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.
i
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.
ii
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 "whatif" 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
i
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.
ii
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
iii
3.
4.
5.
6.
7.
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.
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.
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.
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.
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.
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.
iv
v
Approaches to Drug Overdose Prevention
Analytical Tool (ADOPT):
Evaluating Cost and Health Impacts of a
Medicaid Patient Review & Restriction
Program
Part 1
Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and
Prevention Policies – A Review of the Literature
Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and
Prevention Policies – A Review of the Literature
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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Prevention Policies – A Review of the Literature
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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Prevention Policies – A Review of the Literature
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
1.4
Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and
Prevention Policies – A Review of the Literature
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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Prevention Policies – A Review of the Literature
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
Implement Prescription Drug Monitoring
(PDMP’s)
Databases (PDMPs)
o Work with states to continue developing and
o Focus on patients at high risk (painkiller dosage;
enhancing PDMPs
number of prescriptions for controlled
o Develop incentives for healthcare programs and
substances; number of prescribers) and
providers to use PDMPs when prescribing
prescribers with inappropriate prescribing
patterns (large doses/numbers of controlled
Evaluate Patient Review and Restriction
substances; large proportion of doctor shoppers
Policies/Programs (PRR)
among their patients)
o Evaluate programs requiring high utilizers to use
o Integrate PDMP information into health care by
only one doctor/pharmacy (patient review and
linking PDMPs with electronic health record
restriction)
systems
Evaluate usefulness of Drug Abuse Warning
Implement Patient Review and Restriction (PRR)
Network (DAWN) data
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
Enforce regulatory action against prescribers who
enforcement and prosecutors
do not follow accepted medical guidelines for safe
prescribing of controlled substances
Enforce action against clinics and physicians not
following safe prescribing practices (i.e., pain
Enact, enforce and evaluate state laws to prevent
clinics, etc)
doctor & pharmacy shopping, “pill mill” operation,
and other methods of misuse, abuse and diversion.
Write and disseminate a Model Pain Clinic
Regulation Law
Increase investigations of prescription drug
trafficking at the Federal, state, and local levels
Other Policies/Programs
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White House
CDC
Proper Medication Disposal
Increase access to effective substance abuse
o Increase public awareness and provide education
treatment programs
on safe and effective drug return and disposal
o Engage private sector to support communitybased medication disposal 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, comorbidities, 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 costeffectiveness 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, noncancer 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
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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
Misuse
Nonmedical
Use
Definition
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
Use without a prescription belonging to the respondent or use that occurred
only for the experience or feeling the drug caused
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
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.
Abuse
Dependence
A maladaptive pattern of substance use, leading to clinically significant
impairment or distress as manifested by one or more behaviorally based
criteria
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.
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.
A compulsive pattern of substance use characterized by a loss of control
over substance use and continued use despite the significant substancerelated 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.
Source
Center for Substance
Abuse Treatment
(CSAT), 2006
National Survey on
Drug Use and
Health (NSDUH)
CSAT, 2006
Drug Abuse
Warning Network
(DAWN)
Methodology
Report, 2009
CSAT, 2006
DSM-IV-TR
CSAT, 2006
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
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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
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
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
Schedule I
• High potential for abuse, no currently accepted medical use in the US. Includes illicit
drugs such as heroin and marijuana.
Schedule II
• 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 III
• 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 IV
• Low potential for abuse relative to Schedule III drugs. Includes prescription drugs such
as diazepam.
Schedule V
• 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).
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
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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
a
25.0-40.0
Buprenorphine
7.00
Butorphanol
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
a
Buprenorphine is typically used for opioid detoxification and maintenance 40
b
Transmucosal 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.
c
Data for oxymorphone, hydromorphone and tapentadol obtained from FDA Blueprint for Prescriber Education for
Extended-Release and Long-Acting Opioid Analgesics
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d
Transdermal 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
Lifetime
Past Year
Past Month
N (%)
N (%)
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.
Year
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.
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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 nonregular 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.
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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
Figure 1-3. Sources of Prescription Opioids, 2010
males abusing this opioid
Got from
obtained it primary from a
Other source
drug
dealer
dealer (45.2%), followed by
7%
or stranger
within their social network
4%
(40.6%), or their own
Took from a
prescriptions (25.4%).57
friend or
Obtained
Cicero et al surveyed opioid
relative
free from a
users entering substance abuse
without
friend or
asking
treatment facilities and found
relative
5%
55%
that approximately 90% of
Bought
from
males and females under age
a friend or
20 acquired their prescription
relative
Prescribed
opioids from a dealer, while
12%
by one
less than half of individuals
doctor
over age 51 did so. Cicero et
17%
al also found that males were
Source: Centers for Disease Control and Prevention. Policy Impact:
more likely than females to
Prescription Painkiller Overdose. November 2011
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
Parente et al (2004)
Hall et al (2008)
Katz, Panas et al (2010)
White et al (2009)
Number of Opioid Prescribers
>6 in one year
>5 during the year before death
>1 - >10 over a 12-month period
And/
Or
OR
Number of Pharmacies
Dispensing Opioids
>3 in a year
AND
>1 - >10 over a 12-month period
> 2 over a 3-month period OR
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Citation
U.S. GAO (2009)
U.S. GAO (2011)
Wilsey et al (2010)
Wilsey et al (2011)
Peirce et al (2012)
Number of Opioid Prescribers
>6 over two year period
> 5 in one year
>2 within one month
2-5 in one year
>4 in the 6 months before death
And/
Or
Number of Pharmacies
Dispensing Opioids
> 3 over a 12-month period
AND
>2 within one month
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
Fentanyl
Hydrocodone
Hydromorphone
Methadone
Morphine
Oxycodone
6-10
777
31,364
590
824
810
5,349
Number of Prescribers in Selected States
11-15
16-20
21-50
51+
41
6
1
0
3,518
723
340
9
67
14
11
0
76
9
2
0
50
8
1
0
435
73
18
0
Total
825
35,954
682
911
869
5,875
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Number of Prescribers in Selected States
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 nonbenzodiazepine sleep aids
Prescription Opioid
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
Number of Prescribers in Selected States
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
Prescription Opioid
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
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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
Doctor Shopping
Both Doctor & Pharmacy
Shopper
Pharmacy Shopping
Both Pharmacy &
Doctor Shopper
20%
45%
55%
80%
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).
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Table 1-8. Distribution of Patients by Number of Prescribers and Pharmacies, 2006
#
# Pharmacies Used
Prescribers
1
2
3
4
5
6
7
8
9
10+
Total % (n)
Used
76.9%
1.65%
0.25%
0.06%
0.02%
0.00%
0.00%
0.00%
0.00%
0.00%
78.91 % (443,956)
1
9.75%
3.05%
0.41%
0.11%
0.03%
0.01%
0.00%
0.00%
0.00%
0.00%
13.37% (75,191)
2
2.61%
1.25%
0.41%
0.11%
0.04%
0.01%
0.00%
0.00%
0.00%
0.00%
4.43% (24,919)
3
0.86%
0.54%
0.24%
0.09%
0.03%
0.01%
0.00%
0.00%
0.00%
0.00%
1.77% (9,980)
4
0.30%
0.23%
0.12%
0.06%
0.03%
0.01%
0.01%
0.00%
0.00%
0.00%
0.76% (4,274)
5
0.11%
0.10%
0.06%
0.04%
0.02%
0.01%
0.00%
0.00%
0.00%
0.00%
0.34% (1,887)
6
0.04%
0.05%
0.04%
0.02%
0.02%
0.01%
0.01%
0.00%
0.00%
0.00%
0.18% (1,025)
7
0.02%
0.03%
0.02%
0.01%
0.01%
0.01%
0.00%
0.00%
0.00%
0.00%
0.10% (543)
8
0.01%
0.01%
0.01%
0.01%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.05% (296)
9
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.09% (520)
10+
90.62%
6.91%
1.58%
0.52%
0.20%
0.08%
0.04%
0.02%
0.01%
0.02%
100.00%
Total %
(509,818) (38,865)
(8,870)
(2,917)
(1,138)
(464)
(248)
(108)
(76)
(87)
(562,591)
(n)
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
# Providers Used
1
2
3
4
5
6
7
8
9
10+
Total Population
Source: Katz et al (2010)
n
443,956
75,191
24,919
9,980
4,274
1,887
1,025
543
296
520
562,591
1+
100.00%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.00%
2+
2.5%
27.0%
41.2%
51.2%
61.2%
68.0%
78.0%
81.6%
84.5%
91.2%
9.38%
# Pharmacies Used
4+
6+
0.12%
0.01%
1.12%
0.10%
3.74%
0.44%
7.57%
0.98%
14.09%
2.32%
20.83%
5.14%
30.34%
9.85%
34.44%
13.08%
47.30%
19.26%
69.23%
42.88%
0.90%
0.17%
8+
0.00%
0.01%
0.07%
0.13%
0.28%
1.17%
1.27%
3.87%
7.09%
24.62%
0.05%
10+
0.00%
0.00%
0.02%
0.03%
0.05%
0.21%
0.10%
0.37%
1.35%
11.92%
0.02%
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Table 1-10. Distribution of Patients Using Multiple Prescribers and Pharmacies, 2008
Number of Pharmacies
3
4
5
6
7+
Total (n)
43.74%
3.32%
0.75%
0.19%
0.11%
48.10% (36,178)
2
24.46%
4.67%
1.70%
0.54%
0.26%
31.63% (23,790)
3
3.29%
3.21%
1.67%
0.69%
0.40%
9.26% (6,967)
4
0.77%
1.48%
1.18%
0.63%
0.41%
4.46% (3,357)
5
0.41%
1.23%
1.47%
1.19%
2.25%
6.55% (4,923)
6+
72.67%
13.91%
6.75%
3.24%
.34%
100%
Total
(54,658)
(10,460)
(5,080)
(2,439)
(2,578)
(75,215)
(n)
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
# of Prescribers
50.0%
90.0%
45.0%
80.0%
40.0%
70.0%
Percent Distriubtion
Percent Dispensed
Figure 1-5. Type and Formulation of Prescription Opioids Dispensed to Doctor Shoppers Compared to Non-Doctor Shoppers
Formulation Type
Schedule Type
35.0%
30.0%
25.0%
20.0%
15.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
10.0%
5.0%
0.0%
0.0%
IR
II Only III Only Other
Non-Doctor Shopper
II & III
II & II, III, & III &
Other Other Other
Doctor Shopper
ER
Combo IR & ER IR &
ER & IR, ER,
Combo Combo
&
Combo
Non-Doctor Shopper
Doctor Shopper
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)
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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 noncancer 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 twothirds 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
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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 longstanding 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 longacting 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
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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.
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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
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.
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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
Past Year
Past Month
(n=34,776,000)
(n=12,213,000)
(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)
13.7
Past Year
(n=12,213,000)
4.8
Overall
Region
Northeast
12.2
4.3
Midwest
13.1
4.7
South
13.3
4.4
West
16.4
6.1
County Type
Large Metro (>1 million population)
13.8
4.8
Small Metro (20,000 -999,999 population)
14.3
5.0
Nonmetro (0-19,999 population)
12.5
4.4
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2010
Past Month
(n=5,100,000)
2.0
1.9
2.0
1.9
2.3
1.9
2.1
2.1
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
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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 noncancer 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.
***
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.
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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 Misusea
Arkansas
Commercially
Medicaid
Insured (OR)
(OR)
Probable Misusea
Arkansas
Commercially
Medicaid
Insured (OR)
(OR)
Abuse/Dependenceb
Arkansas
Commerically
Medicaid
Insured (OR)
(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
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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
Ratea
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.
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Type of Drug-Related ED Visit
Total Drug-Related ED Visitsb
Prescription Opioids
Oxycodone
Hydrocodone
Methadone
Illicit Drugs
Number of
Visits
4,916,328
425,247
182,748
115,739
76,237
1,171.024
488,101
224,706
461,028
94,929
Ratea
1589.0
137.4
59.1
37.4
24.6
378.5
157.8
72.6
149.0
30.7
Percent Change
2004-2010
94%
156%
255%
149%
86%
NR
NR
NR
22%
48%
Cocaine
Heroin
Marijuana
Methamphetamine
Source: Drug Abuse Warning Network (2010)
NR = Not Reported
a
Rate is per 100,000 population
b
Includes 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 20042010 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)
# of visits
Ratea
%
Changeb
Oxycodone
# of
visits
Ratea
Hydrocodone
%
Changeb
# of
visits
Ratea
Gender
Male
229,107
150.6
171%
104,028
68.4
257%
55,846
36.7
Female
196,020
124.6
140%
78,651
50.0
253%
59,872
38.1
Race/Ethnicity
White 343,620
NR
186%
155,566
NR
301%
89,330
NR
Black
38,400
NR
188%
13,305
NR
406%
12,966
NR
Hispanic
18,692
NR
197%
4,194
NR
308%
6,612
NR
Other
3,279
NR
471%
1,776
NR
NR
659
NR
Age Group
31,890
36.3
103%
17,420
19.8
204%
8,327
9.5
>21
51,147
297.7
231%
23,561
137.1
264%
16,066
93.5
21-24
58,825
269.0
244%
23,710
112.2
279%
13,761
65.1
25-29
45,524
231.7
126%
18,994
94.6
193%
14,498
72.2
30-34
82,223
200.8
92%
36,100
88.2
211%
21,744
53.1
35-44
89,328
198.3
153%
36,283
80.6
303%
24,048
53.4
45-54
42,290
114.9
278.8
18,111
49.2
425%
10,168
27.6
55-64
24,782
61.3
182%
8,453
20.9
264%
7,118
17.6
65<
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
a
Rate is per 100,000 population
b
Percent change is from 2004 – 2010
%
Changeb
180%
125%
156%
309%
349%
663%
NR
276%
185%
119%
68%
180%
385%
241%
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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 opioidrelated 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
‡‡‡
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
§§§
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.
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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
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Table 1-17. Prevalence of Select Comorbidities among Opioid Abusers compared to Nonabusers
Privately Insured
1998-2002a
Abusers
Controls
n=740
n=1,266
71.1%
8.4%
36.5%
15.0%
National Medicaid
2002-2003b
Abusers
Controls
N=50,162
N=150,485
49.3%
26.1%
31.2%
19.8%
Privately Insured
2003-2007c
Abusers
Controls
n=4,474
n=4,474
74.5%
12.0%
45.5%
18.2%
Florida Medicaid
2003-2007c
Abusers
Controls
n=4,467
n=4,467
68.5%
23.2%
45.5%
12.4%
Psychiatric Disorders
Trauma
Non-Opioid Substance
50.4%
1.2%
45.1%
8.2%
46.6%
1.5%
59.7%
6.6%
Abuse
Non-Opioid Poisoning 17.6%
0.2%
NR
NR
17.1%
0.6%
23.1%
0.8%
Gastrointestinal
8.0%
2.6%
8.6%
6.3%
13.1%
4.5%
16.9%
4.6%
Bleeding
Skin Infections/
10.1%
2.5%
12.7%
5.4%
12.4%
4.0%
17.8%
4.1%
Abscesses
Sexually Transmitted
8.0%
4.3%
8.6%
7.6%
8.1%
4.0%
9.6%
5.6%
Disease
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
NR
NR
NR
NR
21.7%
3.2%
24.8%
3.3%
Pain
Arthritis
NR
NR
27.3%
19.5%
17.7%
5.1%
18.0%
3.9%
Other Back/Neck
NR
NR
27.9%
18.1%
14.5%
2.3%
9.8%
1.4%
Disorders
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.
b
McAdam-Marx et al (2010). Medicaid population based on data from the Medicaid Analytic eXtract (MAX) from the Centers for Medicare and Medicaid
Services (CMS).
c
White 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
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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
17,415
19,394
23,518
25,785
27,424
29,813
34,425
36,010
36,450
Overdose Deaths
Opioid-Related
4,030
5,528
7,456
8,517
9,817
10,928
13,723
14,408
14,800
Overdose Deaths (23.1%) (28.5%) (31.7%) (33.0%) (35.8%) (36.7%) (39.9%) (40.0%) (40.6%)
Methadone986
1,456
2,358
2,972
3,845
4,460
5,406
5,518
4,924
Related Opioid
(5.6%)
(7.5%) (10.0%) (11.5%) (14.0%) (14.0%) (15.0%) (15.3%) (13.5%)
Overdose Deaths
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
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Table 1-19. Prescription Opioid Overdose Death Rates by Selected Demographics, 2000-2008
Overall
Gender
Male
Female
1999a
1.6
2006a
4.6
2008b
4.8
2.1
1.1
5.8
3.3
5.9
3.7
5.8
2.0
2.7
NR
NR
6.3
2.1
1.9
0.5
6.2
0.1
3.8
6.7
8.3
9.7
4.0
0.9
0.1
3.7
7.1
8.3
10.4
5.0
1.0
Race/Ethnicity
Non-Hispanic White
1.6
Hispanic White
1.7
Non-Hispanic Black
0.9
Asian/Native Hawaiian/Pacific Islander
NR
Native American/Alaska Native
NR
Age Group
0-14
0.0
15-24
0.8
25-34
1.9
35-44
3.7
45-54
3.3
55-64
1.1
65 and older
0.3
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
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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 20042006 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 methadonerelated 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
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Table 1-20. Opioid-Related Mortality by State
Citation
Ballesteros,
2003
CDC, 2005
State
North
Carolina
Utah
Years
Studied
1997-2003
Medical examiner
All Residents
Methadone
1997-2003: 198 deaths
566%
1991-2003
Medical examiner
All Residents
Prescription Drugs
385%
Prescription Drugs
Prescription Opioids
Methadone
Hydrocodone
Oxycodone
Prescription Drugs
Prescription Opioids
Methadone
Hydrocodone
Oxycodone
Prescription Opioids
Methadone
Oxycodone
Hydrocodone
Prescription Opioids
1991-1998: 231 deaths
1999-2003: 502 deaths
1991-1998: 18 deaths
1999-2003: 164 deaths
1991-1998: 10 deaths
1999-2003: 111
1991-1998: 31
1999-2003: 83
295 deaths
275 deaths
112 deaths
67 deaths
61 deaths
893 deaths
658 deaths
184 deaths
134 deaths
129 deaths
1,668 deaths
1,068 deaths
382 deaths
232 deaths
724 deaths
Prescription Opioids
34 deaths
NR
All Drugs
1,544 deaths
NR
Prescription Opioids
694 deaths
NR
Data Source
Population
Drug Category
Methadone
Oxycodone
Hydrocodone
Hall, 2008
Wunsch, 2009
CDC, 2009
West Virginia
Virginia
Washington
2006
1997-2003
2004-2007
Medical
examiner; PDMP;
Treatment
Records
All Residents
Medical examiner
Residents of
Western VA
Death certificate
All Residents
Medicaid
Enrollees
PRR Enrollees
Ohio, 2010
Ohio
2010
Death Certificate
All residents
Number of Deaths
% Increase
1,358%
1,676%
328%
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction Program; NR = Not reported in this study
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Table 1-20. Opioid-Related Mortality by State, Continued
Citation
State
Piercefield,
2010
Oklahoma
Years
Studied
1996-2002
Data Source
Medical
Examiner
Population
All Residents
Drug Category
Methadone
Oxycodone
Hydrocodone
Shah, 2011
New Mexico
1990-2005
Number of Deaths
1994-1996: 21 deaths
2004-2006: 377 deaths
1994-1996: 1 death
2004-2006: 174 deaths
1994-1996: 9 deaths
2004-2006: 220 deaths
955 deaths
Medical
All Residents
Prescription Opioids
Examiner
PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction Program; NR = Not reported in this study
% Increase
1,695%
17,300%
2,344%
159%
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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 019mgMED/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
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Figure 1-6. Relationship between Opioid Dosage Level and Fatal/Non-Fatal Overdose Risk
12.00
Overdose Risk
10.00
8.00
6.00
4.00
2.00
Opioid Dosage Level (mg morphine equivelant per day)
0.00
Dunn (i)
Dunn (ii)
Gomes (iii)*
Bohnert (iv)
Bohnert (v)
Bohnert (vi)
1-19mgMED/d
1.00
1.00
1.00
1.00
1.00
1.00
20-49mgMED/d
1.44
1.19
1.32
1.88
1.58
1.42
50-99mgMED/d
3.73
3.11
1.92
4.63
4.73
2.76
>100mgMED/d
8.87
11.18
2.04
7.18
6.64
4.54
>200mgMED/d
2.88
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
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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 opioidrelated 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
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Figure 1-7. Percentage of Opioid Users and Overdoses, by Risk
Group
100%
90%
20%
80%
70%
60%
80%
40%
50%
40%
30%
20%
10%
0%
40%
10%
10%
Opioid Users
Opioid-Related
Overdoses
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
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 (20032007) (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 20032007
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Figure 1-8. Average Annual Direct Health Care Costs* per Opioid Abuse Patient
$30,000
Privately-Insured Sample,
2003-2007
Privately-Insured Sample,
1998-2002
$24,193
$25,000
$26,724
Florida Medicaid Sample,
2003-2007
$3,667
$4,769
$5,820
$20,000
Excess Cost: $15,183
$3,918
$15,884
$15,000
$2,826
$793
$2,034
Excess Cost: $14,054
$10,000
$5,795
Excess Cost: $20,546
$11,541
$5,398
$6,466
$14,410
$5,000
$9,711
$7,659
$386
$318
$1,830
$323
$198
$928
$0
Opioid Abuse Patients
Controls
Hospital Inpatient Costs
Opioid Abuse Patients
Physician/Outpatient Costs
$3,647
$3,104
$877
$1,697
$657
$750
$1,314
Controls
Drug Costs
Opioid Abuse Patients
Controls
Other Costs**
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
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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
Total Prescription
6-10
11-15 16-20
21-50
51+
Total
Cost
777
41
6
1
0
825
$7,810,000
Fentanyl
31,364 3,518 723
340
9
35,954
$9,172,000
Hydrocodone
590
67
14
11
0
682
$983,000
Hydromorphone
824
76
9
2
0
911
$546,000
Methadone
810
50
8
1
0
869
$4,119,000
Morphine
5,349
435
73
18
0
5,875
$10,163,000
Oxycodone
39,714
4,187
833
373
9
45,146
$32,793,000
Total Prescription Opioids
64,239
5,066
926
396
9
70,636
$63,280,000
Total Controlled Substances*
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 nonbenzodiazepine sleep aids
Table 1-22. Prescription Opioid Doctor Shopping among Medicare Beneficiaries and Associated
Costs, 2008
Number of Prescribers in Selected States
Total Prescription
5-10
11-15 16-20
21-50
51+
Total
Cost
21
4
0
0
1,525
$244,930
Codeine with Acetaminophen 1,500
5,043
24
8
2
0
5,077
$19,124,853
Fentanyl
92,801
3,553 700
335
5
97,394
$18,949,677
Hydrocodone
2,453
77
13
8
0
2,551
$1,236,678
Hydromorphone
149
8
0
0
0
157
$90,236
Meperidine
3,414
9
0
0
0
3,423
$859,208
Methadone
6,354
33
4
0
0
6,391
$9,311,773
Morphine
54,183
1,974 440
235
5
56,837
$91,681,281
Oxycodone
4,364
134
33
14
0
4,527
$1,037,423
Tramadol
170,261 5,833 1,202
594
10
177,882 $141,498,636
Total Prescription Opioids
600
10
189,574 $147,948,251
Total Controlled Substances* 181,823 5,927 1,214
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 shortacting 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
Total Societal Cost
Lost Workplace Productivity Costs
2001
Costa
Percentageb
$8,584
100%
$4,545
53.0%
2007
Costa
Percentageb
$55,721
100%
$25,582
45.9%
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2001
Costa
Percentageb
Premature Death
$864
10.1%
Lost Wages/Employment
$3,023
35.2%
Presenteeism
NR
NR
Excess Medically Related Absenteeism
NR
NR
Incarceration Costs
$657.5
7.6%
Excess Disability Costs
NR
NR
Healthcare Costs
$2,607
30.4%
Excess Medical and Drug Costs
$2,481
28.9%
Substance Abuse Treatment
$126
1.4%
Substance Abuse Prevention
NR
NR
Substance Abuse Research
NR
NR
Criminal Justice Costs
$1,430
16.7%
Correctional Facilities
$771
9.0%
Police Protection
$438
5.1%
Legal Adjudication Costs
$221
2.6%
Property Loss due to Crime
NR
NR
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
2007
Costa
Percentageb
$11,218
20.1%
$7,931
14.2%
$2,044
3.7%
$1,814
3.3%
$1,768
3.2%
$807
1.4%
$24,998
44.9%
$23,725
42.6%
$1,119
2.0%
$85
0.2%
$69
0.1%
5,142
9.2%
$2,265
4.1%
$1,526
2.7%
$726
1.3%
$625
1.1%
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
Total
Substance Abuse
Hospital Inpatient
Hospital Outpatient
Physicians
Substance Abuse Facilities
Medical Complications
HIV/AIDS
Chronic Hepatitis C
Oxycodone
$13,276.28
$681.42
(5.1%)
$126.20
$99.85
$111.31
$344.07
$192.52
(1.5%)
$122.12
$69.21
Hydrocodone
$12,732.83
$528.18
(14.1%)
$97.82
$77.39
$86.28
$266.69
$1.40
(0.01%)
NR
NR
Methadone
$6,178.70
$98.50
(1.6%)
$18.24
$14.43
$16.09
$49.74
$30.21
(0.5%)
$19.22
$10.90
Other
$21,032.69
$911.83
(4.3%)
$168.86
$133.59
$148.93
$460.34
$527.90
(2.5%)
$335.79
$190.43
Total
$53,221.50
$2,219.82
(4.2%)
$411.11
$325.26
$362.61
$112.83
$752.04
(1.4%)
$477.14
$270.45
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Neonatal Care
Lost Productivity
Premature Death
Unemployment
Incarceration
Criminal Justice
Oxycodone
$1.19
$10,437.70
(78.6%)
$2,604.26
$4,273.91
$3,559.53
$1,964.64
(14.8%)
$813.81
$406.91
$596.04
$147.89
Hydrocodone
$1.40
$9,811.13
(77.1%)
$2,018.60
$3,458.50
$4,334.03
$2,392.12
(18.8%)
$990.88
$495.45
$725.73
$180.07
Methadone
$0.09
$5,820.45
(94.2%)
$4,614.78
$789.80
$415.87
$229.53
(3.7%)
$95.08
$47.54
$69.64
$17.28
Other
$1.77
$15,959.97
(75.9%)
$3,168.06
$6,209.43
$6,582.46
$3,633.12
(17.3%)
$1,504.94
$752.47
$1,102.22
$273.48
Total
$4.45
$42,029.23
(79.0%)
$12,405.70
$14,731.64
$14,891.89
$8,219.41
(15.4%)
$3,404.70
$1,702.37
$2,493.63
$618.71
Police
Legal Costs
Incarceration
Cost to Crime Victims
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 opioidrelated 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
PMDPs
X
X
X
X
X
X
PRR
Policy Interventions
Laws/Regulations/Policies
Insurers/PBM
X
X
X
X
X
X
X
X
X
X
X
Clinical Guidelines
Pill Mills*
Problem Prescribing
x
General Prescribing
X
EDs & Hospitals
X
Pharmacies
X
X
Insurers & Pharmacy
X
Benefit Managers
High Risk Patients
X
X
X
X
X
General Patients &
X
X
X
Public
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
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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 2005December 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 lockin 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
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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
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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
States without PDMPs
States with PDMPs
States with Proactive PDMPs
States with High-Reporting PDMP
California, New York & Texas
14
All Prescription
Overdose Mortality
Ratea
6.46
7.45
7.64
11.41
5.36
Opioid Overdose
Mortality Rate
MME/Person/Year
2.20
3.13
3.30
6.57
1.65
341.67
362.43
365.67
540.75
251.19
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.
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Taken from: Paulozzi, Kilbourne & Desai (2011). Prescription Drug Monitoring Programs and Death Rates from
Drug Overdose
a
Rates 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
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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 prescriptiondrug 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 upto-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 opioidrelated outcomes.
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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.
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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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Comprehensive Program Integrity Review Final Report. November 2008.
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Simeone R, Holland L. An Evaluation of Prescription Drug Monitoring Programs:
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Review of Implementation of Existing State Controlled Substance Monitoring Programs:
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trends in opioid abuse/misuse? Pain medicine (Malden, Mass.). Mar 2012;13(3):434-442.
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rates from drug overdose. Pain medicine (Malden, Mass.). May 2011;12(5):747-754.
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programs-lack of effectiveness or a call to action? Pain medicine (Malden, Mass.). May
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"Prescription drug monitoring programs and death rates from drug overdose". Pain
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Approaches to Drug Overdose Prevention
Analytical Tool (ADOPT):
Evaluating Cost and Health Impacts of a
Medicaid Patient Review & Restriction
Program
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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
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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
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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, 210239 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
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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.
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STATISTICAL ANALYSIS
Regression Models
We constructed two regression models -- the 90-day exposure window model and the episodebased 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
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Figure 2-1. Regression Model Schematic: Prescriptions, Exposure Windows, and Overdose Events in Models 1 and 2
Subject 1’s prescription history
Subject 1 in model 1
Subject 1 in model 2
30 days
Overdose event
Supply of opioid prescription
Exposure window
Censored
End of episode of opioid use
Subject 2’s prescription history
Subject 2 in model 1
Subject 2 in model 2
Subject 3’s prescription history
Subject 3 in model 1
Subject 3 in model 2
Subject 4’s prescription history
Subject 4 in model 1
Subject 4 in model 2
Fill date of first opioid prescription
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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
4. Obtaining prescriptions from 4 or more pharmacies over a 180-day period
5. Obtaining prescriptions from 3 or more pharmacies over a 90-day period
6. Obtaining prescriptions from 4 or more pharmacies over a 90-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
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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
Mean Months Enrolled
Female
Age
12 – 17
18 – 29
30- 44
45 and over
Race
White
Total
(n=427,411)
28.9
69.2
Long Term Users
(n=90,010)
31.3
70.3
31.7
26.0
20.9
21.4
1.9
14.6
32.0
51.5
<0.001
54.4
67.7
<0.001
P Value
<0.001
<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.
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Total
(n=427,411)
Black
37.1
Hispanic
1.9
Other
6.6
5.0
Depression diagnosis,%
1.2
Alcohol abuse, %
Note: Values are expressed as percentages
Long Term Users
(n=90,010)
24.7
1.0
6.6
10.6
3.0
P Value
<0.001
<0.001
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
Schedule III and IV
Hydrocodone + aspirin/acetaminophen/ibuprofen
Tramadol with or without aspirin
Propoxyphene (with or without
aspirin/acetaminophen/ibuprofen)
Codeine + aspirin/acetaminophen/ibuprofen
Butalbital + codeine (with or without aspirin/acetaminophen/
ibuprofen)
Butorphanol
Pentazocine (with or without aspirin/acetaminophen/ibuprofen)
Schedule II Short-Acting*
Oxycodone (with or without aspirin/acetaminophen/ibuprofen)
Hydromorphone
Fentanyl citrate transmucosal
Morphine sulfate
Codeine Sulfate
Meperidine hydrochloride
Tapentadol
Schedule II Long-Acting*
Oxycodone HCL control release
Morphine sulfate sustained release
Fentanyl transdermal
All use
n
Long-Term Use
%
n
%
1,915,685
508,837
45.7
12.1
1,381,964
378,780
44.6
12.2
274,098
6.5
173,954
5.6
216,409
5.2
80,546
2.6
14,592
0.3
12,755
0.4
6,078
4,394
0.1
0.1
5,851
3,924
0.2
0.1
635,192
43,240
32,052
27,197
17,820
13,984
2,109
15.1
1.0
0.8
0.6
0.4
0.3
0.1
461,802
9,413
14,393
24,501
2,830
79,567
1,929
14.9
0.3
0.5
0.8
0.1
2.6
0.1
232,448
106,442
67,237
5.5
2.5
1.6
220,809
105,429
66,650
7.1
3.4
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
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Prescription Opioid Type
Methadone
Oxymorphone extended release
Dihydrocodeine
Levorphanol tartrate
Total
All use
n
70,507
3,576
1,678
31
4,193,606
Long-Term Use
%
1.7
0.1
0.0
0.0
100.0
n
69,842
3,562
1,447
31
3,320,489
%
2.3
0.1
0.0
0.0
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
Hydrocodone
Tramadol
Oxycodone
Propoxyphene
Oxycodone hydrochloride
Codeine
+aspirin/acetaminophen/ibuprofen
Morphine sulfate sustained release
Meperidine
Other drug type
Number of Long-Term Episodes
65,399
18,151
16,557
8,098
5,317
%
51.6
14.3
13.1
6.4
4.2
3,201
2,943
2,168
4,967
2.5
2.3
1.7
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
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100
90
80
70
60
50
40
30
20
10
0
Percentage, %
>30 d
16-30 d
8-15 d
1-7 d
Note: All
drug types are shown
except thoseEvents
with less than 10,000 prescribed.
Characteristics
of Overdose
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
Included
Definite case
Probable case
Excluded
Suicidal case
Undetermined case
Total
n
%
1,735
3
90.9
0.2
58
112
1,908
3.0
5.9
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
Event type
Hospitalization, with ED visit
Hospitalization, without ED visit
Overdose
n
%
627
36.1
149
8.6
Mean
$12,371
$5,797
Cost
Median
$5,506
$3,241
Interquartile Range
$2658, $13415
$1257, $5479
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ED visit only
Outpatient visit only
Overall
878
84
1738
50.5
4.8
100
$514
$162
$5,376
$315
$149
$2,879
$132, $663
$67, $269
$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
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Table 2-6. Unadjusted Overdose Rates in MarketScan® Medicaid Dataset by Demographic and Clinical Characteristics, 2008-2010
Hospitalization
Overdoses
ED
Outpatient
Total
Person Years
786
848
78
1,738
783,132
12-17 y
62
146
17
225
215,078
18-29 y
124
208
16
348
205,464
30-44 y
259
289
29
577
177,147
45-64 y
Sex
341
218
29
588
185,443
Male
261
268
27
556
227,922
Female
525
Race/Ethnicity
593
64
1,182
555,210
White
635
640
65
1,340
440,992
Black
95
166
21
282
278,918
Hispanic
9
13
0
22
12,890
Other
47
42
History of Depression Diagnosis
5
94
50,332
No
570
55
1,023
738,701
Yes
388
History of Alcohol Abuse
291
36
715
44,431
No
679
753
76
1,508
771,820
Yes
107
108
15
230
11,312
Total
Overdose Rate (95% CI)
Hospitalization
ED
Outpatient
1.00
1.10
0.12
(0.93-1.08)
(1.03-1.18)
(0.09-0.14)
Total
2.22
(2.12-2.33)
Age
398
0.29
(0.22-0.37)
0.60
(0.50-0.72)
1.49
(1.29-1.65)
1.84
(1.65-2.04)
0.68
(0.57-0.80)
1.01
(0.88-1.16)
1.63
(1.45-1.83)
1.18
(1.02-1.34)
0.08
(0.05-0.13)
0.08
(0.04-0.13)
0.16
(0.11-0.24)
0.16
(0.10-0.22)
1.05
(0.91-1.19)
1.69
(1.52-1.88)
3.26
(3.00-3.53)
3.17
(2.92-3.44)
1.15
(1.01-1.29)
0.95
(0.87-1.03)
1.18
(1.04-1.33)
1.07
(0.98-1.16)
0.12
(0.08-0.17)
0.12
(0.09-0.15)
2.44
(2.24-2.65)
2.13
(2.01-2.25)
1.44
(1.33-1.56)
0.34
(0.28-0.42)
0.70
(0.32-1.33)
0.93
(0.69-1.24)
1.45
(1.34-1.57)
0.60
(0.51-0.69)
1.01
(0.54-1.72)
0.83
(0.60-1.13)
0.15
(0.11-0.19)
0.08
(0.05-0.12)
0.00
(0.00-0.29)
0.10
(0.03-0.23)
3.04
(2.88-3.21)
1.01
(0.90-1.14)
1.71
(1.07-2.58)
1.87
(1.51-2.29)
0.54
(0.49-0.59)
8.73
(7.89-9.65)
0.77
(0.71-0.84)
6.55
(5.82-7.35)
0.07
(0.06-0.10)
0.81
(0.57-1.12)
1.38
(1.30-1.47)
16.09
(14.93-17.32)
0.88
(0.81-0.95)
9.46
(7.75-11.43)
0.98
(0.91-1.05)
9.95
(7.83-11.53)
0.10
(0.08-0.12)
1.33
(0.74-2.19)
1.95
(1.86-2.06)
20.33
(17.79-23.14)
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Overdoses
Hospitalization ED
Outpatient
History of Long-Term Opioid Use
Total
Person Years
No
545
573,548
177
333
35
Yes
609
528
56
1,193
Note: Rates are unadjusted and expressed per 1,000 person-years
209,584
Overdose Rate (95% CI)
Hospitalization
ED
Outpatient
0.31
0.26-0.36)
2.91
(2.68-3.15)
0.58
(0.52-0.65)
2.52
(2.31-2.74)
0.06
(0.04-0.08)
0.27
(0.20-0.35)
Total
0.95
(0.87-1.03)
5.69
(5.37-6.02)
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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
During A Long-Term Use Episode
During A Short-Term Use Episode
Outside of Any Episode
Total
n
1,153
248
337
1,738
%
66.3
14.3
19.4
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
PersonYears
Overdose Rate
Adjusted Hazard Ratios (95% CI)
Episode-Based
90-Day Exposure
Model
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
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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.
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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.
35,000
6%
30,000
5%
25,000
4%
20,000
3%
15,000
2%
10,000
5,000
1%
0
0%
1
2
3
4
Peak Number of Pharmacies Visited
# of pharmacies in 1 year setting
# of pharmacies in 90-day setting
% of overdose for 180-day setting
Percentage of Overdose
(indicated in dots)
Number of Long-term Users
(indicated in bars)
Figure 2-3.Overdoses among Long-Term Users by Peak Number of Pharmacies Visited
≥5
# of pharmacies in 180-day setting
% of overdose for 1-year setting
% of overdose for 90-day setting
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
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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
1-year setting,
≥ 3 pharmacies
1-year setting,
≥ 4 pharmacies
180-day setting,
≥ 3 pharmacies
180-day setting,
≥ 4 pharmacies
90-day setting,
≥ 3 pharmacies
90-day setting,
≥ 4 pharmacies
Eligible
Recipients
Overdose
Events
% Overdose among
Eligible Recipients
% of Total
Overdose
DOR
44,266
825
1.86%
69.86%
2.38
29,175
620
2.13%
52.50%
2.30
38,157
818
2.14%
69.26%
3.05
21,351
599
2.81%
50.72%
2.73
28,730
788
2.74%
66.72%
4.28
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
Demographics
Number
Mean age
No shopping,
No overlapping RX
No shopping,
Overlapping RX
Shopping,
No overlapping RX
Shopping,
Overlapping RX
73,042
44.1
3,885
45.6
7,059
38.0
6,024
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.
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Male
Depression diagnosis
History of alcohol
abuse
Overdose Events
Number
Percentage
Overdose Incidence
Total person-years
No shopping,
No overlapping RX
28.2%
9.0%
No shopping,
Overlapping RX
32.1%
11.4%
Shopping,
No overlapping RX
26.8%
16.8%
Shopping,
Overlapping RX
31.2%
18.0%
2.4%
2.8%
5.1%
5.6%
473
0.64%
165
4.25%
188
2.66%
367
6.09%
108,042
438
(401-480)
8,911
1,851
(1,580-2,157)
17,539
1,072
(924-1,237)
13,934
2,634
(2,371-2,918)
1.42
(1.40-1.44)
40.2
(40.0-40.4)
2.83
(2.68-2.98)
100.7
(97.0-104.4)
1.84
(1.76-1.92)
53.9
(52.3-55.4)
2.77
(2.67-2.87)
89.2
(87.1-91.4)
35.4%
44.5%
13.6%
6.5%
14.5%
36.7%
21.3%
27.6%
23.4%
48.6%
17.6%
10.4%
15.2%
38.4%
22.1%
24.4%
82.2%
64.9%
73.6%
65.0%
15.1%
23.4%
21.1%
24.6%
2.7%
11.7%
5.3%
10.4%
Incidence rate*
Opioid Prescriptions
Monthly prescriptions
Average dose
Dose level distribution
0-20mg
20-50mg
50-100mg
100mg or higher
Predominant opioid drug type
Schedule III /IV
Schedule II,
Short Acting
Schedule II,
Long Acting
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
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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-overlappingprescription 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
Opioid dose
1 to <20mg/d
20 to <50mg/d
50 to 100 mg/d
>=100mg/d
Female
Male
1
1.61
3.06
4.02
95% CI
P value
1.24
2.33
3.07
Gender
2.08
4.02
5.26
0.0004
<.0001
<.0001
0.87
1.18
0.8444
0.03
1.53
0.1235
0.74
0.70
Race/Ethnicity
1.19
1.11
0.593
0.2875
1.00
1.02
Age
12-17
18-29
30-44
45 and over
0.21
1.00
0.94
0.88
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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
2.54
1.99
3.23
<.0001
Concurrent sedative/hypnotic use
3.07
2.09
4.50
<.0001
History of alcohol abuse
2.91
2.21
3.83
<.0001
History of depression diagnosis
1.80
1.54
2.10
<.0001
Pharmacy Shoppinga
2.96
2.45
3.68
<.0001
Overlapping Prescriptionsb
a
Pharmacy shopping is defined as having 4 or more unique pharmacies visited within any 90 consecutive days.
b
Overlapping 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.
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APPENDIX
Morphine Equivalent Dose Conversions
Opioid Type
Milligrams Morphine Equivalent
Schedule III and IV
Propoxyphene (with or without
0.23
aspirin/acetaminophen/ibuprofen)
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/
0.15
ibuprofen)
Dihydrocodeine (with or without
0.25
aspirin/acetaminophen/ibuprofen)
Pentazocine (with or without aspirin/acetaminophen/ibuprofen)
0.37
a
25.0-40.0
Buprenorphine
7.00
Butorphanol
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.
a
Buprenorphine is typically used for opioid detoxification and maintenance 16
b
Transmucosal 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.
c
Data for oxymorphone, hydromorphone and tapentadol obtained from FDA Blueprint for Prescriber Education for
Extended-Release and Long-Acting Opioid Analgesics
d
Transdermal 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.
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ICD-9 Codes Indicating Overdose-Related Symptoms
276.4
292.1
292.81
292.8
486
496
Mixed acid–base balance disorder
Drug-induced psychotic disorders (including 292.11 and 292.12)
Drug-induced delirium
Drug-induced mental disorder (excluding 292.81)
Pneumonia, organism unspecified
Chronic airway obstruction, not elsewhere classified
518.81
518.82
780.0
780.97
786.03
786.05
Acute respiratory failure
Other pulmonary insufficiency, not elsewhere classified
Alteration of consciousness
Altered mental state
Apnea
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.
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BIBLIOGRAPHY
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3.
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Katz N, Panas L, Kim M, et al. Usefulness of prescription monitoring programs for
surveillance--analysis of Schedule II opioid prescription data in Massachusetts, 19962006. Pharmacoepidemiology and drug safety. Feb 2010;19(2):115-123.
Parente ST, Kim SS, Finch MD, et al. Identifying controlled substance patterns of
utilization requiring evaluation using administrative claims data. The American journal of
managed care. Nov 2004;10(11 Pt 1):783-790.
White AG, Birnbaum HG, Schiller M, Tang J, Katz NP. Analytic models to identify
patients at risk for prescription opioid abuse. The American journal of managed care.
Dec 2009;15(12):897-906.
Wilsey BL, Fishman SM, Gilson AM, et al. Profiling multiple provider prescribing of
opioids, benzodiazepines, stimulants, and anorectics. Drug and alcohol dependence. Nov
1 2010;112(1-2):99-106.
Peirce GL, Smith MJ, Abate MA, Halverson J. Doctor and pharmacy shopping for
controlled substances. Medical care. Jun 2012;50(6):494-500.
Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Trends in
use of opioids for non-cancer pain conditions 2000-2005 in commercial and Medicaid
insurance plans: the TROUP study. Pain. Aug 31 2008;138(2):440-449.
PMCID:PMC2668925
Dunn KM, Saunders KW, Rutter CM, et al. Opioid prescriptions for chronic pain and
overdose: a cohort study. Annals of internal medicine. Jan 19 2010;152(2):85-92.
PMCID:PMC3000551
Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing
patterns and opioid overdose-related deaths. JAMA : the journal of the American Medical
Association. Apr 6 2011;305(13):1315-1321.
Gomes T, Mamdani MM, Dhalla IA, Paterson JM, Juurlink DN. Opioid dose and drugrelated mortality in patients with nonmalignant pain. Archives of internal medicine. Apr
11 2011;171(7):686-691.
Von Korff M, Saunders K, Thomas Ray G, et al. De facto long-term opioid therapy for
noncancer pain. The Clinical journal of pain. Jul-Aug 2008;24(6):521-527.
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single indicator of test performance. Journal of clinical epidemiology. Nov
2003;56(11):1129-1135.
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
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.
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16.
Sporer KA. Buprenorphine: A primer for emergency physicians. Annals of emergency
medicine. 2004;43(5):580-584.
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2.27
Approaches to Drug Overdose Prevention
Analytical Tool (ADOPT):
Evaluating Cost and Health Impacts of a
Medicaid Patient Review & Restriction
Program
Part 3
The ADOPT Model: An Evidence-Based Tool for Promoting Health Policy
and Disease Prevention - Prescription Opioid Overdose
3.1
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
3.2
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
3.3
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 statespecific 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
Simulate
Individual prescription
history, without policy
intervention
project
Apply the policy to
simulated cohort
Opioid and medical costs,
health outcomes , without
policy intervention
Compare
Individual prescription
history, with policy
intervention
project
Incremental cost and
health outcome of the
policy intervention
Opioid and medical costs,
health outcomes , with
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 nonmodifiable 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.
3.4
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
Demographics
Age
Gender
Race
Prevalence of overdose-related risk factors *
Prescription Behavior
Episode Length
Most Frequently Used Opioid During an Episode
Opioid Type per Prescription
Drug Strength
Days of Supply
Master Form (tablet, solution, elixir)
Generic Drug Type
Pharmacy Information
Prescriber Information
Source
User Modifiable?
MarketScan® Medicaid
Data
Yes
Yes
Yes
Yes
MarketScan® Medicaid
Data: long-term opioid
users only
No
No
No
No
No
No
No
No
Previous study using
Massachusetts’
prescription drug
monitoring database1
No
Overdose
Hazard Ratios
Yes
MarketScan® Medicaid
Data
Encounter Type (inpatient/ED/outpatient)
No
Cost
Prescription Reimbursement Rates
Yes
MarketScan® Medicaid
Data
Overdose-related Medical Cost
No
Program Cost
Informed Assumption †
Yes
PRR Program Eligibility Criteria
Timeframe for Prescription History Review
Yes
Threshold Number of Prescribers
Yes
PRR Program
Threshold Number of Pharmacies
Government Document
Yes
from Multiple States
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
3.5
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, 210239 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
3.6
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 diseasefocused 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,
3.7
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
3.8
Figure 3-2. Overview of the Simulation Process
Simulate individual
profile (step 1)
-age
-gender
-race
-risk factors
Simulate information about initial
episode of opioid use
-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
subsequent episodes (step 6)
- opioid drug use in subsequent
episodes is correlated with
opioid drug use in the previous
episode
Simulate opioid overdose events( step
15)
Check subject’s eligibility for PRR
program (step 16)
Summarize RX history (step 14)
Simulate prescriptions obtained with
in an episode
-daily dose level
and
In a specified length of time
-number of pharmacies
-number of prescribers
-number of RX
-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)
Repeat the process for subsequent
episode(s) (step 12)
Assign absolute dates to all RX (step
13)
Repeat the process for the
entire cohort (step 17)
3.9
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.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
Simulate the basic individual profile
Simulate the predominant drug type
Simulate the episode duration
Simulate the use of concurrent opioid use
Simulate the use of overlapping prescriptions
Simulate the subsequent episodes of opioid use
Simulate the drug type for each prescription in an episode of use
Simulate the prescription details (generic name, strength, master form, quantity, supply
days, dose level , drug price)
Assign relative prescription dates to each prescription
Assign pharmacy IDs to each prescription
Assign prescriber IDs to each prescription
Simulate subsequent episodes of opioid use
Assign absolute dispensing date to each prescription and eliminate irrelevant
prescriptions
Calculate the number of prescriptions/pharmacies/prescribers and the dose level
Calculate the risk of overdose, overdose event type, and overdose-related medical costs
Check the individual eligibility for the PRR program
Summarize the cost and health outcomes of the simulated cohort
3.10
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, %
Female Age Distribution, %
12 - 17
18 - 29
30 - 44
45 and older
Male Age Distribution, %
12 - 17
18 - 29
70.3
Race, %
White
Black
Hispanic
Other
Sedative/Hypnotic drug use, %
Depression diagnosis,%
Alcohol abuse, %
67.7
24.7
1.0
6.6
18.9
10.6
3.0
‡
1.8
15.3
33.2
49.7
2.8
14.3
For more information about our analysis of the MarketScan® data, please refer to Part 2 of this report.
3.11
Population Characteristic
30 - 44
45 and older
%
Population Characteristic
%
27.4
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
Schedule III and IV
Propoxyphene (with or without aspirin/acetaminophen/ibuprofen)
Codeine + aspirin/acetaminophen/ibuprofen
Hydrocodone + aspirin/acetaminophen/ibuprofen
Butalbital + codeine (with or without aspirin/acetaminophen/
ibuprofen)
Butorphanol
Pentazocine (with or without aspirin/acetaminophen/ibuprofen)
Tramadol with or without aspirin
Short-acting, Schedule II
Morphine sulfate
Codeine Sulfate
Oxycodone (with or without aspirin/acetaminophen/ibuprofen)
Tapentadol
Hydromorphone
Meperidine hydrochloride
Fentanyl citrate transmucosal
Long-acting, Schedule II
Morphine sulfate sustained release
Fentanyl transdermal
MarketScan® Long-Term
Users
Prescription
Total
Rate/10,000
(n=90,010)
Patients
Simulated
Cohort
Prescription
Rate/10,000
Patients
75,632
38,355
628,165
5,315
8,403
4,261
69,788
590
11,577
8,387
65,185
507
2,926
2,065
172,173
325
229
19,128
169
423
22,579
9,800
1,048
243,054
772
4,279
33,153
7,381
1,089
116
27,003
86
475
3,683
820
1,499
175
24,146
94
1,457
1,614
1,037
52,715
26,660
5,857
2,962
2,344
1,710
3.12
Prescription Opioid Type
Oxycodone HCL control release
Methadone
Oxymorphone extended release
Dihydrocodeine
Levorphanol tartrate
Total
MarketScan® Long-Term
Users
Prescription
Total
Rate/10,000
(n=90,010)
Patients
92,004
10,222
25,867
2,874
1,549
172
579
64
13
1
3,320,489
158,149
Simulated
Cohort
Prescription
Rate/10,000
Patients
7,309
1,279
79
117
6
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® LongTerm Users
Days’ Supply
>3 d
4-7 d
8-15 d
16-29 d
30 d
<30 d
Dose level*
10 MG
15 MG
20 MG
30 MG
40 MG
50 MG
60 MG
70 MG
80 MG
83.33 MG
90 MG
100 MG
120 MG
160 MG
Generic Drug Type
Methadone Hydrochloride SOL 10 MG/ML
Methadone Hydrochloride SOL 5 MG/5 ML
Methadone Hydrochloride TAB 10 MG
Simulated Cohort
0.2%
5.0%
12.8%
12.6%
68.6%
0.8%
0.3%
6.6%
13.5%
14.6%
65.0%
0.0%
4.2%
3.6%
11.4%
17.6%
18.0%
3.9%
14.3%
1.3%
8.6%
1.7%
3.8%
2.9%
6.9%
1.9%
4.1%
5.7%
8.6%
19.2%
19.6%
4.3%
11.8%
0.6%
10.7%
2.1%
1.9%
3.7%
5.6%
2.1%
0.3%
0.3%
89.6%
0.7%
1.2%
86.6%
3.13
MarketScan® LongTerm Users
Methadone Hydrochloride TAB 40 MG
0.4%
Methadone Hydrochloride TAB 5 MG
9.5%
* Daily dose was not converted to milligrams morphine equivalent
Simulated Cohort
0.7%
10.8%
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
30.1
MarketScan®
Long-Term Users
32.2
2
28.1
33.1
35.8
36.1
3
4
18.3
10.5
14.2
9.6
17.3
8.1
14.4
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
Number of
Pharmacies Visited
1
®
MarketScan
Long-Term Users
22.8
Simulated Cohort
Simulated Cohort
39.2
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
MarketScan®
Simulated Cohort
Long-Term Users
Demographics
n
80,101 (89.0%)
9,126 (91.26%)
Pharmacy Shoppers
MarketScan®
Simulated Cohort
Long-Term Users
9,909 (11.0%)
874 (8.74%)
3.14
Non-Shoppers
MarketScan®
Simulated Cohort
Long-Term Users
43.8
43.3
27.70%
28.90%
9.40%
11.20%
2.52%
3.06%
Pharmacy Shoppers
MarketScan®
Simulated Cohort
Long-Term Users
43.41
43.6
31.55%
30.98%
15.40%
12.20%
4.50%
3.33%
Mean age
Male
Depression diagnosis
History of alcohol abuse
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 (13) 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.
3.15
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
3.16
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
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
20021221091
Service
Date
6/16/2008
6/16/2008
6/25/2008
6/30/2008
7/21/2008
8/4/2008
8/18/2008
8/26/2008
9/19/2008
10/3/2008
10/16/2008
11/14/2008
12/8/2008
12/30/2008
1/26/2009
2/23/2009
3/20/2009
4/16/2009
5/15/2009
6/13/2009
7/13/2009
8/17/2009
9/10/2009
10/3/2009
10/25/2009
11/18/2009
12/15/2009
Days’
Supply
1
1
5
30
7
6
6
30
30
15
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
Quantity
5
1
30
120
30
30
30
120
120
90
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
120
Generic Drug Name
Acetaminophen/Hydrocodone Bitartrate
Meperidine Hydrochloride
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Oxycodone Hydrochloride
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
ASA/Oxycodone HCl/Oxycodone Terephthalate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Acetaminophen/Hydrocodone Bitartrate
Strength
325 MG-7.5 MG
50 MG/ML
325 MG-7.5 MG
325 MG-10 MG
325 MG-7.5 MG
325 MG-7.5 MG
325 MG-7.5 MG
500 MG-10 MG
500 MG-10 MG
325 MG-4.5 MG-0.38 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
500 MG-10 MG
Pharmacy ID
tbTeQFWaheTq
tbTeQFWaheTq
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
tbteTFdaUiTu
tbteTFdaUiTu
tbteTFdaUiTu
abteTFda2NTi
tbteTFdaUiTu
tbteTFdaUiTu
tbteTFdaUiTu
tbteTFdaUiTu
tbteTFdaUiTu
tbteTFdaUiTu
tbteTFdaUiTu
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
abteTFda2NTi
3.17
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 per10-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 $83.55, respectively. This classification process allowed us to reduce the number of
reimbursement rates that users need to specify to 51 (versus 156), as shown in Table 3-9.
Table 3-9. Price Comparison of Commonly Prescribed Opioids
Drug Type and Form *
Schedule III and IV
Propoxyphene
Codeine
Elixir
SOL
TAB
Hydrocodone
With Acetaminophen
Elixir
SOL
TAB
With Ibuprofen, TAB
Butalbital and codeine, CAP
Butorphanol, SOL
Pentazocine, TAB
Price per 10 units ($)†
3.62
0.49
0.78
3.64
0.74
2.97
3.06
7.96
8.47
172.05
9.16
3.18
Drug Type and Form *
Price per 10 units ($)†
Tramadol
With Acetaminophen, TAB
6.93
TAB
1.98
TER, 100MG
27.85
Schedule II Short-Acting
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
Schedule II Long-Acting
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).
3.19
Table 3-10. Price Comparison of Commonly Prescribed Opioids, Price per 50 MME
Drug Category
Schedule III and IV
Propoxyphene
Codeine
Elixir
SOL
TAB
Hydrocodone
w/ Acetaminophen
Elixir
SOL
TAB
w/Ibuprofen, TAB
Butalbital and codeine, CAP
Butorphanol, SOL
Pentazocine, TAB
Tramadol
w /Acetaminophen, TAB
TAB
TER, 100MG
Schedule II Short-Acting
Morphine sulfate
SOL
TAB
Codeine Sulfate 15MG
Oxycodone
TAB, 5MG
SOL
CAP
w/Acetaminophen
CAP
SOL
TAB, 5MG
Tapentadol, TAB, 50MG
Hydromorphone
SOL/SUP
TAB
Representative Generic Name
n
$ per 50
MME
Acetaminophen/Propoxyphene Napsylate TAB 650
MG-100 MG
126,797
0.66
Acetaminophen/Codeine Phosphate ELI 120 MG/5
ML-12 MG/5 ML
Acetaminophen/Codeine Phosphate SOL 120 MG/5
ML-12 MG/5 ML
Acetaminophen/Codeine Phosphate TAB 300 MG-30
MG
1,791
6.81
5,091
10.83
81,793
4.38
Acetaminophen/Hydrocodone Bitartrate ELI 500
MG/15 ML-7.5 MG/15 ML
Acetaminophen/Hydrocodone Bitartrate SOL 325
MG/15 ML-10 MG/15 ML
Acetaminophen/Hydrocodone Bitartrate TAB 500
MG-10 MG
Ibuprofen/Oxycodone Hydrochloride TAB 400 MG-5
MG
Aspirin/Butalbital/Caffeine/Codeine Phosphate CAP
325 MG-50 MG-40 MG-30 MG
Butorphanol Tartrate SPR 10 MG/ML
Naloxone Hydrochloride/Pentazocine Hydrochloride
TAB 0.5 MG-50 MG
6,618
7.40
102
22.28
117,197
1.49
74
11.79
5,027
11.58
3,142
2,325
8.42
2.94
Acetaminophen/Tramadol Hydrochloride TAB 325
MG-37.5 MG
Tramadol Hydrochloride TAB 50 MG
Tramadol Hydrochloride TER 200 MG
20,614
9.24
243,239
2,723
1.98
13.40
Morphine Sulfate SOL 10 MG/ML
Morphine Sulfate TAB 15 MG
Codeine Sulfate TAB 30 MG
1,152
8,202
409
41.58
0.74
5.20
Oxycodone Hydrochloride TAB 15 MG
Oxycodone Hydrochloride SOL 20 MG/ML
Oxycodone Hydrochloride CAP 5 MG
24,041
490
8,620
1.12
1.44
1.79
Acetaminophen/Oxycodone Hydrochloride CAP 500
MG-5 MG
Acetaminophen/Oxycodone Hydrochloride SOL 325
MG/5 ML-5 MG/5 ML
Acetaminophen/Oxycodone Hydrochloride TAB 325
MG-10 MG
Tapentadol Hydrochloride TAB 50 MG
8,047
2.29
889
3.33
85,434
2.52
839
1.92
Hydromorphone Hydrochloride SOL 2 MG/ML
Hydromorphone Hydrochloride TAB 4 MG
5,360
11,339
48.29
1.09
3.20
Drug Category
Meperidine
SOL
TAB, 50MG
Fentanyl citrate transmucosal,
lozenge, 0.8MG
Schedule II Long-Acting
Morphine sulfate sustained release
CAP, 10MG
TAB, 15MG
Fentanyl transdermal,
25MCG/hour
Oxycodone hydrochloride, ER,
TAB, 10MG
Methadone
SOL
TAB
Oxymorphone sustained release,
10MG
Dihydrocodeine
n
$ per 50
MME
Meperidine Hydrochloride SOL 50 MG/ML
Meperidine Hydrochloride TAB 50 MG
Fentanyl Citrate LOZ 0.8 MG
1,886
4,244
154
125.75
24.85
137,12.50
Morphine Sulfate CER 60 MG
Morphine Sulfate TER 15 MG
Fentanyl TDM 100 MCG/HR
2,783
12,233
13,723
6.81
1.77
11.39
Oxycodone Hydrochloride TER 20 MG
19,789
5.46
Methadone Hydrochloride SOL 5 MG/5 ML
Methadone Hydrochloride TAB 5 MG
Oxymorphone Hydrochloride TER 20 MG
141
4,029
505
1.93
0.60
4.59
Representative Generic Name
Acetaminophen/Caffeine/Dihydrocodeine Bitartrate
1,183
9.65
TAB 712.8 MG-60 MG-32 MG
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 statespecific 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
3.21
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.
3.22
Table 3-11. Representative State Patient Review and Restriction Program Policies
Scenario
#
1
2
State
Washington12
West Virginia13
North Carolina14
3
Kentucky15
Current Eligibility Criteria for State Patient Review and Restriction Programs
Modeled Eligibility Criteria
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.
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
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.
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
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 180day period:
Visited >5 providers
Used >3 pharmacies
Received >10 opioid prescriptions
Any of the following conditions in any 60
days:
Visited >3 prescribers
Received > 6 opioid prescriptions
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.
3.23
Scenario
#
4
5
State
Idaho16
Michigan17
Current Eligibility Criteria for State Patient Review and Restriction Programs
Modeled Eligibility Criteria
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
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
Any of the following conditions in any 90
days:
Visited >3 prescribers
Used > 3 pharmacies
Received >11 opioid prescriptions
(The number of pharmacies and prescribers
are not specified in the state criteria. The
numbers used in the modeled criteria are
assumed.)
Other criteria includes convicted fraud and inappropriate use of ED services (content not
shown here)
The number of conditions is not specified in the document. We assume, based on our interpretation, that all conditions need to be met.
3.24
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).
3.25
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)
Brief Description
Number of Eligible
Individuals
Demographics
Mean age (years)
Scenario 1
Scenario 2
Scenario 3
≥4 prescribers or ≥4
pharmacies or ≥10 RX
in 90 days, if any two
conditions met
≥3 prescribers or ≥6
RX in 60 days
≥5 prescribers and ≥3
pharmacies and ≥10
RX in two consecutive
180-day periods
198
(178-218)
1,257
(1,059-1,455)
82
(61-103)
Scenario 4
≥2 prescribers and ≥2
pharmacies and (≥8
RX or (≥3 tramadol
RX or ≥480 tramadol
tablets)) in 60 days
2775
(2,241-3,309)
41.7
(40.1-43.3)
32.8%
(30.2%-35.4%)
43.8
(42.9-44.7)
30.4%
29.7%-31.1%)
43.8
(42.7-44.9)
30.6%
(29.8%-31.4%)
2,186
(1,742 -2,630)
1,232
(941-1,523)
671
(512-830)
4,089
(3,652-4,526)
3.13
(2.64-3.62)
33,957
(29,487-38,427)
11,281
(9,184-13,378)
4,208
(3,598-4,818)
49,446
(42,963-55,929)
2.01
(1.91-2.11)
34,941
(30,598-39,284)
12,632
(9,895-15,369)
4,897
(3,981-5,813)
52,470
(45,392-59,548)
2.14
(2.02-2.26)
81.7
(76.3-87.1)
56.4
(49.8-63.0)
59.3
(52.0-69.6)
976
(813-1,139)
650
(567-733)
368
3,654
(2,917-4,391)
1,994
(1,517-2,471)
1,263
4,229
(3,841-4,617)
2,557
(1,984-3,130)
1,595
41.4
42.5
(40.1-42.7)
(41.6-43.4)
Male (%)
34.1%
31.9%
(30.5%-37.7%)
(30.7%-33.1%)
Opioid Use by the Eligible Cohort, With No PRR Program *
Number of Prescriptions
Used
3,590
16,513
Schedule III and IV
(2,897-4,283)
(14,818 -18,208)
Schedule II, short1,975
5,583
acting
(1,587-2,363)
(4,791-6,375)
Schedule II, long1,189
2,314
acting
(893-1,485)
(2,075-2,553)
6,754
24,410
Total
(5,986-7,522)
(21,672-27,148)
Average Number of RX
2.79
2.27
per Month
(2.63-2.95)
(2.14-2.40)
Average Daily
73.2
67.4
Morphine Equivalent
(65.6-80.8)
(53.4-81.4)
Dose, mg
Opioid Use by the Eligible Cohort, With PRR Program
Eliminated Overlapping
Prescriptions †
1,505
2,147
Schedule III and IV
(1,375-1,635)
(1,875-2,419)
Schedule II, short930
1,233
acting
(811-1,049)
(1,011-1,455)
Schedule II, long572
826
Scenario 5
≥3 prescribers or ≥3
pharmacies in 90 days
2,865
(2,317-3,413)
3.26
Brief Description
acting
Total Number of
Eliminated RX
Total Percentage of
Eliminated RX
RX with Reduced
Strength or Quantity ‡
Schedule III and IV
Schedule II, shortacting
Schedule II, longacting
Total Number of RX
with Reduced
Strength or Quantity
Total Percentage of
RX with Reduced
Strength or Quantity
Average Number of RX
per Month
Average Daily
Morphine Equivalent
Dose, mg
Scenario 1
Scenario 2
Scenario 3
(284-452)
1,994
(1,659-2,329)
48.8%
(95% CI:47.5%-50.1%)
Scenario 4
≥2 prescribers and ≥2
pharmacies and (≥8
RX or (≥3 tramadol
RX or ≥480 tramadol
tablets)) in 60 days
(985-1,541)
6,911
(6,042-7,780)
14.0%
(95% CI:10.2%-17.8%)
≥4 prescribers or ≥4
pharmacies or ≥10 RX
in 90 days, if any two
conditions met
≥3 prescribers or ≥6
RX in 60 days
≥5 prescribers and ≥3
pharmacies and ≥10
RX in two consecutive
180-day periods
(426-718)
3,007
(2,685-3,329)
44.5%
(95% CI: 41.4%-47.6%)
(647-1,005)
4,206
(3,894-4,518)
17.2%
(95% CI:15.1%-19.3%)
165
(132-198)
247
(203-291)
367
(301-433)
Scenario 5
(1,094-2,096)
8,381
(7,201-9,561)
16.2%
(95% CI:13.7%-18.3%)
1,190
(972-1,408)
936
(807-1,065)
1,092
(933-1,251)
113
(72-154)
202
(148-256)
251
(188-314)
2,633
(2,003-3,263)
1,972
(1,572-2,372)
2,221
(1,848-2,594)
4,229
(3,488-4,970)
2,557
(2,557-3,156)
1,595
(1,134-2,056)
779
(705-853)
3,218
(2,895-3,541)
566
(436-696)
6,826
(6,109-7,543)
8,381
(7,593-9,196)
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%)
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)
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)
≥3 prescribers or ≥3
pharmacies in 90 days
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
3.27
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.
3.28
Table 3-13. Annual Health Impact of the PRR Program in a Population of 10,000 Long-Term Users under Different Eligibility
Scenarios*
Baseline
Opioid-overdoserelated event type
No PRR
program
4.8
(3.5, 6.1)
121.4
(108.7, 134.1)
61.2
(54.5,67.9)
Hospitalizations
4.5
(3.6,5.4)
Deaths
* 95% confidence interval in parenthese
Outpatient Visits
Emergency
Department Visits
Scenario 1
≥4 prescribers or ≥4
pharmacies or ≥10
RX in 90 days, with
two conditions met
-0.2
(0, -0.4)
-8.6
(-6.2, -11.0)
-4.8
(-4.1, -5.5)
-0.4
(-0.2, -0.6)
Scenario 2
≥3
prescribers
or ≥6 RX in
60 days
-1.1
(-0.7, -1.5)
-22.1
(-18.6, -25.6)
-11.4
(-9.2, -13.6)
-0.6
(-0.4, -0.8)
Scenario 3
≥5 prescribers and ≥3
pharmacies and ≥10
RX in two consecutive
180-day period
-0.2
(-0.0, -0.5)
-5.4
(-3.9, -6.9)
-3.9
(-3.1, -4.7)
-0.2
(-0.0, -0.4)
Scenario 4
≥2 prescribers and ≥2
pharmacies and (≥8 RX or
(≥3 tramadol RX or ≥480
tramadol tablets)) in 60 days
-2.6
(-1.8, -3.4)
-47.1
(-43.5, -50.7)
-22.4
(-18.5, -26.3)
-1.0
(-0.6, -1.4)
Scenario 5
≥3 prescribers
or ≥3
pharmacies in
90 days
-3.0
(-2.5, -3.5)
-56.3
(-45.7, -58.9)
-36.2
(-30.4, -42.0)
-1.8
(-1.2, -2.4)
3.29
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
3.30
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
3.31
Table 3-14. Cost Analysis of the PRR Program under Different Eligibility Scenarios‡‡
Opioid-overdose-related
event type
Baseline
Scenario 1
Scenario 2
Scenario 3
No PRR
program
≥4 prescribers or ≥4
pharmacies or ≥10
RX in 90 days, with
two conditions met
≥3 prescribers
or ≥6 RX in 60
days
Estimated Cost in US Dollars (per 10,000 Long-term Users)
$4,593,423
$4,364,532
(±$241,029)
(±$200,145)
Opioid Cost
$909,341
$862,402
Overdose-related Medical
(±$105,625)
(±$113,204)
Cost
$339,600
(±$4,000)
Program Cost
$5,502,465
$5,567,532
Total
(±$301,402)
(±$297,145)
$1,395
Average opioid & medical
(±211)
savings per enrollee
Number of enrollees needed
251
for program
(234 - 281)
break-even
Number of total opioid users
60,079
needed for program break(56,010-67,260)
even ††
††
≥5 prescribers and ≥3
pharmacies and ≥10
RX in two consecutive
180-day period
Scenario 4
≥2 prescribers and ≥2
pharmacies and (≥8 RX or
(≥3 tramadol RX or ≥480
tramadol tablets)) in 60
days
Scenario 5
≥3 prescribers or ≥3
pharmacies in 90
days
$4,176,182
(±$246,759)
$713,487
(±$92,328)
$551,400
(±$39,600)
$5,441,069
(±$287,956)
$488
(±157)
$4,443,573
(±$165,321)
$874,606
(±$95,693)
$316,400
(±$4,200)
$5,634,579
(±$289,641)
$2,251
(±352)
$3,857,941
(±$285,695)
$464,792
(±$89,342)
$855,000
(±$106,800)
$5,177,733
(±$321,592)
$425
(±122)
$3,705,989
(±$299,403)
$503,204
(±$84,231)
$873,000
(±$109,600)
$5,082,193
(±$302,524)
$458
(±111)
1,041
(892-1,261)
146
(124-172)
1,333
(942-1,799)
1,162
(868-1,542)
39,249
(31,983-46,829)
84,383
(68,943-98,347)
22,766
(16,954-32,823)
19,222
(13,459-24,593)
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
3.32
‡‡
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
3.33
4.
5.
6.
7.
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.
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.
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.
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.
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.
3.34
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 nonwhite) 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,
3.35
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
Hydrocodone +
aspirin/acetaminophen/ibupro
fen
Oxycodone (with or without
aspirin/acetaminophen/ibupro
fen)
Propoxyphene (with or
without
aspirin/acetaminophen/ibupro
fen)
Tramadol with or without
aspirin
Other Schedule II
Long Acting
Fentanyl transdermal
Morphine sulfate
sustained release
Less Common Opioid Types
Other Schedule II
Other Non-Schedule II
Short Acting
Hydromorphone
Butalbital + codeine
(with or without
aspirin/acetaminophen/
ibuprofen)
Meperidine
Butorphanol
hydrochloride
Oxycodone HCL
control release
Morphine sulfate
Methadone
Codeine Sulfate
Oxymorphone extended
release
Levorphanol
Pentazocine (with or
without
aspirin/acetaminophen/
ibuprofen)
Codeine +
aspirin/acetaminophen/
ibuprofen
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.
3.36
Figure 3-5. Example of Random Sampling of Predominant Drug Type
Age
strata
gender
race
30-44
30-44
30-44
45-64
female
white
male non-white
male
white
female non-white
Hydroco Other_L Other_ Other_ Oxycodo Propoxy Tramado
done
A_II
Non_II SA_II
ne
phene
l
54%
51%
55%
46%
59%
53%
58%
49%
62%
59%
62%
54%
68%
62%
66%
58%
82%
77%
78%
74%
87%
85%
86%
82%
100%
100%
100%
100%
Generated
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+
+
’
gender*gender+
’
age*age_stratum+
’
race*race
’
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.
3.37
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
 Episodes of
drug use
6/1/2009
1/1/2010
12/31/2010
6/1/2011
Reported timespan
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.
3.38
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 longacting 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.
3.39
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
Opioid
Drug type
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
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
3.40
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
3.41
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 MG10 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
3.42
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.
3.43
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
3.44
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
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
Black
Hispanic
Other
1.00
0.60
1.09
1.13
0.48
0.57
0.86
0.74
2.11
1.48
<.0001
0.7959
0.377
Concurrent sedative/hypnotic use
History of alcohol abuse
History of depression diagnosis
Pharmacy Shopping
2.54
3.07
2.91
1.80
1.99
2.09
2.21
1.54
3.23
4.50
3.83
2.10
<.0001
<.0001
<.0001
<.0001
Overlapping Prescriptions
2.96
2.45
3.68
<.0001
Age
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 lognormal distribution with estimated means and standard deviations.
Table 3-17. Distribution of Overdose and Cost Estimates
Event type
Hospitalization, with ED visit
%
Mean
36.1
$12,371
Median
$5,506
Interquartile range
$2658, $13415
3.45
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 recalculated 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.
3.46
APPENDIX II
ICD-9 Codes Indicating Overdose-Related Symptoms
276.4
292.1
292.81
292.8
486
496
Mixed acid–base balance disorder
Drug-induced psychotic disorders (including 292.11 and 292.12)
Drug-induced delirium
Drug-induced mental disorder (excluding 292.81)
Pneumonia, organism unspecified
Chronic airway obstruction, not elsewhere classified
518.81
518.82
780.0
780.97
786.03
786.05
Acute respiratory failure
Other pulmonary insufficiency, not elsewhere classified
Alteration of consciousness
Altered mental state
Apnea
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
1
APAP/Butalbital/Caff/Codeine Phos CAP 325 MG-50 MG-40 MG-30
MG
ASA/Oxycodone HCl/Oxycodone Terephthalate TAB 325 MG-4.5 MG0.38 MG
Acetaminophen/Caffeine/Dihydrocodeine Bitartrate CAP 356.4 MG-30
MG-16 MG
Acetaminophen/Caffeine/Dihydrocodeine Bitartrate TAB 712.8 MG-60
MG-32 MG
Acetaminophen/Codeine Phosphate ELI 120 MG/5 ML-12 MG/5 ML
Acetaminophen/Codeine Phosphate SOL 120 MG/5 ML-12 MG/5 ML
Acetaminophen/Codeine Phosphate SUS 120 MG/5 ML-12 MG/5 ML
Acetaminophen/Codeine Phosphate TAB 300 MG-15 MG
Acetaminophen/Codeine Phosphate TAB 300 MG-30 MG
Acetaminophen/Codeine Phosphate TAB 300 MG-60 MG
Acetaminophen/Hydrocodone Bitartrate ELI 500 MG/15 ML-7.5 MG/15
2
3
4
5
6
7
8
9
10
11
n
3,591
($) Price for 10
units
(95% CI)
6.67( 6.59- 6.74)
918
9.65( 9.44- 9.85)
141
15.67( 15.2116.14)
15.44( 15.2115.67)
0.49( 0.47- 0.51)
0.78( 0.50- 1.06)
4.81( 4.28- 5.34)
3.79( 3.61- 3.96)
3.94( 3.83- 4.04)
3.23( 3.19- 3.28)
0.74( 0.73- 0.76)
1,183
1,791
5,091
79
773
81,793
6,405
6,618
3.47
Obs
Drug Name
n
($) Price for 10
units
(95% CI)
13
ML
Acetaminophen/Hydrocodone Bitartrate SOL 325 MG/15 ML-10 MG/15
ML
Acetaminophen/Hydrocodone Bitartrate TAB 300 MG-10 MG
14
Acetaminophen/Hydrocodone Bitartrate TAB 300 MG-7.5 MG
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
91,583
54,418
31,408
61
117,197
1,599
317,414
109,455
87,809
28,168
4,540
242
64,054
8,047
889
30
31
Acetaminophen/Hydrocodone Bitartrate TAB 325 MG-10 MG
Acetaminophen/Hydrocodone Bitartrate TAB 325 MG-5 MG
Acetaminophen/Hydrocodone Bitartrate TAB 325 MG-7.5 MG
Acetaminophen/Hydrocodone Bitartrate TAB 400 MG-10 MG
Acetaminophen/Hydrocodone Bitartrate TAB 500 MG-10 MG
Acetaminophen/Hydrocodone Bitartrate TAB 500 MG-2.5 MG
Acetaminophen/Hydrocodone Bitartrate TAB 500 MG-5 MG
Acetaminophen/Hydrocodone Bitartrate TAB 500 MG-7.5 MG
Acetaminophen/Hydrocodone Bitartrate TAB 650 MG-10 MG
Acetaminophen/Hydrocodone Bitartrate TAB 650 MG-7.5 MG
Acetaminophen/Hydrocodone Bitartrate TAB 660 MG-10 MG
Acetaminophen/Hydrocodone Bitartrate TAB 750 MG-10 MG
Acetaminophen/Hydrocodone Bitartrate TAB 750 MG-7.5 MG
Acetaminophen/Oxycodone Hydrochloride CAP 500 MG-5 MG
Acetaminophen/Oxycodone Hydrochloride SOL 325 MG/5 ML-5 MG/5
ML
Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-10 MG
Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-2.5 MG
32
33
34
35
36
37
38
Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-5 MG
Acetaminophen/Oxycodone Hydrochloride TAB 325 MG-7.5 MG
Acetaminophen/Oxycodone Hydrochloride TAB 500 MG-7.5 MG
Acetaminophen/Oxycodone Hydrochloride TAB 650 MG-10 MG
Acetaminophen/Pentazocine Hydrochloride TAB 650 MG-25 MG
Acetaminophen/Propoxyphene Hydrochloride TAB 650 MG-65 MG
Acetaminophen/Propoxyphene Napsylate TAB 325 MG-100 MG
196,240
21,295
8,835
27,577
138
276
160
39
40
41
42
43
1,079
180
126,797
20,614
5,027
44
Acetaminophen/Propoxyphene Napsylate TAB 325 MG-50 MG
Acetaminophen/Propoxyphene Napsylate TAB 500 MG-100 MG
Acetaminophen/Propoxyphene Napsylate TAB 650 MG-100 MG
Acetaminophen/Tramadol Hydrochloride TAB 325 MG-37.5 MG
Aspirin/Butalbital/Caffeine/Codeine Phosphate CAP 325 MG-50 MG-40
MG-30 MG
Aspirin/Carisoprodol/Codeine Phosphate TAB 325 MG-200 MG-16 MG
45
Aspirin/Oxycodone Hydrochloride TAB 325 MG-4.8355 MG
481
46
Belladonna Alkaloids/Opium Alkaloids SUP 16.2 MG-60 MG
50
47
Buprenorphine Hydrochloride TAB 2 MG
341
48
Buprenorphine Hydrochloride TAB 8 MG
2,012
49
Buprenorphine Hydrochloride/Naloxone Hydrochloride FIL 8 MG-2 MG
12
102
2.97( 2.83- 3.11)
1,017
20.45( 20.2420.66)
14.91( 14.1215.71)
3.44( 3.42- 3.46)
5.62( 5.57- 5.66)
4.60( 4.57- 4.63)
8.92( 8.74- 9.10)
2.98( 2.96- 2.99)
3.18( 3.07- 3.28)
3.08( 3.06- 3.10)
2.21( 2.19- 2.23)
2.45( 2.44- 2.47)
3.37( 3.34- 3.40)
3.75( 3.67- 3.82)
7.96( 7.75- 8.16)
2.50( 2.49- 2.52)
3.44( 3.32- 3.55)
1.00( 0.96- 1.04)
51
85,434
243
173
168
7.57( 7.55- 7.60)
21.06( 20.3821.74)
2.50( 2.48- 2.51)
7.99( 7.94- 8.04)
6.58( 6.53- 6.63)
8.66( 8.62- 8.70)
8.66( 8.29- 9.03)
2.40( 2.27- 2.54)
21.40( 20.3122.50)
6.64( 6.44- 6.84)
9.36( 8.76- 9.96)
3.03( 3.01- 3.05)
6.93( 6.72- 7.14)
10.42( 10.3410.51)
16.96( 16.3917.53)
11.72( 11.4511.99)
98.39( 85.69111.09)
34.79( 33.7235.86)
66.18( 65.4466.91)
62.73( 61.573.48
Obs
Drug Name
n
52
Buprenorphine Hydrochloride/Naloxone Hydrochloride TAB 2 MG-0.5
MG
Buprenorphine Hydrochloride/Naloxone Hydrochloride TAB 8 MG-2
MG
Butorphanol Tartrate SOL 1 MG/ML
53
Butorphanol Tartrate SOL 2 MG/ML
54
Butorphanol Tartrate SPR 10 MG/ML
55
56
57
58
59
60
Codeine Sulfate TAB 15 MG
Codeine Sulfate TAB 30 MG
Codeine Sulfate TAB 60 MG
Cough/Cold Combination SOL
Cough/Cold Combination SYR
Fentanyl TDM 100 MCG/HR
61
Fentanyl TDM 12 MCG/HR
1,021
62
Fentanyl TDM 25 MCG/HR
8,872
63
Fentanyl TDM 50 MCG/HR
12,442
64
Fentanyl TDM 75 MCG/HR
9,279
65
Fentanyl Citrate LOZ 0.4 MG
51
66
Fentanyl Citrate LOZ 0.8 MG
154
67
Fentanyl Citrate LOZ 1.2 MG
111
68
Fentanyl Citrate LOZ 1.6 MG
90
69
Fentanyl Citrate SOL 0.05 MG/ML
70
Hydrocodone Bitartrate/Ibuprofen TAB 10 MG-200 MG
218
71
72
73
Hydrocodone Bitartrate/Ibuprofen TAB 5 MG-200 MG
Hydrocodone Bitartrate/Ibuprofen TAB 7.5 MG-200 MG
Hydromorphone Hydrochloride SOL 1 MG/ML
106
14,465
4,443
74
Hydromorphone Hydrochloride SOL 10 MG/ML
75
Hydromorphone Hydrochloride SOL 2 MG/ML
5,360
76
Hydromorphone Hydrochloride SOL 4 MG/ML
537
77
Hydromorphone Hydrochloride SUP 3 MG
96
78
Hydromorphone Hydrochloride TAB 2 MG
7,157
50
51
763
18,571
144
306
3,142
51
409
106
212
592
13,723
27,642
70
($) Price for 10
units
(95% CI)
63.88)
32.20( 31.4232.98)
56.05( 55.8556.26)
249.05( 221.47276.62)
125.66( 112.18139.15)
117.88( 116.67119.10)
4.79( 4.39- 5.18)
4.68( 4.47- 4.89)
7.75( 7.44- 8.06)
2.84( 2.71- 2.96)
3.80( 3.75- 3.85)
546.69 ( 542.52550.85)
155.01 ( 152.42157.60)
148.82 ( 147.49150.16)
282.66 ( 280.58284.73)
423.49 ( 419.86427.11)
280.40 ( 259.14301.66)
274.25 ( 256.80291.69)
308.34 ( 277.42339.25)
688.19 ( 650.89725.50)
52.45 ( 51.2953.61)
11.13 ( 10.9111.34)
8.82 ( 8.41- 9.22)
7.83 ( 7.78- 7.87)
82.68 ( 79.1986.17)
266.54 ( 156.73376.34)
96.57 ( 93.8199.33)
69.96 ( 55.9483.98)
87.88 ( 84.4591.31)
4.68 ( 4.41- 4.95)
3.49
Obs
Drug Name
n
79
80
81
Hydromorphone Hydrochloride TAB 4 MG
Hydromorphone Hydrochloride TAB 8 MG
Ibuprofen/Oxycodone Hydrochloride TAB 400 MG-5 MG
11,339
2,840
74
82
83
Meperidine Hydrochloride SOL 10 MG/ML
Meperidine Hydrochloride SOL 100 MG/ML
88
506
84
Meperidine Hydrochloride SOL 25 MG/ML
403
85
Meperidine Hydrochloride SOL 50 MG/ML
1,886
86
Meperidine Hydrochloride SOL 75 MG/ML
98
87
88
89
90
91
92
93
94
Meperidine Hydrochloride TAB 100 MG
Meperidine Hydrochloride TAB 50 MG
Methadone Hydrochloride SOL 10 MG/ML
Methadone Hydrochloride SOL 5 MG/5 ML
Methadone Hydrochloride TAB 10 MG
Methadone Hydrochloride TAB 40 MG
Methadone Hydrochloride TAB 5 MG
Morphine Sulfate C24 120 MG
582
4,244
110
141
38,130
149
4,029
831
95
Morphine Sulfate C24 30 MG
673
96
Morphine Sulfate C24 60 MG
634
97
Morphine Sulfate C24 90 MG
844
98
Morphine Sulfate CER 10 MG
559
99
Morphine Sulfate CER 100 MG
2,191
100
Morphine Sulfate CER 20 MG
2,298
101
Morphine Sulfate CER 200 MG
102
Morphine Sulfate CER 30 MG
2,754
103
Morphine Sulfate CER 50 MG
2,055
104
Morphine Sulfate CER 60 MG
2,783
105
Morphine Sulfate CER 80 MG
1,094
106
Morphine Sulfate SOL 1 MG/ML
107
Morphine Sulfate SOL 10 MG/5 ML
108
Morphine Sulfate SOL 10 MG/ML
109
Morphine Sulfate SOL 2 MG/ML
200
51
203
1,152
382
($) Price for 10
units
(95% CI)
4.35 ( 4.29- 4.41)
9.37 ( 9.26- 9.48)
11.79 ( 11.0112.57)
5.62 ( -0.85- 12.08)
116.00 ( 104.78127.22)
103.67 ( 96.62110.73)
125.75 ( 120.30131.20)
92.67 ( 80.47104.86)
8.06 ( 7.57- 8.55)
4.97 ( 4.82- 5.13)
1.50 ( 1.25- 1.76)
1.16 ( 0.93- 1.40)
1.56 ( 1.55- 1.57)
3.05 ( 2.92- 3.17)
1.79 ( 1.72- 1.86)
123.03 ( 121.69124.37)
33.18 ( 32.3933.97)
70.89 ( 69.9571.82)
100.29 ( 98.68101.90)
35.67 ( 35.0836.26)
132.64 ( 131.41133.88)
37.17 ( 36.8437.49)
275.61 ( 267.89283.34)
40.52 ( 40.2140.82)
67.74 ( 67.1868.29)
81.67 ( 81.1782.17)
108.87 ( 107.65110.09)
79.74 ( 35.66123.83)
13.07 ( 5.7620.38)
83.16 ( 78.3088.03)
81.33 ( 73.403.50
Obs
Drug Name
n
110
111
112
Morphine Sulfate SOL 20 MG/5 ML
Morphine Sulfate SOL 20 MG/ML
Morphine Sulfate SOL 4 MG/ML
75
455
602
113
Morphine Sulfate SOL 5 MG/ML
463
114
115
116
Morphine Sulfate TAB 15 MG
Morphine Sulfate TAB 30 MG
Morphine Sulfate TER 100 MG
8,202
7,548
6,776
117
118
Morphine Sulfate TER 15 MG
Morphine Sulfate TER 200 MG
12,233
831
119
120
Morphine Sulfate TER 30 MG
Morphine Sulfate TER 60 MG
20,014
12,569
121
Nalbuphine Hydrochloride SOL 10 MG/ML
5,006
122
Nalbuphine Hydrochloride SOL 20 MG/ML
2,941
123
2,325
124
Naloxone Hydrochloride/Pentazocine Hydrochloride TAB 0.5 MG-50
MG
Opium TIN 10 MG/ML
125
126
127
128
129
130
131
132
133
134
Opium TIN 10%
Oxycodone Hydrochloride CAP 5 MG
Oxycodone Hydrochloride SOL 20 MG/ML
Oxycodone Hydrochloride SOL 5 MG/5 ML
Oxycodone Hydrochloride TAB 10 MG
Oxycodone Hydrochloride TAB 15 MG
Oxycodone Hydrochloride TAB 20 MG
Oxycodone Hydrochloride TAB 30 MG
Oxycodone Hydrochloride TAB 5 MG
Oxycodone Hydrochloride TER 10 MG
135
Oxycodone Hydrochloride TER 15 MG
804
136
Oxycodone Hydrochloride TER 20 MG
19,789
137
Oxycodone Hydrochloride TER 30 MG
4,199
138
Oxycodone Hydrochloride TER 40 MG
24,061
139
Oxycodone Hydrochloride TER 60 MG
5,875
140
Oxycodone Hydrochloride TER 80 MG
19,890
141
Oxymorphone Hydrochloride TAB 10 MG
855
142
Oxymorphone Hydrochloride TAB 5 MG
421
162
75
8,620
490
824
1,538
24,041
219
23,349
28,648
9,217
($) Price for 10
units
(95% CI)
89.25)
1.43 ( 1.29- 1.57)
7.36 ( 4.73- 9.99)
67.46 ( 62.9971.92)
80.93 ( 43.02118.84)
2.21( 2.18- 2.25)
2.63( 2.60- 2.67)
21.31( 21.0421.57)
5.32( 5.25- 5.39)
40.94( 39.4942.39)
8.65( 8.60- 8.71)
13.95( 13.8114.09)
112.15( 108.49115.81)
68.76( 66.6770.85)
10.87( 10.7211.03)
47.48( 45.3049.66)
8.32( 8.01- 8.63)
2.68( 2.63- 2.72)
8.62( 8.31- 8.93)
0.99( 0.86- 1.11)
5.55( 5.43- 5.68)
5.02( 5.00- 5.05)
8.89( 8.48- 9.29)
8.54( 8.49- 8.58)
2.95( 2.92- 2.98)
17.91( 17.8018.03)
27.08( 26.6227.53)
32.77( 32.6532.89)
48.41( 48.0648.75)
57.40( 57.2257.59)
88.63( 88.1789.10)
107.48( 107.08107.88)
47.45( 46.5948.32)
25.55( 24.723.51
Obs
Drug Name
n
143
Oxymorphone Hydrochloride TER 10 MG
287
144
Oxymorphone Hydrochloride TER 15 MG
81
145
Oxymorphone Hydrochloride TER 20 MG
505
146
Oxymorphone Hydrochloride TER 30 MG
283
147
Oxymorphone Hydrochloride TER 40 MG
561
148
149
Propoxyphene Hydrochloride CAP 65 MG
Propoxyphene Napsylate TAB 100 MG
3,744
473
150
Tapentadol Hydrochloride TAB 100 MG
581
151
Tapentadol Hydrochloride TAB 50 MG
839
152
Tapentadol Hydrochloride TAB 75 MG
457
153
154
Tramadol Hydrochloride TAB 50 MG
Tramadol Hydrochloride TER 100 MG
243,239
1,782
155
Tramadol Hydrochloride TER 200 MG
2,723
156
Tramadol Hydrochloride TER 300 MG
1,797
($) Price for 10
units
(95% CI)
26.38)
29.60( 28.4530.74)
33.45( 28.8038.09)
55.12( 53.8456.40)
82.54( 80.3184.78)
106.83( 104.98108.68)
3.63( 3.57- 3.69)
15.95( 15.6116.30)
30.04( 29.5330.55)
19.21( 18.9419.49)
23.39( 23.0723.72)
1.98( 1.97- 1.99)
32.10( 31.6932.51)
53.61( 53.0954.12)
82.34( 81.5583.12)
3.52
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Magellan Medicaid Adminstration - Idaho. Idaho Drug Utilization Review (DUR)
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3.54