical examiner data, Paulozzi et al compared deaths involving methadone and those involving

other prescription opioids. They found that individuals who overdosed on methadone were more

likely to be younger, less likely to be married, less likely to finish high school or attend college,

and more likely to have been prescribed methadone within 30 days of death.102

In a review of the

literature, Webster et al found that opioid-related death rates were highest among individuals

ages 40-49, males, and individuals living in rural or nonmetropolitan counties.113

Other

indicators of increased likelihood of prescription opioid overdose death include history of mental

health and/or substance abuse disorders.60

From 1999-2004, Paulozzi and Xi reported a shift in the location of the majority of opioid

overdoses in the United States, from occurring in urban areas to rural areas. During that period,

the mortality rate in urban areas increased by 52%, whereas the rate in rural areas had increased

by 371%.120

From 2008-2009, the rate of opioid overdose death ranged from 5.5 deaths per

100,000 population (Nebraska) to 27.0 per 100,000 (New Mexico), compared to the national rate

of 11.9 per 100,000 population. Twenty-seven states had overdose death rates above the national

average, and over three-quarters of those states had a prevalence of nonmedical opioid use that

surpassed the national prevalence. In comparison, of the twenty-four states with death rates at or

below the national level, only one-quarter had a prevalence rate for non-medical opioid use

above the national rate.1 Wunsch et al reviewed all poisoning deaths in western Virginia from

1997-2003 and found hydrocodone, oxycodone, and fentanyl were more likely to be used by

individuals living in rural areas, whereas mortality rates due to methadone did not differ by

geographic location.121

In their review of 2006-2007 North Carolina state death certificate data for Medicaid recipients,

Whitmire and Adams uncovered an association of substance abuse and mental health disorders



1.33

with opioid related mortality.122

Hall et al analyzed West Virginia medical examiner, prescription

drug monitoring program, and opiate treatment program data and found that 94.6% of decedents

had identified substance abuse indicators. In this study, nonmedical routes of exposure and illicit

contributory drugs were particularly prevalent among drug diverters, defined as a death

involving a prescription drug without a documented prescription and having received

prescriptions for controlled substances from five or more clinicians during the year prior to death

(i.e., doctor shopping). Polysubstance abuse was rampant; multiple contributory substances were

implicated in 234 deaths (79.3%).60

Both studies highlight the fact that the majority of overdose

deaths were associated with nonmedical use and diversion of pharmaceuticals, primarily opioid

analgesics. In addition, these studies also suggest that fatal overdose among the Medicaid

populations were associated with mental health and/or substance abuse disorders. 60,122

Routine

medical care for pain management was also mentioned as an associated factor in the North

Carolina study but the authors opined that prescription opioid overdoses may be more closely

associated with substance abuse and mental health disorders than with routine medical care for

pain management.122

State-Specific Opioid-Related Mortality

As displayed in Table 1-20, states have been experiencing a consistent upward trend in

prescription opioid-related mortality.60,121,123-128

State studies have documented significant

increases among specific prescription opioids, including methadone, oxycodone and

hydrocodone. For example, methadone-related deaths have increased anywhere from 566%

(North Carolina)125

to 1,695% (Oklahoma)124

. Examining Oklahoma medical examiner data,

Piercefield et al found only one death involving oxycodone from 1994-1996 (representing less

than 1% of all unintentional prescription drug overdoses), but these deaths increased from 2004-

2006 to 174 deaths (representing nearly 17% of all prescription drug overdoses).124

Opioid-Related Mortality: Opioid Type and Dosing Patterns

Analyses of state data reveal that prescription opioid-related overdose death is not always

associated with a valid prescription for the drug. In an examination of Utah’s medical examiner

data, the authors found that 40% of decedents involved in methadone-related deaths had a valid

prescription for the drug, and 50% of those individuals were taking methadone for the first

time.123

In a review of 2006-2007 North Carolina death certificate data for Medicaid recipients,

Whitmore and Adams found that a large proportion of the methadone deaths occurred

presumably because of taking non-prescribed or illegally purchased methadone. Of the 98

methadone-related deaths among North Carolina Medicaid enrollees, only 26 (26.5%) had

received a Medicaid-paid methadone prescription or methadone clinic services within one year

of death. 122

An analysis of West Virginia’s Controlled Substances Monitoring Program found

that less than half (44.4%) of decedents had a prescription for opioids, and opioids had been

dispensed within 30 days prior to death in 30% of cases. Despite contributing most frequently to

overdose death, only 32.1% of decedents had a valid prescription for methadone, whereas 85.1%

had a valid prescription of hydrocodone and 60.7% of decedents had a valid prescription for

oxycodone.60

Weimer et al and Madden & Shapiro found similar trends analyzing methadone-

related deaths in western Virginia and Vermont, respectively. In the former study, researchers

found that only 28% of methadone-related decedents had a valid prescription for the drug,129

and

33% of decedents in the latter study.130


Literature

1.34

Table 1-20. Opioid-Related Mortality by State

PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction Program; NR = Not reported in this study

Citation State Years

Studied Data Source Population Drug Category Number of Deaths % Increase

Ballesteros,

2003

North

Carolina

1997-2003 Medical examiner All Residents Methadone 1997-2003: 198 deaths 566%

CDC, 2005 Utah

1991-2003 Medical examiner All Residents Prescription Drugs 1991-1998: 231 deaths

1999-2003: 502 deaths

385%

Methadone 1991-1998: 18 deaths

1999-2003: 164 deaths

1,358%

Oxycodone 1991-1998: 10 deaths

1999-2003: 111

1,676%

Hydrocodone 1991-1998: 31

1999-2003: 83

328%

Hall, 2008 West Virginia

2006 Medical

examiner; PDMP;

Treatment

Records

All Residents Prescription Drugs 295 deaths

Prescription Opioids 275 deaths NR

Methadone 112 deaths NR

Hydrocodone 67 deaths NR

Oxycodone 61 deaths NR

Wunsch, 2009 Virginia

1997-2003 Medical examiner Residents of

Western VA

Prescription Drugs 893 deaths


Methadone 184 deaths NR



CDC, 2009 Washington 2004-2007 Death certificate All Residents Prescription Opioids 1,668 deaths NR

Methadone 1,068 deaths NR



Medicaid

Enrollees


PRR Enrollees Prescription Opioids 34 deaths NR

Ohio, 2010 Ohio

2010 Death Certificate All residents All Drugs 1,544 deaths NR



Literature

1.35

Table 1-20. Opioid-Related Mortality by State, Continued

PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction Program; NR = Not reported in this study

Citation State Years

Studied Data Source Population Drug Category

Number of Deaths

% Increase

Piercefield,

2010

Oklahoma 1996-2002 Medical

Examiner

All Residents Methadone 1994-1996: 21 deaths

2004-2006: 377 deaths

1,695%

Oxycodone 1994-1996: 1 death

2004-2006: 174 deaths

17,300%

Hydrocodone 1994-1996: 9 deaths

2004-2006: 220 deaths

2,344%

Shah, 2011 New Mexico 1990-2005 Medical

Examiner

All Residents Prescription Opioids 955 deaths 159%



1.36

As depicted in Figure 1-6, multiple studies demonstrate a relationship between opioid overdose

death and increasing opioid dosage levels, often expressed as milligrams of morphine equivalent

dose per day (mgMED/d). Using data from the CONSORT study – CONsortium to Study Opioid

Risks and Trends – in Washington State, Dunn et al found that patients receiving

>100mgMED/day had a 9.0-fold increase in overdose risk compared to patients receiving the

lowest daily dose (<20mgMED/day).36

Bohnert et al found a similar trend among Veterans

Health Administration patients, with a higher overdose rate among patients with maximum daily

doses of 50-100mgMED/day and >100mgMED/day, as well as among patients who had

concurrent fills for regularly scheduled and as-needed opioids.37

Gomes et al also found a similar

trend among residents of Ontario, Canada who received opioids through a publicly funded

prescription drug coverage program from 1997-2006. Compared with doses between 0-

19mgMED/day, the odds of overdose were twice as high among individuals receiving doses

between 100-199mgMED/day, and the odds were nearly three times as high among individuals

receiving doses in excess of 200mgMED/day.38

In a subsequent analysis of the same population,

Gomes et al found a similar trend in two-year opioid-related mortality rates. Patients with daily

doses at or below 200mgMED/d had a mortality rate of 1.63 deaths per 1,000 population,

whereas patients with doses between 201-400mgMED/d or exceeding 401mgMED/d had

mortality rates of 7.92 and 9.94 per 1,000 population, respectively.131

Paulozzi et al examined the relationship between prescribing history and overdose death by

comparing individuals in New Mexico from 2006-2008 who died of unintentional overdose with

matched controls. Among individuals who died of unintentional drug overdoses, 20% had an

average daily opioid dose exceeding 120mgMED/d, compared with 2.1% of matched controls.

Nearly 30% of decedents had overlapping opioid prescriptions compared with 3.5% of control

patients. The authors defined overlapping prescriptions as those in the same category of drug that

overlapped by 25% or more of the days prescribed.132

Opioid-Related Mortality and Doctor/Pharmacy Shopping

Many studies have analyzed the relationship between engaging in doctor/pharmacy shopping and

the risk of opioid-related mortality. For more information on doctor/pharmacy shopping and the

vary definitions used in research, please refer to the Background section. In 2006, Hall et al

examined data from the West Virginia medical examiner, prescription drug monitoring program,

and opioid treatment programs and measured the prevalence of doctor shopping, defined as

receiving prescriptions from 5 or more prescribers during the year prior to death. Of the 295

unintentional prescription drug overdose deaths, approximately 21% of decedents met criteria for

doctor shopping. The odds of doctor shopping were higher among males and individuals ages

35-44.60

Using data from the West Virginia prescription drug monitoring program and state death

data between 2005 and 2007, Peirce et al analyzed the prevalence of doctor or pharmacy

shopping among Schedule II-IV-related overdose deaths and all other individuals who received a

controlled substance (control group). The authors defined doctor or pharmacy shopping as

receiving prescription opioids from four or more prescribers or filling prescriptions at four or

more pharmacies within the six months before death. The study identified 698 opioid-related

deaths, of which 25% were doctor shoppers and 17.5% were pharmacy shoppers, compared to

3.6% and 1.3% of controls. In addition, 66% of deaths involved individuals who had four or

more prescriptions drugs dispensed by different clinicians within the past 6 months, whereas

only 17% of individuals in the control group did so. Nearly 40% of deaths involved individuals


Literature

1.37

1-19mgMED/d 20-49mgMED/d 50-99mgMED/d >100mgMED/d >200mgMED/d

Dunn (i) 1.00 1.44 3.73 8.87

Dunn (ii) 1.00 1.19 3.11 11.18

Gomes (iii)* 1.00 1.32 1.92 2.04 2.88

Bohnert (iv) 1.00 1.88 4.63 7.18

Bohnert (v) 1.00 1.58 4.73 6.64

Bohnert (vi) 1.00 1.42 2.76 4.54

0.00

2.00

4.00

6.00

8.00

10.00

12.00 O

ver

do

se R

isk

Opioid Dosage Level (mg morphine equivelant per day)

Source: (i) Dunn et al (2010). Risk of opioid-related overdose death or definite/probable opioid-related non-fatal overdose

(ii) Dunn et al (2010). Risk of opioid-related overdose death or serious non-fatal event (serious=requiring hospitalization, unconsciousness, respiratory failure)

(iii) Gomes et al (2011). Risk of opioid-related death expressed as adjusted odds ratio*

(iv) Bohnert et al (2011). Risk of opioid-related death among patients with a chronic pain diagnosis

(v) Bohnert et al (2011). Risk of opioid-related death among patients with an acute pain diagnosis

(vi) Bohnert et al (2011). Risk of opioid-related death among patients with a diagnosed substance use disorder

Note: All risks are expressed as hazard ratios unless otherwise. MED/d = morphine equivilant dose per day

Figure 1-6. Relationship between Opioid Dosage Level and Fatal/Non-Fatal Overdose Risk



1.38

with three or more unique controlled substances dispensed within the past 6 month, compared to

7% of controls. Approximately 29% of deaths involved individuals who only had one controlled

substance dispensed in the past 6 months, compared to three-quarters of the control group.65

In Ohio, there were 1,047 unintentional poisoning deaths in 2008, and prescription opioids were

involved in 37% of these (approximately 837 deaths). According to the state Department of

Health, Violence and Injury Prevention Program, 16% of unintentional overdose deaths in Ohio

involved individuals with a history of doctor shopping, occurring most frequently among females

ages 25-44 (the Department does not break out doctor/pharmacy shoppers by opioid-related

deaths only). One-quarter of all unintentional poisoning deaths involved individuals who

obtained opioids through diversion, with higher rates among males’ ages 15-34 and 65 and older,

as well as females ages 15-24. Methadone was diverted more frequently than other opioids, and

71% of methadone deaths involved diverted methadone.133

Methadone used for pain treatment

and methadone used for opioid substance abuse treatment are not distinguished in Ohio overdose

data. Some experts have conjectured that methadone deaths are more likely to result from

methadone’s use as a pain medication. The underlying rationale is that the timing of the

increased deaths coincided with the increase in use of methadone as a prescription analgesic and

the increased dangers of methadone to opioid naïve patients versus long-term users of opioids in

treatment.134

Gomes et al examined the prevalence of doctor and pharmacy shopping among residents of

Ontario, Canada who received opioids through a publicly funded prescription drug coverage

program and suffered an opioid-related overdose death from 1997-2006 . Of the 593 opioid-

related deaths among individuals receiving opioids for non-cancer pain, they found that

approximately 10% of decedents obtained prescriptions for opioids from four of more prescribers

and 10% filled their opioid prescriptions at four or more pharmacies in the six months before

death (compared to 5.7% and 4.3% of controls, respectively). Nearly 3.5% of decedents obtained

their prescriptions from six or more prescribers and 2.6% filled their opioid prescriptions at six

or more pharmacies during the same period (compared to 1.6% and 0.7% of controls,

respectively).38

As a result of recent research on the relationship between mortality and opioid dose and number

of prescribers, the CDC has identified a subset of high risk patients. This subset consists of the

10% of opioid users seeking care from multiple doctors (i.e., doctor shoppers) and are prescribed

high doses (defined as >100mgMED/day) and account for 40% of all opioid-related overdoses

(see Figure 1-7).35



1.39

Outcomes: Health Care Costs

Health care related to the nonmedical use of opioids has been estimated to cost insurers (both

private and public) approximately $72.5 billion annually.1 Banthin & Miller used data from the

Medical Expenditures Panel Survey (MEPS) to examine trends in prescription opioid utilization

and costs between 1996/1997 and 2001/2002. From 1996/1997 to 2001/2002, Medicaid

expenditures for prescription opioids increased 153% from nearly $257 million to $650 million,

whereas expenditures for all prescription drugs increased 104% during that time, from $11.6

billion to $23.7 billion.14

Prescription opioid expenditures per user increased by 107%, from $75

to $155, but the number of recipients using prescription opioids only increased 22.5% (compared

to increases of 106.6% and 11.5% for all prescription drugs).14

Using data from the U.S. Centers

for Medicare and Medicaid (CMS), Brixner et al also analyzed the trends in prescription opioid

utilization and costs from 1998-2003. The authors found that during that time, Medicaid

expenditures for opioids increased nearly 300%, from $311 million to approximately $1.2

billion, accounting for 4% of total Medicaid prescription drug expenditures.15

In two different studies, White et al compared the costs incurred by patients with a diagnosis of

prescription opioid abuse compared to controls (individuals without this diagnosis) in two

privately insured samples (1998-2002 and 2003-2007) and a Florida Medicaid sample (2003-

2007) (see Figure 1-8). Data for the 1998-2002 privately insured sample came from claims data

for approximately 2 million members from 16 large, nationwide employers; data for the 2003-

2007

10%

40% 10%

40% 80%

20%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

High Risk: Patients seeing multiple doctors for high dose opioids

Low Risk: Patients seeing one doctor for high dose opioids

Low Risk: Patients seeing one doctor for low dose opioids

Taken from: CDC Grand Rounds: Prescription Drug Overdoses - a U.S. Epidemic. Figure 3

Opioid Users Opioid-Related

Overdoses

Figure 1-7. Percentage of Opioid Users and Overdoses, by Risk

Group



1.40


1.41

$7,659

$318

$9,711

$750

$14,410

$1,314

$5,398

$928

$5,795

$1,697

$2,826

$657

$2,034

$386

$3,918

$877

$5,820

$3,104

$793

$198

$4,769

$323

$3,667

$6,466

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

Opioid Abuse Patients Controls Opioid Abuse Patients Controls Opioid Abuse Patients Controls

Hospital Inpatient Costs Physician/Outpatient Costs Drug Costs Other Costs**

Privately-Insured Sample,

1998-2002

Florida Medicaid Sample,

2003-2007

Privately-Insured Sample,

2003-2007

$15,884

$1,830

$3,647

$24,193

$11,541

$26,724

Excess Cost: $14,054



Source: White et al (2005); White et al (2011)

*Costs for the 1998-2002 sample are in 2003 dollars; costs for 2003-2007 sample are in 2009 dollars

**Other costs include ED visits, lab services, and treatment occuring at other places of service for patients ages 12-64 years old

Figure 1-8. Average Annual Direct Health Care Costs* per Opioid Abuse Patient



1.42

privately insured sample came from claims data for approximately 9 million members from 40

self-insured, nationwide employers. The 2003-2007 Florida Medicaid dataset included 6 million

individuals. Researchers identified opioid abusers as patients with at least one ICD-9-CM

(International Classification of Diseases, 9th Revision, Clinical Modification) code for opioid

abuse, and controls were demographically matched individuals without an opioid abuse

diagnosis. From 1998-2002, White et al found that opioid abusers had average per-patient health

care costs of $15,884, more than 8 times that of nonabusers ($1,830).9 While physician visits,

outpatient visits and prescription drugs comprise a smaller percentage of total direct costs for

abusers than nonabusers, the costs associated with these visits for opioid abusers were 5 times

that of nonabusers.9 From 2003-2007, variation in costs between the abusers and controls was

$20,546 in the privately insured population and $15,183 in the Medicaid population.9,105

In the 1998-2002 analysis of the privately insured sample, White et al used a multivariate

regression approach to calculate the costs of abusers vs. nonabusers while controlling for other

factors, such as comorbidities. To accomplish this, they used compared opioid abusers to a

comparison group with a diagnosis of depression, which is a common, consistently diagnosed,

and costly mental health disorder. This analysis found that per-patient costs for opioid abusers

were 1.8-times that of the comparison group ($16,722 vs. $4,875, respectively).9

McAdam-Marx and colleagues compared the costs incurred by Medicaid recipients with an

opioid abuse-related diagnosis (abuse, dependence, or poisoning) to matched controls from

2002-2003 using a multivariate regression analysis adjusted for patient characteristics that could

influence cost outcomes. They found that opioid abuse/dependence patients in the Medicaid

group incurred costs 68% higher than those in the control group ($14,537 vs. $8,663,

respectively). Medicaid opioid abuse/ dependence patients were more likely to have

comorbidities than the control group, and after adjusting for comorbidities (as well as race,

gender, and geographic location), the authors found that the costs incurred by opioid

abuse/dependence patient still exceeded those of the matched controls. The authors reasoned that

effective interventions to manage comorbidities and prevent opioid abuse could help to reduce

costs associated with opioid abuse in the Medicaid population.18

Health Care Costs and Doctor Shopping

The GAO conducted two analyses of claims data for Medicaid and Medicare beneficiaries and

found high numbers of beneficiaries receiving multiple prescriptions for the same controlled

substance from multiple prescribers. They analyzed Medicaid claims from 2006-2007 from five

states (California, Illinois, New York, North Carolina, and Texas) to find patients meeting their

definition of doctor shopping - obtaining prescriptions for the same controlled substance from six

or more prescribers. This analysis found that 65,000 Medicaid beneficiaries met this definition,

representing less than 1% of the total number of beneficiaries in these states. They also analyzed

Medicare claims from 2008 from five states (California, Georgia, Maryland, Massachusetts, and

Texas) and found 170,000 Medicare beneficiaries meeting criteria for doctor shopping (obtaining

prescriptions for the same controlled substance from five or more prescribers), representing only

1.8% of Medicare beneficiaries in these states. The distributions of number of providers and

associated costs are displayed in Table 1-22 and Table 1-22. Costs to both Medicaid and

Medicare for all controlled substances represent 5-6% of the total costs of these drugs to the

programs.17



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Table 1-21. Prescription Opioid Doctor Shopping among Medicaid Beneficiaries and Associated

Costs, 2006-2007 Number of Prescribers in Selected States

6-10 11-15 16-20 21-50 51+ Total Total Prescription

Cost

Fentanyl 777 41 6 1 0 825 $7,810,000

Hydrocodone 31,364 3,518 723 340 9 35,954 $9,172,000

Hydromorphone 590 67 14 11 0 682 $983,000

Methadone 824 76 9 2 0 911 $546,000

Morphine 810 50 8 1 0 869 $4,119,000

Oxycodone 5,349 435 73 18 0 5,875 $10,163,000

Total Prescription Opioids 39,714 4,187 833 373 9 45,146 $32,793,000

Total Controlled Substances* 64,239 5,066 926 396 9 70,636 $63,280,000

Source: United States Government Accountability Office, 2009

Note: The numbers in the columns do not represent unique beneficiaries. There are 64,920 total unique beneficiaries

*Additional analyzed substances included amphetamine derivatives, benzodiazepine, methylphenidate, and non-

benzodiazepine sleep aids

Table 1-22. Prescription Opioid Doctor Shopping among Medicare Beneficiaries and Associated

Costs, 2008 Number of Prescribers in Selected States

5-10 11-15 16-20 21-50 51+ Total Total Prescription

Cost

Codeine with Acetaminophen 1,500 21 4 0 0 1,525 $244,930

Fentanyl 5,043 24 8 2 0 5,077 $19,124,853

Hydrocodone 92,801 3,553 700 335 5 97,394 $18,949,677

Hydromorphone 2,453 77 13 8 0 2,551 $1,236,678

Meperidine 149 8 0 0 0 157 $90,236

Methadone 3,414 9 0 0 0 3,423 $859,208

Morphine 6,354 33 4 0 0 6,391 $9,311,773

Oxycodone 54,183 1,974 440 235 5 56,837 $91,681,281

Tramadol 4,364 134 33 14 0 4,527 $1,037,423

Total Prescription Opioids 170,261 5,833 1,202 594 10 177,882 $141,498,636

Total Controlled Substances* 181,823 5,927 1,214 600 10 189,574 $147,948,251

Source: United States Government Accountability Office, 2011

Note: The numbers in the columns do not represent unique beneficiaries. There are 170,029 unique beneficiaries

*Additional analyzed substances included amphetamine derivatives, benzodiazepine, carisoprodol,

methylphenidate, and non-benzodiazepine sleep aids

State-Specific Health Care Costs

Some states, such as Utah, Ohio and Florida, have higher opioid expenditures per enrollee

compared to the national average, while other states, such as California, New York and Texas,

have lower per enrollee expenditures.15

Using claims data from the Louisiana Workers’

Compensation Corporation, Bernacki et al analyzed the trends in annual cumulative opioid dose

and cost of opioids per claim prescribed for work-related injuries from 1999-2009. The

researchers compared claims for opioids prescribed during the year of the work-related injury

(treatment for acute pain) versus claims for opioids prescribed for up to seven years following

the injury (treatment for chronic pain) but only the chronic pain group had significant increases

in the cost of opioid prescriptions per claim per year. While the average annual cumulative

opioid dose increased significantly for claimants treated for acute and chronic pain, only the



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chronic pain group had significant increases in the cost of opioid prescriptions per claim per

year. Bernacki et al also found that although the cost per opioid dose for long-acting and short-

acting opioids was similar, the cost per claim that involved long-acting opioids for chronic pain

was approximately eight times higher than the costs incurred for claims involving short-acting

opioids for chronic pain. The authors hypothesize that the decision to prescribe long-acting

opioids for chronic pain results in an increase in the annual cumulative opioid dose. 135

Dembe et al reviewed Ohio workers compensation data from 2008-2009 and found nearly a 12%

increase in the average cost per opioid prescription ($81 to $92), a 22% increase in the average

annual cost per opioid prescription ($725 to $895). During that time period, there was a nearly

13% increase in overall prescription opioid expenditures by the workers compensation program

($42.6 million to $47.9 million), compared to a 5% increase prescription drug expenditures

nationally during the same time period.88

Swedlow et al reviewed California’s workers

compensation claims data from 1993-2009 to analyze physician prescribing patterns for

Schedule II opioids. As previously mentioned, they found that the top 1% of prescribers

(approximately 93 physicians) accounted for approximately one-third of the total Schedule II

opioid prescriptions and slightly more that 40% of the total milligrams morphine equivalent

(MME) prescribed. On average, these 93 physicians had more than 53 claims each in which they

prescribed Schedule II opioids, resulting in total payments of $392,667 per physician or $36.5

million for all 93 prescribers combined, accounting for 42% of the total Schedule II opioid

payments from 1993-2009.89

Outcomes: Societal Costs

The societal burden attributable to nonmedical use of prescription opioids was estimated to be

$9.5 billion in 2005.43,56

Societal burden includes direct and indirect health care costs, as well as

costs to the legal system and the costs of foregone productivity.19,20,56

Birnbaum and colleagues

analyzed claims data and secondary sources from 2001 and 2007 to estimate the total societal

burden of prescription opioid abuse and found significant increases in costs within that time

period (see Table 1-23). Birnbaum and colleagues grouped costs associated with prescription

opioid abuse into three categories; 1) Lost workplace productivity costs, 2) Healthcare costs, and

3) Criminal justice costs. The researchers calculated excess medical costs due to absenteeism by

multiplying the “days of lost work due to medical utilization by daily wage.” They calculated

lost productivity due to incarceration by multiplying the “per inmate cost of incarceration, in

terms of lost wages...by the number of inmates incarcerated for crimes attributable to opioid

abuse.”19,20

Presenteeism has been defined as lost productivity due to an employee attending

work despite a medical illness that will inhibit work functioning.136,137

In 2007, the estimated

total economic burden of prescription opioid abuse was approximately $55.7 billion, compared

to nearly $8.6 billion in 2001. In 2001, lost productivity accounted for the majority of costs

(53%), followed by healthcare costs (30.4%), and criminal justice costs (16.7%). By 2007, the

proportion of costs attributable to lost productivity and criminal justice slightly decreased (45.9%

and 9.2%, respectively), whereas healthcare costs increased (44.9%).

Table 1-23. Annual Societal Burden of Prescription Opioid Abuse, 2001 & 2007 2001 2007

Costa

Percentageb

Costa

Percentageb

Total Societal Cost $8,584 100% $55,721 100%

Lost Workplace Productivity Costs $4,545 53.0% $25,582 45.9%



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2001 2007

Costa

Percentageb

Costa

Percentageb

Premature Death $864 10.1% $11,218 20.1%

Lost Wages/Employment $3,023 35.2% $7,931 14.2%

Presenteeism NR NR $2,044 3.7%

Excess Medically Related Absenteeism NR NR $1,814 3.3%

Incarceration Costs $657.5 7.6% $1,768 3.2%

Excess Disability Costs NR NR $807 1.4%

Healthcare Costs $2,607 30.4% $24,998 44.9%

Excess Medical and Drug Costs $2,481 28.9% $23,725 42.6%

Substance Abuse Treatment $126 1.4% $1,119 2.0%

Substance Abuse Prevention NR NR $85 0.2%

Substance Abuse Research NR NR $69 0.1%

Criminal Justice Costs $1,430 16.7% 5,142 9.2%

Correctional Facilities $771 9.0% $2,265 4.1%

Police Protection $438 5.1% $1,526 2.7%

Legal Adjudication Costs $221 2.6% $726 1.3%

Property Loss due to Crime NR NR $625 1.1%

Source: Birnbaum et al (2006); Birnbaum et al (2011)

Notes: (a) All costs are adjusted to 2009 dollars and are in millions

(b) Percentages represent a proportion of the total societal cost

NR= these costs were not calculated in the 2001 analysis

Hansen et al also attempted to quantify the societal burden attributable to the nonmedical use of

prescription opioids and by specific prescription opioid drugs. They used 2006 NSDUH

prevalence data and cost estimates from a variety of secondary sources to estimate the economic

burden (total prescription opioids and by specific drug) in terms of direct substance abuse

treatment, medical complications, lost productivity, and criminal justice. Costs associated with

medical complications were limited to include HIV/AIDS, chronic hepatitis C, and neonatal care.

The researchers calculated costs to crime victims as the “product of the number of crime victims

times the percentage with drug involvement times the average cost per victim.” This analysis

estimated the total economic burden of nonmedical prescription opioid use at approximately $53

billion, consistent with Birnbaum’s analysis of 2007 costs (Table 1-23). As with Birnbaum’s

studies, costs attributable to lost productivity accounted for the majority of costs. As seen in

Table 1-24, these costs generally accounted for about three-quarters of the total costs, except in

the case of methadone, in which case the costs attributable to premature death are significantly

higher than the other prescription opioid categories.21

Table 1-24. Societal Burden of Nonmedical Prescription Opioid use, 2006 Oxycodone Hydrocodone Methadone Other Total

Total $13,276.28 $12,732.83 $6,178.70 $21,032.69 $53,221.50

Substance Abuse $681.42

(5.1%)

$528.18

(14.1%)

$98.50

(1.6%)

$911.83

(4.3%)

$2,219.82

(4.2%)

Hospital Inpatient $126.20 $97.82 $18.24 $168.86 $411.11

Hospital Outpatient $99.85 $77.39 $14.43 $133.59 $325.26

Physicians $111.31 $86.28 $16.09 $148.93 $362.61

Substance Abuse Facilities $344.07 $266.69 $49.74 $460.34 $112.83

Medical Complications $192.52

(1.5%)

$1.40

(0.01%)

$30.21

(0.5%)

$527.90

(2.5%)

$752.04

(1.4%)

HIV/AIDS $122.12 NR $19.22 $335.79 $477.14

Chronic Hepatitis C $69.21 NR $10.90 $190.43 $270.45



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Oxycodone Hydrocodone Methadone Other Total

Neonatal Care $1.19 $1.40 $0.09 $1.77 $4.45

Lost Productivity $10,437.70

(78.6%)

$9,811.13

(77.1%)

$5,820.45

(94.2%)

$15,959.97

(75.9%)

$42,029.23

(79.0%)

Premature Death $2,604.26 $2,018.60 $4,614.78 $3,168.06 $12,405.70

Unemployment $4,273.91 $3,458.50 $789.80 $6,209.43 $14,731.64

Incarceration $3,559.53 $4,334.03 $415.87 $6,582.46 $14,891.89

Criminal Justice $1,964.64

(14.8%)

$2,392.12

(18.8%)

$229.53

(3.7%)

$3,633.12

(17.3%)

$8,219.41

(15.4%)

Police $813.81 $990.88 $95.08 $1,504.94 $3,404.70

Legal Costs $406.91 $495.45 $47.54 $752.47 $1,702.37

Incarceration $596.04 $725.73 $69.64 $1,102.22 $2,493.63

Cost to Crime Victims $147.89 $180.07 $17.28 $273.48 $618.71

Source: Hansen et al, 2011

Notes: All costs are expressed in millions. Prescription opioids in the “Other” category include propxyphene,

codeine, meperidine, hydromorphone, morphine, fentynal and other unspecified prescription opioids.

NR=These costs were not calculated.

Summary

As the prevalence of nonmedical opioid use, misuse and abuse has risen, so has the frequency of

opioid-related outcomes including health care utilization, mortality and costs. Studies have found

that compared to individuals who do not abuse prescription opioids, abusers are more likely to

have a physician visit, ED visit, or an inpatient or outpatient mental health admission. Deaths due

to prescription opioid overdose are now the leading cause of drug-related death, surpassing

heroin and cocaine combined. Methadone accounts for a large proportion of these deaths; studies

have found that methadone accounts for anywhere from 30% to 64% of all prescription opioid-

related deaths.60,121,127

Two factors associated with one’s risk for prescription opioid-related

death are daily dosing levels – studies have found that risk of death significantly increases with

doses exceeding 100mg morphine equivalent dose per day– and obtaining opioids through

diversion (i.e., doctor and/or pharmacy shopping). Multiple studies have documented the

increased prevalence of comorbidities among prescription opioid abusers compared to

nonabusers. This disease burden, which contributes to the higher rates of healthcare utilization

and prescription drug utilization, also contributes to the increased healthcare costs incurred by

abusers. Opioid abusers incur higher costs in other sectors as well, including workplace-related

costs (such as absenteeism) and criminal justice costs.

POLICY OPTIONS TO ELIMINATE OPIOID MISUSE & ABUSE

Both the CDC and White House have suggested policies and programs to reduce the prevalence

and burden of prescription opioid misuse. These strategies include education (provider and

patient), increased law enforcement, improved access to substance abuse treatment programs,

prescription drug monitoring programs (PDMPs), and patient review and restriction programs.

The final two strategies – patient review and restriction programs (PRR) and prescription drug

monitoring programs (PDMPs) – were highlighted in a recent webinar by Ileana Arias, the

Principal Deputy Director of the CDC as two important policy options that can have the greatest

impact (Table 1-25).138

In the following section, we will discuss the characteristics and purpose

of these two programs, as well as literature pertaining to their effectiveness in reducing the

prevalence and/or burden of prescription opioid misuse.



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Table 1-25. Policy Interventions to Reduce the Burden of Prescription Opioid Misuse

Intervention Points Policy Interventions

PMDPs PRR Laws/Regulations/Policies Insurers/PBM Clinical Guidelines

Pill Mills* X X

Problem Prescribing X X X x

General Prescribing X X X X

EDs & Hospitals X X X X

Pharmacies X X X X X

Insurers & Pharmacy

Benefit Managers

X X X X

High Risk Patients X X X X X

General Patients &

Public

X X X

Source: Arias, I (2012). http://www.softconference.com/asam/player.asp?PVQ=HJIH&fVQ=FMKJKD&hVQ

PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction; PBM = Pharmacy

Benefit Manager *”

Pill Mill” is a term used to describe a provider (physician, clinic or pharmacy) that is inappropriately prescribing

and/or dispensing prescription drugs.42

Patient Review and Restriction Programs

The Centers for Medicare and Medicaid Services (CMS) recommend patient review and

restriction programs (PRRs), also referred to as patient review and coordination programs or

“lock-in” programs, as one strategy states can implement to address prescription drug diversion

in the Medicaid program.139

These programs were established pursuant to a federal regulation

(CFR-431.54(e)),140

which states that:

If a Medicaid agency finds that a recipient has utilized Medicaid services at a

frequency or amount that is not medically necessary, as determined in accordance

with utilization guidelines established by the State, the agency may restrict that

recipient for a reasonable period of time to obtain Medicaid services from

designated providers only. The agency may impose these restrictions only if the

following conditions are met:

(1) The agency gives the recipient notice and opportunity for a hearing (in

accordance with procedures established by the agency) before imposing the

restrictions.

(2) The agency ensures that the recipient has reasonable access (taking into

account geographic location and reasonable travel time) to Medicaid services of

adequate quality.

(3) The restrictions do not apply to emergency services furnished to the recipient.

A 2007 internal survey by the CDC’s National Center for Injury Prevention and Control

(Jones, C.M., email correspondence, March 2012) found that 40 states and the District of

Columbia have implemented a lock-in program (see Figure 1-9). The criteria that a

patient must meet to be enrolled in the program varies by state, as well as restrictions

placed on the patient; programs can restrict enrollee access to one physician and/or one

pharmacy and/or one hospital (for non-emergent conditions) for varying lengths of time.

Currently, there is scant peer-reviewed literature on the effectiveness of lock-in programs

http://www.softconference.com/asam/player.asp?PVQ=HJIH&fVQ=FMKJKD&hVQ



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on improving health outcomes and reducing costs, thus most of the data presented below

come from state reports and reports by independent evaluators.

Figure 1-9. State Lock-In Programs by Client Size, 2007

Source: Jones, C.M., email correspondence, March 2012

Note: States shown with active programs may not match the states described in the “Policy Effectiveness

and Outcomes” section. Programs may go in and out of operation due to that states’ budgetary climate.

Policy Effectiveness and Outcomes

Louisiana

Louisiana established a patient review and restriction (PRR) program during the 1970s.

Recipients enrolled in the program may be locked into one primary care provider, one specialist,

one pharmacy (or a combination thereof). Nearly 85% of recipients are locked into one

pharmacy for all nonemergency prescription drugs. From 1994-1996, approximately 2,000

Medicaid recipients were enrolled in the program. Using data from the same period, Blake

examined the effect of the PRR program on opioid use, prescribing patterns, and costs. The

majority of individuals in the PRR program were female (consistent with the Medicaid

population overall), between the ages of 20-59 years old and nearly half were White. Prior to

enrollment in physician-pharmacy lock-in (one physician and one pharmacy) recipients filled

63% of their prescriptions from a single pharmacy versus 92% after enrollment. Similarly, prior

to enrollment in pharmacy-only lock-in, recipients filled nearly 66% of their prescriptions at a

single pharmacy versus 96% after enrollment. Regression analyses found that the PRR program

reduced polypharmacy among enrollees, as well as decreased the use of Schedule II opioids and

pharmacy expenditures. Before enrollment in lock-in, the number of unique prescriptions per

recipient per month ranged from 8-10 compared to six after enrollment. Before enrollment, per



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recipient adjusted monthly pharmacy expenditures ranged from $300-$400, compared to $225-

$250 for enrollees in physician-pharmacy lock-in and approximately $300 for those in

pharmacy-only lock-in.141

Hawaii

In 1980, Hawaii implemented a patient review and restriction (PRR) program, which

allows identified abusers to be restricted to one primary care provider, pharmacy, clinic

or hospital, or to a combination of providers. Patients identified as candidates for the

program must meet one or more of the following criteria:

1. Doctor shopping

a. If multiple providers are consulted for the same reason within a few days

b. If multiple providers specializing in the same area are consulted for the

same or different reasons

c. If providers located in geographically disperse areas are consulted for the

same reason

2. Unnecessary visits to the same provider for the same reason

3. Multiple pharmacies dispensing the same drug prescribed by one physician or by

different physicians

4. Excessive doses or quantities of controlled drugs or drugs with street value

5. Use (particularly long-term use) of prescription drugs, inconsistent or

inappropriate with diagnosis

Chinn analyzed the impact of the PRR program from July 1, 1977 to December 21, 1983. During

that time, 682 unduplicated cases were identified as potentially eligible for the program, and of

those, 137 Medicaid patients were placed into the PRR program (including some individuals who

were restricted more than once). Of those restricted, nearly 21% complied with their restrictions

without any further abuse after one year. Chinn estimated $909,92213

in savings to Medicaid

during 1983 alone.142

Florida

Florida’s patient review and restriction (PRR) program, which began in October 2002, restricts

enrollees to one provider and/or one pharmacy for up to one year. Individuals are enrolled in the

program if they have “utilized prescription drug services with a frequency or amount that is not

medically necessary” or who have been selling or diverting prescription drugs.143

According to a

report on state Medicaid spending control programs, the program added nearly 300 individuals

from January 1, 2005 – March 31, 2005, totaling 1,315 individuals enrolled overall. During that

same time, the program resulted in $739,847 in prescription drug savings and $1,762,636 in

medical savings. Since the program was implemented in October 2002, cumulative savings total

over $12.7 million.144

Oklahoma

13

Calculated per 1982 42 C.F.R 433.213C(1) – total amount expended and paid by the Medicaid program for a

recipient for 4 quarters prior to the restriction and the average determined by quarter, which becomes the base

quarterly amount and is compared with the amount paid quarterly after the recipient is placed in the lock-in

program.88



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Pursuant to the Oklahoma Administrative Code,145

enrollees in the state’s SoonerCare patient

review and restriction (PRR) program (SoonerCare is the state Medicaid program) must meet

three of eight criteria:

1. Increased ED visits

2. Increased use of unique pharmacies

3. Increased number of prescribers and/or physicians

4. Increased number of days supply of opioids

5. Diagnosis of drug dependence or related diagnosis

6. Increased number of hospital discharges

7. Questionable activity noted in previous reviews

8. Noted safety concerns in previous reviews

In 2009, the Oklahoma Health Care Authority published results of an analysis of the impact on

healthcare utilization and costs for individuals enrolled in the program from January 2006

through October 2006 (n=52). They reviewed individual’s utilization history from January 2005-

December 2005 (pre-lock-in) compared to November 2006-December 2007 (post-lock-in). They

found significant decreases in the average number of narcotic and all pharmacy claims,

pharmacies and prescribers used, and ED visits. Average prescription opioid costs decreased by

nearly $13 per month, pharmacy costs decreased by $30 per month, and ED costs decreased by

$259 per month. For the first twelve months post lock-in, Mitchell estimated cumulative savings

of $31,524, and per member annual savings of $606.146,147

Washington

Per the Washington Administrative Code,148

patients enrolled into the state’s patient review and

restriction (PRR) program must meet the following criteria before facing provider restrictions:

1. Two or more of the following within a consecutive 90-day period:

a. Saw >4 physicians

b. Filled prescriptions at >4 pharmacies

c. Received >10 prescriptions

d. Received prescriptions from >4 prescribers

e. Received similar services from >2 providers in the same day

f. Had >10 office visits

2. Any one of the following within a 90-day period:

a. >2 ED visits

b. Questionable utilization patterns documented in medical history

c. Repeated and documented efforts to seek medically unnecessary

services

d. Counseled >1 by a health care provider about inappropriate health care

utilization

3. Received prescriptions for any controlled substances from >2 different

prescribers in any month

4. Billing history documenting the following pattern:

a. Unnecessary, excessive, or contraindicated health care utilization

b. Receiving conflicting health care services, drugs or supplies that are

not medically appropriate



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Individuals meeting this criteria can be locked into one primary care provider, one pharmacy,

one opioid prescriber, one hospital for non-emergent services, or a combination of these

providers for at least 24 months.149

From 2005-2008, the PRR program caseload increased from 200 enrollees to more than 3,000

enrollees and reported savings of more than $39 million since during that period.150

By 2012,

savings reportedly exceeded $109 million.151

Since implementation, the program has seen a 37%

decrease in physician visits, 33% decrease in ED visits, and a 24% decrease in the number of

prescriptions.150,151

Along with the CDC, the state evaluated prescription opioid overdose deaths

occurring in the state from 2004-2007. They found 1,668 deaths due to prescription overdose in

the overall population, and 758 (45.4%) of those deaths occurred among Medicaid recipients.

Additionally, the authors examined deaths occurring among a subset of Medicaid recipients;

those enrolled in the Patient Review and Coordination (PRC) program. The analysis found that

individuals enrolled in this program represented only 0.1% of the entire Medicaid population, but

accounted for 4.5% of opioid-related overdose decedents. The age-adjusted opioid overdose rate

was 30.8 per 100,000 for the overall state Medicaid population, whereas the rate among enrollees

in the patient review and coordination program was 381.4 per 100,000. The annual overdose risk

for individuals in the Medicaid population was one in 6,757, while the annual risk for enrollees

program was one in 172.127

Iowa

Pursuant to Iowa’s Administrative Code, state Medicaid recipients may be placed into patient

review and restriction (PRR) program if they have a documented history of overuse of services.

Overuse of services are defined within the code as “receipt of treatments, drugs, medical

supplies, or other Medicaid benefits from one or multiple providers of service in an amount,

duration, or scope in excess of that which would reasonably be expected to result in a medical or

health benefit to the patient.” Overuse is further defined as receiving “outpatient visits to

physicians, advanced registered nurse practitioners, federally qualified health center, rural health

centers, other clinics, and emergency rooms exceeds 24 visits in any 12-month period.”

Individuals placed into the program are locked into one primary care physician, pharmacy and

hospital/emergency room for a minimum of 24 months.152

According to a 2004 report, the lock-

in program generated state savings of $738,583 from July 2003-December 2003.153

A subsequent

report in November 2008 states that cost savings have increased to approximately $2 million

annually.154

Prescription Drug Monitoring Programs

In order to establish patient eligibility for lock-in programs, states review of Medicaid claims

data to identify patterns of prescription opioid misuse and overutilization. Another data source

states could potentially use to identify patterns of misuse and overutilization are state

Prescription Drug Monitoring Databases (PDMPs). Researcher have used PDMPs to identify

patterns of prescription opioid misuse and overutilization, including identifying doctor and

pharmacy shoppers and individuals receiving high doses of prescription opioids.38,60,61,63-65

Since

1939, states have established PDMPs to collect and evaluate data on prescribed controlled

substances in order to detect and prevent the misuse, abuse and diversion of these drugs.155

The

National Alliance for Model State Drug Laws (NAMSDL), which researches and analyses state



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statutes related to drugs/alcohol and acts as a resource for policymakers, regulators, and other

stakeholders, defines a PDMP as:156

A statewide electronic database which collects designated data on substances

dispensed in the states. The PDMP is housed by a specific statewide regulatory,

administrative or law enforcement agency. The housing agency distributes data

from the database to individuals who are authorized under state law to receive the

information for purposes of their profession.

The NAMSDL has identified seven components that state PDMP should strive to include:157

1. PDMP’s should monitor a) federally controlled substances, b) other state-regulated

substances, and c) other drugs identified by law enforcement and addiction treatment

professionals.

2. PDMP’s should proactively provide data to appropriate individuals, such as law enforcement,

as well as allow de-identified data to be used for public research, policy and education.

3. Allow individuals to request specific information, including law enforcement, prescribers,

and dispensers.

4. Provide training on data use to all individuals requesting data.

5. Programs should conduct evaluations to identify the costs and benefits of the program and

assess opportunities for improvement. This process should include the involvement of an

advisory board or council.

6. Programs must maintain confidentiality and data collected by the program should not be

subject to public or open record laws.

7. Programs should address interstate prescription drug misuse and abuse via statute, regulation,

or interstate agreement.

As of July 2012, 49 states have enacted legislation to create a PDMP, and 41 have programs

currently in operation. Arkansas, Connecticut, Delaware, Maryland, Montana, Nebraska, New

Hampshire and Wisconsin have passed authorizing legislation but do not yet have an operational

program.156

There are several limitations to state PDMPs. Of the 41 states with operational programs, more

than half (25) have only been in operation for the last decade, contributing to the slow rate of

provider utilization of these program. Recognizing the lack of provider familiarity and

subsequent deployment of these programs, the U.S. Department of Health & Human Services is

funding a project called, “Enhancing Access to PDMPs.” This undertaking stems from joint

efforts of public sector and private industry experts that participated in the White House

Roundtable on Health Information Technology (IT) and Prescription Drug Abuse in June

2011.158

“Enhancing Access to PDMPs” will fund pilot studies in Indiana and Ohio to determine

if Health IT can help increase the effectiveness of PDMPs by increasing providers’ real-time

access to the data. In the Indiana pilot, emergency department (ED) physicians will receive

patients' controlled substance histories from a centralized database, a matter of vital importance

because EDs are responsible for almost 25 percent of controlled substance prescriptions. In the

Ohio pilot, drug risk indicators will be included in the electronic health record and will permit

measurement of how this knowledge influences clinical decision-making.159

http://maps.google.com/maps?ll=38.8866666667,-77.0144444444&spn=0.01,0.01&q=38.8866666667,-77.0144444444%20(United%20States%20Department%20of%20Health%20and%20Human%20Services)&t=h



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In addition to lack of timely reporting to end users, another limitation of existing PDMPs

includes the non-uniformity of prescription data. While some states monitor all Schedule II-V

substances, others (such as Pennsylvania) only monitor Schedule II substances, meaning these

states cannot track individuals who may be misusing prescription opioids such as hydrocodone

or tramadol. Another restriction is the timing and method of data acquisition and retrieval. Some

states collect prescription drug dispensing data on a frequent basis (Minnesota and North Dakota

collect data daily), while other states collect data less frequently (New York and Pennsylvania

only collect data once a month). States also restrain the utility of PDMPs by limiting the types of

individuals who are allowed to request data reports from the PDMPs. For example, Pennsylvania

does not allow prescribers to request PDMP data for their patients, New York does not allow

pharmacists to request this data, and Connecticut does not allow pharmacies to request this data.

Other states, such as Vermont, do not allow law enforcement agencies to request PDMP data.160

Additionally, not all PDMPs allow data to be accessed electronically, which can inhibit

utilization. Green et al compared PDMP use in Connecticut and Rhode Island, with the former

having an electronic database and the latter requiring providers to call, fax, or provide a written

request for data. The study found that 50% of physicians in Connecticut used the program at

least once per month, whereas only 16% of physicians in Rhode Island did so.161

Another serious

drawback of several PDMPs is that doctor shoppers living proximate to state boundaries can

travel to see providers in adjoining states.162

The above limitations led to the formation of The National All Schedules Prescription Electronic

Reporting Act (NASPER), enacted in 2005, a U.S. Department of Health and Human Services

grant program for states to implement or enhance prescription drug monitoring programs. The

intent of the law was to encourage the development of PDMPs that would meet consistent

national criteria and have the capability for interstate exchange of information.163

Policy Effectiveness and Outcomes

Using 1997-2003 data from state PDMPs and the Automation of Reports and Consolidated

Orders System (ARCOS) data the U.S. Department of Justice examined the relationship between

the supply and abuse of prescription opioids and the presence of a PDMP. They found that states

with a PDMP have a reduced per capita supply of these drugs, which may therefore decrease the

probability for misuse and abuse.164,165

However, an alternative study by the Substance Abuse

and Mental Health Services Administration (SAMHSA) did not corroborate this finding

presumably because of one or more of the limitations discussed in the preceding paragraph that

led to differences in the PDMPs studied. As part of an implementation evaluation of NASPER,

SAMHSA evaluated the impact of a PDMP in nine states that implemented the program from

1997 - 2004. Using 2004 data from ARCOS, the report found little difference in per capita

distribution of opioids between states with PDMP’s, states without PDMP’s, and national

averages. The same study evaluated the effectiveness of PDMPs by separating the opioids

studied by DEA Schedule using data from the Medical Expenditure Panel Survey (MEPS).

MEPS data showed a decrease in consumption of Schedule II opioids in states with PDMP’s

compared to those without (1.16% of persons in PDMP states vs. 2.90% in states without), but

no significant difference in the consumption of Schedule III opioids. From 1996-2003, there was

an average of 4.7 prescriptions for Schedule II opioids per 100 people in states with a PDMP,

compared to 9.0 per 100 in states without.165



1.54

PDMPs have also been useful in evaluating changes in the consumption of specific opioids.

According to a 2002 report from the United States General Accounting Office (GAO), PDMPs

have influenced the diversion of prescription opioids, especially oxycodone. The report states

that in 2000, eight of the ten states with the highest number of OxyContin prescriptions per

100,000 population did not have a PDMP, while six of the ten states with the lowest number of

prescriptions per 100,000 had PDMPs. The report also notes an unintended negative effect –

when states implement a PDMP, thus making diversion in the state more difficult, diversion

activities had a tendency to spillover to neighboring states without PDMPs. For example, the

presence of Kentucky’s program may have contributed to the rise in diversion in three

neighboring states without programs – Tennessee, West Virginia and Virginia.162

As previously

mentioned, policy makers responded to this type of finding by funding NASPER to promote the

interstate exchange of prescription data.

Some studies appraising the rates of admissions for opioid misuse and abuse suggest that the

establishment of PDMPs is associated with fewer admissions for opioid misuse and abuse in

those states. Using 1997-2003 data from ARCOS and the Treatment Episode Data Set (TEDS),

Reisman et al examined the impact of the presence of a state PDMP on opioid supply and

prescription opioid abuse admissions. They found that while the supply of prescription opioids

and abuse admissions increased during that time, the rate of increase was lower in states with a

PDMP.166

Using 2003-2009 data from the Researched, Abuse, Diversion and Addiction-Related

Surveillance (RADARS) System, Reilfler et al found a similar relationship between the presence

of a PDMP and the state-level rate of opioid misuse and abuse.167

In contrast, Paulozzi, Kilbourne and Desai evaluated the association between the presence of a

PDMP on state-level prescription opioid consumption and overdose from 1999-2005. As

displayed in Table 1-26, they found that, generally, PDMPs were not associated with lower all

drug- or opioid-related overdose deaths or lower opioid consumption rates, even in states with

proactive PDMPs (proactively provide reports to authorized users, such as prescribers,

dispensers, or law enforcement) or high-reporting programs (generate more than 100 solicited or

unsolicited reports per 100,000 authorized users). The researchers found that only three states

with PDMPs (California, New York and Texas) had lower mortality and consumption rates. The

authors theorized this may be due to the longer existence of the PDMP in those states or because

these states continue to use tamper-resistant prescription forms14

, whereas other states have

adopted other methods.168

Table 1-26. The Presence of Prescription Drug Monitoring Programs, Overdose Mortality and

Opioid Consumption Rates All Prescription

Overdose Mortality

Ratea

Opioid Overdose

Mortality Rate MME/Person/Year

States without PDMPs 6.46 2.20 341.67

States with PDMPs 7.45 3.13 362.43

States with Proactive PDMPs 7.64 3.30 365.67

States with High-Reporting PDMP 11.41 6.57 540.75

California, New York & Texas 5.36 1.65 251.19

14

Tamper-resistant prescription forms are designed to prevent (1) unauthorized copying, (2) erasure or modification

of information written by the prescriber and (3) use of counterfeit prescription forms.



1.55

Taken from: Paulozzi, Kilbourne & Desai (2011). Prescription Drug Monitoring Programs and Death Rates from

Drug Overdose

aRates are per 100,000 person years

MME = milligram morphine equivalent per person per year

As outlined in an editorial by Kerlikowske et al, this study had several important limitations.

First, the study did not take into account provider utilization of PDMPs when assessing their

impact; second, federal funding for these programs did not exist until three years into the study

period; third, prescription data from federal programs such as the Department of Veterans

Affairs, Department of Defense, and Indian Health Service, were not included.169

In a subsequent

letter to the editor, Green et al pointed out that many of the 19 state PDMPs studied by Paulozzi

et al did not allow or foster access by health care professionals. As considered above under the

discussion concerning “Enhancing Access to PDMPs”, this lack of input by providers would

seriously undermine the findings of Paulozzi et al.168,170

There is also support for PDMPs arising from assessing healthcare utilization. Using data from a

national pharmaceutical benefit manager for outpatient prescription drug claims, Curtis et al

analyzed the association between the presence of a PDMP and the number opioid prescription

claims. After controlling for sociodemographic characteristics, illicit drug use, and surgical

specialists, they found that PDMPs reduced the number of opioid claims by nearly 40 claims per

1,000 total claims.47

Massachusetts

The Massachusetts PDMP was established in 1992 and monitors prescriptions for Schedule II-V

controlled substances. Using program data from 1996-2006, Katz et al analyzed the number of

prescribers and pharmacies and their relationship with “questionable activity” (a possible

indicator of opioid misuse and/or diversion). Using a threshold of 3 or more prescribers and

pharmacies, 1.6% of individuals and nearly 8% of prescriptions met criteria, but when the

threshold increased to 4 or more prescribers and pharmacies, only 0.5% of individuals and 3.1%

of prescriptions qualified. Increasing the threshold moves to 5 or more prescribers and

pharmacies shrank the number of qualified individuals and prescriptions to 0.2% and 1.5%,

respectively.61

Virginia

Virginia established their PDMP in 2002, and it monitors dispensed prescriptions for Schedule

II-IV controlled substances. In 2004, Barrett conducted an evaluation of the impact of the

program on physicians and prescribing behaviors. Barrett found that 36% of physicians reported

prescribing fewer Schedule II drugs since the PMP was implemented and 57% reported

prescribing more Schedule III and IV drugs instead. Sixty-eight percent of responding physicians

said that the program was useful in decreasing the incidence of doctor shopping.171

Maine

In 2004, Maine implemented a PDMP within in the Office of Substance Abuse (OSA), and the

program monitors dispensed prescriptions for Schedule II-IV drugs. In 2007, Lambert conducted

an impact evaluation of the program to assess utilization by physicians. The PDMP proactively

issues threshold reports to prescribers, which alert the prescriber to individuals who may be

receiving excessively high levels of prescription drugs. Lambert found that three-quarters of



1.56

prescribers had received a threshold report at some point. Of those prescribers receiving a report,

42% found that some of their patients were abusing prescription drugs, 24% entered into a pain

management contract with their patient, 20% referred their patient for substance abuse treatment

and nearly 17% referred their patient for pain management. According to Lambert, 65% of

respondents reported prescribing fewer controlled substances since the program implementation.

The majority of prescribers (53%) found the program useful in controlling doctor shopping.

However, Lambert notes that Maine has yet to analyze the possible relationship between

implementation of the PMP with outcomes such as abuse, healthcare utilization, and overdose

deaths.172

California

California, regrettably, is the one state that is in danger of losing its existing drug-monitoring

system. California has the oldest continuous PDMP in the U.S., dating back over seventy years.

It used to rely on carbon copies – one for the pharmacy, the doctor and the state Department of

Justice – but the system went online in 1998. However, California Gov. Jerry Brown announced

last year that, for budget reasons, he was eliminating the Bureau of Narcotic Enforcement, which

had long managed the prescription drug monitoring program. There is one remaining civil

servant maintaining the system employing year-to-year grants from the state's medical and

pharmacy boards. Without a permanent source of funding, the future of California's prescription-

drug monitoring program is unclear.173

Summary

Numerous policy interventions have been suggested to address the increase in prescription opioid

misuse and abuse, and subsequent health and economic outcomes. One policy, patient review

and restriction programs (or “lock-in” programs) aims to limit access to opioids by restricting the

number of prescribers and/or pharmacies from which patients can obtain opioids. While a

number of states utilize these programs, little has been published on their effectiveness. Some

states, including Louisiana, Hawaii, Florida, Oklahoma, and Iowa, have published data related to

decreased prescription drug costs attributable to the program. However, our review found no

literature that discussed the impact of these programs on other outcomes, including healthcare

utilization and mortality.

Prescription drug monitoring programs (or PDMPs) are a widely touted intervention that can be

used to identify patterns of misuse and overutilization in states. These programs collect data on

controlled substance utilization, and have the capability to share this data health care providers

and law enforcement agencies, but require resources to establish and maintain an accessible up-

to-date data resource. Studies have documented limitations of these programs, including slow

provider update, limited accessibility to the data, and non-uniformity of the drugs monitored.

Rigorous studies evaluating the impact of PDMP’s on outcomes have not been conducted. While

researchers have used the programs to identify doctor shopping patterns and have found that

physicians do alter their prescribing patterns based on PDMP data, our review found no studies

that examined the relationship between the presence of these programs and prescription opioid-

related outcomes.



1.57

SUMMARY

Over the past two decades, as physicians managed pain more aggressively and prescribed

stronger opioids more frequently and at higher doses, studies and surveys at the state and

national level have documented an increase in the prevalence of nonmedical use, misuse, and

abuse of these drugs, particularly products containing hydrocodone, oxycodone, and methadone.

These state- and national-level studies and surveys have also found increased numbers of

patients receiving high doses of prescription opioids (in excess of 100mg MED/d) and who are

chronic users (continuous use for longer than 90 days). The literature suggests that it is this

subset of patients – continuous users receiving high doses – as well as those receiving

prescription opioids through doctor/pharmacy shopping who may account for much of the

increases in frequency of opioid-related health care utilization (i.e., ED visits), mortality, and

costs (healthcare, workplace, criminal justice, etc).

This literature review examined the effectiveness of two policies – patient review and restriction

programs and prescription drug monitoring programs – intended to reduce the increase in

prescription opioid misuse and abuse and subsequent health and economic outcomes, particularly

among Medicaid recipients. While numerous states have implemented one or both of these

policies and have been found to decreases prescription drug costs and able to identify potential

doctor/pharmacy shoppers, little has been published on the impact of these programs on

healthcare utilization and mortality.



1.58

APPENDIX

Literature Review Sources

Databases of Peer-Reviewed Literature

PubMed

Cochrane Library

EconLit

Web of Science

National Data Sources

Drug Abuse Warning Network (DAWN)

National Center for Health Statistics (NCHS)

National Survey on Drug Use and Health (NSDUH)

Federally Maintained Sources of Grey Literature

United States Government Accountability Office (GAO)

Centers for Disease Control and Prevention (CDC)

Department of Justice, Drug Enforcement Administration (DEA)

Substance Abuse and Mental Health Administration (SAMHSA)

Food and Drug Administration (FDA)

Executive Office of the President of the United States, Office of National Drug Control Policy

(ONDCP)

Centers for Medicare and Medicaid Services (CMS)

United States Department of Health and Human Services

State Maintained Sources of Grey Literature

University of Kentucky Institute for Pharmaceutical Outcomes and Policy

University of Southern Maine, Muskie School of Public Health, Cutler Institute of Health and

Social Policy

California Workers Compensation Institute

North Carolina Department of Public Health and Human Services, Division of Public Health,

State Center for Health Statistics

Ohio Department of Health, Violence and Injury Prevention Program

Florida Medicaid, Agency for Healthcare Administration

Oklahoma Health Care Authority

Washington State Health Care Authority

Iowa Foundation for Medical Care

Virginia Commonwealth University Survey and Evaluation Research Laboratory

Nonprofit Organizations

National Alliance for Model State Drug Laws

American Society of Interventional Pain Physicians (ASIPP)

Alliance of States with Prescription Monitoring Programs

National All Schedules Prescription Electronic Reporting Act (NASPER)

National Public Radio (NPR)



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Literature Review Search Terms

Age

Abuse

Demographics

Disparities

Diversion

Doctor shopper/ing

Economic burden

Emergency Department

Ethnicity

Gender

Healthcare Costs

Healthcare Utilization

Hospital Admissions

Hospitalization

Hydrocodone

Insurance

Lock-In

Medicaid

Mental Health Disorders

Methadone

Misuse

Morphine

Mortality

Nonmedical Use

Opioids

Opioid analgesics

Opioid-Related Comorbidities

Opioid-related Costs

Opioid-Related Disorders

Outcomes

Overdose

Oxycodone

Patient review and restriction

Pharmacy shopper/ing

Poisoning

Policy

Premature Death

Prescription Drug Monitoring Programs

Prescription opioids

Race

Risk factor

Socioeconomic status

Substance Abuse



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U.S. Department of Health and Human Services.

166. Reisman RM, Shenoy PJ, Atherly AJ, Flowers CR. Prescription Opioid Usage and Abuse

Relationships: An Evaluation of State Prescription Drug Monitoring Program Efficacy.

Substance Abuse: Research and Treatment. 2009;3(SART-3-Shenoy-et-al):41.

167. Reifler LM, Droz D, Bailey JE, et al. Do prescription monitoring programs impact state

trends in opioid abuse/misuse? Pain medicine (Malden, Mass.). Mar 2012;13(3):434-442.

168. Paulozzi LJ, Kilbourne EM, Desai HA. Prescription drug monitoring programs and death

rates from drug overdose. Pain medicine (Malden, Mass.). May 2011;12(5):747-754.

169. Kerlikowske G, Jones CM, Labelle RM, Condon TP. Prescription drug monitoring

programs-lack of effectiveness or a call to action? Pain medicine (Malden, Mass.). May

2011;12(5):687-689.

170. Green TC, Zaller N, Rich J, Bowman S, Friedmann P. Revisiting Paulozzi et al.'s

"Prescription drug monitoring programs and death rates from drug overdose". Pain

medicine (Malden, Mass.). Jun 2011;12(6):982-985.

171. Barrett K. Prescription Monitoring Program Survey: Report of Findings. Richmond, VA:

Virginia Commonwealth University Survey and Evaluation Research Laboratory August

2004.

172. Lambert D. Impact Evaluation of Maine's Prescription Drug Monitoring Program.

Portland, ME: University of Southern Maine Muskie School of Public Service March

2007.

173. Varney S. Calif.'s Prescription-Drug Monitoring System Feels Pain From Budget Cuts.

April 2012; http://www.npr.org/blogs/health/2012/04/10/149943047/calif-s-prescription-

drug-monitoring-system-feels-pain-from-budget-cuts Accessed July 20, 2012.

http://www.nasper.org/database.htm

http://www.npr.org/blogs/health/2012/04/10/149943047/calif-s-prescription-drug-monitoring-system-feels-pain-from-budget-cuts

http://www.npr.org/blogs/health/2012/04/10/149943047/calif-s-prescription-drug-monitoring-system-feels-pain-from-budget-cuts



1.72





Program

Part 2

Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and

Prevention Policies – MarketScan® Data Analysis


Prevention Policies – MarketScan™ Data Analysis

2.2

Table of Contents

INTRODUCTION ..................................................................................................................................... 2.4

METHODS ................................................................................................................................................ 2.4

Data Source ............................................................................................................................................ 2.4

Definitions.............................................................................................................................................. 2.4

STATISTICAL ANALYSIS ..................................................................................................................... 2.6

Regression Models ................................................................................................................................. 2.6

90-Day Exposure Window ................................................................................................................. 2.6

Episode-Based Model ........................................................................................................................ 2.6

Model Settings ....................................................................................................................................... 2.6

Subgroup Analysis: The Role of Pharmacy Shopping in Overdose Events .......................................... 2.8

Peak Number of Pharmacies .............................................................................................................. 2.8

Pharmacy Shopping Criteria .............................................................................................................. 2.8

Characteristics of Prescription Fill Behaviors.................................................................................... 2.9

Hazard Ratios for Overdose Due to Pharmacy Shopping and Overlapping Prescriptions ................ 2.9

RESULTS .................................................................................................................................................. 2.9

Study Population Characteristics ........................................................................................................... 2.9

Characteristics of Prescription Opioid Use .......................................................................................... 2.10

Opioid Prescriptions by Drug Type ................................................................................................. 2.10

Predominant Opioid Prescriptions among Long-Term Episodes of Opioid Use ............................. 2.11

Supply days of opioid prescriptions by drug type ............................................................................ 2.11

Characteristics of Overdose Events ..................................................................................................... 2.12

Number of Overdose Events ............................................................................................................ 2.12

Overdose Events and Estimated Costs by Encounter Type ............................................................. 2.12

Overdose Rates by Patient Characteristics ....................................................................................... 2.13

Overdoses by Type of Prescription Opioid Use ............................................................................... 2.16

Relationship between Overdose Risk and Prescribed Dose: Results of the 90-Day Exposure Window

Model ............................................................................................................................................... 2.16

Pharmacy Shopping among Long-Term Opioid Users ........................................................................ 2.17

Number of Patients with an Overdose Event, by Peak Number of Pharmacies ............................... 2.17

Comparison between Different Pharmacy Shopping Criteria .......................................................... 2.18

Combined Criteria: Peak Number of Pharmacies and Overlapping Prescriptions .......................... 2.19

Relationship between Potential Pharmacy Shopping and Overdose Risk ....................................... 2.21

SUMMARY ............................................................................................................................................. 2.22

APPENDIX .............................................................................................................................................. 2.23

Morphine Equivalent Dose Conversions ............................................................................................. 2.23

ICD-9 Codes Indicating Overdose-Related Symptoms ....................................................................... 2.24

Type of Overdose Encounters .............................................................................................................. 2.24

BIBLIOGRAPHY .................................................................................................................................... 2.25

List of Tables

Table 2-1.Baseline Characteristics of the Study Population ...................................................................... 2.9

Table 2-2. Commonly Prescribed Opioids in a Sample of the U.S. Adult Medicaid Population, 2010-2011

................................................................................................................................................................. 2.10

Table 2-3. Predominant Drug Types among Long-Term Users .............................................................. 2.11

Table 2-4. Opioid Overdose Events in the MarketScan® Medicaid Dataset, 2008-2012 ....................... 2.12



2.3

Table 2-5. Number of Overdose Events and Estimated Costs by Encounter Type, 2008-1010 .............. 2.12

Table 2-6. Unadjusted Overdose Rates in MarketScan® Medicaid Dataset by Demographic and Clinical

Characteristics, 2008-2010 ...................................................................................................................... 2.14

Table 2-7. Overdoses by Type of Opioid Use ......................................................................................... 2.16

Table 2-8. Overdose Rates and Hazard Ratios by Dose Level and Predominant Drug Type .................. 2.16

Table 2-9. Comparison of Different Pharmacy Shopping Criteria in Medicaid MarketScan® Dataset . 2.19

Table 2-10. Comparison of Different Pharmacy Shopping Characteristics by Demographics, Overdose

Events and Opioid Consumption Patters ................................................................................................. 2.19

Table 2-11. Hazard Ratios of Overdose, Including Indicators for Pharmacy Shoppinga and Overlapping

Prescriptions in Medicaid MarketScan® Dataset, 2008-2010 ................................................................ 2.21

List of Figures

Figure 2-1. Regression Model Schematic: Prescriptions, Exposure Windows, and Overdose Events in

Models 1 and 2 ........................................................................................................................................... 2.7

Figure 2-2. Percentage of Supply Days for Prescription Opioids ............................................................ 2.11

Figure 2-3.Overdoses among Long-Term Users by Peak Number of Pharmacies Visited ...................... 2.18



2.4

INTRODUCTION

To respond to the CDC’s request for a model to examine the effectiveness of Medicaid patient

review and restriction (PRR) programs, we developed a micro-simulation model of an adult

Medicaid enrollee cohort to explore the impact and the cost-effectiveness of such programs. The

model was informed by an analysis of the MarketScan® Medicaid sample, in conjunction with

the literature review presented separately.

METHODS

Data Source

This study uses MarketScan® data, a commercially available administrative claims dataset that

includes information on demographics (age, race and gender), Medicaid enrollment duration,

diagnosis, and health care utilization (i.e., prescription drugs, hospital and emergency department

visits). The study population for this analysis consisted of Medicaid beneficiaries who received

at least one opioid analgesic prescription for non-cancer pain between January 2008 and

December 2010. We excluded individuals:

With less than 24-months continuous Medicaid enrollment;

Younger than age 12 years at the start of continuous enrollment;

With history of cancer diagnosis (ICD-9 CM neoplasms 140-293.2, excluding 173.X, 210-

239 and 232);

Residing in any long-term care facilities;

Who filled any opioid prescription in the first 3 months of the continuous enrollment period

(this is necessary to exclude subjects whose time-to-event estimation is subject to left

truncation).

We identified 427,411 Medicaid beneficiaries in the MarketScan® data during the 24-month

period who met the inclusion criteria.

Definitions

Episode of Opioid Use. We defined an “episode of opioid use” as commencing with the

dispensing date of an opioid prescription with no previous prescription in the dataset, or having a

gap longer than 31 days from the end run-out date of a previous opioid prescription. “Episode

duration” is defined as the number of days from the first fill date to the end date of the last opioid

prescription with no prescription gaps exceeding 31 days after the previous refill.

Long-term Episode of Opioid Use. An episode is defined as “long-term” if the duration is longer

than 90 days with 3 or more prescriptions dispensed in that time.

Pharmacy Shopping. Pharmacy shopping defined as visiting multiple pharmacies to obtain

medically unnecessary prescription opioids and contributes to nonmedical opioid use, misuse and

abuse. Pharmacy shopping has been defined in the literature using a variety of cut-off points for

classifying a patient as having potential controlled substance misuse or mismanagement that

would warrant further evaluation. Published thresholds vary by number of pharmacies seen by a

single patient to obtain any opioid over a given time period. 1-5

Peak Number of Pharmacies. Within a long-term episode of use, we defined the “peak number

of pharmacies” visited as the maximum number of unique pharmacies IDs that appeared in



2.5

opioid prescription claims during any 90 days in that episode. The peak number of pharmacies

visited may be a more accurate indicator of prescription opioid consumption patterns than the

total number of pharmacies visited for the entire episode, which is affected by the episode length.

In other words, long-term continuous opioid use over many months may have multiple

pharmacies due to a change in residence or a pharmacy switch, but if multiple pharmacies are

used in a shorter (90-day) period, this is more likely to represent opioid misuse or abuse.

Morphine Equivalent Dose and Average Daily Dose. Consistent with previous studies,6-10

we

compared the effects of multiple types of opioid drugs using a drug conversion method known as

the “morphine equivalent dose.” The morphine equivalent dose (MED) is calculated by

multiplying the strength of the opioid prescription by the quantity and by a drug-specific

conversion factor (expressed in milligrams morphine equivalent, or MME). The majority of these

conversation factors are based on Von Korff’s CONSORT (CONsortium to Study Opioid Risks

and Therapeutics) study.10

For details on the drug-specific conversion factors used, please refer

to the Appendix. The total MED is calculated by adding MEDs for all opioid prescriptions within

an episode. The average daily dose is the total MED divided by episode duration. The average

milligrams morphine equivalent daily dose (mgMED/d) is categorized into 4 levels: 0-

<20mgMED/d; 20-<50mgMED/d; 50-<100mg/d; and 100mg/d or more.

Overlapping Prescriptions. Overlapping prescriptions was defined as two prescriptions of the

same drug type that overlapped by 25% or more of the days prescribed and the former of the two

prescriptions had a supply time of 5 days or longer. The origin of the 25% cutoff point is from

the clinical opinions of an expert panel in which early opioid refills were defined as patients who

filled opioid prescriptions when 25 percent or more of an existing prescription should have

remained available.11

We restricted it to the same opioid category because patients could have

legitimate concomitant use of two or more different types of opioids. We required the

prescription dispensed earlier than the other have at least 5 days of supply, because the 25%

cutoff point was too sensitive for prescriptions with short supply days – a refill on the same date

as the run-off day of a previous fill with less than 5-day supply would be mistakenly considered

as overlapping prescription.

Opioid Overdose Events. Opioid overdose events were identified using inpatient and outpatient

claims data for the study population. We defined “definite cases of overdose” as claims with

ICD-9 codes indicating opioid-related poisoning (965.0, 965.00, 965.02 and 965.09) or

accidental poisoning (E935.1 and E935.2). We defined “probable cases of overdose” as claims

with ICD-9 codes indicating adverse effects of opioid use (E935.1 and E935.2) plus at least one

ICD-9 code indicating overdose-related symptoms on the same day (see Appendix for the full

list). We included both definite and probable cases in the analysis. We excluded suicidal

poisoning by opioid drugs (E950.0), poisoning undetermined whether accidentally or

purposefully inflicted (E980.0), and opioid drug dependence (304.X and 305.X). We grouped

inpatient and outpatient claims into overdose encounters and classified the encounters into 3

types: hospitalizations, ED visits, and outpatient visits (see Appendix for detailed rules for

grouping and classification). If an individual had multiple overdose encounters during his/her

continuous Medicaid enrollment period, only the earliest one (i.e. initial overdose) was counted.



2.6

STATISTICAL ANALYSIS

Regression Models

We constructed two regression models -- the 90-day exposure window model and the episode-

based model -- to estimate the relationship between the risk of overdose and daily opioid dose.

Each regression model explored a different question.

90-Day Exposure Window

The 90-day exposure window model includes all initial overdoses regardless of the drug source

(prescribed to patient or obtained through diversion), thereby providing a more accurate

estimation of the population at risk of overdose from any prescription opioid. The exposure

window model is used in other studies7,9

to examine the population overdose risk, thus providing

cross-validation with our analytical results.

Episode-Based Model

Administrative data cannot capture the real pattern of opioid use, which includes illicit use

supported through diversion. Preliminary analysis of our study population indicated some

evidence of drug diversion contributing to overdose episodes.* Therefore, we designed the

episode-based model to examine overdoses that happened within an episode of opioid use, but

not those that happened when no documented prescription opioid was in use.

Model Settings

The episode-based model treats each episode as a separate observation period. In each episode, a

patient was exposed to opioids at one of four average daily MED levels (0-20mg/d, 20-50 mg/d,

50-100 mg/d, and >100 mg/d) for the whole episode. We used categorical rather than continuous

variables to describe average daily MED because a continuous variable model could be subject

to bias caused by patients obtaining opioids with extremely high dose. The time at risk for

overdose lasts until the end of an episode, or the day of the first overdose (if any) that occurred

within the episode, or the censoring date.†Gaps between episodes were not included in

calculating the time at risk. Exposure windows were defined as 90 days prior to an initial

overdose (including the event date) for each overdose patient, and 90 days past the fill date of the

first prescription for all other patients remaining at risk for overdose at the time of that patient’s

event.

Error! Reference source not found. depicts how the exposure windows and the time at risk are

sed in each model. Subject 1’s prescriptions are clustered into two episodes of opioid use, and

therefore Subject 1 has two observation periods in the episode-based model (model 1) but only

one in the 90-day exposure window model (model 2). Subject 3’s overdose, which occurred with

no legitimate prescription on record, is not included in the episode-based model. Subject 4 has

three exposure windows in the 90-day exposure model, because she was at risk for overdose

when three overdose events (including hers) occurred.

* We conducted a preliminary analysis in STATA which shows that some overdose happens when there was no

opioid prescription in use, which suggests opioid from other sources (probably through diversion). † The censoring date is used if the prescription lasts beyond the end of the enrollment period

Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies – MarketScan™ Data Analysis

2.7

Figure 2-1. Regression Model Schematic: Prescriptions, Exposure Windows, and Overdose Events in Models 1 and 2

Subject 4 in model 1


Overdose event

CensoredSupply of opioid prescription

Exposure window

30 daysSubject 1’s prescription history







Subject 2’s prescription history



Fill date of first opioid prescription

End of episode of opioid use



2.8

Both models adopt Cox proportional hazards regression analysis12

to estimate the risk of

overdose as a function of average daily MED levels. In the episode-based model, a patient’s

multiple observation periods (i.e. multiple episodes) are clustered into one group. In the 90-day

exposure window model, the average daily MED level is treated as a time-varying covariate. The

regression analysis for each model is adjusted for demographic variables including gender, age,

and race as well as clinical variables including history of depression diagnosis, history of alcohol

abuse, and concurrent use of sedative/hypnotics.

Subgroup Analysis: The Role of Pharmacy Shopping in Overdose Events

Patient review and restriction programs rely on identifying opioid users at risk for misuse or

abuse based on various criteria, including (but not limited to) the number of pharmacies used, the

number of physicians providing opioid prescriptions, and the number of emergency department

visits. The MarketScan® database contained pharmacy but not physician identifiers, so we

focused on the role of use of multiple pharmacies on risk of overdose events.

Peak Number of Pharmacies

We restricted the study population to long-term opioid users because patient review and

restriction programs are not applicable to short-term opioid use, as the program is unlikely to use

prescription history in the past to regulate future prescription behavior if the period of opioid use

is short (i.e., less than 90 days based on our definition). The study population was classified by

the peak number of different pharmacies visited during a specified timeframe. The number of

pharmacies was calculated by counting the number of unique, de-identified pharmacy IDs from

the prescription claims database within a specified timeframe. The peak number of pharmacies

was the highest number of pharmacies visited for that patient. The peak number was classified

into 1, 2, 3, 4, and 5 or more pharmacies and three time periods were used - 90 days, 180 days

and 1 year. We calculated the number and percentage of patients having an opioid-related

overdose event(s) in each subgroup classified by the peak number of pharmacies based on each

of the three time periods.

Pharmacy Shopping Criteria

Based on the number of pharmacies and the timeframe, we created six different definitions for

pharmacy shopping:

1. Obtaining prescriptions from 3 or more pharmacies over a 1 year period

2. Obtaining prescriptions from 4 or more pharmacies over a 1 year period

3. Obtaining prescriptions from 3 or more pharmacies over a 180-day period




We calculated how many long-term users met each definition of pharmacy shopping, and how

many pharmacy shoppers had one or more opioid-related overdose event during the follow-up

period. We used opioid-related overdose as a surrogate measure of opioid misuse and abuse and

calculated the diagnostic odds ratio (DOR) for each definition. Typically, DOR is a measure of

the effectiveness of a diagnostic test. DOR is defined as the ratio of the odds of testing positive if

the subject has a disease relative to the odds of testing positive if the subject does not have the

disease.13

We replaced “test” with the criterion for pharmacy shopping and “disease” with



2.9

opioid-related overdose, thereby using DOR to assess the efficacy of each definition to identify

opioid users at high risk of overdose, and, presumably, also at high risk of abuse and misuse.

Characteristics of Prescription Fill Behaviors

We combined the two items - pharmacy shopping definition with the highest DOR (the indicator

for overlapping prescriptions) – to categorize the study population into four subgroups:

1. Patients without pharmacy shopping behavior and without overlapping prescriptions

2. Patients without pharmacy shopping behavior but with overlapping prescriptions

3. Patients with pharmacy shopping behavior but without overlapping prescription

4. Patients with both pharmacy shopping behavior and overlapping prescriptions

We calculated the demographic characteristics, overdose risk, and prescription fill patterns (in

terms of prescription frequency, dose level, and drug type by DEA classification) of each

subgroup.

Hazard Ratios for Overdose Due to Pharmacy Shopping and Overlapping Prescriptions

We examined whether pharmacy shopping behavior and overlapping prescriptions were

associated with an increased risk of opioid-related overdose. The 90-day exposure model, which

was modified to include two additional indicators for the history of pharmacy shopping and

having overlapping prescriptions, was used for this purpose. The hazard ratios of pharmacy

shopping and overlapping prescriptions were calculated.

RESULTS

Study Population Characteristics

Among the 427,411 Medicaid patients included in the study population, 69.2% were female,

68.3% were 18 years of age and older and 54.4% were white (see. The prevalence of diagnosed

depression and alcohol abuse was 5.0% and 1.2%, respectively. Long-term users‡ accounted for

21.1% of the study population (90,010 individuals). The majority of long-term users (51.5%)

were over 45 years of age, whereas only 1.9% were between ages 12 and18 years. Long-term

users had a significantly higher prevalence of depression (10.6%) and alcohol abuse (3.0%) than

the overall study population.

Table 2-1.Baseline Characteristics of the Study Population Total

(n=427,411)

Long Term Users

(n=90,010) P Value

Mean Months Enrolled 28.9 31.3 <0.001

Female 69.2 70.3 <0.001

Age

12 – 17 31.7 1.9

<0.001 18 – 29 26.0 14.6

30- 44 20.9 32.0

45 and over 21.4 51.5

Race

White 54.4 67.7 <0.001

‡ Long-term opioid users were defined as those who have at least one episode of opioid use longer than 90 days

with at least 3 prescriptions.



2.10

Total

(n=427,411)

Long Term Users

(n=90,010) P Value

Black 37.1 24.7

Hispanic 1.9 1.0

Other 6.6 6.6

Depression diagnosis,% 5.0 10.6 <0.001

Alcohol abuse, % 1.2 3.0 <0.001

Note: Values are expressed as percentages

Characteristics of Prescription Opioid Use

Opioid Prescriptions by Drug Type

Error! Reference source not found. lists the number of each opioid drug prescribed during the

tudy period. Most prescriptions (79.2%) were dispensed to long-term users. Hydrocodone, a

schedule III opioid, is by far the most commonly prescribed opioid in both the overall study

population (45.7%) and the subset of long-term users (44.6%). The most commonly prescribed

short-acting§ Schedule II opioid is oxycodone (15.1% and 14.9% in the overall population and

long-term users, respectively). Prescriptions for long-acting§ Schedule II drugs are higher among

long-term users (11.5%) than in the rest of the study population (0.5%, data not shown).

Table 2-2. Commonly Prescribed Opioids in a Sample of the U.S. Adult Medicaid Population,

2010-2011

Prescription Opioid Type All use Long-Term Use

n % n %

Schedule III and IV

Hydrocodone + aspirin/acetaminophen/ibuprofen 1,915,685 45.7 1,381,964 44.6

Tramadol with or without aspirin 508,837 12.1 378,780 12.2

Propoxyphene (with or without

aspirin/acetaminophen/ibuprofen) 274,098 6.5 173,954 5.6

Codeine + aspirin/acetaminophen/ibuprofen 216,409 5.2 80,546 2.6

Butalbital + codeine (with or without aspirin/acetaminophen/

ibuprofen) 14,592 0.3 12,755 0.4

Butorphanol 6,078 0.1 5,851 0.2

Pentazocine (with or without aspirin/acetaminophen/ibuprofen) 4,394 0.1 3,924 0.1

Schedule II Short-Acting*

Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 635,192 15.1 461,802 14.9

Hydromorphone 43,240 1.0 9,413 0.3

Fentanyl citrate transmucosal 32,052 0.8 14,393 0.5

Morphine sulfate 27,197 0.6 24,501 0.8

Codeine Sulfate 17,820 0.4 2,830 0.1

Meperidine hydrochloride 13,984 0.3 79,567 2.6

Tapentadol 2,109 0.1 1,929 0.1

Schedule II Long-Acting*

Oxycodone HCL control release 232,448 5.5 220,809 7.1

Morphine sulfate sustained release 106,442 2.5 105,429 3.4

Fentanyl transdermal 67,237 1.6 66,650 2.2

§ Prescription opioids are classified as short- or long-acting based on their duration. Short-acting opoids result in a

more rapid increase and decrease in blood serum levels, where as long-acting opioids release gradually into the

bloodstream or have a long half-life for prolonged activity.14



2.11

Prescription Opioid Type All use Long-Term Use

n % n %

Methadone 70,507 1.7 69,842 2.3

Oxymorphone extended release 3,576 0.1 3,562 0.1

Dihydrocodeine 1,678 0.0 1,447 0.0

Levorphanol tartrate 31 0.0 31 0.0

Total 4,193,606 100.0 3,320,489 100.0

Predominant Opioid Prescriptions among Long-Term Episodes of Opioid Use

As displayed in Table 2-3, hydrocodone, tramadol and oxycodone are the most predominantly

used opioids (in terms of total MED per episode) among long-term episodes of opioid use

(51.6%, 14.3% and 13.1%, respectively).

Table 2-3. Predominant Drug Types among Long-Term Users Prescription Opioid Type Number of Long-Term Episodes %

Hydrocodone 65,399 51.6

Tramadol 18,151 14.3

Oxycodone 16,557 13.1

Propoxyphene 8,098 6.4

Oxycodone hydrochloride 5,317 4.2

Codeine

+aspirin/acetaminophen/ibuprofen 3,201 2.5

Morphine sulfate sustained release 2,943 2.3

Meperidine 2,168 1.7

Other drug type 4,967 3.9

Supply days of opioid prescriptions by drug type

Figure 2-2 shows the distribution of supply days of all prescriptions (including short-term users’)

of each drug type (opioid types with less than 10,000 prescriptions are not shown). Very few

prescriptions (<0.3%, or 11,310 out of 4.1 million prescriptions) had supplies greater than 30

days.

Figure 2-2. Percentage of Supply Days for Prescription Opioids



2.12

Characteristics of Overdose Events

Number of Overdose Events

Table 2-4 lists the number of initial overdoses meeting or not meeting our inclusion criteria.

Among the 1,908 overdose events, 90.9% were definite cases of unintentional overdose (having

at least one ICD-9 code indicating poisoning or accidental poisoning by opioid) and 0.2% were

probable cases (having at least one ICD-9 code indicating adverse effects of opioid plus at least

one ICD9 code indicating an overdose-related symptom on the same day). The remaining 8.9%

of cases were excluded due to ICD-9 codes indicating suicide or undetermined causes. Among

the 1,738 patients who had an initial unintentional overdose, 313 (18.0%, data not shown) had at

least one more subsequent overdose in the study period.

Table 2-4. Opioid Overdose Events in the MarketScan® Medicaid Dataset, 2008-2012 Overdose Event Type n %

Included

Definite case 1,735 90.9

Probable case 3 0.2

Excluded

Suicidal case 58 3.0

Undetermined case 112 5.9

Total 1,908 100

Overdose Events and Estimated Costs by Encounter Type

Table 2-5 lists the number of initial overdoses by encounter types and cost estimation for each

encounter type. The majority of overdoses resulted in an ED visit (50.5%) or hospitalization

(44.7%, with or without ED visit). Cost estimation was based on Medicaid payments, as recorded

in the inpatient and outpatient MarketScan® data.

Table 2-5. Number of Overdose Events and Estimated Costs by Encounter Type, 2008-1010 Overdose Cost

Event type n % Mean Median Interquartile Range

Hospitalization, with ED visit 627 36.1 $12,371 $5,506 $2658, $13415

Hospitalization, without ED visit 149 8.6 $5,797 $3,241 $1257, $5479

0 10 20 30 40 50 60 70 80 90

100

Per

cen

tage

, %

>30 d

16-30 d

8-15 d

1-7 d

Note: All drug types are shown except those with less than 10,000 prescribed.



2.13

ED visit only 878 50.5 $514 $315 $132, $663

Outpatient visit only 84 4.8 $162 $149 $67, $269

Overall 1738 100 $5,376 $2,879 $407, $6945

Overdose Rates by Patient Characteristics

Table 2-6 lists overdose rates by patients’ characteristics. The overdose rate for the overall study

population was 2.22 per 1,000 person-years, and 2.10 per 1,000 person-years for overdoses

resulting in ED visits or hospitalizations, respectively. Individuals ages 30 years and older had

much higher overdose rates than persons younger than age 30. Both males and females had

similar rates. African Americans had a significantly lower overdose risk; about one-third of the

overdose rate of white users. Hispanics and other races also showed lower rates than whites;

however these rates were not statistically significant after adjusting for other characteristics

(adjustment is realized through the regression analysis; see Table 2-11 for more details). This is

likely due to the small sample size of overdoses. Users with a history of depression or alcohol

abuse had substantially higher overdose rates (16.9 per 1,000 person-years and 20.3 per 1,000

Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies – MarketScan™ Data

Analysis

2.14

Table 2-6. Unadjusted Overdose Rates in MarketScan® Medicaid Dataset by Demographic and Clinical Characteristics, 2008-2010 Overdoses Overdose Rate (95% CI)

Hospitalization ED Outpatient Total Person Years Hospitalization ED Outpatient Total

Total

786 848 78 1,738 783,132

1.00

(0.93-1.08)

1.10

(1.03-1.18)

0.12

(0.09-0.14)

2.22

(2.12-2.33)

Age

12-17 y 62 146 17 225

215,078

0.29

(0.22-0.37)

0.68

(0.57-0.80)

0.08

(0.05-0.13)

1.05

(0.91-1.19)

18-29 y 124 208 16 348 205,464

0.60

(0.50-0.72)

1.01

(0.88-1.16)

0.08

(0.04-0.13)

1.69

(1.52-1.88)

30-44 y 259 289 29 577 177,147

1.49

(1.29-1.65)

1.63

(1.45-1.83)

0.16

(0.11-0.24)

3.26

(3.00-3.53)

45-64 y 341 218 29 588 185,443

1.84

(1.65-2.04)

1.18

(1.02-1.34)

0.16

(0.10-0.22)

3.17

(2.92-3.44)

Sex

Male 261 268 27 556

227,922

1.15

(1.01-1.29)

1.18

(1.04-1.33)

0.12

(0.08-0.17)

2.44

(2.24-2.65)

Female 525 593 64 1,182 555,210

0.95

(0.87-1.03)

1.07

(0.98-1.16)

0.12

(0.09-0.15)

2.13

(2.01-2.25)

Race/Ethnicity

White 635 640 65 1,340

440,992

1.44

(1.33-1.56)

1.45

(1.34-1.57)

0.15

(0.11-0.19)

3.04

(2.88-3.21)

Black 95 166 21 282 278,918

0.34

(0.28-0.42)

0.60

(0.51-0.69)

0.08

(0.05-0.12)

1.01

(0.90-1.14)

Hispanic 9 13 0 22 12,890

0.70

(0.32-1.33)

1.01

(0.54-1.72)

0.00

(0.00-0.29)

1.71

(1.07-2.58)

Other 47 42 5 94 50,332

0.93

(0.69-1.24)

0.83

(0.60-1.13)

0.10

(0.03-0.23)

1.87

(1.51-2.29)

History of Depression Diagnosis

No 398 570 55 1,023

738,701

0.54

(0.49-0.59)

0.77

(0.71-0.84)

0.07

(0.06-0.10)

1.38

(1.30-1.47)

Yes 388 291 36 715 44,431

8.73

(7.89-9.65)

6.55

(5.82-7.35)

0.81

(0.57-1.12)

16.09

(14.93-17.32)

History of Alcohol Abuse

No 679 753 76 1,508

771,820

0.88

(0.81-0.95)

0.98

(0.91-1.05)

0.10

(0.08-0.12)

1.95

(1.86-2.06)

Yes 107 108 15 230 11,312

9.46

(7.75-11.43)

9.95

(7.83-11.53)

1.33

(0.74-2.19)

20.33

(17.79-23.14)

Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies – MarketScan™ Data

Analysis

2.15

Overdoses Overdose Rate (95% CI)

Hospitalization ED Outpatient Total Person Years Hospitalization ED Outpatient Total

History of Long-Term Opioid Use

No 177 333 35 545

573,548

0.31

0.26-0.36)

0.58

(0.52-0.65)

0.06

(0.04-0.08)

0.95

(0.87-1.03)

Yes 609 528 56 1,193 209,584

2.91

(2.68-3.15)

2.52

(2.31-2.74)

0.27

(0.20-0.35)

5.69

(5.37-6.02)

Note: Rates are unadjusted and expressed per 1,000 person-years



2.16

person-years, respectively) than those with no such history. Almost 70% of overdoses (1,193 of

1,738) occurred among users with at least one episode of long-term opioid use. The overdose

rate among long-term users was 5.69 per 1,000 person-years compared with 0.95 per 1,000

person-years in short-term users.

Overdoses by Type of Prescription Opioid Use

We further examined the type of episode of opioid use when an overdose occurred. As shown in

Table 2-7, about 19% (337) of overdoses occurred outside of any episode of opioid use. More

than half of the overdoses (66.3%) occurred during a period of long-term opioid use, while only

14.3% occurred during a period of short-term use.

Table 2-7. Overdoses by Type of Opioid Use Overdose Type n %

During A Long-Term Use Episode 1,153 66.3

During A Short-Term Use Episode 248 14.3

Outside of Any Episode 337 19.4

Total 1,738 100.0

Relationship between Overdose Risk and Prescribed Dose: Results of the 90-Day Exposure

Window Model

Table 2-8 shows the relationship between dose level, predominant opioid types and the risk of

overdose. Patients who had any opioid use in most recent 90-days had an overall overdose risk of

8.20 per 1,000 person-years, compared to 0.44 per 1,000 person-years for who did not have

opioid use during this time period.

Table 2-8. Overdose Rates and Hazard Ratios by Dose Level and Predominant Drug Type

Overdoses Person-

Years Overdose Rate

Adjusted Hazard Ratios (95% CI)

Episode-Based

Model

90-Day Exposure

Model

Opioid Dose

None 272 612,379 0.44 (0.39-0.50) N/A 0.31 (0.23-0.44)

1 to <20mg/d 189 47,991 3.93 (3.39-4.54) 1 [reference] 1 [reference]

20 to <50mg/d 485 71,388 6.79 (6.20-7.42) 1.83 (1.53-2.17) 1.69 (1.27-2.26)

50 to 100 mg/d 292 26,837 10.88 (9.67-12.20) 3.18 (2.60-3.89) 2.10 (1.48-2.98)

>=100mg/d 435 24,537 17.72 (16.10-19.48) 4.76 (3.83-5.91) 4.89 (3.67-6.52)

Any opioid use 1,401 170,753 8.20 (7.78-8.65) N/A 2.42 (1.75-3.31)

Predominant Opioid Type

Hydrocodone 641 80,242 7.99 (7.38-8.63) 1 [reference] N/A

Oxycodone 205 23,212 8.83 (7.66-10.13) 0.94 (0.80-1.10) N/A

Codeine 33 5,848 5.64 (3.88-7.92) 1.02 (0.71-1.46) N/A

Tramadol 120 16,174 7.42 (6.15-8.87) 1.09 (0.90-1.33) N/A

Morphine ER 95 10,229 9.29 (7.51-11.35) 1.35 (1.06-1.72) N/A

Propoxyphene 26 3,340 7.78 (5.08-11.40) 0.85 (0.67-1.09) N/A

Other * 281 31,708 8.86 (7.86-9.96) 0.93 (0.78-1.12) N/A

Include: butalbital + codeine (with or without aspirin/acetaminophen/ ibuprofen), butorphanol, pentazocine

(with or without aspirin/acetaminophen/ibuprofen), hydromorphone, fentanyl citrate transmucosal, morphine

sulfate, meperidine hydrochloride, tapentadol, oxycodone HCL control release, fentanyl transdermal,

methadone, oxymorphone extended release, dihydrocodeine, levorphanol tartrate



2.17

Hazard ratios (HR) for dosage levels were adjusted for gender, age, race, history of depression,

history of alcohol abuse, and concurrent use of sedative/hypnotics. In both the episode-based

model and the 90-day exposure model, higher dosage level was associated with a significantly

increased risk of overdose. Patients who received an average daily dose of 100mg/d or higher

had almost a 5-fold increase in overdose risk (4.76 [95% CI: 3.83 to 5.91] in the episode-based

model and 4.89 [95% CI: 3.67 to 6.52] in the 90-day exposure model, compared with the group

of patients who received the lowest dosage level (1 to <20 mg/d). Compared with the same group

(1 to <20 mg/d), patients who did not receive any recent prescription had hazard ratios of 0.31

(95% CI: 0.23 to 0.44) in the 90-day exposure model (note that no opioid use is not included in

the episode-based model).

The episode-based model also included the most frequently prescribed opioid type in an episode

(i.e., predominant opioid type) as covariates. Compared with the episodes where hydrocodone

was the most frequently prescribed opioid, episodes with a different predominant drug type did

not show any statistically significant difference in terms of adjusted overdose risk, except for

morphine in extended-release format which showed a 35% increase (HR: 1.35, 95% CI: 1.06 to

1.72) in overdose risk. Overdoses involving codeine had the lowest unadjusted overdose rate of

5.64 per 1,000 person-years (95% CI: 3.88 to 7.92 per 1,000 person-years), whereas its adjusted

risk was very close (HR: 1.02 [95% CI: 0.71 to 1.46]) to that of the reference drug, hydrocodone.

Pharmacy Shopping among Long-Term Opioid Users

The study population in this subgroup analysis is restricted to the 90,010 long-term users.**

The

main analysis found that approximately one in five opioid users met criteria for long-term use

(21.1% of all opioid users). Inclusion of short-term users would substantially bias the

comparison between shoppers and non-shoppers.

Number of Patients with an Overdose Event, by Peak Number of Pharmacies

Over a 1-year period, the numbers of patients using 3, 4, and 5 or more pharmacies were 15,901

(16.8%), 9,766 (10.8%), and 19,409 (20.6%), respectively. That means nearly half (49.2%) of

long-term users visited three or more different pharmacies in a 1-year period. If the time span is

narrowed to any 180 consecutive days, the numbers of patients using 3, 4, and 5 or more

pharmacies changed to 16,806 (18.7%), 9,012 (10.0%), and 12,339 (13.7%), respectively. After

further restricting the time setting to any 90 consecutive days, the corresponding numbers and

percentages fell to 15,647 (17.3%), 7,564 (8.4%), and 5,519 (6.1%), respectively. That means

only 14.5% of the long-term users used 4 or more pharmacies during any 90 consecutive days,

compared with 31.2% over a 1-year period.

In all three time periods (90-days, 180-days, and 1 year), the percentage of patients having

opioid-related overdoses in each category monotonically increases as the peak number of

pharmacies increases. However, the most dramatic increase is seen among patients using 4 and 5

or more pharmacies in the 90-day period. We found that 3.4% of patients who ever visited 4

pharmacies in any 90 consecutive days and 5.4% of patients who ever visited 5 or more

pharmacies in any 90 consecutive days had at least 1 overdose event in the follow-up time,

compared with only 0.4% to 1.5% among those visiting 1 to 3 pharmacies in any 90 consecutive

**

Those who had at least 1 episode of opioid use for 90 days or longer with at least 3 opioid prescriptions dispensed

in that episode.



2.18

days. In the 180-day and 1-year timeframes, the difference between the high and low numbers of

pharmacies visited was not as significant.

Figure 2-3 presents the distribution of the number of patients (bars, left y axis) and the

percentage of overdose (dots, right y axis) in each category based on the peak number of

pharmacies. Three sets of bars and dots represent three different time spans (1 year, 180-days

and 90-days) in which the numbers of pharmacies were counted.

Figure 2-3.Overdoses among Long-Term Users by Peak Number of Pharmacies Visited

Comparison between Different Pharmacy Shopping Criteria

A comparison of the six different definitions of pharmacy shopping is listed in Table 2-9.

Corresponding to the information provided in Figure 2-3, 49.2% (44,266 of 90,010) of sample

Medicaid recipients are categorized as pharmacy shoppers when using the definition of 3 or more

pharmacies during a one-year period; whereas only 14.5% (13,083) are eligible when using the

definition of 4 or more pharmacies over 90 consecutive days. This shows that the more

restrictive criteria is less sensitive in identifying overdose cases – less than half (47.0%) of

overdosed subjects are included in the pharmacy shopping group defined as “≥4 pharmacies in

any 90 consecutive days,” compared with nearly 70% (69.9%) when defined as “≥3 pharmacies

0%

1%

2%

3%

4%

5%

6%

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

1 2 3 4 ≥ 5

Percentage o

f Overd

ose

(in

dicated

in d

ots)

Nu

mb

er o

f Lo

ng-

term

Use

rs

(i

nd

icat

ed in

bar

s)

Peak Number of Pharmacies Visited

# of pharmacies in 1 year setting # of pharmacies in 180-day setting # of pharmacies in 90-day setting % of overdose for 1-year setting % of overdose for 180-day setting % of overdose for 90-day setting



2.19

in a year.” To quantitatively compare the different criteria, the diagnostic odds ratio (DOR)††

is

calculated for each criterion, a higher value of which is indicative of better test performance. The

criterion of “≥4 pharmacies in any 90 consecutive days” has the highest value of 5.23.

Table 2-9. Comparison of Different Pharmacy Shopping Criteria in Medicaid MarketScan®

Dataset Pharmacy Shopping

Criteria

Eligible

Recipients

Overdose

Events

% Overdose among

Eligible Recipients

% of Total

Overdose DOR

1-year setting,

≥ 3 pharmacies 44,266 825 1.86% 69.86% 2.38

1-year setting,

≥ 4 pharmacies 29,175 620 2.13% 52.50% 2.30

180-day setting,

≥ 3 pharmacies 38,157 818 2.14% 69.26% 3.05

180-day setting,

≥ 4 pharmacies 21,351 599 2.81% 50.72% 2.73

90-day setting,

≥ 3 pharmacies 28,730 788 2.74% 66.72% 4.28

90-day setting,

≥ 4 pharmacies 13,083 555 4.24% 46.99% 5.23

Combined Criteria: Peak Number of Pharmacies and Overlapping Prescriptions

To identify the group of prescription opioid users at the highest risk for an overdose event (and

perhaps those most likely to benefit from a patient review and restriction program), we combined

our results from prescription utilization patterns and peak pharmacy use. As the criterion of “4

or more pharmacies in any 90 consecutive days” had the highest DOR, we used it to define high

risk pharmacy shopping behavior. In addition to this definition, we used an indicator for

overlapping opioid prescriptions in order to further distinguish high risk patterns. Table 2-10

shows that, among the 90,010 long-term users, 6,024 (6.7%) had both pharmacy shopping

behavior and overlapping prescriptions; 3,885 (4.3%) did not exhibit pharmacy shopping

behavior, but did have overlapping prescriptions; 7,059 (7.8%) did have shopping behavior but

no overlapping prescriptions; the rest (81.1%) had neither shopping behavior nor overlapping

prescriptions.

Table 2-10. Comparison of Different Pharmacy Shopping Characteristics by Demographics,

Overdose Events and Opioid Consumption Patters

No shopping,

No overlapping RX

No shopping,

Overlapping RX

Shopping,

No overlapping RX

Shopping,

Overlapping RX

Demographics

Number 73,042 3,885 7,059 6,024

Mean age 44.1 45.6 38.0 42.0

††

DOR is a measure of the effectiveness of a diagnostic test. DOR is defined as the ratio of the odds of testing

positive if the subject has a disease relative to the odds of testing positive if the subject does not have the disease.13

We replaced ‘test’ with criterion for pharmacy shopping and ‘disease’ with opioid-related overdose, thereby using

DOR to assess the efficacy of each definition to identify opioid users at high risk of overdose, and, presumably, also

at high risk of abuse and misuse.



2.20

No shopping,

No overlapping RX

No shopping,

Overlapping RX

Shopping,

No overlapping RX

Shopping,

Overlapping RX

Male 28.2% 32.1% 26.8% 31.2%

Depression diagnosis 9.0% 11.4% 16.8% 18.0%

History of alcohol

abuse 2.4% 2.8% 5.1% 5.6%

Overdose Events

Number 473 165 188 367

Percentage 0.64% 4.25% 2.66% 6.09%

Overdose Incidence

Total person-years 108,042 8,911 17,539 13,934

Incidence rate*

438

(401-480)

1,851

(1,580-2,157)

1,072

(924-1,237)

2,634

(2,371-2,918)

Opioid Prescriptions

Monthly prescriptions

1.42

(1.40-1.44)

2.83

(2.68-2.98)

1.84

(1.76-1.92)

2.77

(2.67-2.87)

Average dose

40.2

(40.0-40.4)

100.7

(97.0-104.4)

53.9

(52.3-55.4)

89.2

(87.1-91.4)

Dose level distribution

0-20mg 35.4% 14.5% 23.4% 15.2%

20-50mg 44.5% 36.7% 48.6% 38.4%

50-100mg 13.6% 21.3% 17.6% 22.1%

100mg or higher 6.5% 27.6% 10.4% 24.4%

Predominant opioid drug type

Schedule III /IV 82.2% 64.9% 73.6% 65.0%

Schedule II,

Short Acting 15.1% 23.4% 21.1% 24.6%

Schedule II,

Long Acting 2.7% 11.7% 5.3% 10.4% Note: RX = opioid prescriptions

* per 100,000 person-years

There is no clear trend across the four categories in terms of age. Males appeared to be higher in

the two categories with overlapping prescription (32.1% without shopping and 31.2% with

shopping). Patients classified as conducting pharmacy shopping tended to have higher

prevalence of depression (16.8% without overlapping prescription and 18.0% with overlapping

prescription) and alcohol abuse (5.1% without overlapping prescription and 5.6% with

overlapping prescription).

Our analysis found that 40% of overdose events (473 out of 1,193 overdoses) occurred in the

subgroup of patients without any shopping behavior or overlapping prescriptions, whereas the

remaining 60% overdosed users (720 out of 1,193 overdoses) included either pharmacy

shopping, overlapping prescriptions or both. The percentage of overdoses in the group with both

shopping behavior and overlapping prescriptions is almost 10 times higher (6.09%/0.64%=9.52)

than the group with neither condition. Even without shopping behavior, patients who had

overlapping prescriptions still had a high percentage of overdoses (4.25%) -- even higher than

those who had shopping behavior but did not have overlapping prescriptions (2.66%). The

incidence rates of overdose tell a similar story. The group meeting both criteria is about 6-times

more likely to overdose compared with the group with neither condition (2.634 vs. 0.438 per

1,000 person-years); patients with overlapping prescriptions but no shopping behavior had a



2.21

higher incidence rate (1,851 per 100,000 person years) than patients with shopping behavior, but

no overlapping prescriptions (1,072 per 100,000 person-years).

Interestingly, patients who had overlapping prescriptions but were not considered pharmacy

shoppers had the highest average monthly number of prescriptions (2.83 prescriptions per

month) and average daily dose (100.7mgMED/d). In comparison, patients who were pharmacy

shoppers without overlapping prescriptions had only had a moderate increase in both measures

(1.84 per month and 53.9mgMED/d), compared with the no-shopping-and-no-overlapping-

prescription group (1.42 per month and 40.2mg/d MED). The distribution of dose levels and the

distribution of frequently prescribed opioid types appear to be associated with whether a patient

had overlapping prescriptions rather than a history of pharmacy shopping. Patients who had

overlapping prescriptions are more likely to use high doses and schedule II opioids, especially

long-acting formulations.

Relationship between Potential Pharmacy Shopping and Overdose Risk

The elevated risk of overdose in the subgroups of patients with either pharmacy shopping

behavior or overlapping prescriptions or both could be attributable to increased opioid use and

higher rates of pre-existing conditions. We wanted to examine whether pharmacy shopping

behavior and overlapping prescriptions were associated with a higher risk of overdose after

controlling for dose level, demographic characteristics, and pre-existing conditions. To

accomplish this, two dummy variables (each representing whether a patient visited 4 or more

pharmacies within any 3 months and whether a patient had overlapping prescriptions) were

added to the episode-based model. The result of this regression analysis (Table 11) showed that,

after adjusting for daily doses and other characteristics, patients who visited 4 or more

pharmacies were 1.80 (95% CI: 1.54 to 2.10) times more likely to have an overdose than those

who did not. Overlapping prescriptions were associated with an almost 3-fold increase in

overdose risk (2.96, 95% CI: 2.45 to 3.68) higher risk compared with those who did not have

overlapping prescriptions. This result implies that pharmacy shopping and overlapping

prescriptions are associated with a higher risk of overdose for reasons beyond higher dose of

opioid use, such as concurrent sedative/hypnotic use, or history of alcohol abuse or depression.

Table 2-11. Hazard Ratios of Overdose, Including Indicators for Pharmacy Shoppinga and

Overlapping Prescriptions in Medicaid MarketScan® Dataset, 2008-2010 Hazard Ratio 95% CI P value

Opioid dose

1 to <20mg/d 1

20 to <50mg/d 1.61 1.24 2.08 0.0004

50 to 100 mg/d 3.06 2.33 4.02 <.0001

>=100mg/d 4.02 3.07 5.26 <.0001

Gender

Female 1.00

Male 1.02 0.87 1.18 0.8444

Age

12-17 0.21 0.03 1.53 0.1235

18-29 1.00

30-44 0.94 0.74 1.19 0.593

45 and over 0.88 0.70 1.11 0.2875

Race/Ethnicity



2.22

Hazard Ratio 95% CI P value

White 1.00

Black 0.60 0.48 0.74 <.0001

Hispanic 1.09 0.57 2.11 0.7959

Other 1.13 0.86 1.48 0.377

Concurrent sedative/hypnotic use 2.54 1.99 3.23 <.0001

History of alcohol abuse 3.07 2.09 4.50 <.0001

History of depression diagnosis 2.91 2.21 3.83 <.0001

Pharmacy Shoppinga 1.80 1.54 2.10 <.0001

Overlapping Prescriptionsb 2.96 2.45 3.68 <.0001

aPharmacy shopping is defined as having 4 or more unique pharmacies visited within any 90 consecutive days.

bOverlapping prescriptions are defined as defined as two prescriptions of the same drug type that overlapped by 25%

or more of the days prescribed and the former of the two prescriptions had a supply time of 5 days or longer.

SUMMARY

Our analysis found that the overall overdose rate among Medicaid opioid users was 2.22 per

1,000 person years. Patients with older ages were more likely to have an opioid overdose.

Gender did not have significant effect. Whites had the highest overdose rates (3.04 per 1,000

person years), whereas blacks had the lowest (1.01. per 1,000 person years). The difference

between ethnicities mirrored the reported difference in overdose-related mortality by CDC

indicating that whites and American natives have three times higher opioid overdose death rates

than blacks and Hispanics.15

Our analysis also showed that comorbidities including depression

and alcohol abuse were associated with a three-fold higher overdose risk. Patients who had a

long-term opioid use (≥90 d with 3 or more prescriptions) were at a higher risk than patients who

did not.

We used two regression models to conduct multivariate analysis. Both models had comparable

hazard ratios for opioid dose. Patients who had an average daily dose of over 100 mg morphine

equivalent had 4.8-fold and 4.9-fold higher risk of overdose than those having 20 mg or less in

the episode-based model and in the 90-day exposure model, respectively. In addition, the

episode-based model examined how opioid type affected overdose risk. We found that there was

no statistically significant difference in adjusted overdose risk between different opioid types,

except sustained-release morphine which had 35% higher overdose risk, compared to the

baseline of hydrocodone.

Our analysis was the first attempt we are aware of to compare the performance between different

cutoff numbers of pharmacies and timeframes. We found that the 3-month setting and the cutoff

number of 4 pharmacies had a higher diagnostic odds ratio, that is, a better test performance,

than the other criteria. The present study also examined another risk factor- the history of having

overlapping prescriptions. The new criterion for pharmacy shopping that combined both

pharmacy number and the history of overlapping prescription yielded two novel findings. First,

overlapping prescriptions were associated with higher daily opioid dose and more monthly

prescriptions, even in absence of pharmacy shopping. Secondly, patients who exhibited

pharmacy shopping compared with those who exhibited both factors. This suggests that PRR

program eligibility criteria could be improved by including the history of overlapping

prescriptions.



2.23

APPENDIX

Morphine Equivalent Dose Conversions Opioid Type Milligrams Morphine Equivalent

Schedule III and IV

Propoxyphene (with or without

aspirin/acetaminophen/ibuprofen)

0.23

Codeine + aspirin/acetaminophen/ibuprofen 0.15

Hydrocodone + aspirin/acetaminophen/ibuprofen 1.00

Tramadol with or without aspirin 0.10


ibuprofen)

0.15

Dihydrocodeine (with or without

aspirin/acetaminophen/ibuprofen)

0.25

Pentazocine (with or without aspirin/acetaminophen/ibuprofen) 0.37

Buprenorphinea

25.0-40.0

Butorphanol 7.00

Schedule II Short-Acting

Morphine sulfate 1.00

Codeine sulfate 0.15

Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 1.50

Hydromorphone 4.00

Meperidine hydrochloride 0.10

Oxymorphone 3.00

Fentanyl citrate transmucosalb 0.125

Tapendatol short actingc

not established

Schedule II Long-Acting

Morphine sulfate sustained release 1.00

Fentanyl transdermald 2.40

Levorphanol tartrate 11.0

Oxycodone HCL control release 1.50

Methadone 3.00

Oxymorphone extended releasec

3.00

Hydromorphone extended releasec

5.00

Tapentadol extended releasec

not established

Sources: Von Korff et al (2008); FDA Blueprint for Prescriber Education for Extended-Release and Long-Acting

Opioid Analgesics (2012)

Note: The majority of these conversation factors are based on Von Korff’s CONSORT (CONsortium to Study

Opioid Risks and Therapeutics) study. Opioids delivered by pill, capsule, liquid, transdermal patch, and

transmucosal administration were included in the data, but opioids formulated for administration by injection or

suppository were not included. aBuprenorphine is typically used for opioid detoxification and maintenance

16

bTransmucosal fentanyl conversion to morphine equivalents assumes 50% bioavailability of transmucosal fentanyl

and 100 micrograms transmucosal fentanyl is equivalent to 12.5 to 15 mg of oral morphine. cData for oxymorphone, hydromorphone and tapentadol obtained from FDA Blueprint for Prescriber Education for

Extended-Release and Long-Acting Opioid Analgesics

dTransdermal fentanyl conversion to morphine equivalents is based on the assumption that one patch delivers the

dispensed micrograms per hour over a 24 hour day and remains in place for 3 days.



2.24

ICD-9 Codes Indicating Overdose-Related Symptoms

276.4 Mixed acid–base balance disorder

292.1 Drug-induced psychotic disorders (including 292.11 and 292.12)

292.81 Drug-induced delirium

292.8 Drug-induced mental disorder (excluding 292.81)

486 Pneumonia, organism unspecified

496 Chronic airway obstruction, not elsewhere classified

518.81 Acute respiratory failure

518.82 Other pulmonary insufficiency, not elsewhere classified

780.0 Alteration of consciousness

780.97 Altered mental state

786.03 Apnea

786.05 Shortness of breath

786.09 Dyspnea and respiratory abnormalities—other

786.52 Painful respiration

799.0 Asphyxia and hypoxemia

Type of Overdose Encounters

Emergency department (ED) visits are identified from both inpatient and outpatient claims data

as claims having emergency room as service place and/or having emergency medicine or

emergency services as service type. Inpatient claims with the same admission dates and

outpatient claims occurring in 2 preceding days are grouped into one overdose encounter.

Overdose encounters are divided into 3 types: hospitalization if any non-ED inpatient claims

appear in that encounter; ED encounter if there are any ED claims and no non-ED inpatient

claims; and outpatient encounter if there are non-ED outpatient claims and no inpatient or ED

outpatient claims.



2.25

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or service names are registered trademarks or trademarks of SAS Institute Inc. [computer

program]. Cary, NC: SAS Institute Inc.; Copyright © 2002-2008.

13. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a

single indicator of test performance. Journal of clinical epidemiology. Nov

2003;56(11):1129-1135.

14. Argoff CE, Silvershein DI. A comparison of long- and short-acting opioids for the

treatment of chronic noncancer pain: tailoring therapy to meet patient needs. Mayo Clinic

proceedings. Mayo Clinic. Jul 2009;84(7):602-612. PMCID:PMC2704132

15. Centers for Disease Control and Prevention. Vital signs: overdoses of prescription opioid

pain relievers---United States, 1999--2008. MMWR. Morbidity and mortality weekly

report. Nov 4 2011;60:1487-1492.



2.26

16. Sporer KA. Buprenorphine: A primer for emergency physicians. Annals of emergency

medicine. 2004;43(5):580-584.



2.27

3.1





Program

Part 3

The ADOPT Model: An Evidence-Based Tool for Promoting Health Policy

and Disease Prevention - Prescription Opioid Overdose

3.2

Table of Contents INTRODUCTION ..................................................................................................................................... 3.4

METHODS ................................................................................................................................................ 3.4

Data Sources .......................................................................................................................................... 3.4

Definitions for the MarketScan®

Data Analysis ..................................................................................... 3.6

Overview: The Micro-Simulation Process and Simulation Process ...................................................... 3.7

MODEL CALIBRATION ....................................................................................................................... 3.11

Number of Prescriptions ...................................................................................................................... 3.12

Days’ Supply, Dose Level, and Generic Drug Type ............................................................................ 3.13

Distribution of Number of Pharmacies Visited .................................................................................... 3.14

Demographic Characteristics and Opioid Use Patterns of Potential Pharmacy Shoppers ................... 3.14

Individual-Level Comparison .............................................................................................................. 3.15

COST ESTIMATION .............................................................................................................................. 3.18

MODEL OUTPUT ................................................................................................................................... 3.21

Patient Review and Restriction Program Policies ................................................................................ 3.21

Impact on Prescription Opioid Use ...................................................................................................... 3.25

Impact on Opioid Overdose-Related Events ........................................................................................ 3.28

Cost Analysis of Different Patient Review and Restriction Scenarios ................................................ 3.30

DISCUSSION .......................................................................................................................................... 3.33

Model Limitations ................................................................................................................................ 3.33

APPENDIX I ........................................................................................................................................... 3.35

The Simulation Process ........................................................................................................................ 3.35

Step 1: Simulate the Basic Individual Profile .................................................................................. 3.35

Step 2: Simulate Predominant Drug Type in An Episode of Drug Use ........................................... 3.35

Step 3: Simulate Episode Length ..................................................................................................... 3.37

Step 4: Simulate Concurrent Prescription Opioid Use ..................................................................... 3.37

Step 5: Simulate Overlapping Prescriptions .................................................................................... 3.38

Step 6: Simulate Subsequent Episodes of Prescription Opioid Use ................................................. 3.38

Step 7: Simulate the Opioid Type of Each Prescription in an Episode ............................................ 3.39

Step 8: Simulate the Prescription Details: Generic Name, Strength, Master Form, Quantity, Supply

Days, Dose Level and Drug Price .................................................................................................... 3.41

Step 9: Assign Prescription Dates .................................................................................................... 3.42

Step 10: Assign Pharmacy IDs to Each Prescription ....................................................................... 3.43

Step 11: Assign Prescriber IDs to Each Prescription ....................................................................... 3.43

Step 12: Simulate Subsequent Episodes of Opioid Use ................................................................... 3.44

Step 13: Assign Absolute Dispensing Date to Each Prescription .................................................... 3.44

Step 14: Calculate Number of Prescription/Pharmacies/Prescribers and Dose Level ..................... 3.44

Step 15: Calculate Risk of Overdose, Overdose Event Type, and Overdose-Related Medical Costs

......................................................................................................................................................... 3.44

Step 16: Check Individual Eligibility for the Patient Review and Restriction Program .................. 3.46

Step 17: Summarize the Cost and Health Outcomes of the Simulated Cohort ................................ 3.46

APPENDIX II .......................................................................................................................................... 3.47

ICD-9 Codes Indicating Overdose-Related Symptoms ....................................................................... 3.47

Type of Overdose Encounters .............................................................................................................. 3.47

Numbers and Estimated Cost for Each Generic Opioid Drug Type .................................................... 3.47

BIBLIOGRAPHY .................................................................................................................................... 3.53

3.3

List of Tables

Table 3-1.ADOPT Input Parameters: Data Sources and Modifiability of Input Category ........................ 3.5

Table 3-2. ADOPT Model Assumptions .................................................................................................. 3.11

Table 3-3. Baseline Characteristics of MarketScan® Medicaid Long-Term Users ................................. 3.11

Table 3-4. Comparison between MarketScan® Medicaid Opioid Prescriptions among Long-Term Users in

2009 and the Simulated Yearly Number of Prescriptions ........................................................................ 3.12

Table 3-5. Comparison between the Percent Distributions of Days’ Supply, Daily Dose,* and Generic

Drug Type for Methadone in the MarketScan®

and Simulated Populations ............................................ 3.13

Table 3-6. Unique Pharmacy Visits per Year and Within Any 90-Day Period among MarketScan®

and

Simulated Cohorts .................................................................................................................................... 3.14

Table 3-7. Demographic Characteristics, Overdose Rates, and Opioid Use Patterns of Patients with

Different Pharmacy Shopping Characteristics ......................................................................................... 3.14

Table 3-8. Prescription History of a Representative Individual from the MarketScan®

Long-Term User

Population for Comparison With Figure 3-3 Data ................................................................................... 3.17

Table 3-9. Price Comparison of Commonly Prescribed Opioids ............................................................. 3.18

Table 3-10. Price Comparison of Commonly Prescribed Opioids, Price per 50 MME ........................... 3.20

Table 3-11. Representative State Patient Review and Restriction Program Policies ............................... 3.23

Table 3-12. Demographic and Drug Use Patterns of PRR Program Enrollees in a Simulated Population of

10,000 Long-Term Users under Different Eligibility Scenarios (with 95% confidence interval in

parentheses).............................................................................................................................................. 3.26

Table 3-13. Annual Health Impact of the PRR Program in a Population of 10,000 Long-Term Users under

Different Eligibility Scenarios* ............................................................................................................... 3.29

Table 3-14. Cost Analysis of the PRR Program under Different Eligibility Scenarios ........................... 3.32

Table 3-15. Most Frequently Used Opioid Types in Market Scan Data .................................................. 3.36

Table 3-16. Hazard Ratios for Prescription Opioid Overdose ................................................................. 3.45

Table 3-17. Distribution of Overdose and Cost Estimates ....................................................................... 3.45

List of Figures

Figure 3-1. Overview of ADOPT Micro-Simulation Model ...................................................................................... 3.4

Figure 3-2. Overview of the Simulation Process ........................................................................................................ 3.9

Figure 3-3. Simulated Individual’s Prescription History from ADOPT Model for Comparison with Table 3-8 Data

.................................................................................................................................................................................. 3.16

Figure 3-4. ADOPT Output: Program Summary ...................................................................................................... 3.25

Figure 3-5. Example of Random Sampling of Predominant Drug Type .................................................................. 3.37

Figure 3-6. Subsequent Episodes of Opioid Use ...................................................................................................... 3.38

Figure 3-7. Example of Opioid Type Distribution Table, for Predominant Drug Type of Hydrocodone and Episode

Length between 180- and 364-Days ......................................................................................................................... 3.40

3.4

INTRODUCTION

The Approaches to Drug Overdose Prevention Analytical Tool (ADOPT) is an evidence-based

tool created by UC Davis to help inform policy decisions regarding policies to prevent

prescription opioid misuse/abuse and consequent adverse health outcomes. Specifically, it is an

Excel-based, micro-simulation model that simulates patterns of prescription opioid use by

Medicaid recipients to evaluate associated health outcomes and costs.* It compares the

counterfactual scenarios of implementing a prescription drug misuse/abuse prevention policy

versus the absence of such a policy, and evaluates the cost and health impact of the policy. The

model’s interactive features allow users to customize the population demographics and policy

details, and perform a "what-if" analysis to project the outcomes of the specified policy within

that population (see Figure 3-1). Although ADOPT has the potential to analyze and compare

different approaches to drug misuse/abuse prevention (such as prescriber/patient education or

monitoring strategies), the current version focuses on the Medicaid patient review and restriction

(PRR) program sometimes referred to as a “lock-in” program).

This section of the report (Part 3) explains the design, calibration, cost estimation, and basic

operation of the model. It also provides an example of the projected program policy results

(using MarketScan® Medicaid data to inform the ADOPT model) on prescription opioid use,

related over-dose events, and their cost impact. The strength of this model is its ability to be

customized to state-specific data, as these findings will lead to more valid conclusions for state-

specific populations and policies than the example provided here.

Figure 3-1. Overview of ADOPT Micro-Simulation Model

User-defined patient population

characteristics

User-defined Policy Intervention

Individual prescription history, without policy

intervention

Individual prescription history, with policy

intervention

Simulate

Apply the policy to simulated cohort

Opioid and medical costs, health outcomes , without

policy intervention

project

Opioid and medical costs, health outcomes , with

policy intervention

project

CompareIncremental cost and

health outcome of the policy intervention

METHODS

Data Sources

The ADOPT model allows users to specify the values of some of input parameters, while others

are not user-modifiable. The default values of the modifiable parameters and the values of non-

modifiable parameters come from multiple sources, including the analysis of MarketScan®

* The model was informed by an analysis of the MarketScan® Medicaid dataset and a literature review; these

analyses are presented in Parts 1 and 2, respectively, of this report.

3.5

Medicaid data, the literature (for prescriber information), content experts (for PRR program

cost),† and government documents (for PRR program eligibility criteria) (see Table 3-1).

Table 3-1.ADOPT Input Parameters: Data Sources and Modifiability of Input Category Input Parameter Type Source User Modifiable?

Demographics

Age

MarketScan® Medicaid

Data

Yes

Gender Yes

Race Yes

Prevalence of overdose-related risk factors * Yes

Prescription Behavior

Episode Length

MarketScan® Medicaid

Data: long-term opioid

users only

No

Most Frequently Used Opioid During an Episode No

Opioid Type per Prescription No

Drug Strength No

Days of Supply No

Master Form (tablet, solution, elixir) No

Generic Drug Type No

Pharmacy Information No

Prescriber Information

Previous study using

Massachusetts’

prescription drug

monitoring database1

No

Overdose

Hazard Ratios MarketScan® Medicaid

Data

Yes

Encounter Type (inpatient/ED/outpatient) No

Cost

Prescription Reimbursement Rates MarketScan® Medicaid

Data

Yes

Overdose-related Medical Cost No

Program Cost Informed Assumption † Yes

PRR Program Eligibility Criteria

Timeframe for Prescription History Review

PRR Program

Government Document

from Multiple States

Yes

Threshold Number of Prescribers Yes

Threshold Number of Pharmacies Yes

Threshold Number of Prescriptions Yes

Number of Above Conditions Required for Eligibility Yes

* Risk factors include depression diagnosis, alcohol use, and concurrent sedative/hypnotic drug use.

† Cost estimations are based on estimates from Oklahoma and Washington state PRR programs, per

content expert discussions with the CDC; Jones, C.M., Roy, K. Email correspondence, August 2012

The MarketScan® Medicaid data are a commercially available administrative claims dataset that

include information on demographics (age, race and gender), Medicaid enrollment duration,

diagnosis, and health care utilization (i.e., prescription drugs, hospital and emergency department

visits). It contains approximately 7 million Medicaid enrollees from multiple states (the number

of states varies by year; in 2012 there were 12 states). The study population for this analysis

consisted of Medicaid beneficiaries who received at least one opioid analgesic prescription for

non-cancer pain between January 2008 and December 2010. We excluded individuals:

with less than 24 months continuous Medicaid enrollment;

† Cost estimations are based on estimates from Oklahoma and Washington state PRR programs, per content expert

discussions with the CDC; Jones, C.M., Roy, K. Email correspondence, August 2012

3.6

younger than age 12 years at the start of continuous enrollment;

with history of cancer diagnosis (ICD-9 CM neoplasms 140-293.2, excluding 173.X, 210-

239 and 232);

residing in any long-term care facilities;

having any opioid prescription filled in the first 3 months of the continuous enrollment

period. This last criterion enabled us to accurately identify the period of continuous opioid

use.

We identified 427,411 Medicaid beneficiaries in the MarketScan® data during the 24 month

period who met the inclusion criteria.

Definitions for the MarketScan®

Data Analysis

Episode of Opioid Use. An “episode of opioid use” commenced with the dispensing date of an

opioid prescription with no previous opioid prescription in the dataset, or having a gap longer

than 31 days from the end run-out date of a previous opioid prescription. “Episode duration” was

the number of days from the first fill date to the end date of the last opioid prescription with no

prescription gaps exceeding 31 days after the previous refill.

Long-term Episode of Opioid Use. An episode was “long-term” if the duration is longer than 90

days with 3 or more prescriptions dispensed, concurrently or in succession, in that time.

Pharmacy Shopping. Pharmacy shopping was defined as visiting multiple pharmacies to obtain

prescription opioids, which contribute to medically unnecessary opioid use, misuse and abuse.

Published thresholds (for identifying misuse/abuse) vary by number of pharmacies visited by a

single patient to obtain any opioid over a given time period.1-5

Peak Number of Pharmacies. Within a long-term episode of opioid use, we defined the “peak

number of pharmacies” visited as the maximum number of unique pharmacy IDs that appeared

in opioid prescription claims during any 90 days in that episode. The peak number of

pharmacies visited may be a more accurate indicator of prescription opioid consumption patterns

than the total number of pharmacies visited for the entire episode, which is affected by the

episode length. In other words, long-term, continuous opioid use over many months may include

multiple pharmacies due to a change in residence or a pharmacy switch, but multiple pharmacies

used in a shorter (90-day) period are more likely to represent opioid misuse or abuse.

Morphine Equivalent Dose and Average Daily Dose. Consistent with previous studies,6-10

we

compared the effects of multiple types of opioids using a drug conversion method known as the

“morphine equivalent dose.” The morphine equivalent dose (MED) is calculated by multiplying

the strength of the opioid prescription by the quantity and by a drug-specific conversion factor

(expressed in milligrams morphine equivalent, or MME). The majority of these conversion

factors are based on Von Korff’s CONSORT (CONsortium to Study Opioid Risks and

Therapeutics) study.10

The total MED was calculated by adding MEDs for all opioid

prescriptions within an episode. The average daily dose was the total MED divided by episode

duration. The average daily dose was categorized into 4 levels: 0-<20mg/d; 20-<50mg/d; 50-

<100mg/d; and 100mg/d or more.

Overlapping Prescriptions. Overlapping prescriptions were two prescriptions of the same opioid

type, one of which had a supply for 5 days or longer, that overlapped by 25% or more of the days

3.7

prescribed. The 25% cutoff point originated from the clinical opinions of an expert panel in

which early opioid refills were defined as patients who filled opioid prescriptions when 25

percent or more of an existing prescription should have remained available.11

We restricted it to

the same opioid category because patients could have legitimate concomitant use of two or more

different types of opioids. We required the earliest prescription dispensed to have at least 5 days

of supply, because the 25% cutoff point was too sensitive for prescriptions with short supply

days – a refill on the same date as the run-out day of a previous fill with less than 5-day supply

would be mistakenly considered as overlapping prescription.

Opioid Overdose Events. “Opioid overdose events” were based on inpatient and outpatient

claims data for the study population. We defined “definite cases of overdose” as claims with

ICD-9 codes indicating opioid-related poisoning (965.0, 965.00, 965.02 and 965.09) or

accidental poisoning (E935.1 and E935.2). We defined “probable cases of overdose” as claims

with ICD-9 codes indicating adverse effects of opioid use (E935.1 and E935.2) plus at least one

ICD-9 code indicating overdose-related symptoms on the same day (see Appendix II for full

list). We included both definite and probable cases in the analysis. We excluded suicidal

poisoning by opioid drugs (E950.0), poisoning undetermined whether accidentally or

purposefully inflicted (E980.0), and opioid drug dependence (304.X and 305.X). We grouped

inpatient and outpatient claims into overdose encounters and classified the encounters into 3

types: hospitalizations, ED visits, and outpatient visits (see Appendix II for detailed rules for

grouping and classification). If an individual had multiple overdose encounters, only the earliest

one (i.e. initial overdose) was counted.

Overview: The Micro-Simulation Process and Simulation Process

We chose a micro-simulation model to study a Medicaid patient review and restriction (PRR)

program. Although micro-simulation models are complex and time consuming to build and to

run, they accommodate heterogeneity better than cohort models. For example, in some disease-

focused models, the individual heterogeneity may not be important; the disease prevalence,

incidence and mortality rates are based on population-level data (or age/gender stratified) and

using a micro-simulation may not add enough additional information to justify the additional

effort and cost required. In the case of evaluating prescription drug abuse prevention policies, it

is critical to account for the individual heterogeneity. Individuals differ in the types of drugs

used, dosage, length of drug use, number of pharmacies/prescribers used to obtain drugs, and so

on. Whether an individual meets the criteria of the preventive program depends on his/her

personal behavior. A cohort model cannot evaluate the cost and efficacy of a policy driven by

individual behavior. For this reason, we built the ADOPT as a micro-simulation model.

Micro-simulation is a valuable tool used to project the impact of a policy. Micro-simulation

modeling is often rigid in its evaluation of a pre-determined range of policy options in a specific

population (often at a national level) with a number of fixed assumptions; the conclusion applied

to another policy context may not be generalizable. To make our analysis more flexible, timely,

and relevant to state-specific concerns, we designed the model interface to allow users to

customize the analysis. The model is Excel-based and can be used by any computer running

Excel 97 or more recent versions.

For each simulated Medicaid enrollee, the model starts with simulating the basic profile

including age, gender, race, and overdose-related risk factors (such as depression diagnosis,

3.8

alcohol use, and concurrent sedative/hypnotic drug use), based on user-defined distributions of

population characteristics (See Figure 3-2, step 1). With the profile information, ADOPT then

simulates the characteristics of each episode of opioid use for each enrollee. This includes the

predominant drug type (i.e., the most frequently prescribed drug in an episode [step 2]), the

3.9

Figure 3-2. Overview of the Simulation Process

-daily dose levelandIn a specified length of time -number of pharmacies -number of prescribers-number of RX

-age-gender-race-risk factors

Simulate individual profile (step 1)

-predominant drug type (step 2)-Length of episode (step 3)-whether have concurrent drug use (step 4)-whether have overlapping RX (step 5)

Simulate information about initial episode of opioid use

-opioid drug type of each RX (step 7)-more details: strength, master form, supply days, etc (step 8)-RX date relative to the start date of the episode (step 9)-Pharmacy ID of each RX (step 10)-Prescriber ID of each RX (step 11)

Simulate prescriptions obtained with in an episode

Simulate information about subsequent episodes (step 6)

- opioid drug use in subsequent episodes is correlated with opioid drug use in the previous episode

Repeat the process for subsequent episode(s) (step 12)

Assign absolute dates to all RX (step 13)

Summarize RX history (step 14)

Simulate opioid overdose events( step 15)

Check subject’s eligibility for PRR program (step 16)

Repeat the process for the entire cohort (step 17)

3.10

length of episode (step 3), and whether the subject has concurrent prescriptions (step 4) and/or

overlapping prescriptions (step 5) in each of the episodes.

The ADOPT assumes the characteristics of a subsequent episode are correlated with those of a

previous episode (step 6). For example, an enrollee who frequently uses hydrocodone in the

previous episode is likely to use hydrocodone more than any other opioid drugs in the following

episode. After characteristics of an episode are created, the ADOPT simulates a list of

prescriptions and adds details of each prescription through multiple steps (including drug names

in step 7, prescription details [strength, quantity, supply days, generic name, and reimbursement]

in step 8, dispensing date relative to the start date of the episode in step 9, pharmacy IDs in step

10, and prescriber IDs in step 11). This process (steps 2-11) is repeated for all episodes in step

12. After all prescriptions in all episodes of opioid use are simulated for a hypothetical enrollee,

ADOPT assigns calendar dates (i.e., converting a relative date such as “6 days after the

beginning date of the second episode” to an absolute date such as “Jan-10-2010”) to all

prescriptions and sorts them into a chronological order (step 13). The daily dose and the

maximum number of pharmacies and prescribers that a subject obtains prescriptions from, as

well as the maximum number of prescriptions in a specific time frame (e.g., any 90 days or any

60 days) are calculated (step 14). The risk of overdose events is based on the daily dose (step

15). The numbers of pharmacies, prescribers, and prescriptions are then compared with the PRR

program eligibility criteria in order to determine whether the hypothetical enrollee is eligible for

program enrollment (step 16). If eligible, the enrollee’s prescription history and the risk of

having an overdose event under the PRR program will be calculated (step 16). The ADOPT

repeats the above process (step 1-16) for the entire cohort and calculates the aggregated cost and

number of overdose events (step 17).

The simulation process used by the ADOPT model requires a series of steps, which are outlined

below and presented in greater detail in Appendix I.

1. Simulate the basic individual profile

2. Simulate the predominant drug type

3. Simulate the episode duration

4. Simulate the use of concurrent opioid use

5. Simulate the use of overlapping prescriptions

6. Simulate the subsequent episodes of opioid use

7. Simulate the drug type for each prescription in an episode of use

8. Simulate the prescription details (generic name, strength, master form, quantity, supply

days, dose level , drug price)

9. Assign relative prescription dates to each prescription

10. Assign pharmacy IDs to each prescription

11. Assign prescriber IDs to each prescription

12. Simulate subsequent episodes of opioid use

13. Assign absolute dispensing date to each prescription and eliminate irrelevant

prescriptions

14. Calculate the number of prescriptions/pharmacies/prescribers and the dose level

15. Calculate the risk of overdose, overdose event type, and overdose-related medical costs

16. Check the individual eligibility for the PRR program

17. Summarize the cost and health outcomes of the simulated cohort

3.11

Table 3-2 describes key assumptions underlying the model functions and results. These

assumptions may not apply when state-specific data inputs are used.

Table 3-2. ADOPT Model Assumptions Assumptions

Outcome probabilities derived from MarketScan data are generalizable to individual state Medicaid

programs

PRR programs are not applied to patients with cancer

Drug pricing derived from MarketScan data is generalizable to individual state Medicaid programs

Patterns of prescription opioid use found in the MarketScan data are generalizable to individuals enrolled

individual state Medicaid programs.

PRR enrollees stay enrolled in Medicaid and the PRR program for the duration of the policy period]

Enrollees consumed prescriptions “as prescribed” – does not consider opioid diversion

Overdose risk is based on acquisition of prescription opioids, not use of illegal opioids

Characteristics of a subsequent episode of opioid use are correlated with those of a previous episode

Correlation between the number of pharmacies and the number of prescribers in the simulated cohort

follows the correlation found in a previous study using the Massachusetts’ prescription drug monitoring

database.1

(The following assumptions are only for the analysis of the PRR program cost and health effects, and user-ad

justifiable).

All PRR program enrollees’ overlapping prescriptions (i.e., two prescriptions of the same drug type, one of

which had a supply for 5 days or longer, overlapped by 25% or more of the days prescribed) are eliminated

in the scenario of having the PRR program

Because of the higher risk of overdose events at higher doses of opioids, All PRR program enrollees’

prescriptions that contribute to an aggregate daily dose over 80mg morphine equivalent will have reduced

quantity or strength to an aggregate daily dose of 80mg morphine equivalent in the scenario of having the

PRR program

MODEL CALIBRATION

The ADOPT model was calibrated by comparing the simulated outcomes with the values derived

from our MarketScan®

data‡ analysis. The population characteristics and the prevalence of risk

factors of the simulated cohort are set to match those of the long-term users identified in the

MarketScan®

data analysis. As seen in Table 3, the majority of the population is white, female,

and age 45 years and older with about 19% using sedative hypnotics and about 11% diagnosed

with depression. Parameter values are listed in Table 3-3

Table 3-3. Baseline Characteristics of MarketScan® Medicaid Long-Term Users Population Characteristic % Population Characteristic %

Female, % 70.3 Race, %

Female Age Distribution, % White 67.7

12 - 17 1.8 Black 24.7

18 - 29 15.3 Hispanic 1.0

30 - 44 33.2 Other 6.6

45 and older 49.7 Sedative/Hypnotic drug use, % 18.9

Male Age Distribution, % Depression diagnosis,% 10.6

12 - 17 2.8 Alcohol abuse, % 3.0

18 - 29 14.3

‡ For more information about our analysis of the MarketScan® data, please refer to Part 2 of this report.

3.12

Population Characteristic % Population Characteristic %

30 - 44 27.4

45 and older 55.5

The simulation results reported in this section are the mean values after running 10 simulation

rounds with 10,000 subjects simulated in each round. The real-world comparison group consists

of long-term users in the MarketScan®

dataset during the 2009 calendar year. We chose this year

because the MarketScan®

data that we used spans from 2008 to 2010 and one of our initial

inclusion criteria was at least 24-month continuous enrollment. By choosing 2009, the middle of

the 3-year time span, we were able to include all long-term users (90,010 subjects).

Number of Prescriptions

The numbers of prescriptions in the simulated cohort were compared with the numbers of

prescriptions dispensed among long-term users in the MarketScan®

dataset in 2009 (Table 3-4).

The simulated total number of prescriptions is close to the MarketScan®

values. The three most

frequently prescribed opioids are, in descending order, hydrocodone, oxycodone, and tramadol in

both the MarketScan® population and the simulated cohort. Some disparities exist between the

simulated numbers of prescriptions and those in the MarketScan®

dataset, especially for some

less commonly prescribed opioid types (e.g., meperidine). In general, the long-acting schedule II

drugs are under-sampled in the simulation, compared with the actual values. This may result in

an underestimate of the risk of overdose events in the simulated cohort.

Table 3-4. Comparison between MarketScan® Medicaid Opioid Prescriptions among Long-Term

Users in 2009 and the Simulated Yearly Number of Prescriptions

Prescription Opioid Type

MarketScan®

Long-Term

Users

Simulated

Cohort

Total

(n=90,010)

Prescription

Rate/10,000

Patients

Prescription

Rate/10,000

Patients

Schedule III and IV

Propoxyphene (with or without aspirin/acetaminophen/ibuprofen) 75,632 8,403 11,577

Codeine + aspirin/acetaminophen/ibuprofen 38,355 4,261 8,387

Hydrocodone + aspirin/acetaminophen/ibuprofen 628,165 69,788 65,185


ibuprofen)

5,315 590 507

Butorphanol 2,926 325 169

Pentazocine (with or without aspirin/acetaminophen/ibuprofen) 2,065 229 423

Tramadol with or without aspirin 172,173 19,128 22,579

Short-acting, Schedule II

Morphine sulfate 9,800 1,089 1,499

Codeine Sulfate 1,048 116 175

Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 243,054 27,003 24,146

Tapentadol 772 86 94

Hydromorphone 4,279 475 1,457

Meperidine hydrochloride 33,153 3,683 1,614

Fentanyl citrate transmucosal 7,381 820 1,037

Long-acting, Schedule II

Morphine sulfate sustained release 52,715 5,857 2,344

Fentanyl transdermal 26,660 2,962 1,710

3.13

Prescription Opioid Type

MarketScan®

Long-Term

Users

Simulated

Cohort

Total

(n=90,010)

Prescription

Rate/10,000

Patients

Prescription

Rate/10,000

Patients

Oxycodone HCL control release 92,004 10,222 7,309

Methadone 25,867 2,874 1,279

Oxymorphone extended release 1,549 172 79

Dihydrocodeine 579 64 117

Levorphanol tartrate 13 1 6

Total 3,320,489 158,149 151,693 Note: Prescription rate is the number of opioid prescriptions per 10,000 patients

Days’ Supply, Dose Level, and Generic Drug Type

We compared drug-specific details, including the distribution of supply days, dose level, and

generic drug type for a particular opioid type between the MarketScan® dataset and the simulated

cohort. Table 3-5 shows the details of such comparison for methadone. The simulated supply

days is distributed similarly to that of the MarketScan® experience, except that the ADOPT

model does not simulate any prescription with a supply longer than 30 days, which only accounts

for 0.8% of the total MarketScan®

prescriptions. The distribution of doses is comparable between

the MarketScan® data and the simulated cohort.

Table 3-5. Comparison between the Percent Distributions of Days’ Supply, Daily Dose,* and

Generic Drug Type for Methadone in the MarketScan®

and Simulated Populations MarketScan

® Long-

Term Users Simulated Cohort

Days’ Supply

>3 d 0.2% 0.3%

4-7 d 5.0% 6.6%

8-15 d 12.8% 13.5%

16-29 d 12.6% 14.6%

30 d 68.6% 65.0%

<30 d 0.8% 0.0%

Dose level*

10 MG 4.2% 4.1%

15 MG 3.6% 5.7%

20 MG 11.4% 8.6%

30 MG 17.6% 19.2%

40 MG 18.0% 19.6%

50 MG 3.9% 4.3%

60 MG 14.3% 11.8%

70 MG 1.3% 0.6%

80 MG 8.6% 10.7%

83.33 MG 1.7% 2.1%

90 MG 3.8% 1.9%

100 MG 2.9% 3.7%

120 MG 6.9% 5.6%

160 MG 1.9% 2.1%

Generic Drug Type

Methadone Hydrochloride SOL 10 MG/ML 0.3% 0.7%

Methadone Hydrochloride SOL 5 MG/5 ML 0.3% 1.2%

Methadone Hydrochloride TAB 10 MG 89.6% 86.6%

3.14

MarketScan®

Long-

Term Users Simulated Cohort



* Daily dose was not converted to milligrams morphine equivalent

Distribution of Number of Pharmacies Visited

We compared the distributions of the number of unique pharmacies visited in the entire year and

peak number of unique pharmacies visited in any 90 days (Table 3-6). In general, the ADOPT

model tends to slightly overestimate the proportion of patients using one or two pharmacies and

slightly underestimate the proportion using three or more pharmacies.

Table 3-6. Unique Pharmacy Visits per Year and Within Any 90-Day Period among

MarketScan®

and Simulated Cohorts Total per Year Peak Number in During Any 90-Days

Number of

Pharmacies Visited

MarketScan®

Long-Term Users Simulated Cohort

MarketScan®


1 22.8 30.1 32.2 39.2

2 28.1 33.1 35.8 36.1

3 18.3 14.2 17.3 14.4

4 10.5 9.6 8.1 5.7

5 7.9 6.4 3.0 2.3

6 5.0 2.3 1.1 0.8

>7 7.4 4.3 2.4 1.5

Demographic Characteristics and Opioid Use Patterns of Potential Pharmacy Shoppers

The simulated cohort is separated into two groups: patients who used 4 or more pharmacies in

any 90 days (shopper group) and those who used fewer than 4 (non-shopper group). We then

calculated each group’s demographic characteristics and drug use patterns, including monthly

average number of prescriptions, average dose level, and drug type (schedule II/non-schedule II,

long or short acting), and compared the results of the simulated cohort with MarketScan®

values

(Table 3-7). In general, the simulated proportion of pharmacy shoppers is close to those in the

MarketScan®

group, as are the mean ages and the male proportions in both the pharmacy

shopping and non-pharmacy shopping groups. The proportion of depression diagnosis and

history of alcohol abuse between the two groups is smaller in the simulated cohort than in the

MarketScan® population. The ADOPT model tends to estimate a higher monthly number of

prescriptions and a higher average dose level in both shopper and non-shopper groups. The

distribution of the predominant drug type is comparable between the MarketScan®

and simulated

population.

Table 3-7. Demographic Characteristics, Overdose Rates, and Opioid Use Patterns of Patients

with Different Pharmacy Shopping Characteristics

Non-Shoppers Pharmacy Shoppers

MarketScan®


MarketScan®


Demographics

n 80,101 (89.0%) 9,126 (91.26%) 9,909 (11.0%) 874 (8.74%)

3.15

Non-Shoppers Pharmacy Shoppers

MarketScan®


MarketScan®


Mean age 43.8 43.3 43.41 43.6

Male 27.70% 28.90% 31.55% 30.98%

Depression diagnosis 9.40% 11.20% 15.40% 12.20%

History of alcohol abuse 2.52% 3.06% 4.50% 3.33%

Opioid Use Pattern

Monthly prescriptions 1.68 1.84 2.79 3.42

Average dose 43.7 52.3 93.7 102.7

Dose Distribution Level

0-20mg 34.60% 26.50% 14.92% 11.34%

20-50mg 45.80% 54.20% 37.73% 44.60%

50-100mg 12.20% 8.93% 21.79% 16.70%

100mg or higher 7.40% 10.37% 25.65% 27.36%

Predominant Opioid Type

Schedule III or IV 80.20% 83.50% 73.60% 66.20%

Schedule II, short-acting 16.70% 11.70% 21.10% 24.60%

Schedule II, long-acting 3.10% 4.80% 5.30% 9.20%

Note: Non-Shoppers=less than 4 pharmacies visited in any 90 days; Shoppers = 4 or more pharmacies visited in any

90 days.

Individual-Level Comparison

We also evaluated whether the simulated prescription history resembles the MarketScan®

prescription history. However, it is impossible to find a simulated enrollee that shares the exact

same prescription history with a MarketScan®

counterpart. Instead, we analyzed two

representative examples from the simulated cohort and the MarketScan®

cohort.

Figure 3-3 shows a screenshot of a simulated enrollee’s prescription history. The simulated

enrollee is a 37-year old female, with a history of long-term prescription opioid use, primarily

hydrocodone. At the beginning of her episode of use,§ the first two prescriptions had fewer

supply days, lower strength per pill (5MG hydrocodone) and lower daily dose, compared with

subsequent prescriptions. A similar pattern was also observed in the MarketScan® dataset, as

shown inTable 3-8. The MarketScan® patient received a prescription opioid with lower strength

and fewer supply days at the beginning of the episode, and received monthly prescriptions with a

higher strength once the prescription use stablized. The MarketScan® patient visited multiple

pharmacies, but had no overlapping (>25% of supply days) prescriptions during the two-year

period (the prescriptions with the longest overlapping days supply were prescribed in December

2008, with a 7-day overlap, and in August and September 2009, with a 7-day overlap). The

simulated patient also had no overlapping prescriptions; however, prescriptions with several (1-

3) overlapping supply days were common. The simulated patient switched between pharmacy

“A” and “B”, but is unlikely to be a pharmacy shopper.

In this example, the comparison at the individual level was not conclusive, as we were unable to

show all representative scenarios. However, the point of this description is to alert ADOPT users

that the model permits verification of the details of each simulated individual’s prescription

§ Episode of use is defined as commencing with the dispensing date of an opioid prescription with no previous

prescription in the dataset, or having a gap longer than 31 days from the end run-out date of a previous opioid

prescription.

3.16

history. Users are encouraged make these comparisons to judge whether the simulated cohort

resembles their state-specific cohort.

Figure 3-3. Simulated Individual’s Prescription History from ADOPT Model for Comparison

with Table 3-8 Data

3.17

Table 3-8. Prescription History of a Representative Individual from the MarketScan®

Long-Term User Population for Comparison

With Figure 3-3 Data

Enrollee ID Service

Date

Days’

Supply Quantity Generic Drug Name Strength Pharmacy ID

20021221091 6/16/2008 1 5 Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG tbTeQFWaheTq

20021221091 6/16/2008 1 1 Meperidine Hydrochloride 50 MG/ML tbTeQFWaheTq

20021221091 6/25/2008 5 30 Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG abteTFda2NTi

20021221091 6/30/2008 30 120 Acetaminophen/Oxycodone Hydrochloride 325 MG-10 MG abteTFda2NTi




20021221091 8/26/2008 30 120 Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abteTFda2NTi

20021221091 9/19/2008 30 120 Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbteTFdaUiTu

20021221091 10/3/2008 15 90 ASA/Oxycodone HCl/Oxycodone Terephthalate 325 MG-4.5 MG-0.38 MG tbteTFdaUiTu


















3.18

COST ESTIMATION

Estimating the reimbursement rates for prescription opioids using the MarketScan®

dataset

presented three challenges. First, the reimbursement rates varied by brand. The same generic

drug type could have different prices. Second, the reimbursement rates vary by state. Lastly, the

reimbursement rate is also determined by patient’s capitation status. These three factors

contribute to the variance in the per-unit reimbursement rate for a generic drug type. Since the

MarketScan® database did not contain identifiers for brand name or geographic location, we

could not directly account for the first two challenges by stratifying the data. We were able to

address the third challenge by excluding those records with a capitation status of “1” (i.e., “yes”).

We calculated the mean per-10-unit reimbursement rate for the 156 generic prescription opioids

that appeared in the MarketScan®

database, as shown in Appendix II. We used the estimated per-

10-unit reimbursement rates to directly populate the ADOPT model. However, because the

ADOPT model is customizable and it is a time-consuming task for users to specify the

reimbursement rate for each of the 156 drugs, we tried to shorten the list by grouping drugs by

the effective component type (e.g., hydrocodone or codeine), then by master form (e.g., tablet,

solution, or elixir), auxiliary component (e.g., acetaminophen, ibuprofen, or aspirin), and

strength of the opioid component (e.g., 5MG or 10MG). For example, the per-10-unit

reimbursement rates for hydrocodone were first classified by auxiliary component type

(acetaminophen versus ibuprofen), then the acetaminophen group was further classified by

master form (elixir vs. solution vs. tablet). As this analysis found neither a clear trend nor

significant proportional difference in reimbursement rates in relation to the strength of

hydrocodone, no further classification was done for strength. By contrast, the per-10-unit

reimbursement rates for tramadol extended release tablets were found to be strength-related. We

pooled together all records (excluding capitation patients) of tramadol extended release tablets at

all strength levels, and then calculated a baseline reimbursement rate for baseline strength – in

this case, $27.85 per 10 tramadol extended release tablets with strength of 100mg. Based on this

baseline reimbursement rate, the rates for 10 tablets with strength of 200mg and 300mg are $55.7

and $83.55, respectively. This classification process allowed us to reduce the number of

reimbursement rates that users need to specify to 51 (versus 156), as shown in Table 3-9.

Table 3-9. Price Comparison of Commonly Prescribed Opioids Drug Type and Form * Price per 10 units ($)†

Schedule III and IV

Propoxyphene 3.62

Codeine

Elixir 0.49

SOL 0.78

TAB 3.64

Hydrocodone

With Acetaminophen

Elixir 0.74

SOL 2.97

TAB 3.06

With Ibuprofen, TAB 7.96

Butalbital and codeine, CAP 8.47

Butorphanol, SOL 172.05

Pentazocine, TAB 9.16

3.19

Drug Type and Form * Price per 10 units ($)†

Tramadol

With Acetaminophen, TAB 6.93

TAB 1.98

TER, 100MG 27.85


Morphine sulfate

SOL 61.73

TAB 2.33

Codeine Sulfate 15MG 2.92

Oxycodone

TAB, 5MG 2.95

SOL 2.15

CAP 2.68

With Acetaminophen

CAP 3.44

SOL 1.00

TAB, 5MG 3.05

Tapentadol, TAB, 50MG 17.45

Hydromorphone

SOL/SUP 87.12

TAB 4.92

Meperidine

SOL 105.31

TAB, 50MG 4.81

Fentanyl citrate transmucosal, lozenge, 0.8MG 284.52


Morphine sulfate sustained release

CAP, 10MG 12.75

TAB, 15MG 4.92

Fentanyl transdermal, 25MCG/hour 137.35

Oxycodone hydrochloride, ER, TAB, 10MG 16.12

Methadone

SOL 1.24

TAB 1.68

Oxymorphone sustained release, 10MG 26.40

Dihydrocodeine 15.53

Levorphanol, TAB 10.01

Abbreviation: TAB: tablet; SOL: solution; CAP: capsule: ER: extended

release. Note: * If the item shows dose level (e.g. TAB, 5MG), it means that the price is dose-related. If a tablet with strength of 5MG

costs $12, then a tablet with strength of 10MG costs $24.

† 10 units are 10 pills or 10mL.

Table 3-10 shows the price per 50 mg morphine equivalent for a representative (i.e., mostly

prescribed generic drug type in a category) drug name in each category. The per 50 MME prices

vary widely, with the most expensive one being oral fentanyl citrate transmucosal ($13,712.50

per 50 MME).

3.20

Table 3-10. Price Comparison of Commonly Prescribed Opioids, Price per 50 MME

Drug Category Representative Generic Name n $ per 50

MME

Schedule III and IV

Propoxyphene Acetaminophen/Propoxyphene Napsylate TAB 650

MG-100 MG

126,797 0.66

Codeine

Elixir Acetaminophen/Codeine Phosphate ELI 120 MG/5

ML-12 MG/5 ML

1,791 6.81

SOL Acetaminophen/Codeine Phosphate SOL 120 MG/5

ML-12 MG/5 ML

5,091 10.83

TAB Acetaminophen/Codeine Phosphate TAB 300 MG-30

MG

81,793 4.38

Hydrocodone

w/ Acetaminophen

Elixir Acetaminophen/Hydrocodone Bitartrate ELI 500

MG/15 ML-7.5 MG/15 ML

6,618 7.40

SOL Acetaminophen/Hydrocodone Bitartrate SOL 325

MG/15 ML-10 MG/15 ML

102 22.28

TAB Acetaminophen/Hydrocodone Bitartrate TAB 500

MG-10 MG

117,197 1.49

w/Ibuprofen, TAB Ibuprofen/Oxycodone Hydrochloride TAB 400 MG-5

MG

74 11.79

Butalbital and codeine, CAP Aspirin/Butalbital/Caffeine/Codeine Phosphate CAP

325 MG-50 MG-40 MG-30 MG

5,027 11.58

Butorphanol, SOL Butorphanol Tartrate SPR 10 MG/ML 3,142 8.42

Pentazocine, TAB Naloxone Hydrochloride/Pentazocine Hydrochloride

TAB 0.5 MG-50 MG

2,325 2.94

Tramadol

w /Acetaminophen, TAB Acetaminophen/Tramadol Hydrochloride TAB 325

MG-37.5 MG

20,614 9.24

TAB Tramadol Hydrochloride TAB 50 MG 243,239 1.98

TER, 100MG Tramadol Hydrochloride TER 200 MG 2,723 13.40


Morphine sulfate

SOL Morphine Sulfate SOL 10 MG/ML 1,152 41.58

TAB Morphine Sulfate TAB 15 MG 8,202 0.74

Codeine Sulfate 15MG Codeine Sulfate TAB 30 MG 409 5.20

Oxycodone

TAB, 5MG Oxycodone Hydrochloride TAB 15 MG 24,041 1.12

SOL Oxycodone Hydrochloride SOL 20 MG/ML 490 1.44

CAP Oxycodone Hydrochloride CAP 5 MG 8,620 1.79

w/Acetaminophen

CAP Acetaminophen/Oxycodone Hydrochloride CAP 500

MG-5 MG

8,047 2.29

SOL Acetaminophen/Oxycodone Hydrochloride SOL 325

MG/5 ML-5 MG/5 ML

889 3.33

TAB, 5MG Acetaminophen/Oxycodone Hydrochloride TAB 325

MG-10 MG

85,434 2.52

Tapentadol, TAB, 50MG Tapentadol Hydrochloride TAB 50 MG 839 1.92

Hydromorphone

SOL/SUP Hydromorphone Hydrochloride SOL 2 MG/ML 5,360 48.29

TAB Hydromorphone Hydrochloride TAB 4 MG 11,339 1.09

3.21

Drug Category Representative Generic Name n $ per 50

MME

Meperidine

SOL Meperidine Hydrochloride SOL 50 MG/ML 1,886 125.75

TAB, 50MG Meperidine Hydrochloride TAB 50 MG 4,244 24.85

Fentanyl citrate transmucosal,

lozenge, 0.8MG

Fentanyl Citrate LOZ 0.8 MG 154 137,12.50


Morphine sulfate sustained release

CAP, 10MG Morphine Sulfate CER 60 MG 2,783 6.81

TAB, 15MG Morphine Sulfate TER 15 MG 12,233 1.77

Fentanyl transdermal,

25MCG/hour

Fentanyl TDM 100 MCG/HR 13,723 11.39

Oxycodone hydrochloride, ER,

TAB, 10MG

Oxycodone Hydrochloride TER 20 MG 19,789 5.46

Methadone

SOL Methadone Hydrochloride SOL 5 MG/5 ML 141 1.93

TAB Methadone Hydrochloride TAB 5 MG 4,029 0.60

Oxymorphone sustained release,

10MG

Oxymorphone Hydrochloride TER 20 MG 505 4.59

Dihydrocodeine Acetaminophen/Caffeine/Dihydrocodeine Bitartrate

TAB 712.8 MG-60 MG-32 MG

1,183 9.65

Levorphanol, TAB Levorphanol Tartrate 2 MG 4,59 2.28

Abbreviation: MME: mg morphine equivalent; TAB: tablet; SOL: solution; CAP: capsule: ER: extended release.

MODEL OUTPUT

We intended to use the ADOPT model to evaluate the costs and outcomes (in terms of health

impact and return on investment) of patient review and restriction (PRR) programs in different

states. However, we were unable to obtain state-specific model inputs, such as state-specific

opioid user demographics, state-specific opioid reimbursement rates, and state PRR program

spending. Due to the lack of state-specific data, we used values derived from the MarketScan®

database, to analyze and compare different program eligibility criteria adopted by several

representative states. Because we used a hypothetical population derived from a database

combining multiple, unknown states, our analytic output is exploratory rather than deterministic

or predictive. Although the lack of state-specific inputs prohibits us from carrying out state-

specific analysis at this stage, more relevant analyses can be conducted by users, especially state

officials, who have better access to and knowledge of state-specific data.

Patient Review and Restriction Program Policies

Although the patient review and restriction (PRR) programs exist in many states, the enrollment

criteria vary across states. Based on a brief review of accessible sources of the state PRR

program criteria, we selected 6 representative states and modeled 5 different scenarios of

program eligibility criteria (the criteria in West Virginia and North Carolina are similar and,

therefore, modeled as one) (Table 10). It is noteworthy that some state PRR program eligibility

criteria involve non-quantifiable items, such as “referral by provider,” “excessive emergency

room use,” “noncompliance with narcotics contract,” or “demonstrated inappropriate utilization.”

These situations cannot be modeled using the ADOPT model. Additionally, the current version

of the ADOPT model does not simulate frequent use of emergency departments or office visits

3.22

can render a patient eligible for the PRR program, nor does it simulate misuse of other

prescription drugs including stimulants, and carisoprodol (however, these can be added to the

model when/if supporting data becomes available).

The modeled eligibility criteria used combinations of the number of prescribers seen, the number

of pharmacies visited, and the number of prescriptions filled over a given time span.

Washington, West Virginia, and North Carolina specify the number of PRR eligibility criteria a

patient has to meet, while the other three states (Kentucky, Idaho, and Michigan) do not mention

a required number of criteria. For example, upon referral to the Idaho program, patients may be

restricted based on an analysis of potential overuse of targeted medications including opioids,

tramadol and benzodiazepines, as well as the number of prescribers and pharmacies used,

excessive ER use and history of drug abuse. However, the program does not specify a cutoff

number of pharmacies/prescribers or require meeting a specific number of criteria (Table 3-11).

In these cases, we modeled similar eligibility criteria based on our interpretation.

3.23

Table 3-11. Representative State Patient Review and Restriction Program Policies Scenario

#

State Current Eligibility Criteria for State Patient Review and Restriction Programs Modeled Eligibility Criteria

1 Washington12

Two or more of the following conditions occurred in a period of ninety consecutive

calendar days in the previous twelve months.

Received services from four or more different providers, including physicians,

advanced registered nurse practitioners (ARNPs), and physician assistants (PAs);

Had prescriptions filled by four or more different pharmacies;

Received ten or more (opioid) prescriptions;

Had prescriptions written by four or more different prescribers;

Received similar services from two or more providers in the same day;

Had ten or more office visits.

Two or more of the following conditions in a

period of ninety consecutive calendar days:

Visited >4 prescribers

Used >4 pharmacies

Received >10 opioid prescriptions

2 West Virginia13

Any of the following conditions (note: the program is not limited to the listed criteria):

Overutilization: ≥ 6 claims for ≥ 3 different agents (listed below) in the past 60 days

o Opiates

o Benzodiazepines

o Stimulants

o Tramadol

o Carisoprodol

Multiple Prescribers: ≥ 3 prescribers for the agents, or combinations of the agents,

listed below in the past 60 days

o Opiates

o Benzodiazepines

o Stimulants

o Tramadol

o Carisoprodol

Any of the following conditions in any 60

days:


Received > 6 opioid prescriptions

North Carolina14

One or more of the following criteria:

Filled > 6 prescriptions for either opioid pain relievers or anti-anxiety

(benzodiazepine) medications within a two month period

Prescribed opioid pain relievers and/or benzodiazepine medications by >3 prescribers

within a two month period

Referral from a provider, DMA or CCNC.

3 Kentucky15

The recipient has the following conditions in consecutive 180 calendar day periods:

Received services from > 5different providers

Received >10 different (opioid) prescription drugs

Received (opioid) prescriptions from >3different pharmacies

All of the following conditions in any 180-

day period:

Visited >5 providers

Used >3 pharmacies


Meet above conditions in two consecutive

180-day periods.

The number of conditions is not specified in the document. We assume that all conditions need to be met.

3.24

Scenario

#

State Current Eligibility Criteria for State Patient Review and Restriction Programs Modeled Eligibility Criteria

4 Idaho16

Upon referral, the following are analyzed:

Medication profile for the potential overuse of target medications

o ≥ 6 Benzodiazepines claims in last 60 days

o ≥ 8 opiate claims within last 60 days

o ≥ 3 Tramadol claims or 480 tablets within last 60 days

o Continuous use of skeletal muscle relaxants for > 6 months

Multiple providers

Multiple pharmacies

Excessive emergency room use

Screening of health conditions for a history of drug dependence or abuse

All of the following conditions in any 60-day

period:

Prescription overuse:

o Received >8 opioid prescriptions, OR

o 3 tramadol claims or 480 tablets in any

60 days;

Visited >2 or more pharmacies

Visited >2 or more prescribers

(The number of pharmacies and prescribers

are not specified in the state criteria. The

numbers used in the modeled criteria are

assumed.)

5 Michigan17

Any of the following conditions:

Visited >3 different physicians in one quarter

Visited >2 different physicians to obtain duplicate services for the same

health condition or prescriptions the following drug categories:

o Narcotic Analgesics

o Barbiturates

o Sedative-Hypnotic, Non-Barbiturates

o Central Nervous System Stimulants/Anti-Narcoleptics

o Anti-Anxieties

o Amphetamines

o Skeletal Muscle Relaxants

Visited multiple physicians for vague diagnosis (e.g., myalgia, myositis, sinusitis,

lumbago, migraine) to obtain any of the drugs listed above

Used >3 different pharmacies in one quarter

Received > 11 prescriptions in the listed categories in one quarter

Other criteria includes convicted fraud and inappropriate use of ED services (content not

shown here)

Any of the following conditions in any 90

days:


Used > 3 pharmacies


The number of conditions is not specified in the document. We assume, based on our interpretation, that all conditions need to be met.

3.25

Impact on Prescription Opioid Use

After the simulation is completed, users will be directed to the output screen where the impact of

the patient review and restriction (PRR) program on prescription opioid use, overdose-related

events, and cost (including prescription reimbursement, overdose-related medical services and

PRR program cost) is summarized. Figure 3-4 shows a screenshot of the model output using

criteria from Scenario #1 (i.e., based on the Washington program). The tables shown hereinafter

are based on a summary of the output after 10 simulation rounds. The population characteristics

are set to be the same as those used in the model calibration (as shown in Table 3-3).

Figure 3-4. ADOPT Output: Program Summary

Table 3-12 summarizes the demographics and the opioid use pattern of the eligible cohort under

the five scenarios of eligibility criteria. The size of the eligible cohort varies substantially among

the different scenarios. Scenario #3 (based on the Kentucky program) uses the most stringent

criteria, with only 82 (95% CI: 61-103) out of 10,000 simulated patients eligible for the program.

Scenarios #4 and #5 are less stringent, with over a quarter of the simulated population (2,775

[95% CI: 2,241-3,309] and 2,865 [95% CI: 2,317-3,413] patients for scenarios 4 and 5,

respectively) eligible for the program. Under all five simulated scenarios, the PRR program

eligible cohorts are younger than the entire simulated population, with a mean age of 47.9 (95%

CI: 47.4 – 48.4). Similarly, all scenarios show that the proportion of males in the eligible cohort

is slightly, but statistically significantly higher than the entire population (29.7% [95% CI:

29.6%-29.8%] male).

3.26

Table 3-12. Demographic and Drug Use Patterns of PRR Program Enrollees in a Simulated Population of 10,000 Long-Term Users

under Different Eligibility Scenarios (with 95% confidence interval in parentheses) Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Brief Description

≥4 prescribers or ≥4

pharmacies or ≥10 RX

in 90 days, if any two

conditions met


RX in 60 days

≥5 prescribers and ≥3

pharmacies and ≥10

RX in two consecutive

180-day periods


pharmacies and (≥8

RX or (≥3 tramadol

RX or ≥480 tramadol

tablets)) in 60 days


pharmacies in 90 days

Number of Eligible

Individuals

198

(178-218)

1,257

(1,059-1,455)

82

(61-103)

2775

(2,241-3,309)

2,865

(2,317-3,413)

Demographics

Mean age (years) 41.4

(40.1-42.7)

42.5

(41.6-43.4)

41.7

(40.1-43.3)

43.8

(42.9-44.7)

43.8

(42.7-44.9)

Male (%) 34.1%

(30.5%-37.7%)

31.9%

(30.7%-33.1%)

32.8%

(30.2%-35.4%)

30.4%

29.7%-31.1%)

30.6%

(29.8%-31.4%)

Opioid Use by the Eligible Cohort, With No PRR Program *

Number of Prescriptions

Used

Schedule III and IV 3,590

(2,897-4,283)

16,513

(14,818 -18,208)

2,186

(1,742 -2,630)

33,957

(29,487-38,427)

34,941

(30,598-39,284)

Schedule II, short-

acting

1,975

(1,587-2,363)

5,583

(4,791-6,375)

1,232

(941-1,523)

11,281

(9,184-13,378)

12,632

(9,895-15,369)

Schedule II, long-

acting

1,189

(893-1,485)

2,314

(2,075-2,553)

671

(512-830)

4,208

(3,598-4,818)

4,897

(3,981-5,813)

Total 6,754

(5,986-7,522)

24,410

(21,672-27,148)

4,089

(3,652-4,526)

49,446

(42,963-55,929)

52,470

(45,392-59,548)

Average Number of RX

per Month

2.79

(2.63-2.95)

2.27

(2.14-2.40)

3.13

(2.64-3.62)

2.01

(1.91-2.11)

2.14

(2.02-2.26)

Average Daily

Morphine Equivalent

Dose, mg

73.2

(65.6-80.8)

67.4

(53.4-81.4)

81.7

(76.3-87.1)

56.4

(49.8-63.0)

59.3

(52.0-69.6)

Opioid Use by the Eligible Cohort, With PRR Program

Eliminated Overlapping

Prescriptions †

Schedule III and IV 1,505

(1,375-1,635)

2,147

(1,875-2,419)

976

(813-1,139)

3,654

(2,917-4,391)

4,229

(3,841-4,617)

Schedule II, short-

acting

930

(811-1,049)

1,233

(1,011-1,455)

650

(567-733)

1,994

(1,517-2,471)

2,557

(1,984-3,130)

Schedule II, long- 572 826 368 1,263 1,595

3.27

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Brief Description


pharmacies or ≥10 RX

in 90 days, if any two

conditions met


RX in 60 days




180-day periods


pharmacies and (≥8

RX or (≥3 tramadol

RX or ≥480 tramadol

tablets)) in 60 days


pharmacies in 90 days

acting (426-718) (647-1,005) (284-452) (985-1,541) (1,094-2,096)

Total Number of

Eliminated RX

3,007

(2,685-3,329)

4,206

(3,894-4,518)

1,994

(1,659-2,329)

6,911

(6,042-7,780)

8,381

(7,201-9,561)

Total Percentage of

Eliminated RX

44.5%

(95% CI: 41.4%-47.6%)

17.2%

(95% CI:15.1%-19.3%)

48.8%

(95% CI:47.5%-50.1%)

14.0%

(95% CI:10.2%-17.8%)

16.2%

(95% CI:13.7%-18.3%)

RX with Reduced

Strength or Quantity ‡

Schedule III and IV 165

(132-198)

1,190

(972-1,408)

113

(72-154)

2,633

(2,003-3,263)

4,229

(3,488-4,970)

Schedule II, short-

acting

247

(203-291)

936

(807-1,065)

202

(148-256)

1,972

(1,572-2,372)

2,557

(2,557-3,156)

Schedule II, long-

acting

367

(301-433)

1,092

(933-1,251)

251

(188-314)

2,221

(1,848-2,594)

1,595

(1,134-2,056)

Total Number of RX

with Reduced

Strength or Quantity

779

(705-853)

3,218

(2,895-3,541)

566

(436-696)

6,826

(6,109-7,543)

8,381

(7,593-9,196)

Total Percentage of

RX with Reduced

Strength or Quantity

11.5%

(95% CI:10.2%-12.8%)

13.2%

(95% CI:12.4%-14.0%)

13.8%

(95% CI:11.7%-15.9%)

13.8%

(95% CI:12.6%-15.0%)

16.0%

(95% CI:15.3%-16.7%)

Average Number of RX

per Month

1.33

(1.14-1.52)

1.80

(1.72-1.88)

1.46

(1.15-1.77)

1.76

(1.71-1.81)

1.79

1.72-1.86)

Average Daily

Morphine Equivalent

Dose, mg

46.2

(38.7-53.7)

50.2

(45.7-54.7)

44.5

(37.9-51.1)

48.7

(47.4-50.0)

49.2

(48.6-49.8)

Note: RX = prescription(s).

* The model first simulates the opioid use if there is no patient review and restriction program in place, then simulates how the opioid use is changed by the program. The items

under this sub-title show the opioid use of the potential eligible patients in the scenario of no PRR program.

† Eliminated overlapping prescriptions=overlapping with a previous prescription of the same drug type for 25% or more of the total supply days of the previous prescription

‡ Prescriptions with reduced strength or quantity are those contributing to an aggregate daily dose over 80mgmorphine equivalent on any day

3.28

The ADOPT model compares the prescription opioid use by the PRR eligible cohort in the case

of having a PRR program versus the absence of a program. Without the PRR program, the

eligible cohort has more per-person-prescriptions than the remaining, non-eligible population

under all scenarios, especially Scenarios #1 and #3. Moreover, the eligible cohort has more

frequent use of long-acting Schedule II opioids (17.6% [95% CI: 14.7%-20.5%] under Scenario

#1, for example) than the remaining, non-eligible population (5.4% [95% CI: 4.7%-6.1%]).

Expectedly, the eligible cohort also has higher numbers of monthly prescriptions and average

doses than the remaining, non-eligible population. In general, more stringent, selective eligibility

criteria (such as in Scenarios #1 and # 3) yield a smaller pool of heavier opioid users (i.e. more

monthly prescriptions and higher average dose) than less selective eligibility criteria (such as

Scenarios #4 and #5).

With the PRR program, many overlapping prescriptions are eliminated, and the prescriptions

with originally excessive doses are now reduced in strength or quantity. Table 3-12 shows the

proportions of total eliminated prescriptions under Scenarios 1 through 5 as 44.5%, 17.2%,

48.8%, 14.0%, and 16.2%, respectively. The corresponding percentages for prescriptions with

reduced strength or drug quantity are 11.5%, 13.2%, 13.8%, 13.8%, and 16.0%. These values

show that the percentages of prescriptions with reduced strength or drug quantity are comparable

between all scenarios, whereas Scenarios #1 and #3 have significantly higher percentages of

eliminated prescriptions, likely because they target heavier opioid users than other scenarios. The

number of monthly prescriptions is reduced under all scenarios. The highest reduction is seen

under Scenario #3, from 3.13 (95% CI: 2.64-3.62) prescriptions per month to 1.46 (95% CI:

1.15-1.77), followed by Scenario #1 where the number is reduced from 2.79 (95% CI: 2.63-2.95)

to 1.33 (95% CI: 1.14-1.52). The reduction under Scenarios #2, #4, and #5 is relatively small.

The average dose is reduced to a comparable level (ranging from 44.5 to 50.2 g morphine

equivalent per day) across all scenarios, with Scenarios #1 and #3 showing a relatively greater

reduction in dose.

Impact on Opioid Overdose-Related Events

Table 3-13 shows baseline estimates of annual opioid-overdose-related outpatient visits, ED

visits, hospitalization, and deaths and compares these outcomes with the estimated reduction in

each event according to different PRR program criteria. In general, the less selective scenarios

result in a greater reduction in these events. However, if the absolute reduction is divided by the

number of program enrollees, the more selective scenarios have a greater per-person reduction,

indicating that the more selective scenarios better identify the patients at highest risk of an

overdose event.

3.29

Table 3-13. Annual Health Impact of the PRR Program in a Population of 10,000 Long-Term Users under Different Eligibility

Scenarios* Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Opioid-overdose-

related event type

No PRR

program


pharmacies or ≥10

RX in 90 days, with

two conditions met

≥3

prescribers

or ≥6 RX in

60 days




180-day period


pharmacies and (≥8 RX or

(≥3 tramadol RX or ≥480

tramadol tablets)) in 60 days

≥3 prescribers

or ≥3

pharmacies in

90 days

Outpatient Visits

4.8

(3.5, 6.1)

-0.2

(0, -0.4)

-1.1

(-0.7, -1.5)

-0.2

(-0.0, -0.5)

-2.6

(-1.8, -3.4)

-3.0

(-2.5, -3.5)

Emergency

Department Visits

121.4

(108.7, 134.1)

-8.6

(-6.2, -11.0)

-22.1

(-18.6, -25.6)

-5.4

(-3.9, -6.9)

-47.1

(-43.5, -50.7)

-56.3

(-45.7, -58.9)

Hospitalizations

61.2

(54.5,67.9)

-4.8

(-4.1, -5.5)

-11.4

(-9.2, -13.6)

-3.9

(-3.1, -4.7)

-22.4

(-18.5, -26.3)

-36.2

(-30.4, -42.0)

Deaths

4.5

(3.6,5.4)

-0.4

(-0.2, -0.6)

-0.6

(-0.4, -0.8)

-0.2

(-0.0, -0.4)

-1.0

(-0.6, -1.4)

-1.8

(-1.2, -2.4)

* 95% confidence interval in parenthese

3.30

Cost Analysis of Different Patient Review and Restriction Scenarios

This cost analysis of a patient review and restriction (PRR) program is considered conservative

because the model does not consider additional cost-savings achieved through reductions in

office or emergency department visits to obtain opioid prescriptions or from “external” effects

such as reduced drug diversion and, consequently, reduced overdose risk for others. At this stage,

we lack data about the cost and frequency of office/emergency department visits for obtaining

excessive opioid prescriptions. To our best knowledge, there has been no evidence regarding the

extent to which patient review and restriction programs can help to control access to prescription

opioids through diversion. Therefore, this cost analysis only considers cost-saving achieved

through reduced prescription opioid use and reduced overdose-related medical costs attributable

to the patient holding the prescription.

We included administrative costs for the program, which will vary by state, by year and by

program caseload. Our assumed program costs were derived from email communication with

content experts.**

The emails gave an estimated labor cost of $700,000 for the Washington state

PRR program in current year with a caseload of 3,800. In the ADOPT model, we assumed a

fixed annual program cost of $300,000, as well as assigning a variable cost of $200 per program

enrollee to represent the additional labor and material expenditures that increases as the program

caseload increases. Using such setting, the modeled program cost for Washington state is about

$1 million ($300,000+3,800*$200=$1,060,000), which is close to the real program cost if other

cost components (besides labor cost) were taken into account.

The results of the cost analysis are shown in Table 3-14. Without a PRR program, the estimated

total prescription opioid costs and the overdose-related medical costs are approximately $4.59

million (Standard Error: ±0.24) and $0.91 million (SE: ±0.11) per year per 10,000 long-term

users, respectively. The major cost savings of implementing the PRR program are attributable to

reduced opioid expenditure. For example, under Scenario #5, the cost savings for opioids and

overdose-related medical services are around $0.73 million (SE: ±0.17) and $0.45 million (SE:

±0.64) per 10,000 long-term users annually. In a state with 10,000 long-term users, a PRR

program implemented under Scenarios #2, #4 or #5 saves money, whereas programs under

Scenarios #1 and #3 cost more than not implementing the program.

Although Scenarios #2, #4 and #5 have the highest overall savings, their average savings per

enrollee are much lower than those achieved in Scenarios #1 and #3. On average, each program

enrollee under Scenarios #1 and #3 saves $1,395 (SE: ±211) and $2,251 (SE: ±352), compared

with less than $500 under Scenarios #2, #4 and #5. Based on average savings, we calculated the

number of enrollees needed for the PRR program to break even (i.e., beyond which, the program

starts to yield positive total savings). Under Scenarios #1 and #3, the program needs to enroll 251

and 146 patients, respectively; under Scenarios #2, #4, and #5, the program must enroll more

than 1,000 patients.

We also calculated the total number of prescription opioid users needed for the PRR program to

break even. This number is important because PRR program staff can compare this threshold

with the actual number of Medicaid opioid users in the state to project whether the program has a

**

Jones, C.M., Roy, K. Email correspondence, August 2012

3.31

large enough pool of users to have a positive financial impact. To determine the minimal number

of Medicaid prescription opioid users needed to break even requires the following information:

1) how many the PRR program enrollees are needed to break even 2) what proportion of opioid users are long-term users 3) how many users per 10,000 long-term users meet the PRR program criteria

3.32

††

The calculation is based on the assumption that the PRR program has annual fixed cost of $300,000 and variable cost of $200 per enrollee per year on top of it. ‡‡

numbers in parentheses with “±” are standard error; numbers in parentheses in a range format such as “234-281” are the estimated range based on the upper and lower bounds of

95% confidence interval of cost estimates

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Opioid-overdose-related

event type

No PRR

program


pharmacies or ≥10

RX in 90 days, with

two conditions met

≥3 prescribers

or ≥6 RX in 60

days




180-day period


pharmacies and (≥8 RX or

(≥3 tramadol RX or ≥480

tramadol tablets)) in 60

days


pharmacies in 90

days

Estimated Cost in US Dollars (per 10,000 Long-term Users)

Opioid Cost

$4,593,423

(±$241,029)

$4,364,532

(±$200,145)

$4,176,182

(±$246,759)

$4,443,573

(±$165,321)

$3,857,941

(±$285,695)

$3,705,989

(±$299,403)

Overdose-related Medical

Cost

$909,341

(±$105,625)

$862,402

(±$113,204)

$713,487

(±$92,328)

$874,606

(±$95,693)

$464,792

(±$89,342)

$503,204

(±$84,231)

Program Cost -

$339,600

(±$4,000)

$551,400

(±$39,600)

$316,400

(±$4,200)

$855,000

(±$106,800)

$873,000

(±$109,600)

Total

$5,502,465

(±$301,402)

$5,567,532

(±$297,145)

$5,441,069

(±$287,956)

$5,634,579

(±$289,641)

$5,177,733

(±$321,592)

$5,082,193

(±$302,524)

Average opioid & medical

savings per enrollee -

$1,395

(±211)

$488

(±157)

$2,251

(±352)

$425

(±122)

$458

(±111)

Number of enrollees needed

for program

break-even -

251

(234 - 281)

1,041

(892-1,261)

146

(124-172)

1,333

(942-1,799)

1,162

(868-1,542)

Number of total opioid users

needed for program break-

even ††

-

60,079

(56,010-67,260)

39,249

(31,983-46,829)

84,383

(68,943-98,347)

22,766

(16,954-32,823)

19,222

(13,459-24,593)

Table 3-14. Cost Analysis of the PRR Program under Different Eligibility Scenarios‡‡

3.33

In the example in Table 3-14, we derived the proportion of long-term users from the

MarketScan®

database (90,010 long-term users out of 427,411 opioid users, which equals to

21.1%). The result shows that PRR programs using less selective criteria (such as in Scenarios

#4 and #5) can break even with a smaller population of Medicaid prescription opioid users

(about 20,000), whereas PRR programs employing more selective criteria- (such as in Scenarios

#1 and #3) require a larger population (about 60,000 and 85,000) to break even.

DISCUSSION

Our analysis is exploratory, as the inputs do not reflect specific state-level data, and the

MarketScan®

Medicaid data has major limitations, including the fact that states from which it is

derived are not identified, may change from year to year, and may or may not have existing PRR

programs in place. In addition, these analyses include the same fixed cost for implementing the

program not matter how many individuals are enrolled. We cannot conclude that one set of

patient review and restriction program eligibility criteria is superior to another. In this example,

the difference in cost-effectiveness appears to be driven primarily by the program denominator.

The less selective criteria produces a larger population from which greater reductions in total

opioid use and overdose prevention may be realized, and over which the fixed program cost

($300K) may be spread with only adding the nominal $200/enrollee (variable) cost, whereas the

programs with more selective criteria capture a smaller, but higher risk, population. This

produces higher per enrollee fixed program costs; however their averted (presumably higher)

health care costs and smaller total variable costs demonstrate a cost-effective alternative.

Our analysis has a number of limitations, as listed below. These included the fact that the model

did not account for all possible eligibility criteria, such as excessive emergency department use

or excessive office visits. The cost analysis only included cost estimation in a few aspects,

including prescription reimbursement and overdose-related medical costs, but did not include the

cost of outpatient and ED visits to obtain opioid prescriptions, nor cost savings due to reduced

overdose risk of recipients using diverted opioids.

Model Limitations

1. Geographic variation

In the current version of ADOPT, most default input values are based on the

MarketScan®

Medicaid dataset, which does not contain a geographic identifier. Although

MarketScan®

data comes from multiple states (12 states in 2012), it may not be

representative of the national data. It is possible that in certain states, the Medicaid opioid

users behave differently than the MarketScan®

population – in which case the analysis

may not be accurate.

2. Baseline Scenario: Under-estimated prevalence of opioid abuse/misuse

ADOPT compares two scenarios of having a PRR program versus not having a program.

It uses the MarketScan®

Medicaid dataset to simulate the scenario of not having the

program, then identifying the subjects who meet the program enrollment criteria and

calculating the health and financial impact if the PRR was established. However, it is

possible that some states already had a PRR program when the MarketScan®

data were

collected, in which case the prevalence of opioid abuse or misuse (including drug

shopping) would be under-estimated. This may cause an undervaluation of a PRR

program in our analysis.

3. Prescriber Information is imputed

3.34

The MarketScan®

data do not contain prescriber information.Because many PRR

progams use the number of opioid prescribers as an eligibility criterion, ADOPT uses the

previously reported correlation between numbers of pharmacies and prescribers from the

Massachusetts’ PRR program database to simulate prescriber IDs. It is possible that this

correlation may not reflect the experience of the MarketScan®

population. The lack of

prescriber information in the MarketScan®

data also did not allow us to calibrate the

simulated prescriber information.

4. Incomplete representation of PRR criteria

The current version of ADOPT can only analyze some of the criteria that may be used in

a PRR program, (i.e., numbers of prescribers, prescriptions, and pharmacies, as well as

the average dose level). In practice, a PRR program often includes other criteria such as

emergency department use, number of office visits, history of substance abuse, or

prescriptions of other restricted agents. ADOPT lacks the capacity to analyze these

additional PRR program criteria; however, they could be incorporated if the data were

available.

5. Uncertainty in estimation of overdose risk

ADOPT uses the hazard ratios for opioid overdose that are derived from the MarketScan®

inpatient and outpatient datasets. However, overdose rates may be higher than observed

in these data because patients may have expired before entering the hospital. In addition,

overdose events were identified by using the diagnostic codes. Misclassification of

diagnostic codes may cause under-estimation or over-estimation of the overdose risk.

6. Uncertainty about PRR program cost The program costs are adjustable by users. Our exploratory analysis of PRR program

effect is based on assumed program fixed and variable costs, which likely to deviate from

the actual state program costs.

7. Assumption about PRR program effects

The model assumes that the PRR program will eliminate the overlapping prescriptions

and control excessive use (>=80mg MME per day) for all enrollees. This over-simplifies

the real impact of the PRR program. Reducing all opioid prescriptions to this maximum

dose per day may not be feasible. Nevertheless, ADOPT can potentially be modified to

simulate more realistic and complex impact of the PRR program if such evidence

becomes available.

Despite these limitations, the ADOPT model demonstrates the potential to simulate individual

prescription consumption behavior with satisfactory similarity to real prescription consumption

behavior based on calibration with MarketScan® data. Using the current model structure and

interface, it is possible to add new functions if and when future data becomes available. The

features of this interactive tool make it feasible for state-level program staff to conduct timely

and specific analysis with state-level data to inform state policy decisions about PRR programs.

3.35

APPENDIX I

The Simulation Process

Appendix I describes the details of each step that the ADOPT model takes to simulate individual

prescription behavior. Unlike the methods sections described in other studies, no table of input

values is given because the number of input parameters used by the ADOPT model is so huge

(>8,000) that it is impossible to show every single value in this report. However, it cannot be

expected that a micro-simulation model would effectively represent the diversity of individual

prescription behaviors using a handful of input parameters. To account for the diversity, most of

this model’s input tables are very specific. For example, there are 126 tables used for a single

step of simulating the frequency of the appearance of drug types in an episode of opioid use,

each containing 12 columns and up to 21 rows and corresponding to a specific combination of

predominant drug type and episode length type. Such detailed specification ensures that the

ADOPT mimics the real prescription behavior as closely as possible. Although this report does

not have the capacity to list all input parameters, all input values used by the ADOPT can be

found in the model (i.e., the Excel file) itself.

Step 1: Simulate the Basic Individual Profile

Creating the prescription history of a hypothetical opioid user begins with creating the basic

individual profile. The user defines the population age, gender, and racial/ethnic distribution and

the prevalence of risk factors (including depression diagnosis, history of alcohol abuse, and

concurrent sedative/hypnotic drug use). To facilitate our following discussion, we will focus on a

hypothetical opioid user, “Jane,” who is a 41-year old, white female, with diagnosed depression,

no history of alcohol abuse, and concurrent sedative/hypnotic drug use.

Step 2: Simulate Predominant Drug Type in An Episode of Drug Use

This step involves predicting what kind(s) of drugs Jane uses, which can be difficult since Jane

can use different types of prescription opioids, either concurrently or successively. Instead of

predicting every single opioid that she uses, the ADOPT model first predicts the predominant

drug type that she uses for the initial episode of drug use. An episode of drug use is defined as

the dispensing date of an opioid prescription with no previous prescription or with a gap longer

than 31 days from the run-out date of previous prescription. Episode duration is defined as the

number of days from the date of first fill to the run-out date of the last opioid prescription,

without any lapses longer than 31 days after the previous refill. The predominant drug type is

defined as the most frequently prescribed drug type within an episode.

Predicting the most frequently used opioids (“predominant drug types”) in an episode is achieved

through a multinomial logistic regression model. The predictor variables are age stratum

(including 12-17, 18-29, 30-44, and 45-64), gender (male and female), and race (white and non-

white) and the predicted variable is the predominant drug type. This model is based on analyses

of the MarketScan®

data as described in Part 2 of the report. The logic behind the model is that

the specific opioid prescription type is associated with these demographic characteristics;

therefore, we can use the demographic characteristics to predict, indirectly, the type of opioids

used.

The predominant drug types in the MarketScan®

data include: hydrocodone, oxycodone,

propoxyphene, and tramadol. Each of these four drugs accounts for >10% of the distribution of

the most commonly used opioids. Other Schedule II long-acting, other Schedule II short-acting,

3.36

and other non-Schedule II opioids are less commonly used and, therefore, grouped into the latter

3 categories, in order to ensure that the regression model has sufficient predictive power. If an

individual falls into any of the latter three categories, a further sampling process based on the

age-, gender-, and race-specific distribution of drug types in that category will be done to predict

the specific drug used. The drug types under the latter three categories are shown in Table 3-15

below.

Table 3-15. Most Frequently Used Opioid Types in Market Scan Data

Predominant Opioid Types

Less Common Opioid Types

Other Schedule II

Long Acting

Other Schedule II

Short Acting Other Non-Schedule II

Hydrocodone +

aspirin/acetaminophen/ibupro

fen Fentanyl transdermal

Hydromorphone Butalbital + codeine

(with or without

aspirin/acetaminophen/

ibuprofen)

Oxycodone (with or without


fen)

Morphine sulfate

sustained release

Meperidine

hydrochloride

Butorphanol

Propoxyphene (with or

without


fen)

Oxycodone HCL

control release

Morphine sulfate Pentazocine (with or

without


ibuprofen)

Tramadol with or without

aspirin

Methadone Codeine Sulfate Codeine +


ibuprofen

Oxymorphone extended

release

Levorphanol

Opium

Dihydrocodeine

Fentanyl citrate

transmucosal

Tapentadol

The multinomial logistic regression model gives the predictive value (in percentage) for each

category of commonly used opioid types for all possible combinations of the explanatory

variables. In the model, the predictive values are translated into cumulative probabilities, as

shown in Figure 3-5. The model then generates a random number between 0 and 1 and this

number is compared with the cumulative probabilities to decide which interval (i.e., category of

predominant drug type) the random number falls in. As shown in Figure 3-5, a row of cumulative

probabilities is located for Jane’s age, gender and race. The randomly generated number is 0.72,

which is greater than 0.68 (the upper bound for the category of “other Schedule II short-acting”)

and smaller than 0.82 (the upper bound for the category of “oxycodone”); therefore, the

predominant drug type for Jane’s initial episode is oxycodone. The output of this simulation

process reflects the MarketScan®

distribution of predominant drug types.

3.37

Figure 3-5. Example of Random Sampling of Predominant Drug Type

30-44 female white 54% 59% 62% 68% 82% 87% 100%

30-44 male non-white 51% 53% 59% 62% 77% 85% 100%

30-44 male white 55% 58% 62% 66% 78% 86% 100%

45-64 female non-white 46% 49% 54% 58% 74% 82% 100%

Age

strata

gender race Hydroco

done

Other_L

A_II

Other_

Non_II

Other_

SA_II

Oxycodo

ne

Propoxy

phene

Tramado

lGenerated

random number

0.72

0.72>0.68 and <0.82

Oxycodone

A similar technique is used repeatedly in the following steps. No detailed description is provided

again.

Step 3: Simulate Episode Length

Predicting episode length is achieved through another multinomial logistic regression model

based on the MarketScan®

database. The predictor variables are age, gender, race and the

predominant drug type sampled in Step 2. The logic behind this regression model is that drug use

duration is related to both demographic characteristics (which are associated with pain

type/severity and likelihood of drug abuse) and the drug type(s) used. The predicted variable is

the episode length, categorized into 0-29 days (short term), 30-59 days, 60-89 days (episodic),

90-179 days, 180-364 (long-term), and >365 (persistent). The simulation process to determine

the episode length is similar to the simulation process used to determine the predominant drug

type in Step 2. After the episode length is determined, another random sampling process is

conducted to determine number of days for an episode, which is based on the distribution of the

number of days in each episode length category in the MarketScan®

data. For example, based on

Jane’s profile and her predominant drug of oxycodone, the sampled category of episode length is

“30-59 days” and the subsequently sampled number of days is 46 days.

Step 4: Simulate Concurrent Prescription Opioid Use

Concurrent opioid use is defined as receiving two or more different types of prescription opioids

from the same pharmacy with an overlapping prescription period. For example, a patient could

regularly receive codeine and tramadol from the same pharmacy on the same day, which is

considered to be concurrent opioid use.

Predicting concurrent opioid use is also based on a multinomial logistic regression model, with

the predictive variables being age, gender, race, predominant drug type and length of episode.

Unlike the aforementioned regression models, this one does not rely on predictive values to

sample which category the individual is in, because the predicted value is a binomial variable.

The likelihood of having concurrent opioid use is as follows

Pconcurrent use=1/(1-exp( 0+’age*age_stratum+

’race*race

+’gender*gender+

’length*length_type+

’drug*predominant_drug_type))

where is the vector of corresponding coefficient for the vector of covariates. The calculated

likelihood is compared with a randomly sampled probability. A likelihood smaller than the

randomly generated probability means not having concurrent opioid use in the episode. For

example, if Jane’s likelihood is 2.5% and the random generated number is 21.6% (larger than

2.5%), she does not have concurrent opioid use in this episode.

3.38

Step 5: Simulate Overlapping Prescriptions

Overlapping prescriptions are defined as (1) receiving the same type of opioid drug from the

same pharmacy with an overlapping prescription period and/or, (2) receiving opioid prescriptions

(the same type or not) from multiple pharmacies with an overlapping prescription period. For

example, a patient would meet the criteria of having overlapping prescriptions if she receives a

30-day oxycodone prescription from “Pharmacy A” on 6/1/2010 and another 30-day

hydrocodone prescription from “Pharmacy B” on 6/12/2010.

Predicting overlapping prescriptions is based on a multinomial logistic regression model with the

same structure as that for concurrent use. The sampling process is also the same. The presence of

overlapping prescription and concurrent drug use are assumed to be independent.

Step 6: Simulate Subsequent Episodes of Prescription Opioid Use

The ADOPT model reports all opioid prescriptions that a patient receives during a calendar year

(the current version uses 2010). As shown in Figure 3-6, a patient could have multiple episodes

of opioid use in 2010. In order to illustrate the prescription history within 2010, the model

simulates a 2-year time period from 6/1/2009 to 6/1/2011. The date of the initial episode can

begin be any day between 6/1/2009 and 6/1/2010. The length of the gap between two

consecutive episodes is randomly sampled from the distribution of gaps in the MarketScan®

data.

The model continues to simulate episodes until the end date of the last episode extends beyond

6/1/2011. Only the prescriptions with at least one day’s supply between 1/1/2010 and 12/31/2010

are reported in the model. Eligibility for the PRR program is determined based only on the

reported prescriptions. Values reported for cost and efficacy of PRR policy alternatives are for

one-year implementation.

Figure 3-6. Subsequent Episodes of Opioid Use

Simulated timespan

Reported timespan

6/1/2009 1/1/2010 12/31/2010 6/1/2011

Episodes of drug use

To simulate subsequent episodes, the model repeats Steps 2-5. The difference is that one

additional variable is added to each regression model –the predicted opioid in the previous

episode. For example, if Jane’s previous episode of opioid use is predominantly hydrocodone,

she is more likely to use hydrocodone in the subsequent episode. Adding the status of the

predicted variable in the previous episode enables the model to account for the association

between episodes.

3.39

Step 7: Simulate the Opioid Type of Each Prescription in an Episode

The ADOPT model simulates the opioid type of every prescription in an episode based on the

information collected by the model thus far – the predominant opioid drug type, the episode

length (number of days), the presence of concurrent drug use, and the presence of overlapping

opioid use. For each predominant opioid type and each episode length, ADOPT refers to a

specific drug type distribution table. For example, in an episode involving long-acting

oxycodone as the predominant drug type for more than 3 months (i.e. long-term), there is a 7.2%,

4.3%, and 6.8% chance of also having prescriptions for hydrocodone, tramadol, and short-acting

oxycodone, respectively (among other unmentioned opioid drugs). The reason for using a

specific drug type distribution table is that every predominant opioid has a specific spectrum of

associated drugs that are prescribed during the same episode and with specific frequencies. In

addition, the spectrum and the frequency distributions of associated drugs are also related to

episode length – for example, long-term use of long-acting oxycodone may have a different

spectrum and frequency distribution of associated drugs compared to a short-term use of long-

acting oxycodone ER.

ADOPT uses a total of 126 opioid type distribution tables (21 predominant opioid types by 6

episode length types). In each distribution table, there are 12 columns, each corresponding to a

specific combination of concurrent drug use and overlapping drug use. These 12 columns are

organized into four sections (most commonly used, second, third and fourth pharmacy) that

present possible overlapping drug use. Each of the four sections contain 3 columns showing

different concurrent drug use status including one for no concurrent drug use, one for the primary

prescription when concurrent use, and one for companion prescriptions of concurrent use. The

primary prescription is defined as follows:

1. the prescribed drug type (could be any opioid type) if only one prescription is in use.

Note that in an episode of concurrent drug use, a subject may still have days using only

one drug.

2. the predominant drug type if concurrent but different drugs are in use and one of

concurrent drugs is predominant

3. either of concurrent drugs if concurrent but different drugs are in use and none of

concurrent drugs is predominant. In this case the primary prescription is randomly

selected from concurrent drugs.

Companion prescriptions are those not of the primary drug type. For example, if the predominant

drug type of Jane’s first episode of opioid use is oxycodone and she has concurrent drug use,

then oxycodone is the primary prescription and any concurrent prescription, say hydrocodone, is

a companion prescription. If none of two or more concurrent prescriptions is of the predominant

drug type, then the order (primary or companion) is randomly assigned.

3.40

Figure 3-7. Example of Opioid Type Distribution Table, for Predominant Drug Type of

Hydrocodone and Episode Length between 180- and 364-Days

The most commonly used

pharmacy

Second pharmacy

Third pharmacy

Fourth pharmacy

Drug type distributions in second/third/forth pharmacy are used if the subject has overlapping prescriptions

Drug type distribution if no concurrent prescription

Drug type distribution for primary prescriptions if with concurrent prescription

Drug type distribution for companion prescriptions if with concurrent prescription

Same structure as in primary pharmacy group

Opioid Drug type

If the episode does not have concurrent drug use, then the drug type distribution is based on the

column of no concurrent drug use. Otherwise, the drug type distribution is sampled from both the

column for primary prescriptions and the column for companion prescriptions.

The four drug type distribution groups for overlapping drug use are prescriptions from the most

commonly used, then the second, third, and fourth pharmacies. The most commonly used

pharmacy is the one from where the opioid user receives the most prescriptions in a certain

period; the second to fourth pharmacies are the places where the user receives numbers of

prescriptions in a descending order. The most commonly used pharmacy does not have to be of a

single pharmacy ID in an episode. For example, if Jane receives 3 prescriptions from pharmacy

A and 2 from pharmacy B in January, and 2 from pharmacy C and 1 from pharmacy B in

February, then the most commonly used pharmacy is A for January and C for February and the

second pharmacy is B for both months. In the MarketScan®

data we did not observe any users

visiting more than 4 pharmacies to obtain overlapping prescriptions. The maximum number of

pharmacies in an episode is sampled from the real distribution derived from the MarketScan®

data.

Consider Jane’s first episode with the following criteria:

1) Predominant drug type: oxycodone

2) Episode length: 42 days

3) Both concurrent and overlapping drug use (with overlapping prescriptions from a

maximum of two pharmacies).

First, the model identifies the drug type distribution table specific to oxycodone and episodic use

(30-59 days). Four drug type distribution columns - the second column for primary prescriptions

3.41

and the third column for companion prescriptions in the primary and second pharmacy groups -

are used to each sample 40 prescriptions (i.e. 160 prescriptions with assigned drug types in total).

The model sampled 40 prescriptions for each prescription type (primary vs. companion) and

pharmacy type (primary vs. second vs. third vs. fourth), because no episode in the MarketScan®

database exceeded 40 prescriptions in any category of prescription type and pharmacy type in

any 2-year period (i.e. the simulated time span). Each 40 prescriptions are stored in a separate

area with clear indicators about prescription type and pharmacy type.

Step 8: Simulate the Prescription Details: Generic Name, Strength, Master Form, Quantity,

Supply Days, Dose Level and Drug Price

This step involves simulating the details for the list of prescriptions with drug type assigned in

the Step 7. The first item to be simulated is the dose level, for two reasons. First, the dose level is

key information as it is directly associated with the risk of overdose. Second, the simulated items

downstream in the simulation chain are more likely to bear biases because biases may

accumulate during the process. Therefore, the dose level is placed on top of the simulation chain.

The second item is supply days. We allow the supply days of an opioid prescription to be any

duration between 1-day and 30-days. Prescriptions with supplies exceeding 30-days are very rare

(<0.4%) in the MarketScan®

data. The distribution of the supply is specific to both dose level and

episode length. It is dose-specific because the MarketScan®

data show that the supply is

correlated (positively or negatively, depending on drug type) with dose level. For example, a

prescription for acute pain may require a prescription with limited days’ supply yet high dosage.

It is episode length-specific because a long episode is more likely to be associated with

established, stable prescriptions with greater supply days (e.g. monthly supply).

After the days’ supply is simulated, the model simulates the opioid’s generic name, formulation,

and strength in a single step. Each unique combination of generic name, formulation, and

strength for an opioid prescription that appeared in the MarketScan®

database is considered as a

sub-type of that opioid drug. A dose-level-specific distribution of the subtypes is calculated for

each dose level of each drug type. For example, for a daily dose of 187.5mg butalbital and

codeine, the chance of having ‘APAP/BUTAL/CAFF/CODEINE’ in a capsule form with

strength of “30MG” is 34.2% and the chance of having “ASA/BUTAL/CAFF/ CODEINE” in a

capsule form with strength of ‘30MG’ is 65.8%.

The quantity (or the volume, if in solution form) of the prescription opioid is then determined by

multiplying the daily dose with the number of supply days, and then divided by the strength. The

estimated Medicaid reimbursement for the prescription is calculated by multiplying per unit drug

cost (prices per 10 units are listed in Table 3-9) with the quantity. The estimation of per unit

costs is detailed in the “Cost Analysis” section of this report.

The simulation of prescription details for each prescription is not always independent. For a

long-term episode of opioid use, it is likely that a patient may have an established prescription

pattern, meaning a repeated monthly supply of a particular opioid with a stable daily dose. The

ADOPT model recognizes an established prescription pattern by allowing any prescription of a

predominant drug type with 30-day supply to trigger a stable prescription chain. The prescription

chain consists of prescriptions with the same generic name, daily dosage, and days’ supply. The

chance of triggering the chain is based on the drug type-, episode length- and dose-specific

3.42

probabilities derived from the MarketScan®

data. The number of prescriptions in the chain is

sampled from a drug type-, episode length- and dose-specific distribution.

For example, consider Jane’s second episode which lasts 142 days and has hydrocodone as the

preliminary drug type. The 6th

prescription is “Acetaminophen/ Hydrocodone Bitartrate 325 MG-

10 MG” with 30-day supply and triggers the stable prescription chain. Recall that there is a list of

40 simulated prescriptions with drug types assigned during last step. The next 8 hydrocodone

prescriptions are not necessarily the next 8 prescription on the list. For example, they could be

the 9th

, 13th

, 14th

, 17th

and so on.

Step 9: Assign Prescription Dates

At this point, the simulated prescriptions are undated. Before adding dates to these prescriptions,

ADOPT sorts the simulated prescriptions to mimic the chronological order of prescription

history in the MarketScan®

dataset. By reviewing the individual prescription histories in the

MarketScan®

data, we observed that for most (>80%) episodic and long-term use, patients

received prescriptions with limited days’ supply and widely varying dose level at the beginning

of an episode. Over time, the dose level stabilized and the days’ supply increased. For about 30%

of episodic and long-term episodes of use, a reduced dose and days’ supply were observed when

close to the end of an episode. However, no such pattern was observed in short-term episodes.

Therefore, ADOPT re-orders the randomly ordered prescriptions based on dose level and days’

supply. The prescriptions with stable dose and longer days supply are placed toward the middle

of an episode. However, the ordering process is not strict – which means that it creates a general

trend, but not a strict order. For example, a patient could have acute, escalating pain and a

receive prescription opioid type with short days’ supply or the same opioid type with increased

dose level during his/her established stable prescription period. The model should allow the

existence of such random events. The program that executes the ordering process strives for a

balance between order and randomness. The percentages of patients having the pattern of

varying to stable dose level and short to long supply days, and the percentage of prescriptions in

an episode that does not follow such pattern were derived from the MarketScan®

data and were

predominant drug type- and episode length-specific.

The ordered prescriptions are then assigned prescription dates relative to the beginning of the

episode (i.e. Day 1). Overlapping days between two consecutive prescriptions are allowed for no

more than 25% of total days’ supply of the preceding prescription. Similarly, gaps (i.e. days

without any prescription opioid in use) between two consecutive prescriptions are allowed. A

predominant-drug-type-specific distribution of overlapping days (if negative value) or gap days

(if positive value) from the MarketScan® data are used to adjust the relative prescription date.

For example, if the preceding prescription was received on day 21 and had 30 days’ supply and

the sample adjusting day is -2, then the date for the next prescription is 21+(30-1)-2= day 48.

Any prescriptions received at a date that is beyond the assigned episode length are eliminated.

The mechanism for assigning dates to concurrent prescriptions is different. Because concurrent

prescriptions, by definition, are dispensed on the same dates when the primary prescriptions are

dispensed, their dates are based on the dates assigned to the primary prescriptions of the

predominant drug type. For example, if Jane has hydrocodone concurrently prescribed with

oxycodone ER, and the dispensing date for an oxycodone ER is day 21, then the dispensing date

for a concurrently prescribed hydrocodone is also day 21. It is rare, but possible that a concurrent

drug is dispensed on a different date – the ADOPT model can mimic this rare event by sampling

3.43

from a predominant-drug-type-specific distribution of day difference between concurrent

prescriptions. Overlapping prescriptions have date-assigning systems independent of each other.

Step 10: Assign Pharmacy IDs to Each Prescription

Patients having no overlapping prescriptions only use the primary pharmacy. However, the

primary pharmacy does not mean a single pharmacy ID. In the aforementioned example, Jane

has pharmacy A as her primary pharmacy in January and pharmacy B as her primary pharmacy

in February. The ADOPT considers two types of primary pharmacy change: occasional change

and extended switching. Occasional change of primary pharmacy is defined as the condition of

having non-overlapping prescriptions dispensed by another pharmacy for no more than two times

followed by a successive prescription being dispensed by the previous pharmacy.

For example, Jane had all 4 prescriptions dispensed by pharmacy A in March and April, then had

one prescription dispensed by pharmacy B in May, then had the next round of prescriptions

dispensed by A again in June. The one prescription from pharmacy B in May is considered an

‘occasional change’. Occasional change could be due, for example, to a temporary change in

location, or temporary lack of supply at pharmacy A. Extended switching of primary pharmacy is

defined as the condition of having non-overlapping prescriptions dispensed by a second

pharmacy for more than two times or the condition of having non-overlapping prescriptions

dispensed by a second pharmacy for two times or less but receiving the successive prescription

from a third pharmacy. An example for the latter condition is that Jane received 4 prescriptions

from pharmacy A, then 2 prescriptions from pharmacy B, then 5 prescriptions from pharmacy C.

The reason for this type of pharmacy switching may be due to change in primary residence, and

is not uncommon in the MarketScan®

data.

Both occasional change and extended switching of primary pharmacy are included in the

ADOPT model. The probabilities of having occasional change and having extended switching

are episode-length- and overlapping-drug-use-status-specific. For each prescription, a random

number is generated and compared with the probability. If the random number is smaller, then

switching in pharmacy ID occurs. The pharmacy IDs are assigned in alphabetic order.

If a patient has overlapping prescriptions from multiple pharmacies, assigning pharmacy IDs for

the prescriptions from the second, third, and fourth pharmacies is similar to that for the

prescriptions from the primary pharmacy, except the initial pharmacy ID starts from the letter

next to the last assigned one for the previous pharmacy.

Step 11: Assign Prescriber IDs to Each Prescription

Assigning prescriber ID is trickier than assigning pharmacy ID because the MarketScan®

data do

not contain prescriber information. We had to rely on the reported correlation between the

number of prescribers and pharmacies based on the analysis of the Massachusetts’ prescription

drug monitoring database.1 In the paper by Katz et al, (re-presented as Table 8 in Part 1 of this

report) a table shows the number of pharmacies with a corresponding distribution of number of

prescribers. ADOPT calculates the total number of pharmacies the enrollee visited in the

episode, and then searches the correlation table for the corresponding number of prescribers.

Using the distribution of number of prescribers, ADOPT randomly samples the total number of

prescribers in that episode.

3.44

Step 12: Simulate Subsequent Episodes of Opioid Use

At this point, the ADOPT model finishes the simulation of a single episode of opioid use for a

hypothetical individual. As stated in Step 6, an individual can have multiple episodes. At this

stage the ADOPT model repeats Steps 7 to 11 for all subsequent episodes that an individual has.

The details of each prescription in each episode are stored in a designated area.

Step 13: Assign Absolute Dispensing Date to Each Prescription

So far the details of all prescriptions that a hypothetical individual receives are simulated, and

each prescription is assigned a dispensing date relative to the initial starting day of an episode.

The ADOPT model summarizes the details of all prescriptions in a single list and assigns an

absolute dispensing date to each prescription. Calculation of the absolute dispensing date is the

absolute date of the beginning of an episode plus the relative dispensing date.

For example, if Jane’s second episode started Feb 2, 2010, the second prescription dispensed on

day 21 of this episode has an absolute date of Feb 22. The prescriptions are ordered in an

ascending order of absolute date. Those dispensed outside of calendar year 2010 are eliminated

from the list.

Step 14: Calculate Number of Prescription/Pharmacies/Prescribers and Dose Level

With an absolute date assigned to each prescription, the ADOPT is able to calculate the total

number of pharmacies and the total number of prescribers that provided a hypothetical individual

with prescription opioids, as well as the total number of prescriptions in a given time span.

Because the program eligibility criteria adopted by states often involve the number of

pharmacies/ prescribers/prescriptions in ANY time span of a specified length, the total numbers

of pharmacies, prescribers, and prescriptions are counted from the dispensing date of every

prescription onwards to ensure an exhaustive search of any possible condition that meets the

eligibility criterion. For example, if an individual has 10 prescriptions, the model calculates total

numbers of pharmacies, prescribers, and prescriptions in 30, 60, 90, and 180 days starting from

the dispensing date of prescription No. 1, then calculates the same numbers from the dispensing

date of prescription No. 2, and so on and so forth.

The ADOPT model also calculates the average morphine equivalent dose level for each month.

The conversion factors for drug strength are the same as the ones used in the MarketScan®

data

analysis report. The monthly morphine equivalent dose level is the total morphine equivalent

dose of all prescriptions used in a month divided by the total days with opioid in use in that

month.

Step 15: Calculate Risk of Overdose, Overdose Event Type, and Overdose-Related Medical

Costs

The baseline risk of having an opioid-related overdose event (56 per 100,000 person-months) - is

the estimated risk for a hypothetical individual who is at age 18-29, white, female, having less

than 20mg/d, no concurrent sedative or hypnotic use, no history of alcohol abuse or depression

diagnosis, no overlapping prescription or pharmacy shopping behavior (defined as greater than

or equal to pharmacies in 3 months). The risk of overdose for individuals with other

characteristics is adjusted by multiplying the baseline risk with hazard ratios for the

characteristics. To account for the uncertainty of estimated hazard ratios, the ADOPT allows the

hazard ratio to be drawn from an estimated distribution. For example, if another hypothetical

individual has MME of 86 mg/d and other characteristics equal to the baseline, the model draws

3.45

a random number, say 3.11, from the distribution with the mean equal to 3.06 and the 95% CI

being 2.33 to 4.02 (the point estimates and the CIs for hazard ratios are shown in Table 3-16 and

multiplies the baseline risk of 56 per 100,000 person-month with 3.11 to calculate the adjusted

risk.

Table 3-16. Hazard Ratios for Prescription Opioid Overdose Hazard Ratio 95% CI P value

Opioid dose

1 to <20mg/d 1

20 to <50mg/d 1.61 1.24 2.08 0.0004

50 to 100 mg/d 3.06 2.33 4.02 <.0001

>=100mg/d 4.02 3.07 5.26 <.0001

Gender

Female 1.00

Male 1.02 0.87 1.18 0.8444

Age

12-17 0.21 0.03 1.53 0.1235

18-29 1.00

30-44 0.94 0.74 1.19 0.593

45 and over 0.88 0.70 1.11 0.2875

Race/Ethnicty

White 1.00

Black 0.60 0.48 0.74 <.0001

Hispanic 1.09 0.57 2.11 0.7959

Other 1.13 0.86 1.48 0.377

Concurrent sedative/hypnotic use 2.54 1.99 3.23 <.0001

History of alcohol abuse 3.07 2.09 4.50 <.0001

History of depression diagnosis 2.91 2.21 3.83 <.0001

Pharmacy Shopping 1.80 1.54 2.10 <.0001

Overlapping Prescriptions 2.96 2.45 3.68 <.0001

The risk of overdose is calculated month by month, as monthly average dosage changes over

time. A random number is generated each month and compared with the adjusted risk. If the

random number is smaller than the adjusted risk, then the hypothetical individual is considered

having an overdose event.

The type of overdose encounter – hospitalization, ED visit, or outpatient visit – is determined by

random sampling from the distribution of overdose encounter type derived from the

MarketScan®

data, as shown in Table 3-17. The estimated related cost is sampled from a log-

normal distribution with estimated means and standard deviations.

Table 3-17. Distribution of Overdose and Cost Estimates Event type % Mean Median Interquartile range

Hospitalization, with ED visit 36.1 $12,371 $5,506 $2658, $13415

3.46

Event type % Mean Median Interquartile range

Hospitalization, without ED visit 8.6 $5,797 $3,241 $1257, $5479

ED visit only 50.5 $514 $315 $132, $663

Outpatient visit only 4.8 $162 $149 $67, $269

Overall 100 $5,376 $2,879 $407, $6945

Step 16: Check Individual Eligibility for the Patient Review and Restriction Program

The ADOPT model checks whether a hypothetical individual meets the user-specified patient

review and restriction (PRR) program criteria. In Step 14, total numbers of

prescriptions/pharmacies/prescribers have been calculated. The maximum numbers of

prescriptions/pharmacies/prescribers in the hypothetical individual’s prescription history are

compared with the threshold numbers specified by a user. The model then counts how many

conditions the individual meets, and compares the number of conditions met with the required

number of conditions to be met to be eligible for program enrollment.

If a hypothetical individual is considered eligible for the PRR program, the ADOPT model will

go through the prescription list to identify unnecessary prescriptions and prescriptions with

excessive dosage. The overlapping prescriptions are those overlapping with a previous

prescription of the same drug type for 25% or more of the total supply days of the previous

prescription and at least 5 days supply for one of the prescriptions. The prescriptions with

excessive dosage are those contributing to an aggregate daily dose over 80mg morphine

equivalent on any day. For example, if a patient took 70mg morphine equivalent from

prescription A and 40mg from prescription B, then both A and B are considered as having

excessive dosage.

The identified overlapping prescriptions will be eliminated, and the identified prescriptions with

excessive dosage will be reduced to an aggregate daily dose of 80mg morphine equivalent. The

new monthly dose level will be re-calculated, and the new risk of overdose event will be re-

calculated based on new monthly average dose level.

Step 17: Summarize the Cost and Health Outcomes of the Simulated Cohort

So far, the ADOPT model simulated one individual’s prescription history. Next, it will repeat

Steps 1-16 to create the entire simulated cohort. The numbers of total prescriptions and

prescriptions in three drug categories (Schedule III and IV, Schedule II short-acting, and

Schedule II long-acting) for the entire cohort are summarized for two scenarios: having a PRR

program versus not having the program. Costs of drug reimbursement and overdose-related

medical costs for the entire cohort are summarized for both scenarios. The total numbers of

overdose events by event type (hospitalization, ED visit, and outpatient visit) are summarized for

both scenarios. The cost and the health outcomes for both scenarios are presented in tables and

figures in the output screen.

3.47

APPENDIX II

ICD-9 Codes Indicating Overdose-Related Symptoms

276.4 Mixed acid–base balance disorder

292.1 Drug-induced psychotic disorders (including 292.11 and 292.12)

292.81 Drug-induced delirium

292.8 Drug-induced mental disorder (excluding 292.81)

486 Pneumonia, organism unspecified

496 Chronic airway obstruction, not elsewhere classified

518.81 Acute respiratory failure

518.82 Other pulmonary insufficiency, not elsewhere classified

780.0 Alteration of consciousness

780.97 Altered mental state

786.03 Apnea

786.05 Shortness of breath

786.09 Dyspnea and respiratory abnormalities—other

786.52 Painful respiration

799.0 Asphyxia and hypoxemia

Type of Overdose Encounters

Emergency department (ED) visits are identified from both inpatient and outpatient claims data

as claims having emergency room as service place and/or having emergency medicine or

emergency services as service type. Inpatient claims with the same admission dates and

outpatient claims occurring in 2 preceding days are grouped into one overdose encounter.

Overdose encounters are divided into 3 types: hospitalization if any non-ED inpatient claims

appear in that encounter; ED encounter if there are any ED claims and no non-ED inpatient

claims; and outpatient encounter if there are non-ED outpatient claims and no inpatient or ED

outpatient claims.

Numbers and Estimated Cost for Each Generic Opioid Drug Type Obs Drug Name n ($) Price for 10

units

(95% CI)

1 APAP/Butalbital/Caff/Codeine Phos CAP 325 MG-50 MG-40 MG-30

MG

3,591 6.67( 6.59- 6.74)

2 ASA/Oxycodone HCl/Oxycodone Terephthalate TAB 325 MG-4.5 MG-

0.38 MG

918 9.65( 9.44- 9.85)

3 Acetaminophen/Caffeine/Dihydrocodeine Bitartrate CAP 356.4 MG-30

MG-16 MG

141 15.67( 15.21-

16.14)

4 Acetaminophen/Caffeine/Dihydrocodeine Bitartrate TAB 712.8 MG-60

MG-32 MG

1,183 15.44( 15.21-

15.67)

5 Acetaminophen/Codeine Phosphate ELI 120 MG/5 ML-12 MG/5 ML 1,791 0.49( 0.47- 0.51)

6 Acetaminophen/Codeine Phosphate SOL 120 MG/5 ML-12 MG/5 ML 5,091 0.78( 0.50- 1.06)

7 Acetaminophen/Codeine Phosphate SUS 120 MG/5 ML-12 MG/5 ML 79 4.81( 4.28- 5.34)

8 Acetaminophen/Codeine Phosphate TAB 300 MG-15 MG 773 3.79( 3.61- 3.96)

9 Acetaminophen/Codeine Phosphate TAB 300 MG-30 MG 81,793 3.94( 3.83- 4.04)

10 Acetaminophen/Codeine Phosphate TAB 300 MG-60 MG 6,405 3.23( 3.19- 3.28)

11 Acetaminophen/Hydrocodone Bitartrate ELI 500 MG/15 ML-7.5 MG/15 6,618 0.74( 0.73- 0.76)

3.48

Obs Drug Name n ($) Price for 10

units

(95% CI)

ML

12 Acetaminophen/Hydrocodone Bitartrate SOL 325 MG/15 ML-10 MG/15

ML

102 2.97( 2.83- 3.11)

13 Acetaminophen/Hydrocodone Bitartrate TAB 300 MG-10 MG 1,017 20.45( 20.24-

20.66)

14 Acetaminophen/Hydrocodone Bitartrate TAB 300 MG-7.5 MG 51 14.91( 14.12-

15.71)

15 Acetaminophen/Hydrocodone Bitartrate TAB 325 MG-10 MG 91,583 3.44( 3.42- 3.46)


17 Acetaminophen/Hydrocodone Bitartrate TAB 325 MG-7.5 MG 31,408 4.60( 4.57- 4.63)

18 Acetaminophen/Hydrocodone Bitartrate TAB 400 MG-10 MG 61 8.92( 8.74- 9.10)








26 Acetaminophen/Hydrocodone Bitartrate TAB 750 MG-10 MG 242 7.96( 7.75- 8.16)


28 Acetaminophen/Oxycodone Hydrochloride CAP 500 MG-5 MG 8,047 3.44( 3.32- 3.55)

29 Acetaminophen/Oxycodone Hydrochloride SOL 325 MG/5 ML-5 MG/5

ML

889 1.00( 0.96- 1.04)

30 Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-10 MG 85,434 7.57( 7.55- 7.60)

31 Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-2.5 MG 243 21.06( 20.38-

21.74)


33 Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-7.5 MG 21,295 7.99( 7.94- 8.04)

34 Acetaminophen/Oxycodone Hydrochloride TAB 500 MG-7.5 MG 8,835 6.58( 6.53- 6.63)


36 Acetaminophen/Pentazocine Hydrochloride TAB 650 MG-25 MG 138 8.66( 8.29- 9.03)

37 Acetaminophen/Propoxyphene Hydrochloride TAB 650 MG-65 MG 276 2.40( 2.27- 2.54)

38 Acetaminophen/Propoxyphene Napsylate TAB 325 MG-100 MG 160 21.40( 20.31-

22.50)

39 Acetaminophen/Propoxyphene Napsylate TAB 325 MG-50 MG 1,079 6.64( 6.44- 6.84)

40 Acetaminophen/Propoxyphene Napsylate TAB 500 MG-100 MG 180 9.36( 8.76- 9.96)

41 Acetaminophen/Propoxyphene Napsylate TAB 650 MG-100 MG 126,797 3.03( 3.01- 3.05)

42 Acetaminophen/Tramadol Hydrochloride TAB 325 MG-37.5 MG 20,614 6.93( 6.72- 7.14)

43 Aspirin/Butalbital/Caffeine/Codeine Phosphate CAP 325 MG-50 MG-40

MG-30 MG

5,027 10.42( 10.34-

10.51)

44 Aspirin/Carisoprodol/Codeine Phosphate TAB 325 MG-200 MG-16 MG 173 16.96( 16.39-

17.53)

45 Aspirin/Oxycodone Hydrochloride TAB 325 MG-4.8355 MG 481 11.72( 11.45-

11.99)

46 Belladonna Alkaloids/Opium Alkaloids SUP 16.2 MG-60 MG 50 98.39( 85.69-

111.09)

47 Buprenorphine Hydrochloride TAB 2 MG 341 34.79( 33.72-

35.86)

48 Buprenorphine Hydrochloride TAB 8 MG 2,012 66.18( 65.44-

66.91)

49 Buprenorphine Hydrochloride/Naloxone Hydrochloride FIL 8 MG-2 MG 168 62.73( 61.57-

3.49


units

(95% CI)

63.88)

50 Buprenorphine Hydrochloride/Naloxone Hydrochloride TAB 2 MG-0.5

MG

763 32.20( 31.42-

32.98)

51 Buprenorphine Hydrochloride/Naloxone Hydrochloride TAB 8 MG-2

MG

18,571 56.05( 55.85-

56.26)

52 Butorphanol Tartrate SOL 1 MG/ML 144 249.05( 221.47-

276.62)

53 Butorphanol Tartrate SOL 2 MG/ML 306 125.66( 112.18-

139.15)

54 Butorphanol Tartrate SPR 10 MG/ML 3,142 117.88( 116.67-

119.10)

55 Codeine Sulfate TAB 15 MG 51 4.79( 4.39- 5.18)



58 Cough/Cold Combination SOL 212 2.84( 2.71- 2.96)

59 Cough/Cold Combination SYR 592 3.80( 3.75- 3.85)

60 Fentanyl TDM 100 MCG/HR 13,723 546.69 ( 542.52-

550.85)


157.60)


150.16)


284.73)


427.11)

65 Fentanyl Citrate LOZ 0.4 MG 51 280.40 ( 259.14-

301.66)


291.69)


339.25)


725.50)

69 Fentanyl Citrate SOL 0.05 MG/ML 27,642 52.45 ( 51.29-

53.61)

70 Hydrocodone Bitartrate/Ibuprofen TAB 10 MG-200 MG 218 11.13 ( 10.91-

11.34)

71 Hydrocodone Bitartrate/Ibuprofen TAB 5 MG-200 MG 106 8.82 ( 8.41- 9.22)

72 Hydrocodone Bitartrate/Ibuprofen TAB 7.5 MG-200 MG 14,465 7.83 ( 7.78- 7.87)

73 Hydromorphone Hydrochloride SOL 1 MG/ML 4,443 82.68 ( 79.19-

86.17)

74 Hydromorphone Hydrochloride SOL 10 MG/ML 70 266.54 ( 156.73-

376.34)

75 Hydromorphone Hydrochloride SOL 2 MG/ML 5,360 96.57 ( 93.81-

99.33)

76 Hydromorphone Hydrochloride SOL 4 MG/ML 537 69.96 ( 55.94-

83.98)

77 Hydromorphone Hydrochloride SUP 3 MG 96 87.88 ( 84.45-

91.31)

78 Hydromorphone Hydrochloride TAB 2 MG 7,157 4.68 ( 4.41- 4.95)

3.50


units

(95% CI)



81 Ibuprofen/Oxycodone Hydrochloride TAB 400 MG-5 MG 74 11.79 ( 11.01-

12.57)

82 Meperidine Hydrochloride SOL 10 MG/ML 88 5.62 ( -0.85- 12.08)

83 Meperidine Hydrochloride SOL 100 MG/ML 506 116.00 ( 104.78-

127.22)


110.73)

85 Meperidine Hydrochloride SOL 50 MG/ML 1,886 125.75 ( 120.30-

131.20)


104.86)

87 Meperidine Hydrochloride TAB 100 MG 582 8.06 ( 7.57- 8.55)

88 Meperidine Hydrochloride TAB 50 MG 4,244 4.97 ( 4.82- 5.13)

89 Methadone Hydrochloride SOL 10 MG/ML 110 1.50 ( 1.25- 1.76)

90 Methadone Hydrochloride SOL 5 MG/5 ML 141 1.16 ( 0.93- 1.40)

91 Methadone Hydrochloride TAB 10 MG 38,130 1.56 ( 1.55- 1.57)

92 Methadone Hydrochloride TAB 40 MG 149 3.05 ( 2.92- 3.17)

93 Methadone Hydrochloride TAB 5 MG 4,029 1.79 ( 1.72- 1.86)

94 Morphine Sulfate C24 120 MG 831 123.03 ( 121.69-

124.37)


33.97)


71.82)


101.90)

98 Morphine Sulfate CER 10 MG 559 35.67 ( 35.08-

36.26)

99 Morphine Sulfate CER 100 MG 2,191 132.64 ( 131.41-

133.88)


37.49)

101 Morphine Sulfate CER 200 MG 200 275.61 ( 267.89-

283.34)


40.82)


68.29)


82.17)


110.09)

106 Morphine Sulfate SOL 1 MG/ML 51 79.74 ( 35.66-

123.83)

107 Morphine Sulfate SOL 10 MG/5 ML 203 13.07 ( 5.76-

20.38)

108 Morphine Sulfate SOL 10 MG/ML 1,152 83.16 ( 78.30-

88.03)


3.51


units

(95% CI)

89.25)

110 Morphine Sulfate SOL 20 MG/5 ML 75 1.43 ( 1.29- 1.57)

111 Morphine Sulfate SOL 20 MG/ML 455 7.36 ( 4.73- 9.99)


71.92)


118.84)

114 Morphine Sulfate TAB 15 MG 8,202 2.21( 2.18- 2.25)

115 Morphine Sulfate TAB 30 MG 7,548 2.63( 2.60- 2.67)

116 Morphine Sulfate TER 100 MG 6,776 21.31( 21.04-

21.57)

117 Morphine Sulfate TER 15 MG 12,233 5.32( 5.25- 5.39)

118 Morphine Sulfate TER 200 MG 831 40.94( 39.49-

42.39)

119 Morphine Sulfate TER 30 MG 20,014 8.65( 8.60- 8.71)

120 Morphine Sulfate TER 60 MG 12,569 13.95( 13.81-

14.09)

121 Nalbuphine Hydrochloride SOL 10 MG/ML 5,006 112.15( 108.49-

115.81)

122 Nalbuphine Hydrochloride SOL 20 MG/ML 2,941 68.76( 66.67-

70.85)

123 Naloxone Hydrochloride/Pentazocine Hydrochloride TAB 0.5 MG-50

MG

2,325 10.87( 10.72-

11.03)

124 Opium TIN 10 MG/ML 162 47.48( 45.30-

49.66)

125 Opium TIN 10% 75 8.32( 8.01- 8.63)

126 Oxycodone Hydrochloride CAP 5 MG 8,620 2.68( 2.63- 2.72)

127 Oxycodone Hydrochloride SOL 20 MG/ML 490 8.62( 8.31- 8.93)

128 Oxycodone Hydrochloride SOL 5 MG/5 ML 824 0.99( 0.86- 1.11)

129 Oxycodone Hydrochloride TAB 10 MG 1,538 5.55( 5.43- 5.68)


131 Oxycodone Hydrochloride TAB 20 MG 219 8.89( 8.48- 9.29)



134 Oxycodone Hydrochloride TER 10 MG 9,217 17.91( 17.80-

18.03)

135 Oxycodone Hydrochloride TER 15 MG 804 27.08( 26.62-

27.53)


32.89)


48.75)


57.59)


89.10)


107.88)

141 Oxymorphone Hydrochloride TAB 10 MG 855 47.45( 46.59-

48.32)

142 Oxymorphone Hydrochloride TAB 5 MG 421 25.55( 24.72-

3.52


units

(95% CI)

26.38)

143 Oxymorphone Hydrochloride TER 10 MG 287 29.60( 28.45-

30.74)


38.09)


56.40)


84.78)


108.68)

148 Propoxyphene Hydrochloride CAP 65 MG 3,744 3.63( 3.57- 3.69)

149 Propoxyphene Napsylate TAB 100 MG 473 15.95( 15.61-

16.30)

150 Tapentadol Hydrochloride TAB 100 MG 581 30.04( 29.53-

30.55)


19.49)


23.72)

153 Tramadol Hydrochloride TAB 50 MG 243,239 1.98( 1.97- 1.99)

154 Tramadol Hydrochloride TER 100 MG 1,782 32.10( 31.69-

32.51)


54.12)


83.12)

3.53

BIBLIOGRAPHY









Dec 2009;15(12):897-906.



1 2010;112(1-2):99-106.






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Association. Apr 6 2011;305(13):1315-1321.



11 2011;171(7):686-691.



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11. Goldberg GA, Kim SS, Seifeldin R, Haberman M, Robinson D, Jr. Identifying

suboptimal management of persistent pain from integrated claims data: a feasibility

study. Managed care (Langhorne, Pa.). Aug 2003;12(8 Suppl Improving pain):8-13.

12. Washington Administrative Code. Patient Review and Restiction Criteria. . Washington

State Health Care Authority 182-501-0315.

http://apps.leg.wa.gov/WAC/default.aspx?cite=182-501-0135 Accessed May 15, 2012.

13. West Virginia Department of Health and Human Resources. West Virginia Lock-In

Criteria. 2011;

http://www.dhhr.wv.gov/bms/pharmacy/dur/documents/wv_lock_incriteria20110527.pdf.

Accessed December 19th, 2012.

14. North Carolina Department of Health and Human Services. North Carolina Medicaid

Pharmacy Newsletter: Implementation of a Recipient Management Lock-In Program.

http://apps.leg.wa.gov/WAC/default.aspx?cite=182-501-0135

http://www.dhhr.wv.gov/bms/pharmacy/dur/documents/wv_lock_incriteria20110527.pdf

3.54

Sept 2010;

http://www.ccwjc.com/Forms/Pharmacy/Medicaid%20Narcotic%20Lock%20in%20Polic

y.pdf. Accessed December 19th, 2012.

15. Kentucky Administrative Regulations. 907 KAR 1:677. Medicaid recipient lock-in

program. http://www.lrc.state.ky.us/kar/907/001/677.htm. Accessed December 19th,

2012.

16. Magellan Medicaid Adminstration - Idaho. Idaho Drug Utilization Review (DUR)

Quarterly February 2011;

http://www.healthandwelfare.idaho.gov/Portals/0/Medical/PrescriptionDrugs/DUR%20N

ewsletter%20Feb%202011.pdf. Accessed December 19th, 2012.

17. Michigan Department of Community Health. Section 8: Beneficiary Monitoring

Program. 2011;

http://www.sccmha.org/Documents/Library%20of%20Resources/2011%20-%2004%20-

01%20Medicaid%20Provider%20Manual-4-1-2011.pdf. Accessed December 19th, 2012.

http://www.ccwjc.com/Forms/Pharmacy/Medicaid%20Narcotic%20Lock%20in%20Policy.pdf

http://www.ccwjc.com/Forms/Pharmacy/Medicaid%20Narcotic%20Lock%20in%20Policy.pdf

http://www.lrc.state.ky.us/kar/907/001/677.htm

http://www.healthandwelfare.idaho.gov/Portals/0/Medical/PrescriptionDrugs/DUR%20Newsletter%20Feb%202011.pdf

http://www.healthandwelfare.idaho.gov/Portals/0/Medical/PrescriptionDrugs/DUR%20Newsletter%20Feb%202011.pdf

http://www.sccmha.org/Documents/Library%20of%20Resources/2011%20-%2004%20-01%20Medicaid%20Provider%20Manual-4-1-2011.pdf

http://www.sccmha.org/Documents/Library%20of%20Resources/2011%20-%2004%20-01%20Medicaid%20Provider%20Manual-4-1-2011.pdf

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