778
ORIGINAL ARTICLE
Patients With Chronic Disabling Occupational Musculoskeletal
Disorder Failing to Complete Functional Restoration: Analysis
of Treatment-Resistant Personality Characteristics
Krista J. Howard, MS, Tom G. Mayer, MD, Brian R. Theodore, MS, Robert J. Gatchel, PhD, ABPP
ABSTRACT. Howard KJ, Mayer TG, Theodore BR,
Gatchel RJ. Patients with chronic disabling occupational musculoskeletal disorder failing to complete functional restoration:
analysis of treatment-resistant personality characteristics. Arch
Phys Med Rehabil 2009;90:778-85.
Key Words: Personality disorders; Rehabilitation; Risk
factors.
© 2009 by the American Congress of Rehabilitation
Medicine
Objective: To identify the risk factors for noncompletion of
a functional restoration program for patients with chronic disabling occupational musculoskeletal disorders.
Design: Prospective cohort study.
Setting: Consecutive patients undergoing functional restoration treatment in a regional rehabilitation referral center.
Participants: A sample of 3052 consecutive patients, classified as either completers (n⫽2367) or noncompleters (n⫽685),
who entered a functional restoration program.
Interventions: Not applicable.
Main Outcome Measures: The measures used included
medical evaluations, demographic data, Diagnostic and Statistical Manual of Mental Disorders psychiatric diagnoses, the
Minnesota Multiphasic Personality Inventory, and validated
questionnaires evaluating pain, depression, and occupational
factors.
Results: The findings revealed that patients who did not
complete the program had a longer duration of total disability
between injury and admission to treatment (completers⫽20mo
vs noncompleters⫽13mo; P⬍.001). Furthermore, patients who
were opioid-dependent were 1.5 times more likely to drop out
of rehabilitation, and patients diagnosed with a socially problematic Cluster B Personality Disorder were 1.6 times more
likely to drop out.
Conclusions: Although some risk factors associated with
program noncompletion may be addressed in treatment, socially maladaptive personality disorders, long-neglected disability, and chronic opioid dependence are the major barriers to
successful treatment completion. The patients identified with
personality disorders may display resistance to treatment and
may be difficult for the treatment staff to deal with. Early
recognition of these treatment-resistant personality characteristics in the functional restoration process may assist the treatment team in developing more effective strategies to help this
dysfunctional group.
HE PREVALENCE OF CHRONIC occupational muscuT
loskeletal disorders is both a medical and an economic
concern. In fact, it is estimated that costs for health care and
From the Productive Rehabilitation Institute of Dallas for Ergonomics (PRIDE),
Research Foundation, Dallas (Howard, Theodore); Department of Orthopedic Surgery, University of Texas Southwestern Medical Center at Dallas, Dallas (Mayer);
and Department of Psychology, University of Texas at Arlington, Arlington
(Gatchel), TX.
No commercial party having a direct financial interest in the results of the research
supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated.
Correspondence to Tom G. Mayer, MD, 5701 Maple Ave #100, Dallas, TX 75235,
e-mail: [email protected]. Reprints are not available from the author.
0003-9993/09/9005-00708$36.00/0
doi:10.1016/j.apmr.2008.11.009
Arch Phys Med Rehabil Vol 90, May 2009
lost productivity relating to chronic pain are approximately $70
to $100 billion annually.1 Yet common treatments for chronic
pain disorders, including surgery or medication maintenance,
often lack evidence of success.2-4 Furthermore, patients experiencing chronic pain are faced with multiple barriers to recovery, including the physical injury itself (and the loss of mobility
and strength associated with it), general deconditioning, and a
variety of psychosocial and financial issues. For such patients
for whom early intervention is not effective, an interdisciplinary functional restoration program is often recommended.5,6
Chronic pain patients admitted into a functional restoration
program are afforded physical and occupational therapy, psychosocial assessments and counseling, assistance with workrelated factors, and education on health-related issues.7 Evidence-based outcome measures not only include physical and
psychosocial improvement but also take into account posttreatment outcomes, such as return to work, work retention, and
decreasing both unnecessary health care use and surgeries.
It has repeatedly been shown that patients completing the
prescribed regimen within a functional restoration program
yield positive outcomes.8,9 However, those who drop out of the
treatment prematurely are highly likely to experience poorer
outcomes.8,10 Mayer et al8 showed that patients who completed
the functional restoration program were more successful at
returning to work and retaining work relative to those who did
not complete the program. This foundational study determined
that only 13% of the noncompleters successfully returned to
work, compared with 85% of the program completers who
continued work 1-year posttreatment. In a subsequent study
published in Archives, Proctor et al10 assessed the outcomes of
completers and noncompleters, and they found that patients
who dropped out of a functional restoration program were 9.7
times less likely to return to work compared with those who
successfully completed the prescribed treatment. Additionally,
at the 1-year follow-up, of those who did initially return to
List of Abbreviations
BDI
DSM-IV
MMPI
MVAS
OR
PD
Beck Depression Inventory
Diagnostic and Statistical Manual of Mental
Disorders, 4th Edition
Minnesota Multiphasic Personality Inventory
million visual analog scale
odds ratio
personality disorder
779
RISK FACTORS FOR NONCOMPLETION, Howard
work, the noncompleters were 7 times less likely to retain work
than were the completers. That study also showed that the
noncompleters were more likely to exhibit excessive treatmentseeking behaviors, and were more likely to undergo surgery to
the same injured area (both of which are seen as poor treatment
outcomes).
Studies evaluating risk factors for completion status of effective tertiary treatment programs for patients with chronic
pain are rare. Similar studies that have focused on negative
treatment outcomes have provided inconclusive or contradictory determinants with respect to demographic and injuryspecific factors related to poor outcomes.10-21 Work-related
factors, such as the relationship between the patient and the
employer, job characteristics and demands, and the availability
of the current position postinjury, have been examined in
outcome studies.10,22-26 The general findings support that patients who reported a positive relationship with their respective
employers, and a desire to return to work, were more likely to
have positive treatment outcomes. Furthermore, patients who
continued to work postinjury, or knew that their original job
was still available, were also more likely to exhibit better
outcomes.
Aside from the demographic, injury-specific, and occupational factors characteristic of chronic pain patients, measures
of physical pain and disability are also strongly correlated with
treatment outcomes.5,27-31 Furthermore, self-report ratings indicating higher levels of depression have been found to be
predictive of poor treatment outcomes, specifically completion
status.10,11,16,32-37 Although most patients entering a functional
restoration program report elevated levels of depression, the
Proctor et al10 study showed that patients who prematurely
dropped out of the program had reported significantly higher
levels of depression at admission than did those who completed
the program.
Chronic pain populations also tend to exhibit higher rates of
substance use and PDs than the general population.32-34 The
DSM-IV uses a multiaxial system to classify psychiatric diagnoses.38 Axis I diagnoses involve clinical disorders, such as
major depressive disorder, anxiety disorders, and substance use
disorders. Axis II diagnoses involve PDs, which are divided
into 3 specific clusters: Cluster A includes paranoid, schizoid,
and schizotypal PDs; Cluster B includes antisocial, borderline,
histrionic, and narcissistic PDs; and Cluster C includes avoidant,
dependent, and obsessive-compulsive PDs. Axis I and Axis II
diagnoses have been shown to be prevalent within the chronic
pain populations, and patients with these disorders have repeatedly been found to exhibit poor posttreatment outcomes and are
more likely to drop out of treatment.32 When considering the
Axis I clinical disorders, substance use is identified to be highly
prevalent in chronic pain populations. Dersh et al32 found that
opioid dependence was strongly associated with poor treatment
outcomes. Patients with this diagnosis were found to be 2.7
times less likely to return to work, and 2.6 times less likely to
retain work, at 1-year posttreatment. Furthermore, Proctor10
found a correlation between patients identified as highly at risk
for opioid dependence and program noncompletion.
While the prevalence of PDs in the general population, as
assessed by the DSM-IV Axis II classification, is estimated at
9%,39 PDs have a greater representation in chronic pain populations.32 Various PDs have also been tied to poor treatment
outcomes. Dersh et al40 found an association between PDs and
treatment completion status. Patients exhibiting no PDs were
significantly more likely to complete the rehabilitation program
than those who were diagnosed with at least 1 PD. Furthermore, Proctor10 identified a significant difference in completion
status of a functional restoration program for schizotypal, an-
tisocial, borderline, histrionic, narcissistic, and dependent PDs,
along with significant differences comparing Clusters A, B,
and C. Although substance use and PDs fall into separate
axes in the DSM-IV classification system, there appears to
be a high level of comorbidity between these diagnostic
categories.39,41-45
The purpose of the present study was to examine simultaneously whether an array of potential risk factors, individually
found to be associated with chronic pain in the review by
Proctor,10 was associated with noncompletion of a tertiary
functional restoration program. Historically, treatment noncompletion has been relatively low (20%–25%), yet it has been
shown that patients who complete the program exhibit better
posttreatment outcomes than those who drop out prematurely.
The intent of this study was to compare the outcome groups
(completers and noncompleters) across 4 dimensions: (1) demographic and injury-specific variables, (2) work-related variables, (3) pain and depression self-reports, and (4) personality
inventories and DSM-IV diagnoses. A logistic regression analysis was planned to help determine the combination of key
variables among these levels that may be highly associated
with predicting noncompletion.
METHODS
Participants
The study consisted of a consecutive cohort of 3052 patients
presenting with a chronic disabling occupational musculoskeletal disorder. These patients consented to, and started, treatment at a functional restoration treatment facility. The criteria
for participation in this treatment program were as follows: (1)
the duration between date of injury and treatment was at least
3 months, (2) primary acute care and/or secondary care
failed or were determined to be unnecessary, (3) surgery was
either not an option or did not produce relief from the injury,
(4) severe pain and functional limitations remained, and (5)
the patient was able to communicate in English or Spanish. The
participants in this study were patients discharged during the
period of January 1996 through December 2004. This cohort
was divided into the following 2 groups: the noncompleter
group consisted of 685 patients who were admitted to the
functional restoration program and underwent the initial evaluations. Noncompletion status was determined by failure to
complete the full prescribed treatment regimen. The completer
group consisted of 2367 patients who successfully completed
the prescribed functional restoration treatment program.
Procedure
The participants consented to collection of information for
treatment management and research purposes at the time of
admission. The medically supervised treatment program consisted of quantitatively directed exercise progression, which
was under the supervision of certified physical and occupational therapists. In addition, patients participated in other
activities aimed at disability management, such as counseling,
stress management, biofeedback, and coping skills training.
Furthermore, education support and assistance was provided
for injury prevention and occupational factors.2,8
Several measures were used to assess the demographic,
injury-specific, and occupational factors, along with the perceived levels of pain, disability, depression, and Axis I and
Axis II diagnoses. Although many of these measures were
administered multiple times throughout treatment and at posttreatment, the measures considered in this study were assessed
at the initial phase of treatment. At the initial interview, demoArch Phys Med Rehabil Vol 90, May 2009
780
RISK FACTORS FOR NONCOMPLETION, Howard
graphic data were collected and physical and functional capacity measurements were performed by appropriate staff members. The psychosocial instruments administered at admission
to the program included the Quantified Pain Drawing, which is
a self-report of perceived pain; the MVAS,46 which is a visual
analog questionnaire measuring disability; the BDI47; the
MMPI48; and the Structured Clinical Interview for DSM-IV
Axis I and Axis II diagnoses. In particular for the MMPI, the
Disability Profile was considered, which is represented by an
elevation of 4 or more of the 10 scales assessed. The Structured
Clinical Interview for DSM Diagnosis–Non-Patient Form49 is a
structured interview that yields Axis I diagnoses that correspond with the DSM-IV criteria. The diagnoses considered in
this study included major depressive disorder, generalized anxiety disorder, and substance use disorders. The Structured
Clinical Interview for DSM Diagnosis–II50 is a structured
interview that identifies Axis II PDs defined with the DSM-IV
criteria. The 10 PDs, each assessed as a dichotomous variable,
are grouped into 3 clusters.38 Cluster A includes paranoid,
schizoid, and schizotypal PDs. Cluster B includes antisocial,
borderline, histrionic, and narcissistic PDs. Cluster C includes
avoidant, dependent, and obsessive-compulsive PDs.
Statistical Analyses
The initial univariate analyses were used to develop a profile
of the typical noncompleter. This was accomplished by comparing the completer group with the noncompleter group on the
basis of demographic, injury-specific, and occupational variables, along with self-report measures of pain, disability, depression, and Axis I and Axis II diagnoses. In this study,
sample sizes varied for certain variables because of either
procedural changes in assessments administered over the
9-year span or issues with missing data. Many noncompleters
dropped out prior to completing all the initial assessments. For
the categorical variables, tests of association were conducted
based on the Pearson chi-square test statistic for all analyses of
the differences between the completer and noncompleter
groups, with effect sizes reported as the OR. Independent t tests
were conducted on all analyses of the differences between the
completer and noncompleter groups on continuous variables,
with effect sizes reported as the Cohen d. A Holm-Bonferroni
step-down method was used to correct for any potential type I
errors.
A multivariate logistic regression model was then created
based on the attributes found at the univariate level that determined the variables most associated with noncompletion status.
A sequential logistic regression analysis was performed in this
study with the intent to identify the specific variables most
associated with noncompletion. The variables considered for
the logistic regression model were based on those found significant at the univariate level. The first block contained the
demographic and injury-specific variables, along with selfreport measures of depression (BDI) and perceived disability
(MVAS), followed by the occupational variables in block 2.
The third block assessed the Axis I diagnoses. Finally, Axis II
PDs were added in block 4. The total number of patients used
in the logistic regression analysis was reduced to 1845 (completer group, n⫽1545; noncompleter group, n⫽300) because
any patient with missing or invalid data was eliminated. (Valid
data on all variables entered into the model were required for
each patient to be considered in the multivariate analysis.) A
Pearson chi-square statistic was assessed after the addition of
each block in the sequential logistic regression model to evaluate the association of each set of variables with noncompletion status. The significance criterion for the logistic regression
analysis was set at alpha equal to .05.
Arch Phys Med Rehabil Vol 90, May 2009
RESULTS
The demographic and injury-specific data are presented in
table 1. There was a significant difference between the 2
comparison groups for both the length of disability (t⫽4.933;
P⬍.001) and the temporary-total disability (t⫽6.602; P⬍.001),
which indicated that noncompleters took longer to get into the
rehabilitation program, and that they were not working for longer
periods than the completers. Furthermore, noncompleters were
more likely to have undergone surgery (␹2⫽8.251; P⫽.004;
OR⫽1.3) and were more likely to have settled their workers’
compensation case (␹2⫽6.887; P⫽.009; OR⫽1.3) prior to treatment.
Table 2 depicts the findings for the occupational variables
considered in this study. Although there were no differences
between the 2 groups in terms of the type of work and physical
demands required, the groups did differ significantly with respect to their work status at admission and the knowledge of
the availability of their original job at postrehabilitation. Patients currently working at admission to the functional restoration program were found to be 2.2 times more likely to
complete the program than those not working (␹2⫽24.84;
P⬍.001). Additionally, patients who knew their original jobs
were still available were 1.9 times more likely to complete the
program than those who had been replaced in their jobs
(␹2⫽48.37; P⬍.001).
The various psychologic and social variables considered in
this study are presented in tables 3 and 4. Patients in the
noncompleter group displayed significantly higher scores on
the BDI (t⫽7.730; P⬍.001) and the MVAS (t⫽7.981; P⬍.001)
relative to the completers. Patients displaying the disability
profile on the MMPI were 1.6 times more likely to drop out
prematurely than those with fewer than 3 scale elevations
(␹2⫽12.68; P⬍.001). Axis I and Axis II DSM-IV diagnoses
also differed significantly between the 2 groups. The noncompleters were identified to be 1.6 times more likely to be diagnosed with an anxiety disorder (␹2⫽8.272; P⫽.004) and were
twice as likely to be diagnosed with a substance use disorder
(␹2⫽29.668; P⬍.001). More specifically, patients entering the
rehabilitation program with a diagnosis of opioid dependence
were 2.1 times more likely to drop out of the program than
patients not dependent on opioids (␹2⫽30.263; P⬍.001). The 2
groups were also compared on the prevalence of Axis II PDs,
and 74.7% of the noncompleters were diagnosed with an Axis
II PD, compared with 59.8% of the completers (␹2⫽30.608;
P⬍.001; OR⫽2.0). In addition, patients diagnosed with any
Cluster B PD (antisocial, borderline, histrionic, narcissistic)
were twice as likely to drop out of the program than patients
without a Cluster B PD (␹2⫽40.777; P⬍.001).
The multivariate logistic regression analysis (table 5) was
found to be significant with the addition of each block, and
revealed the following key risk factors associated with noncompletion status: (1) length of disability, (2) high score on the
MVAS, (3) not working at admission to treatment, (4) opioid
dependence, and (5) any Cluster B PD. The overall classification rate of this model was 78.5%.
DISCUSSION
The present study represents a comprehensive examination
of patients with chronic disabling occupational musculoskeletal
(specifically spinal) disorders who were admitted to a tertiary
functional restoration program. The purpose of this study was
to identify key risk factors associated with noncompletion of a
functional restoration treatment program, then create a logistic
regression model that would predict patients who were less
likely to complete the program based on selected criteria at
781
RISK FACTORS FOR NONCOMPLETION, Howard
Table 1: Demographic Characteristics and Injury-Specific Variables (Nⴝ3052)
Variables
Sex (% male)
Age (y)*
Ethnicity (%)
Black
White
Hispanic
Asian
Other
Education (y)*
Length of disability (mo)†
0–5mo
6–11mo
12–17mo
18–23mo
24–35mo
36–47mo
ⱖ48mo
Temporary-total disability (mo)*
Pretreatment surgeries (%)
Attorney retained (%)
Case settlement, pretreatment (%)
Valid n⫽2987
Compensable body parts*
Area of injury
Cervical (%)
Thoracic/lumbar
Multiple spinal
Multiple-musculoskeletal
Upper extremity
Lower extremity
Upper and lower (no spine)
Other
Completers (1996–2004)
n⫽2367
77.6%
Noncompleters (1996–2004)
n⫽685
22.%
P
(␹2 or t test)
53.7
45.1⫾9.622
53.6
45.2⫾10.482
NS
NS
23.2
52.7
20.6
1.5
2.1
11.6⫾3.054
16.6⫾19.03
23.6
28.6
17.5
11.0
9.6
4.2
5.5
12.6⫾13.41
40.4
18.5
24.4
n⫽565/2317
1.6⫾1.05
24.4
52.7
19.1
1.0
2.8
11.6⫾3.264
21.6⫾24.32
16.1
27.9
16.5
12.0
12.6
3.4
11.5
19.6⫾22.98
46.6
18.6
29.4
n⫽197/670
1.5⫾1.22
NS
4.3
41.0
7.9
21.6
17.6
6.0
0.9
0.7
5.1
40.4
11.2
21.4
14.5
6.7
0.7
0.0
NS
⬍.001
⬍.001
.004
NS
.009
OR and 95% CI or
Cohen d
d⫽.228
d⫽.374
1.28 (1.09–1.54)
1.30 (1.06–1.56)
NS
NS
Abbreviations: CI, confidence interval; NS, nonsignificant.
*Values are mean ⫾ SD.
†
Values are mean ⫾ SD; percent breakdown.
admission. As highlighted, a psychosocial risk factor profile
was identified. Because it has been shown that patients who
complete a functional restoration program have much higher
success rates of work return, work retention, and other positive
outcomes,10 the ultimate future goal would be to devise and
implement appropriate interventions that would assist these
Table 2: Occupational Variables (Nⴝ3052)
Variables
Currently working (%)
Original job available (% yes)
Job satisfaction (%)
1: Very satisfied
2: Satisfied
3: Neither
4: Dissatisfied
5: Very dissatisfied
Job code (% blue collar)
Job demand (%)
1: Sedentary/light
2: Light/medium
3: Medium/heavy
4: Heavy/very heavy
Completers (1996–2004)
n⫽2367
77.6%
Noncompleters (1996–2004)
n⫽685
22.4%
P (␹2)
OR (95% CI)
14.4
53.6
7.2
37.5
⬍.001
⬍.001
2.18 (1.59–2.98) (for completion)
1.93 (1.60–2.32) (for completion)
56.2
24.9
12.5
3.4
2.9
71.7
58.4
24.6
10.8
2.5
3.7
70.1
NS
14.7
27.3
35.1
22.9
14.4
25.5
34.8
25.3
NS
NS
Abbreviations: CI, confidence interval; NS, nonsignificant.
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RISK FACTORS FOR NONCOMPLETION, Howard
Table 3: Pretreatment BDI, Pain Intensity, MVAS, and MMPI (Nⴝ3052)
Variables
Completers
(1996–2004)
Noncompleters
(1996–2004)
P
(␹2 or t test)
OR and 95% CI or Cohen d
BDI (pretreatment)
No depression (%)
Mild (%)
Moderate (%)
Severe (%)
Extreme (%)
Pain intensity (pretreatment)*
MVAS (pretreatment)†
Mildly disabling (%)
Moderately disabling (%)
Severely disabling (%)
MMPI Normal profile (0 scale elevations) (%)
MMPI Disability profile (4 or more scale elevations) (%)
17.10⫾10.61
26.6
21.0
30.0
9.2
13.2
7.4⫾12.30
90.7⫾25.24
3.5
33.8
62.7
9.8
45.1
20.85⫾12.34
17.9
17.5
31.5
10.5
22.6
8.0⫾7.46
99.6⫾25.19
3.0
19.5
77.5
1.9
56.8
⬍.001
d⫽.326
NS
⬍.001
d⫽.353
⬍.001
⬍.001
5.64 (2.30–13.9) (for completion)
1.59 (1.23–2.08)
†
Abbreviations: CI, confidence interval; NS, nonsignificant.
*Values are mean ⫾ SD.
Values are mean ⫾ SD; percent breakdown.
†
predetermined dropouts in completing the prescribed treatment.
In comparing the completers to the noncompleters, it was
evident that the noncompletion group had a greater length of
disability than the completer group. Although the specific reasons there was a greater delay for treatment for the noncompleter group is unknown with this sample, it can be speculated
that some patients might encounter difficulties getting funding
for the multidisciplinary treatment program; some patients
might resist treatment because of fear of reinjury or secondary
gains; and some patients may develop conditions, such as drug
dependency, that could interfere with proper medical treatment.
Regardless of the reason, though, a greater duration between
injury and treatment has been associated with poor outcomes in
numerous studies.7,10,12,51
Because most patients in this study had experienced a workrelated injury, the occupational variables taken into consideration when comparing the risk factors for noncompletion included whether patients were working at admission to the
treatment, whether patients had insight that their original jobs
were still available, and whether patients were satisfied with
their employers. The present analysis identified that patients working at admission (specifically working full-time) were more likely
to complete the treatment. Furthermore, those patients whose
original jobs were still available were more likely to complete
the program. For employers, keeping the injured employee at
work, or at least keeping the prospect of allowing the employee
to return to the original position posttreatment, is an important
consideration to increase the likelihood of positive treatment
outcomes.
Physical and psychosocial factors that have been found to be
associated with poor treatment outcomes include higher levels
of depression,10,11,33-36 higher levels of pain intensity,16,28,29,31
and higher levels of perceived disability.5,52 The findings from
the current study align with prior research with respect to both
depression and perceived disability. Noncompleters were more
likely to report severe/extreme depression symptoms on the
BDI (as indicated by a score of ⱖ25), and were more likely to
rate themselves as severely disabled on the MVAS (as indicated by a score of ⱖ85). This coincides with the MMPI
analysis, for which noncompleters were significantly more
likely to be diagnosed with disability profile than the completers. Psychosocial factors, such as depression and perceived
disability, are seen to impact treatment dropout regardless of
the level of pain associated directly with the injury.
Thus, psychosocial factors seem to be an integral part of
determining completion status regardless of the physical condition itself. Postinjury psychopathologies (Axis I), along with
PDs (Axis II), were also found to be risk factors for noncompletion of treatment. A prior study has linked Axis I disorders
to negative treatment outcomes.5 In this present study, incidences of major depressive disorder did not differ significantly
between the completers and noncompleters, compared with the
depression symptoms assessed by the BDI. However, more
than 50% of the patients in the entire cohort studied (both
completers and noncompleters) were diagnosed with major
Table 4: Postinjury Axis I and Axis II Diagnoses (nⴝ2395)
Variables
Completers
(1996–2003)
Noncompleters
(1996–2003)
P
(␹2 or t test)
OR and 95% CI
50.7
10.0
15.8
14.1
59.8
24.1
38.8
27.6
53.5
14.9
27.2
25.8
74.7
33.9
56.3
34.6
NS
.004
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
.005
1.59 (1.15–2.17)
2.04 (1.56–2.56)
2.04 (1.59–2.70)
2.00 (1.54–2.56)
1.61 (1.28–2.04)
2.04 (1.64–2.56)
1.39 (1.10–1.75)
Major depressive disorder
Anxiety disorder
Substance use disorder
Opioid dependency
Any Axis II diagnosis
Any Cluster A diagnosis at admission
Any Cluster B diagnosis at admission
Any Cluster C diagnosis at admission
NOTE: Values are percentages or as otherwise indicated.
Abbreviations: CI, confidence interval; NS, not significant.
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RISK FACTORS FOR NONCOMPLETION, Howard
Table 5: Logistic Regression Analysis of Noncompletion Rate
Variables
␤
SE
Wald
P
OR
95% CI for OR Lower
95% CI for OR Upper
Length of disability
Pretreatment surgery
Case settlement (pretreatment)
BDI (pretreatment)
MVAS (pretreatment)
Work status (pre)
Job availability
Anxiety disorder
Opioid dependency
Any Cluster A Dx
Any Cluster B Dx
Any Cluster C Dx
Any Cluster D Dx
Constant
0.009
0.082
⫺0.201
0.010
0.011
⫺0.904
⫺0.239
⫺0.029
0.391
0.182
0.482
0.116
0.123
⫺3.200
0.003
0.140
0.173
0.007
0.003
0.287
0.142
0.194
0.162
0.153
0.143
0.148
0.190
0.327
6.344
0.345
1.352
2.229
11.742
9.888
2.834
0.022
5.815
1.408
11.404
0.615
0.422
95.501
0.012
0.557
0.245
0.135
0.001
0.002
0.092
0.882
0.016
0.235
0.001
0.433
0.516
0.000
1.009
1.086
0.818
1.010
1.011
0.405
0.787
0.972
1.478
1.200
1.619
1.123
1.131
0.041
1.002
0.825
0.582
0.997
1.005
0.231
0.596
0.665
1.076
0.888
1.224
0.840
0.780
1.016
1.429
1.148
1.023
1.017
0.711
1.040
1.420
2.031
1.620
2.142
1.501
1.641
NOTE. Classification table: overall⫽78.5%; specificity⫽87.1%; sensitivity⫽34.3%.
Abbreviations: ␤, beta; CI, confidence interval; Dx, diagnosis.
depressive disorder. With respect to anxiety disorders, although
the prevalence rate is not very high overall, there is a significant difference between the 2 groups, with noncompleters 1.6
times more likely to have an anxiety disorder than the completer group. These psychosocial factors are important to consider when assessing and treating patients with chronic pain
conditions.
Substance use disorders have been associated with poor
outcomes in numerous studies.39,41,42,53-54 Specifically, opioid
dependence is of particular interest in chronic pain populations.
Although this diagnosis includes any type of opioid drug, most
patients report a dependency on prescription narcotics. Considering that the patients entering a tertiary rehabilitation setting are being treated for chronic pain conditions, it makes
sense that they may develop an addiction or dependence to
these types of pain medications. In this study, there was a
strong distinction between completers and noncompleters related to opioid dependence at admission. In fact, a patient
entering the treatment program who was diagnosed with opioid
dependency was twice as likely to drop out of the program as
one who was not diagnosed with opioid dependency. Even
though attempts are made to help the patient detoxify during
the program, the drug dependency is often so overwhelming
that it interferes with treatment.
It was also found that program noncompleters were more
likely to be diagnosed with any Axis II PD. Unlike depression
and anxiety, PDs cannot easily be treated in a short period.
However, knowing that a patient has a particular PD may help
with treatment. By identifying these disorders at admission to
the treatment program, the interdisciplinary team can adopt
various methods or strategies to treat these particular individuals better with their specific vagaries.
Study Limitations
The main study limitation to identifying the risk factors for
noncompletion was that not all of the patients who dropped out
prematurely completed all of the initial assessments. If all the
data for each patient who failed to complete the program were
available, this would help develop an even better profile of the
typical noncompleter of functional restoration.
CONCLUSIONS
The purpose of this study was to identify the key risk factors
for noncompletion of a functional restoration program for
patients with chronic disabling occupational musculoskeletal
disorders. The study started by comparing the 2 subgroups
(completers, noncompleters) on various physical, psychologic,
and social factors at the univariate level. The results from the
subsequent logistic regression analysis indicated that the main
risk factors for noncompletion were (1) having an extended
length of disability, (2) indicating higher ratings of perceived
disability on the MVAS, (3) not currently working at time of
admission to treatment, (4) being diagnosed with an opioid
dependence disorder, and (5) being diagnosed with any Cluster
B PD. It is also important to note that the measurement for
depression symptoms (BDI) remained significant in the sequential logistic regression model until the fourth block, when
the Axis II PD clusters were added. For practical purposes, if
a proper DSM-IV evaluation is not administered at admission
to a functional restoration program, then it would be appropriate to consider an elevation in the BDI as a risk factor for
noncompletion.
Knowing that individuals differ on every domain, it is implausible to integrate every possible physiologic, psychologic,
social, and behavioral indicator into a single model. However,
by assessing the characteristics that reliably identify a particular behavior, such as dropping out of a treatment program, it
is possible to flag these patients with treatment-resistant personality characteristics at admission and provide an intervention tailored to their specific needs that will foster positive
treatment outcomes.
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