Approaches to Drug Overdose Prevention Analytical Tool (ADOPT): Evaluating Cost and Health Impacts of a Medicaid Patient Review & Restriction Program

Transcription

1 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: 1U58CD 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

2 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

3 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

4 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

5 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. correspondence, August 2012 iii

6 program, then identifies the subjects who meet the program enrollment criteria and calculates the health and financial impact if the PRR was established. However, it is possible that some states already had a PRR program when the MarketScan data were collected, in which case the prevalence of opioid abuse or misuse (including drug shopping) would be under-estimated. This may cause an undervaluation of a PRR program in the analysis. 3. Prescriber information is imputed: Many PRR programs use the number of opioid prescribers as an eligibility criterion; however, the MarketScan data do not contain prescriber information. Therefore the model uses the reported correlation between numbers of pharmacies and prescribers from the Massachusetts PRR program database. It is possible that this correlation may not reflect the experience of the MarketScan population. 4. Incomplete representation of PRR criteria: The current version of ADOPT can only analyze some of the criteria that may be used in a PRR program, but in practice, PRR programs often use other criteria such as emergency department use, number of office visits, or history of substance abuse. If data become available, they could be incorporated into the model. 5. Uncertainty in estimation of overdose risk: ADOPT uses the hazard ratios for opioid overdose that are derived from the MarketScan inpatient and outpatient datasets. However, overdose rates may be higher than observed because patients may have expired before entering the hospital. In addition, overdose events were identified by using the diagnostic codes. Misclassification of diagnostic codes may cause under-estimation or over-estimation of the overdose risk. 6. Uncertainty of PRR program costs: The model uses a fixed program cost of $300,000 annually and a variable cost of $200/program enrollee, however, these costs may not reflect the actual cost incurred by states operating PRR programs and they do not include the start-up costs for states newly implementing programs. 7. Assumptions about effects of PRR programs may be inaccurate: For example, assumptions that these programs reduce dosage or overlapping prescriptions. Despite these limitations, the ADOPT model demonstrates the potential to simulate individual prescription consumption behavior with satisfactory similarity to real prescription consumption behavior based on calibration with MarketScan data. Using the current model structure and interface, it is possible to add new functions if and when future data becomes available. Ultimately, the strength of the ADOPT is its ability to be customized with state-specific data, which will produce more timely, accurate, and relevant conclusions than those reached using the MarketScan data. Policy makers now have the opportunity to introduce valid, evidence-based information into their decision making process about state-specific patient review and restriction programs to ensure that the most cost-effective policies target those enrollees who will benefit the most. iv

7 v

8 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

9 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 Rationale METHODS Definitions BACKGROUND: PRESCRIPTION OPIOID MISUSE & ABUSE Prevalence of Prescription Opioid Misuse, Abuse and Dependence State-Specific Prevalence Rates Medicaid-Specific Prevalence Rates Sources of Misused and Abused Opioids Doctor and Pharmacy Shopping Opioid Prescribing Patterns State-Specific Opioid Prescribing Patterns Medicaid-Specific Prescribing Patterns Risk Factors Associated with Prescription Opioid Misuse and Abuse Sociodemographic Risk Factors Behavioral Risk Factors Pain Level & Comorbidities Mental Health Opioid Dose and Supply OUTCOMES RELATED TO OPIOID MISUSE AND ABUSE Health Care Utilization Characteristics Associated with Prescription Opioid-Related Healthcare Utilization Health Outcomes: Opioid-Related Comorbidities Health Outcomes: Opioid-Related Mortality Characteristics Associated with Opioid-Related Mortality State-Specific Opioid-Related Mortality Opioid-Related Mortality: Opioid Type and Dosing Patterns Opioid-Related Mortality and Doctor/Pharmacy Shopping Outcomes: Health Care Costs Health Care Costs and Doctor Shopping State-Specific Health Care Costs Outcomes: Societal Costs POLICY OPTIONS TO ELIMINATE OPIOID MISUSE & ABUSE Patient Review and Restriction Programs Policy Effectiveness and Outcomes Prescription Drug Monitoring Programs Policy Effectiveness and Outcomes SUMMARY APPENDIX Literature Review Sources Databases of Peer-Reviewed Literature National Data Sources Federally Maintained Sources of Grey Literature State Maintained Sources of Grey Literature Nonprofit Organizations Literature Review Search Terms BIBLIOGRAPHY

10 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature List of Tables Table 1-1. Selected Policies and Programs to Reduce Prescription Drug Misuse and Abuse Table 1-2: Defining Prescription Drug Use Patterns Table 1-3. Morphine Equivalent Dose Conversion Table Table 1-4. Prevalence of Nonmedical Prescription Opioid Use, Table 1-5. Thresholds for Defining Doctor and Pharmacy Shopping Table 1-6. Medicaid Beneficiaries Receiving Prescription Opioids from Multiple Prescribers, FY 2006 and Table 1-7. Medicare Beneficiaries Obtaining Prescription Opioids from Multiple Prescribers, Table 1-8. Distribution of Patients by Number of Prescribers and Pharmacies, Table 1-9. Distribution of Patients Using Multiple Prescribers and Pharmacies, Table Distribution of Patients Using Multiple Prescribers and Pharmacies, Table Prevalence of Nonmedical Prescription Opioid Use by Gender, 2002 & Table Prevalence of Nonmedical Opioid Use by Race/Ethnicity, Table Prevalence of Nonmedical Opioid Use by Geographic Location, Table Odds of Opioid Misuse and Abuse by Average Daily Dose and Days Supply Table ED Visits for Prescription Opioids Compared to Illicit Drugs, Table Opioid-Related ED Visits by Gender and Race/Ethnicity, Table Prevalence of Select Comorbidities among Opioid Abusers compared to Nonabusers Table Deaths Involving Prescription Opioids, Table Prescription Opioid Overdose Death Rates by Selected Demographics, Table Opioid-Related Mortality by State Table Prescription Opioid Doctor Shopping among Medicaid Beneficiaries and Associated Costs, Table Prescription Opioid Doctor Shopping among Medicare Beneficiaries and Associated Costs, Table Annual Societal Burden of Prescription Opioid Abuse, 2001 & Table Societal Burden of Nonmedical Prescription Opioid use, Table Policy Interventions to Reduce the Burden of Prescription Opioid Misuse Table The Presence of Prescription Drug Monitoring Programs, Overdose Mortality and Opioid Consumption Rates List of Figures Figure 1-1. Relationship between Nonmedical Opioid Use and Misuse Figure 1-2. Schedule I-V Controlled Substances Figure 1-3. Sources of Prescription Opioids,

11 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Figure 1-4. Dual Doctor and Pharmacy Shoppers in West Virginia, Figure 1-5. Type and Formulation of Prescription Opioids Dispensed to Doctor Shoppers Compared to Non-Doctor Shoppers Figure 1-6. Relationship between Opioid Dosage Level and Fatal/Non-Fatal Overdose Risk Figure 1-7. Percentage of Opioid Users and Overdoses, by Risk Group Figure 1-8. Average Annual Direct Health Care Costs* per Opioid Abuse Patient Figure 1-9. State Lock-In Programs by Client Size,

12 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 , 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 healthcare and societal 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: 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 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 1.5

13 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and 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 Evaluate Patient Review and Restriction patterns (large doses/numbers of controlled Policies/Programs (PRR) substances; large proportion of doctor shoppers o Evaluate programs requiring high utilizers to use among their patients) only one doctor/pharmacy (patient review and o Integrate PDMP information into health care by restriction) linking PDMPs with electronic health record Evaluate usefulness of Drug Abuse Warning systems Network (DAWN) data Implement Patient Review and Restriction (PRR) o State Medicaid and workers compensation programs should implement PRR programs to monitor inappropriate use of controlled prescription drugs o Require patients using multiple prescribers and/or pharmacies (without medical justification) to use a single prescriber and/or pharmacy for their controlled prescription drugs Enforcement/Regulation Increase training and education for law enforcement and prosecutors Enforce action against clinics and physicians not following safe prescribing practices (i.e., pain clinics, etc) Write and disseminate a Model Pain Clinic Regulation Law Increase investigations of prescription drug trafficking at the Federal, state, and local levels Other Policies/Programs Enforce regulatory action against prescribers who do not follow accepted medical guidelines for safe prescribing of controlled substances Enact, enforce and evaluate state laws to prevent doctor & pharmacy shopping, pill mill operation, and other methods of misuse, abuse and diversion. 1.6

14 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 1.7

15 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 Table 1-2: Defining Prescription Drug Use Patterns Misuse Nonmedical Use Abuse Dependence 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. 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 1.8

16 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 Schedule II Schedule III Schedule IV Schedule V High potential for abuse, no currently accepted medical use in the US. Includes illicit drugs such as heroin and marijuana. High potential for abuse leading to severe psychological or physical dependence. Includes prescription drugs such as oxycodone, as well as drugs such as cocaine. 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 Low potential for abuse relative to Schedule III drugs. Includes prescription drugs such as diazepam. 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 Morphine Equivalent Dose. Milligrams morphine equivalent (MME), morphine equivalent dose (MED) and morphine equivalent dose per day (MED/d) are used repeatedly in studies to 1.9

17 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 Buprenorphine a Butorphanol 7.00 Schedule II Short-Acting* Morphine sulfate 1.00 Codeine sulfate 0.15 Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 1.50 Hydromorphone 4.00 Meperidine hydrochloride 0.10 Oxymorphone 3.00 Fentanyl citrate transmucosal b Tapendatol short acting c not established Schedule II Long-Acting* Morphine sulfate sustained release 1.00 Fentanyl transdermal d 2.40 Levorphanol tartrate 11.0 Oxycodone HCL control release 1.50 Methadone 3.00 Oxymorphone extended release c 3.00 Hydromorphone extended release c 5.00 Tapentadol extended release c 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 1.10

18 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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, Year Lifetime Past Year Past Month N (%) N (%) N (%) , 611 (12.6) 10,992 (4.7) 4,377 (1.9) ,207 (13.1) 11,671 (4.9) 4,693 (2.0) ,768 (13.2) 11,256 (4.7) 4,404 (1.8) ,692 (13.4) 11,815 (4.9) 4,658 (1.9) ,472 (13.6) 12,649 (5.1) 5,220 (2.1) ,060 (13.3) 12,466 (5.0) 5,174 (2.1) ,861 (14.0) 11,885 (4.8) 4,747 (1.9) ,046 (13.9) 12,405 (4.9) 5,257 (2.1) ,776 (13.7) 12,213 (4.8) 5,100 (2.0) Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, Note: Prevalence among individuals ages 12 and older. Numbers are in thousands. Multiple iterations of the NSDUH, 46 as well as surveys of patients hospitalized for opioid withdrawal 48 and enrolled into methadone maintenance programs 49 have found that the most commonly used opioids were compounds containing hydrocodone and oxycodone. Of those reporting past-year nonmedical opioid use in the NSDUH, Becker et al found that the Administered by the Substance Abuse and Mental Health Services Administration, this survey is based on a target of 67,500 face-to-face interviews with a representative sample of civilian, non-institutionalized individuals ages 12 and older. 1.11

19 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 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 , 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 data from the Medicaid Analytic extract (MAX) database, McAdam-Marx et al estimated the prevalence of prescription opioid abuse in the Medicaid population at 87 per 10,000 population and found the majority of Medicaid abusers (59%) lived in the Eastern region of the United States. 18,54 Sources of Misused and Abused Opioids Individuals misusing and abusing prescription opioids acquire their drugs from a variety of sources. 11,55 The 2010 NSDUH found that 17% of users obtained their opioids through a prescription from one doctor, whereas 83% obtained their prescription opioids from other sources (Figure 1-3). 11,26,56 In comparison, the 2006 NSDUH found that 11% of respondents obtained their opioids through a prescription from one doctor. 46 Rosenblum et al surveyed nearly 5,700 individuals entering into methadone treatment facilities across the country and found similar results 28% of individuals reported their most frequent source of opioids was through a doctor s prescription. 49 Studies have found that the source of abused opioids varies by gender and age. Back et al analyzed results from the 2006 NSDUH and found that men were more likely to obtain prescription opioids from family/friends (either purchased or for free) or from a drug dealer or Administered by the Sloane Epidemiology Center of Boston University, this is a telephone survey administered from February 1998 through September 2006 on prescription and non-prescription medication use during the previous 7 days. The sample consists of 19,150 randomly identified civilian, non-institutionalized individuals ages 18 and older. The Behavioral Risk Factor and Surveillance System (BRFSS) survey is a cross-sectional telephone survey of adults ages 18 years and older conducted by state health departments, with support from the CDC. 1.12

20 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature stranger, while women were more likely to take prescription opioids from family/friends without asking. 46 Green et al found that women abusing hydrocodone/ acetaminophen primarily obtained the drug from within their social network, such as friends or family members (44.6%), followed by a dealer (37.5%), or their own prescription (28.8%), whereas males abusing this opioid obtained it primary from a dealer (45.2%), followed by within their social network (40.6%), or their own prescriptions (25.4%). 57 Cicero et al surveyed opioid users entering substance abuse treatment facilities and found that approximately 90% of males and females under age 20 acquired their prescription opioids from a dealer, while less than half of individuals over age 51 did so. Cicero et al also found that males were more likely than females to acquire their opioids from a dealer, (OR=1.64), whereas Figure 1-3. Sources of Prescription Opioids, 2010 Took from a friend or relative without asking 5% Bought from a friend or relative 12% Got from drug dealer or stranger 4% Prescribed by one doctor 17% 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 Other source 7% Source: Centers for Disease Control and Prevention. Policy Impact: Prescription Painkiller Overdose. November 2011 Obtained free from a friend or relative 55% Citation Number of Opioid Prescribers And/ Number of Pharmacies Or Dispensing Opioids Parente et al (2004) >6 in one year OR >3 in a year Hall et al (2008) >5 during the year before death Katz, Panas et al (2010) >1 - >10 over a 12-month period AND >1 - >10 over a 12-month period White et al (2009) > 2 over a 3-month period OR 1.13

21 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Citation Number of Opioid Prescribers And/ Or Number of Pharmacies Dispensing Opioids > 3 over a 12-month period U.S. GAO (2009) >6 over two year period U.S. GAO (2011) > 5 in one year Wilsey et al (2010) >2 within one month AND >2 within one month Wilsey et al (2011) 2-5 in one year Peirce et al (2012) >4 in the 6 months before death OR >4 in the 6 months before death There may be justifiable reasons that patients use multiple providers. Wilsey et al recognized the potential for patients to either (1) substitute clinicians, (2) obtain medications from a practitioner covering for the patient's customary provider, or (3) receive treatment from another practitioner that could be entirely appropriate (dentist, emergency room doctor, etc.). Assuming this is accurate, they defined the occurrence of a multiple provider episode as occurring when an individual received a prescription for the same medication from two or more practitioners filled by two or more pharmacies within a 30-day period. This practice negates the traditional gatekeeper role of a single pharmacist who would know if the patient was obtaining a medication in a justifiable manner (e.g., seeing a physician on-call for the patient s customary doctor). Using these criteria, they found that opioid prescriptions (12.8%) were most frequently involved in multiple provider episodes, followed by benzodiazepines (4.2%), stimulants (1.4%), and anorectics (0.9%), respectively. The greatest associations with multiple provider episodes were simultaneously receiving prescriptions for different controlled substances (polypharmacy of controlled substances). 64 A second study by this group attempted to find a threshold for identifying patients who used multiple providers. Using data from the California prescription drug monitoring program, this study found that patients who used two to five providers to obtain opioids did not differ consequentially in terms of their demographics and prescription utilization characteristics from patients who used only one provider during a one-year period. This was consistent with the proposition that many patients who use up to five prescribers in a one-year period might have justifiable reasons for doing so. 63 A 2009 United States Government Accountability Office (GAO) report on Medicaid fraud and abuse of controlled substances reviewed claims during Fiscal Years (FY) 2006 and 2007 in five states - California, Illinois, New York, North Carolina, and Texas. The GAO defined doctor shopping as seeing six or more different prescribers to obtain prescriptions for the same type of controlled substance during FY 2006 and 2007, and found that nearly 65,000 Medicaid recipients met these criteria. Table 1-6 shows the number of beneficiaries receiving prescription opioids from multiple prescribers. 16 Table 1-6. Medicaid Beneficiaries Receiving Prescription Opioids from Multiple Prescribers, FY 2006 and 2007 Prescription Opioid Number of Prescribers in Selected States Total Fentanyl Hydrocodone 31,364 3, ,954 Hydromorphone Methadone Morphine Oxycodone 5, ,

22 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Number of Prescribers in Selected States Prescription Opioid Total Total Prescription Opioids 39,714 4, ,146 Total Controlled Substances* 64,239 5, ,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 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 Prescription Opioid Number of Prescribers in Selected States Total Codeine with Acetaminophen 1, ,525 Fentanyl 5, ,077 Hydrocodone 92,801 3, ,394 Hydromorphone 2, ,551 Meperidine Methadone 3, ,423 Morphine 6, ,391 Oxycodone 54,183 1, ,837 Tramadol 4, ,527 Total Prescription Opioids 170,261 5,833 1, ,882 Total Controlled Substances* 181,823 5,927 1, ,574 Source: United States Government Accountability Office (2011) Note: The numbers in the columns do not represent unique beneficiaries. There are 170,029 unique beneficiaries *Additional substances included amphetamine derivatives, benzodiazepine, carisoprodol, methylphenidate, and non-benzodiazepine sleep aids Epidemiologists have also explored the relationship between shopping behavior and drug-related death. Using data from West Virginia s prescription drug monitoring program and drug-related death data compiled in their state forensic database, Peirce et al analyzed trends in doctor and pharmacy shopping among living and deceased individuals. They found a significantly greater proportion of deceased subjects were doctor shoppers and pharmacy shoppers (25% and 17%) than living subjects (4% and 1%). As depicted in Figure 1-4, these researchers reported that 55% of pharmacy shoppers also met criteria for doctor shoppers, whereas only 20% of doctor shoppers met criteria for pharmacy shopping. 65 Thus, as the authors 1.15

23 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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, Doctor Shopping Both Doctor & Pharmacy Shopper Pharmacy Shopping Both Pharmacy & Doctor Shopper 80% 20% 45% 55% 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 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). 1.16

24 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table 1-8. Distribution of Patients by Number of Prescribers and Pharmacies, 2006 # # Pharmacies Used Prescribers Used Total % (n) % 1.65% 0.25% 0.06% 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% % (443,956) % 3.05% 0.41% 0.11% 0.03% 0.01% 0.00% 0.00% 0.00% 0.00% 13.37% (75,191) % 1.25% 0.41% 0.11% 0.04% 0.01% 0.00% 0.00% 0.00% 0.00% 4.43% (24,919) % 0.54% 0.24% 0.09% 0.03% 0.01% 0.00% 0.00% 0.00% 0.00% 1.77% (9,980) % 0.23% 0.12% 0.06% 0.03% 0.01% 0.01% 0.00% 0.00% 0.00% 0.76% (4,274) % 0.10% 0.06% 0.04% 0.02% 0.01% 0.00% 0.00% 0.00% 0.00% 0.34% (1,887) % 0.05% 0.04% 0.02% 0.02% 0.01% 0.01% 0.00% 0.00% 0.00% 0.18% (1,025) % 0.03% 0.02% 0.01% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% 0.10% (543) % 0.01% 0.01% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.05% (296) % 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.09% (520) Total % (n) 90.62% (509,818) 6.91% (38,865) 1.58% (8,870) 0.52% (2,917) 0.20% (1,138) 0.08% (464) 0.04% (248) 0.02% (108) 0.01% (76) 0.02% (87) % (562,591) Taken From: Katz et al (2010). Usefulness of prescription monitoring programs for surveillance analysis of Schedule II opioid prescription data in Massachusetts, Figure 2A. Table 1-9. Distribution of Patients Using Multiple Prescribers and Pharmacies, 2006 # Pharmacies Used # Providers Used n , % 2.5% 0.12% 0.01% 0.00% 0.00% 2 75, % 27.0% 1.12% 0.10% 0.01% 0.00% 3 24, % 41.2% 3.74% 0.44% 0.07% 0.02% 4 9, % 51.2% 7.57% 0.98% 0.13% 0.03% 5 4, % 61.2% 14.09% 2.32% 0.28% 0.05% 6 1, % 68.0% 20.83% 5.14% 1.17% 0.21% 7 1, % 78.0% 30.34% 9.85% 1.27% 0.10% % 81.6% 34.44% 13.08% 3.87% 0.37% % 84.5% 47.30% 19.26% 7.09% 1.35% % 91.2% 69.23% 42.88% 24.62% 11.92% Total Population 562, % 9.38% 0.90% 0.17% 0.05% 0.02% Source: Katz et al (2010) 1.17

25 Percent Dispensed Percent Distriubtion Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Distribution of Patients Using Multiple Prescribers and Pharmacies, 2008 # of Prescribers Number of Pharmacies Total (n) % 3.32% 0.75% 0.19% 0.11% 48.10% (36,178) % 4.67% 1.70% 0.54% 0.26% 31.63% (23,790) % 3.21% 1.67% 0.69% 0.40% 9.26% (6,967) % 1.48% 1.18% 0.63% 0.41% 4.46% (3,357) % 1.23% 1.47% 1.19% 2.25% 6.55% (4,923) Total (n) 72.67% (54,658) 13.91% (10,460) 6.75% (5,080) 3.24% (2,439).34% (2,578) 100% (75,215) Taken from: Cepeda et al (2012). Opioid Shopping Behavior: How Often, How Soon, Which Drugs, and What Payment Method? Table 2. Note: Total population was 25,161,024; of that, 72,215 (0.30% met criteria for doctor shopping Figure 1-5. Type and Formulation of Prescription Opioids Dispensed to Doctor Shoppers Compared to Non-Doctor Shoppers Formulation Type Schedule Type 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% II Only III Only Other II & III II & Other II, III, & Other III & Other 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% IR ER Combo IR & ER IR & Combo ER & Combo IR, ER, & Combo Non-Doctor Shopper Doctor Shopper 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) 1.18

26 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 to 12.2 million in , 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 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 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 , Kenan et al found that the number of opioid prescriptions per 100 individuals increased 35.3%, from 61.9 to Additionally, the average prescription size (expressed as morphine milligram equivalent [MME] per day multiplied by the prescription duration) of both hydrocodone and oxycodone prescriptions increased nearly 70%, from 170MME to 288MEE and 923MME to 1566MME, respectively. 86 In 2005, nearly 100 million prescriptions were written for hydrocodone, making it the most commonly prescribed drug in the United States. In 1.19

27 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 , 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 CDC 43 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 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 , 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 , 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 KASPER data revealed that the top 10% of prescribers were responsible for the vast majority of all prescriptions for controlled substances

28 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 , 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 , 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 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 DDD/1000/d and increased to DDD/1000/d in From , 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 95 th 99 th percentile in the Medicaid population consumed approximately 26% of total opioids and the top 1% (99 th 100 th 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 (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 The study compared trends and risks of opioid use, misuse and abuse in two populations a national commercially insured population (HealthCore Blue Cross and Blue Shield) and the Arkansas Medicaid population. 1.21

29 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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= ) 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, 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 , however, prevalence rates for females remain below those of males (see Table 1-11). 27 Table Prevalence of Nonmedical Prescription Opioid Use by Gender, 2002 & 2010 Lifetime (%) Past Year (%) Past Month (%) Males Females Males Females Males Females Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 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 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 The study compared trends and risks of opioid use, misuse and abuse in two populations a national commercially insured population (HealthCore Blue Cross and Blue Shield) and the Arkansas Medicaid population. 1.22

30 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 Prevalence of Nonmedical Opioid Use by Race/Ethnicity, 2010 Lifetime (n=34,776,000) Past Year (n=12,213,000) Past Month (n=5,100,000) Overall White Black or African American Hispanic or Latino Asian Native American or Alaska Native Native Hawaiian or Other Pacific Islander 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 Prevalence of Nonmedical Opioid Use by Geographic Location, 2010 Lifetime (n=34,776,000) Past Year (n=12,213,000) Past Month (n=5,100,000) Overall Region Northeast Midwest South West County Type Large Metro (>1 million population) Small Metro (20, ,999 population) Nonmetro (0-19,999 population) Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2010 Behavioral Risk Factors Multiple studies and surveys have identified non-opioid substance abuse as a risk factor for prescription opioid misuse and abuse. 46,50,93,99,103 The NSDUH repeatedly identifies illicit substance and alcohol abuse as a risk factor for past-year nonmedical opioid use and abuse. 50,103 Among males, other illicit drug abuse or dependence (such as cocaine, heroin, hallucinogens, or inhalants) and alcohol abuse or dependence are significantly associated with past-year opioid 1.23

31 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 % 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 The study compared trends and risks of opioid use, misuse and abuse in two populations a national commercially insured population (HealthCore Blue Cross and Blue Shield) and the Arkansas Medicaid population. 1.24

32 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 Odds of Opioid Misuse and Abuse by Average Daily Dose and Days Supply Possible Misuse a Probable Misuse a Abuse/Dependence b Arkansas Arkansas Commercially Commerically Medicaid Medicaid Insured (OR) Insured (OR) (OR) (OR) Commercially Insured (OR) Arkansas Medicaid (OR) Daily Dose <Median mg/day Median-120 mg/day >120 mg/day Days Supply days NR NR NR NR days NR NR NR NR >185 days NR NR NR NR Source: a Sullivan et al (2010); b Edlund et al (Nov 2010) Note: Median daily dose was 32mg and 35 mg morphine equivalent in the commercially insured and Arkansas Medicaid samples, respectively. NR=not reported. Summary Over the past two decades, as physicians managed pain more aggressively and prescribed stronger opioids more frequently and at higher doses, studies and surveys at the state and national level have documented an increase in the prevalence of nonmedical use, misuse, and abuse of these drugs, particularly products containing hydrocodone, oxycodone and methadone. Risk factors identified for prescription opioid misuse and abuse include (but are not limited to) demographic factors (gender, race/ethnicity, etc), non-opioid substance use, and comorbid mental health disorders. State- and national-level surveys show increased numbers of patients who receive high doses of prescription opioids (in excess of 100mgMED/d), and who are chronic users (continuous use for longer than 90 days). Analyses of state- and Medicaid/Medicare data 1.25

33 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 , 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 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 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 ED Visits for Prescription Opioids Compared to Illicit Drugs, 2010 Type of Drug-Related ED Visit Number of Visits Rate a Percent Change Patients were identified as abusers if they had at least one claim with an ICD-9 code related to prescription opioid abuse from (304.0, 304.7, 305.5, and 965, but excluding ). A group of matched controls without an opioid abuse diagnosis served as the comparison group. 1.26

34 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Type of Drug-Related ED Visit Number of Rate a Percent Change Visits Total Drug-Related ED Visits b 4,916, % Prescription Opioids 425, % Oxycodone 182, % Hydrocodone 115, % Methadone 76, % Illicit Drugs 1, NR Cocaine 488, NR Heroin 224, NR Marijuana 461, % Methamphetamine 94, % Source: Drug Abuse Warning Network (2010) NR = Not Reported 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 In addition to having the highest number of opioid-related visits, the rate of increase from was highest among individuals ages 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 Opioid-Related ED Visits by Gender and Race/Ethnicity, 2010 Opiates (Total) Oxycodone Hydrocodone # of visits Rate a % # of Change b Rate a % # of visits Change b visits Rate a % Change b Gender Male 229, % 104, % 55, % Female 196, % 78, % 59, % Race/Ethnicity White 343,620 NR 186% 155,566 NR 301% 89,330 NR 156% Black 38,400 NR 188% 13,305 NR 406% 12,966 NR 309% Hispanic 18,692 NR 197% 4,194 NR 308% 6,612 NR 349% Other 3,279 NR 471% 1,776 NR NR 659 NR 663% Age Group >21 31, % 17, % 8, NR , % 23, % 16, % , % 23, % 13, % , % 18, % 14, % , % 36, % 21, % , % 36, % 24, % , , % 10, % 65< 24, % 8, % 7, % 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

35 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 and found that opioid abusers were more likely to suffer multiple co-morbidities. 9 In a similar vein, using data from , 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 , 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 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. 1.28

36 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 1.29

37 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Prevalence of Select Comorbidities among Opioid Abusers compared to Nonabusers Privately Insured a National Medicaid b Privately Insured c Florida Medicaid c Abusers n=740 Controls n=1,266 Abusers N=50,162 Controls N=150,485 Abusers n=4,474 Controls n=4,474 Abusers n=4,467 Controls n=4,467 Psychiatric Disorders 71.1% 8.4% 49.3% 26.1% 74.5% 12.0% 68.5% 23.2% Trauma 36.5% 15.0% 31.2% 19.8% 45.5% 18.2% 45.5% 12.4% Non-Opioid Substance Abuse 50.4% 1.2% 45.1% 8.2% 46.6% 1.5% 59.7% 6.6% Non-Opioid Poisoning 17.6% 0.2% NR NR 17.1% 0.6% 23.1% 0.8% Gastrointestinal Bleeding 8.0% 2.6% 8.6% 6.3% 13.1% 4.5% 16.9% 4.6% Skin Infections/ Abscesses 10.1% 2.5% 12.7% 5.4% 12.4% 4.0% 17.8% 4.1% Sexually Transmitted Disease 8.0% 4.3% 8.6% 7.6% 8.1% 4.0% 9.6% 5.6% Hepatitis A, B or C 6.5% 0.2% 17.1% 2.4% 4.1% 0.2% 12.4% 1.1% Pancreatitis 0.9% 0.05% 1.7% 0.6% 2.5% 0.2% 4.8% 0.4% Chronic Low Back Pain NR NR NR NR 21.7% 3.2% 24.8% 3.3% Arthritis NR NR 27.3% 19.5% 17.7% 5.1% 18.0% 3.9% Other Back/Neck 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: a White 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 (excluding ) NR = Not Reported 1.30

38 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 year olds, and, behind motor vehicle accidents, the second leading cause overall. 1,43,105,117 Table Deaths Involving Prescription Opioids, All Drug Overdose Deaths Opioid-Related Overdose Deaths Methadone- Related Opioid Overdose Deaths 17,415 19,394 23,518 25,785 27,424 29,813 34,425 36,010 36,450 4,030 (23.1%) 986 (5.6%) 5,528 (28.5%) 1,456 (7.5%) 7,456 (31.7%) 2,358 (10.0%) 8,517 (33.0%) 2,972 (11.5%) 9,817 (35.8%) 3,845 (14.0%) 10,928 (36.7%) 4,460 (14.0%) 13,723 (39.9%) 5,406 (15.0%) 14,408 (40.0%) 5,518 (15.3%) 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. 14,800 (40.6%) 4,924 (13.5%) From , 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 , 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 (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,

39 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Prescription Opioid Overdose Death Rates by Selected Demographics, a 2006 a 2008 b Overall Gender Male Female Race/Ethnicity Non-Hispanic White Hispanic White Non-Hispanic Black Asian/Native Hawaiian/Pacific Islander NR NR 0.5 Native American/Alaska Native NR NR 6.2 Age Group and older Source: a Warner et al (2009); b CDC (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 , 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 , 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 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 North Carolina state death certificate data for Medicaid recipients, Whitmire and Adams uncovered an association of substance abuse and mental health disorders 1.32

40 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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, 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 (representing less than 1% of all unintentional prescription drug overdoses), but these deaths increased from 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 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

41 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Opioid-Related Mortality by State Citation State Years Studied Data Source Population Drug Category Number of Deaths % Increase Ballesteros, North Medical examiner All Residents Methadone : 198 deaths 566% 2003 Carolina CDC, 2005 Utah Medical examiner All Residents Prescription Drugs : 231 deaths 385% : 502 deaths Methadone : 18 deaths 1,358% : 164 deaths Oxycodone : 10 deaths 1,676% : 111 Hydrocodone : % : 83 Hall, 2008 West Virginia 2006 Medical All Residents Prescription Drugs 295 deaths examiner; PDMP; Prescription Opioids 275 deaths NR Treatment Methadone 112 deaths NR Records Hydrocodone 67 deaths NR Oxycodone 61 deaths NR Wunsch, 2009 Virginia Medical examiner Residents of Prescription Drugs 893 deaths Western VA Prescription Opioids 658 deaths NR Methadone 184 deaths NR Hydrocodone 134 deaths NR Oxycodone 129 deaths NR CDC, 2009 Washington Death certificate All Residents Prescription Opioids 1,668 deaths NR Methadone 1,068 deaths NR Oxycodone 382 deaths NR Hydrocodone 232 deaths NR Medicaid Prescription Opioids 724 deaths NR Enrollees PRR Enrollees Prescription Opioids 34 deaths NR Ohio, 2010 Ohio 2010 Death Certificate All residents All Drugs 1,544 deaths NR Prescription Opioids 694 deaths NR PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction Program; NR = Not reported in this study 1.34

42 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Opioid-Related Mortality by State, Continued Citation State Years Number of Deaths Data Source Population Drug Category Studied % Increase Piercefield, Oklahoma Medical All Residents Methadone : 21 deaths 1,695% 2010 Examiner : 377 deaths Oxycodone : 1 death 17,300% : 174 deaths Hydrocodone : 9 deaths 2,344% : 220 deaths Shah, 2011 New Mexico Medical Examiner All Residents Prescription Opioids 955 deaths 159% PDMP = Prescription Drug Monitoring Program; PRR = Patient Review and Restriction Program; NR = Not reported in this study 1.35

43 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 mgMED/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 Compared with doses between 0-19mgMED/day, the odds of overdose were twice as high among individuals receiving doses between mgMED/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 mgMED/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 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 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 1.36

44 Overdose Risk Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Figure 1-6. Relationship between Opioid Dosage Level and Fatal/Non-Fatal Overdose Risk Opioid Dosage Level (mg morphine equivelant per day) 1-19mgMED/d 20-49mgMED/d 50-99mgMED/d >100mgMED/d >200mgMED/d Dunn (i) Dunn (ii) Gomes (iii)* Bohnert (iv) Bohnert (v) Bohnert (vi) 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 1.37

45 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 (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 and 65 and older, as well as females ages 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 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)

46 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Figure 1-7. Percentage of Opioid Users and Overdoses, by Risk Group 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 80% 10% 10% Opioid Users 20% 40% 40% 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 Opioid-Related Overdoses 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 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 ( and ) and a Florida Medicaid sample ( ) (see Figure 1-8). Data for the privately insured sample came from claims data for approximately 2 million members from 16 large, nationwide employers; data for the

47 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 1.40

48 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Figure 1-8. Average Annual Direct Health Care Costs* per Opioid Abuse Patient $30,000 Privately-Insured Sample, Privately-Insured Sample, $26,724 Florida Medicaid Sample, $25,000 $24,193 $3,667 $4,769 $20,000 $5,820 $15,000 $15,884 $793 $2,034 Excess Cost: $14,054 $3,918 $5,795 Excess Cost: $20,546 $2,826 Excess Cost: $15,183 $11,541 $10,000 $5,398 $14,410 $6,466 $5,000 $0 $9,711 $323 $3,647 $7,659 $386 $3,104 $1,830 $877 $198 $1,697 $657 $318 $928 $750 $1,314 Opioid Abuse Patients Controls Opioid Abuse Patients Controls Opioid Abuse Patients Controls Hospital Inpatient Costs Physician/Outpatient Costs Drug Costs Other Costs** Source: White et al (2005); White et al (2011) *Costs for the sample are in 2003 dollars; costs for sample are in 2009 dollars **Other costs include ED visits, lab services, and treatment occuring at other places of service for patients ages years old 1.41

49 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature privately insured sample came from claims data for approximately 9 million members from 40 self-insured, nationwide employers. The 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 , 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 , 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 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 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 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 Costs to both Medicaid and Medicare for all controlled substances represent 5-6% of the total costs of these drugs to the programs

50 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Prescription Opioid Doctor Shopping among Medicaid Beneficiaries and Associated Costs, Number of Prescribers in Selected States Total Total Prescription Cost Fentanyl $7,810,000 Hydrocodone 31,364 3, ,954 $9,172,000 Hydromorphone $983,000 Methadone $546,000 Morphine $4,119,000 Oxycodone 5, ,875 $10,163,000 Total Prescription Opioids 39,714 4, ,146 $32,793,000 Total Controlled Substances* 64,239 5, ,636 $63,280,000 Source: United States Government Accountability Office, 2009 Note: The numbers in the columns do not represent unique beneficiaries. There are 64,920 total unique beneficiaries *Additional analyzed substances included amphetamine derivatives, benzodiazepine, methylphenidate, and nonbenzodiazepine sleep aids Table Prescription Opioid Doctor Shopping among Medicare Beneficiaries and Associated Costs, 2008 Number of Prescribers in Selected States Total Total Prescription Cost Codeine with Acetaminophen 1, ,525 $244,930 Fentanyl 5, ,077 $19,124,853 Hydrocodone 92,801 3, ,394 $18,949,677 Hydromorphone 2, ,551 $1,236,678 Meperidine $90,236 Methadone 3, ,423 $859,208 Morphine 6, ,391 $9,311,773 Oxycodone 54,183 1, ,837 $91,681,281 Tramadol 4, ,527 $1,037,423 Total Prescription Opioids 170,261 5,833 1, ,882 $141,498,636 Total Controlled Substances* 181,823 5,927 1, ,574 $147,948,251 Source: United States Government Accountability Office, 2011 Note: The numbers in the columns do not represent unique beneficiaries. There are 170,029 unique beneficiaries *Additional analyzed substances included amphetamine derivatives, benzodiazepine, carisoprodol, methylphenidate, and non-benzodiazepine sleep aids State-Specific Health Care Costs Some states, such as Utah, Ohio and Florida, have higher opioid expenditures per enrollee compared to the national average, while other states, such as California, New York and Texas, have lower per enrollee expenditures. 15 Using claims data from the Louisiana Workers Compensation Corporation, Bernacki et al analyzed the trends in annual cumulative opioid dose and cost of opioids per claim prescribed for work-related injuries from 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 1.43

51 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 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 Outcomes: Societal Costs The societal burden attributable to nonmedical use of prescription opioids was estimated to be $9.5 billion in ,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 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 Annual Societal Burden of Prescription Opioid Abuse, 2001 & Cost a Percentage b Cost a Percentage b Total Societal Cost $8, % $55, % Lost Workplace Productivity Costs $4, % $25, % 1.44

52 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Cost a Percentage b Cost a Percentage b Premature Death $ % $11, % Lost Wages/Employment $3, % $7, % Presenteeism NR NR $2, % Excess Medically Related Absenteeism NR NR $1, % Incarceration Costs $ % $1, % Excess Disability Costs NR NR $ % Healthcare Costs $2, % $24, % Excess Medical and Drug Costs $2, % $23, % Substance Abuse Treatment $ % $1, % Substance Abuse Prevention NR NR $85 0.2% Substance Abuse Research NR NR $69 0.1% Criminal Justice Costs $1, % 5, % Correctional Facilities $ % $2, % Police Protection $ % $1, % Legal Adjudication Costs $ % $ % 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 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 Societal Burden of Nonmedical Prescription Opioid use, 2006 Oxycodone Hydrocodone Methadone Other Total Total $13, $12, $6, $21, $53, Substance Abuse $ (5.1%) $ (14.1%) $98.50 (1.6%) $ (4.3%) $2, (4.2%) Hospital Inpatient $ $97.82 $18.24 $ $ Hospital Outpatient $99.85 $77.39 $14.43 $ $ Physicians $ $86.28 $16.09 $ $ Substance Abuse Facilities $ $ $49.74 $ $ Medical Complications $ $1.40 $30.21 $ $ (1.5%) (0.01%) (0.5%) (2.5%) (1.4%) HIV/AIDS $ NR $19.22 $ $ Chronic Hepatitis C $69.21 NR $10.90 $ $

53 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Oxycodone Hydrocodone Methadone Other Total Neonatal Care $1.19 $1.40 $0.09 $1.77 $4.45 Lost Productivity $10, (78.6%) $9, (77.1%) $5, (94.2%) $15, (75.9%) $42, (79.0%) Premature Death $2, $2, $4, $3, $12, Unemployment $4, $3, $ $6, $14, Incarceration $3, $4, $ $6, $14, Criminal Justice $1, (14.8%) $2, (18.8%) $ (3.7%) $3, (17.3%) $8, (15.4%) Police $ $ $95.08 $1, $3, Legal Costs $ $ $47.54 $ $1, Incarceration $ $ $69.64 $1, $2, Cost to Crime Victims $ $ $17.28 $ $ 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. 1.46

54 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature Table Policy Interventions to Reduce the Burden of Prescription Opioid Misuse Intervention Points Policy Interventions PMDPs PRR Laws/Regulations/Policies Insurers/PBM Clinical Guidelines Pill Mills * X X Problem Prescribing X X X x General Prescribing X X X X EDs & Hospitals X X X X Pharmacies X X X X X Insurers & Pharmacy X X X X Benefit Managers High Risk Patients X X X X X General Patients & X X X Public Source: Arias, I (2012). 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 (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., 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 1.47

55 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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., 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 , 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 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 1.48

56 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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, 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, 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 C.F.R C(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

57 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 December 2005 (pre-lock-in) compared to November 2006-December 2007 (post-lock-in). They found significant decreases in the average number of narcotic and all pharmacy claims, pharmacies and prescribers used, and ED visits. Average prescription opioid costs decreased by nearly $13 per month, pharmacy costs decreased by $30 per month, and ED costs decreased by $259 per month. For the first twelve months post lock-in, Mitchell estimated cumulative savings of $31,524, and per member annual savings of $ ,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 1.50

58 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 , 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 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 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 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 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 1.51

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

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

61 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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 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 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 forms 14, whereas other states have adopted other methods. 168 Table The Presence of Prescription Drug Monitoring Programs, Overdose Mortality and Opioid Consumption Rates All Prescription Overdose Mortality Rate a Opioid Overdose Mortality Rate MME/Person/Year States without PDMPs States with PDMPs States with Proactive PDMPs States with High-Reporting PDMP California, New York & Texas Tamper-resistant prescription forms are designed to prevent (1) unauthorized copying, (2) erasure or modification of information written by the prescriber and (3) use of counterfeit prescription forms. 1.54

62 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 , Katz et al analyzed the number of prescribers and pharmacies and their relationship with questionable activity (a possible indicator of opioid misuse and/or diversion). Using a threshold of 3 or more prescribers and pharmacies, 1.6% of individuals and nearly 8% of prescriptions met criteria, but when the threshold increased to 4 or more prescribers and pharmacies, only 0.5% of individuals and 3.1% of prescriptions qualified. Increasing the threshold moves to 5 or more prescribers and pharmacies shrank the number of qualified individuals and prescriptions to 0.2% and 1.5%, respectively. 61 Virginia Virginia established their PDMP in 2002, and it monitors dispensed prescriptions for Schedule II-IV controlled substances. In 2004, Barrett conducted an evaluation of the impact of the program on physicians and prescribing behaviors. Barrett found that 36% of physicians reported prescribing fewer Schedule II drugs since the PMP was implemented and 57% reported prescribing more Schedule III and IV drugs instead. Sixty-eight percent of responding physicians said that the program was useful in decreasing the incidence of doctor shopping. 171 Maine In 2004, Maine implemented a PDMP within in the Office of Substance Abuse (OSA), and the program monitors dispensed prescriptions for Schedule II-IV drugs. In 2007, Lambert conducted an impact evaluation of the program to assess utilization by physicians. The PDMP proactively issues threshold reports to prescribers, which alert the prescriber to individuals who may be receiving excessively high levels of prescription drugs. Lambert found that three-quarters of 1.55

63 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 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. 1.56

64 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature SUMMARY Over the past two decades, as physicians managed pain more aggressively and prescribed stronger opioids more frequently and at higher doses, studies and surveys at the state and national level have documented an increase in the prevalence of nonmedical use, misuse, and abuse of these drugs, particularly products containing hydrocodone, oxycodone, and methadone. These state- and national-level studies and surveys have also found increased numbers of patients receiving high doses of prescription opioids (in excess of 100mg MED/d) and who are chronic users (continuous use for longer than 90 days). The literature suggests that it is this subset of patients continuous users receiving high doses as well as those receiving prescription opioids through doctor/pharmacy shopping who may account for much of the increases in frequency of opioid-related health care utilization (i.e., ED visits), mortality, and costs (healthcare, workplace, criminal justice, etc). This literature review examined the effectiveness of two policies patient review and restriction programs and prescription drug monitoring programs intended to reduce the increase in prescription opioid misuse and abuse and subsequent health and economic outcomes, particularly among Medicaid recipients. While numerous states have implemented one or both of these policies and have been found to decreases prescription drug costs and able to identify potential doctor/pharmacy shoppers, little has been published on the impact of these programs on healthcare utilization and mortality. 1.57

65 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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) 1.58

66 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 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 1.59

67 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature BIBLIOGRAPHY 1. Centers for Disease Control and Prevention. Vital signs: overdoses of prescription opioid pain relievers---united States, MMWR. Morbidity and mortality weekly report. Nov ;60: Centers for Disease Control and Prevention. Drug overdose deaths--florida, MMWR. Morbidity and mortality weekly report. Jul ;60(26): Maxwell JC. The prescription drug epidemic in the United States: a perfect storm. Drug and alcohol review. May 2011;30(3): DuPont RL. Prescription drug abuse: an epidemic dilemma. Journal of psychoactive drugs. Jun 2010;42(2): Manchikanti L, Helm S, 2nd, Fellows B, et al. Opioid epidemic in the United States. Pain physician. Jul 2012;15(3 Suppl):ES Substance and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. Highlights of the 2009 Drug Abuse Warning Network (DAWN) Findings on Drug-Related Emergency Department Visits. Rockville, MD Centers for Disease Control and Prevention NCfHS. Underlying Causes of Death, CDC WONDER Online Database. Accessed July 31, Chou R, Fanciullo GJ, Fine PG, et al. Clinical Guidelines for the Use of Chronic Opioid Therapy in Chronic Noncancer Pain. The journal of pain : official journal of the American Pain Society. 2009;10(2): e White AG, Birnbaum HG, Mareva MN, et al. Direct costs of opioid abuse in an insured population in the United States. Journal of managed care pharmacy : JMCP. Jul-Aug 2005;11(6): Drug Abuse Warning Network. Selected Tables of National Estimates of Drug-Related Emergency Department Visits. Rockville, MD:: Center for Behavioral Health Statistics and Quality, SAMHSA; Substance Abuse and Mental Health Services Administration. Results from the 2010 National Survey on Drug Use and Health: National Findings. 2011;Series H-36, (HHS Publication No. SMA ). 12. Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. Highlights of the 2010 Drug Abuse Warning Network (DAWN) Findings on Drug-Related Emergency Department Visits. Rockwille, MD. July Warner M, Chen LH, Makuc DM, Anderson RN, Minino AM. Drug poisoning deaths in the United States, NCHS data brief. Dec 2011(81): Banthin JS, Miller GE. Trends in prescription drug expenditures by Medicaid enrollees. Medical care. May 2006;44(5 Suppl):I Brixner DI, Oderda GM, Roland CL, Rublee DA. Opioid expenditures and utilization in the Medicaid system. Journal of pain & palliative care pharmacotherapy. 2006;20(1): United States Government Accountability Office. Medicaid: Fraud and Abuse Related to Controlled Substances Identifiied in Selected States. September 2009; GAO

68 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 17. United States Government Accountability Office. Report to Congressional Requesters. Medicare Part D: Instances of Questionable Access to Prescription Drugs. September 2011; GAO McAdam-Marx C, Roland CL, Cleveland J, Oderda GM. Costs of opioid abuse and misuse determined from a Medicaid database. Journal of pain & palliative care pharmacotherapy. Mar 2010;24(1): Birnbaum HG, White AG, Reynolds JL, et al. Estimated costs of prescription opioid analgesic abuse in the United States in 2001: a societal perspective. Clin J Pain. Oct 2006;22(8): Birnbaum HG, White AG, Schiller M, Waldman T, Cleveland JM, Roland CL. Societal costs of prescription opioid abuse, dependence, and misuse in the United States. Pain Med. Apr 2011;12(4): Hansen RN, Oster G, Edelsberg J, Woody GE, Sullivan SD. Economic costs of nonmedical use of prescription opioids. The Clinical journal of pain. Mar-Apr 2011;27(3): Volkow ND. Efforts of the National Institute on Drug Abuse to prevent and treat prescription drug abuse Subcommittee on criminal justince, drug policy, and human resources, Committee on government reform. Washington, DC: National Institute on Drug Abuse Department of Justice, Drug Enforcement Administration. 21 CFR Part 1300, 1301, 1304, et al. Implementation of the Ryan Haight Online Pharmacy Consumer Protection Act of 2008; Final Rule. April Jena AB, Goldman DP. Growing Internet use may help explain the rise in prescription drug abuse in the United States. Health affairs (Project Hope). Jun 2011;30(6): Executive Office of the President of the United States. Epidemic: Responding to America's Prescription Drug Abuse Crisis Accessed February 7, Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Unintentional Injury Prevention. Policy Impact: Prescription Painkiller Overdose. November 2011; PrescriptionPainkillerOD.pdf. Accessed February 6th, Substance Abuse and Mental Health Services Administration, Office of Applied Studies. National Survey on Drug Use and Health Accessed April 26, Center for Substance Abuse Treatment. Prescription Medications: Misuse, Abuse, Dependence and Addiction.. Substance Abuse Treatment Advisory. May 2006;5(2). 29. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed, text revision. Washington, DC: American Psychiatric Press Substance Abuse and Mental Health Services Administration. Drug Abuse Warning Network, 2009: Methodology Report. Rockville, MD: Substance Abuse and Mental Health Services Administration

69 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 31. United States Department of Justice, Drug Enforcement Administration, Office of Diversion Control. Controlled Substance Schedules Accessed July 11, 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): PMCID:PMC Franklin GM, Mai J, Wickizer T, Turner JA, Fulton-Kehoe D, Grant L. Opioid dosing trends and mortality in Washington State workers' compensation, American journal of industrial medicine. Aug 2005;48(2): Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Trends in use of opioids for non-cancer pain conditions in commercial and Medicaid insurance plans: the TROUP study. Pain. Aug ;138(2): PMCID:PMC Centers for Disease Control and Prevention. CDC Grand Rounds: Prescription Drug Overdoses - a U.S. Epidemic. MMWR. Morbidity and mortality weekly report. Jan ;61: Dunn KM, Saunders KW, Rutter CM, et al. Opioid prescriptions for chronic pain and overdose: a cohort study. Annals of internal medicine. Jan ;152(2): PMCID:PMC 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 ;305(13): 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 ;171(7): 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): PMCID:PMC Sporer KA. Buprenorphine: A primer for emergency physicians. Annals of emergency medicine. 2004;43(5): Section 309, Diversion Prevention and Control. Uniform Controlled Substances Act: National Conference of Commissioners on Uniform State Laws, Rigg KK, March SJ, Inciardi JA. Prescription Drug Abuse & Diversion: Role of the Pain Clinic. Journal of drug issues. 2010;40(3): PMCID:PMC Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Unintentional Drug Poisoning in the United States. July 2010; Cicero TJ, Inciardi JA, Munoz A. Trends in abuse of Oxycontin and other opioid analgesics in the United States: The journal of pain : official journal of the American Pain Society. Oct 2005;6(10): Gilson AM, Ryan KM, Joranson DE, Dahl JL. A reassessment of trends in the medical use and abuse of opioid analgesics and implications for diversion control: Journal of pain and symptom management. Aug 2004;28(2): Back SE, Payne RL, Simpson AN, Brady KT. Gender and prescription opioids: findings from the National Survey on Drug Use and Health. Addictive behaviors. Nov 2010;35(11): PMCID:PMC

70 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 47. Curtis LH, Stoddard J, Radeva JI, et al. Geographic variation in the prescription of schedule II opioid analgesics among outpatients in the United States. Health services research. Jun 2006;41(3 Pt 1): PMCID:PMC Canfield MC, Keller CE, Frydrych LM, Ashrafioun L, Purdy CH, Blondell RD. Prescription opioid use among patients seeking treatment for opioid dependence. Journal of addiction medicine. Jun 2010;4(2): PMCID:PMC Rosenblum A, Parrino M, Schnoll SH, et al. Prescription opioid abuse among enrollees into methadone maintenance treatment. Drug and alcohol dependence. Sep ;90(1): Becker WC, Sullivan LE, Tetrault JM, Desai RA, Fiellin DA. Non-medical use, abuse and dependence on prescription opioids among U.S. adults: psychiatric, medical and substance use correlates. Drug and alcohol dependence. Apr ;94(1-3): Parsells Kelly J, Cook SF, Kaufman DW, Anderson T, Rosenberg L, Mitchell AA. Prevalence and characteristics of opioid use in the US adult population. Pain. Sep ;138(3): Jones CM. Frequency of Prescription Pain Reliever Nonmedical Use: and Archives of internal medicine. Jun : Centers for Disease Control and Prevention. Adult use of prescription opioid pain medications - Utah, MMWR. Morbidity and mortality weekly report. Feb ;59(6): Ghate SR, Haroutiunian S, Winslow R, McAdam-Marx C. Cost and comorbidities associated with opioid abuse in managed care and Medicaid patients in the United Stated: a comparison of two recently published studies. Journal of pain & palliative care pharmacotherapy. Sep 2010;24(3): Inciardi JA, Surratt HL, Cicero TJ, Kurtz SP, Martin SS, Parrino MW. The "black box" of prescription drug diversion. Journal of addictive diseases. Oct 2009;28(4): PMCID:PMC Hernandez SH, Nelson LS. Prescription drug abuse: insight into the epidemic. Clin Pharmacol Ther. Sep 2010;88(3): Green TC, Grimes Serrano JM, Licari A, Budman SH, Butler SF. Women who abuse prescription opioids: findings from the Addiction Severity Index-Multimedia Version Connect prescription opioid database. Drug and alcohol dependence. Jul ;103(1-2): PMCID:PMC Cicero TJ, Lynskey M, Todorov A, Inciardi JA, Surratt HL. Co-morbid pain and psychopathology in males and females admitted to treatment for opioid analgesic abuse. Pain. Sep 2008;139(1): 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): Hall AJ, Logan JE, Toblin RL, et al. Patterns of abuse among unintentional pharmaceutical overdose fatalities. JAMA : the journal of the American Medical Association. Dec ;300(22): Katz N, Panas L, Kim M, et al. Usefulness of prescription monitoring programs for surveillance--analysis of Schedule II opioid prescription data in Massachusetts, Pharmacoepidemiology and drug safety. Feb 2010;19(2):

71 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 62. 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): Wilsey BL, Fishman SM, Gilson AM, et al. An analysis of the number of multiple prescribers for opioids utilizing data from the California Prescription Monitoring Program. Pharmacoepidemiology and drug safety. Dec 2011;20(12): PMCID:PMC Wilsey BL, Fishman SM, Gilson AM, et al. Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics. Drug and alcohol dependence. Nov ;112(1-2): Peirce GL, Smith MJ, Abate MA, Halverson J. Doctor and pharmacy shopping for controlled substances. Medical care. Jun 2012;50(6): Cepeda MS, Fife D, Chow W, Mastrogiovanni G, Henderson SC. Assessing opioid shopping behaviour: a large cohort study from a medication dispensing database in the US. Drug safety : an international journal of medical toxicology and drug experience. Apr ;35(4): Cepeda MS, Fife D, Chow W, Mastrogiovanni G, Henderson SC. Opioid Shopping Behavior: How Often, How Soon, Which Drugs, and What Payment Method. Journal of clinical pharmacology. Feb Inciardi JA, Surratt HL, Kurtz SP, Cicero TJ. Mechanisms of prescription drug diversion among drug-involved club- and street-based populations. Pain medicine (Malden, Mass.). Mar 2007;8(2): PMCID:PMC Inciardi JA, Surratt HL, Cicero TJ, Beard RA. Prescription opioid abuse and diversion in an urban community: the results of an ultrarapid assessment. Pain medicine (Malden, Mass.). Apr 2009;10(3): PMCID:PMC Davis WR, Johnson BD. Prescription opioid use, misuse, and diversion among street drug users in New York City. Drug and alcohol dependence. Jan ;92(1-3): PMCID:PMC Paulozzi LJ, Budnitz DS, Xi Y. Increasing deaths from opioid analgesics in the United States. Pharmacoepidemiol Drug Saf. Sep 2006;15(9): Strassels SA. Economic burden of prescription opioid misuse and abuse. J Manag Care Pharm. Sep 2009;15(7): Toblin RL, Mack KA, Perveen G, Paulozzi LJ. A population-based survey of chronic pain and its treatment with prescription drugs. Pain. Jun 2011;152(6): Franklin GM, Rahman EA, Turner JA, Daniell WE, Fulton-Kehoe D. Opioid use for chronic low back pain: A prospective, population-based study among injured workers in Washington state, The Clinical journal of pain. Nov-Dec 2009;25(9): Von Korff M, Kolodny A, Deyo RA, Chou R. Long-term opioid therapy reconsidered. Annals of internal medicine. Sep ;155(5): PMCID:PMC Manchikanti L, Abdi S, Atluri S, et al. American Society of Interventional Pain Physicians (ASIPP) Guidelines for Responsible Opioid Prescribing in Chronic Non- Cancer Pain: Part I - Evidence Assessment. Pain physician. Jul 2012;15(3 Suppl):S1-S Ballantyne JC, Shin NS. Efficacy of opioids for chronic pain: a review of the evidence. The Clinical journal of pain. Jul-Aug 2008;24(6):

72 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 78. Kalso E, Edwards JE, Moore RA, McQuay HJ. Opioids in chronic non-cancer pain: systematic review of efficacy and safety. Pain. Dec 2004;112(3): Martell BA, O'Connor PG, Kerns RD, et al. Systematic review: opioid treatment for chronic back pain: prevalence, efficacy, and association with addiction. Annals of internal medicine. Jan ;146(2): Trescot AM, Glaser SE, Hansen H, Benyamin R, Patel S, Manchikanti L. Effectiveness of opioids in the treatment of chronic non-cancer pain. Pain physician. Mar 2008;11(2 Suppl):S Trescot AM, Helm S, Hansen H, et al. Opioids in the management of chronic non-cancer pain: an update of American Society of the Interventional Pain Physicians' (ASIPP) Guidelines. Pain physician. Mar 2008;11(2 Suppl):S5-S Caudill-Slosberg MA, Schwartz LM, Woloshin S. Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs Pain. Jun 2004;109(3): Dorn SD, Meek PD, Shah ND. Increasing frequency of opioid prescriptions for chronic abdominal pain in US outpatient clinics. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. Dec 2011;9(12): e Olsen Y, Daumit GL, Ford DE. Opioid prescriptions by U.S. primary care physicians from 1992 to The journal of pain : official journal of the American Pain Society. Apr 2006;7(4): Volkow ND, McLellan TA, Cotto JH, Karithanom M, Weiss SR. Characteristics of opioid prescriptions in JAMA : the journal of the American Medical Association. Apr ;305(13): PMCID:PMC Kenan K, Mack KA, Paulozzi LJ. Trends in prescriptions for oxycodone and other commonly used opioids in the United States, Open Medicine. 2012;6(2): Manchikanti L. National drug control policy and prescription drug abuse: facts and fallacies. Pain physician. May 2007;10(3): Dembe A, Wickizer T, Sieck C, Partridge J, Balchick R. Opioid use and dosing in the workers' compensation setting. A comparative review and new data from Ohio. American journal of industrial medicine. Apr 2012;55(4): Swedlow A, Ireland J, Johnson G. Prescribing Patterns of Schedule II Opiois in California Workers' Compensation California Workers' Compensation Institute,;March Blumenschein K, Fink JL, Freeman PR, Kirsch K, Steinke DT, Talbert J. Independent Evaluation of the Kentucky All Schools Prescription Electronic Reporting (KASPER) Program: Executive Summary. Lexington, KY: University of Kentucky Institute for Pharmeutical Outcomes and Policy October Zerzan JT, Morden NE, Soumerai S, et al. Trends and geographic variation of opiate medication use in state Medicaid fee-for-service programs, 1996 to Medical care. Nov 2006;44(11): World Health Organization's Collaborating Centre for Drug Statistics Methodology. DDD - Definitions and General Considerations 2012; Accessed October 17,

73 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 93. Edlund MJ, Martin BC, Fan MY, Braden JB, Devries A, Sullivan MD. An analysis of heavy utilizers of opioids for chronic noncancer pain in the TROUP study. Journal of pain and symptom management. Aug 2010;40(2): PMCID:PMC Braden JB, Fan MY, Edlund MJ, Martin BC, DeVries A, Sullivan MD. Trends in use of opioids by noncancer pain type among Arkansas Medicaid and HealthCore enrollees: results from the TROUP study. The journal of pain : official journal of the American Pain Society. Nov 2008;9(11): PMCID:PMC Braker LS, Reese AE, Card RO, Van Howe RS. Screening for potential prescription opioid misuse in a michigan medicaid population. Fam Med Nov-Dec 2009;41(10): Edlund MJ, Steffick D, Hudson T, Harris KM, Sullivan M. Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic noncancer pain. Pain. Jun 2007;129(3): Ives TJ, Chelminski PR, Hammett-Stabler CA, et al. Predictors of opioid misuse in patients with chronic pain: a prospective cohort study. BMC Health Serv Res. 2006;6: Braden JB, Russo J, Fan MY, et al. Emergency department visits among recipients of chronic opioid therapy. Arch Intern Med. Sep 2010;170(16): Edlund MJ, Martin BC, Fan MY, Devries A, Braden JB, Sullivan MD. Risks for opioid abuse and dependence among recipients of chronic opioid therapy: results from the TROUP study. Drug Alcohol Depend. Nov 2010;112(1-2): Sullivan MD, Edlund MJ, Zhang L, Unutzer J, Wells KB. Association between mental health disorders, problem drug use, and regular prescription opioid use. Archives of internal medicine. Oct ;166(19): Reid MC, Engles-Horton LL, Weber MB, Kerns RD, Rogers EL, O'Connor PG. Use of opioid medications for chronic noncancer pain syndromes in primary care. Journal of general internal medicine. Mar 2002;17(3): PMCID:PMC Paulozzi LJ, Logan JE, Hall AJ, McKinstry E, Kaplan JA, Crosby AE. A comparison of drug overdose deaths involving methadone and other opioid analgesics in West Virginia. Addiction (Abingdon, England). Sep 2009;104(9): Tetrault JM, Desai RA, Becker WC, Fiellin DA, Concato J, Sullivan LE. Gender and non-medical use of prescription opioids: results from a national US survey. Addiction (Abingdon, England). Feb 2008;103(2): Wu LT, Woody GE, Yang C, Blazer DG. How do prescription opioid users differ from users of heroin or other drugs in psychopathology: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of addiction medicine. Mar 2011;5(1): PMCID:PMC White AG, Birnbaum HG, Schiller M, Waldman T, Cleveland J, Roland CL. Economic Impact of Opioid Abuse, Dependence, and Misuse. American Journal of Pharmacy Benefits. 2011;3(4):e59-e Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Risks for possible and probable opioid misuse among recipients of chronic opioid therapy in commercial and medicaid insurance plans: The TROUP Study. Pain. Aug 2010;150(2): PMCID:PMC Huang B, Dawson DA, Stinson FS, et al. Prevalence, correlates, and comorbidity of nonmedical prescription drug use and drug use disorders in the United States: Results of 1.66

74 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature the National Epidemiologic Survey on Alcohol and Related Conditions. The Journal of clinical psychiatry. Jul 2006;67(7): Thielke SM, Simoni-Wastila L, Edlund MJ, et al. Age and sex trends in long-term opioid use in two large American health systems between 2000 and Pain medicine (Malden, Mass.). Feb 2010;11(2): Young AM, Havens JR, Leukefeld CG. A comparison of rural and urban nonmedical prescription opioid users' lifetime and recent drug use. The American journal of drug and alcohol abuse. May 2012;38(3): Cicero TJ, Dart RC, Inciardi JA, Woody GE, Schnoll S, Munoz A. The development of a comprehensive risk-management program for prescription opioid analgesics: researched abuse, diversion and addiction-related surveillance (RADARS). Pain medicine (Malden, Mass.). Mar 2007;8(2): Sehgal N, Manchikanti L, Smith HS. Prescription opioid abuse in chronic pain: a review of opioid abuse predictors and strategies to curb opioid abuse. Pain physician. Jul 2012;15(3 Suppl):ES Martins SS, Fenton MC, Keyes KM, Blanco C, Zhu H, Storr CL. Mood and anxiety disorders and their association with non-medical prescription opioid use and prescription opioid-use disorder: longitudinal evidence from the National Epidemiologic Study on Alcohol and Related Conditions. Psychological medicine. Jun 2012;42(6): Webster LR, Cochella S, Dasgupta N, et al. An analysis of the root causes for opioidrelated overdose deaths in the United States. Pain medicine (Malden, Mass.). Jun 2011;12 Suppl 2:S Hartung DM, Middleton L, Haxby DG, Koder M, Ketchum KL, Chou R. Rates of adverse events of long-acting opioids in a state Medicaid program. Ann Pharmacother. Jun 2007;41(6): Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Unintentional Injury Prevention. Vital Signs: Prescription Painkiller Overdoses in the US. November 2011; Accessed February 7, Centers for Disease Control and Prevention. Vital signs: risk for overdose from methadone used for pain relief - United States, MMWR. Morbidity and mortality weekly report. Jul ;61: Tanabe P, Paice JA, Stancati J, Fleming M. How Do Emergency Department Patients Store and Dispose of Opioids After Discharge? A Pilot Study. Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association. Dec Manchikanti L, Singh A. Therapeutic opioids: a ten-year perspective on the complexities and complications of the escalating use, abuse, and nonmedical use of opioids. Pain physician. Mar 2008;11(2 Suppl):S Warner M, Chen LH, Makuc DM. Increase in fatal poisonings involving opioid analgesics in the United States, NCHS Data Brief. Sep 2009(22): Paulozzi LJ, Xi Y. Recent changes in drug poisoning mortality in the United States by urban-rural status and by drug type. Pharmacoepidemiology and drug safety. Oct 2008;17(10): Wunsch MJ, Nakamoto K, Behonick G, Massello W. Opioid deaths in rural Virginia: a description of the high prevalence of accidental fatalities involving prescribed 1.67

75 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature medications. The American journal on addictions / American Academy of Psychiatrists in Alcoholism and Addictions. Jan-Feb 2009;18(1):5-14. PMCID:PMC Whitmire JT, Adams GW. Unintentional Overdose Deaths in the North Carolina Medicaid Population: Prevalence, Prescription Drug Use, and Medical Care Services.: SCHS Studies: A Publication of the State Center for Health Statistics. State of North Carolina, Department of Health and Human Services, Division of Public Health August Centers for Disease Control and Prevention. Increase in poisoning deaths caused by nonillicit drugs--utah, MMWR. Morbidity and mortality weekly report. Jan ;54(2): Piercefield E, Archer P, Kemp P, Mallonee S. Increase in unintentional medication overdose deaths: Oklahoma, American journal of preventive medicine. Oct 2010;39(4): Ballesteros MF, Budnitz DS, Sanford CP, Gilchrist J, Agyekum GA, Butts J. Increase in deaths due to methadone in North Carolina. JAMA : the journal of the American Medical Association. Jul ;290(1): Shah NG, Lathrop SL, Reichard RR, Landen MG. Unintentional drug overdose death trends in New Mexico, USA, : combinations of heroin, cocaine, prescription opioids and alcohol. Addiction (Abingdon, England). Jan 2008;103(1): Centers for Disease Control and Prevention. Overdose deaths involving prescription opioids among Medicaid enrollees - Washington, MMWR. Morbidity and mortality weekly report. Oct ;58(42): Ohio Department of Health, Violence and Injury Prevention Program Ohio Drug Overdose Data: General Findings. 2010; 5BDB3924.ashx. Accessed August 28, Weimer MB, Korthuis PT, Behonick GS, Wunsch MJ. The source of methadone in overdose deaths in Western Virginia in Journal of addiction medicine. Sep 2011;5(3): PMCID:PMC Madden ME, Shapiro SL. The methadone epidemic: methadone-related deaths on the rise in Vermont. The American journal of forensic medicine and pathology. Jun 2011;32(2): Gomes T, Juurlink DN, Dhalla IA, Mailis-Gagnon A, Paterson JM, Mamdani MM. Trends in opioid use and dosing among socio-economically disadvantaged patients. Open medicine : a peer-reviewed, independent, open-access journal. 2011;5(1):e PMCID:PMC Paulozzi LJ, Kilbourne EM, Shah NG, et al. A history of being prescribed controlled substances and risk of drug overdose death. Pain medicine (Malden, Mass.). Jan 2012;13(1): Ohio Department of Health, Violence and Injury Prevention Program. Epidemic of Prescription Drug Overdose in Ohio. Accessed February 7, Ohio Department of Health, Violence and Injury Prevention Program. Data Highlight: Methadone Deaths in Ohio D3FE52D0E.ashx. Accessed August 14th,

76 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 135. Bernacki EJ, Yuspeh L, Lavin R, Tao XG. Increases in the use and cost of opioids to treat acute and chronic pain in injured workers, 1999 to Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine. Feb 2012;54(2): Widera E, Chang A, Chen HL. Presenteeism: a public health hazard. Journal of general internal medicine. Nov 2010;25(11): PMCID:PMC Merrill RM, Aldana SG, Pope JE, et al. Presenteeism According to Healthy Behaviors, Physical Health, and Work Environment. Population health management. Aug Arias I. Prescription Drug Overdoses: Public Health Policy Approach: Centers for Disease Control and Prevention.; April Centers for Medicare and Medicaid Services, Integrity CfP. Drug Diversion in the Medicaid Program: State Strategies for Reducing Prescription Drug Diversion in Medicaid. January Prevention/MedicaidIntegrityProgram/Downloads/drugdiversion.pdf. Accessed September 18, "Exceptions to certain State plan requirements". Code of Federal Regulations, Title 42, Pt , 2007 ed, Blake SG, Feldhaus JF, Hunter TS, Rappaport H, Holt G, Medon PJ. PMD2 The Effect of the Louisiana Medicaid Lock-In on Prescription Drug Utilization and Expenditures. Value in Health. 1998;1(1): Chinn FJ. Medicaid recipient lock-in program--hawaii's experience in six years. Hawaii medical journal. Jan 1985;44(1): Florida Medicaid, Agency for Healthcare Administration. Prescribed Drug Services Coverage, Limitations, and Reimbursement Handbook. _080501_Prescribed_Drug_ver1.2.pdf. Accessed May 16, Florida Medicaid. Medicaid Prescribed Drug Spending Control Program Initiatives: Quarterly Report January 1-March 31, 2005, Accessed February 7, Oklahoma Administrative Code. Freedom of Choice - Recipient Lock-In. Oklahoma Health Care Authority 317: Mitchell L. Pharmacy lock-in program promotes appropriate use of resources. The Journal of the Oklahoma State Medical Association. Aug 2009;102(8): Oklahoma Health Care Authority. SoonerCare Lock-in Program. 0CCgQFjAB&url=http%3A%2F%2Fwww.okhca.org%2FWorkArea%2Flinkit.aspx%3F LinkIdentifier%3Did%26ItemID%3D10414&ei=o5WFUNLMCKKRiQLS1YHYCA&us g=afqjcnfb3idmeldllhrnv4irdiwcnmxxmg&sig2=abmwc21hj6vbsuwpjxgxi A. Accessed October 22, Washington Administrative Code. Patient Review and Restiction Criteria.. Washington State Health Care Authority Accessed May 15, Washington State Health Care Authority. Patient Review & Coordination Program. Accessed May 15,

77 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 150. Kuss T. Narcotic Review Program, Patient Review and Coordination Program: Opioid Misuse among Medicaid and General Assistance Enrollees in Washington State. November 2008; Opioid_PresKuss.pdf. Accessed May 15, Wagner C, Theel A. Best Practices: PRC Clients and Care Plans Accessed May 15, Iowa Administrative Code, Human Services Department. Member lock-in (249a) pdf. Accessed May 16, Iowa Foundation for Medical Care. IFMC Response to the Iowa Department of Human Services Medical Services with Preferred Drug List. March 2004; AA&url=http%3A%2F%2Fwww.dhs.state.ia.us%2Fpublications%2FIMEPro%2FPharM edproposal%2fifmc%2520bafo% %2ftab%25209%2520bafo.doc&ei=geazt4_2nmaw2gxh5el7&usg=afqjcnf VZZdQVF-UfXFea9wxg9IqUkCTOQ&sig2=SgOtsFsdotgVk0PNFpFRtw. Accessed May 16, Colburn D, Coady J, Ellis A, Griffin H, Tripp M. Medicaid Integrity Report: Iowa Comprehensive Program Integrity Review Final Report. November Prevention/FraudAbuseforProfs/downloads//iacompfy08pireviewfinalreport.pdf. Accessed May 16, Blumenschein K, Fink JL, Freeman PR, et al. Review of Prescription Drug Monitoring Programs in the United States. Lexington, KY: University of Kentucky Institute for Pharmaceutical Outcomes and Policy;June National Alliance for Model State Drug Laws. Prescription Drug Monitoring Project. Accessed February 8, National Alliance for Model State Drug Laws. Components of a Strong Prescription Monitoring Statute/Program. November 2004; 0monitoring%20statute.pdf Accessed May 8, Office of National Coordinator (ONC), Substance Abuse and Mental Health Services Administration. Action Plan for Improving Access to Prescription Drug Monitoring Programs Through Health Information Technology June 2012; p%3b/wcipubcontent/publish/onc/public_communities/_content/files/063012_final_action_plan_cle arance.pdf. Accessed September 12, U.S. Department of Health and Human Services. New health IT effort aimed at reducing prescription drug abuse to be tested in Indiana and Ohio. June 2012; Accessed September 12, Alliance of States with Prescription Monitoring Programs. State Profiles. Accessed August 22,

78 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 161. Green TC, Mann MR, Bowman SE, et al. How Does Use of a Prescription Monitoring Program Change Medical Practice? Pain medicine (Malden, Mass.). Jul United States General Accounting Office. Report to the Subcommittee on Oversight and Investigations, Committee on Energy and Commerce, House of Representatives. Prescription Drugs: State Monitoring Programs Provide Useful Tool to Reduce Diversion. May 2002; GAO National All Schedules Prescription Electronic Reporting Act. Facts on NASPER: National Drug Control Policy and Prevention of Prescription Drug Abuse Reauthorization Act of Accessed September 12, Simeone R, Holland L. An Evaluation of Prescription Drug Monitoring Programs: Simeone Associates, Inc. ;Septemper Center for Substance Abuse Treatment, Substance and Mental Health Services Administration. National All Schedules Prescription Electronic Reporting Act of 2005: A Review of Implementation of Existing State Controlled Substance Monitoring Programs: U.S. Department of Health and Human Services Reisman RM, Shenoy PJ, Atherly AJ, Flowers CR. Prescription Opioid Usage and Abuse Relationships: An Evaluation of State Prescription Drug Monitoring Program Efficacy. Substance Abuse: Research and Treatment. 2009;3(SART-3-Shenoy-et-al): Reifler LM, Droz D, Bailey JE, et al. Do prescription monitoring programs impact state trends in opioid abuse/misuse? Pain medicine (Malden, Mass.). Mar 2012;13(3): Paulozzi LJ, Kilbourne EM, Desai HA. Prescription drug monitoring programs and death rates from drug overdose. Pain medicine (Malden, Mass.). May 2011;12(5): Kerlikowske G, Jones CM, Labelle RM, Condon TP. Prescription drug monitoring programs-lack of effectiveness or a call to action? Pain medicine (Malden, Mass.). May 2011;12(5): Green TC, Zaller N, Rich J, Bowman S, Friedmann P. Revisiting Paulozzi et al.'s "Prescription drug monitoring programs and death rates from drug overdose". Pain medicine (Malden, Mass.). Jun 2011;12(6): Barrett K. Prescription Monitoring Program Survey: Report of Findings. Richmond, VA: Virginia Commonwealth University Survey and Evaluation Research Laboratory August Lambert D. Impact Evaluation of Maine's Prescription Drug Monitoring Program. Portland, ME: University of Southern Maine Muskie School of Public Service March Varney S. Calif.'s Prescription-Drug Monitoring System Feels Pain From Budget Cuts. April 2012; Accessed July 20,

79 Part 1 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies A Review of the Literature 1.72

80 Approaches to Drug Overdose Prevention Analytical Tool (ADOPT): Evaluating Cost and Health Impacts of a Medicaid Patient Review & Restriction Program Part 2 Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis

81 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Table of Contents INTRODUCTION METHODS Data Source Definitions STATISTICAL ANALYSIS Regression Models Day Exposure Window Episode-Based Model Model Settings Subgroup Analysis: The Role of Pharmacy Shopping in Overdose Events Peak Number of Pharmacies Pharmacy Shopping Criteria Characteristics of Prescription Fill Behaviors Hazard Ratios for Overdose Due to Pharmacy Shopping and Overlapping Prescriptions RESULTS Study Population Characteristics Characteristics of Prescription Opioid Use Opioid Prescriptions by Drug Type Predominant Opioid Prescriptions among Long-Term Episodes of Opioid Use Supply days of opioid prescriptions by drug type Characteristics of Overdose Events Number of Overdose Events Overdose Events and Estimated Costs by Encounter Type Overdose Rates by Patient Characteristics Overdoses by Type of Prescription Opioid Use Relationship between Overdose Risk and Prescribed Dose: Results of the 90-Day Exposure Window Model Pharmacy Shopping among Long-Term Opioid Users Number of Patients with an Overdose Event, by Peak Number of Pharmacies Comparison between Different Pharmacy Shopping Criteria Combined Criteria: Peak Number of Pharmacies and Overlapping Prescriptions Relationship between Potential Pharmacy Shopping and Overdose Risk SUMMARY APPENDIX Morphine Equivalent Dose Conversions ICD-9 Codes Indicating Overdose-Related Symptoms Type of Overdose Encounters BIBLIOGRAPHY List of Tables Table 2-1.Baseline Characteristics of the Study Population Table 2-2. Commonly Prescribed Opioids in a Sample of the U.S. Adult Medicaid Population, Table 2-3. Predominant Drug Types among Long-Term Users Table 2-4. Opioid Overdose Events in the MarketScan Medicaid Dataset,

82 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Table 2-5. Number of Overdose Events and Estimated Costs by Encounter Type, Table 2-6. Unadjusted Overdose Rates in MarketScan Medicaid Dataset by Demographic and Clinical Characteristics, Table 2-7. Overdoses by Type of Opioid Use Table 2-8. Overdose Rates and Hazard Ratios by Dose Level and Predominant Drug Type Table 2-9. Comparison of Different Pharmacy Shopping Criteria in Medicaid MarketScan Dataset Table Comparison of Different Pharmacy Shopping Characteristics by Demographics, Overdose Events and Opioid Consumption Patters Table Hazard Ratios of Overdose, Including Indicators for Pharmacy Shopping a and Overlapping Prescriptions in Medicaid MarketScan Dataset, List of Figures Figure 2-1. Regression Model Schematic: Prescriptions, Exposure Windows, and Overdose Events in Models 1 and Figure 2-2. Percentage of Supply Days for Prescription Opioids Figure 2-3.Overdoses among Long-Term Users by Peak Number of Pharmacies Visited

83 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 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 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 , excluding 173.X, and 232); Residing in any long-term care facilities; Who filled any opioid prescription in the first 3 months of the continuous enrollment period (this is necessary to exclude subjects whose time-to-event estimation is subject to left truncation). We identified 427,411 Medicaid beneficiaries in the MarketScan data during the 24-month period who met the inclusion criteria. Definitions Episode of Opioid Use. We defined an episode of opioid use as commencing with the dispensing date of an opioid prescription with no previous prescription in the dataset, or having a gap longer than 31 days from the end run-out date of a previous opioid prescription. Episode duration is defined as the number of days from the first fill date to the end date of the last opioid prescription with no prescription gaps exceeding 31 days after the previous refill. Long-term Episode of Opioid Use. An episode is defined as long-term if the duration is longer than 90 days with 3 or more prescriptions dispensed in that time. Pharmacy Shopping. Pharmacy shopping defined as visiting multiple pharmacies to obtain medically unnecessary prescription opioids and contributes to nonmedical opioid use, misuse and abuse. Pharmacy shopping has been defined in the literature using a variety of cut-off points for classifying a patient as having potential controlled substance misuse or mismanagement that would warrant further evaluation. Published thresholds vary by number of pharmacies seen by a single patient to obtain any opioid over a given time period. 1-5 Peak Number of Pharmacies. Within a long-term episode of use, we defined the peak number of pharmacies visited as the maximum number of unique pharmacies IDs that appeared in 2.4

84 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 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, , and ) or accidental poisoning (E935.1 and E935.2). We defined probable cases of overdose as claims with ICD-9 codes indicating adverse effects of opioid use (E935.1 and E935.2) plus at least one ICD-9 code indicating overdose-related symptoms on the same day (see Appendix for the full list). We included both definite and probable cases in the analysis. We excluded suicidal poisoning by opioid drugs (E950.0), poisoning undetermined whether accidentally or purposefully inflicted (E980.0), and opioid drug dependence (304.X and 305.X). We grouped inpatient and outpatient claims into overdose encounters and classified the encounters into 3 types: hospitalizations, ED visits, and outpatient visits (see Appendix for detailed rules for grouping and classification). If an individual had multiple overdose encounters during his/her continuous Medicaid enrollment period, only the earliest one (i.e. initial overdose) was counted. 2.5

85 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 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 studies 7,9 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, mg/d, 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 2.6

86 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 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 Supply of opioid prescription Exposure window Overdose event 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 2.7

87 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Both models adopt Cox proportional hazards regression analysis 12 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 2.8

88 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis opioid-related overdose, thereby using DOR to assess the efficacy of each definition to identify opioid users at high risk of overdose, and, presumably, also at high risk of abuse and misuse. Characteristics of Prescription Fill Behaviors We combined the two items - pharmacy shopping definition with the highest DOR (the indicator for overlapping prescriptions) to categorize the study population into four subgroups: 1. Patients without pharmacy shopping behavior and without overlapping prescriptions 2. Patients without pharmacy shopping behavior but with overlapping prescriptions 3. Patients with pharmacy shopping behavior but without overlapping prescription 4. Patients with both pharmacy shopping behavior and overlapping prescriptions We calculated the demographic characteristics, overdose risk, and prescription fill patterns (in terms of prescription frequency, dose level, and drug type by DEA classification) of each subgroup. Hazard Ratios for Overdose Due to Pharmacy Shopping and Overlapping Prescriptions We examined whether pharmacy shopping behavior and overlapping prescriptions were associated with an increased risk of opioid-related overdose. The 90-day exposure model, which was modified to include two additional indicators for the history of pharmacy shopping and having overlapping prescriptions, was used for this purpose. The hazard ratios of pharmacy shopping and overlapping prescriptions were calculated. RESULTS Study Population Characteristics Among the 427,411 Medicaid patients included in the study population, 69.2% were female, 68.3% were 18 years of age and older and 54.4% were white (see. The prevalence of diagnosed depression and alcohol abuse was 5.0% and 1.2%, respectively. Long-term users accounted for 21.1% of the study population (90,010 individuals). The majority of long-term users (51.5%) were over 45 years of age, whereas only 1.9% were between ages 12 and18 years. Long-term users had a significantly higher prevalence of depression (10.6%) and alcohol abuse (3.0%) than the overall study population. Table 2-1.Baseline Characteristics of the Study Population Total Long Term Users (n=427,411) (n=90,010) P Value Mean Months Enrolled <0.001 Female <0.001 Age < and over Race White <0.001 Long-term opioid users were defined as those who have at least one episode of opioid use longer than 90 days with at least 3 prescriptions. 2.9

89 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Total Long Term Users (n=427,411) (n=90,010) P Value Black Hispanic Other Depression diagnosis,% <0.001 Alcohol abuse, % <0.001 Note: Values are expressed as percentages Characteristics of Prescription Opioid Use Opioid Prescriptions by Drug Type Error! Reference source not found. lists the number of each opioid drug prescribed during the tudy period. Most prescriptions (79.2%) were dispensed to long-term users. Hydrocodone, a schedule III opioid, is by far the most commonly prescribed opioid in both the overall study population (45.7%) and the subset of long-term users (44.6%). The most commonly prescribed short-acting Schedule II opioid is oxycodone (15.1% and 14.9% in the overall population and long-term users, respectively). Prescriptions for long-acting Schedule II drugs are higher among long-term users (11.5%) than in the rest of the study population (0.5%, data not shown). Table 2-2. Commonly Prescribed Opioids in a Sample of the U.S. Adult Medicaid Population, Prescription Opioid Type All use Long-Term Use n % n % Schedule III and IV Hydrocodone + aspirin/acetaminophen/ibuprofen 1,915, ,381, Tramadol with or without aspirin 508, , Propoxyphene (with or without aspirin/acetaminophen/ibuprofen) 274, , Codeine + aspirin/acetaminophen/ibuprofen 216, , Butalbital + codeine (with or without aspirin/acetaminophen/ ibuprofen) 14, , Butorphanol 6, , Pentazocine (with or without aspirin/acetaminophen/ibuprofen) 4, , Schedule II Short-Acting* Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 635, , Hydromorphone 43, , Fentanyl citrate transmucosal 32, , Morphine sulfate 27, , Codeine Sulfate 17, , Meperidine hydrochloride 13, , Tapentadol 2, , Schedule II Long-Acting* Oxycodone HCL control release 232, , Morphine sulfate sustained release 106, , Fentanyl transdermal 67, , 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

90 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Prescription Opioid Type All use Long-Term Use n % n % Methadone 70, , Oxymorphone extended release 3, , Dihydrocodeine 1, , Levorphanol tartrate Total 4,193, ,320, Predominant Opioid Prescriptions among Long-Term Episodes of Opioid Use As displayed in Table 2-3, hydrocodone, tramadol and oxycodone are the most predominantly used opioids (in terms of total MED per episode) among long-term episodes of opioid use (51.6%, 14.3% and 13.1%, respectively). Table 2-3. Predominant Drug Types among Long-Term Users Prescription Opioid Type Number of Long-Term Episodes % Hydrocodone 65, Tramadol 18, Oxycodone 16, Propoxyphene 8, Oxycodone hydrochloride 5, Codeine +aspirin/acetaminophen/ibuprofen 3, Morphine sulfate sustained release 2, Meperidine 2, Other drug type 4, Supply days of opioid prescriptions by drug type Figure 2-2 shows the distribution of supply days of all prescriptions (including short-term users ) of each drug type (opioid types with less than 10,000 prescriptions are not shown). Very few prescriptions (<0.3%, or 11,310 out of 4.1 million prescriptions) had supplies greater than 30 days. Figure 2-2. Percentage of Supply Days for Prescription Opioids 2.11

91 Percentage, % Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis >30 d d 8-15 d 1-7 d Note: All drug types are shown except those with less than 10,000 prescribed. Characteristics of Overdose Events Number of Overdose Events Table 2-4 lists the number of initial overdoses meeting or not meeting our inclusion criteria. Among the 1,908 overdose events, 90.9% were definite cases of unintentional overdose (having at least one ICD-9 code indicating poisoning or accidental poisoning by opioid) and 0.2% were probable cases (having at least one ICD-9 code indicating adverse effects of opioid plus at least one ICD9 code indicating an overdose-related symptom on the same day). The remaining 8.9% of cases were excluded due to ICD-9 codes indicating suicide or undetermined causes. Among the 1,738 patients who had an initial unintentional overdose, 313 (18.0%, data not shown) had at least one more subsequent overdose in the study period. Table 2-4. Opioid Overdose Events in the MarketScan Medicaid Dataset, Overdose Event Type n % Included Definite case 1, Probable case Excluded Suicidal case Undetermined case Total 1, 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, Overdose Cost Event type n % Mean Median Interquartile Range Hospitalization, with ED visit $12,371 $5,506 $2658, $13415 Hospitalization, without ED visit $5,797 $3,241 $1257, $

92 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis ED visit only $514 $315 $132, $663 Outpatient visit only $162 $149 $67, $269 Overall $5,376 $2,879 $407, $6945 Overdose Rates by Patient Characteristics Table 2-6 lists overdose rates by patients characteristics. The overdose rate for the overall study population was 2.22 per 1,000 person-years, and 2.10 per 1,000 person-years for overdoses resulting in ED visits or hospitalizations, respectively. Individuals ages 30 years and older had much higher overdose rates than persons younger than age 30. Both males and females had similar rates. African Americans had a significantly lower overdose risk; about one-third of the overdose rate of white users. Hispanics and other races also showed lower rates than whites; however these rates were not statistically significant after adjusting for other characteristics (adjustment is realized through the regression analysis; see Table 2-11 for more details). This is likely due to the small sample size of overdoses. Users with a history of depression or alcohol abuse had substantially higher overdose rates (16.9 per 1,000 person-years and 20.3 per 1,

93 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Table 2-6. Unadjusted Overdose Rates in MarketScan Medicaid Dataset by Demographic and Clinical Characteristics, Total Age Overdoses Overdose Rate (95% CI) Hospitalization ED Outpatient Total Person Years Hospitalization ED Outpatient Total , ,132 ( ) ( ) ( ) ( ) y , y , y , y ,443 Sex Male ,922 Female , ,210 Race/Ethnicity White , ,992 Black ,918 Hispanic ,890 Other ,332 History of Depression Diagnosis No , ,701 Yes ,431 History of Alcohol Abuse No , ,820 Yes , ( ) 0.60 ( ) 1.49 ( ) 1.84 ( ) 1.15 ( ) 0.95 ( ) 1.44 ( ) 0.34 ( ) 0.70 ( ) 0.93 ( ) 0.54 ( ) 8.73 ( ) 0.88 ( ) 9.46 ( ) 0.68 ( ) 1.01 ( ) 1.63 ( ) 1.18 ( ) 1.18 ( ) 1.07 ( ) 1.45 ( ) 0.60 ( ) 1.01 ( ) 0.83 ( ) 0.77 ( ) 6.55 ( ) 0.98 ( ) 9.95 ( ) 0.08 ( ) 0.08 ( ) 0.16 ( ) 0.16 ( ) 0.12 ( ) 0.12 ( ) 0.15 ( ) 0.08 ( ) 0.00 ( ) 0.10 ( ) 0.07 ( ) 0.81 ( ) 0.10 ( ) 1.33 ( ) 1.05 ( ) 1.69 ( ) 3.26 ( ) 3.17 ( ) 2.44 ( ) 2.13 ( ) 3.04 ( ) 1.01 ( ) 1.71 ( ) 1.87 ( ) 1.38 ( ) ( ) 1.95 ( ) ( ) 2.14

94 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Overdoses Overdose Rate (95% CI) Hospitalization ED Outpatient Total Person Years Hospitalization ED Outpatient Total History of Long-Term Opioid Use No , ) 0.58 ( ) 0.06 ( ) 0.95 ( ) Yes , , ( ) 2.52 ( ) 0.27 ( ) 5.69 ( ) Note: Rates are unadjusted and expressed per 1,000 person-years 2.15

95 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis person-years, respectively) than those with no such history. Almost 70% of overdoses (1,193 of 1,738) occurred among users with at least one episode of long-term opioid use. The overdose rate among long-term users was 5.69 per 1,000 person-years compared with 0.95 per 1,000 person-years in short-term users. Overdoses by Type of Prescription Opioid Use We further examined the type of episode of opioid use when an overdose occurred. As shown in Table 2-7, about 19% (337) of overdoses occurred outside of any episode of opioid use. More than half of the overdoses (66.3%) occurred during a period of long-term opioid use, while only 14.3% occurred during a period of short-term use. Table 2-7. Overdoses by Type of Opioid Use Overdose Type n % During A Long-Term Use Episode 1, During A Short-Term Use Episode Outside of Any Episode Total 1, Relationship between Overdose Risk and Prescribed Dose: Results of the 90-Day Exposure Window Model Table 2-8 shows the relationship between dose level, predominant opioid types and the risk of overdose. Patients who had any opioid use in most recent 90-days had an overall overdose risk of 8.20 per 1,000 person-years, compared to 0.44 per 1,000 person-years for who did not have opioid use during this time period. Table 2-8. Overdose Rates and Hazard Ratios by Dose Level and Predominant Drug Type Overdoses Person- Years Overdose Rate Adjusted Hazard Ratios (95% CI) Episode-Based 90-Day Exposure Model Model Opioid Dose None , ( ) N/A 0.31 ( ) 1 to <20mg/d , ( ) 1 [reference] 1 [reference] 20 to <50mg/d , ( ) 1.83 ( ) 1.69 ( ) 50 to 100 mg/d , ( ) 3.18 ( ) 2.10 ( ) >=100mg/d , ( ) 4.76 ( ) 4.89 ( ) Any opioid use 1, , ( ) N/A 2.42 ( ) Predominant Opioid Type Hydrocodone , ( ) 1 [reference] N/A Oxycodone , ( ) 0.94 ( ) N/A Codeine 33 5, ( ) 1.02 ( ) N/A Tramadol , ( ) 1.09 ( ) N/A Morphine ER 95 10, ( ) 1.35 ( ) N/A Propoxyphene 26 3, ( ) 0.85 ( ) N/A Other * , ( ) 0.93 ( ) N/A Include: butalbital + codeine (with or without aspirin/acetaminophen/ ibuprofen), butorphanol, pentazocine (with or without aspirin/acetaminophen/ibuprofen), hydromorphone, fentanyl citrate transmucosal, morphine sulfate, meperidine hydrochloride, tapentadol, oxycodone HCL control release, fentanyl transdermal, methadone, oxymorphone extended release, dihydrocodeine, levorphanol tartrate 2.16

96 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Hazard ratios (HR) for dosage levels were adjusted for gender, age, race, history of depression, history of alcohol abuse, and concurrent use of sedative/hypnotics. In both the episode-based model and the 90-day exposure model, higher dosage level was associated with a significantly increased risk of overdose. Patients who received an average daily dose of 100mg/d or higher had almost a 5-fold increase in overdose risk (4.76 [95% CI: 3.83 to 5.91] in the episode-based model and 4.89 [95% CI: 3.67 to 6.52] in the 90-day exposure model, compared with the group of patients who received the lowest dosage level (1 to <20 mg/d). Compared with the same group (1 to <20 mg/d), patients who did not receive any recent prescription had hazard ratios of 0.31 (95% CI: 0.23 to 0.44) in the 90-day exposure model (note that no opioid use is not included in the episode-based model). The episode-based model also included the most frequently prescribed opioid type in an episode (i.e., predominant opioid type) as covariates. Compared with the episodes where hydrocodone was the most frequently prescribed opioid, episodes with a different predominant drug type did not show any statistically significant difference in terms of adjusted overdose risk, except for morphine in extended-release format which showed a 35% increase (HR: 1.35, 95% CI: 1.06 to 1.72) in overdose risk. Overdoses involving codeine had the lowest unadjusted overdose rate of 5.64 per 1,000 person-years (95% CI: 3.88 to 7.92 per 1,000 person-years), whereas its adjusted risk was very close (HR: 1.02 [95% CI: 0.71 to 1.46]) to that of the reference drug, hydrocodone. Pharmacy Shopping among Long-Term Opioid Users The study population in this subgroup analysis is restricted to the 90,010 long-term users. ** The main analysis found that approximately one in five opioid users met criteria for long-term use (21.1% of all opioid users). Inclusion of short-term users would substantially bias the comparison between shoppers and non-shoppers. Number of Patients with an Overdose Event, by Peak Number of Pharmacies Over a 1-year period, the numbers of patients using 3, 4, and 5 or more pharmacies were 15,901 (16.8%), 9,766 (10.8%), and 19,409 (20.6%), respectively. That means nearly half (49.2%) of long-term users visited three or more different pharmacies in a 1-year period. If the time span is narrowed to any 180 consecutive days, the numbers of patients using 3, 4, and 5 or more pharmacies changed to 16,806 (18.7%), 9,012 (10.0%), and 12,339 (13.7%), respectively. After further restricting the time setting to any 90 consecutive days, the corresponding numbers and percentages fell to 15,647 (17.3%), 7,564 (8.4%), and 5,519 (6.1%), respectively. That means only 14.5% of the long-term users used 4 or more pharmacies during any 90 consecutive days, compared with 31.2% over a 1-year period. In all three time periods (90-days, 180-days, and 1 year), the percentage of patients having opioid-related overdoses in each category monotonically increases as the peak number of pharmacies increases. However, the most dramatic increase is seen among patients using 4 and 5 or more pharmacies in the 90-day period. We found that 3.4% of patients who ever visited 4 pharmacies in any 90 consecutive days and 5.4% of patients who ever visited 5 or more pharmacies in any 90 consecutive days had at least 1 overdose event in the follow-up time, compared with only 0.4% to 1.5% among those visiting 1 to 3 pharmacies in any 90 consecutive ** Those who had at least 1 episode of opioid use for 90 days or longer with at least 3 opioid prescriptions dispensed in that episode. 2.17

97 Number of Long-term Users (indicated in bars) Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis days. In the 180-day and 1-year timeframes, the difference between the high and low numbers of pharmacies visited was not as significant. Figure 2-3 presents the distribution of the number of patients (bars, left y axis) and the percentage of overdose (dots, right y axis) in each category based on the peak number of pharmacies. Three sets of bars and dots represent three different time spans (1 year, 180-days and 90-days) in which the numbers of pharmacies were counted. Figure 2-3.Overdoses among Long-Term Users by Peak Number of Pharmacies Visited 35,000 6% 30,000 5% 25,000 20,000 15,000 10,000 5, Peak Number of Pharmacies Visited # of pharmacies in 1 year setting # of pharmacies in 180-day setting # of pharmacies in 90-day setting % of overdose for 1-year setting % of overdose for 180-day setting % of overdose for 90-day setting 4% 3% 2% 1% 0% Percentage of Overdose (indicated in dots) 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 2.18

98 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 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 Table 2-9. Comparison of Different Pharmacy Shopping Criteria in Medicaid MarketScan Dataset Pharmacy Shopping Criteria Eligible Recipients Overdose Events % Overdose among Eligible Recipients % of Total Overdose DOR 1-year setting, 3 pharmacies 44, % 69.86% year setting, 4 pharmacies 29, % 52.50% day setting, 3 pharmacies 38, % 69.26% day setting, 4 pharmacies 21, % 50.72% day setting, 3 pharmacies 28, % 66.72% day setting, 4 pharmacies 13, % 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 Comparison of Different Pharmacy Shopping Characteristics by Demographics, Overdose Events and Opioid Consumption Patters No shopping, No overlapping RX No shopping, Overlapping RX Shopping, No overlapping RX Shopping, Overlapping RX Demographics Number 73,042 3,885 7,059 6,024 Mean age DOR is a measure of the effectiveness of a diagnostic test. DOR is defined as the ratio of the odds of testing positive if the subject has a disease relative to the odds of testing positive if the subject does not have the disease. 13 We replaced test with criterion for pharmacy shopping and disease with opioid-related overdose, thereby using DOR to assess the efficacy of each definition to identify opioid users at high risk of overdose, and, presumably, also at high risk of abuse and misuse. 2.19

99 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis No shopping, No overlapping RX No shopping, Overlapping RX Shopping, No overlapping RX Shopping, Overlapping RX Male 28.2% 32.1% 26.8% 31.2% Depression diagnosis 9.0% 11.4% 16.8% 18.0% History of alcohol abuse 2.4% 2.8% 5.1% 5.6% Overdose Events Number Percentage 0.64% 4.25% 2.66% 6.09% Overdose Incidence Total person-years 108,042 8,911 17,539 13,934 Incidence rate* Opioid Prescriptions Monthly prescriptions 438 ( ) 1.42 ( ) 40.2 ( ) 1,851 (1,580-2,157) 2.83 ( ) ( ) 1,072 (924-1,237) 1.84 ( ) 53.9 ( ) 2,634 (2,371-2,918) 2.77 ( ) 89.2 ( ) Average dose Dose level distribution 0-20mg 35.4% 14.5% 23.4% 15.2% 20-50mg 44.5% 36.7% 48.6% 38.4% mg 13.6% 21.3% 17.6% 22.1% 100mg or higher 6.5% 27.6% 10.4% 24.4% Predominant opioid drug type Schedule III /IV 82.2% 64.9% 73.6% 65.0% Schedule II, Short Acting 15.1% 23.4% 21.1% 24.6% Schedule II, Long Acting 2.7% 11.7% 5.3% 10.4% Note: RX = opioid prescriptions * per 100,000 person-years There is no clear trend across the four categories in terms of age. Males appeared to be higher in the two categories with overlapping prescription (32.1% without shopping and 31.2% with shopping). Patients classified as conducting pharmacy shopping tended to have higher prevalence of depression (16.8% without overlapping prescription and 18.0% with overlapping prescription) and alcohol abuse (5.1% without overlapping prescription and 5.6% with overlapping prescription). Our analysis found that 40% of overdose events (473 out of 1,193 overdoses) occurred in the subgroup of patients without any shopping behavior or overlapping prescriptions, whereas the remaining 60% overdosed users (720 out of 1,193 overdoses) included either pharmacy shopping, overlapping prescriptions or both. The percentage of overdoses in the group with both shopping behavior and overlapping prescriptions is almost 10 times higher (6.09%/0.64%=9.52) than the group with neither condition. Even without shopping behavior, patients who had overlapping prescriptions still had a high percentage of overdoses (4.25%) -- even higher than those who had shopping behavior but did not have overlapping prescriptions (2.66%). The incidence rates of overdose tell a similar story. The group meeting both criteria is about 6-times more likely to overdose compared with the group with neither condition (2.634 vs per 1,000 person-years); patients with overlapping prescriptions but no shopping behavior had a 2.20

100 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 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 Hazard Ratios of Overdose, Including Indicators for Pharmacy Shopping a and Overlapping Prescriptions in Medicaid MarketScan Dataset, Hazard Ratio 95% CI P value Opioid dose 1 to <20mg/d 1 20 to <50mg/d to 100 mg/d <.0001 >=100mg/d <.0001 Gender Female 1.00 Male Age and over Race/Ethnicity 2.21

101 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Hazard Ratio 95% CI P value White 1.00 Black <.0001 Hispanic Other Concurrent sedative/hypnotic use <.0001 History of alcohol abuse <.0001 History of depression diagnosis <.0001 Pharmacy Shopping a <.0001 Overlapping Prescriptions b <.0001 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. 2.22

102 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis Morphine Equivalent Dose Conversions APPENDIX 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 Buprenorphine a Butorphanol 7.00 Schedule II Short-Acting Morphine sulfate 1.00 Codeine sulfate 0.15 Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 1.50 Hydromorphone 4.00 Meperidine hydrochloride 0.10 Oxymorphone 3.00 Fentanyl citrate transmucosal b Tapendatol short acting c not established Schedule II Long-Acting Morphine sulfate sustained release 1.00 Fentanyl transdermal d 2.40 Levorphanol tartrate 11.0 Oxycodone HCL control release 1.50 Methadone 3.00 Oxymorphone extended release c 3.00 Hydromorphone extended release c 5.00 Tapentadol extended release c 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. 2.23

103 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis ICD-9 Codes Indicating Overdose-Related Symptoms Mixed acid base balance disorder Drug-induced psychotic disorders (including and ) Drug-induced delirium Drug-induced mental disorder (excluding ) 486 Pneumonia, organism unspecified 496 Chronic airway obstruction, not elsewhere classified Acute respiratory failure Other pulmonary insufficiency, not elsewhere classified Alteration of consciousness Altered mental state Apnea Shortness of breath Dyspnea and respiratory abnormalities other Painful respiration Asphyxia and hypoxemia Type of Overdose Encounters Emergency department (ED) visits are identified from both inpatient and outpatient claims data as claims having emergency room as service place and/or having emergency medicine or emergency services as service type. Inpatient claims with the same admission dates and outpatient claims occurring in 2 preceding days are grouped into one overdose encounter. Overdose encounters are divided into 3 types: hospitalization if any non-ed inpatient claims appear in that encounter; ED encounter if there are any ED claims and no non-ed inpatient claims; and outpatient encounter if there are non-ed outpatient claims and no inpatient or ED outpatient claims. 2.24

104 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis BIBLIOGRAPHY 1. Katz N, Panas L, Kim M, et al. Usefulness of prescription monitoring programs for surveillance--analysis of Schedule II opioid prescription data in Massachusetts, Pharmacoepidemiology and drug safety. Feb 2010;19(2): 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): 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): Wilsey BL, Fishman SM, Gilson AM, et al. Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics. Drug and alcohol dependence. Nov ;112(1-2): Peirce GL, Smith MJ, Abate MA, Halverson J. Doctor and pharmacy shopping for controlled substances. Medical care. Jun 2012;50(6): Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Trends in use of opioids for non-cancer pain conditions in commercial and Medicaid insurance plans: the TROUP study. Pain. Aug ;138(2): PMCID:PMC Dunn KM, Saunders KW, Rutter CM, et al. Opioid prescriptions for chronic pain and overdose: a cohort study. Annals of internal medicine. Jan ;152(2): PMCID:PMC 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 ;305(13): 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 ;171(7): 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): PMCID:PMC !!! INVALID CITATION!!! 12. Version 9.2 of the SAS System for Windows. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. [computer program]. Cary, NC: SAS Institute Inc.; Copyright Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. Journal of clinical epidemiology. Nov 2003;56(11): 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): PMCID:PMC Centers for Disease Control and Prevention. Vital signs: overdoses of prescription opioid pain relievers---united States, MMWR. Morbidity and mortality weekly report. Nov ;60:

105 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 16. Sporer KA. Buprenorphine: A primer for emergency physicians. Annals of emergency medicine. 2004;43(5):

106 Part 2 of 3: Prevalence of Prescription Opioid Misuse and Abuse, Related Outcomes, and Prevention Policies MarketScan Data Analysis 2.27

107 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

108 Table of Contents INTRODUCTION METHODS Data Sources Definitions for the MarketScan Data Analysis Overview: The Micro-Simulation Process and Simulation Process MODEL CALIBRATION Number of Prescriptions Days Supply, Dose Level, and Generic Drug Type Distribution of Number of Pharmacies Visited Demographic Characteristics and Opioid Use Patterns of Potential Pharmacy Shoppers Individual-Level Comparison COST ESTIMATION MODEL OUTPUT Patient Review and Restriction Program Policies Impact on Prescription Opioid Use Impact on Opioid Overdose-Related Events Cost Analysis of Different Patient Review and Restriction Scenarios DISCUSSION Model Limitations APPENDIX I The Simulation Process Step 1: Simulate the Basic Individual Profile Step 2: Simulate Predominant Drug Type in An Episode of Drug Use Step 3: Simulate Episode Length Step 4: Simulate Concurrent Prescription Opioid Use Step 5: Simulate Overlapping Prescriptions Step 6: Simulate Subsequent Episodes of Prescription Opioid Use Step 7: Simulate the Opioid Type of Each Prescription in an Episode Step 8: Simulate the Prescription Details: Generic Name, Strength, Master Form, Quantity, Supply Days, Dose Level and Drug Price Step 9: Assign Prescription Dates Step 10: Assign Pharmacy IDs to Each Prescription Step 11: Assign Prescriber IDs to Each Prescription Step 12: Simulate Subsequent Episodes of Opioid Use Step 13: Assign Absolute Dispensing Date to Each Prescription Step 14: Calculate Number of Prescription/Pharmacies/Prescribers and Dose Level Step 15: Calculate Risk of Overdose, Overdose Event Type, and Overdose-Related Medical Costs Step 16: Check Individual Eligibility for the Patient Review and Restriction Program Step 17: Summarize the Cost and Health Outcomes of the Simulated Cohort APPENDIX II ICD-9 Codes Indicating Overdose-Related Symptoms Type of Overdose Encounters Numbers and Estimated Cost for Each Generic Opioid Drug Type BIBLIOGRAPHY

109 List of Tables Table 3-1.ADOPT Input Parameters: Data Sources and Modifiability of Input Category Table 3-2. ADOPT Model Assumptions Table 3-3. Baseline Characteristics of MarketScan Medicaid Long-Term Users Table 3-4. Comparison between MarketScan Medicaid Opioid Prescriptions among Long-Term Users in 2009 and the Simulated Yearly Number of Prescriptions 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 Table 3-6. Unique Pharmacy Visits per Year and Within Any 90-Day Period among MarketScan and Simulated Cohorts Table 3-7. Demographic Characteristics, Overdose Rates, and Opioid Use Patterns of Patients with Different Pharmacy Shopping Characteristics Table 3-8. Prescription History of a Representative Individual from the MarketScan Long-Term User Population for Comparison With Figure 3-3 Data Table 3-9. Price Comparison of Commonly Prescribed Opioids Table Price Comparison of Commonly Prescribed Opioids, Price per 50 MME Table Representative State Patient Review and Restriction Program Policies Table 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) Table Annual Health Impact of the PRR Program in a Population of 10,000 Long-Term Users under Different Eligibility Scenarios* Table Cost Analysis of the PRR Program under Different Eligibility Scenarios Table Most Frequently Used Opioid Types in Market Scan Data Table Hazard Ratios for Prescription Opioid Overdose Table Distribution of Overdose and Cost Estimates List of Figures Figure 3-1. Overview of ADOPT Micro-Simulation Model Figure 3-2. Overview of the Simulation Process Figure 3-3. Simulated Individual s Prescription History from ADOPT Model for Comparison with Table 3-8 Data Figure 3-4. ADOPT Output: Program Summary Figure 3-5. Example of Random Sampling of Predominant Drug Type Figure 3-6. Subsequent Episodes of Opioid Use Figure 3-7. Example of Opioid Type Distribution Table, for Predominant Drug Type of Hydrocodone and Episode Length between 180- and 364-Days

110 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 Simulate Individual prescription history, without policy intervention project Opioid and medical costs, health outcomes, without policy intervention User-defined Policy Intervention Apply the policy to simulated cohort Compare Incremental cost and health outcome of the policy intervention Individual prescription history, with policy intervention project 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

111 Medicaid data, the literature (for prescriber information), content experts (for PRR program cost), and government documents (for PRR program eligibility criteria) (see Table 3-1). Table 3-1.ADOPT Input Parameters: Data Sources and Modifiability of Input Category Input Parameter Type Source User Modifiable? Demographics Age 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 MarketScan Medicaid Data MarketScan Medicaid Data: long-term opioid users only Previous study using Massachusetts prescription drug monitoring database 1 Overdose Hazard Ratios MarketScan Medicaid Yes Encounter Type (inpatient/ed/outpatient) Data No Cost Prescription Reimbursement Rates MarketScan Medicaid Yes Overdose-related Medical Cost Data No Program Cost Informed Assumption Yes PRR Program Eligibility Criteria Timeframe for Prescription History Review Yes Threshold Number of Prescribers PRR Program Yes Threshold Number of Pharmacies Government Document Yes Threshold Number of Prescriptions from Multiple States 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. 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 We excluded individuals: with less than 24 months continuous Medicaid enrollment; Yes Yes Yes Yes No No No No No No No No No 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. correspondence, August

112 younger than age 12 years at the start of continuous enrollment; with history of cancer diagnosis (ICD-9 CM neoplasms , excluding 173.X, 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

113 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, , and ) 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

114 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

115 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 -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) -daily dose level and In a specified length of time -number of pharmacies -number of prescribers -number of RX 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

116 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 ) to all prescriptions and sorts them into a chronological order (step 13). The daily dose and the maximum number of pharmacies and prescribers that a subject obtains prescriptions from, as well as the maximum number of prescriptions in a specific time frame (e.g., any 90 days or any 60 days) are calculated (step 14). The risk of overdose events is based on the daily dose (step 15). The numbers of pharmacies, prescribers, and prescriptions are then compared with the PRR program eligibility criteria in order to determine whether the hypothetical enrollee is eligible for program enrollment (step 16). If eligible, the enrollee s prescription history and the risk of having an overdose event under the PRR program will be calculated (step 16). The ADOPT repeats the above process (step 1-16) for the entire cohort and calculates the aggregated cost and number of overdose events (step 17). The simulation process used by the ADOPT model requires a series of steps, which are outlined below and presented in greater detail in Appendix I. 1. Simulate the basic individual profile 2. Simulate the predominant drug type 3. Simulate the episode duration 4. Simulate the use of concurrent opioid use 5. Simulate the use of overlapping prescriptions 6. Simulate the subsequent episodes of opioid use 7. Simulate the drug type for each prescription in an episode of use 8. Simulate the prescription details (generic name, strength, master form, quantity, supply days, dose level, drug price) 9. Assign relative prescription dates to each prescription 10. Assign pharmacy IDs to each prescription 11. Assign prescriber IDs to each prescription 12. Simulate subsequent episodes of opioid use 13. Assign absolute dispensing date to each prescription and eliminate irrelevant prescriptions 14. Calculate the number of prescriptions/pharmacies/prescribers and the dose level 15. Calculate the risk of overdose, overdose event type, and overdose-related medical costs 16. Check the individual eligibility for the PRR program 17. Summarize the cost and health outcomes of the simulated cohort 3.10

117 Table 3-2 describes key assumptions underlying the model functions and results. These assumptions may not apply when state-specific data inputs are used. Table 3-2. ADOPT Model Assumptions Assumptions Outcome probabilities derived from MarketScan data are generalizable to individual state Medicaid programs PRR programs are not applied to patients with cancer Drug pricing derived from MarketScan data is generalizable to individual state Medicaid programs Patterns of prescription opioid use found in the MarketScan data are generalizable to individuals enrolled individual state Medicaid programs. PRR enrollees stay enrolled in Medicaid and the PRR program for the duration of the policy period] Enrollees consumed prescriptions as prescribed does not consider opioid diversion Overdose risk is based on acquisition of prescription opioids, not use of illegal opioids Characteristics of a subsequent episode of opioid use are correlated with those of a previous episode Correlation between the number of pharmacies and the number of prescribers in the simulated cohort follows the correlation found in a previous study using the Massachusetts prescription drug monitoring database. 1 (The following assumptions are only for the analysis of the PRR program cost and health effects, and user-ad justifiable). All PRR program enrollees overlapping prescriptions (i.e., two prescriptions of the same drug type, one of which had a supply for 5 days or longer, overlapped by 25% or more of the days prescribed) are eliminated in the scenario of having the PRR program Because of the higher risk of overdose events at higher doses of opioids, All PRR program enrollees prescriptions that contribute to an aggregate daily dose over 80mg morphine equivalent will have reduced quantity or strength to an aggregate daily dose of 80mg morphine equivalent in the scenario of having the PRR program MODEL CALIBRATION The ADOPT model was calibrated by comparing the simulated outcomes with the values derived from our MarketScan data analysis. The population characteristics and the prevalence of risk factors of the simulated cohort are set to match those of the long-term users identified in the MarketScan data analysis. As seen in Table 3, the majority of the population is white, female, and age 45 years and older with about 19% using sedative hypnotics and about 11% diagnosed with depression. Parameter values are listed in Table 3-3 Table 3-3. Baseline Characteristics of MarketScan Medicaid Long-Term Users Population Characteristic % Population Characteristic % Female, % 70.3 Race, % Female Age Distribution, % White Black Hispanic Other and older 49.7 Sedative/Hypnotic drug use, % 18.9 Male Age Distribution, % Depression diagnosis,% Alcohol abuse, % For more information about our analysis of the MarketScan data, please refer to Part 2 of this report. 3.11

118 Population Characteristic % Population Characteristic % and older 55.5 The simulation results reported in this section are the mean values after running 10 simulation rounds with 10,000 subjects simulated in each round. The real-world comparison group consists of long-term users in the MarketScan dataset during the 2009 calendar year. We chose this year because the MarketScan data that we used spans from 2008 to 2010 and one of our initial inclusion criteria was at least 24-month continuous enrollment. By choosing 2009, the middle of the 3-year time span, we were able to include all long-term users (90,010 subjects). Number of Prescriptions The numbers of prescriptions in the simulated cohort were compared with the numbers of prescriptions dispensed among long-term users in the MarketScan dataset in 2009 (Table 3-4). The simulated total number of prescriptions is close to the MarketScan values. The three most frequently prescribed opioids are, in descending order, hydrocodone, oxycodone, and tramadol in both the MarketScan population and the simulated cohort. Some disparities exist between the simulated numbers of prescriptions and those in the MarketScan dataset, especially for some less commonly prescribed opioid types (e.g., meperidine). In general, the long-acting schedule II drugs are under-sampled in the simulation, compared with the actual values. This may result in an underestimate of the risk of overdose events in the simulated cohort. Table 3-4. Comparison between MarketScan Medicaid Opioid Prescriptions among Long-Term Users in 2009 and the Simulated Yearly Number of Prescriptions Prescription Opioid Type MarketScan Long-Term Users Prescription Total Rate/10,000 (n=90,010) Patients Simulated Cohort Prescription Rate/10,000 Patients Schedule III and IV Propoxyphene (with or without aspirin/acetaminophen/ibuprofen) 75,632 8,403 11,577 Codeine + aspirin/acetaminophen/ibuprofen 38,355 4,261 8,387 Hydrocodone + aspirin/acetaminophen/ibuprofen 628,165 69,788 65,185 Butalbital + codeine (with or without aspirin/acetaminophen/ 5, ibuprofen) Butorphanol 2, Pentazocine (with or without aspirin/acetaminophen/ibuprofen) 2, Tramadol with or without aspirin 172,173 19,128 22,579 Short-acting, Schedule II Morphine sulfate 9,800 1,089 1,499 Codeine Sulfate 1, Oxycodone (with or without aspirin/acetaminophen/ibuprofen) 243,054 27,003 24,146 Tapentadol Hydromorphone 4, ,457 Meperidine hydrochloride 33,153 3,683 1,614 Fentanyl citrate transmucosal 7, ,037 Long-acting, Schedule II Morphine sulfate sustained release 52,715 5,857 2,344 Fentanyl transdermal 26,660 2,962 1,

119 Prescription Opioid Type MarketScan Long-Term Users Prescription Total Rate/10,000 (n=90,010) Patients Simulated Cohort Prescription Rate/10,000 Patients Oxycodone HCL control release 92,004 10,222 7,309 Methadone 25,867 2,874 1,279 Oxymorphone extended release 1, Dihydrocodeine Levorphanol tartrate Total 3,320, , ,693 Note: Prescription rate is the number of opioid prescriptions per 10,000 patients Days Supply, Dose Level, and Generic Drug Type We compared drug-specific details, including the distribution of supply days, dose level, and generic drug type for a particular opioid type between the MarketScan dataset and the simulated cohort. Table 3-5 shows the details of such comparison for methadone. The simulated supply days is distributed similarly to that of the MarketScan experience, except that the ADOPT model does not simulate any prescription with a supply longer than 30 days, which only accounts for 0.8% of the total MarketScan prescriptions. The distribution of doses is comparable between the MarketScan data and the simulated cohort. Table 3-5. Comparison between the Percent Distributions of Days Supply, Daily Dose,* and Generic Drug Type for Methadone in the MarketScan and Simulated Populations MarketScan Long- Term Users Simulated Cohort Days Supply >3 d 0.2% 0.3% 4-7 d 5.0% 6.6% 8-15 d 12.8% 13.5% d 12.6% 14.6% 30 d 68.6% 65.0% <30 d 0.8% 0.0% Dose level* 10 MG 4.2% 4.1% 15 MG 3.6% 5.7% 20 MG 11.4% 8.6% 30 MG 17.6% 19.2% 40 MG 18.0% 19.6% 50 MG 3.9% 4.3% 60 MG 14.3% 11.8% 70 MG 1.3% 0.6% 80 MG 8.6% 10.7% MG 1.7% 2.1% 90 MG 3.8% 1.9% 100 MG 2.9% 3.7% 120 MG 6.9% 5.6% 160 MG 1.9% 2.1% Generic Drug Type Methadone Hydrochloride SOL 10 MG/ML 0.3% 0.7% Methadone Hydrochloride SOL 5 MG/5 ML 0.3% 1.2% Methadone Hydrochloride TAB 10 MG 89.6% 86.6% 3.13

120 MarketScan Long- Term Users Simulated Cohort Methadone Hydrochloride TAB 40 MG 0.4% 0.7% Methadone Hydrochloride TAB 5 MG 9.5% 10.8% * Daily dose was not converted to milligrams morphine equivalent Distribution of Number of Pharmacies Visited We compared the distributions of the number of unique pharmacies visited in the entire year and peak number of unique pharmacies visited in any 90 days (Table 3-6). In general, the ADOPT model tends to slightly overestimate the proportion of patients using one or two pharmacies and slightly underestimate the proportion using three or more pharmacies. Table 3-6. Unique Pharmacy Visits per Year and Within Any 90-Day Period among MarketScan and Simulated Cohorts Total per Year Peak Number in During Any 90-Days Number of MarketScan MarketScan Simulated Cohort Simulated Cohort Pharmacies Visited Long-Term Users Long-Term Users > 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 MarketScan Long-Term Users Non-Shoppers Simulated Cohort MarketScan Long-Term Users Pharmacy Shoppers Simulated Cohort Demographics n 80,101 (89.0%) 9,126 (91.26%) 9,909 (11.0%) 874 (8.74%) 3.14

121 Non-Shoppers Pharmacy Shoppers MarketScan MarketScan Simulated Cohort Long-Term Users Long-Term Users Simulated Cohort Mean age Male 27.70% 28.90% 31.55% 30.98% Depression diagnosis 9.40% 11.20% 15.40% 12.20% History of alcohol abuse 2.52% 3.06% 4.50% 3.33% Opioid Use Pattern Monthly prescriptions Average dose Dose Distribution Level 0-20mg 34.60% 26.50% 14.92% 11.34% 20-50mg 45.80% 54.20% 37.73% 44.60% mg 12.20% 8.93% 21.79% 16.70% 100mg or higher 7.40% 10.37% 25.65% 27.36% Predominant Opioid Type Schedule III or IV 80.20% 83.50% 73.60% 66.20% Schedule II, short-acting 16.70% 11.70% 21.10% 24.60% Schedule II, long-acting 3.10% 4.80% 5.30% 9.20% Note: Non-Shoppers=less than 4 pharmacies visited in any 90 days; Shoppers = 4 or more pharmacies visited in any 90 days. Individual-Level Comparison We also evaluated whether the simulated prescription history resembles the MarketScan prescription history. However, it is impossible to find a simulated enrollee that shares the exact same prescription history with a MarketScan counterpart. Instead, we analyzed two representative examples from the simulated cohort and the MarketScan cohort. Figure 3-3 shows a screenshot of a simulated enrollee s prescription history. The simulated enrollee is a 37-year old female, with a history of long-term prescription opioid use, primarily hydrocodone. At the beginning of her episode of use, the first two prescriptions had fewer supply days, lower strength per pill (5MG hydrocodone) and lower daily dose, compared with subsequent prescriptions. A similar pattern was also observed in the MarketScan dataset, as shown intable 3-8. The MarketScan patient received a prescription opioid with lower strength and fewer supply days at the beginning of the episode, and received monthly prescriptions with a higher strength once the prescription use stablized. The MarketScan patient visited multiple pharmacies, but had no overlapping (>25% of supply days) prescriptions during the two-year period (the prescriptions with the longest overlapping days supply were prescribed in December 2008, with a 7-day overlap, and in August and September 2009, with a 7-day overlap). The simulated patient also had no overlapping prescriptions; however, prescriptions with several (1-3) overlapping supply days were common. The simulated patient switched between pharmacy A and B, but is unlikely to be a pharmacy shopper. In this example, the comparison at the individual level was not conclusive, as we were unable to show all representative scenarios. However, the point of this description is to alert ADOPT users that the model permits verification of the details of each simulated individual s prescription Episode of use is defined as commencing with the dispensing date of an opioid prescription with no previous prescription in the dataset, or having a gap longer than 31 days from the end run-out date of a previous opioid prescription. 3.15

122 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

123 Table 3-8. Prescription History of a Representative Individual from the MarketScan Long-Term User Population for Comparison With Figure 3-3 Data Enrollee ID Service Days Date Supply Quantity Generic Drug Name Strength Pharmacy ID /16/ Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG tbteqfwahetq /16/ Meperidine Hydrochloride 50 MG/ML tbteqfwahetq /25/ Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG abtetfda2nti /30/ Acetaminophen/Oxycodone Hydrochloride 325 MG-10 MG abtetfda2nti /21/ Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG abtetfda2nti /4/ Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG abtetfda2nti /18/ Acetaminophen/Hydrocodone Bitartrate 325 MG-7.5 MG abtetfda2nti /26/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /19/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /3/ ASA/Oxycodone HCl/Oxycodone Terephthalate 325 MG-4.5 MG-0.38 MG tbtetfdauitu /16/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /14/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /8/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /30/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /26/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /23/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /20/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /16/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /15/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG tbtetfdauitu /13/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /13/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /17/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /10/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /3/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /25/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /18/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti /15/ Acetaminophen/Hydrocodone Bitartrate 500 MG-10 MG abtetfda2nti 3.17

124 COST ESTIMATION Estimating the reimbursement rates for prescription opioids using the MarketScan dataset presented three challenges. First, the reimbursement rates varied by brand. The same generic drug type could have different prices. Second, the reimbursement rates vary by state. Lastly, the reimbursement rate is also determined by patient s capitation status. These three factors contribute to the variance in the per-unit reimbursement rate for a generic drug type. Since the MarketScan database did not contain identifiers for brand name or geographic location, we could not directly account for the first two challenges by stratifying the data. We were able to address the third challenge by excluding those records with a capitation status of 1 (i.e., yes ). We calculated the mean per-10-unit reimbursement rate for the 156 generic prescription opioids that appeared in the MarketScan database, as shown in Appendix II. We used the estimated per- 10-unit reimbursement rates to directly populate the ADOPT model. However, because the ADOPT model is customizable and it is a time-consuming task for users to specify the reimbursement rate for each of the 156 drugs, we tried to shorten the list by grouping drugs by the effective component type (e.g., hydrocodone or codeine), then by master form (e.g., tablet, solution, or elixir), auxiliary component (e.g., acetaminophen, ibuprofen, or aspirin), and strength of the opioid component (e.g., 5MG or 10MG). For example, the per-10-unit reimbursement rates for hydrocodone were first classified by auxiliary component type (acetaminophen versus ibuprofen), then the acetaminophen group was further classified by master form (elixir vs. solution vs. tablet). As this analysis found neither a clear trend nor significant proportional difference in reimbursement rates in relation to the strength of hydrocodone, no further classification was done for strength. By contrast, the per-10-unit reimbursement rates for tramadol extended release tablets were found to be strength-related. We pooled together all records (excluding capitation patients) of tramadol extended release tablets at all strength levels, and then calculated a baseline reimbursement rate for baseline strength in this case, $27.85 per 10 tramadol extended release tablets with strength of 100mg. Based on this baseline reimbursement rate, the rates for 10 tablets with strength of 200mg and 300mg are $55.7 and $83.55, respectively. This classification process allowed us to reduce the number of reimbursement rates that users need to specify to 51 (versus 156), as shown in Table 3-9. Table 3-9. Price Comparison of Commonly Prescribed Opioids Drug Type and Form * Price per 10 units ($) Schedule III and IV Propoxyphene 3.62 Codeine Elixir 0.49 SOL 0.78 TAB 3.64 Hydrocodone With Acetaminophen Elixir 0.74 SOL 2.97 TAB 3.06 With Ibuprofen, TAB 7.96 Butalbital and codeine, CAP 8.47 Butorphanol, SOL Pentazocine, TAB

125 Drug Type and Form * Price per 10 units ($) Tramadol With Acetaminophen, TAB 6.93 TAB 1.98 TER, 100MG Schedule II Short-Acting Morphine sulfate SOL 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 Hydromorphone SOL/SUP TAB 4.92 Meperidine SOL TAB, 50MG 4.81 Fentanyl citrate transmucosal, lozenge, 0.8MG Schedule II Long-Acting Morphine sulfate sustained release CAP, 10MG TAB, 15MG 4.92 Fentanyl transdermal, 25MCG/hour Oxycodone hydrochloride, ER, TAB, 10MG Methadone SOL 1.24 TAB 1.68 Oxymorphone sustained release, 10MG Dihydrocodeine Levorphanol, TAB 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 $ 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, per 50 MME). 3.19

126 Table Price Comparison of Commonly Prescribed Opioids, Price per 50 MME Drug Category Representative Generic Name n $ per 50 MME Schedule III and IV Propoxyphene Acetaminophen/Propoxyphene Napsylate TAB 650 MG-100 MG Codeine Elixir Acetaminophen/Codeine Phosphate ELI 120 MG/5 ML-12 MG/5 ML SOL Acetaminophen/Codeine Phosphate SOL 120 MG/5 TAB ML-12 MG/5 ML Acetaminophen/Codeine Phosphate TAB 300 MG-30 MG Hydrocodone w/ Acetaminophen Elixir Acetaminophen/Hydrocodone Bitartrate ELI 500 MG/15 ML-7.5 MG/15 ML 126, , , , , SOL Acetaminophen/Hydrocodone Bitartrate SOL MG/15 ML-10 MG/15 ML TAB Acetaminophen/Hydrocodone Bitartrate TAB , MG-10 MG w/ibuprofen, TAB Ibuprofen/Oxycodone Hydrochloride TAB 400 MG MG Butalbital and codeine, CAP Aspirin/Butalbital/Caffeine/Codeine Phosphate CAP 5, MG-50 MG-40 MG-30 MG Butorphanol, SOL Butorphanol Tartrate SPR 10 MG/ML 3, Pentazocine, TAB Naloxone Hydrochloride/Pentazocine Hydrochloride TAB 0.5 MG-50 MG 2, Tramadol w /Acetaminophen, TAB Acetaminophen/Tramadol Hydrochloride TAB 325 MG-37.5 MG 20, TAB Tramadol Hydrochloride TAB 50 MG 243, TER, 100MG Tramadol Hydrochloride TER 200 MG 2, Schedule II Short-Acting Morphine sulfate SOL Morphine Sulfate SOL 10 MG/ML 1, TAB Morphine Sulfate TAB 15 MG 8, Codeine Sulfate 15MG Codeine Sulfate TAB 30 MG Oxycodone TAB, 5MG Oxycodone Hydrochloride TAB 15 MG 24, SOL Oxycodone Hydrochloride SOL 20 MG/ML CAP Oxycodone Hydrochloride CAP 5 MG 8, w/acetaminophen CAP Acetaminophen/Oxycodone Hydrochloride CAP 500 MG-5 MG 8, SOL Acetaminophen/Oxycodone Hydrochloride SOL MG/5 ML-5 MG/5 ML TAB, 5MG Acetaminophen/Oxycodone Hydrochloride TAB , MG-10 MG Tapentadol, TAB, 50MG Tapentadol Hydrochloride TAB 50 MG Hydromorphone SOL/SUP Hydromorphone Hydrochloride SOL 2 MG/ML 5, TAB Hydromorphone Hydrochloride TAB 4 MG 11,

127 Drug Category Representative Generic Name n $ per 50 MME Meperidine SOL Meperidine Hydrochloride SOL 50 MG/ML 1, TAB, 50MG Meperidine Hydrochloride TAB 50 MG 4, Fentanyl citrate transmucosal, Fentanyl Citrate LOZ 0.8 MG ,12.50 lozenge, 0.8MG Schedule II Long-Acting Morphine sulfate sustained release CAP, 10MG Morphine Sulfate CER 60 MG 2, TAB, 15MG Morphine Sulfate TER 15 MG 12, Fentanyl transdermal, Fentanyl TDM 100 MCG/HR 13, MCG/hour Oxycodone hydrochloride, ER, Oxycodone Hydrochloride TER 20 MG 19, TAB, 10MG Methadone SOL Methadone Hydrochloride SOL 5 MG/5 ML TAB Methadone Hydrochloride TAB 5 MG 4, Oxymorphone sustained release, Oxymorphone Hydrochloride TER 20 MG MG Dihydrocodeine Acetaminophen/Caffeine/Dihydrocodeine Bitartrate 1, TAB MG-60 MG-32 MG Levorphanol, TAB Levorphanol Tartrate 2 MG 4, 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

128 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

129 Table Representative State Patient Review and Restriction Program Policies Scenario State Current Eligibility Criteria for State Patient Review and Restriction Programs Modeled Eligibility Criteria # 1 Washington 12 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; 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 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. 2 West Virginia 13 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 Any of the following conditions in any 60 days: Visited >3 prescribers Received > 6 opioid prescriptions 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 North Carolina 14 One or more of the following criteria: Filled > 6 prescriptions for either opioid pain relievers or anti-anxiety (benzodiazepine) medications within a two month period Prescribed opioid pain relievers and/or benzodiazepine medications by >3 prescribers within a two month period Referral from a provider, DMA or CCNC. 3 Kentucky 15 The recipient has the following conditions in consecutive 180 calendar day periods: Received services from > 5different providers Received >10 different (opioid) prescription drugs Received (opioid) prescriptions from >3different pharmacies All of the following conditions in any 180- day period: Visited >5 providers Used >3 pharmacies Received >10 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

130 Scenario State Current Eligibility Criteria for State Patient Review and Restriction Programs Modeled Eligibility Criteria # 4 Idaho 16 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 5 Michigan 17 Any of the following conditions: Visited >3 different physicians in one quarter Visited >2 different physicians to obtain duplicate services for the same health condition or prescriptions the following drug categories: o Narcotic Analgesics o Barbiturates o Sedative-Hypnotic, Non-Barbiturates o Central Nervous System Stimulants/Anti-Narcoleptics o Anti-Anxieties o Amphetamines o Skeletal Muscle Relaxants Visited multiple physicians for vague diagnosis (e.g., myalgia, myositis, sinusitis, lumbago, migraine) to obtain any of the drugs listed above Used >3 different pharmacies in one quarter Received > 11 prescriptions in the listed categories in one quarter Other criteria includes convicted fraud and inappropriate use of ED services (content not shown here) All of the following conditions in any 60-day period: Prescription overuse: o Received >8 opioid prescriptions, OR o 3 tramadol claims or 480 tablets in any 60 days; Visited >2 or more pharmacies Visited >2 or more prescribers (The number of pharmacies and prescribers are not specified in the state criteria. The numbers used in the modeled criteria are assumed.) Any of the following conditions in any 90 days: Visited >3 prescribers Used > 3 pharmacies Received >11 opioid prescriptions 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

131 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: ) 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: ). 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

132 Table 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 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 2 prescribers and 2 5 prescribers and 3 pharmacies and ( 8 3 prescribers or 6 pharmacies and 10 RX or ( 3 tramadol RX in 60 days RX in two consecutive RX or 480 tramadol 180-day periods tablets)) in 60 days 4 prescribers or 4 pharmacies or 10 RX in 90 days, if any two conditions met 198 ( ) Number of Eligible Individuals Demographics Mean age (years) 41.4 ( ) Male (%) 34.1% (30.5%-37.7%) Opioid Use by the Eligible Cohort, With No PRR Program * Number of Prescriptions Used Schedule III and IV Schedule II, shortacting Schedule II, longacting Total 3,590 (2,897-4,283) 1,975 (1,587-2,363) 1,189 (893-1,485) 6,754 (5,986-7,522) 2.79 ( ) Average Number of RX per Month Average Daily 73.2 Morphine Equivalent ( ) Dose, mg Opioid Use by the Eligible Cohort, With PRR Program Eliminated Overlapping Prescriptions 1,257 (1,059-1,455) 42.5 ( ) 31.9% (30.7%-33.1%) 16,513 (14,818-18,208) 5,583 (4,791-6,375) 2,314 (2,075-2,553) 24,410 (21,672-27,148) 2.27 ( ) 67.4 ( ) 82 (61-103) 41.7 ( ) 32.8% (30.2%-35.4%) 2,186 (1,742-2,630) 1,232 (941-1,523) 671 ( ) 4,089 (3,652-4,526) 3.13 ( ) 81.7 ( ) 2775 (2,241-3,309) 43.8 ( ) 30.4% 29.7%-31.1%) 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 ( ) 56.4 ( ) 3 prescribers or 3 pharmacies in 90 days 2,865 (2,317-3,413) 43.8 ( ) 30.6% (29.8%-31.4%) 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 ( ) 59.3 ( ) Schedule III and IV 1,505 2, ,654 4,229 (1,375-1,635) (1,875-2,419) (813-1,139) (2,917-4,391) (3,841-4,617) Schedule II, shortacting 930 (811-1,049) 1,233 (1,011-1,455) 650 ( ) 1,994 (1,517-2,471) 2,557 (1,984-3,130) Schedule II, long ,263 1,

133 Brief Description Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 2 prescribers and 2 5 prescribers and 3 pharmacies and ( 8 3 prescribers or 6 pharmacies and 10 RX or ( 3 tramadol RX in 60 days RX in two consecutive RX or 480 tramadol 180-day periods tablets)) in 60 days 4 prescribers or 4 pharmacies or 10 RX in 90 days, if any two conditions met 3 prescribers or 3 pharmacies in 90 days acting ( ) (647-1,005) ( ) (985-1,541) (1,094-2,096) Total Number of Eliminated RX 3,007 (2,685-3,329) 4,206 (3,894-4,518) 1,994 (1,659-2,329) 6,911 (6,042-7,780) 8,381 (7,201-9,561) Total Percentage of Eliminated RX 44.5% (95% CI: 41.4%-47.6%) 17.2% (95% CI:15.1%-19.3%) 48.8% (95% CI:47.5%-50.1%) 14.0% (95% CI:10.2%-17.8%) 16.2% (95% CI:13.7%-18.3%) RX with Reduced Strength or Quantity Schedule III and IV 165 ( ) 247 ( ) 367 ( ) 1,190 (972-1,408) 936 (807-1,065) 1,092 (933-1,251) 113 (72-154) 202 ( ) 251 ( ) 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) Schedule II, shortacting Schedule II, longacting Total Number of RX 779 3, ,826 8,381 with Reduced ( ) (2,895-3,541) ( ) (6,109-7,543) (7,593-9,196) Strength or Quantity Total Percentage of 11.5% 13.2% 13.8% 13.8% 16.0% RX with Reduced (95% CI:10.2%-12.8%) (95% CI:12.4%-14.0%) (95% CI:11.7%-15.9%) (95% CI:12.6%-15.0%) (95% CI:15.3%-16.7%) Strength or Quantity Average Number of RX per Month ( ) ( ) ( ) ( ) ) Average Daily Morphine Equivalent ( ) ( ) ( ) ( ) ( ) Dose, mg 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

134 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: ) prescriptions per month to 1.46 (95% CI: ), followed by Scenario #1 where the number is reduced from 2.79 (95% CI: ) to 1.33 (95% CI: ). 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

135 Table Annual Health Impact of the PRR Program in a Population of 10,000 Long-Term Users under Different Eligibility Scenarios* Opioid-overdoserelated event type 4.8 Outpatient Visits (3.5, 6.1) Emergency Department Visits (108.7, 134.1) 61.2 Hospitalizations (54.5,67.9) 4.5 Deaths (3.6,5.4) * 95% confidence interval in parenthese Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 4 prescribers or prescribers and 3 2 prescribers and 2 3 prescribers No PRR pharmacies or 10 prescribers pharmacies and 10 pharmacies and ( 8 RX or or 3 program RX in 90 days, with or 6 RX in RX in two consecutive ( 3 tramadol RX or 480 pharmacies in two conditions met 60 days 180-day period tramadol tablets)) in 60 days 90 days -0.2 (0, -0.4) -8.6 (-6.2, -11.0) -4.8 (-4.1, -5.5) -0.4 (-0.2, -0.6) -1.1 (-0.7, -1.5) (-18.6, -25.6) (-9.2, -13.6) -0.6 (-0.4, -0.8) -0.2 (-0.0, -0.5) -5.4 (-3.9, -6.9) -3.9 (-3.1, -4.7) -0.2 (-0.0, -0.4) -2.6 (-1.8, -3.4) (-43.5, -50.7) (-18.5, -26.3) -1.0 (-0.6, -1.4) -3.0 (-2.5, -3.5) (-45.7, -58.9) (-30.4, -42.0) -1.8 (-1.2, -2.4) 3.29

136 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 communication with content experts. ** The s 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 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. correspondence, August

137 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

138 Table Cost Analysis of the PRR Program under Different Eligibility Scenarios Opioid-overdose-related event type Estimated Cost in US Dollars (per 10,000 Long-term Users) Opioid Cost $4,593,423 (±$241,029) $4,364,532 (±$200,145) Overdose-related Medical $909,341 $862,402 Cost (±$105,625) (±$113,204) $339,600 Program Cost - (±$4,000) $5,502,465 $5,567,532 Total (±$301,402) (±$297,145) Average opioid & medical $1,395 savings per enrollee - (±211) Number of enrollees needed for program 251 break-even - ( ) Number of total opioid users needed for program breakeven - Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 2 prescribers and 2 4 prescribers or 4 5 prescribers and 3 3 prescribers pharmacies and ( 8 RX or 3 prescribers or 3 No PRR pharmacies or 10 pharmacies and 10 or 6 RX in 60 ( 3 tramadol RX or 480 pharmacies in 90 program RX in 90 days, with RX in two consecutive days tramadol tablets)) in 60 days two conditions met 180-day period days 60,079 (56,010-67,260) $4,176,182 (±$246,759) $713,487 (±$92,328) $551,400 (±$39,600) $5,441,069 (±$287,956) $488 (±157) 1,041 (892-1,261) 39,249 (31,983-46,829) $4,443,573 (±$165,321) $874,606 (±$95,693) $316,400 (±$4,200) $5,634,579 (±$289,641) $2,251 (±352) 146 ( ) 84,383 (68,943-98,347) $3,857,941 (±$285,695) $464,792 (±$89,342) $855,000 (±$106,800) $5,177,733 (±$321,592) $425 (±122) 1,333 (942-1,799) 22,766 (16,954-32,823) $3,705,989 (±$299,403) $503,204 (±$84,231) $873,000 (±$109,600) $5,082,193 (±$302,524) $458 (±111) 1,162 (868-1,542) 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 are the estimated range based on the upper and lower bounds of 95% confidence interval of cost estimates 3.32

139 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

140 The MarketScan data do not contain prescriber information.because many PRR progams use the number of opioid prescribers as an eligibility criterion, ADOPT uses the previously reported correlation between numbers of pharmacies and prescribers from the Massachusetts PRR program database to simulate prescriber IDs. It is possible that this correlation may not reflect the experience of the MarketScan population. The lack of prescriber information in the MarketScan data also did not allow us to calibrate the simulated prescriber information. 4. Incomplete representation of PRR criteria The current version of ADOPT can only analyze some of the criteria that may be used in a PRR program, (i.e., numbers of prescribers, prescriptions, and pharmacies, as well as the average dose level). In practice, a PRR program often includes other criteria such as emergency department use, number of office visits, history of substance abuse, or prescriptions of other restricted agents. ADOPT lacks the capacity to analyze these additional PRR program criteria; however, they could be incorporated if the data were available. 5. Uncertainty in estimation of overdose risk ADOPT uses the hazard ratios for opioid overdose that are derived from the MarketScan inpatient and outpatient datasets. However, overdose rates may be higher than observed in these data because patients may have expired before entering the hospital. In addition, overdose events were identified by using the diagnostic codes. Misclassification of diagnostic codes may cause under-estimation or over-estimation of the overdose risk. 6. Uncertainty about PRR program cost The program costs are adjustable by users. Our exploratory analysis of PRR program effect is based on assumed program fixed and variable costs, which likely to deviate from the actual state program costs. 7. Assumption about PRR program effects The model assumes that the PRR program will eliminate the overlapping prescriptions and control excessive use (>=80mg MME per day) for all enrollees. This over-simplifies the real impact of the PRR program. Reducing all opioid prescriptions to this maximum dose per day may not be feasible. Nevertheless, ADOPT can potentially be modified to simulate more realistic and complex impact of the PRR program if such evidence becomes available. Despite these limitations, the ADOPT model demonstrates the potential to simulate individual prescription consumption behavior with satisfactory similarity to real prescription consumption behavior based on calibration with MarketScan data. Using the current model structure and interface, it is possible to add new functions if and when future data becomes available. The features of this interactive tool make it feasible for state-level program staff to conduct timely and specific analysis with state-level data to inform state policy decisions about PRR programs. 3.34

141 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

142 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 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 Oxycodone HCL control 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 Morphine sulfate Pentazocine (with or without aspirin/acetaminophen/ ibuprofen) Methadone Codeine Sulfate Codeine + aspirin/acetaminophen/ ibuprofen Oxymorphone extended release Levorphanol Opium Dihydrocodeine Fentanyl citrate transmucosal Tapentadol The multinomial logistic regression model gives the predictive value (in percentage) for each category of commonly used opioid types for all possible combinations of the explanatory variables. In the model, the predictive values are translated into cumulative probabilities, as shown in Figure 3-5. The model then generates a random number between 0 and 1 and this number is compared with the cumulative probabilities to decide which interval (i.e., category of predominant drug type) the random number falls in. As shown in Figure 3-5, a row of cumulative probabilities is located for Jane s age, gender and race. The randomly generated number is 0.72, which is greater than 0.68 (the upper bound for the category of other Schedule II short-acting ) and smaller than 0.82 (the upper bound for the category of oxycodone ); therefore, the predominant drug type for Jane s initial episode is oxycodone. The output of this simulation process reflects the MarketScan distribution of predominant drug types. 3.36

143 Figure 3-5. Example of Random Sampling of Predominant Drug Type Age strata gender race Hydroco Other_L Other_ done A_II Non_II Other_ Oxycodo SA_II ne Propoxy Tramado phene l female white 54% 59% 62% 68% 82% 87% 100% male non-white 51% 53% 59% 62% 77% 85% 100% male white 55% 58% 62% 66% 78% 86% 100% female non-white 46% 49% 54% 58% 74% 82% 100% Generated random number >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), days, days (episodic), days, (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 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 P concurrent use =1/(1-exp( 0 + age*age_stratum+ race*race + gender*gender+ length*length_type+ drug*predominant_drug_type)) where is the vector of corresponding coefficient for the vector of covariates. The calculated likelihood is compared with a randomly sampled probability. A likelihood smaller than the randomly generated probability means not having concurrent opioid use in the episode. For example, if Jane s likelihood is 2.5% and the random generated number is 21.6% (larger than 2.5%), she does not have concurrent opioid use in this episode. 3.37

144 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 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 6/1/2009 1/1/ /31/2010 6/1/2011 Episodes of drug use 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

145 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

146 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