A SYSTEM DYNAMICS MODEL OF PHARMACEUTICAL OPIOIDS:
MEDICAL USE, DIVERSION, AND NONMEDICAL USE
Teresa D. Schmidt, M.A.
Portland State University
Systems Science Graduate Program
P.O. Box 751, Portland, OR 97207
Phone: 509-592-3588
tds @pdx.edu
Wayne Wakeland, Ph.D.
Portland State University
J. David Haddox, D.D.S., M.D.
Purdue Pharma L.P.
Abstract: A dramatic rise in the nonmedical of pharmaceutical opioids has presented the United
States with a substantial public health problem. Nonmedical use of prescription pain relievers has
become increasingly prevalent in the US over the last two decades, and diversion of medicines
obtained by prescription is assumed to be a major source of supply for nonmedical opioid use.
Policymakers striving to protect population health by ameliorating the adverse outcomes of
nonmedical use of opioid analgesics could benefit from a systems-level model which reflects the
complexity of the system and incorporates the full range of available data. To address this need,
the current project describes the conceptualization and development of a System Dynamics
model that is used to complement and leverage results from existing research. Additional testing
is needed to authenticate preliminary intervention simulation results, which suggest that a
reduction in the initiation of nonmedical use may have a more profound impact on the total
number of opioid overdose deaths than more tamper-resistant formulations, decreases in opioid
prescribing, or decreases in rates of abuse among medical users. Results indicate that System
Dynamics can help to identify points of high leverage for policy interventions as well as bring
attention to the unanticipated negative consequences of these interventions.
Introduction
A dramatic rise in the nonmedical use of pharmaceutical opioids has presented the United
States with a substantial public health problem (Comptom & Volkow, 2006). Results from the
National Survey of Drug Use and Health suggest that in 2009, 5.3 million individuals (2.1% of
the U.S. population age 12 and up) had used pharmaceutical opioids for nonmedical purposes
within the past month (SAMHSA, 2010a). Trend data suggest that nonmedical use is becoming
increasingly severe and remains largely unabated by current policies and regulations (e.g.,
Fishman et al., 2004). Tools and interventions are sorely needed to reduce nonmedical use of
opioids, and practitioners are in need of efficacious strategies to detect, prevent, and treat the
nonmedical use of opioid medications (Compton & Volkow, 2006). This article describes a
system dynamics model which is designed to inform efforts to intervene in the epidemic of
nonmedical pharmaceutical opioid use by identifying points of high-leverage in the system of
nonmedical pharmaceutical opioid consumption in the United States.
Background
Nonmedical use of prescription pain relievers has become increasingly popular in the
United States over the last two decades. Pharmaceutical opioids are now among the most popular
drugs for nonmedical use in the United States, second only to marijuana (NCASA, 2005). The
rate of initiation has increased drastically from the early 90s through the early 2000s (SAMHSA,
2006), with 2.2 million individuals initiating the nonmedical use of pain relievers in 2008
(SAMHSA, 2009a). The rate of opioid-related overdose has also been escalating in the United
States, with more than a threefold increase between 1999 and 2006, and more than a fivefold
increase among youth aged 15-24 (Warner et al., 2009). Overdose deaths due to pharmaceutical
opioids have outnumbered deaths due to both cocaine and heroin since 2001, and in 2007
outnumbered heroin by more than five times (CDC, 2010).
The diversion of pharmaceutical opioids is assumed to be a major source of supply for
nonmedical use. Results from the 2009 National Survey on Drug Use and Health (SAMHSA,
2010) demonstrate that friends or relatives commonly reported as the source of the pain reliever
used nonmedically most recently. Fifty-five percent of respondents said they obtained the pain
reliever they nonmedically used most recently from a friend or relative for free, and another
14.9% said they either purchased or stole them from a friend or relative. Among survey
respondents who received the drugs for free, 80% of them also reported that their friend or
relative had originally received the drugs from a doctor, although the survey didn’t ask whether
the source’s relationship with that doctor was a legitimate one. These results suggest that the
largest source of pharmaceutical opioids for nonmedical use is the reservoir found in the home
medicine cabinet kept by patients and others.
Recent increases in the prescribing of pharmaceutical opioids stems in part from recent
surges in the diagnosis of and recognition of the need to treat chronic noncancer pain. A
longitudinal analysis of chronic pain prevalence at a primary care facility in Seattle, Washington
suggested that 11.2% of primary care patients who were previously free from chronic pain
(defined as pain symptoms lasting 6 months or longer) were found to suffer from chronic pain
during the year of the study (Gureje et al., 2001). Data from NHANES (Hardt et al., 2008),
coupled with data from the U.S. Census Bureau, support an estimated prevalence of 29 million
Americans aged 20 or older with chronic pain (defined as pain lasting three months or longer) in
the period 1999-2002, indicating that pain is a significant cause of disability (National Center for
Health Statistics, 2006). The increased rate of diagnosis of chronic pain has increased the
demand for medical treatment. And while opioid treatment for chronic, noncancer pain is
considered by some to be controversial (Collett, 2001), pharmaceutical opioids have been found
to be more effective at ameliorating pain than alternative medications (see Furlan et al., 2006 for
a review), and their prescription and medical use has become increasingly common over the last
decade (Governale, 2007, 2008a, 2008b).
It is not entirely understood why pharmaceutical opioids have become so popular for
nonmedical use, especially relative to other licit and illicit substances. Users may feel a false
sense of safety when using these products bec , unlike illicit drugs, they are a medication that
is endorsed and frequently prescribed by physicians (Compton & Volkow, 2006). In addition, the
surge in chronic pain treatment may provide non-patients with access to pharmaceutical opioids
through friends or relatives, and may also provide many youth with role models who are taking
pain medication in the context of everyday activities (Compton & Volkow, 2006).
As of February 2009, the Food and Drug Administration (FDA) has begun requiring Risk
Evaluation and Mitigation Strategies (REMS) for all Schedule II long-acting pharmaceutical
opioids as a way to ensure that the benefits of these medications outweigh their risks (Leiderman,
2009). REMS requirements vary for each opioid product, depending on the level of risk, but all
REMS must include an evaluation of their effectiveness and may additionally require specific
interventions, including: (a) a medication guide, (b) a patient package insert, (c) a
communication plan, (d) other “elements to assure safe use,” and (e) an implementation system.
Unfortunately, prior research has found little evidence to suggest that these types of interventions
are effective in reducing the risk of medication misuse or abuse (see Chou et al., 2009 for a
systematic review). There remains a need for tools and interventions that effectively reduce the
adverse outcomes associated with the misuse of pharmaceutical opioids.
System Dynamics
The system dynamics (SD) modeling approach uses a set of differential equations to
simulate system behavior over time. Such models are well suited to health policy analysis
involving complex chains of influence and feedback loops which are beyond the capabilities of
statistical models (Sterman, 2006), and have been successfully applied to the evaluation of policy
alternatives for a variety of public health problems, such as for cocaine abuse prevention
(Homer, 1993), health care reform (Milstein, Homer, & Hirsch, 2010), diabetes population
dynamics (Jones et al., 2006), and tobacco policy options (Cavana & Tobias, 2008).
The SD approach can help to identify points of high-leverage for policy interventions as
well as unanticipated negative consequences of these interventions, providing policymakers with
information that is not available through research that is focused on individual aspects of a
system (Sterman, 2006). In the current project, the development of an SD model complements
and leverages results from an extensive amount of research based on surveys and statistical
analyses, such as Fleming et al. (2007) who identify factors associated with opioid abuse, Butler
et al. (2010) who use factor analysis to identify the aspects of opioid product formulations that
are related to attractiveness for abuse, and Davis and Johnson (2007) who report on opioid use,
abuse & diversion, concluding with a call for models to be developed.
Policymakers striving to protect population health by ameliorating the adverse outcomes
of nonmedical opioid use could benefit from a systems-level model of pharmaceutical opioid
medical and nonmedical use which reflects the complexity of the system and incorporates the
full range of available data. This article describes a SD model designed to increase understanding
of the system of pharmaceutical opioid nonmedical use and to help identify and assess leverage
points for reducing the associated adverse outcomes. The following sections describe the
conceptual approach and research method employed and outlines how the model represents the
fundamental dynamics of pharmaceutical opioids as they are prescribed, diverted, used
nonmedically, and involved in overdose morbidity and mortality. Model testing is then discussed
briefly, followed by a description of several simulated interventions and their effectiveness in
reducing the adverse outcomes and population risk in the model. The discussion section
highlights the main contributions of the model findings, future directions for continued research
in this area, and the likely implications of the study’s preliminary findings.
Model Creation Process
The model creation process began in May, 2009, with a core modeling team and a panel
of leading experts in various related fields. The core modeling team included Lewis Lee, M.S.,
Louis Macovsky, D.V.M., M.S., and Wayne Wakeland, Ph.D., and was joined by Teresa
Schmidt, M.A., as of January, 2010. Advisory panel members included leading authorities on the
use of pharmaceutical opioids to treat chronic pain, prescription drug diversion and addiction,
health policy analysis, and SD modeling (see Table 1). Purdue Pharma L.P., which markets some
pharmaceutical opioids, provided the funding and support nec y for the team to
independently develop an epidemiological model of pharmaceutical opioid use, diversion, and
nonmedical use.
Table 1. Advisory Panel Members
John Fitzgerald, Ph.D., L.P.C., C.A.S., Associate Director, Risk Management & Epidemiology,
Purdue Pharma L. P.
Aaron Gilson, M.S., M.S.S.W., Ph.D., Director of the Pain & Policy Studies Group at the
University of Wisconsin Carbone Comprehensive Center.
J. David Haddox, D.D.S., M.D., D.A.B.P.M., Vice President of Health Policy for Purdue
Pharma L.P.
Dennis McCarty, M.A., Ph.D., professor and Vice Chair in the Department of Public Health &
Preventive Medicine at Oregon Health and Science University
Lynn Webster, M.D., F.A.C.P.M., F.A.S.A.M., cofounder and Chief Medical Director of the
Lifetree Clinical Research and Pain Clinic
Jack Homer, M.S., Ph.D., a nationally renowned expert in the application of system dynamics
to public health policy evaluation
Model development began with a thorough review of existing literature so that empirical
evidence could be found to support key model parameters. Literature sources included a broad
spectrum of data sources, survey results, and scholarly articles covering the period from 1995 to
2007. The advisory panel met with the modeling team on several occasions in 2009 (May, July,
August, and December) and 2010 (February and June) to oversee the model logic and the
representation of parameters and interventions in the model.
In the early meetings, panel members discussed areas of particular importance to the
pharmaceutical opioid nonmedical use epidemic and shared professional presentations on these
areas. Key topics included chronic pain treatment, diversion, dependence and abuse, and the
FDA REMS. The modeling team drew from panel members’ discussions and presentations to
shape the initial model structure and to define the boundaries of inclusion. Starting in August,
2009, the modeling team shared drafts of the model with panelists, along with overview
presentations that summarized how the model logic had been developed and informed. The
panel was invited to critique the model and was presented with dilemmas regarding the most
accurate way to represent various aspects of treatment, diversion, and nonmedical use. During
these meetings, panel members referred the modeling team to additional resources and drew
from their professional knowledge to inform the team’s design of the model.
Multiple data gaps were identified that could not be adequately addressed by existing
literature (see Wakeland et al., 2010). In these cases, panel members provided their expert
judgment to help fill these data gaps, and rigorous model testing was used to determine whether
the model’s performance was contingent upon the accuracy of these data. In May 2010, the
model was reviewed by Homer, who carefully evaluated the model logic and provided a detailed
critique to Lee and Wakeland. Model logic and parameters were refined according to Homer’s
critiques and were then reviewed by the expert panel in June, 2010. Over the subsequent months,
the model was subjected to rigorous testing to identify it strengths and weaknesses.
In August, 2010, model testing revealed the need for a fundamental change in model
logic. Up until that point, the model had been built under the implicit assumption that the
epidemic of nonmedical use was essentially driven by increases in opioid prescribing. But model
testing revealed that increases in prescribing and sharing simply could not account for the full
magnitude of the epidemic. Although sharing and other forms of diversion are necessary to fuel
the epidemic, model testing results indicated that the upsurge in nonmedical use must have been
driven at least in part by increased popularity and demand for opioid products in the nonmedical
use (NMU) sector. This insight led to substantial revision of the model, including additional
consultation with the expert panel and revisions to much of the model logic.
Dynamics of the Pharmaceutical Opioid Epidemic
The following sections describe major areas of the system dynamics model. The model
encompasses the dynamics of medical treatment with opioid analgesics, the initiation and
prevalence of nonmedical usage, drug supply and demand, and opioid-related overdose fatalities.
Three major sections of the model are discussed, each of which includes a description of
empirical support, a narrative description of the model’s behavior, and a causal loop diagram to
illustrate the structure and logic. The verbal descriptions contain parenthetical numbers that
correspond to specific points along the feedback loops in the diagrams. The model contains 8
state variables and their associated differential equations, 62 auxiliary variables and their
associated algebraic equations or graphical functions, and 57 parameters.
Nonmedical Use of Pharmaceutical Opioids
Empirical findings in the literature support key model parameters and help to clarify the
model’s logic and many of its assumptions. Within the ‘Nonmedical Use Sector,’ DSM-IV
criteria have been used to differentiate persons who engage in problematic substance use
according to whether or not they meet diagnostic criteria for “opioid abuse” or “opioid
dependence” (American Psychiatric Association, 1994). NSDUH data from the year 2000 to
2004 suggests that around 12-14% of individuals who use pharmaceutical opioids nonmedically
meet the criteria for abuse or dependence (Colliver, Kroutil, Dai & Gfroerer, 2006), either of
which is associated with a significantly higher frequency of nonmedical use. Specifically,
analyses of NSDUH 2007 data by Lee in 2010 (Lee et al., 2010) indicate that high frequency
nonmedical users (who meet the DSM-IV criteria for dependence or abuse) use opioids around
220 days per year, whereas low frequency nonmedical users do so about 30 days per year.
Extrapolation from heroin findings indicates that higher frequency of opioid use is associated
with a significantly higher all-cause mortality rate (WHO; see Degenhardt et al., 2004; and Hser
et al., 2001). This information was interpreted to indicate that low and high frequency users
constitute two separate populations in the nonmedical use sector.
The supply of pharmaceutical opioids for nonmedical use often comes from friends or
relatives, but can also come from leftover medicine obtained by prescription, prescription
forgery, ‘doctor shopping’ (visiting multiple doctors for the purpose of obtaining the same or
similar drugs ), theft, or drug dealers (SAMHSA, 2007). Drug trafficking, theft, and forgery
among non-patients are not included in the current model, as they would add considerable
complexity to model logic and do not relate directly to the dynamics of chronic pain treatment,
diversion from patients, and nonmedical use. Extrapolation of results from the 2006 NSDUH
survey (SAMHSA, 2007) suggests around 25% of the nonmedical demand for pharmaceutical
opioids is ‘trafficked,’ or bought or stolen from patients with pain conditions who are receiving
these products ostensibly for treatment. The empirical evidence described above, as well as other
findings (see Appendix A), informs the model logic in the nonmedical sector. This logic and
many of its underlying assumptions are described with the following narrative and are illustrated
in Figure 1. In the current model, a percentage of the U.S. population {1} is assumed to initiate
nonmedical use each year {2}, all of whom start out in a stock (i.e., population) of ‘Low
Frequency Nonmedical Users’ and a small percentage of whom advance to a stock of ‘High
Frequency Nonmedical Users’ {3} during each subsequent year. The total number of individuals
using opioids nonmedically {4} is divided by the current number of individuals in the US who
are using other drugs nonmedically {5} to calculate the relative popularity of pharmaceutical
opioids for nonmedical use {6}. As the popularity of using pharmaceutical opioids for
nonmedical use increases, the rate of initiation increases, creating a positive feedback loop that
results in exponential increases in the rate of initiation.
Demand for pharmaceutical opioids is calculated from the number of individuals in low-
and high-frequency populations {7}. Much of this demand is assumed to be met through illicit
channels (e.g., theft, forgery, or interpersonal sharing), but about 25% of it is assumed to depend
on diversion from chronic pain patients {8}. When this 25% represents an ample amount
compared to demand, the rate of initiation {2} is assumed to be unaffected, as well as the rate of
advancement from low frequency to high frequency use {3}. However, when the [trafficking-
related] supply is limited, rates of initiation and advancement are assumed to decrease. The
number of dosage units diverted from patients divided by the U.S. population, indicates the
degree to which opioids are accessible for nonmedical use {9}. As the populations of nonmedical
users increase beyond what the patient-diverted supply can support, accessibility becomes
limited, decreasing initiation and advancement, and creating a negative feedback loop that
eventually equilibrates the otherwise exponential increases in nonmedical use.
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Figure 1. Causal Loop Diagram of the Nonmedical Use of Pharmaceutical Opioids. Circled numbers correspond to parenthetical notations in
the text. Numbers in boxes correspond to model parameters in Appendix A.
Medical Use of Pharmaceutical Opioids
An overview of some key empirical findings helps to clarify the logic and assumptions of
the ‘Medical Use Sector’ as well. Historically, increases in opioid abuse, defined as the self-
administered use of a pharmaceutical opioid medication for a nonmedical purpose (Katz et al.,
2010), and increases in addiction, which involves uncontrollable compulsions and significant
adverse consequences (Compton, Darakjian, & Miotto, 1998), have led to the implementation of
regulatory policies for pharmaceutical opioids (FDA, 2008). These regulatory policies have been
shown to lead many physicians to avoid prescribing opioids to patients out of fear of overzealous
regulatory scrutiny (Joranson, Gilson, Dahl, & Haddox, 2002). In addition, prescribers who are
fearful of regulatory scrutiny of their opioid analgesic prescribing practices have been found to
decrease the amount of opioids they prescribe, limiting quantities and refills, and to shift
prescribing to opioid products with a presumably lower risk of abuse, addiction, or overdose
(i.e., products in less-restrictive schedules under the federal Controlled Substances Act; Wolfert,
Gilson, Dahl, & Cleary, 2010).
Regarding the relative risks of different opioid products, immediate-release, short-acting
formulations (single-entity and opioid + non-opioid combination analgesics) are prescribed much
more frequently and are, therefore, implicated in a larger number of overdose deaths (Cicero,
Surratt, Inciardi, & Munoz, 2007). Another way of evaluating relative risk, however, is to use
the number of persons exposed via prescription, as opposed to the population as a whole. The
latter denominator estimates the general overall health burden of a particular drug-associated
problem, while the former provides a metric whereby the harm is normalized on the basis of
exposure, since not all persons in a given location will be exposed to a pharmaceutical opioid
analgesic and exposure rates can vary by geographic location and over time. When abuse rates
are normalized for the number of individuals exposed via out-patient, retail dispensing of these
drugs, a metric referred to as Unique Recipients of Dispensed Drug (URDD), long-acting opioids
(products that are pharmacologically long-acting, such as methadone, and those which are
pharmaceutically-designed to be long-acting, such as transdermal delivery systems and
modified-release oral opioid analgesic formulations) have a higher rate of abuse per 1000 URDD
than do the immediate-release opioid analgesics. For example, from 2003 to 2006, 5-8 cases of
long-acting opioid abuse were found per 1,000 URDD, compared to <1 case of abuse per 1,000
URDD for short-acting opioids (Cicero et al., 2007). Support materials for a recent FDA
meeting, using numbers of prescriptions, as distinct from numbers of URDD, included an
analysis of emergency department (ED) data which showed that “the rate of ED visits per 10,000
prescriptions was about five times higher for OxyContin [a long-acting formulation] compared to
oxycodone [a short-acting formulation] over a recent three-year period” (FDA, 2010). Physicians
have been found to be sensitive to this information regarding relative risk, and exhibit more
caution in prescribing long-acting opioids (Potter et al., 2001).
The empirical evidence described above, as well as other findings (see Appendix B)
informs the model logic in the medical sector. This logic and its underlying assumptions are
described with the following narrative and are illustrated in Figure 2. In the current model, a
proportion of the U.S. population is diagnosed with a chronic pain condition each year {1}.
Patients are subsequently treated with either short-acting {2} or long-acting {3} opioid
formulations, and become members of one of the stocks (populations) of chronic pain patients
under treatment. Patients who begin treatment with short-acting formulations may cease
treatment if their condition improves, or they may switch to long-acting formulations if their pain
conditions worsen {4}.
Each year a portion of patients in both the short-acting and long-acting populations begin
to abuse and/or become addicted to the prescribed opioids, causing their membership to transfer
to either the stock of long-acting patients with opioid abuse or addiction {5} or the stock of
short-acting patients with opioid abuse or addiction {6}. Note that this stock {6} does not
include people who initiate nonmedical use of opioids without having been prescribed opioids,
and, therefore, the people in this stock are also considered to be another category of opioid user.
The fraction of patients with abuse or addiction{7} influences physicians’ perception of the risk
involved in prescribing opioids {8}, as does the total number of opioid overdose deaths each
year {9}. As physicians perceive higher levels of risk {8} they become increasingly biased
toward prescribing short-acting (lower risk) formulations {10}, and their overall rates of opioid
prescribing decrease {11}. Because of these balancing feedback loops, increases in the amount
of abuse and addiction {7} is slowed when physicians begin to perceive higher levels of risk.
Thus, the model variables move towards a state of dynamic equilibrium, stabilized by
physicians’ response to increasing rates of abuse, addiction, and overdose.
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Figure 2. Causal Loop Diagram of the Medical Usage of Pharmaceutical Opioids. Circled numbers correspond to parenthetical notations in
the text. Numbers in boxes correspond to model parameters in Appendix B.
Diversion of Pharmaceutical Opioids
An overview of some key empirical findings helps clarify the model’s logic and
assumptions regarding the diversion of pharmaceutical opioids to nonmedical use via chronic
pain patients. Within the ‘Diversion Sector,’ results from the 2009 National Survey on Drug Use
and Health (SAMHSA, 2010) indicate that among individuals who reported using pain relievers
nonmedically in the past year, 17.6% of them stated that they acquired their most recent supply
through a prescription from a doctor, indicating that they may have been a patient (medical user).
Among non-patient, nonmedical users who have not received a prescription, about 6% acquired
their most recent supply through theft or forgery, and 69% received opioids for free from friends
or family members, the majority of whom did receive prescriptions directly from a doctor,
although the survey does not gather information on the nature of the relationship between the
source and the doctor (legitimate therapeutic relationship, doctor being deceived, doctor
cooperating with source). Based on this information, the current model assumes that the
remaining 25% of the demand generated by low and high frequency users is met through buying
or stealing it from chronic pain patients (trafficking, as opposed to sharing).
Research suggests that around 5% of chronic pain patients engage in doctor shopping and
around 4% engage in forgery (Manchikanti et al., 2006). In the current model, forgery and doctor
shopping are assumed to be exhibited entirely by patients with abuse or addiction. Stocks of
patients with abuse or addiction constitute around 10% of the total population of chronic pain
patients, so 40% of these patients are assumed to exhibit forgery, and 50% of these patients are
assumed to exhibit doctor shopping. The proportion of additional prescriptions that are
successfully acquired through these methods remains unknown, but is assumed in the model to
be 12% and 14% for doctor shopping and forgery, respectively.
The empirical evidence described above, as well as other findings (see Appendix C)
informs the model logic in the diversion sector. This logic and its underlying assumptions are
described with the following narrative and are illustrated in Figure 3. A fixed proportion of the
patients with abuse or addiction are assumed to engage in trafficking each year, including doctor
shopping {1} and forgery {2}. The number of extra prescriptions acquired {3} is calculated as a
product of (a) the average number of prescriptions given to patients with abuse or addiction, (b)
the number of patients who engaging in trafficking, and (c) the fraction of excess prescriptions
acquired through forgery and doctor shopping. Some proportion of these excess prescriptions is
assumed to be used by the patients themselves, rather than diverted to non-patient users {4}. This
number is calculated as a product of (a) the number of patients with abuse or addiction and (b)
the average number of extra prescriptions used per year by each patient with abuse or addiction.
The number of prescriptions that are used “in excess” by pain patients is subtracted from the
number of extra prescriptions acquired, and the rest is converted to dosage units {5} and
assumed to be diverted to nonmedical users through trafficking channels {6}.
Diverted pharmaceutical opioids accumulate in a stock of dosage units {7} that are
consumed according to the demand in the Nonmedical Use Sector not met by sharing, forgery, or
stealing. Supply can also be expressed as ‘months of supply available’ {8}, which indicates the
extent to which the diverted supply is able to meet the demand at any given time. When the
supply of opioids becomes limited, a profit motive emerges {9} and patients’ motivation for
prescription forgery and doctor shopping increases. As this motivation fluctuates, the fraction of
prescriptions acquired through forgery and doctor shopping follows {10}, effectively stabilizing
the amount of diversion through a balancing feedback loop.
Average Number of
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Patients with Abuse or <Average
Addiction Number of Base Fraction
Number of Days of Dosage Units Base Fraction _ of Excess
Extra Opioid Usage per Opioid of Excess _ Prescriptions
Samaivet Among Patients with Prescription> profit Motive Papsociptions ae
Pupp ye? Multiplier pqumtes
Patient- Abuse or : , Through Dr ‘Forgery
Diverted Shopping
Opioids
Profit Motive as a
Largest Possible
>
Total Demand for
Opioids Among All
Nonmedical Users
Mae Function of Profit Motive.
Available ——— Months of Supply Multiplier
@)
Table Function
for Profit Motive
Figure 3. Causal Loop Diagram of the Diversion of Pharmaceutical Opioids. Circled numbers correspond to parenthetical notations in the
text. Numbers in boxes correspond to model parameters in Appendix C.
Model Testing
The model was tested in detail to determine its robustness and to gain an overall sense of
its validity. As is often the case with system dynamics models, the empirical support for some of
the parameters was limited. (See Appendices for key support references.) System Dynamics
models are generally more credible when their behavior is not highly sensitive to changes in the
parameters that have limited empirical support. Therefore, to determine sensitivity of primary
outcomes to changes in parameter values, each parameter in turn was increased by 30% and then
decreased by 30%, and the outcome was recorded in terms of cumulative overdose deaths.
Several parameters with limited empirical support did have a substantial influence on model
behavior, meaning a 30% change in the parameter resulted in a greater than 30% change in the
cumulative number of overdose deaths of the number of patients treated for pain with long-acting
opioids.
Parameters that were both sensitive and empirically limited included the impact of
perceived risk on prescribing behavior, the percentage of diagnosed chronic pain patients who
are treated with opioids, the average number of dosage units taken per day of nonmedical usage,
the impact of a limited nonmedical supply on forgery and doctor shopping behavior, and the
impact of three intervention alternatives (discussed in more detail below), including (a) increased
perceived risk on the part of physicians, (b) increased tamper resistance of opioid formulations,
and (c) decreased popularity of pharmaceutical opioids for nonmedical use. (See Wakeland et al.,
2010 for more information regarding data gaps.) Some of the parameters that strongly influenced
model behavior did have sufficient empirical support, such as baseline rates of opioid
abuse/addiction, the average number of prescriptions given to chronic pain patients, the average
number of dosage units per prescription, and overdose mortality rates. However, because model
testing revealed a high degree of sensitivity to parameters for which empirical support is limited,
study results must be considered preliminary and exploratory.
In addition to sensitivity analyses, the model was also tested to ensure that its behavior
remained plausible when subjected to tests involving extreme conditions (i.e., abnormal
parameter values), and model results were compared to historical reference data, where
available. The results of these tests were generally favorable, which indicated to us at least a
preliminary degree of model validity.
Simulated Interventions
To illustrate the potential for evaluating interventions, several areas in the model were
identified as likely to exhibit high-leverage. Several possible interventions were added to the
simulation to explore their potential effects on the number of opioid overdose deaths in the U.S.
population. All interventions were represented as simple toggles that would double beneficial
parameters or halve harmful parameters in the model. While somewhat unrealistic, these
dramatic interventions help to illustrate the dynamics of the model and the system’s response to
interventions at each point of leverage.
Tamper resistance intervention. This intervention tested the introduction of new, highly
effective tamper-resistant formulations for long-acting pharmaceutical opioids. In the model, this
was simulated by increasing the tamper resistance by a factor of two, which caused two proximal
effects in the medical sector: a) the rate at which opioid-treated chronic pain patients become
abusers or addicts was reduced by 50%, and b) physicians perceived there to be much less risk of
abuse and therefore prescribed opioid therapy for a higher fraction of their patients, including
more prescribing of tamper-resistant long-acting formulations. Tamper resistance also reduced
the rate of nonmedical use initiation by 50% in the nonmedical sector.
Prescriber intervention. This intervention simulated the possible outcome of a highly
effective prescriber education program by doubling physicians’ perception of risk and therefore
reducing rates of treatment with opioids. This intervention reduced the percentage of patients
who develop abuse or addiction because the interventions assumed that educated prescribers
would be much more selective in the use of opioid treatment and would monitor treatment more
effectively. In the model, when physicians’ perception of risk doubles, the fraction of patients
treated is decreased by 50%, and the fraction of patients developing abuse or addiction is
decreased by 50%.
Patient intervention. This intervention simulated a reduction in the rate at which patients
develop abuse or addiction but maintained the baseline level of physician risk perception. The
fraction of patients developing abuse/addiction was decreased by 50%, similar to the prescriber
intervention. However, this third intervention isolated the effects of patient behavior from the
behavior of prescribers, so the results could be interpreted separately.
Popularity intervention. This intervention simulated a reduction in the popularity of
pharmaceutical opioids for nonmedical use, which is calculated as the total number of
individuals using pharmaceutical opioids divided by the total number of individuals using other
illicit substances in the U.S. When this ratio is halved, it effectively reduces the rate of initiation
by 50%.
Results
Figure 4 shows a baseline model run for the historical period from 1995 to 2008, plus a
policy evaluation period from 2008 to 2015. Reference data is scant, but total opioid-related
deaths, resulting from all types of medical and nonmedical use, was reported to be 13, 755 in
2006, and historical data suggests the pattern of increase has been almost exponential, increasing
more gradually in the late 90s and more rapidly throughout the early 2000s (Warner, Chen &
Makuc, 2009).
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
30,000
20,000
people/year
10,000
|
sees
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Time (year)
Total Overdose Deaths : baseline
Nonmedical Overdose Deaths : baseline
Medical Overdose Deaths : baseline
Figure 4. Baseline calculated opioid-related deaths
As shown in Figure 4, the model’s baseline behavior exhibits a nearly exponential shape
between the late 90s and early 2000s, and calculates a total number of opioid overdose deaths to
be around 13,400 in 2006, with 2,823 overdose deaths among medical users and 10,580 overdose
deaths among nonmedical users. So these baseline results can be considered plausible, but
additional support is needed to know how accurately the model reproduces the proportion of
overdose deaths suffered by medical and nonmedical users. The fraction of deaths associated
with only medical or nonmedical use is not known, and must be estimated. One study of opioid
overdose deaths found that less than half of the decedents had ever been prescribed opioids (Hall
et al., 2008), suggesting that medical users probably account for much less than half of the
overdose deaths. This rough estimate is captured by the model’s baseline behavior, but additional
validation of the proportion of opioid overdose deaths attributable to medical users is needed.
Even with limited reference data, the current model illustrates the potential usefulness of
SD model for policy analysis. The model was configured to show the response to the four
interventions described in the methods section. All four interventions were modeled as having a
very high degree of effectiveness in order to exaggerate their impacts; and for each case, the
simulated intervention began in 2008 and persisted until the end of the simulation, 2015.
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
Tamper Resistance Intervention Prescriber Intervention
30,000
20,000 Cees
& aa
= 10,000 ieee
>= ed Fess Pa Fe GD
see
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2007 2009 2011 2013 2015
Time (year)
Patient Intervention Popularity Intervention
30,000
= 20.000 Cercle
& Loo
= 40,000 eal sane
Heer —————————
0
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2007 2009 2011 2013 2015
‘Time (year)
Total Overdose Deaths
Nonmedical Overdose Deaths
Medical Overdose Deaths
Figure 5. Effect of simulated interventions (tamper resistance, prescriber, patient, and popularity)
in 2008 on opioid-related overdose deaths
Tamper Resistance Intervention
Figure 5 shows the number of opioid overdose deaths per year among chronic pain
patients, including the historical period from 1995 to 2008, plus the policy evaluation period
from 2008 to 2012. As might be expected, doubling the degree to which tamper resistance
prevents abuse has an impact on the number of nonmedical users who suffer overdose deaths.
Interestingly, increased tamper resistance also leads to an increase in the number of patients who
die of overdose. This is because, as shown in Figure 6, the prescribers’ perception of the risk of
these medicines drops sharply, which significantly increases the total number of patients who
received opioid therapy. So although tamper resistance leads to a smaller percentage of patients
dying, its implementation causes the percentage of individuals receiving opioids to increase by
an even greater percentage.
Perceived Risk of Treating with Opioid Products Total Number of Patients Receiving Opioid Treatment
2 40M
ral
165 30M vi
Po. eee 20M
ie
0.95 10M
eee Lery
al =
a6 0
1995 1997 1999 Zoo 2003 2005 2007 200s O11 2012 2015 1998 1997 198 a0d1 2009 9005 2007 2009 9011 2013 DOTS
Tine (eat) Time (ea)
cin i fin wih Opa Pate tsp ta tbe Pai Re Ot eet neve
Prescriber and Patient Interventions
The tamper resistance intervention influences both the perception of risk and the death
fraction, and these effects are somewhat confounding. To isolate perceived risk from the total
number of opioid overdose deaths, two additional simulations were implemented in the medical
sector. The prescriber intervention, as shown in Figure 5, leads to an immediate increase in
perceived risk that reduced rates of opioid treatment and leads to an immediate reduction in the
annual number of medical overdose deaths. The reduction in total patients eventually leads to a
reduced supply of diverted opioids and reduces the number of nonmedical users who die of
overdose. Unfortunately, this delay takes several decades to effectively reduce nonmedical
overdose deaths, and much of the nonmedical supply is acquired outside of diversion from
chronic pain patients, so the number of nonmedical overdose deaths stabilizes at around 14,000
by the year 2025.
The patient intervention simply reduces the abuse/addiction rate of chronic pain patients
by 50% without directly influencing prescriber risk. As shown in Figure 5, this reduces the
steepness of the slope of medical overdose deaths, but they still increase gradually through 2015
(and stabilize at around 3850 deaths per year by 2030). The effect of this patient intervention on
the number of overdose deaths in the nonmedical sector is negligible.
Popularity Intervention
Lastly, the popularity intervention decreases the popularity of pharmaceutical opioids for
nonmedical use (and subsequently the initiation rate) by 50%. As might be expected, this does
not have an impact on the number of chronic pain patients who suffer from overdose deaths, but
it has a dramatic effect on the number of nonmedical users who do. By the year 2025,
nonmedical overdose deaths are calculated to reach the level of medical overdose deaths, at
around 4,200 deaths per year. Because a larger proportion of the overdose deaths are attributed to
nonmedical users, these results indicate that the rate of initiation is a more powerful leverage
point for reducing the total number of opioid overdose deaths than the rate of abuse/addiction
among chronic pain patients.
Discussion
The preliminary results from the model indicate that system dynamic modeling has
promise as a tool for understanding the epidemic of nonmedical use of pharmaceutical opioids. It
holds promise for contributing to the evaluation of policy options and could be used to address
opioid-related mortality and morbidity. More specifically, the results of the prescriber and patient
interventions suggest that changes in the number of patients who are prescribed opioids have a
greater impact on the total number of medical overdose deaths than changes in the fraction of
patients who develop abuse or addiction. So long as tamper-resistant formulations cannot offer
perfect protection against overdose deaths, this may be a valid concern for the implementation of
tamper-resistant formulations into the pharmaceutical market. However, tamper-resistant
formulations may have other public health benefits, such as reduced transmission of infectious
diseases by injection drug use (eg, HIV/AIDS, hepatitis B and C), thromboembolic events from
injection of tablet particulates, abscesses, and septicemia. This model did not attempt to estimate
such other public health benefits.
In addition, previous research has indicated that over half of opioid overdose deaths are
suffered by individuals who have never been prescribed pharmaceutical opioids directly (Hall et
al., 2008). The preliminary results of the model indicate that reducing the initiation of
nonmedical use is indeed a more powerful point of leverage than reducing the number of chronic
pain patients who develop addiction or abuse. Furthermore, results from NSDUH 2006 data
(SAMHSA, 2007) indicate that a substantial fraction of the opioids that are used among
nonmedical populations are acquired through sources outside of diversion from patients. Results
from the simulated prescriber intervention suggest that the impact of efforts to reduce overdose
deaths among non-patient populations may take a long time to manifest.
A key strength of this study is its system-level perspective and deliberate recognition of
the complex interconnections and feedback loops associated with pharmaceutical opioid
consumption and the adverse outcomes that are associated with it. Although the current model
has not yet been sufficiently calibrated to predict the absolute impact of the four simulated
interventions, the present study serves well to demonstrate how a systems-level model may help
to evaluate the potential efficacy of interventions to reduce opioid-related overdose deaths. The
model demonstrates a comparison of the relative impacts of three alternative interventions, and
illuminates the complex interactions associated with pharmaceutical treatment of chronic pain,
the risk of abuse and addiction, prescriber perceptions, diversion, and adverse outcomes such as
overdose mortality. From a systems perspective, it is likely that highly effective tamper-resistant
opioid formulations could significantly reduce the fraction of medical users who die from
accidental overdose, but it may be less likely that the total number of overdose deaths among
medical users would be reduced. And while prescriber and patient interventions have the
potential for reducing the number of medical overdose deaths, they are likely to be less effective
in reducing the total number of opioid-related overdose deaths when compared to interventions
that reduce the rate of initiation of nonmedical use among non-patient populations.
Limitations
Despite great efforts to find empirical support for all model parameters, validity remains
a primary limitation in the current study. Several parameters have weak empirical support, as
mentioned previously, and a number of potentially important factors have been excluded, often
because support remains elusive. For example, the model is limited in that it focuses exclusively
on prescribing and diversion of pharmaceutical opioids for the treatment of chronic pain, without
representing acute pain patients and their treatment with pharmaceutical opioids. The nonmedical
use sector is designed to capture the overall demand for and nonmedical use of all types of
pharmaceutical opioids and the associated adverse consequences. The prescribing of opioids to
treat acute pain accounts for a significant fraction of the opioids dispensed annually, so it is
likely to contribute the supply of opioids for the nonmedical use sector, as well as to physician’s
perception of risk in the medical use sector. For both of these reasons, the exclusion of acute pain
treatment may threaten the validity of the model. A closely related limitation in the medical
sector is that all “new” chronic pain patients in the model are considered to have legitimate
medical need for analgesics, when in reality some of the people presenting with purported pain
are motivated by illicit intent (see Weaver & Schnoll, 2007).
Beyond the limited representation of variations in opioid treatment, the model also does
not account for either poly-drug use or poly-drug abuse, either of which may involve other
opioids or non-opioids with additive effects in the central nervous system (eg, benzodiazepines,
cocaine, heroin), both of which dramatically increase the risk of unintentional overdose, nor does
the model account for the tendency for drug abusers to switch between or to combine
pharmaceutical opioids and other drugs, both pharmaceutical and illicit, due to supply, cost, and
other factors. The model excludes the influence of opioid addiction treatment programs and
common non-pharmacologic alternatives to using opioids for chronic pain treatment, such as
cognitive behavioral therapy (Morley, Williams, & Hussain, 2008). And institutional factors that
impact opioid use, such as payor policies and formularies, as well as cost constraints, are also
excluded from the model at this time. Because poly-drug use and abuse, opioid treatment
programs, alternative treatments, and institutional factors can all influence rates of both the
medical and nonmedical use of opioids and the negative outcomes associated with such use, the
exclusion of these many factors imposes limitations on the model’s ability to provide conclusive
inferences.
Work is underway to expand the scope of the model to address many of the above
limitations. Still, it is hoped that the insights achieved by applying a system dynamics approach
to this important public health concern can inform policy makers about the value of system
dynamics for analyzing alternative points of intervention. It is believed that system dynamics has
much to offer in evaluating interventions and policy alternatives that are intended to ameliorate
the adverse outcomes associated with pharmaceutical opioids.
Acknowledgments
Funding for this project was provided by Purdue Pharma L.P. The authors appreciate the
significant contributions from John Fitzgerald, Ph.D. regarding drug abuse and addiction, from
Jack Homer, Ph.D., who critiqued the technical aspects of the SD model, from Lewis Lee, M.S.
who developed the primary model logic and located much of the needed data, from Louis
Macovsky, DVM, who created an initial prototype model and helped locate data sources, and
from Dennis McCarty regarding drug abuse and health policy. Together, these contributors
provided many in provided many insights and considerable guidance on the development of the
model and this manuscript.
Disclosures
Wayne Wakeland, PhD, Teresa Schmidt, MA, and Aaron Gilson, PhD, were compensated
through a research grant funded by Purdue Pharma L.P. J. David Haddox, DDS, MD is a full-
time employee of Purdue Pharma L.P. Lynn R. Webster, MD, has served as a consultant for
Cephalon, Covidien, King, Labopharm, MedXcel, Neuromed, and Purdue Pharma L.P.; on the
advisory boards of BDSI, Cephalon, King, Labopharm, Neuromed, Pharmacofore, Purdue
Pharma L.P., and Janssen Pharmaceutical K.K; and as investigator in research for Cephalon,
Collegium, Endo Pharmaceuticals, King Pharmaceuticals, QRx Pharmaceutical, and Reckitt
Benckiser.
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Managed Care Pharmaceuticals 11(6), 469-479.
Wolfert, M. Z., Gilson, A. M., Dahl, J. L., & Cleary, J. F. (2010). Opioid analgesics for pain
control: Wisconsin physicians’ knowledge, beliefs, attitudes, and prescribing practices. Pain
Medicine, 11(3), 425-34.
Appendix A. References of Support for Model Parameters in the Nonmedical Use Sector
Parameter Value Support
NONMEDICAL USE SECTOR
1 Base Level of Abuse Potential of Pharmaceutical Opioids 1.3 Panel Consensus
2. Fraction of Demand Met from Chronic Pain Trafficking 25 Extrapolation from NSDUH 2006 results (SAMHSA,
2007)
3. Fraction of Low Freq Users who switch to High Freq 0.01 Extrapolation from MTF data (Monitoring the Future; see
Johnston et al., 2007) and results (Mack et al., 2003)
4 High Frequency User All-Cause Mortality Rate 0.02 Extrapolation from heroin research findings (WHO; see
Degenhard et al., 2004; and Hser et al., 2001)
5 High Frequency User Cessation Rate 0.2 Imputation from NSDUH data (National Survey on Drug
Use and Health, 2007; see SAMHSA 2009b)
6 Low Frequency User All-Cause Mortality Rate 0.012 Extrapolation from heroin research findings (Rehm et al.,
2005)
7 Low Frequency User Cessation Rate 0.06 Imputation from NSDUH data (National Survey on Drug
Use and Health, 2007; see SAMHSA 2009b)
8 Number of Days of Nonmedical Use Among High Freq 220 Extrapolation from NSDUH 2007 results (Lee et al., 2010)
Users
9 Number of Days of Nonmedical Use Among Low Freq 30 Extrapolation from NSDUH 2007 results (Lee et al., 2010)
Users
10 Number of Dosage Units Taken per Day 2 Modeling Team Judgment, reviewed by Panel
11 Overdose Mortality Rate for High Freq Nonmedical Users 0.002 Extrapolation from research findings (Fisher et al., 2004;
Warner et al., 2009; Warner-Smith et al., 2000)
12 Overdose Mortality Rate for Low Freq Nonmedical 0.0002 Extrapolation from research findings (Fisher et al., 2004;
Users Warner et al., 2009; Warner-Smith et al., 2000)
13 Rate of Initiation of Nonmedical Opioid Use 0.005 Imputed from National Drug Use and Health Survey Data
(NDUHS, 1995; see SAMHSA, 1996)
14 Table Function for the Impact of Limited Accessibility [(0,0)-(4,1)] Modeling Team Judgment, reviewed by Panel
on Initiation
15 Table Function for the Number of Individuals Using 6.7M in ‘95 Calculated from NSDUH 2006 results, see SAMHSA,
Illicit Drugs Excluding Marijuana and Pharmaceutical to 8.6M in ‘10 2007
Opioids
16 US Population Ages 12 and Older 211M in ‘95 to Imputed from NSDUH data (National Survey on Drug Use
357M in ‘07
and Health 1995, 2002; see SAMHSA, 1996, 2002)
"A Table Function is a series of XY coordinates representing a relationship (usually nonlinear) between two variables
Appendix B. References of Support for Model Parameters in the Medical Sector
Parameter
Value
Support
MEDICAL USE SECTOR
DA nbkwn ye
14
15
16
17
All Cause Mortality Rate for Patients on Long-acting Opioids
All Cause Mortality Rate for Patients on Short-acting Opioids
All Cause Mortality Rate for Patients with Abuse/Addiction
Average Long-acting Treatment Duration (in years)
Average Short-acting Treatment Duration (in years)
Base Level of Abuse Potential for Pharmaceutical Opioids
Base Rate for Adding or Switching (to Long-acting)
Base Rate of Treatment
Base Risk Factor (degree Tx reduced in 1995 due to
perceived risk)
Diagnosis Rate for Chronic Pain
Overdose Mortality Rate for Patients Abusing Opioids
Overdose Mortality Rate for Patients on Long-acting
Overdose Mortality Rate for Patients on Short-acting
Rate of Addiction for Patients on Long-acting
Rate of Addiction for Patients on Short-acting
Table Function’ for Short-acting Bias (as function of
perceived risk)
Tamper Resistance (baseline value)
0.012
0.01
0.015
0.03
.06 in ‘95 to
.21 in ‘00
1.3
.05 in ’95 to
15 in ‘05
0.0015
0.0025
0.00005
0.05
0.02
[C,0)-(4,D]
1
U.S. Population mortality data, adjusted by panel consensus
U.S. Population mortality data, adjusted by panel consensus
U.S. Population mortality data, adjusted by panel consensus
Panel Consensus
Panel Consensus
Modeling Team Judgment, reviewed by Panel
Extrapolation from outcome data: Verispan, LLC, SDI Vector
One®: National (VONA; see Governale, 2008a)
Panel Consensus, informed by Potter et al., 2001
Modeling Team Judgment, reviewed by panel
Panel Consensus, informed by WHO (World Health
Organization; see Gureje et al., 2001)
Extrapolation from Heroin Research (see Sullivan, 2007)
CONSORT study (Consortium to Study Opioid Risks and
Trends; see Potter et al., 2001)
CONSORT study (Consortium to Study Opioid Risks and
Trends; see Potter et al., 2001)
Meta-Analyses (see Dunn et al., 2010; Hgjsted & Sjggren,
2007)
VISN16 data (South Central Veterans Affairs Health Care
Network; see Fishbain et al., 2008)
Modeling Team Judgment, reviewed by panel
Policy variable (1=status quo)
TA Table Function is a series of XY coordinates representing a relationship (usually nonlinear) between two variables
Appendix C. References of Support for Model Parameters in the Diversion Sector
Parameter Value Support
DIVERSION SECTOR
1 Average Number of Dosage Units Per Opioid Prescription 86 Extrapolation from dispensing data: Verispan, LLC, SDI Vector
One®: National (VONA; see Governale, 2008a, 2008b)
2 Average Number of Extra Dosage Units taken per Day Among 1.5 Panel Consensus
Patients with Abuse or Addiction
3 Average Number of Prescriptions Prescribed to Patients with 10 Extrapolation from DURM data (Drug Use and Research
Abuse or Addiction Management, 2001) and dispensing data: Verispan, LLC, SDI
Vector One®: National (VONA; see Governale, 2008a, 2008b)
4 Base Fraction of Excess Prescriptions Acquired Through Dr 0.13 Modeling Team Judgment, reviewed by Panel
Shopping
5 Base Fraction of Excess Prescriptions Acquired Through 0.13 Modeling Team Judgment, reviewed by Panel
Forgery
6 Fraction of Patients with Abuse/Addict who Engage in Dr 2) Extrapolation from study results (Manchikanti et al., 2006)
Shopping
7 Fraction of Patients with Abuse/Addict who Engage in Forgery 4 Extrapolation from study results (Manchikanti et al., 2006)
8 Largest Possible Motive Multiplier 5 Panel Consensus
9 Number of Days of Extra Opioid Usage Among Patients with 50 Generalized from NSDUH data (National Survey on Drug Use
Abuse/Addiction and Health 2002, 2003, & 2004; see Table 2.18B in Colliver et
al., 2006)
10 Table Function for Profit Motive [(0,0)— Modeling Team Judgment, reviewed by Panel
(20,1)]
"A Table Function is a series of XY coordinates representing a relationship (usually nonlinear) between two variables
8/4/2011
Manuscript Authors
Teresa Schmidt, m.a.
Graduate Student at Systems Science,
Portland State University
Wayne Wakeland, Ph.p.
" Professor of Systems Science,
Portland State University
J. David Haddox, p.D.s., M.D., D.A.B.P.M.
Vice President of Health Policy
for Purdue Pharma L.P.
Jack Homer, M.S., Ph.D.,
Nationally renowned expert in the application of
SD to public health policy evaluation
| Lewis Lee, M.s., s.M.,
Graduate of the Systems Science Program at
Portland State University
\] Louis Macovsky, D.v.M., MS.,
: Graduate of the Systems Science Program at
Portland State University
Portland State
Advisory Panel __ J. David Haddox
@) DDS. MD. D.ABP.M.,
i. Vice President of Health Policy,
John Fitzgerald
Ph.D., L-P.C., C.A.S.,
Associate Director, Risk
Management & Dennis McCarty
Epidemiology, Purdue "~<) M.A, Ph.D.,
Pharma LP. | Professor and Vice Chair in the
Department of Public Health &
Preventive Medicine at Oregon
Aaron Gilson Health and Science University
MS., M.S.S.W., Ph.D.,
Director of the Pain & Lynn Webster
Policy Studies Group M_D., F.A.C.P.M., FAS.AM.,
University of Wisconsin Cofounder and Chief Medical
Carbone Comprehensive Director of Lifetree Clinical
Center Research and Pain Clinic
8/4/2011
Portland
Total Number of Opioid Analgesic
Poisoning Deaths in the United States
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
oh
PPS
ee F
S) o
S SF Ss
s Ss
Oo
oF
» +
Sy
o 9
FP
Total Number of Opioid Analgesic Poisoning Deaths
Warner, M., Chen, L. H., & Makuc, D. M. (2009). Increase in fatal poisonings involving
opioid analgesics in the United States, 1999-2006. NCHS Data Brief, 22.
Portland
Pharmaceutical Opioids
* Immediate-release (“short-acting”)
+ Single-entity (opioid is only active ingredient)
* Combinations (opioid + nonopioid analgesic)
* Long-acting & Extended-release (LA/ER)
+ Pharmacologically Long-acting
* methadone
+ Pharmaceutically Long-acting
+ Single-entity opioids in formulations that
retard release of the active ingredient
8/4/2011
Total Number of Opioid Prescriptions
Dispensed by US Retail Pharmacies
Q
2
2
2
B
2
a
SDI Vector On
"Oxycodone ™ Hydrocodone —_® Total Number of Opioid Rx
e®: National (VONA) 09-30-10 Hydrocodone & Oxycodone 1991-2009
land
3,000 5
2,500 +
2,000 +
1,500 +
1,000 +
500 |
Number of New Nonmedical Users of Opioid Analgesics
(Thousands)
0
1965 1970 1975 1980 ©1985 = 1990 1995 2000-2005
Source: SAMHSA (2006). Overview of findings from the 2005 National Survey
on Drug Use and Health. (Office of Applied Studies, NSDUH Series H-30,
DHSS Publication No. SMA 06-4194). Rockville, MD.
i
8/4/2011
Nonmedical Use
Popularity of R)
Opioids for (2)
Treating
w Patients
Chronic] ( Bf
Pain | \_7
Patients
hopping
Forgery
Profit
Motive
Overdose
yNe Deaths
ig &
Perceived
Risk
i
8/4/2011
Parameter Support
MEDICAL USE SECTOR DIRECT INDIRECT PANEL
‘All Cause Mortality Rate for Patients on Long-acting Opioids
All Cause Mortality Rate for Patients on Short-acting Opioids
All Cause Mortality Rate for Patients with Abuse/Addiction
Average Long-acting Treatment Duration (in years)
Base Level of Abuse Potential for Pharmaceutical Opioids
Base Rate for Adding or Switching (to Long-acting)
Base Rate of Treatment | |
1
2
3
4
5 Average Short-acting Treatment Duration (in years)
6
7
8
9
Base Risk Factor (Tx reduced in “95 due to perceived risk)
10 Diagnosis Rate for Chronic Pain
11 Overdose Mortality Rate for Patients Abusing Opioids
12 Overdose Mortality Rate for Patients on Long-acting
13 Overdose Mortality Rate for Patients on Short-acting
14 Rate of Addiction for Patients on Long-acting
15 Rate of Addiction for Patients on Short-acting
16 Table Function! for Short-acting Bias (function of perceived risk)
17 Tamper Resistance (baseline value)
TA Table Function is a series of XY coordinates representing a relationship (usually nonlinear) between two variables
Parameter Support
DIVERSION SECTOR DIRECT INDIRECT PANEL
1 Average Number of Dosage Units Per Opioid Prescription
2 Average Number of Extra Dosage Units taken per Day Among
Patients with Abuse or Addiction
3. Average Number of Prescriptions Prescribed to Patients with
Abuse or Addiction
4 Base Fraction of Excess Prescriptions Acquired Through Dr
Shopping
5. Base Fraction of Excess Prescriptions Acquired Through Forgery
6 Fraction of Patients with Abuse/Addict who Engage in Dr
Shopping
7 Fraction of Patients with Abuse/Addict who Engage in Forgery
8 Largest Possible Motive Multiplier
9 Number of Days of Extra Opioid Usage Among Patients with
Abuse/Addiction
10 Table Function! for Profit Motive
“A Table Function is a series of XY coordinates representing a relationship (usually nonlinear) between two variables
8/4/2011
Parameter Support
NONMEDICAL USE SECTOR DIRECT INDIRECT PANEL
Base Level of Abuse Potential of Pharmaceutical Opioids |
Low Frequency User All-Cause Mortality Rate
Low Frequency User Cessation Rate
Number of Days of Nonmedical Use Among High Freq Users
9 Number of Days of Nonmedical Use Among Low Freq Users
1
2
3
4
5 High Frequency User Cessation Rate
6
7
8
Fraction of Demand Met from Chronic Pain Trafficking
Fraction of Low Freq Users who switch to High Freq
High Frequency User All-Cause Mortality Rate
10 Number of Dosage Units Taken per Day ||
11 Overdose Mortality Rate for High Freq Nonmedical Users
12 Overdose Mortality Rate for Low Freq Nonmedical Users ||
13. Rate of Initiation of Nonmedical Opioid Use
14. Table Function for the Impact of Limited Accessibility |
15 Table Function! for the Number of Individuals Using Hlicit Drugs
Excluding Marijuana and Pharmaceutical Opioids
17_ US Population Ages 12 and Older
TA Table Function is a series of XY coordinates representing a relationship (usually nonlinear) between two variables
Abuse, Addiction, —_—_
and Overdose
Deaths Among
Megieal Users
New Chronic
Pain Patients
[Short Acting
Patent ith \X“Tcoming Nauta on
Abas om Short Acting [Short Acting
Addiction Opioids
Long Acting Patients on | "Adding OF oe
|Patients with| Long Acting| S Patients with
‘Abuse or or Short and Tang Actin
Addiction += Long Acting S ‘Treatment
Becoming tee
Addicted to Treatment Short Acting
‘Long Acting fs ‘ Rate for
a Bias Toward Acting
2 ar
ie) Seven
Pareating with Risk Adjusted
Pharmaceutical Opioids ‘Treatment Rate’
8/4/2011
‘ ) Portland State
sumer Diversion Sector
Prescriptions Diverted
from Patients with Number of F ;
Abuse or Addiction aed Number of Patients
> Prescriptions ‘yhoiEagage ia De
oes Prescriptions pan through Shopping or Forgery
Units per Used by Patients Shopping
Prescription with Abuse or
Number of Addiction Number of Prescriptions
Dosage Units Given to Patients with
Diverted \ ‘Abuse or Addiction
Excess Prescriptions Fiacitont’
{ used by a Patient with prescriptions
| Abuse or Addiction —_cquired Through
Mh Divert Forgery and Dr
| Diverting Daily Excess Use we Shopping Multiplied
by Patients with by the Profit Motive
Abuse or Addiction G i} Multiplier
s Demand Among Profit ail Base Fraction of
Diverted by | Nonmedical Users ie a Fiction Excess Prescriptions
Patients \ of Months of ‘Acquired Through
Months of ‘Supply Forgery or Dr.
Supply — Shopping
Available
Sp Portland State
Nonmedical Use Sector
‘Aged Twelve Plus Paatily a:
Rate of Initiation. Total Number of
of Nonmedical Sinden field Individuals Using
Opwid Use umber of individu ioids Nonmedie
i i henbeotie y, Opioids Nonmedically
Rate of Initiation (echuae Marines
During Unlimited Pharmaceutical Opioids) ai
Accessibility fei Ay
pueiitine — Increasing
Nonmedica Tow pension High
Ue Frequency Frequency || Frequency
Nonmedical Nonmedical
Opioid Users Opioid Users
1D) Raion Demand
tp ) Met fromChronic ‘Total Demand
car Far reins for Opioids
Supply of
Pharmaceutical Opioids
Opioids y —— Diverted by sj
Patients_| Distributing
<US Population
‘Aged Twelve Plus>
8/4/2011
Baseline Model Results (vs. RBP)
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
30,000
10,000 io
— —E—
=
oF
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
‘Tie (year)
Total Overdose Deaths : baseline
‘Nonmedical Overdose Deaths : baseline
‘Medical Overdose Deaths : baseline
Reference Behavior Data from NCHS
Sp Portland State
Interventions
* Tamper-Resistant Drug Formulations
* Prescriber Intervention
* Patient Intervention
* Popularity Intervention
8/4/2011
Portland
Results: Tamper-Resistant Drugs
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
30,000
20,000
people/year
10,000
G
pT
[Toe]
pe
—
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Time (year)
Total Overdase Deaths: tamper resistance
Nonmedical Overdose Deaths : tamper resistance
Medical Overdose Deaths : tamper resistance
Portland Stat
Results: Tamper-Resistant Drugs
Total Number of Patients Receiving Opioid Treatment
40
30M
si apa
ee ae
Perceived Risk of Treating with Opioid Products
Tine Gea)
‘bie ice ening dT ap in
43
1995 1997 1999 2001 203 2005 2007 2008] EEE eee
095
=.
1995 1897 1999 2007 2003 2005 2007 2009 201 a013 2015
Tine (yea)
Pind heheh pi Ps teety
8/4/2011
10
8/4/2011
Results: Prescriber Intervention
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
30,000
20,000 Las]
= a
= 10,000
o eee Peco
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Time (year)
Total Overdose Deaths : prescriber intervention
Nonmedical Overdose Deaths : prescriber intervention
Medical Overdose Deaths: prescriber intervention
Portland Stat.
Results: Patient Intervention
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
30,000
people/year
10,000
0
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Time (year)
Total Overdose Deaths: patient intervention
Nonmedical Overdose Deaths : patient intervention
Medical Overdose Deaths: patient intervention
11
Results: Popularity Intervention
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
30,000
20,000
10,000 Siiiace
people/year
S|
; =F 7 |p tp
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Time (year)
Total Overdose Desths: populaity intervention
Nonmedical Overdase Deaths : popularity intervention
Medical Overdose Deaths : popularity intervention
Portland Stat
Discussion: Implications
Medical, Nonmedical, and Total Opioid Overdose Deaths per Year
‘Tamper Resistance Intervention Prescriber Intervention _Patient Intervention Popularity Intervention
BIG7 2009 2011 2O7S 2015 2007 Z0O% BOTT 2013 Zls 2007 20 BDI 213 2015 2007 2OO9 2011 2013 O15
8/4/2011
12
F ‘ DATA NEEDED
Discussion:
Limitations
Key Data Gaps
+ Treatment Rates
+ Perceived Risk (TA AVAILABLE AND USED
+ Profit Motive
+ Forgery Fraction
+ Nonmedical Dosage
+ Intervention Effects
DATA AVAILABLE
Wakeland et al. CPDD Poster, 2010
Sp Portland
Discussion: Limitations
Boundary Exclusions
+ Acute Pain Medications
¢ Alternative Treatments and Insurance
¢ Alternative Routes of Diversion
* Balloon Effects and Poly-Drug Abuse
8/4/2011
13
Discussion: Future Research
* Data Gaps (see Wakeland et al. CPDD Poster, 2010)
* Verification and Validation
¢ Expansion to Address Boundary Exclusions
2,500 | Number of New Nonmedical Users
of Opioid Analgesics (Thousands)
pt
1965 197019751980 «19851990 1995 2000 2008
Sp Portland State
Acknowledgments
¢ This research was funded by a contract from
Purdue Pharma L. P.
* Special thanks to Aaron Gilson, Jack Homer,
John Fitzgerald, Lewis Lee, Louis Macovsky,
Dennis McCarty, and Lynn Webster for their
guidance, data support, and critical review.
8/4/2011
14
