Caces, Ph.D. served as reviewer and Project Manager at ONDCP; Terry Zobeck, Ph.D. and. Michael ..... 2007) and drug-related harms (McAuliffe & Dunn, 2004).
Design Considerations for a Prescription Drug Consequences Index
April 2015
DESIGN CONSIDERATIONS FOR A PRESCRIPTION DRUG CONSEQUENCES INDEX
April 2015
Office of National Drug Control Policy Executive Office of the President
Acknowledgements This publication is the result of a Cooperative Agreement between the Executive Office of the President, Office of National Drug Control Policy (ONDCP) and the South Carolina Research Foundation, University of South Carolina (USC) under Cooperative Agreement #G10USCAROLINA. Eric Sevigny, Ph.D. (Principal Investigator) produced this publication. Fe Caces, Ph.D. served as reviewer and Project Manager at ONDCP; Terry Zobeck, Ph.D. and Michael Cala, Ph.D. also served as reviewers.
Disclaimer The information and opinions expressed herein are the views of the authors and do not necessarily represent the views of the Office of National Drug Control Policy or any other agency of the Federal Government.
Notice This report may be reproduced in whole or in part without permission from ONDCP. Citation of the source is appreciated. Suggested citation: Sevigny, Eric L. (2015). Design Considerations for a Prescription Drug Consequences Index. Office of National Drug Control Policy. Washington, DC: Executive Office of the President.
Electronic Access to Publication This document and associated data can be accessed electronically through the following World Wide Web address: http://www.whitehouse.gov/ondcp
Originating Office Executive Office of the President Office of National Drug Control Policy Washington, DC 20503 April 2015
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Executive Summary Prescription drug abuse is the fastest growing drug problem in the United States, and has rightfully risen to the forefront of federal and state drug control agendas. Against this backdrop, there is a critical need to develop surveillance and monitoring systems that can assist policymakers and other stakeholders in responding to this entrenched problem. Drawing on previous research supported by the Office of National Drug Control Policy (Sevigny & Saisana, 2013), this report identifies and reviews key design considerations toward the development of a Prescription Drug Consequences Index (PDCI) as a useful policy tool. Many of these index design considerations would best be resolved by a panel of experts on prescription drug misuse convened for this purpose. Conceptually, the panel would first need to articulate the purpose and scope of the PDCI. Other important design considerations include identifying the prescription drugs to be monitored and at what level of specificity (e.g., formulation, product, substance, class), deciding upon the operating definition of prescription drug misuse, and selecting which prescription drug consequences to measure and how to operationalize them. The report then inventories prescription drug data systems that could potentially supply valid and reliable indicators for constructing the PDCI, while also presenting a data quality framework for assessing the utility of these data systems according to their availability and accessibility, timeliness and continuity, and coverage and specificity. Methodological issues are taken up next, including considerations concerning the statistical treatment of indicators, weighting, and aggregation—critical technical decisions that would best be resolved through statistical consultation with an expert in index construction. The report concludes with a discussion concerning the potential for leveraging existing prescription drug surveillance systems for developing the PDCI.
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Table of Contents Introduction ..................................................................................................................................... 1 The Epidemiology of Nonmedical Prescription Drug Use ............................................................. 1 Availability and Misuse .............................................................................................................. 2 Abuse, Dependence, and Treatment ........................................................................................... 3 Emergency Department Admissions........................................................................................... 4 Child Exposures .......................................................................................................................... 4 Overdose Deaths ......................................................................................................................... 5 Rationale for a Prescription Drug Consequences Index ................................................................. 5 Conceptual Issues in Constructing a Prescription Drug Consequences Index ............................... 6 What Is the Purpose of the Index? .............................................................................................. 6 Which Prescription Drugs Will Be Monitored and at What Level of Specificity? .................... 9 How Will Prescription Drug Misuse Be Conceptualized and Measured? ................................ 10 Which Prescription Drug Consequences Will Be Included in the Index? ................................ 12 How Should Consequence Indicators Be Normalized? ............................................................ 13 Prescription Drug Data Systems ................................................................................................... 15 Inventory of Prescription Drug Data Systems .......................................................................... 16 Data Quality Framework........................................................................................................... 16 Validity and Reliability ......................................................................................................... 18 Availability and Accessibility ............................................................................................... 19 Timeliness and Continuity .................................................................................................... 20 Coverage and Specificity ...................................................................................................... 21 Methodological Choices in Constructing a Prescription Drug Consequences Index ................... 22 Statistical Treatment of Selected Indicators ............................................................................. 23 Weighting and Aggregation ...................................................................................................... 23 The Potential for Leveraging Existing Prescription Drug Surveillance Systems ......................... 25 Summary ....................................................................................................................................... 26 References ..................................................................................................................................... 28
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List of Tables and Figures Figure 1. Prescription Drug Consequences Performance Measurement Conceptual Framework ..7 Table 1. Toward a Taxonomy of Prescription Drug Consequences .............................................13 Table 2. Prescription Drug Data Systems .....................................................................................17
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Introduction Over the past 15 years, prescription drug abuse has emerged as the fastest growing drug problem in the United States (Dart et al., 2015; Kolodny et al., 2015; Maxwell, 2011; Paulozzi, 2012). Much of this increase has been driven by the abuse of opioid medications, which generates more than $50 billion in societal costs annually (Birnbaum et al., 2011; Hansen, Oster, Edelsberg, Woody, & Sullivan, 2011). Consequently, preventing and reducing prescription drug abuse has become a key national drug control priority. The Office of National Drug Control Policy (ONDCP, 2011) has outlined national efforts to reduce nonmedical prescription drug use in its Prescription Drug Abuse Prevention Plan, with targeted action along four key policy fronts: education, monitoring, proper disposal, and enforcement. Assessing progress and informing policy developments in these areas requires the timely collection, analysis, and dissemination of data on prescription drug-related outcomes. Building upon previous ONDCP-sponsored work that developed a series of national and state Drug Consequences Indices (DCIs) for illegal drugs (Sevigny & Saisana, 2013), this report addresses design considerations for constructing an analogous Prescription Drug Consequences Index (PDCI).
The Epidemiology of Nonmedical Prescription Drug Use The percentage of the U.S. population who reported using at least one prescription drug during the past 30 days has increased significantly since the late 1990s (Frenk, Porter, & Paulozzi, 2015; Gu, Dillon, & Burt, 2010). Rates of nonmedical use of prescription drugs (NMUPD), especially for opioid medications, have also risen dramatically alongside legitimate use
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(Manchikanti, 2006; Maxwell, 2011; Paulozzi, 2012). This section briefly reviews the current epidemiology of NMUPD before addressing key index design considerations. Availability and Misuse The availability of prescription drugs with abuse potential has increased greatly in recent decades (Garfield et al., 2012; Kolodny et al., 2015; Olfson, Blanco, Wang, & Greenhill, 2013). Much of the public health concern over rising prescription drug use rates has centered on opioid medications, with both the number of opioid prescriptions and the total volume dispensed increasing significantly since the 1990s (Atluri, Sudarshan, & Manchikanti, 2014; Imtiaz, Shield, Fischer, & Rehm, 2014; Kenan, Mack, & Paulozzi, 2012; Kolodny et al., 2015; Manchikanti, 2006; Manchikanti, Fellows, & Ailinani, 2010; Mazer-Amirshahi, Mullins, Rasooly, van den Anker, & Pines, 2014; Olfson, Wang, Iza, Crystal, & Blanco, 2013). Although recent evidence points to a leveling and even slight decline in prescription opioid abuse and related harms, the problem remains entrenched at historically high rates (Dart et al., 2015; Hedegaard, Chen, & Warner, 2015; Kolodny et al., 2015). Exact trends vary by substance, but the nonmedical use of prescription drugs among both household and student populations generally rose throughout the 1990s, peaked during the 2000s, and then declined steadily through 2013 (Johnston, O'Malley, Bachman, Schulenberg, & Miech, 2014; Manchikanti, 2006; Substance Abuse and Mental Health Services Administration, 2014a). Nevertheless, despite stable and even falling prevalence, Americans initiate and continue to use prescription medications for nonmedical reasons at higher rates than most other drugs. According to the Monitoring the Future (MTF) survey, for instance, the annual incidence of illicit prescription opioid, amphetamine, and tranquilizer use among high school students is surpassed only by marijuana (Johnston et al., 2014). Likewise, according to the National Survey of Drug Use and 2
Health (NSDUH), more individuals aged 12 and older (6.5 million) reported past-month nonmedical use of prescription drugs in 2013 than any other illicit substance except marijuana (19.8 million users) (Substance Abuse and Mental Health Services Administration, 2014b). Abuse, Dependence, and Treatment Frequent nonmedical use of prescription drugs has been increasing since at least the early 2000s. For example, Jones (2012) found that among the general household population the chronic nonmedical use of opioids (defined as using on 200 or more days) increased 75% between 20022003 and 2009-2010, whereas moderate use (30-199 days) increased only about 8% and infrequent use (1-29 days) actually declined by 6%. General population rates of prescription drug abuse and dependence, especially involving opioid medications, are also generally increasing. For example, McCabe, Cranford, and West (2008) report that the prevalence of prescription drug abuse and dependence in the U.S. population aged 18 and older increased 65% and 67%, respectively, between 1991-1992 and 2001-2002—significant increases driven mainly by opioid and sedative use disorders. More recently, the prevalence of prescription drug dependence in the US. population aged 12 and older increased significantly by 40% between 2002 and 2013 (although combined prevalence for prescription drug abuse or dependence remained stable over the same period) (Substance Abuse and Mental Health Services Administration, 2014a). Rates of prescription drug abuse are also increasing among treatment populations. Data from the Treatment Episode Data Set (TEDS) show that between 2002 and 2012 the percentage of primary admissions involving prescription opioids increased more than four-fold (2.4% to 9.7%) and more than doubled for benzodiazepines (0.4% to 1.0%), a period when the share of heroin admissions remained relatively flat (15.1% to 16.3%) and cocaine admissions fell by nearly half (13.0% to 6.9%) (Substance Abuse and Mental Health Services Administration, 2014c). Thus, the 3
evidence points to increasing rates of chronic and dependent nonmedical use of prescription drugs among both general and special populations. Emergency Department Admissions Acute reactions to prescription drugs as measured by emergency department (ED) visits contribute substantially to drug-related morbidity in the United States. According to the Drug Abuse Warning Network (DAWN), in 2011 more drug-related ED visits involved the nonmedical use of prescription drugs (58%), whether alone or in combination, than all other illicit drugs combined (51%) (Substance Abuse and Mental Health Services Administration, 2013). Moreover, whereas ED visits involving just illicit drugs showed no significant increase between 2004 and 2011, ED visits involving only the misuse of prescription drugs increased a significant 148% over the same time period (Substance Abuse and Mental Health Services Administration, 2013). Child Exposures Exposure to prescription drugs is a nontrivial cause of morbidity and mortality among children. Bailey, Campagna, and Dart (2009) documented 9,179 exposures to prescription opioids between 2003 and 2006 as reported to poison centers participating in the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS) System. About 5% of these exposures resulted in serious symptoms either requiring treatment or causing disability or death. Increasing rates of prenatal exposure to prescription opioids have also been documented. For example, Patrick et al. (2012) analyzed the Healthcare Cost and Utilization Project’s (HCUP) Kids’ Inpatient Database (KID) to reveal that the incidence of neonatal abstinence syndrome caused by maternal opiate use increased from 1.20 to 3.39 per 1,000 hospital births per year between 2000 and 2009.
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Overdose Deaths Between 1999 and 2011, the rate of prescription opioid poisoning deaths nearly quadrupled from 1.4 to 5.4 per 100,000, before declining slightly to 5.1 per 100,000 in 2013 (Chen, Hedegaard, & Warner, 2014; Hedegaard et al., 2015). Notably, overdose deaths involving prescription opioids, which in 2011 accounted for 41% of all overdose fatalities, first outnumbered collective heroin and cocaine fatalities in 2003; by 2006, opioids were more commonly involved in overdose deaths than cocaine, heroin, and psychostimulants combined (Calcaterra, Glanz, & Binswanger, 2013; Chen et al., 2014; Jones, Mack, & Paulozzi, 2013). Benzodiazepines are also increasingly contributing to prescription drug poisoning deaths, especially in combination with opioids. Between 2006 and 2011, the number of poisoning deaths involving only prescription opioids did not increase significantly, but deaths from a combination of opioids and benzodiazepines increased an average of 14% annually over this time period (Chen et al., 2014; Jones et al., 2013).
Rationale for a Prescription Drug Consequences Index NMUPD produces a broad array of costly negative consequences. When confronted with multifaceted social problems that transect multiple domains and interests, such as the problem of prescription drug misuse, government agencies face the difficult tasks of both monitoring policy and program performance. Given these challenges, composite indicators (CIs) have been proposed and deployed in the drug field as practical tools for performance measurement, policy analysis, benchmarking, and public communication (Ritter, 2009; Sevigny & Saisana, 2013). Broadly, a CI is a statistical aggregation of diverse social indicators into a single numerical index, which facilitates the monitoring and assessment of outcomes across both jurisdictions and time. Thus, a key advantage of the envisioned Prescription Drug Consequences Index is the ability to summarize a range of theoretically relevant prescription drug consequences in a more efficient and 5
parsimonious manner than is possible with a chart book of social indicators taken separately. Importantly, the PDCI is intended to enhance the utility of the underlying indicators, and should be considered complementary information along with the overall matrix of prescription drug consequence indicators. In short, as discussed in the remainder of this report, developing a composite indicator to monitor a phenomenon as complex and multidimensional as prescription drug consequences presents numerous conceptual and methodological issues that must be addressed for the effort to be successful and accepted by stakeholders.
Conceptual Issues in Constructing a Prescription Drug Consequences Index This section examines a number of important considerations regarding purpose and scope that an interdisciplinary team of scholars would need to address in developing a Prescription Drug Consequences Index. What Is the Purpose of the Index? The development and construction of a Prescription Drug Consequences Index must be guided by an overarching logic model that clearly articulates its purpose and objectives. In the drug policy field, CIs have been used to rank drugs according to harm (Nutt, King, & Phillips, 2010), monitor progress toward strategic objectives (Home Office, 2009), and make crossjurisdictional comparisons of both drug policies (Brand, Saisana, Rynn, Pennoni, & Lowenfels, 2007) and drug-related harms (McAuliffe & Dunn, 2004). In previous work supported by ONDCP, the U.S. Drug Consequences Indices were developed within a performance measurement framework to monitor national trends and facilitate interstate comparisons of the consequences of illicit drugs (Sevigny & Saisana, 2013). A similar performance measurement approach in which prescription drug consequences are assessed against policy objectives articulated in the national
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Prescription Drug Abuse Prevention Plan could provide a similar guiding framework for the PDCI. The conceptual framework presented in Figure 1, which was developed under the Arizona Rx Drug Misuse and Abuse Initiative, provides an example of a logic model developed within a state policy monitoring context, where the arrows indicate direction of effect and the pluses (+) and minuses (-) reflect targeted increases and decreases, respectively (Gardner & Orosco, 2015). A possible innovation in this area would be the development of the PDCI as a sentinel surveillance system that aggregates information from multiple data streams in near real-time. Although this would be a technically challenging endeavor, it is more viable than for illicit drugs because many government and industry pharmacosurveillance systems already monitor adverse events with timely reporting capabilities (Arfken & Cicero, 2003; Dart, 2009; Dasgupta & Schnoll, 2009; Hughes, Bogdan, & Dart, 2007). For example, the FDA Adverse Event Reporting System (FAERS) releases quarterly information on prescription drug-related adverse outcomes on approximately a three-quarter delay, and the National Poison Data System (NPDS) monitors human exposures to drugs in near real-time based on calls to the nation’s poison centers. Surveillance systems often have complex structures (e.g., FAERS is a relational database) or are
Figure 2. Prescription Drug Consequences Performance Measurement Conceptual Framework Source: Gardner and Orosco (2015)
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proprietary and expensive to access, so a PDCI that aims for sentinel surveillance would confront unique implementation and maintenance challenges. Notably, the Washington State Prescription Monitoring Program Data Mapping Project, which received funding through the federal Harold Rogers Prescription Drug Monitoring Program, is in the early stages of developing a composite indicator that aims to integrate prescription drug data from multiple state sources (Baumgartner & Leichtling, 2015). Although not mutually exclusive of assessment and surveillance functions, other potential applications of a PDCI include public communication, resource allocation, policy analysis, and strategic planning. As it relates to index design, one objective might seek to identify communities with entrenched levels of abuse, whereas another might prioritize the detection of emergent drug problems. Similarly, depending on the particular objective, monitoring could proceed prospectively or start from some prior benchmark. Data system discontinuities should be considered when deciding upon appropriate anchor points. The intended purpose of the PDCI will also inform its geographic resolution (e.g., national, state, substate). Performance assessment may only require a national and/or state level of analysis, but effective surveillance would probably require finer granularity (e.g., county, three-digit zip code). Fortunately, many drug data systems have small area geospatial resolutions, but this capability is not universal (Brownstein, Green, Cassidy, & Butler, 2010; King & Essick, 2013; McDonald, Carlson, & Izrael, 2012; Wangia & Shireman, 2013). The key point is that the aims and objectives of the index need to be clearly articulated, as these considerations will drive subsequent index design. Although surveillance would be an innovative application, current data limitations suggest performance measurement should be a primary function of the PDCI.
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Which Prescription Drugs Will Be Monitored and at What Level of Specificity? Prescription medications with anabolic-androgenic, analgesic, anesthetic, anxiolytic, sedative, and stimulant properties are commonly misused and abused in the United States (Barrett, Meisner, & Stewart, 2008; Bokor & Anderson, 2014; Kanayama, Hudson, & Pope Jr, 2010). Consequently, a major design decision for the PDCI concerns the range of abusable prescription drugs that will be monitored. If the index narrowly focuses on a single drug class that may be causing the most serious public health consequences (e.g., opioids), it risks becoming outdated as that problem subsides and other dangerous drugs emerge. Conversely, if coverage is too broad, certain low prevalence drug classes (e.g., anabolic steroids) may not produce a strong enough signal to meaningfully contribute to the index. Thus, the selection of prescription drugs for monitoring purposes must strike a reasonable balance in its breadth of coverage. Within each broad prescription drug class, consequences may also be measured at various levels of product specificity. For example, ranging from most to least specific, OxyContin abuse could be measured at the following levels of specificity: formulation (OxyContin ER), product (OxyContin), substance (oxycodone), subclass (semisynthetic opioid), and class (opioid). Thus, depth of coverage represents another critical measurement decision for the PDCI. Product differentiation is essential because abuse liability varies by substance, branding, and formulation (Butler, Black, Cassidy, Dailey, & Budman, 2011). Practically, however, product specificity differs across drug data systems (Dart, 2009; Wysowski, 2007). Moreover, the terminology describing prescription drugs is often ambiguous and antiquated, with shifting definitions across both time and contexts. In a medical database, for instance, the term “narcotic” might refer to opioid medications, but in a legal database it likely encompasses other controlled substances such as cocaine. Thus, although it may be desirable to monitor prescription drug consequences at the
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product level, measurement differences across data systems may severely limit this ability and necessitate monitoring at a higher level of aggregation (Hernandez & Nelson, 2010). How Will Prescription Drug Misuse Be Conceptualized and Measured? Prescription drug misuse encompasses a wide variety of substance-using behaviors. At the broadest level, Smith et al. (2013) conceptualize prescription drug consequences by differentiating misuse, abuse, and related events (MAREs). They define MARE elements as follows: misuse is intentional but inappropriate therapeutic use; abuse is intentional nontherapeutic use for euphoric effect; suicide-related is attempted or successful self-injurious behavior involving prescription drugs; therapeutic error is a mistake in medication administration (e.g., wrong drug, wrong dose, wrong person). All of these are adverse events, but whether they should be measured by the PDCI ultimately depends on its governing scope and purpose. Most drug abuse research uses a definition of nonmedical use of prescription drugs (NMUPD) that combines misuse (i.e., nonprescribed use) and abuse (i.e., use to get high) into a single measured construct (Barrett et al., 2008; Boyd & McCabe, 2008; Dart, 2009; Hernandez & Nelson, 2010; McCabe, Boyd, & Teter, 2009; Zacny & Lichtor, 2008). However, national surveys that ask about NMUPD employ question wording that makes it virtually impossible to unpack this “heterogeneous group of motivations and related behaviors” (Boyd & McCabe, 2008:1; McCabe et al., 2009; Voon & Kerr, 2013). For example, MTF defines NMUPD as using medications “without a doctor’s orders” and NSDUH as using medications “not prescribed for you or that you took only for the experience or feeling it caused.” Under these definitions, it is not possible to distinguish the person who injects diverted product for the psychoactive effect from the uninsured individual who obtains prescription pills from a friend to treat a legitimate pain condition.
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Consequently, official prevalence estimates of NMUPD may overstate the magnitude of the problem because they capture quasi-legitimate self-treatment behaviors along with more serious forms of abuse (McCabe et al., 2009). However, there is also countervailing evidence that a single question format actually underestimates NMUPD relative to a decomposed multipart question because respondents may find the combined question confusing or may refrain from affirming misuse if it could be construed as abuse (Hubbard, Pantula, & Lessler, 1992). To avoid such question effects, Boyd and McCabe (2008) propose a four-category classification scheme for asking about nonmedical prescription drug use that crosses motivation (recreational use vs. selftreatment) with legality (own prescription vs. diverted product). Thus, within this framework, it would be possible to clearly distinguish four problem behaviors: (i) recreational use with diverted product, (ii) recreational use with own prescription, (iii) self-treatment with diverted product, and (iv) self-treatment with own prescription (outside of approved use). Ideally, given the diversity of the adverse events that can be attributed to prescription drugs, it would be desirable to preserve or account for this heterogeneity in the measurement process. However, in light of existing conventions in measuring NMUPD and the different ways this information is collected across data systems, the feasibility of attending to these distinctions in constructing the PDCI would need to be determined as part of the conceptualization and indicator selection process. The initial consideration would begin with conceptualizing the phenomenon of prescription drug consequences and defining whether this will include abuse, misuse, and/or other related events. The follow-on consideration concerns the actual definitions and measurement operations used within existing data systems. Practically, this process will be iterative, but the proposed conceptual model should drive measurement rather than vice versa.
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Which Prescription Drug Consequences Will Be Included in the Index? As already noted, development of the PDCI should be guided by an overarching logic model that articulates the purpose of the index and clearly defines the phenomenon of interest. Thus, the core conceptual task entails formulating the problem of prescription drug consequences. A taxonomic approach that organizes relevant consequences into a meaningful representation of the problem is a convenient method. For example, Sevigny and Saisana (2013) developed a taxonomy of drug-related consequences for illegal drugs that structured consequences into the following three domains (and nine subdomains): health consequences (mortality, morbidity, drugexposed infants), social and economic consequences (family disruption and child maltreatment, reduced attainment and productivity, stigmatization and marginalization), and crime and disorder consequences (drugged driving, crime and nuisance, community and environmental harms). This taxonomy, which was developed based on a comprehensive review of the literature and feedback obtained from a network of drug policy scholars and addictions experts, could serve as a viable starting point for a ‘taxonomy of prescription drug consequences.’ However, a targeted literature review on prescription drug consequences and related outcomes will be required to supplement and modify this earlier taxonomy. Convening an interdisciplinary panel of experts on prescription drug abuse to engage in further concept mapping would be beneficial for achieving consensus on this topic. Table 1 presents a range of outcomes involving prescription drugs that begins the process of developing a more detailed prescription drug consequences taxonomy.
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Table 1. Toward a Taxonomy of Prescription Drug Consequences Adverse Event Misuse
Potential Constructs and Indicators Using more or longer than prescribed Nonprescribed use Nonadherence to regimen
Self‐treatment without valid prescription Overutilization Off‐label use
Abuse and Dependence
Overdose Tampering Harmful routes of administration Impaired healthcare workers Low educational bonding and attainment Low employability Poor parental monitoring EMS naloxone deployments
Increased risky behaviors delinquency Polysubstance use Tolerance and addiction Morbidity and mortality Reduced quality of life Impaired driving Early onset and initiation
Diversion
Doctor shopping Pharmacy robbery Pill mills Fraud and forgery Theft from family Medical diversion and malpractice
Internet distribution Access and availability Inappropriate sharing Overlapping prescriptions Multiple prescription providers Use of extended release formulations
Other Adverse Outcomes
Suicide Medication errors Drug interactions Underutilization and undertreatment
Child exposure Neonatal withdrawal syndrome Side effects Public water system contamination
and
How Should Consequence Indicators Be Normalized? Making valid comparisons of prescription drug consequences across either time or jurisdictions requires standardization by the target population or some metric of product availability. Adjusting outcomes by a suitable denominator permits valid comparison of consequences according to a defined population at risk (Smith et al., 2007). The general population residing within a specific geographic area is the most frequently used denominator in public health and other areas. At the patient-level, the denominator most commonly used in the literature is unique recipients of dispensed drug (URDD), which measures the number of distinct patients who fill a prescription for a given drug (Dart, 2009; Secora, Dormitzer, Staffa, & Dal Pan, 2014). Other common prescription-level denominators include the number of prescriptions dispensed,
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kilograms distributed, tablets dispensed, and total patient days of therapy (Dart, 2009; Secora et al., 2014; Smith et al., 2007; Wangia & Shireman, 2013). Another potential but as yet unutilized denominator in prescription drug abuse research is pharmaceutical company expenditures on provider-targeted promotion and direct-to-consumer advertising (Kornfield, Donohue, Berndt, & Alexander, 2013). Each denominator has advantages and weaknesses in application, and some may be more useful than others for particular audiences or specific purposes. The drawback of a populationbased denominator is that it does not account for product availability and assumes similar risk across the defined population. However, normalizing by the general population provides an estimate of overall prescription drug burden that enables comparison with other social ills, such as illicit drug abuse (Secora et al., 2014; Smith et al., 2007). The patient-level denominator, URDD, frames risks and benefits according to the distinct number of individuals who fill a prescription for a particular drug, but the main limitation of this measure is that it does not account for the total volume of drugs dispensed, including refills and multiple prescriptions. Thus, high-volume diversion either within a community or by any particular individual will be missed by this measure (Dart, 2009; Secora et al., 2014; Smith et al., 2007). The prescription-level denominators measure availability at different degrees of granularity. Thus, the key advantage of these measures is that they account for drug utilization by normalizing adverse events per a defined exposure opportunity. Their main limitation is incommensurability between different product types. In other words, the use of these measures assumes equivalent abuse potential across drugs for a given amount of substance (e.g., one kilogram, prescription, or bottle of pills). This is rarely a safe assumption, even within the same drug class (Secora et al., 2014). For example, the number of tablets that can be produced from one
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kilogram varies by drug; likewise, the amount of active ingredient per tablet varies by substance and formulation. Attempts to overcome this limitation include converting the denominator to a defined daily dose (DDD), which is defined by the World Health Organization’s Collaborating Centre for Drug Statistics Methodology as the “average maintenance dose per day for a drug used for its main indication in adults,” or, for opioids, to morphine equivalents (Imtiaz et al., 2014; Paulozzi, Budnitz, & Xi, 2006; Wangia & Shireman, 2013; Zerzan et al., 2006). It is unclear which of these metrics will be suitable for the PDCI. Because consequences from a diverse group of prescription drugs will be aggregated, the use of a standardized metric seems justified. However, the use of these standardized metrics as valid measures of abuse liability has been questioned (Walsh, Nuzzo, Lofwall, & Holtman Jr, 2008; Zacny & Gutierrez, 2009). Moreover, standardization is performed at the substance level (e.g., oxycodone, clonazepam), so this adjustment will be problematic for data systems that collect drug information at a higher level of aggregation. Assessing the applicability and generality of these various denominators represents a critical design decision for the PDCI.
Prescription Drug Data Systems With the conceptual framework complete, the next critical task entails populating the taxonomy with measureable indicators of prescription drug consequences. This section inventories prescription drug data systems that might supply relevant indicators for purposes of index construction, and it reviews key data quality considerations that should inform this type of assessment.
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Inventory of Prescription Drug Data Systems Data on prescription drug outcomes are available from many sources, including government, industry, and other private entities (Dart, 2009; Dasgupta et al., 2013; Dasgupta & Schnoll, 2009; Hernandez & Nelson, 2010; Secora et al., 2014; Smith, Graham, Haddox, & Steffey, 2008). The major data systems that collect information on prescription drug utilization, misuse, abuse, and diversion are listed in Table 2. These particular data systems, along with other systems from government insurance programs (e.g., Medicaid, Medicare) and fee-based industry sources (e.g., IMS Health, MarketScan, Verispan), will require close evaluation for their ability to provide valid and reliable indicators of prescription drug consequences. For each data system, Table 2 provides information on the resolution of drug and geographic information contained within each database. For drugs, specificity designations include product, substance, class, and none (i.e., “prescription drugs”); for geography, specificity designations include national, state, and substate. Data Quality Framework Validity and reliability are paramount considerations when evaluating the usefulness of both whole data systems and specific indicators for monitoring and evaluation. However, data system utility is also a function of other considerations, including data availability and accessibility, timeliness and sustainability, and coverage and specificity. Each of these areas is addressed below with consideration for index construction.
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Table 2. Prescription Drug Data Systems Data System
Sponsor
Data Description
Drug Specificity
Geographic Specificity
Automation of Reports and Consolidated Orders System (ARCOS)
DEA
Commercial distribution of controlled substances
Substance
State
Criminal Cases Against Doctors
DEA
Federal criminal investigations of physicians
Product
Substate
Drug Abuse Warning Network (DAWN)
SAMHSA (discontinued)
Emergency department drug mentions and episodes and drug‐ related deaths
Substance
National, substate (limited)
Drug Testing Index (DTI)
Quest Diagnostics
Drug positivity rates in the workforce
Substance
State
Fatality Analysis Reporting System (FARS)
NHTSA
Drug‐involved fatal vehicle accidents
Substance
Substate
FDA Adverse Events Reporting System (FAERS)
FDA
Adverse drug reports
Product
National
Healthcare Cost and Utilization Project (HCUP)
AHRQ
Hospital/ED admissions
Class
State
Medical Expenditure Panel Survey (MEPS)
AHRQ
Prescription drug utilization
Product
National
Monitoring the Future (MTF)
NIDA
Nonmedical use and risk perceptions
Product
National
National Ambulatory Medical Care Survey (NAMCS)
CDC
Office‐based ambulatory medical care services
Product
National, Substate (restricted)
National Forensic Laboratory Information System (NFLIS)
DEA
Forensic analysis of seized drugs
Substance
Substate
National Hospital Ambulatory Medical Care Survey (NHAMCS)
CDC
Hospital‐based ambulatory medical care services
Product
National, Substate (restricted)
National Poison Data System (NPDS)
AAPCC
Poison center calls
Substance
Substate
National Survey of Substance Abuse Treatment Services (NSSATS)
SAMHSA
Substance abuse treatment services and utilization
Product/ Substance
State
National Survey on Drug Use and Health (NSDUH)
SAMHSA
Nonmedical use, abuse, dependence, diversion
Product
National, Substate (restricted)
National Addictions Vigilance Intervention and Prevention Program (NAVIPPRO)
Inflexxion, Inc.
Clinical assessment of treatment populations
Product
Substate
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Data System
Sponsor
Data Description
Drug Specificity
Geographic Specificity
National Electronic Injury Surveillance System (NEISS)
CPSC
Drug exposures in children under 5
None
National
National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)
NIAAA
Nonmedical use
Class
National
National Health and Nutrition Examination Survey (NHANES)
CDC
Prescription Drug use
Substance
National
National Precursor Log Exchange (NPLEx)
NADDI
Tracks sales of over‐the‐ counter cold and allergy medications
Product
Substate
National Vital Statistics System (NVSS)
CDC
Drug‐related deaths
Class
State
Prescription Behavior Surveillance System (PDSS)
Brandeis University
Prescription drug utilization
Product
State
Researched Abuse, Diversion and Addiction‐ Related Surveillance (RADARS) System
Denver Health and Hospital Authority
Poison center reports, special population surveys
Product
Substate
Rx Pattern Analysis Tracking Robberies & Other Losses (RxPATROL)
Purdue Pharma L.P.
Pharmacy theft
Product
Local
ToxIC Registry
ACMT
Toxicological data from patients
Substance
Local
Treatment Episode Data Set (TEDS)
SAMHSA
Treatment admissions
Class
State
Youth Risk Behavior Survey (YRBS)
CDC
Nonmedical use
None
State, Local (limited)
Abbreviations: Agency for Healthcare Research and Quality (AHRQ), American Association of Poison Control Centers (AAPCC), American College of Medical Toxicology (ACMT), Centers for Disease Control and Prevention (CDC), Consumer Product Safety Commission (CPSC), Drug Enforcement Administration (DEA), Food and Drug Administration (FDA), National Association of Drug Diversion Investigators (NADDI), National Highway Traffic Safety Administration (NHTSA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA), Substance Abuse and Mental Health Services Administration (SAMHSA)
Validity and Reliability Good data is the foundation of effective monitoring (General Accounting Office, 1993; Mounteney, Fry, McKeganey, & Haugland, 2010). An overarching question concerning any data system is how well it measures the constructs that fall within its domain (Caulkins, Ebener, & 18
McCaffrey, 1995). Validity must be understood as a function of overall data system integrity as much as the measurement qualities of specific indicators that may inform a composite index. For example, the Healthcare Cost and Utilization Project is the largest ongoing collection of nationaland state-level hospital care data in the United States, facilitating research on a range of important health policy issues. However, the reporting of specific health diagnoses is based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), which is a classification system that enables identification of only broad classes of prescription drug use disorders (e.g., amphetamines, barbiturates, opioids) (Kassed, Levit, & Hambrick, 2007). Reliability is another integral feature of drug data systems, wherein concern lies with the comparability of indicators across time or units. For example, Warner et al. (2013) investigated the completeness of drug-involved deaths reported in the National Vital Statistics System (NVSS) and found that between 2008 and 2010 the percentage of drug intoxication deaths attributable to a specific drug ranged from 35% to 99% across the 50 states, potentially biasing cross-state comparisons of drug-specific overdose rates. Thus, assessing the validity and reliability of both whole data systems and specific indicators is a first-order imperative for index design. Availability and Accessibility Despite broad-based improvements in drug data availability and accessibility over the past quarter-century, it remains difficult for investigators to access many key policy-relevant datasets (National Research Council, 2010). For example, onetime plans to make data on drug seizures from the DEA’s National Forensic Laboratory Information System (NFLIS) “available to approved requestors via the Internet” have yet to be realized (Office of National Drug Control Policy, 2003:94). Moreover, access to many drug-related government datasets (e.g., the
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Automation of Reports and Consolidated Orders System) is not governed by any clearly defined data release protocol. High end-user costs also impede access to many useful drug data systems. For example, the expense of obtaining microlevel data on poison center calls from the National Poison Data System can be prohibitive, potentially running into the tens of thousands of dollars.1 Government datasets can also be costly; the expense of obtaining the most recent year of state-level data on inpatient hospitalizations from the federal Healthcare Cost and Utilization Project ranges from $35 to $1,610 depending on the state, which can sum to a substantial figure for all states across multiple years. Finally, access to many key databases with policy relevance is summarily blocked for research purposes. For example, the Rx Pattern Analysis Tracking Robberies & Other Losses (RxPATROL) system, which collects information on pharmacy theft, restricts access to all but law enforcement agencies and pharmacy professionals (Smith et al., 2008). In short, the use of many public and private data holdings continues to be limited by access restrictions, unclear data-sharing protocols, and high cost barriers. Such access and availability barriers must inform indicator selection for the PDCI. Timeliness and Continuity Delayed reporting has long been a weakness of federal drug data systems (Ebener, Caulkins, Geschwind, McCaffrey, & Saner, 1993; National Research Council, 2001). Collecting and preparing data for public release is time-intensive, and there are justifiable reasons for delaying
1
It is not possible to supply a more specific estimate because the fees for accessing NPDS data from the American Association of Poison Control Centers (AAPCC) “are dependent upon the type of organization (e.g., corporation, nonprofit, individual), the number of substance categories requested to be displayed, the frequency of data delivery, and the time frame of interest, among other selections.”
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the release of sensitive data (e.g., to ensure the integrity of law enforcement investigations). However, unreasonable or unexpected delays in data dissemination could impede the timeliness of the PDCI and delay future updates. ONDCP has identified the increased timeliness of federal drug datasets as a key objective in its Performance Reporting System (PRS) (Office of National Drug Control Policy, 2011). The demand for speedy reporting of drug information has spurred the development of “real-time” data systems. For example, the NPDS monitors human exposures to illicit drugs in near real-time, uploading case information from the nation’s poison control centers several times per hour (e.g., Hoyte et al., 2012). As mentioned above, however, the drawback is that access to the NPDS and similar proprietary data systems is costly. The utility of drug data systems is also reflected in their maintenance and sustainability, which is essential for tracking drug trends and evaluating long-term policy effects. Federal drug data systems undergo periodic adjustments to improve the validity, reliability, and generalizability of the information collected, which often produces methodological discontinuities and even long gaps in coverage. For example, the termination of the Drug Abuse Warning Network (DAWN) in 2011 has created a multiyear gap in the surveillance of drug-related emergency department visits that will not be closed until its intended replacement system becomes operational (Office of National Drug Control Policy, 2014). Continuity considerations are vital to any ongoing PDCI monitoring effort. Coverage and Specificity Coverage refers either to the geographic units or drug types captured within a particular drug data system. Geographic coverage reflects the unit or units of analysis at which a data system collects and reports information, including the national, regional, state, and local levels. Most data systems produce national estimates, but many cannot generate subnational estimates, greatly 21
limiting their usefulness. In contrast, many systems report data only for the coterminous U.S., select subsets of states, or specific counties or cities, and thus cannot produce nationally representative statistics (Collins & Zawitz, 1990; Sevigny & Saisana, 2013). National-level data systems that allow users to drill down to finer geographic units of analysis are ideal, but obtaining full coverage across all aggregations is rare. For example, the Youth Risk Behavioral System (YRBS) is capable of producing both national and state-level estimates, but not all states participate in YRBS and many that do fail to achieve representativeness. To cite another example, NVSS data available through CDC WONDER allow users to obtain county-level overdose death counts, but achieving this geographic granularity often requires aggregating multiple years of data. Coverage also refers to the array of measured substances, but an equally important consideration is the specificity with which data systems record drug type information (Mounteney et al., 2010; Sevigny & Saisana, 2013). Some data systems capture product level information on prescription drugs, but many others only report broad (e.g., “prescription drugs”) or highly aggregated drug outcomes (e.g., opioids) that limit their utility for substance-specific surveillance and policy analysis. Sorting through coverage and specificity issues will be a key initial step to the successful development of a Prescription Drug Consequences Index.
Methodological Choices in Constructing a Prescription Drug Consequences Index This section reviews key technical decisions in composite indicator construction, including the statistical treatment of indicators and weighting and aggregation methods. Implementing a technical plan for creating a composite indicator will benefit from consultation with an expert in index design and construction.
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Statistical Treatment of Selected Indicators The statistical treatment of individual social indicators is intended to address potential methodological issues in index development, including issues of missing data, outliers, and incommensurate scaling. First, addressing both item and unit missingness in a rigorous way will reduce data loss and its attendant biases. For example, Sevigny and Saisana (2013) first employed multiple imputation to fill in item missingness within specific datasets and then imputed unit missingness within the overall state-year matrix of illicit drug consequence indicators. Second, to avoid biasing index calculations, it is important to minimize the impact of outliers and other influential data points (e.g., log transformation, Winsorization). Finally, because the various indicators informing the index will be measured using different metrics, they must be standardized and commensurately rescaled prior to aggregation, for which there are a range of options (see Organisation for Economic Co-operation and Development, 2008). Weighting and Aggregation Weighting reflects the relative importance placed on the individual indicators underlying an index, and it therefore represents one of the most controversial issues in index construction because alternate sets of weights can produce different rankings for states or other units of analysis. There are a variety of potential weighting schemes, including equal weighting, but a common approach in the drug field relies on the expert judgment of scholars and practitioners to rate index dimensions according to their perceived severity or harmfulness (Nutt et al., 2010; Sevigny & Saisana, 2013; van Amsterdam, Opperhuizen, Koeterb, & van den Brink, 2010). Because of the possible bias inherent in subjective value assessments, however, statistical weighting approaches such as data envelopment analysis have also been used in composite indicator construction (Hermans, Brijs, Wets, & Vanhoof, 2009; Zhou, Ang, & Zhou, 2010). For example, Zhou et al. 23
(2010) describe an optimization method that first derives the “best” and “worst” possible weights for each unit, and then combines these into an overall matrix (Zhou et al., 2010). Perhaps the most desirable approach to generating weights is to apply both methods whereby expert judgments serve to bound a statistically optimized set of weights. Thus, an important task in the development of a PDCI would be to convene a national panel of experts on prescription drug misuse to engage in a weighting exercise in which participants compare and rank prescription drug consequences indicators and/or domains according to their harmfulness (e.g., Nutt et al., 2010; Sevigny & Saisana, 2013). There are also a variety of aggregation methods for index construction, the choice of which depends on the intended objectives and functions of the index (Munda, 2012; Organisation for Economic Co-operation and Development, 2008). A key decision concerns whether compensability among the index’s underlying indicators or dimensions is desirable, that is, if a relative deficiency in one area can be offset or compensated by a surplus in another. If the index aggregates several highly dissimilar dimensions—such as the crime, health, and productivity consequences of prescription drug misuse—then a noncompensatory logic might be desirable where “trade-offs” across indicators are disallowed (e.g., a lower rate of prescription drug related emergency department visits cannot offset a greater rate of pharmacy theft). On the other hand, if weights are operationalized as “importance coefficients” or if independence between indicators or dimensions cannot be assumed, as is often the case in a policy context, then compensability might be appropriate and even compulsory (Munda & Nardo, 2009). In this instance, the modeling decision of whether to employ a linear or nonlinear aggregation approach rests on theoretical considerations. Linear aggregation assumes complete compensability, implying that high performance in one area can fully offset poor performance in another, which may or may not be a
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reasonable assumption. Nonlinear aggregation methods are only partially compensable, such that poor performance in one domain cannot be fully offset by strong performance in another. Thus, the effect is to penalize unbalanced performances (Floridi, Pagni, Falorni, & Luzzati, 2011). Although the technical details and mechanics governing aggregation methods warrants consultation with a statistical expert, stakeholders need to be well versed in the conceptual distinctions between noncompensability, partial compensability, and full compensability in order to understand and interpret the realized Prescription Drug Consequences Index.
The Potential for Leveraging Existing Prescription Drug Surveillance Systems The discussion to this point has assumed the development of a Prescription Drug Consequences Index would proceed from the ground up beginning with conceptualization and indicator selection. However, there are several existing proprietary surveillance systems that regularly collect and monitor multifaceted data on prescription drug misuse, abuse, and diversion that could potentially be leveraged for developing a PDCI. Currently, the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS) System collects information from poison center reports, user-submitted street price reports, and regular surveys of drug diversion investigators, methadone patients, college students, healthcare workers, and other key informants (Cicero et al., 2007), and the National Addictions Vigilance Intervention and Prevention Program (NAVIPPRO) collects data from clinical assessments of adults and adolescents entering substance abuse treatment, electronic media (e.g., internet forums and chat rooms), and other nonproprietary sources (e.g., the FDA Adverse Events Reporting System) (Butler et al., 2008). A third system presently still in development is the Prescription Behavior Surveillance System (PBSS), which is being implemented as an early warning surveillance and evaluation tool drawing on data from state Prescription Drug Monitoring Programs (PDMPs) (Strickler et al., 2014). And as already 25
mentioned, officials in Washington State are in the early stages of developing a composite indicator that measures the consequences of prescription drugs (Baumgartner & Leichtling, 2015). The main advantage of leveraging one or more of these existing systems for index construction is that the data collection and management procedures are either in process or already well-established, requiring less up-front investment to identify and retrieve data system indicators. These systems also collect and disseminate data in near-real time, a feature that supports both surveillance and performance measurement functions. In addition, these systems collect geolocated and product specific information on prescription drugs, allowing for fine gradations in monitoring. However, there are a number of potential downsides to using these systems. First, the RADARS, NAVIPPRO, and PBSS system components are already established and therefore unlikely to fully and coherently map on to an exhaustive conceptual framework of prescription drug consequences. Second, not all drug classes may be equally represented across these systems. Third, although the data have broad geographic coverage, the systems are not nationally representative (although the PBSS is driving toward the universe of PDMPs). Fourth, and perhaps most problematic, the data systems are proprietary and require subscription access (NAVIPPRO, RADARS) or may be accessible only to certain authorities (PBSS). To the extent these data are accessible by outside investigators, obtaining this information on a consistent and ongoing basis is likely to be cost prohibitive unless suitable data use agreements can be reached.
Summary Prescription drug abuse, which is the fastest growing drug problem in the United States, has risen to the forefront of federal and state drug control agendas. In light of current 26
epidemiological trends in the nonmedical use of prescription drugs, there is a critical need to develop sound surveillance and monitoring systems to assist policymakers in responding to this entrenched problem. This report reviewed justifications and design considerations for the development of a Prescription Drug Consequences Index that could serve as a useful policy tool toward these efforts. First, a number of open conceptual issues were identified, including the intended purpose of the index, the breadth and depth of the prescription drugs to be monitored, the operating definition of prescription drug misuse, the specific prescription drug consequences to be measured, and appropriate normalization metrics. Second, a prescription drug data system inventory was prepared to provide background information and provide a structure for considering data access and quality issues. Third, methodological considerations regarding the statistical treatment of indicators and approaches to index weighting and aggregation were raised and discussed. Finally, the potential for leveraging existing prescription drug surveillance systems for developing a Prescription Drug Consequences Index was explored. The open questions surrounding these various design considerations will best be resolved by a panel of scholars and practitioners with expertise in the area of prescription drug misuse.
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