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Title: Panic disorder and agoraphobia_ A direct comparison of their multivariate comorbidity patterns
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Journal of Affective Disorders 190 (2016) 75–83

Contents lists available at ScienceDirect

Journal of Affective Disorders
journal homepage: www.elsevier.com/locate/jad

Research report

Panic disorder and agoraphobia: A direct comparison of their
multivariate comorbidity patterns
Ashley L. Greene, Nicholas R. Eaton n
Department of Psychology, Stony Brook University, Stony Brook, NY, USA

art ic l e i nf o

a b s t r a c t

Article history:
Received 14 July 2015
Received in revised form
4 September 2015
Accepted 25 September 2015
Available online 9 October 2015

Background: Scientific debate has long surrounded whether agoraphobia is a severe consequence of
panic disorder or a frequently comorbid diagnosis. Multivariate comorbidity investigations typically treat
these diagnoses as fungible in structural models, assuming both are manifestations of the fear-subfactor
in the internalizing–externalizing model. No studies have directly compared these disorders' multivariate
associations, which could clarify their conceptualization in classification and comorbidity research.
Methods: In a nationally representative sample (N ¼43,093), we examined the multivariate comorbidity
of panic disorder (1) without agoraphobia, (2) with agoraphobia, and (3) regardless of agoraphobia; and
(4) agoraphobia without panic. We conducted exploratory and confirmatory factor analyses of these and
10 other lifetime DSM-IV diagnoses in a nationally representative sample (N¼ 43,093).
Results: Differing bivariate and multivariate relations were found. Panic disorder without agoraphobia
was largely a distress disorder, related to emotional disorders. Agoraphobia without panic was largely a
fear disorder, related to phobias. When considered jointly, concomitant agoraphobia and panic was a fear
disorder, and when panic was assessed without regard to agoraphobia (some individuals had agoraphobia while others did not) it was a mixed distress and fear disorder.
Limitations: Diagnoses were obtained from comprehensively trained lay interviewers, not clinicians and
analyses used DSM-IV diagnoses (rather than DSM-5).
Conclusions: These findings support the conceptualization of agoraphobia as a distinct diagnostic entity
and the independent classification of both disorders in DSM-5, suggesting future multivariate comorbidity studies should not assume various panic/agoraphobia diagnoses are invariably fear disorders.
& 2015 Elsevier B.V. All rights reserved.

Keywords:
Panic disorder
Agoraphobia
Comorbidity
Internalizing–externalizing
Classification

1. Introduction
Controversy has long surrounded the classification of panic
disorder and agoraphobia. Issues of etiology, nosology, and treatment mark the complexity of the relationship between these
disorders (see Goisman et al., 1994; Maier et al., 1991), which are
associated with chronicity, significant distress, and impairment
(Pollack and Smoller, 1995; Wittchen et al., 2010). Much of the
scientific debate surrounding these disorders' classification reflects two mutually exclusive views. First, Klein (1980) posits that
agoraphobia is a sequela of recurrent spontaneous panic attacks,
which are the result of a biological defect (Klein and Gorman,
1987). This characterizes agoraphobia with recurring panic attacks
as a severe subtype of panic disorder. Second, is a cognitive-behavioral perspective, largely developed by Marks (1987), which
holds that agoraphobia is a separate diagnostic entity from panic
n

Corresponding author.
E-mail address: nicholas.eaton@stonybrook.edu (N.R. Eaton).

http://dx.doi.org/10.1016/j.jad.2015.09.060
0165-0327/& 2015 Elsevier B.V. All rights reserved.

disorder that may include concurrent panic attacks but may also
present alone. In this view, panic attacks are conceptualized as
nonspecific symptoms that can appear alongside several psychiatric illnesses.
Taken together, these opposing interpretations beg the following question: is agoraphobia a severe consequence of panic
disorder or a frequently comorbid diagnosis? Individuals experiencing co-occurring panic disorder and agoraphobia report higher
levels of panic symptom severity, lower rates of symptom remission, longer durations of illness episodes, and increased risk for the
development of other comorbid mental disorders (Bruce et al.,
2005; Pané-Farré et al., 2013). This suggests the presence of
agoraphobia may be a proxy for panic severity, more social and
occupational dysfunction, and greater levels of situational avoidance (Kessler et al., 2006; Nay et al., 2013), and may imply agoraphobia is not a separate entity. Another interpretation is agoraphobia has a direct impact on impairment (Kessler et al., 2006).
That is, due to the positive relationship between comorbidity and
harmful dysfunction, those with agoraphobia may report greater

76

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

distress and impairment because it is a potential marker of substantial comorbidity with a broad range of severe psychopathology
other than panic. Following such research, examining comorbidity
patterns between agoraphobia and mental disorders other than
panic disorder would be illuminating.
Consequences of the debate between these two conceptualizations of panic disorder and agoraphobia are also reflected in our classification systems' historical origins and its revisions, beginning with DSM-III (see Asmundson et al., 2014;
Craske et al., 2010; Wittchen et al., 2010). The DSM-IV-TR (American Psychiatric Association, 2000) hierarchical classification of
panic disorder over agoraphobia required an assessment of both,
yielding diagnoses of panic disorder without agoraphobia (PD),
panic disorder with agoraphobia (PD þAG), and agoraphobia
without a history of panic disorder (AG). Under this rubric, a diagnosis of panic disorder was prioritized over agoraphobia despite
any definitive pathognomonic evidence for conceptualizing agoraphobia as a residual of panic disorder. Even so, there is substantial
empirical support for agoraphobia as an independent disorder,
leading to a modified separation from PD in DSM-5 (American
Psychiatric Association, 2013). Now panic disorder and agoraphobia are defined as two separate, yet still associated, conditions. The remaining conceptual overlap is seen in both sets of
these disorders' diagnostic criteria, which include symptoms of
the other (this issue is reviewed in detail by Asmundson et al.,
2014). This shift highlights the questionable nosological status of
these diagnoses and illustrates the importance of systematic investigation to clarify their optimal classification and
conceptualization.
One means of investigating construct validity is to embed
variables of interest within a broader network and assess convergent and discriminant associations with other variables
(Cronbach and Meehl, 1955). For psychopathological constructs,
this includes investigations of disorders' comorbidity patterns.
Therefore, examining convergent and discriminant patterns of
comorbidity for agoraphobia and panic disorder to ascertain the
(dis)similarity of these diagnoses may be informative. Epidemiological studies suggest that these phenomena can, but do not necessarily, manifest comorbidly: lifetime prevalence estimates are
1.2–4.2% for PD, .5–1.8% for PD þAG, .2–1.6% for AG (Grant et al.,
2006; Kessler et al., 2005, 2006; Nay et al., 2013; Wittchen et al.,
2010, 2008). Moreover, Eaton et al. (1994) found 50% of individuals
with panic disorder had no history of agoraphobia, and others
suggest more than 50% of agoraphobia cases have no history of
panic disorder as defined by both DSM-III (Angst and Dobler-Mikola, 1985; Joyce et al., 1989; Weissman et al., 1985) and DSM-IV
(Wittchen et al., 2008). With regard to comorbidity with other
mental disorders, higher rates of comorbidity are seen in AG (97%)
and co-occurring PD þ AG (100%) than in PD (80%) (Kessler et al.,
2005, 2006).
1.1. Multivariate comorbidity and transdiagnostic factors
The bivariate comorbidity patterns discussed above provide an
informative, but limited, pair-wise perspective on diagnostic
construct validity. In contrast, multivariate comorbidity patterns
can provide important evidence about these diagnostic constructs'
possible independence by investigating how agoraphobia and
panic disorder relate to numerous common mental disorders simultaneously. To our knowledge, no multivariate comorbidity
studies have focused explicitly on contrasting panic disorder and
agoraphobia, which leaves the question of their potentially informative multivariate comorbidity patterns unresolved.
Latent variable modeling of multimorbidity among common
mental disorders has converged on a structure with two transdiagnostic factors: internalizing and externalizing (Eaton et al.,

2012, 2015; Kessler et al., 2011a, 2011b; Krueger, 1999; Krueger
and Eaton, 2015; Slade and Watson, 2006; Vollebergh et al., 2001).
The internalizing factor represents comorbidity among mood and
anxiety disorders, and the externalizing factor represents comorbidity among impulsivity-related disorders such as substance
use and antisocial personality disorder (ASPD). While internalizing
can be modeled as a unitary factor, multiple studies have replicated a bifurcated structure, wherein higher-order internalizing
subsumes two lower-order comorbidity factors: distress (indicated
by major depressive disorder [MDD], dysthymic disorder, generalized anxiety disorder [GAD], etc.) and fear (indicated by social
phobia, specific phobia, posttraumatic stress disorder, etc.) (Eaton
et al., 2013b; Kim and Eaton, in press; Krueger, 1999; Slade and
Watson, 2006; Vollebergh et al., 2001). Moreover, multiple studies
have now established the latent internalizing–externalizing model
as being invariant across gender, race/ethnicity, and the adult
lifespan, both in cross-sectional age groups followed over time and
in individuals followed longitudinally (see Eaton et al., 2015,, 2010;
Krueger and Markon, 2006).
Among PD, PD þAG, PD þ / AG, and AG diagnoses, little information is available regarding their saturation by latent transdiagnostic factors, and thus their optimal placement in the internalizing–externalizing structure. All four diagnostic combinations
have been modeled in the structural literature as indicators of
internalizing or fear (Eaton, 2014; Eaton et al., 2013a; Keyes et al.,
2013; Krueger and Markon, 2006; Miller et al., 2012; RodriguezSeijas et al., 2015b; Slade and Watson, 2006; Vollebergh et al.,
2001). Such models reflect a tenuous assumption that these four
manifestations are invariably fear disorders, which no study has
tested. Some structural psychopathology studies have modeled
only AG, PD, or PD þAG; other studies have modeled panic disorder without regard to agoraphobia (PD þ/ AG). The issue is
complicated by skip-out rules in many datasets, biased towards a
temporally primary role of panic attacks and panic-like symptoms.
Directly exploring each diagnostic construct's association to
latent transdiagnostic factors of psychopathology has marked
implications for structural research—particularly in models requiring a priori assignment of disorders to latent factors (e.g.,
confirmatory factor analysis (CFA))—and may also inform classification. For instance, PD has potential as a marker of distress: although panic attacks are fear-based paroxysmal episodes, evidence points to elevated risk of subsequent depression regardless
of whether panic is active or in remission (Kessler et al., 1998). This
leads us to question whether the consequences associated with
panic attacks (e.g., generalized and longstanding apprehension
and worry) are better reflected as distress as compared to fear. In
contrast, there is evidence that AG is more specifically associated
with anxiety disorders, especially phobias (Asmundson et al.,
2014; Wittchen et al., 2010).
1.2. The current study
A direct comparison of these four diagnostic constructs' multivariate comorbidity patterns informs two questions: (1) How do
panic disorder and agoraphobia, alone and in combination, relate
to other common mental disorders? Such analyses have the potential to inform these constructs' classification by considering
their patterns of convergent and discriminant relations. (2) How
best should transdiagnostic comorbidity structural models treat
various forms of panic disorder and agoraphobia? A multivariate
comorbidity investigation may also inform classification by embedding these diagnoses in a broader network, allowing for examination of potential (cross-)loadings that have been historically
assumed rather than empirically adjudicated.

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

77

2. Method

comprehensive comparisons.

2.1. Participants

2.3.2. Evaluation of fit
CFAs were evaluated based on the comparative fit index (CFI),
the Tucker–Lewis index (TLI), the root mean squared error of approximation (RMSEA), the number of freely estimated parameters
in the model, the Bayesian information criterion (BIC), and the
sample-size adjusted BIC. Values of CFI/TLI 4.95, and RMSEA o.06,
are commonly used guidelines for inferring reasonably good
model fit (Hu and Bentler, 1999). The number of freely estimated
parameters in a model represents how many parameters were
estimated, rather than being fixed to a certain value (e.g., zero) or
constrained to equality. Models with a smaller number of freely
estimated parameters have greater parsimony. BIC is an information-theoretic fit statistic, allowing direct comparison of nonnested models and balances model fit and parsimony by penalizing complex models. Lower BIC values suggest better fit. Overall, in
the CFAs we defined the optimal model as the model with the best
fit as judged by CFI, TLI, RMSEA, BIC, and adjusted BIC. In the case
where two models might have very similar fit indices, the optimal
model was defined as the most parsimonious (model with the
fewest freely estimated parameters).

This study used data from 43,093 individuals who participated
in the National Epidemiologic Survey on Alcohol and Related
Conditions (NESARC; for a full description of the sampling frame
see Grant and Dawson, 2006) fielded in 2001–2002 (81% response
rate of eligible sample). The NESARC is a sample of the civilian,
non-institutionalized United States population aged 18 years to
over 98 years, and was representative of the age, gender, and race/
ethnicity distributions of the 2000 United States Census (Grant
and Kaplan, 2005; Grant et al., 2005). African–Americans, Hispanics, and young adults were oversampled. The weighted prevalence rates for all four of the panic/agoraphobia diagnostic
combinations in this sample were quite similar across three age
cohorts: 18–40, 41–59, and 60þ years, respectively. Lifetime prevalence rates for these three groups were highly similar for PD (4%,
5%, 3%), AG (.19%, .19%, and .12%), PD þAG (1%, 1%, and .47%), and
PD þ/ AG (5%, 7%, and 4%). Thus, prevalence rates generally differed by less than 1% (M ¼.93%; median ¼.77%) between the age
bands for all disorders. 57 percent (n ¼24,575) of participants were
women. Non-Hispanic Whites made up 56.9% of the sample, Hispanics/Latinos 19.3%, African–Americans 19.1%, Asians/Native Hawaiians/Pacific Islanders 3.1%, and American Indians/Alaska Natives 1.6%. After complete description of the study, written informed consent was obtained. All procedures were reviewed and
fully approved for human subjects by the United States Census
Bureau and Office of Management and Budget.
2.2. Assessment
Diagnoses were made using the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version (AUDADIS-IV) (Grant et al., 1995), a fully-structured interview for experienced lay interviewers. We examined lifetime diagnoses of
PD þAG, PD, PD þ/ AG, AG, MDD, dysthymic disorder, GAD, social
phobia, specific phobia, alcohol dependence, nicotine dependence,
marijuana dependence, other drug dependence, and ASPD. Prevalence rates for the four panic and agoraphobia diagnoses were
1% of the sample for PD þAG, 4% of the sample for PD, 5% of the
sample for PD þ / AG, and .17% of the sample for AG, which are
congruent with previously reported prevalence rates from other
studies. Reliability reports of the AUDADIS diagnoses examined are
acceptable to quite good (e.g., kappas ¼.42–.84; Grant et al., 2003;
Hasin et al., 2007; Ruan et al., 2008). The other drug dependence
variable collapsed relatively uncommon forms of drug dependence
(i.e., stimulants, opioids, sedatives, tranquilizers, cocaine, solvents,
hallucinogens, heroin, and any other drug not assessed) into a
variable with variance sufficient for covariance structure modeling. Internal consistency of this other drug variable was good
(alpha ¼ .77; Eaton et al., 2012, 2011, 2010).
2.3. Statistical analyses
2.3.1. Estimators
All analyses were conducted in Mplus version 7 (Muthén and
Muthén, 2010). Exploratory factor analyses (EFAs) used a robust
maximum likelihood estimator (MLR) and an oblique geomin rotation. CFAs used the MLR estimator as well as a weighted least
squares estimator (WLSMV) in separate analyses. Both estimators
allowed us to incorporate the NESARC's complex sampling design
(e.g., sampling weights), and treat all diagnoses as categorical.
Because these estimators provide different indices of fit, thus
permitting different sorts of comparisons across models, we conducted analyses using both estimators in the interest of

2.3.3. Modeling approach
Our primary aim was to explore panic and agoraphobia's
multivariate associations in a CFA framework. We first conducted
EFAs to gain a comprehensive understanding of the multivariate
relationships in these data and provide a supplementary perspective to that of CFA. That is, we hypothesized the four panic and
agoraphobia variables would relate differentially to the latent
transdiagnostic comorbidity factors, and EFAs provided an exploratory means to examine all loadings and cross-loadings
without CFA's requirements of simple structure. EFAs extracted
two- and three-factor solutions, based on previous findings of
internalizing–externalizing and distress-fear-externalizing solutions in these and other data (Eaton et al., 2015). EFAs were conducted separately for PD, PDþ AG, PD þ/ AG, and AG. In each
case, all other (non-panic/agoraphobia) diagnoses listed above
were included.
In CFAs, a higher-order internalizing–externalizing model was
parameterized, where internalizing had two subfactors: distress
(indicated by MDD, dysthymic disorder, and GAD) and fear (indicated by social phobia and specific phobia), following previous
research in this and other samples (Eaton et al., 2011, 2010;
Krueger, 1999; Slade and Watson, 2006; Vollebergh et al., 2001).
Externalizing was indicated by ASPD, alcohol, nicotine, marijuana,
and other drug dependence. Internalizing's distress and fear-subfactors were constrained to equality for model identification, and
higher-order internalizing–externalizing factors were correlated.
After creating this baseline model, four separate sets of CFAs were
conducted, wherein each set of CFAs modeled one of the four
panic disorder/agoraphobia diagnostic variables and tested all
seven of its possible loadings on latent transdiagnostic factors.
Those seven models tested (cross-)loadings of the panic/agoraphobia diagnoses on: (1) distress, (2) fear, (3) externalizing,
(4) distress and fear, (5) distress and externalizing, (6) fear and
externalizing, and (7) distress, fear, and externalizing. This produced 28 CFA models in total. While it seemed unlikely that any of
our four panic/agoraphobia variables would meaningfully (cross-)
load on externalizing, we tested and report all CFA loading combinations in the interest of comprehensiveness.

78

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

3. Results
3.1. Bivariate relations
First, we investigated the tetrachoric correlations of PD,
PD þAG, PD þ/ AG, and AG with all other disorders. Resulting
correlations (Table 1) indicated that the four panic/agoraphobia
variables showed different bivariate correlation patterns. For
simplicity, we focus on the two highest correlations for each disorder. The two highest correlations for PD þ/ AG and for PD were
with distress disorders: MDD (r ¼.54 and r ¼.49, respectively) and
GAD (r ¼ .53 and r ¼.42, respectively). The two highest correlations
for PD þAG were with fear disorders: social phobia (r ¼.67) and
specific phobia (r ¼.64). The two highest correlations for AG were
with the fear disorder of social phobia (r ¼.55) and the distress
disorder of MDD (r ¼.45). Mean correlations were also calculated
for each panic/agoraphobia diagnostic variable with the distress,
fear, and externalizing disorders. The highest average correlations
for PD and PD þ/ AG were with distress disorders, and for
PD þAG and AG, fear disorders. The highest average correlation for
each disorder differed significantly (p o.001) from the second
highest average correlation for all four variables.

Table 1
Tetrachoric correlations among panic, agoraphobia, and other diagnoses.
Diagnosis
Distress disorders
MDD
Dysthymia
GAD
Fear disorders
Social phobia
Specific phobia
Externalizing disorders
ASPD
Alcohol dep
Other drug dep
Nicotine dep
Marijuana dep
Average correlations
Distress disorders
Fear disorders
Externalizing disorders

PD

PD þAG

PDþ / AG

AG

.49
.40
.42

.54
.48
.57

.54
.47
.53

.45
.35
.39

.25
.30

.67
.64

.46
.45

.55
.44

.27
.25
.34
.30
.28

.32
.27
.41
.28
.41

.32
.28
.41
.33
.37

.29
.24
.18
.24
.15

.44
.28
.29

.53
.66
.34

.51
.46
.34

.40
.50
.22

Note: The highest correlation for each panic/agoraphobia combination diagnostic
variable is bolded to facilitate interpretation. MDD: major depressive disorder.
GAD: generalized anxiety disorder. ASPD: antisocial personality disorder. Dep:
dependence. See text for description of panic and agoraphobia variables.

3.2. Multivariate relations
3.2.1. EFAs
Across EFAs, two-factor extractions represented internalizing
(factor #1) and externalizing (#2), and three-factor extractions
represented distress (#1), externalizing (#2), and fear (#3) (Table 2). In all two-factor models, the panic/agoraphobia variables
loaded primarily on the first (internalizing) factor. In three-factor
models, PD and PD þ / AG loaded primarily on distress (loadings:
.45 and .64, respectively) with minor cross-loadings; alternatively,
PD þAG and AG loaded primarily on fear (.90 and .78, respectively)
with trivial cross-loadings.
3.2.2. CFAs
Four separate sets of CFAs compared models that each allowed
one of the four panic/agoraphobia combination diagnostic variables to (cross-)load on the seven combinations of distress, fear,
and externalizing factors (Table 3). We began by parameterizing an
internalizing–externalizing latent factor model using all disorders
except the panic/agoraphobia-related diagnoses. This structure fit
the data well (CFI¼.994, TLI ¼.991, RMSEA ¼.011) and served as a
framework for subsequent CFA analyses (Fig. 1).
For PD, all models met thresholds for acceptable fit based on
traditional fit indices (i.e., CFI, TLI, RMSEA), and three models on
which PD loaded on distress (i.e., the distress, distress/externalizing, and distress/fear/externalizing models) provided the
best fit. Of these, the distress model was the most parsimonious.
BIC and adjusted BIC all supported a distress/externalizing model
as optimal, closely followed by a distress/fear/externalizing model;
this suggested that the improvement in fit (although miniscule
according to traditional fit indices) was not offset by penalization
for one or two additional parameters. Of the distress, distress/fear,
and distress/fear/externalizing models, all three indices supported
distress. Investigations of factor loadings in these three distress
models showed that, when cross-loadings occurred, the strongest
loadings were always on distress. Taken together, these results
suggested PD was primarily a distress disorder. The loading on
distress was .56 and this model accounted for 31.1% of the variance
in the PD diagnosis (see Fig. 1).
For PD þAG, all models involving a loading on fear showed
identical optimal fit by traditional fit indices, and thus the fear
model was optimal due to its parsimony. BIC and adjusted BIC both
selected the model with PD þAG loading solely on fear. The

loading of PD þAG on fear was high (.87), and this model accounted for 76.4% of PD þAG's diagnostic variance. Further, in both
models containing distress and fear loadings, the distress loading
was negative.
For PD þ/ AG, two models produced identical, optimal fit by
traditional fit indices—the distress/fear model and the distress/
fear/externalizing model. BIC and adjusted BIC selected the least
parsimonious model (distress/fear/externalizing), and the second
best model by BIC and adjusted BIC was the distress/fear model.
The distress/fear/externalizing model showed a small factor
loading on externalizing (.07), however, compared to larger loadings on distress (.29) and fear (.39), suggesting a primarily distress
and fear latent structure. Given this low loading on externalizing,
and the fit and parsimony of the distress/fear model relative to
distress/fear/externalizing, it appears that PD þ/ AG is best conceptualized as a mixed distress/fear disorder. Loadings in the
model for distress and fear were .29 and .45, respectively, and it
accounted for 47.7% of PD þ/ AG's diagnostic variance.
For AG, all models fit well by traditional fit indices, and a model
with AG loading solely on fear was optimal based on best fit and
parsimony. Both BIC and adjusted BIC selected the fear model as
well. Further, in both models containing distress and fear loadings,
the distress loading was negative. AG had a loading of .66 on the
fear factor, which accounted for 43.3% of its variance.

4. Discussion
This study was the first to investigate separately the multivariate associations of agoraphobia and panic disorder, alone and
in combination, within a transdiagnostic comorbidity factor
model. These disorders' multivariate comorbidity patterns, and
their optimal placement within the internalizing–externalizing
structure, were not well understood previously, because (1) investigations typically focused on bivariate associations, and
(2) multivariate comorbidity studies typically assumed that agoraphobia and panic-related variables both relate similarly to the
internalizing fear-subfactor.
4.1. Implications for classification
Our findings support the conceptualization of agoraphobia and

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

79

Table 2
Two- and three-factor EFAs for PD, PD þ AG, PD þ/-AG, and AG
Diagnosis

MDD
Dysthymia
GAD
Social Phobia
Specific Phobia
ASPD
Alcohol Dep
Other Drug Dep
Nicotine Dep
Marijuana Dep
PD
PDþ AG
PDþ /-AG
AG
Factor
Correlations
1
2
3

PD þAG

PD

PDþ /-AG

AG

1

2

1

2

3

1

2

1

2

3

1

2

1

2

3

1

2

1

2

3

.87
.87
.85
.59
.46
.05
-.11
.03
.00
-.01
.51

.01
-.01
-.04
.10
.13
.70
.85
.87
.69
.88
.14

.87
.97
.66
.23
.01
.04
-.10
.08
-.02
-.01
.45

.02
.01
-.07
.02
.02
.69
.84
.88
.68
.87
.14

.01
-.14
.28
.56
.74
.02
.00
-.07
.04
.02
.09

.85
.86
.87
.65
.51
.05
-.12
.02
-.01
.00

.00
-.01
-.07
.05
.10
.70
.85
.87
.69
.87

.77
.90
.58
.12
-.04
.04
-.07
.08
-.00
-.01

.04
.03
-.05
.01
.04
.68
.83
.86
.67
.85

.13
-.02
.36
.68
.73
.04
-.01
-.03
.04
.05

.86
.86
.86
.62
.49
.05
-.11
.03
.00
-.01

.01
-.01
-.06
.08
.11
.70
.85
.87
.69
.87

.85
.85
.88
.68
.60
.05
-.12
.01
.00
-.01

.01
-.00
-.10
-.01
.00
.70
.85
.88
.68
.88

-.21
-.36
-.00
.27
.43
-.01
.01
-.07
.03
-.01

.86
.87
.85
.60
.47
.05
-.11
.02
-.00
-.00

.01
-.01
-.05
.09
.12
.70
.85
.87
.69
.87

.75
.90
.55
.11
-.05
.03
-.09
.08
-.02
-.01

.04
.03
-.07
.01
.03
.68
.83
.87
.67
.86

.16
-.01
.41
.68
.74
.05
.00
-.06
.05
.03

.77

.06

.02

.00

.90
.61

.12

.64

.07

.12

1

2

1

2

3

.69
1

.09
2

.03
1

.01
2

.78
3

1
.60

1

1
.62
.03

1
.04

1
.59

1

1
.53
.60

1
.50

1

1
1
.59

2

1

1

2

1
.57
.62

1
.47

3

1

2

1

2

1
.61

1

1
.52
.59

1
.47

1

3

1

1

Note: MDD: major depressive disorder. GAD: generalized anxiety disorder. ASPD: antisocial personality disorder. Dep: dependence. See text for description of panic/agoraphobia variables. Factor loadings 4 .30 are bolded to facilitate substantive interpretation.

Table 3
CFAs for PD, PD þAG, PD þ /-AG, and AG
Model
PD
Distress
Fear
EXT
Distress/Fear
Distress/EXT
Fear/EXT
Distress/Fear/EXT
PDþ AG
Distress
Fear
EXT
Distress/Fear
Distress/EXT
Fear/EXT
Distress/Fear/EXT
PDþ /-AG
Distress
Fear
EXT
Distress/Fear
Distress/EXT
Fear/EXT
Distress/Fear/EXT
AG
Distress
Fear
EXT
Distress/Fear
Distress/EXT
Fear/EXT
Distress/Fear/EXT

RMSEA

CFI

TLI

No. of free parameters

R-Squared

Distress Load

.011
.013
.020
.012
.011
.013
.011

.992
.989
.975
.992
.993
.990
.993

.989
.986
.968
.989
.991
.986
.991

24
24
24
25
25
25
26

.311
.355
.287
.301
.284
.317
.287

.56

.018
.011
.027
.011
.018
.011
.011

.980
.993
.953
.993
.979
.993
.993

.974
.991
.939
.991
.972
.991
.991

24
24
24
25
25
25
26

.647
.764
.725
.822
.638
.811
.856

.014
.012
.027
.011
.013
.012
.011

.989
.992
.955
.993
.990
.992
.993

.986
.989
.942
.990
.987
.989
.990

24
24
24
25
25
25
26

.465
.553
.476
.477
.437
.527
.456

.010
.009
.012
.010
.011
.010
.010

.993
.994
.990
.994
.993
.994
.994

.991
.992
.987
.992
.990
.992
.992

24
24
24
25
25
25
26

.341
.433
.338
.468
.329
.470
.498

Fear Load

EXT Load

.60
.54
.47
.45
.47

.10
.49
-.04

.14
.12
.14

.81
.87
.85
-.13
.78
-.10

1.00
.95
1.04

.04
-.10
-.09

.68
.74
.69
.29
.58
.29

.45
.69
.39

.13
.06
.07

.58
.66
.58
-.08
.54
-.07

.74
.73
.79

.06
-.08
-.08

BIC

Adjusted BIC

206169.237
206350.144
206742.132
206172.909
206122.350
206315.271
206133.042

206092.965
206273.872
206665.859
206093.459
206042.899
206235.821
206050.414

196520.175
196000.748
197074.986
196007.898
196515.300
196003.956
196012.345

196443.903
195924.476
196998.714
195928.448
196435.850
195924.506
195929.717

207760.715
207626.896
208805.832
207557.240
207698.198
207620.482
207549.911

207684.443
207550.624
208729.560
207477.789
207618.747
207541.032
207467.283

193423.088
193358.479
193490.613
193368.353
193430.471
193368.623
193378.700

193346.816
193282.207
193414.341
193288.903
193351.021
193289.173
193296.071

Note: k: number of freely estimated parameters. Load: loading. See text for description of models, panic/agoraphobia variables, and additional fit index information. Best-fit
factor loadings are bolded to facilitate interpretation.

80

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

Fig. 1. Multivariate associations of the four panic and agoraphobia variables.

panic disorder as distinct diagnostic entities and the reclassification strategy adopted by DSM-5. In a multivariate context, panic
disorder without agoraphobia (PD) was primarily associated with
the distress-subfactor of internalizing, suggesting that PD might
best be conceptualized as a disorder more similar to MDD and
GAD, rather than the fear disorders. Agoraphobia, on the other
hand, appears best included with other fear disorders, and seems
particularly related to the phobias. When diagnostic variables required that agoraphobia be present (AG and PD þAG), the resulting
diagnostic construct was indeed associated with the fear-subfactor
of internalizing. When panic disorder was modeled without regard
to whether or not agoraphobia was present (PD þ / AG), the resulting diagnostic construct cross-loaded on distress and fear. We
recognize that these findings run contrary to previous reports in
the structural psychopathology literature, which consistently treat
agoraphobia and panic similarly: as indicators of fear (Keyes et al.,
2013; Krueger and Markon, 2006; Miller et al., 2012).
The current results support the idea that individuals who experience panic disorder without agoraphobic avoidance share
characteristics with other emotional disorders, such as worry and
dysphoria. Previous epidemiological research has demonstrated
that depression co-occurs in most panic cases and panic occurs in
a considerable number of those with depression (Andrade et al.,
1996; Merikangas et al., 1996). The panic–depression association
has also been reported by Kessler et al. (1998), who found a robust
reciprocal predictive relationship between panic and depression.
These authors concluded that the absence of a dose–response
relationship is suggestive of primary depression as a risk factor for
subsequent panic attacks (indicative of depression severity rather
than comorbidity), whereas primary panic may serve as a marker
of a shared underlying causal risk factor that can also lead to
subsequent depression. Research on suicidality has also shown
that PD and panic attacks may be markers of an especially severe
form of depression with ensuing suicide ideation and attempts
(Goodwin and Roy‐Byrne, 2006), which can be elevated in the
presence of substance dependence and borderline personality
disorder (Friedman et al., 1992; Ozkan and Altindag, 2005; RoyByrne et al., 2000). Furthermore, a biometric twin study conducted

by Kendler et al. (2003) indicated that the genetic risk factors
underlying panic disorder share more overlap with those of disorders of distress (MDD and GAD) than fear (animal and situational phobias). Although there is evidence for a strong genetic
correlation between agoraphobia and panic disorder, the specific
genetic etiological (in)dependence of these diagnostic constructs
is still to be determined (Mosing et al., 2009).
Agoraphobia, on the other hand, appears to be an indicator of
fear. Consistent with previous research (Kessler et al., 2005, 2006;
Wittchen et al., 2010), we found agoraphobia to be more in line
with the phobias than with the distress disorders, which is aligned
with the agoraphobic experience of anticipatory fear and avoidance response. Furthermore, agoraphobia can include fears of social embarrassment and inability to escape a situation in which a
panic attack might occur so it follows that our two highest bivariate correlations for PD þ AG were social phobia and specific
phobia, and the highest correlation for AG was social phobia. This
is congruent with previous findings that social phobia predicted
the onset and relapse of PD þAG to a greater extent than PD (Nay
et al., 2013). Conversely, in the National Comorbidity Survey Replication, social phobia’s prevalence rate more than doubled in the
context of comorbid agoraphobia, as it was present in 18.8% of
individuals with panic attacks (and no agoraphobia), 31.1% of individuals with PD, 65.5% of individuals with agoraphobia and
panic attacks (but not panic disorder), and 66.5% of individuals
with PD þAG (Kessler et al., 2006). Given that phobias are highly
comorbid, and that social phobia typically has an earlier age of
onset (Magee et al., 1996), it appears that the presence of social
phobia is associated with notable risk for agoraphobia, regardless
of panic attacks/disorder. Further, our finding a high degree of
shared variance between social phobia and agoraphobia also
supports reports of neuroticism accounting for these disorders'
shared genetic liability (Bienvenu et al., 2007). Results from previous research and the current study should be viewed in light of
documented concerns about the high degree of overlap between
agoraphobia and specific phobia within structured clinical interviews (Asmundson et al., 2014; Kessler et al., 2006; Wittchen et al.,
2010). However, our findings indicate that such overlap may be

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

better accounted for by the saturation of both constructs by
transdiagnostic fear and less so by blurred diagnostic boundaries.
4.2. Implications for transdiagnostic structural modeling
Our results indicate that care must be used when incorporating
various panic disorder and agoraphobia diagnostic combinations
into latent structural models of psychopathology requiring a priori
assumptions about parameterization, such as CFA. While there is
limited evidence for PD as a distress disorder in previous structural
work (e.g., Kotov et al., 2015; Watson et al., 2012; Wright et al.,
2013), PD is almost invariably utilized as an indicator of the fearsubfactor of internalizing in structural modeling. The current study
indicates that this is not necessarily the case, and the diagnostic
constructs' links to transdiagnostic factors reflect their definition.
PD þ/ AG appears to be the diagnostic construct most often used
in multivariate comorbidity research, but it is the least helpful in
parameterizing the simple structure (i.e., limited cross-loadings)
required by CFA. In the case of AG, our results warrant the parameterization of this diagnosis as a fear disorder. This is congruent
with an underlying dimensional structure reflecting avoidance
(Slade and Grisham, 2009).
The current findings run counter to much of the structural
psychopathology literature and highlight an important direction
for future studies, rather than definitive evidence. It is possible
these findings reflect covariance patterns specific to this sample or
assessment instrument. However, it is also possible that there are
subtle differences in the comorbidity patterns of these four disorders, requiring nuanced analyses in other samples to determine
how best to parameterize latent transdiagnostic structural models.
Resolving this issue will be of critical importance to latent comorbidity structure modeling, particularly with regard to evidentiary support of distress and fear-subfactors of internalizing.
Studies have replicated (Eaton et al., 2013b; Slade and Watson,
2006; Vollebergh et al., 2001), failed to replicate (Kessler et al.,
2011b), and produced equivocal results (Seeley et al., 2011) regarding these subfactors. Several reasons for this are apparent.
First, different sets of indicator diagnoses invariably lead to
somewhat different structures. Second, the correlation between
distress and fear is typically high, and identifying distress and fearsubfactors can thus be an exercise in determining whether or not
this correlation is at unity (i.e., a unitary internalizing model
without subfactors) or less than unity (i.e., discernible subfactors
can be identified). Third, the simple structure requirements of
some modeling efforts, such as CFA (versus EFA and exploratory
structural equation modeling), do not permit potentially important cross-loadings to be modeled (Eaton et al., 2013b). However, our results suggest an additional reason for the structural
ambiguity of distress and fear-subfactors of internalizing: the
method of modeling panic disorder, a common indicator of internalizing, has major repercussions for the resulting latent structure.
If panic disorder is defined in a way that makes it primarily a
distress disorder, fewer indicators of fear than typically expected
will be present in the data, and the resulting fear-subfactor will be
saturated to some extent by the distress of the panic diagnosis,
likely inflating the correlation between the subfactors and decreasing resolution for a distress and fear substructure. Further,
forcing PD in this case to load solely on fear could represent model
misspecification. In situations where it is possible to choose one or
more diagnoses for modeling, researchers should choose the
variable that best meets their modeling and identification needs.
For instance, if three distress indicators are available but only two
fear indicators, it would often be preferable to use a PD þAG or AG
diagnosis for a third fear indicator.

81

4.3. Clinical implications
Latent structural models are now being used to frame assessment as well as intervention, thus providing a reliable, valid, effective, and efficient means for clinicians to address psychopathology and comorbidity (Rodriguez-Seijas et al., 2015a). In
terms of assessment, our results suggest that panic disorder and
agoraphobia should be assessed separately, without skip-outs or
hierarchical diagnostic algorithms. In terms of practice, transdiagnostic treatments are being developed and may allow for
amelioration of a variety of related disorders by targeting core
symptoms across diagnostic boundaries (e.g., Barlow et al., 2010,
2014; Kushner et al., 2013). As described by Stewart et al. (2015) in
their review of anxiolytic drug development for anxiety spectrum
disorders (ASDs), targeting the spectrum nature of central nervous
system disorders in animal models may complement traditional
unidimensional symptom models. The authors suggested a focus
on modeling the overlapping comorbidities among ASDs to test
the ability of anxiolytic drugs to target the pathogenic overlap
between different symptoms of ASDs. Thus, our results may be
helpful for such translational preclinical-models that aim to
identify domain-specific treatments (e.g., AG and other fear disorders versus PD and other distress disorders), which may ultimately facilitate the identification of novel biomarkers and behavioral phenotypes in treatment development research.
Insofar as transdiagnostic treatments may focus on more finegrained levels of psychopathological processes in the future, interventions targeting transdiagnostic fear will perhaps be less effective for PD, and interventions targeting transdiagnostic distress
will perhaps be less effective for AG. However, transdiagnostic
interventions that target the core of the internalizing disorders
generally may prove beneficial for treatment of panic and agoraphobia separately or comorbidly. This may be especially true for
those with comorbid PD þAG, given that severe AG is thought to
represent a marker of PD severity and risk of relapse once it has
remitted, besides decreasing the likelihood of full remission (see
Wittchen et al., 2010). Indeed, the longest clinical trial of exposure
therapy for PD þAG found that residual AG symptoms strongly
predicted the recurrence of panic (Fava et al., 2001). This suggests
treatments for comorbid cases may achieve more success by targeting both elements of internalizing: fear and distress. Future
treatment outcome studies may benefit from the current results by
properly modeling these diagnoses, which could provide illuminating comparisons of how their differing multivariate patterns of
comorbidity relate to chronicity and relapse risk over time.
In addition, our findings have potential to inform longitudinal
research on the development of these disorders, which also has
implications for transdiagnostic treatment. For example, Ritchie
et al. (2013) examined prevalence and incidence rates of late-onset
agoraphobia in an elderly cohort and found those participants
exhibited a clinically atypical presentation (compared to younger
cases) that may have been overlooked due to a lack of previous
panic attacks. That is, late-onset agoraphobia was not associated
with panic disorder (only two of their 132 later-life first-onset
cases had previous or recent panic attacks), but it was associated
with comparatively younger age, severe depression, trait anxiety,
and poor visuospatial memory. Conversely, the staging model of
psychiatric illness posits that the onset of panic disorder may not
represent a specific disease, but a stage of development for anxiety
disorders and hypochondriasis (Cosci and Fava, 2012; Fava et al.,
2008). In this model, panic and agoraphobia represent different
stages of the same disorder (e.g., prodromal symptoms of agoraphobia and/or social phobia and/or GAD that become acute
manifestations, followed by the onset of panic disorder and worsening anxiety symptoms and/or depression that can become
chronic) that may be more responsive to stage-oriented treatment.

82

A.L. Greene, N.R. Eaton / Journal of Affective Disorders 190 (2016) 75–83

Congruent with the staging model, our analyses suggest a distinction between the disorders, which are not limited to the fear
spectrum. Understanding their multivariate relations over time
will be vital to understanding of how they can be ameliorated.
4.4. Limitations
The present study's results should be interpreted in light of its
limitations. First, diagnoses were obtained from comprehensively
trained lay interviewers, not clinicians. Second, our statistical
analysis used dichotomous diagnoses, addressing a different level
of analysis compared to those used with symptom-level data
(Markon, 2010; Wright et al., 2013). Third, we used DSM-IV rather
than DSM-5 diagnoses, rendering us unable to capture recent revisions to the diagnostic criteria. Nevertheless, our results appear
fully compatible with DSM-5's disassociation of these disorders.
Fourth, NESARC does not contain age of onset information, precluding temporal analysis of disorder development for comorbid
PD þAG. Even so, previous research using age of onset information
in a latent variable survival analysis framework has established
that transdiagnostic latent comorbidity factors (e.g., internalizing)
are present and account for the development of lifetime comorbidity (Kessler et al., 2011a, 2011b). Subsequent studies should
examine the multivariate associations of comorbid PD þAG based
on which disorder onset first since these presentations may show
differing multivariate comorbidity patterns. Finally, it is noteworthy that the fear-subfactor had two consistent non-panic/
agoraphobia indicators (i.e., social phobia and specific phobia).
While a third fear-factor indicator would strengthen the model,
we did not have access to such indicators (e.g., OCD) in this nationally representative sample. Even so, we used the same set of
non-panic/agoraphobia indicators in all analyses, and thus each
model attempted to reproduce highly similar correlation matrices
in the CFA analyses. By varying the pathways by which our four
diagnostic variables related to latent factors (and thus other diagnoses), we found different models were able to reproduce these
observed correlations optimally. Future studies with different
samples and more indicators may help parse these subtle differences in multivariate associations.
4.5. Conclusion
This study is the first to directly examine agoraphobia and
panic disorder's multivariate comorbidity patterns, separately and
comorbidly. Panic disorder was primarily a distress disorder, while
agoraphobia was primarily a fear disorder. Our findings are informative for nosology; they support the conceptualization of
agoraphobia as a distinct diagnostic entity and the independent
classification of both disorders in DSM-5. These results also inform
intervention efforts and structural psychopathology research,
highlighting the need to model panic disorder and agoraphobia
diagnoses with care, particularly models requiring simple
structure.

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