RESEARCH REPORT
doi:10.1111/j.1360-0443.2010.03052.x
Risk factors for drug dependence among out-patients
on opioid therapy in a large US health-care system
add_3052
1776..1782
Joseph A. Boscarino1,2,3, Margaret Rukstalis1, Stuart N. Hoffman4, John J. Han5,
Porat M. Erlich1,6, Glenn S. Gerhard7 & Walter F. Stewart1,8
Center for Health Research, Geisinger Health System, Danville, PA, USA,1 Department of Medicine and Pediatrics, Mount Sinai School of Medicine, New York, NY,
USA,2 Department of Psychiatry,Temple University School of Medicine, Philadelphia, PA, USA,3 Department of Neurology, Geisinger Health System, Danville, PA,
USA,4 Department of Pain Medicine, Geisinger Health System, Danville, PA, USA,5 Department of Medicine,Temple University School of Medicine, Philadelphia,
PA, USA,6 Weis Center for Research, Geisinger Health System, Danville, PA, USA7 and Department of Epidemiology, Johns Hopkins Bloomberg School of Public
Health, Baltimore, MD, USA8
ABSTRACT
Aims Our study sought to assess the prevalence of and risk factors for opioid drug dependence among out-patients on
long-term opioid therapy in a large health-care system. Methods Using electronic health records, we identified
out-patients receiving 4+ physician orders for opioid therapy in the past 12 months for non-cancer pain within a large
US health-care system. We completed diagnostic interviews with 705 of these patients to identify opioid use disorders
and assess risk factors. Results Preliminary analyses suggested that current opioid dependence might be as high as
26% [95% confidence interval (CI) = 22.0–29.9] among the patients studied. Logistic regressions indicated that
current dependence was associated with variables often in the medical record, including age <65 [odds ratio
(OR) = 2.33, P = 0.001], opioid abuse history (OR = 3.81, P < 0.001), high dependence severity (OR = 1.85,
P = 0.001), major depression (OR = 1.29, P = 0.022) and psychotropic medication use (OR = 1.73, P = 0.006). Four
variables combined (age, depression, psychotropic medications and pain impairment) predicted increased risk for
current dependence, compared to those without these factors (OR = 8.01, P < 0.001). Knowing that the patient
also had a history of severe dependence and opioid abuse increased this risk substantially (OR = 56.36, P < 0.001).
Conclusion Opioid misuse and dependence among prescription opioid patients in the United States may be higher
than expected. A small number of factors, many documented in the medical record, predicted opioid dependence
among the out-patients studied. These preliminary findings should be useful in future research efforts.
Keywords
Drug abuse, drug dependence, opioids, out-patients, pain management, prescription drugs.
Correspondence to: Joseph A. Boscarino, Senior Investigator, Center for Health Research, Geisinger Clinic, 100 N. Academy Avenue, Danville, PA 178224400, USA. E-mail: jaboscarino@geisinger.edu
Submitted 28 September 2009; initial review completed 4 December 2009; final version accepted 6 April 2010
INTRODUCTION
The prevalence of opioid prescription drug use in the
United States has increased in the past decade [1–4]. In
part, recent use of prescription opioids in the United
States are the results of past clinical debates related to the
wider use of these medications for pain [5–7]. In the
1980s, the use of long-term opioid therapy for cancer
pain in the United States prompted a re-evaluation of its
use for non-malignant pain [8]. Experts at the time supported a view that opioid maintenance therapy could be
prescribed safely [9–13]. Previous research, however, has
been limited to studies that have relied on medical record
documentation or other less rigorous methods of defining
drug misuse [14–16]. Thus, the actual estimates of and
risk factors for prescription opioid misuse are uncertain
[17]. In the current study, we undertook a survey of outpatients in a large US multi-specialty group practice and
used the Diagnostic and Statistical Manual of Mental
Disorder, 4th edition (DSM-IV) to define substance use
disorders [18].
METHODS
We completed diagnostic interviews among a random
sample of patients with a history of opioid prescriptions
Preliminary results from this study were presented at the 15th Annual HMO Research Network Conference, Danville, PA, April 2009.
© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction
Addiction, 105, 1776–1782
Risk factors for drug dependence among out-patients
for non-malignant cancer pain. Patients were selected
from among longer-term opioid users, defined as those
with 4+ opioid drug prescriptions in the past 12 months
[mean = 10.72, standard deviation (SD) = 4.96]. Telephone interview data were used to identify opioid use
disorders and to collect data on risk factors. This study
was approved by the Geisinger Health System Institutional Review Board.
Study sample
Individuals for this study were selected randomly from
primary and specialty care patients seen in the Geisinger
Clinic, part of the Geisinger Health System (GHS), an
integrated system that serves residents in 31 central and
northeastern Pennsylvania counties. The Geisinger
Clinic includes hospital-based primary and specialty care
clinics, as well as ~40 free-standing Community Practice
Clinics. All ambulatory clinics have used the Epic (Epic
System Corporation, Verona, WI, USA) out-patient electronic health record (EHR) system since 2001.
Patients were eligible for this study if they were 18+
years of age, received care from one of nine community
practice clinics or from the three specialty clinics, including a pain management, orthopedics and a rheumatoid
clinic, and were prescribed opioid medications 4+ times
for non-malignant pain any time from 30 June 2006 to
1 July 2007. Because the patients for our study were
recruited locally, we selected enough clinics to meet our
planned sample size objectives. Altogether, 12 clinics participated in the study, providing a sample pool of 2459
prescription opioid patients for study.
Data collection and study measures
Telephone interviews were completed from August 2007
to November 2008. Following a patient notification letter,
telephone recruitment was initiated. A total of 2373
patients were contacted by telephone, with up to 15 calls
made to complete a patient survey. Of these patients, 234
were determined to be ineligible for study, due to death,
institutionalization, language barriers, illness, denial of
opioid use or due to being in the last survey batch not
contacted because the study quota was completed. Thus,
our survey completion rate was 705/2139 = 33% [19].
Our survey cooperation rate (i.e. percentage interviewed
after patient contact) was 51% (705/1390) [19].
Following consent, interviewers administered structured diagnostic interviews that included: (i) a modified
Composite International Diagnostic Interview (CIDI)
[20,21]; (ii) assessment of depression, post-traumatic
stress disorder (PTSD), general anxiety and psychological
trauma using a diagnostic interview designed for this
purpose [22–24]; and (iii) questions relevant to severity
of opioid dependence [25], tobacco dependence [26,27]
© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction
1777
and childhood neglect [28,29]. We examined these
domains because we wanted to find the best factors to
predict substance dependence in clinical practice [30].
The survey was administered using a computer-assisted
telephone interviewing (CATI) system [WinCati, version
4.2 (Sawtooth Technologies, Northbrook, IL, USA)].
Substance dependence/abuse measures
Substance misuse was defined based on DSM-IV criteria
which were collected using the CIDI instrument
[18,20,21], modified to capture data relevant to prescription opioid use. Because we assessed prescription opioid
dependence at the beginning of the interview, we used a
substance dependence scale adapted for telephone
administration that had been validated previously
[31–33]. The criteria for dependence on this scale were
concordant with DSM-IV nomenclature for dependence
[18]. Alcohol dependence was assessed using the CIDI
instrument. Because history of prescription opioid abuse
is thought to be a predictor of current opioid dependence
[34], this was defined as the presence of one or more
prescription opioid abuse problems (e.g. health, family,
functional or legal problems) in the patient’s history, consistent with DSM-IV.
Nicotine dependence
Fagerstrom Tolerance Scale (FTS) was used to assess nicotine dependence [26,27,35]. We used the diagnostic cutoff point for this scale, defined as a score of 7 or higher
[26]. The FTS has good concurrent and predictive validity
for nicotine dependence and has been used widely in
research [35,36].
Mental health disorders
Major depression was assessed using a depression
measure developed from the Structured Clinical Interview for DSM-IV instrument [37], validated in other
telephone surveys [22,38–42]. Post-traumatic stress disorder (PTSD) was also based on the DSM-IV and developed for telephone administration, as used in previous
trauma studies [22,38,40–43]. Results of the validity
of this instrument are good and have been reported
elsewhere [22,23,42].
Pain measures
We used the Brief Pain Inventory (BPI) to assess current
pain status in the past 7 days [43,44]. The BPI is a widely
used pain scale and is now employed world-wide to assess
chronic, non-malignant pain.
Trauma exposure
Trauma exposure was assessed using a history of childhood neglect and a life-time trauma exposure scale,
Addiction, 105, 1776–1782
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Joseph A. Boscarino et al.
respectively. Childhood neglect was assessed using a scale
developed by Felitti and others [28,29,45]. This scale has
been used in previous studies and validated previously
[28,45,46]. For life-time trauma exposure we used a
stressor measure that focused upon major traumatic
events (e.g. forced sexual contact, being attacked with a
weapon) that occurred prior to the interview. This scale
was part of a trauma instrument that has been used in
previous studies [22,23,37,40,47].
Other study measures
Other measures included history of substance abuse
treatments, history of psychiatric care, current psychotropic medication use, history of illicit drug use (e.g.
amphetamines, marijuana, cocaine, etc.) and selfreported health status. These assessments were part of
the CIDI medical/drug history. Our study also included
the Severity of Dependence Scale (SDS), adopted for
opiates, to assess life-time opioid dependence severity
[25]. The SDS has been used in addiction research and
validated previously [48]. We used a score of 7+ to define
high life-time opioid addiction severity.
Statistical analyses
We analyzed response bias using EHR data to compare
study respondents (n = 705) to eligible non-respondents
(n = 1434). We used these results to develop weights to
adjust for potential response bias. Next we examined
descriptive statistics for opioid dependence by
demographic/medical characteristics. Following this, we
completed logistic regressions to identify risk factors for
opioid dependence. To identify useful models, only variables with bivariate P-values < 0.10 were selected. In
addition, only variables that remained significant at
P < 0.10 were retained in the final models. All multivariate logistic models were assessed for ‘goodness-of-fit’
using the area under the receiver operating characteristic
(ROC) curve and the Hosmer–Lemeshow c2 test [49].
Also, as patients were clustered within 12 clinics, we used
the survey module in STATA (version 9.2) to adjust for
patient clustering [50]. To calculate population attributable risk [51], we used PEPI version 4.0 [52]. All statistical results shown were based on two-tailed tests.
RESULTS
Analyses suggested that non-participants tended to be
male, not married, current smokers, seen in primary care
clinics and less ill than participants. However, no differences were found in participation by race, employment
status, obesity status or the number of prescriptions
received in the past 3 years. Based upon these results,
case weights were developed to adjust for differences in
© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction
participation by gender and clinic setting and these
weights were used in subsequent analyses.
Analyses suggested that 35.5% [95% confidence
interval (CI) = 31.1–40.2] of the longer-term opioid
users studied, defined as those who received 4+ prescriptions in the past 12 months (mean = 10.72, SD = 4.96),
appeared to meet the criteria for life-time and 25.8%
(95% CI = 22.0–29.9) appeared to meet the criteria for
current opioid dependence, respectively. Of those with
life-time opioid dependence, 72.2% (95% CI = 70.1–
78.0) also met the criteria for current opioid dependence.
Life-time opioid dependence was associated with age
less than 65 years (P < 0.001), non-white race
(P < 0.05), being seen in a specialty clinic (P < 0.05),
reporting poorer health status (P < 0.01) and reporting
higher pain impairment (P < 0.01). Life-time dependence
was also associated with having received a higher
number of opioid drug orders in the past 3 years
(P < 0.001) (detailed results of the above available upon
request).
Those who met life-time criteria for opioid abuse were
more likely to meet life-time criteria for opioid dependence (P < 0.001) (Table 1). Also, those with a history of
higher opioid dependence severity also had a higher
prevalence of opioid dependence (P < 0.001). Life-time
opioid dependence was also associated with life-time
alcohol dependence (P < 0.01), tobacco dependence
(P < 0.01), major depression (P < 0.001), generalized
anxiety disorder (P < 0.001) and life-time PTSD
(P < 0.001). Those with life-time opioid dependence also
had a history of childhood neglect (P < 0.01), exposure to
psychological trauma (P < 0.001), illicit drug use
(P < 0.001), substance abuse treatment (P < 0.001),
anti-social personality (P < 0.001) and had a history of
recent psychotropic medication use (P < 0.001).
Based on these results, multivariate models were
developed for life-time and current opioid dependence,
respectively. Using the selection criteria discussed, six predictor variables were identified for life-time and current
dependence (Table 2). For the life-time regression
model, opioid dependence was associated with age
<65 (OR = 2.80, P < 0.001), current pain impairment
(OR = 1.94, P = 0.01), history of opioid abuse
(OR = 3.95, P < 0.001), higher life-time opioid dependence severity (OR = 3.00, P = 0.003), higher numbers
of drug orders (OR = 1.75, P = 0.009) and history of
antisocial personality disorder (OR = 1.44, P = 0.015).
Current opioid dependence was associated with age
<65 (OR = 2.33, P = 0.001), history of opioid abuse
(OR = 3.81, P < 0.001), higher life-time opioid dependence severity (OR = 1.85, P = 0.001), history of major
depression (OR = 1.29, P = 0.022) and psychotropic
medication use (OR = 1.73, P = 0.006). In addition,
higher pain impairment also met the inclusion criteria
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Risk factors for drug dependence among out-patients
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Table 1 Mental health/psychological characteristics of patients meeting DSM-IV criteria for life-time prescription opioid dependencea.
Study variables
Life-time history of prescription opioid abuse
% Yes
% No
Life-time high prescription opioid dependence severity
% Yes
% No
Life-time alcohol dependence
% Yes
% No
Life-time tobacco dependence
% Yes
% No
Life-time major depressive disorder
% Yes
% No
Life-time generalized anxiety disorder
% Yes
% No
Life-time post-traumatic stress disorder
% Yes
% No
History of high childhood neglect
% Yes
% No
History of high exposure to psychological trauma
% Yes
% No
History of illicit drug use
% Yes
% No
History of any substance abuse treatment
% Yes
% No
Current psychotropic medication use
% Yes
% No
Antisocial personality disorder (positive screen)
% Yes
% No
(n=)
Total sample % (n)
Life-time drug
dependence % (n)
No life-time drug
dependence % (n)
32.8 (230)
67.2 (475)
58.2 (147)
41.8 (104)
18.7 (83)***
81.3 (371)
15.0 (108)
85.0 (597)
29.8 (77)
70.2 (174)
6.9 (31)***
93.1 (423)
9.8 (68)
90.2 (637)
14.2 (36)
85.8 (215)
7.4 (32)**
92.6 (442)
36.8 (251)
63.2 (454)
42.6 (103)
57.4 (148)
33.7 (148)**
66.3 (306)
34.6 (249)
65.4 (456)
51.4 (129)
49.6 (122)
25.9 (120)***
74.1 (334)
12.6 (89)
87.4 (616)
20.2 (50)
79.8 (201)
8.4 (39)***
91.6 (415)
13.3 (97)
86.7 (608)
21.5 (55)
78.5 (196)
8.8 (42)***
91.2 (412)
24.9 (178)
75.1 (527)
33.2 (84)
66.8 (167)
20.3 (94)**
79.7 (360)
23.0 (161)
77.0 (544)
32.0 (80)
68.0 (171)
18.0 (81)***
82.0 (373)
39.3 (273)
60.7 (432)
51.2 (127)
48.8 (124)
32.8 (146)***
67.2 (308)
22.5 (153)
77.5 (552)
37.0 (90)
63.0 (161)
14.5 (63)***
85.5 (391)
61.1 (434)
38.9 (271)
71.7 (181)
28.3 (70)
55.3 (253)***
44.7 (201)
23.8 (167)
76.2 (538)
(705)
32.7 (83)
67.3 (168)
(251)
18.8 (84)***
81.2 (370)
(454)
**P < 0.01; ***P < 0.001. aAll percentage results adjusted/weighted for response bias and data clustering, ns are unweighted.
(P = 0.079). For the life-time dependence model, the area
under the ROC curve was equal to 0.79, with a Hosmer–
Lemeshow c2 test of 4.3, P = 0.75 (Table 2). For current
dependence, the area under the ROC curve was equal to
0.77, with a Hosmer–Lemeshow c2 test of 13.1, P = 0.11
(Table 2).
As seen in Figure 1, based on the results of model 2,
current opioid dependence was associated strongly with
the combination of younger age, pain impairment,
history of depression, current psychotropic medication
use, history of higher opioid dependence severity and
© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction
history of opioid abuse. Notably, knowing the patient was
less than 65 years old, had current pain impairment, had
a history of depression and was currently taking psychotropic medications was associated with an elevated risk
for current dependence (OR = 8.01, 95% CI = 4.5–
14.26). In addition, knowing that the patient also had a
history of higher opioid dependence severity increased
this combined risk (OR = 14.8, 95% CI = 8.65–25.31).
Finally, having information related to the patient’s
opioid abuse history increased this risk even higher
(OR = 56.36, 95% CI = 32.49–97.76) (Fig. 1).
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Joseph A. Boscarino et al.
Table 2 Multivariate logistic regressions predicting life-time and current prescription opioid dependence based on DSM-IV criteria
(n = 705).a
Model 1: life-time dependence*
Model 2: current dependence**
Predictor variables
OR
95% CI
P-value
OR
95% CI
P-value
Less than 65 years old
Reported pain interferes with life/work
History of opioid abuse
History of high dependence severity
Opioid orders past 3 years (highest quintile)
Positive screen for antisocial personality
History of major depression
Currently use psychotropic medications
2.80
1.94
3.95
3.00
1.75
1.44
–
–
1.83–4.28
1.21–3.10
2.39–6.53
1.58–5.69
1.18–2.58
1.09–1.91
–
–
<0.001
0.010
<0.001
0.003
0.009
0.015
–
–
2.33
1.54
3.81
1.85
_
_
1.29
1.73
1.55–3.53
0.94–2.50
2.56–5.67
1.38–2.46
_
_
1.05–1.60
1.21–2.47
0.001
0.079
<0.001
0.001
_
_
0.022
0.006
a
All results adjusted/weighted for response bias and data clustering. *Area under ROC curve = 0.79; Hosmer–Lemeshow c2 = 4.3; P = 0.75. **Area under
ROC curve = 0.77; Hosmer–Lemeshow c2 test = 13.1; P = 0.11. CI: confidence interval; OR: odds ratio.
Figure 1 Odds ratios represent the combined odds of having current prescription
opioid dependence (based on DSM-IV criteria) for each respective combination of
risk factors, compared to those without
these risk factors, respectively [49].
Age = less than 65 years old; pain = high
current pain impairment; depression =
history of depression; meds = current psychotropic medication use; severe = history
of high prescription opioid dependence
severity; abuse = history of prescription
opioid abuse
DISCUSSION
Our study suggests that as many as 36% of the patients
interviewed met criteria for life-time opioid dependence
and 26% met criteria for current dependence, respectively. One of the best predictors of opioid dependence
(both for current and life-time dependence) is having a
history of opioid abuse.
In addition, other risk factors for life-time opioid
dependence include younger age, pain impairment,
higher drug dependence severity, a greater number of
opioid orders in the EHR and history of anti-social personality. For current opioid dependence, in addition to
history of drug abuse, significant variables include
younger age, pain impairment, history of higher dependence severity, history of depression and current psychotropic medication use. Unfortunately, not all this
information might be available in the patient’s medical
record. Nevertheless, the following information should be
documented, especially a patient being evaluated for
© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction
opioid therapy: age, current pain impairment, history of
depression and current psychotropic medications. As
noted, these four variables suggest an increased likelihood of current dependence (OR = 8.01, 95% CI = 4.5–
14.26).
The estimated population attributable risk percentage
for current dependence among opioid users exposed to all
four of these risk factors (19% of patients) is 62.5% (95%
CI = 54.2–71.0) [52,53]. Adding severity of opioid
dependence and history of opioid abuse to this, increases
this risk even higher (OR = 56.36, 95% CI = 32.49–
97.76). This results in a population attributable risk percentage for current addiction among opioid users
exposed to these risk factors (10% of patients) equal to
83.9% (95% CI = 77.2–90.6) [51,52].
This study has strengths and limitations. Study
strengths are that it was based upon a random
sample of out-patients seen in a large multi-specialty
group practice; that drug dependence was assessed
based on DSM-IV; and that subjects were identified
Addiction, 105, 1776–1782
Risk factors for drug dependence among out-patients
through drug orders in the EHR, not patient self-report
or treatment records. Study limitations include that our
diagnostic data were based on patient self-report; that
our survey completion rate was less than optimal, thus
study estimates may be biased; and, as patients were
drawn from a predominately Caucasian population in
one US region, it may not be possible to generalize these
findings.
Despite these limitations, our study suggests that physicians prescribing opioids to chronic pain patients may
be assisted by collecting information related to the
patient’s mental health history, current psychotropic
medication use and pain status, before prescribing these
medications. While some of these factors are known to be
associated with drug dependence, others are not so
obvious (e.g. psychotropic medication use). These data
may be useful to better determine susceptibility for opioid
use disorders in clinical practice and for improving
patient management. It did not go unnoticed that a small
number of fairly common variables predicted drug
dependence in the current study. Given the battery of
predictors examined, we did not expect this outcome. In
addition, our study suggests that opioid dependence may
be higher than expected among chronic pain patients.
Additional research is planned to confirm and expand
upon these findings.
Declarations of interest
None.
Acknowledgements
Funding for this study was by a grant from the Administrative Committee for Research (ACR), Geisinger Clinic,
grant no. TRA-015 (Dr Boscarino, Principle Investigator).
The ACR had no role in study design, data collection,
analysis, interpretation, writing or in the decision to
submit the manuscript for publication.
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