Dynamic Simulation of Opioid Misuse Outcomes

Alexandra Nielsen and Wayne Wakeland

Systems Science Graduate Program, Portland State University, 1604 S.W. 10

Av., Portland, OR 97201, U.S.A.

Keywords: Prescription Drug Abuse, System Dynamics Modelling, Opioid Analgesics.

Abstract: The objective of the study was to develop a system dynamics model of the medical use of pharmaceutical

opioids, and the associated diversion and nonmedical use of these drugs. The model was used to test the

impact of simulated interventions in this complex system. The study relied on secondary data obtained from

the literature and from other public sources for the period 1995 to 2008. In addition, an expert panel

provided recommendations regarding model parameters and model structure. The behaviour of the resulting

systems-level model compared favourably with reference behaviour data (R

=.95). After the base model

was tested, logic to simulate interventions was added and the impact on overdose deaths was evaluated over

a seven-year period, 2008-2015. Principal findings were that the introduction of a tamper resistant

formulation unexpectedly increased total overdose deaths. This was due to increased prescribing which

counteracted the drop in the death rate. We conclude that it is important to choose metrics carefully, and

that the system dynamics modelling approach can help to evaluate interventions intended to ameliorate the

adverse outcomes in the complex system associated with treating pain via opioids.

1 INTRODUCTION

A dramatic rise in the nonmedical use of

pharmaceutical opioid pain medicine has presented

the United States with a substantial public health

problem (Compton and Volkow, 2006). Despite the

increasing prevalence of negative outcomes, such as

nonfatal and fatal overdoses, nonmedical use of

pharmaceutical opioids remains largely unabated by

current policies and regulations (see Fishman et al.,

2004). Resistance to policy interventions likely

stems from the complexity of medical and

nonmedical use of pharmaceutical opioids, as

evidenced by the confluence of the many factors that

play a role in medical treatment, diversion, and

abuse of these products in the United States.

Complex social systems are well known to resist

to policy interventions, often resulting in unintended

consequences or unanticipated sources of impedance

(Sterman, 2000). These undesirable outcomes can

result from our inability to simultaneously consider

a large number of interconnected variables,

feedback mechanisms, and complex chains of

causation (Hogarth, 1987). Prescription opioid use,

diversion, and nonmedical use constitute a complex

system with many interconnected components,

including prescribers, pharmacists, persons

obtaining opioids from prescribers for medical use,

persons obtaining drugs from illicit sources, and

people giving away or selling drugs. Interactions

among these actors result in chains of causal

relationships and feedback loops in the system. For

example, prescribing behaviours affect patients’

utilization of opioids; adverse consequences of

medical and nonmedical use influence physicians’

perceptions of the risks associated with prescribing

opioids; and physicians’ perception of risk affects

subsequent prescribing behaviours (Potter et al.,

2001); (Joranson et al., 2002).

This paper presents a system dynamics model

which simulates the system described above. The

model is designed to provide a more complete

understanding of how medical use, nonmedical use,

and trafficking are interrelated, and to identify

points of high leverage for policy interventions to

reduce the adverse consequences associated with the

epidemic of nonmedical use. An intervention

corresponding to the introduction of relatively less-

abusable, tamper-resistant formulation is simulated,

and possible downstream effects are highlighted.

397

Nielsen A. and Wakeland W..

Dynamic Simulation of Opioid Misuse Outcomes.

DOI: 10.5220/0004062103970408

In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2012),

pages 397-408

ISBN: 978-989-8565-20-4

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 BACKGROUND

Between 1999 and 2006, the number of U. S.

overdose deaths attributed to opioids tripled–

increasing more than five-fold among youth aged 15

to 24 (Warner et al., 2009)–signalling the onset of a

major public health concern. Overdose deaths

involving opioid analgesics have outnumbered

cocaine and heroin overdoses since 2001 (CDC,

2010), and estimates from the 2009 National Survey

on Drug Use and Health (NSDUH) suggest that 5.3

million individuals (2.1% of the U.S. population

aged 12 and older) used opioids for nonmedical

purposes within the previous month (SAMHSA,

2010). Earlier data from NSDUH suggest that the

rate of initiating nonmedical usage increased

drastically from 1994 to 1999 (SAMHSA, 2006),

and has continued at high rates, with over 2 million

individuals reporting the initiation of nonmedical

use of pain relievers in 2009 (SAMHSA, 2010).

Recent increases in prescribing opioids stem in part

from increases in chronic pain diagnosis and the

development of highly effective long-acting

pharmaceutical opioid analgesics.

One problem that arose with these new long-

acting formulations was the ease with which they

could be tampered with to enhance the effects when

used non-medically.

Policymakers striving to ameliorate the adverse

outcomes associated with opioids could benefit from

a systems-level model that reflects the complexity of

the system and incorporates the full range of

available data. Such a model could be used to study

the possible effectiveness of a tamper resistant drug.

3 SYSTEM DYNAMICS

SIMULATION MODEL

The current work features a system dynamics

simulation model that represents the fundamental

dynamics of opioids as they are prescribed,

trafficked, used medically and nonmedically, and

involved in overdose mortality. The model was

developed over a two-year period through

collaborative efforts of a modelling team and a panel

of pain care and policy experts. The SD modelling

approach uses a set of differential equations to

simulate system behaviour over time. SD models are

well suited to health policy analyses involving

complex chains of influence and feedback loops that

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 (Cavana and Tobias,

2008; Homer, 1993; Jones et al., 2006; Milstein,

Homer, & Hirsch, 2010). The SD approach can help

identify points of high leverage for interventions, as

well as possible unanticipated negative

consequences of those interventions. This provides

policymakers with information that is not available

from research focused on individual aspects of a

system (Sterman, 2006).

Model development began with a thorough

literature review to locate empirical evidence to

support key model parameters. Literature sources

included a broad spectrum of data sources, survey

results, and scholarly articles covering data collect

between 1995 and 2009. Multiple data gaps were

identified that could not be adequately addressed by

existing literature (see Wakeland et al., 2010).

Expert panel members provided expert judgment to

help fill these data gaps.

The model was developed iteratively, starting

with a brainstorming session that included both

subject matter experts (SMEs) and computer

simulation team members. The system boundary for

the initial sketch model describing the various stocks

and flows was very broad and included populations

of people receiving opioid analgesics to treat pain,

people using these prescription drugs nonmedically,

people using illicit drugs, plus the overall demand

and supply for various drugs. The simulation team

then refined the diagram, searched for parameter

data, and specified possible equations. Model scope

was reduced and the model was simplified due to the

lack of data and requisite knowledge. The data and

model were reviewed again by the SMEs, and the

simulation team further refined the model and

sought additional data based on the SME feedback.

This process was repeated multiple times.

The model encompasses the dynamics of the

medical treatment of pain with opioids, the initiation

and prevalence of nonmedical usage; the diversion

of pharmaceutical opioids from medical to

nonmedical usage; and adverse outcomes such as

overdose fatalities. Discussion of each sector

includes a description of empirical support, a

narrative on model behaviour, and a causal loop

diagram showing model structure. Bracketed

numbers in the text correspond to specific points in

the diagrams. The model contains 40 parameters, 41

auxiliary variables, and 7 state variables, as well as

their associated equations and graphical functions.

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Figure 1: Causal loop diagram of the nonmedical use sector. Circled numbers correspond to bracketed notations in the text.

Numbers in boxes correspond to model parameters in the Appendix, Table 1.

Figure 2: Causal loop diagram of the medical use sector. Circled numbers correspond to bracketed notations in the text.

Numbers in boxes correspond to model parameters in the Appendix, table 2.

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3.1 Nonmedical Use Sector

12%-14% of individuals who use opioids

nonmedically meet the criteria for opioid abuse or

dependence (Colliver et al., 2006), either of which is

associated with a high frequency of nonmedical use.

Extrapolation from heroin findings indicates that

higher frequency opioid use is associated with a

significantly higher mortality rate (WHO; see

Degenhardt et al., 2004); (Hser et al., 2001) and

supports a distinction between two subpopulations

of nonmedical users (low- and high-frequency) in

this model sector.

As illustrated in Figure 1 (with supporting data in

the Appendix, Table 1), a percentage of the US

population {1} is assumed to initiate nonmedical use

each year {2}, all of whom start out in a stock 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 opioids for nonmedical use

{6}. As the popularity of using opioids

nonmedically increases, the rate of initiation

increases, creating a positive feedback loop that

ceteris paribus would result in an exponential

increase in the rate of initiation.

Nonmedically used opioids are obtained through

many routes, but of key interest for the current

research is opioid ‘trafficking’ (i.e., buying or

selling) via persons who are receiving these products

ostensibly for treatment. Extrapolation of results

from the 2006 NSDUH survey (SAMHSA, 2007)

suggests around 25% of the nonmedical demand for

opioids is met via trafficking.

In the model, demand for opioids is calculated

from the number of individuals in low- and high-

frequency populations {7}. As noted above, 25% of

demand is assumed to be met by trafficking {8},

with the rest coming from sources not modelled

explicitly (mostly interpersonal sharing among

friends and relatives, per SAMHSA, 2007). When

the trafficking supply is ample relative to demand,

the rate of initiation {2} and the rate of advancement

from low-frequency to high-frequency use {3} are

assumed to be somewhat enhanced. When the

trafficking supply is limited, however, rates of

initiation and advancement are assumed to decrease

dramatically. The ratio of supply to demand {9}

indicates the degree to which opioids are accessible

for nonmedical use. As the populations of

nonmedical users increase beyond what trafficking

can support, accessibility becomes limited,

decreasing initiation and advancement. This creates

a negative feedback loop that eventually equilibrates

the otherwise exponential increase in nonmedical

use driven by the popularity feedback loop.

3.2 Medical Use Sector

Increases in opioid abuse and addiction have led to

regulatory policies which have lead many physicians

to avoid prescribing opioids to patients out of fear of

overzealous regulatory scrutiny (Joranson et al.,

2002). Or, they may decrease the amount of opioids

they prescribe, and shift their prescribing towards

short-acting opioid products because long-acting

opioids have been shown to have a higher rate of

abuse than immediate-release opioid analgesics

when abuse rates are normalized for the number of

individuals exposed (Cicero, Surratt, Inciardi, &

Munoz, 2007). Thus, physicians exhibit more

caution in prescribing long-acting opioids (Potter et

al., 2001).

As illustrated in Figure 2, the system model

assumes that a proportion of the US population is

diagnosed with a chronic pain condition each year

{1}. A fraction of these people are subsequently

treated with either short-acting {2} or long-acting

{3} opioid formulations, and become members of

one of the stocks (populations) of patients under

opioid treatment ostensibly for chronic pain. 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 appear to worsen {4}.

Each year some individuals move from the

stocks of ‘individuals receiving opioids’ {2-3} to the

stocks of ‘individuals receiving opioids with abuse

or addiction’ {5-6}. The fraction of opioid-

prescribed individuals with abuse or addiction {7}

influences physicians’ perception of the risk

involved in opioid prescribing {8}, as does the total

number of overdose deaths among medical users

each year {9}. As physicians perceive higher levels

of risk {8} they become increasingly biased toward

prescribing short-acting formulations {10}, and their

overall rates of opioid prescribing decrease {11}.

Because of these balancing feedback loops, the

increase in the amount of abuse and addiction {7} is

slowed. Physicians’ responses to increasing rates of

abuse, addiction, and overdose effectively move the

model towards a state of dynamic equilibrium.

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Figure 3: Causal loop diagram of the trafficking sector. Circled numbers correspond to bracketed notations in the text.

Numbers in boxes correspond to the model parameters in the Appendix, Table 3.

3.3 Trafficking Sector

Findings from Manchikanti et al. (2006) indicate

that 5% of chronic pain patients engage in doctor

shopping and around 4% engage in forgery. In the

model, forgery and doctor shopping by persons

interacting with prescribers are assumed to be

exhibited entirely by those with abuse or addiction,

which constitute around 7% of individuals receiving

opioid prescriptions for chronic pain. This would

imply that about 70% of persons with abuse or

addiction (5 out of 7) engage in doctor shopping and

over half (4 out of 7) engage in forgery. More

research is needed to support these parameters and

the associated logic.

As shown in Figure 3, a fixed fraction of persons

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

total number of individuals engaging in trafficking

and (b) the number of extra prescriptions obtained

per trafficker {11}. Some proportion of these excess

prescriptions is assumed to be used by the traffickers

themselves, rather than diverted to other nonmedical

users {4}. This number is calculated as a product of

(a) the number of individuals with abuse or

addiction and (b) the average number of extra

prescriptions used per year by such individuals. The

number of prescriptions that are used “in excess” by

medical users is subtracted from the number of extra

prescriptions acquired. The remainder is converted

to dosage units {5} and assumed to be diverted to

nonmedical users {6}.

Trafficked opioids accumulate in a stock of

dosage units {7} that are consumed according to

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401

demand from the nonmedical use sector. Supply can

also be expressed as ‘months of supply available’

{8}, which indicates the extent to which the

trafficked supply is able to meet the demand at any

given time. When the supply of opioids becomes

limited, a profit motive emerges {9} and motivation

to forge and doctor shop increases. When supply is

large compared to demand, motivation to commit

fraud for the purpose of sale is small. As this

motivation fluctuates, the number of extra

prescriptions each trafficker would like to obtain

{10} also changes. But the number of prescriptions

that can be successfully trafficked is attenuated by

cautious dispensing when perceived risk is high

among physicians and pharmacies {11}, which

creates a balancing feedback loop that stabilizes the

amount of trafficking.

4 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, as indicated in Tables 1-3 in

the Appendix. System Dynamics models are

generally more credible when their behaviour is not

overly 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. One parameter with

limited empirical support which has a substantial

influence on model behaviour is the impact of

limited accessibility on the initiation rate. Another

parameter, the rate of initiation of nonmedical use,

also strongly influenced model behaviour but is less

worrisome because it does have sufficient empirical

support. Because model testing revealed a high

degree of sensitivity to certain parameters for which

empirical support is limited, study results should be

considered exploratory and viewed with caution.

In addition to sensitivity analyses, the model was

also carefully checked for dimensional consistency

and appropriate integration step-size, subjected to a

rigorous model walk through to uncover logical

flaws, and subjected to a variety of hypothesis tests.

The model walk-through revealed logical flaws that

required substantial model revision. Several

parameters with a high degree of sensitivity and

limited empirical support were replaced, and all tests

were re-run. The results of these tests were generally

favourable, indicating at least a preliminary degree

of model validity.

(a) Total overdose deaths (persons/yr.)

(b) Total nonmedical users (persons)

Figure 4: Model output versus reference behaviour. (a)

total prescription opioid overdose deaths per year, (b) total

nonmedical users of prescription opioids, (c) total number

of individuals initiating nonmedical opioid use per year.

When empirical support was available, model

outputs were validated against reference data for the

historical period. While this reference period is

relatively short, the model does fit the data well, as

shown in Figure 4.

Figure 4a shows the number of prescription

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opioid overdose deaths from a baseline model run

for the historical period overlaid on a plot of the

reported number of overdose deaths obtained from

the CDC multiple cause of death database.

The total opioid-related deaths resulting from all

types of medical and nonmedical use has been

reported to be 13,755 in 2006 and approximately

14,000 in 2007. The data suggest that the pattern

has been an S-shaped, with modest growth in the

late 90s and more rapid growth throughout the early

2000s before levelling off between 2006 and 2007

(Warner, Chen & Makuc, 2009). The baseline model

behaviour, also shown in Figure 4a, shows a similar

S-shaped growth curve, with the number of opioid

overdose deaths calculated to be approximately

13,200 in 2006 and 14,300 in 2007 (R

= .90).

While additional data are needed to validate these

results, the model behaviour does exhibit a

preliminary level of credibility for this metric.

Figure 4b shows the total number of individuals

using prescription opioids non-medically overlaid on

reference data for the historical time period. The

graph of historical data is not smooth, but again, the

general pattern of growth is S-shaped. The graphical

output from a baseline model run is a smooth S-

shaped curve that is a good fit for the limited time

series data available (R

= .95).

Figure 4c gives model output and reference data

for the number of individuals initiating nonmedical

use of prescription opioids. The reference behaviour

pattern here is highly non-linear with the number of

initiates more than doubling from 1995 to 2000, then

approximately no change between 2000 and 2004,

followed by a decrease and levelling from 2004 to

2007. The baseline model run matches the reference

behaviour pattern very closely (R

=.95).

Overall, model results closely track the complex

patterns graphs of empirical data despite exhibited.

Thus, baseline results were deemed sufficiently

plausible to proceed with intervention analysis.

5 RESULTS

To test the intervention, the model time horizon was

extended to 2015 and a baseline run was made. The

intervention was then formulated and tested.

5.1 Tamper Resistant Formulation

Logic representing the introduction of a tamper

resistant drug formulation was added to the model.

The model was run over a time period of twenty

years, which was divided into an historical period

from 1995 to 2008, and an evaluation period from

2008 to 2015. The intervention was represented as

simple toggle switch that doubled beneficial

parameters and/or halved harmful parameters. The

response of the model to this simulated intervention

is shown in Figure 5. This intervention of a new

drug formulation being introduced in 2008 was

implemented as a 50% decrease in: 1) the rate of

abuse or addiction among opioid-treated persons, 2)

the fraction of low-frequency nonmedical opioid

users who become high-frequency users per year, 3)

the rate of initiation of nonmedical opioid use, and

4) the perceived risk of opioid abuse amongst

prescribers (this increased the prescribing rates for

all opioids). Figure 5 shows that this change caused

an increase in the total number of overdose deaths

in the model, due to a sizable increase in deaths

among medical users and a small decrease in deaths

among nonmedical users. This was not expected.

Figure 6 explains why this happened. Subplot (a)

shows that the number of individuals receiving

treatment increased sharply, and this increase, even

when coupled with lower death rates, led to the net

increase in the total number of overdose deaths

compared to baseline (Figure 5). Figure 6, subplot

(b) shows the number of deaths divided by number

of individuals receiving opioid treatment (and then

divided by 10,000 to yield an indicator in the 0 to 10

range). This indicator, which was beginning to

increase as of 2008, declined as a result of the

intervention, especially in the nonmedical sector. So,

although the fraction of deaths among patients did

decrease as anticipated, the rise in patient

populations (due to the lower risk perception

associated with tamper resistant formulations

amongst prescribers) obscured the benefits of the

lower death fraction.

Figure 5: Projected total opioid deaths (top), nonmedical

(middle), and medical (bottom). Baseline is shown, plus

the impact of a tamper-resistant formulation introduced in

2008.

DynamicSimulationofOpioidMisuseOutcomes

403

(a) Total Number of Patients Receiving Opioid Therapy

(b) Deaths per 10,000 Patients, Medical, Nonmedical

Figure 6: A dramatic increase in (a) the number of patients

who receive opioids results in a smaller ratio (b) of the

overdose deaths divided by the number of patients

receiving opioid therapy/10000.

6 DISCUSSION

Results from the model indicate that SD modelling

holds promise as a tool for understanding the

complex challenges associated with the epidemic of

nonmedical use of opioids, and for evaluating the

potential impact (on overdose deaths) of

interventions to minimize the risks of opioid

analgesics. By deliberately exaggerating the direct

effects, downstream effects were also accentuated to

make as obvious as possible any unintended

consequences or counterintuitive results.

Since previous research has indicated that over

half of opioid overdose deaths are individuals who

have never been prescribed opioids directly (Hall et

al., 2008), it is important to consider distal effects of

medical sector-related interventions on nonmedical

use and overdose deaths. Results of the intervention

that simulated the introduction of tamper-resistant

formulations also show that it is important to be

aware of the metrics used to judge effectiveness.

When using the metric deaths per 10,000 treated

patients, tamper resistance appears to be effective at

reducing the rate of overdose deaths as proportion of

the medical users.

6.1 Limitations

Tamper resistance is only one possible intervention

in the system of opioid misuse. Other interventions

not referenced in this article include prescriber and

patient education interventions which affect

prescribing behavior and the onset of addiction

among patients, a prescription monitoring program,

which reduces fraud and nonmedical supply, and a

popularity intervention which disrupts the vicious

cycle of initiation, nonmedical use, and peer

pressure that drives up nonmedical use of opioids.

Tamper resistance is a pharmaceutical intervention,

and as such does not take into account the social

forces that influence health behavior or drug use.

Despite great efforts to find empirical support for

all model parameters, parameter validity remains a

primary limitation in the study (see Wakeland, et al.

2010). Several parameters have weak empirical

support, and a number of potentially important

factors have been excluded. For example, the model

is limited because it focuses on chronic pain, and

ignores the vastly-larger number of persons who

receive opioids to treat acute pain. The prescribing

of opioids to treat acute pain accounts for a much

larger 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.

The model may also be exaggerating the notion

of profit as a motive for trafficking. Since the

fraction of demand met by interpersonal sharing is

large, it may be necessary to model this mechanism

in a more detailed fashion.

Additionally, poly-drug use and abuse, opioid

treatment programs, alternative treatments, and

institutional factors that impact opioid use, such as

payer policies and formularies, can all influence

rates of medical and nonmedical use of opioids and

the outcomes associated with such use. The

exclusion of these factors imposes limitations on the

model’s ability to provide conclusive inferences.

7 CONCLUSIONS

The principal strength of this study is its system-

level perspective and deliberate recognition of the

complex interconnections and feedback loops

associated with the use of opioids to treat pain and

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the associated adverse outcomes. From a systems

perspective it is clear that interventions focused on

prescribing behaviour can have implications beyond

the medical aspects of the system, and that a

multifaceted approach which also addresses illicit

use is warranted. The present study serves well to

demonstrate how a systems-level model may help to

evaluate the relative potential efficacy of

interventions to reduce opioid-related overdose

deaths.

ACKNOWLEDGEMENTS

Funding was provided by Purdue Pharma L.P., and

NIDA grant number 1R21DA031361-01A1. The

authors also gratefully acknowledge support from J.

David Haddox, John Fitzgerald, Jack Homer, Lewis

Lee, Louis Macovsky, Dennis McCarty, Lynn R.

Webster, and Aaron Gilson.

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APPENDIX

Table 1: References of support for model parameters in the nonmedical use sector.

Parameter Value Support

1 Base Level of Abuse Potential of Opioids 1.3 Panel Consensus

2 Fraction of Demand Met from Chronic Pain Trafficking .25 Extrapolation from NSDUH (SAMHSA, 2007)

3 Fraction of Low-Freq Users who switch to High-Freq 0.06

Extrapolated from MTF data (Johnston et al.,

2007) and results (Mack et al., 2003)

4 High-Frequency User All-Cause Mortality Rate 0.02

Extrapolated from heroin research findings (WHO;

Degenhard et al., 2004; Hser et al., 2001)

5 High-Frequency User Cessation Rate 0.08 Imputation from NSDUH SAMHSA 2009b)

6 Low-Frequency User All-Cause Mortality Rate 0.012 Extrapolated from (Rehm et al., 2005)

7 Low-Frequency User Cessation Rate 0.15 Imputation from NSDUH data (SAMHSA 2009b)

Number of Days of Nonmedical Use Among High-Freq

Users

220

Extrapolation from NSDUH 2007 results (Lee et

al., 2010)

9 Nbr. of Days of Nonmedical Use Among Low-Freq Users 30 Extrapolated from NSDUH 2007 (Lee et al., 2010)

10 Number of Dosage Units Taken per Day 2 Modeling Team Judgment, reviewed by Panel

Overdose Mortality Rate for High-Freq Nonmedical

Users

0.002

Extrapolated from Fisher et al., 2004; Warner et

al., 2009; Warner-Smith et al., 2000

Overdose Mortality Rate for Low-Freq Nonmedical

Users

0.0002

Extrapolated from Fisher et al., 2004; Warner et

al., 2009; Warner-Smith et al., 2000

13 Rate of Initiation of Nonmedical Opioid Use 0.006 Imputed from NDUHS (SAMHSA, 1996)

Table Function for the Impact of Limited Accessibility

on Initiation and Increasing Use

[(0,0)-(5,2)] Modeling Team Judgment, reviewed by Panel

Table Function for the Number of Individuals Using

Illicit Drugs Excluding Marijuana and Opioids

6.7M to 8.6M

Calculated from NSDUH 2006 results (SAMHSA,

2007)

16 US Population Ages 12 and Older 211M to 357M Imputed from NSDUH (SAMHSA, 1996, 2002)

Table 2: References of support for model parameters in the medical sector.

Parameter Value Support

All-Cause Mortality Rate for those receiving Long-

acting Opioids

0.012

US Population mortality data, adjusted by panel

consensus

All-Cause Mortality Rate for those receiving Short-

acting Opioids

0.01

US Population mortality data, adjusted by panel

consensus

All-Cause Mortality Rate for those with

Abuse/Addiction

0.015

US Population mortality data, adjusted by panel

consensus

4 Average Long-acting Treatment Duration 7 yrs. Panel Consensus

5 Average Short-acting Treatment Duration 5 yrs. Panel Consensus

6 Base Level of Abuse Potential for Opioids 1.3 Modeling Team Judgment, reviewed by Panel

7 Base Rate for Adding or Switching (to Long-acting) 0.03

Extrapolation from outcome data: Verispan, LLC,

SDI Vector One®: National (Governale, 2008a)

8 Base Rate of Treatment .05 to .23 Panel Consensus, informed by Potter et al., 2001

Base Risk Factor (degree Tx reduced in 1995 due to

perceived risk)

1.3 Modeling Team Judgment, reviewed by panel

10 Diagnosis Rate for Chronic Pain .05 to .15 Panel Consensus & Gureje et al., 2001

11 Overdose Mortality Rate if Abusing Opioids 0.0015 Extrapolation from Heroin data (Sullivan, 2007)

12 Overdose Mortality Rate if on Long-acting 0.0025 CONSORT study (Potter et al., 2001)

13 Overdose Mortality Rate if on Short-acting 0.00005 CONSORT study (Potter et al., 2001)

14 Rate of Addiction for those on Long-acting 0.05

Meta-Analyses (Dunn et al., 2010; Højsted &

Sjøgren, 2007)

15 Rate of Addiction for those on Short-acting 0.02 VISN16 data (Fishbain et al., 2008)

Table Function

for Short-acting Bias (as function of

perceived risk)

[(1,0)-(4,1)] Modeling Team Judgment, reviewed by panel

17 Tamper Resistance (baseline value) 1 Policy variable (1=status quo)

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Table 3: References of support for model parameters in the trafficking sector.

Parameter Value Support

1 Avg. Nbr. of Dosage Units Per Opioid Prescription 86 Extrapolation from VONA (Governale, 2008)

Avg. Nbr. of Extra Dosage Units taken per Day Among

those with Abuse or Addiction

1.5 Panel Consensus

3 Fract. of Abuse/Addicted who Engage in Dr Shopping .5 Extrapolated from (Manchikanti et al., 2006)

4 Fraction of Abuse/Addiction who Engage in Forgery .4 Extrapolated from (Manchikanti et al., 2006)

Number of Days of Extra Opioid Usage Among those

with Abuse/Addiction

Generalized from NSDUH 2002, 2003, & 2004

(Table 2.18B in Colliver et al., 2006)

6 Profit Multiplier 15 Modeling team judgment

Table Function for the Effect of Rec Risk on Extra Rx

Obtained

[(0,0) – (2,1)] Modeling team judgment

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