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Categorical vs dimensional classifications of psychotic disorders

Authors:

Abstract

Objective: Both categorical and dimensional methods appear relevant to classifying psychotic disorders; however, there is no clear consensus on the most appropriate categories and dimensions or on the best approach for constructing nosologic criteria that integrate these 2 methods. This review examines the evidence on specific dimensions and categories that would best characterize psychoses. Method: Entries in the MEDLINE database between 1980 and 2011 were searched for studies of the dimensional and/or categorical structure of psychosis. Studies were included if samples represented a spectrum of psychotic disorders and dimensions/categories were empirically derived using principal components analysis, factor analysis, or latent class analysis. Results: Most dimensional studies observed 4 or 5 dimensions within psychosis, with positive, negative, disorganization, and affective symptom domains most frequently reported. Substance abuse, anxiety, early onset/developmental, insight, cognition, hostility, and behavioral/social disturbance dimensions appeared in some studies. Categorical studies suggested 3 to 7 major classes within psychosis, including a class similar to Kraepelin's dementia praecox and one or more classes with significant mood components. Only 2 studies compared the relative fit of empirically derived dimensions and categories within the same data set, and each had significant limitations. Conclusion: There is relatively consistent evidence on appropriate categories and dimensions for characterizing psychoses. However, the lack of studies directly comparing or combining these approaches provides insufficient evidence for definitive conclusions about their relative merits and integration. The authors provide specific recommendations for designing future studies to identify valid dimensions and/or categories of the psychoses and investigate hybrid approaches to model the structure of the underlying illnesses.
Categorical vs dimensional classifications of psychotic
disorders
Melissa Potuzaka, Caitlin Ravichandranb,c, Kathryn E. Lewandowskia,b, Dost Ongüra,b, and
Bruce M. Cohena,b,*
aMcLean Hospital, Psychotic Disorders Division, Belmont, MA, 02478, USA
bHarvard Medical School, Department of Psychiatry, Boston, MA, 02215, USA
cMcLean Hospital, Psychiatric Biostatistics Laboratory, Belmont, MA 02478, USA
Abstract
Objective—Both categorical and dimensional methods appear relevant to classifying psychotic
disorders; however, there is no clear consensus on the most appropriate categories and dimensions
or on the best approach for constructing nosologic criteria that integrate these 2 methods. This
review examines the evidence on specific dimensions and categories that would best characterize
psychoses.
Method—Entries in the MEDLINE database between 1980 and 2011 were searched for studies of
the dimensional and/or categorical structure of psychosis. Studies were included if samples
represented a spectrum of psychotic disorders and dimensions/categories were empirically derived
using principal components analysis, factor analysis, or latent class analysis.
Results—Most dimensional studies observed 4 or 5 dimensions within psychosis, with positive,
negative, disorganization, and affective symptom domains most frequently reported. Substance
abuse, anxiety, early onset/developmental, insight, cognition, hostility, and behavioral/social
disturbance dimensions appeared in some studies. Categorical studies suggested 3 to 7 major
classes within psychosis, including a class similar to Kraepelin’s dementia praecox and one or
more classes with significant mood components. Only 2 studies compared the relative fit of
empirically derived dimensions and categories within the same data set, and each had significant
limitations.
Conclusion—There is relatively consistent evidence on appropriate categories and dimensions
for characterizing psychoses. However, the lack of studies directly comparing or combining these
approaches provides insufficient evidence for definitive conclusions about their relative merits and
integration. The authors provide specific recommendations for designing future studies to identify
valid dimensions and/or categories of the psychoses and investigate hybrid approaches to model
the structure of the underlying illnesses.
1. Introduction
Current diagnostic systems for psychiatric disorders, including the
Diagnostic and Statistical
Manual of Mental Disorders, Fourth Edition
(
DSM-IV
), use signs and symptoms of illness
to assign individuals to distinct, nonoverlapping categories. This approach was taken in part
© 2012 Elsevier Inc. All rights reserved.
*Corresponding author. Frazier Research Institute, McLean Hospital, 115 Mill Street, Mail Stop 304, Belmont, MA 02474, USA. Tel.:
+1 617 855 3227; fax: +1 617 855 3670. bcohen@mclean.harvard.edu (B.M. Cohen).
Disclosures: The authors declare that they have no other disclosures of conflicts of interest in connection with the present work.
NIH Public Access
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so that the validity and utility of the criteria and categories could be tested. In practice,
explicit categorical criteria have improved reliability; however, the validity of current
nosological systems remains under debate. Do these systems accurately reflect the complex
underlying etiological and pathophysiologic structure of the illnesses observed in patients?
Categorization of psychiatric disorders attempts to “carve nature at its joints.” However, it is
not clear if there are “joints” between psychiatric disorders; and dimensional (as opposed to
categorical) approaches, characterizing patients based on their most prominent symptoms,
have been proposed. In drafting the
DSM, Fifth Edition
(
DSM-V
), criteria (www.dsm5.org),
vigorous discussion is under way about the relative roles of categorical and dimensional
measures [1–4]. Specifically, in response to the
DSM
research planning conference on
dimensional approaches [2], the
DSM-V
developers have proposed the incorporation of
dimensional assessments, alongside the categorical diagnostic criteria, which are currently
being tested in
DSM-V
field trials. The National Institute of Mental Health has contributed
by incorporating into their 2007 Strategic Plan the need to “develop, for research purposes,
new ways of classifying mental disorders based on dimensions of observable behavior and
neurobiological measures” (Strategy 1.4) [5]. In response to this goal, the Research Domain
Criteria project was initiated in 2009 (http://www.nimh.nih.gov/research-funding/rdoc/
index.shtml) to organize directed research efforts to advance our understanding of the
etiology and underlying mechanisms of psychopathology through a dimensional approach,
“agnostic with respect to contemporary diagnostic classifications,” using different units of
analysis (eg, genes, cells, behavior) [6].
This long-standing debate largely reflects the fact that the evidence available on illness
comes from assessment of high-level features: observed behaviors and self-report of
problems. Diagnoses remain similar to those made 100 years ago because there are no
accepted alternatives, such as genetic or other biological markers, although overwhelming
evidence suggests that such factors underlie illness risk and expression. Kraepelin’s
dichotomy of dementia praecox and manic-depressive illness has persisted because course
and treatment outcome can be roughly predicted from his distinctions and some patients fit
within its strictures. Although perhaps not the best measure, absolute boundaries between
discrete diagnostic categories do lead to many patients being classified as “not otherwise
specified” (NOS); following a careful evaluation, they do not fit into the
DSM
or related
diagnosis buckets. In the current
DSM
nosology, the checklist approach to diagnosis, based
on the presence or absence of symptoms, has led to grouping cases of varying severity under
one category, with subclinical cases classified as not ill. This results in a sharp line between
individuals meeting criteria for a disorder and those not meeting criteria, who may
nonetheless have a form of illness. Clinicians and investigators acknowledge limitations in
the current psychotic diagnoses and agree that a redefined classification system,
incorporating dimensional elements, could be beneficial for better exploring the etiology of
psychosis and improving the choice of treatments. However, to construct better nosologic
criteria, one needs evidence on exactly what dimensions and categories characterize patients
with psychotic disorders and how they should be combined in a model that best fits this
population.
Despite the controversy surrounding the relative merits, and possible complementarities, of
categorical and dimensional approaches to diagnosis, little attention has been given to
directly comparing the alternatives using evidence-based strategies or to investigating the
utility of combined approaches. Herein, we review findings from a comprehensive literature
search of published studies exploring the dimensional and/or categorical structure of
psychosis. We conclude with recommendations for future studies needed to compare
classification systems for use in clinical care and research.
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2. Method
We searched entries in MEDLINE from January 1, 1980, to January 1, 2011, for articles that
met the following criteria: contained search words in title or abstract (* denotes truncated):
(
a
) psychosis, psychotic or psychoses; (
b
) dimension*, categor*, latent class, or latent factor;
and (
c
) diagnosis, classification, or nosology. This search identified 439 primary articles;
title/abstracts were read, and relevant publications were selected for in-depth review. In
addition, we screened citations for possible inclusion of relevant articles.
Studies were included if they met the following criteria: (
a
) The study sample contained
more than one type of psychotic disorder. We excluded studies conducted with a mainly
schizophrenia [7] or bipolar sample [8] and those with nonclinical samples [9] because they
did not address differential classification schemes for the broad spectrum of psychotic
disorders, the question we were reviewing. The studies included in our review focus on
idiopathic psychotic disorders in large part because they are the most common causes of
psychosis and for which the causes and relationships of different presentations are still
unknown. (
b
) Reported dimensions and/or categories were empirically derived to describe
the symptom structure and/or subgroups with shared symptom profiles within the sample.
We excluded studies that empirically examined the factor structure of varying definitions of
schizophrenia from different diagnostic systems [10,11]. (
c
) The statistical methods used to
derive dimensions were principal components analysis (PCA) or factor analysis, and the
method used to derive empirical categories was latent class analysis. We excluded studies
using multidimensional scaling [12], which could not be compared with the overwhelming
majority of studies using PCA or factor analysis.
Of note, there are many studies before 1980 that attempted to empirically derive psychotic
syndromes (dimensions) and types (clusters), such as the work performed by Lorr and
colleagues in 1963 [13] and the World Health Organization’s International Pilot Study of
Schizophrenia in 1974 [14]. Although these and other important studies are relevant, we
included only those performed after 1980 to coincide with the introduction of the
DSM,
Third Edition
(
DSM-III
). Studies using the more reliable and consensually based diagnostic
criteria after this date are more easily compared with one another, which was an explicit
point of introducing
DSM-III
.
Using the criteria outlined above, we identified 41 primary articles addressing aspects of
dimensional vs categorical criteria as the preferred nosology of psychotic disorders. The
findings of these studies are discussed below and summarized in the Table. To our
knowledge, a literature review examining this issue has not been previously published.
Linscott et al [15] systematically reviewed studies to evaluate whether criterion symptoms
of schizophrenia are categorical, but did not review dimensional approaches.
3. Results
3.1. Dimensional studies
All studies of symptom dimensions in psychotic disorders used factor analysis or a closely
related method. In 39 studies that examined dimensional structure in patients with a broad
spectrum of psychotic disorders, the number of empirically derived factors/dimensions
ranged from 2 to 11 (Table). The majority of the studies agreed that either 4 or 5 dimensions
describe the psychosis construct, with positive, negative, disorganization, and affective
symptom dimensions most frequently reported. Additional dimensions and clustering of
symptoms within dimensions were unique to individual studies.
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All studies found a dimension that encompassed positive symptoms, although they named
this dimension differently, eg,
Schneiderian
,
reality distortion
,
delusions
, and
psychotic
. The
symptoms loading highly on this dimension varied based on the instrument(s) used in each
study, but largely consisted of specific delusions and hallucinations as well as, in some
studies, bizarre behavior and thought disorder. Several studies [16–24] reported 2 or more
independent positive dimensions. Peralta et al [20] suggested that more complex
dimensional models, subdividing broad symptom dimensions into ones that represent
specific psychopathology, may shed light on the neurobiology of psychoses (Table).
All studies observed key negative symptoms, as a combined symptom dimension [16–
19,22,24–48], as multiple specific negative symptom dimensions [20,21], or as part of a
disorganization or Bleulerian dimension [23,49–54]. When negative symptoms loaded
together, the items most often found were restricted/blunted/flat affect, restricted/retarded
thinking, alogia, and slowed activity. Two studies selecting complex factor solutions
reported a higher number of dimensions and found that negative symptoms were distributed
among other dimensions instead of forming an independent dimension [20,21]. McGorry et
al [52] and Salvatore et al [51] reported a dimension encompassing negative, catatonic/
motor, and disorganization symptoms, consistent with Bleuler’s early conceptualization of
schizophrenias. Twenty-two studies [16,18–22,24,27–35,37,39,44,46–48] reported
independent disorganization and negative dimensions. Symptoms most often loading on the
disorganization dimension were incoherence, inappropriate affect, tangentiality,
circumstantial thinking/speech, illogicality, rumination, bizarre behavior, and derailment.
One potential confounding factor in determining whether negative and disorganization
symptoms are independent or combined dimensions is that rating scales used by the studies
do not clearly differentiate between primary and secondary negative symptoms. Perhaps
future studies, making use of improved negative symptom scales—such as the Clinical
Assessment Interview for Negative Symptoms currently under development—will clarify
this issue [55].
Thirty-one of the 39 studies reported an affective symptom dimension. Five studies that did
not report this dimension used the Scale for the Assessment of Positive Symptoms (SAPS)
and Scale for the Assessment of Negative Symptoms (SANS) as the only assessments
[16,19,20,34,35], which may not include enough items covering affective symptoms to
observe a separate dimension. Three other studies not reporting an affective dimension
chose to focus on nonaffective symptoms in their analyses [17,37,39]. Of the studies
reporting affective dimensions, all but 7 found separate manic and depressive dimensions
[31,33,38,41–43,45]. McGrath et al [31] included limited coverage of affective symptoms,
which resulted in a single inclusive affective dimension. Ehmann et al [33] used the Routine
Assessment of Patient Progress, which does not include items specific to depression or
mania and reported anxiety/somatization and aggression dimensions. In fact, the item
“mood/affect” fell under the aggression dimension. Bell et al [38] reported that affective
symptoms subdivided into 2 dimensions: an “emotional discomfort” dimension, which
included anxiety, depression, and guilt items from the Positive and Negative Syndrome
Scale (PANSS), and a “hostility” dimension, which included hostility, poor impulse control,
uncooperativeness, and excitement items from the PANSS. These may be analogous to
depressed and manic dimensions. Daneluzzo et al [41] and Rapado-Castro et al [43] used the
PANSS as well and did not report a separate manic dimension.
Twelve studies reported dimensions not found in other studies or reported in only a small
number of studies. Rosenman et al [25] found that substance abuse was common in their
population and included it in their analysis. Most studies chose to view substance abuse as a
comorbid condition instead of a potential dimension of psychoses. McGrath et al [31]
reported an early onset/developmental dimension, which was not examined in any other
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studies. This unique finding is attributed to inclusion of items specific to characterization of
onset and course/chronicity of illness, such as poor premorbid functioning, school
deterioration, prodromal signs, psychosis onset less than 16, and remitting course. Several
studies reported a lack of insight dimension [18,21,22]. Van Os et al [18] acknowledged that
this may not be a “true dimension of symptomatology” because it loaded on only one item
from their chosen assessment. Cuesta et al [21,22] reported an insight dimension, which
included 3 items with high factor loadings but obvious overlap: lack of feeling of illness,
lack of insight, and refusal of treatment. Two studies reported an independent anxiety
dimension [22,33], whereas other studies either have found anxiety symptoms to load under
a depression or mania dimension or did not address anxiety in their assessments. Several
studies reported dimensions typically characterized as nonspecific symptoms: (
a
) cognitive
functioning or cognition [36,38,43], (
b
) hostility [38,40,43], and (
c
) behavioral/social
disturbance [20,36,42].
Methodological differences, particularly choice of assessments and symptoms included in
the analyses, likely explain much of the variation in findings. Peralta and Cuesta [56]
suggested that “item selection is perhaps the most important decision in the whole process.”
Ten studies used the Operational Criteria Checklist for Psychotic Illness (OPCRIT or
OCCPI) checklist as their only assessment [18,23,24,27,30,48–50,53,54], which has
incomplete coverage of negative symptoms. Five studies used only the SAPS/SANS
assessments, which do not specifically assess affective symptoms [16,19,20,34,35]. When
multiple items covering overlapping aspects of psychopathology were available for analysis,
selected items were often chosen in an effort to prevent overrepresentation of individual
symptoms (eg, restricted vs blunted affect). Three studies [27,49,50] excluded items
endorsed by only a small percentage of their sample and items that did not seem directly
related to psychopathology. These choices, unique to each analysis, complicate comparison
across studies.
Variation in findings may also be explained by the choice of statistical methods. Most
studies used PCA, which some researchers equate with exploratory factor analysis (EFA)
[57]. However, the motivations underlying the 2 approaches differ substantially; PCA is a
data reduction technique, whereas EFA explains the correlation structure of observed items
as a result of their associations with underlying latent (unobserved) factors [57]. Although
the 2 approaches often lead to similar conclusions, the choice between them can
substantially impact results. Even among studies using PCA or EFA, choices such as criteria
for determining the number of factors/components and the rotation method used to obtain
solutions vary [57]. Researchers using factor analysis can further choose between EFA,
which avoids prior assumptions about the number of underlying factors and the items which
contribute to those factors, and confirmatory factor analysis (CFA), which requires those
prior assumptions [57,58]. Confirmatory factor analysis can be useful for replicating or
extending results of a prior EFA using an independent sample. Six studies used CFA either
following PCA in a split-half design (or in an independent sample) to validate results of the
PCA performed on the first half of their sample [28,38,44,49] or alone to compare goodness
of fit in their samples with competing factor models reported in the literature [27,37].
Methods relaxing distributional assumptions required for most implementations of factor
analysis are available, although only one study used them [31].
Although use of multiple and varied assessments across studies provides evidence that some
robust findings (such as a positive symptom dimension) are not sensitive to choice of
instrument(s), findings on the dimensional structure of psychosis would be strengthened by
replication of results using the same instruments and methods in different populations.
Comparison of different assessment tools across studies is also necessary before consensus
can be achieved on the optimal assessment of symptom dimensions. One study [38]
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performed a CFA comparing factor solutions resulting from PANSS assessment of their
sample (schizophrenia and schizoaffective disorder patients) with the sample of Kay et al
[59] (only schizophrenia patients). Bell et al [38] reported similar dimensions for each
sample: negative, positive, cognitive, emotional discomfort, and hostility; however, the
amount of variance explained by each factor varied between samples. Meta-analyses
combining data across studies [60,61] have the potential to yield insights, although variation
in items and assessments used limits their application.
Studies designed to investigate factor structure, using comprehensive assessments of a wide
variety of symptoms, should lead to the most accurate dimensional model. The optimal
number of dimensions may depend on the intended purpose. For example, a model
endorsing a large number of symptom dimensions may be untenable for use in routine
clinical practice but may be useful in seeking constructs for research. Cuesta et al [21], who
presented a hierarchical model of psychosis, discussed the possibility of incorporating
lower- or higher-order levels of dimensionality based on the focus of study. The setting in
which competing models are applied will ultimately determine usefulness in each instance.
3.2. Categorical studies
Empirically derived categories within psychotic disorders have been studied less than
dimensions, presumably because current nosologic systems are already categorical.
Ironically, the most widely used diagnostic system,
DSM-III
, and its successor,
DSM-IV
,
were explicitly designed with well-defined sign and symptom items and syndromic
groupings intended to be tested for validity. We encountered 7 studies that investigated
whether empirically derived categories within psychotic disorders differ from current
operational classification systems (ie,
DSM-IV
and
International Classification of Diseases,
10th Revision
[
ICD-10
] [62]). The statistical method in these studies is typically latent class
analysis (LCA). Latent class analysis, in this application, categorizes individuals based on
responses to items from instruments that assess symptoms. Latent class analysis does not
prove the existence of classes but rather provides a model for subgroups of the sample that
must be independently replicated [63]. Some studies initially performed factor analysis of
symptoms in their sample and then applied the resulting factor scores for each individual in
the LCA [44–46]. This enabled the authors to investigate the association of dimensional
score distribution within the resulting latent classes.
The number of classes identified ranged from 3 to 7; however, composition of classes varied
among studies (Table). All but one study agreed on a class reminiscent of Kraepelin’s
description of
dementia praecox
, although the nomenclature of this empirically derived class
varied among studies:
classic schizophrenia
[44,63,64];
Kraepelinian schizophrenia
[46];
prominent delusions, flat affect, thought disorder
[45]; and
mixed psychotic
[47]. This class
was characterized by poor outcome and high levels of positive and negative symptoms,
whereas varying levels of disorganization and affective symptoms were observed among
studies. All studies reported one or more classes with a significant mood component and
agreed that mood symptoms play an important role in delineating classes of psychotic
patients. Five studies agreed on a class characterized by moderate to high levels of positive,
depressive, and manic symptoms, and low to moderate levels of negative symptoms, named
differently across studies:
bipolar-schizomania
[44,63],
schizobipolar
[64],
affective
psychosis
[46], and
schizoaffective
[45]. Four studies [44,47,63,64] identified a class,
agreeing on the name
schizodepression
, with high levels of depressive and negative
symptoms and moderate to high levels of positive symptoms. Two studies found a class,
schizomania
, comprising high levels of manic and positive symptoms, low levels of negative
symptoms, and variable levels of disorganization [47,64]. Four studies [44,46,53,63]
reported a class with high levels of depressive symptoms and almost no other symptoms,
resembling the
DSM-IV
diagnosis of major depression. The
deficit nonpsychosis
class
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described by Derks et al [46] resembles the
disorganization
class reported by Murray et al
[53] in that both were marked by high levels of negative and disorganization symptoms.
Two studies [47,64] reported a
psychosis
class exhibiting primarily positive symptoms,
resembling the
reality distortion/depression
class found by Murray et al [53]. Two studies
[44,63] identified a
hebephrenia
class with moderate to high levels of positive, negative, and
disorganization symptoms. However, Kendler et al [63] reported high levels of manic
symptoms in this class, whereas Boks et al [44] reported very low levels. Two of the studies
included nonpsychotic patients; and both reported the presence of a
nonpsychotic
[44] or
healthy
class [46], defined by low scores on each of the dimensions of psychopathology.
Four studies attempted to compare their empirically derived classes with diagnostic
categories from existing nosological systems (eg,
DSM-IV
,
ICD-10
). Kendler et al [63] and
Murray et al [53] found that their classes exhibited high concordance with
DSM, Revised
Third Edition
(
DSM-III-R
), categories, demonstrated by a high percentage of subjects within
each class meeting criteria for a single
DSM-III-R
diagnosis. For example, of subjects
belonging to the
classic schizophrenia
class in Kendler’s study [63], 84% met criteria for
DSM-III-R
schizophrenia; and 96% of subjects in the
major depression
class met criteria for
DSM-III-R
major depression. In the study of Murray et al [53], 79% of those diagnosed with
DSM-III-R
depression with psychosis fell under the
depression
latent class; and more than
90% of those diagnosed with
DSM-III-R
mania, mania with psychosis, or bipolar with
psychosis fell under the
bipolar
latent class. In contrast, Derks et al [46] reported that the
empirically derived classes in their study “cut across traditional
DSM
diagnosis” and may
represent an alternative depiction of psychoses. Peralta et al [64] reported that their
empirically derived classes demonstrated poor concordance with both
DSM-IV
and
ICD-10
classifications overall, including only moderate concordance for schizophrenia. The authors
describe a “vicious circle in that we do not possess robust extraclinical markers for
disentangling the nosological structure of psychotic illness, and at the same time the blurred
boundaries between disorders hinder the physiopathologic and etiologic research.” Of note,
diagnostic interviews used in research are often lists of items from
DSM
and
ICD
or based
on the structure of
DSM
or
ICD
nosology; so they may tautologically return answers
validating those structures.
3.3. Studies comparing categorical vs dimensional classification
Direct comparisons of the fit of alternative models and external validation of the models are
lacking among studies exploring dimensional and categorical approaches. Of all the studies
reviewed, only 2 [47,53] fit both dimensional and categorical models to the same data set.
Murray et al [53] noted concordance between PCA and LCA results applied to the same
data, but did not attempt to compare the fit of the 2 models. Peralta et al [47] studied 3
different time frames (index episode, lifetime course, and interepisode symptoms) in
comparing dimensional and categorical approaches and found that their factor solution
(dimensions) explained a greater proportion of the variance in a chosen set of external
clinical variables than their latent classes (categories), irrespective of time frame. Significant
limitations of this unique and interesting study are the relatively small sample size (110
patients) and the limited number of symptoms in the analysis. These parameters can have a
large impact on the resulting factor structure. Using instruments that cover a broader range
of symptoms could improve the complexity and validity of derived classifications.
Several studies compared the predictive ability of empirically derived dimensions with
existing diagnostic categories (ie,
DSM-IV
,
ICD
-10, Research Diagnostic Criteria (RDC)
[65]) using clinical/outcome measures as external validators [18,23,29,30,32,66]. However,
these comparisons may place well-established categories at a disadvantage because only the
dimensions, and not the categories, were derived using data from the same samples used for
external validation. As exploratory “bottom-up approaches” for empirically deriving
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dimensions or categories are dependent on the chosen sample population and statistical
methods/assumptions used, Helzer et al [3] emphasize the importance of well-chosen
external validators for assessing clinical validity. Van Os and colleagues found stronger
associations of derived dimensions than of
DSM-III
,
ICD-10
[18], and RDC [32] diagnostic
categories with outcome measures, such as quality of life, social disability, duration of
hospitalization, and treatment history. Demjaha et al [29] and Dikeos et al [30] found that
empirically derived dimensions, in conjunction with traditional diagnostic categories,
explained significantly more variability in clinical measures, such as mode of onset,
neurological soft signs, duration of untreated psychosis, poor premorbid work and social
adjustment, and course, than diagnostic categories alone. However, the converse was not
true, in that diagnostic categories alone did not explain more variability in clinical measures
than the information provided when categories and empirically derived dimensions were
used together [30]. Similarly, Rosenman et al [66] found that empirically derived
dimensions explained significantly more variability in clinical measures than categories
alone. Allardyce et al [23] examined similar clinical characteristics but found less consistent
results in favor of either dimensional or categorical approaches. However, all of these
authors agree that a complementary approach incorporating both dimensions and categories
may provide the best system of classification.
4. Discussion
The majority of dimensional studies agree that 4 or 5 dimensions describe the psychosis
construct, with positive, negative, disorganization, and affective symptoms most frequently
reported. Of studies reporting an affective dimension(s), manic and depressive symptoms
were frequently found to comprise this dimension(s). It remains to be determined if other
less frequently reported dimensions can be considered useful: substance abuse, early onset/
developmental, lack of insight, anxiety, cognitive functioning/cognition, hostility, and
behavioral/social disturbance.
Categorical studies suggest that 3 to 7 major classes can be found within the spectrum of
psychosis. Six of 7 studies reported a class characterized by high levels of positive and
negative symptoms and poor outcome, similar to Kraepelin’s dementia praecox. All of the
studies agree that there are one or more classes involving a significant mood component,
with or without cooccurring positive and negative symptoms.
The 2 studies comparing the fit of dimensional and categorical models within the same data
set support the value of dimensions. However, we were unable to find published studies
investigating specific hybrid approaches. The field needs explicit guidance on how
categorical or dimensional classification, or their combination, best explains the naturally
occurring variance in clinical presentations.
4.1. Future studies
Research is needed to provide evidence for dimensions and categories that best characterize
patients with psychotic disorders and to validate combined models that best fit this
population. A key design element in evaluating categorical and dimensional approaches is
instrument selection. Many questionnaires and symptom scales are available, but each was
designed for specific purposes that may not fit the needs of a nosological study. It may be
necessary to add questions not included in standard interviews for
DSM
diagnoses; for
example, one could consider course, comorbidities, cultural background, and sex. Because
we do not know where to draw diagnostic lines, both core and peripheral symptoms must be
considered. For example, many patients with psychotic disorders exhibit comorbid anxiety
and substance use disorders. It should be questioned whether these symptoms are best
conceptualized as categorically different from psychosis or, rather, as a related dimension.
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Another important factor is time frame of the study, which can greatly impact the results.
There are characteristics and symptoms unique to a first-episode population that will not be
present in a chronically ill population, and vice versa. A cross-sectional design may reveal
very different results from a longitudinal design. Whereas future longitudinal studies can
best address changes in symptom profiles over the course of illness, cross-sectional studies
can account for chronicity of illness and compare symptom dimensions or classes among
groups of patients of different age and symptom duration.
Illness severity must be considered. Evaluating only severe, tertiary care hospitalized
patients can limit generalizability of the results to community-based samples, or vice versa.
However, it is rarely feasible to study a population of subjects representing the full spectrum
of severity from “healthy” controls to those with minor symptoms, those requiring minimal
outpatient care, and those requiring inpatient or custodial care. Ultimately, a comparison of
results between studies using different subject populations is necessary. Similar issues of
heterogeneity apply to many other aspects of interindividual variation, such as ethnicity,
education, and past and current treatment, all of which can affect symptoms and course of
illness. For this reason, the most valuable epidemiologic studies usually have very large
sample sizes.
Further research should directly compare the performance of dimensional and categorical
approaches in the same patient population and make specific recommendations for hybrid
approaches. In addition to comparing the predictive validity of empirically derived
dimensions and categories, this research should take advantage of modern statistical
techniques, such as latent class factor analysis and factor mixture modeling, which combine
aspects of dimensional and categorical modeling [67] and of methods for comparing the
relative goodness of fit of dimensional, categorical, and hybrid models applied to the same
data sets [68]. Hybrid modeling approaches provide a specific framework for combining
dimensions and categories within the same data set. Factor mixture modeling, for example,
assumes individuals fall into distinct classes but allows individuals within classes to differ
along dimensional continua. Standard and well-established statistics can be used to compare
the fits of alternative models (eg, FA model vs factor mixture modeling model vs LCA
model) to the same data set to help select the approach providing the best fit [68]. Hybrid
strategies have been applied successfully to support established or alternative classification
systems for other psychiatric disorders, including alcohol and substance use disorders
[69,70], attention-deficit/hyperactivity disorder [71], and posttraumatic stress disorder [72].
Although the most useful data for investigating hybrid approaches are likely to come from
studies designed specifically for that purpose, the reanalysis of existing data could provide a
starting point for suggesting promising models at relatively little cost. Cooperation among
researchers in sharing data to allow the testing of alternate models in different populations
could facilitate the development and validation of candidate models. The evaluation of these
candidate hybrid models should be accomplished by developing them in one sample and
then ensuring that they are tested in other, independent, samples.
If results from initial studies suggest that dimensional or hybrid approaches provide superior
fit to empirical data and have superior external validity compared to categories alone,
subsequent work must propose and validate specific strategies for incorporating dimensional
aspects of psychotic disorders into standard clinical and research practice. To be practically
useful, a classification approach must be general enough to be applied across a range of
clinical or research settings and simple enough to be applied routinely. This requires the
identification or development of scales to assess the categories or dimensions identified in
research, guidelines for their use, and validation in subpopulations. These steps will provide
concrete and empirically validated means for integrating dimensional and categorical
aspects of psychotic symptomatology into clinical and research practice.
Potuzak et al. Page 9
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Few would deny that our psychiatric nosology could be improved. It will not be easy to
identify the right dimensional and categorical elements. Nevertheless, even small advances
may lead to improved research and treatment. Better models of psychiatric classification
may suggest new mechanisms to explore in research on pathophysiology and new targets for
research on improved treatments. In addition, better models might allow researchers to
classify patients into more homogeneous syndromic groups, which should improve signal to
noise for measurements of etiology or pathology. Better models may similarly allow more
accurate testing of whether particular treatments target certain dimensions or categories of
illness, reducing the variance that arises from mixing different populations and outcomes.
Someday, we may be able to rely on clearer evidence of psychopathologic distinctions from
biomarkers; but current technology is not yet adequate to that task. Continuing to study
dimensions and categories of illness may appear “low tech”; but signs and symptoms are
how we tell people are ill, and better syndromic models of these illnesses may strengthen the
signals observed in “high tech” studies of etiology and pathophysiology.
Acknowledgments
Funding: Frazier Research Institute, McLean Hospital.
Dr Dost Ongür has received research funding from Rules Based Medicine.
References
1. First MB. Deconstructing psychosis. 2006 Feb 15–17. http://www.dsm5.org/Research/Pages/
DeconstructingPsychosis(February15-17,2006).aspx.
2. First MB. Dimensional aspects of psychiatric diagnosis. 2006 Jul 26–28. http://www.dsm5.org/
Research/Pages/DimensionalAspectsofPsychiatricDiagnosis(July26-28,2006).aspx.
3. Helzer JE, Kraemer HC, Krueger RF. The feasibility and need for dimensional psychiatric
diagnoses. Psychol Med. 2006; 36:1671–1680. [PubMed: 16907995]
4. Allardyce J, Suppes T, Van Os J. Dimensions and the psychosis phenotype. Int J Methods Psychiatr
Res. 2007; 16(Suppl 1):S34–S40. [PubMed: 17623393]
5. The National Institute of Mental Health Strategic Plan. 2007. http://www/nimh.nih.gov/about/
strategic-planning-reports/index.shtml.
6. Sanislow CA, Pine DS, Quinn KJ, Kozak MJ, Garvey MA, Heinssen RK, et al. Developing
constructs for psychopathology research: research domain criteria. J Abnorm Psychol. 2010;
119:631–639. [PubMed: 20939653]
7. Emsley R, Rabinowitz J, Torreman M. The factor structure for the Positive and Negative Syndrome
Scale (PANSS) in recent-onset psychosis. Schizophr Res. 2003; 61:47–57. [PubMed: 12648735]
8. Lindenmayer JP, Bossie CA, Kujawa M, Zhu Y, Canuso CM. Dimensions of psychosis in patients
with bipolar mania as measured by the positive and negative syndrome scale. Psychopathology.
2008; 41:264–270. [PubMed: 18441528]
9. Krabbendam L, Myin-Germeys I, De Graaf R, Vollebergh W, Nolen WA, Iedema J, et al.
Dimensions of depression, mania and psychosis in the general population. Psychol Med. 2004;
34:1177–1186. [PubMed: 15697044]
10. Bell RC, Dudgeon P, McGorry PD, Jackson HJ. The dimensionality of schizophrenia concepts in
first-episode psychosis. Acta Psychiatr Scand. 1998; 97:334–342. [PubMed: 9611083]
11. Peralta V, Cuesta MJ. The underlying structure of diagnostic systems of schizophrenia: a
comprehensive polydiagnostic approach. Schizophr Res. 2005; 79:217–229. [PubMed: 15993566]
12. Minas IH, Stuart GW, Klimidis S, Jackson HJ, Singh BS, Copolov DL. Positive and negative
symptoms in the psychoses: multidimensional scaling of SAPS and SANS items. Schizophr Res.
1992; 8:143–156. [PubMed: 1457393]
13. Lorr, M.; Klett, C.; McNair, D. Syndromes of psychosis. New York: Macmillan Company; 1963.
p. 286
Potuzak et al. Page 10
Compr Psychiatry
. Author manuscript; available in PMC 2013 November 01.
$watermark-text $watermark-text $watermark-text
14. Sartorius N, Shapiro R, Jablensky A. The international pilot study of schizophrenia. Schizophr
Bull. 1974:21–34. [PubMed: 4619919]
15. Linscott RJ, Allardyce J, van Os J. Seeking verisimilitude in a class: a systematic review of
evidence that the criterial clinical symptoms of schizophrenia are taxonic. Schizophr Bull. 2010;
36:811–829. [PubMed: 19176472]
16. Minas IH, Klimidis S, Stuart GW, Copolov DL, Singh BS. Positive and negative symptoms in the
psychoses: principal components analysis of items from the Scale for the Assessment of Positive
Symptoms and the Scale for the Assessment of Negative Symptoms. Compr Psychiatry. 1994;
35:135–144. [PubMed: 8187478]
17. Bassett AS, Bury A, Honer WG. Testing Liddle’s three-syndrome model in families with
schizophrenia. Schizophr Res. 1994; 12:213–221. [PubMed: 8054313]
18. van Os J, Fahy TA, Jones P, Harvey I, Sham P, Lewis S, et al. Psychopathological syndromes in
the functional psychoses: associations with course and outcome. Psychol Med. 1996; 26:161–176.
[PubMed: 8643756]
19. Toomey R, Kremen WS, Simpson JC, Samson JA, Seidman LJ, Lyons MJ, et al. Revisiting the
factor structure for positive and negative symptoms: evidence from a large heterogeneous group of
psychiatric patients. Am J Psychiatry. 1997; 154:371–377. [PubMed: 9054785]
20. Peralta V, Cuesta MJ. Dimensional structure of psychotic symptoms: an item-level analysis of
SAPS and SANS symptoms in psychotic disorders. Schizophr Res. 1999; 38:13–26. [PubMed:
10427607]
21. Cuesta MJ, Peralta V. Integrating psychopathological dimensions in functional psychoses: a
hierarchical approach. Schizophr Res. 2001; 52:215–229. [PubMed: 11705715]
22. Cuesta MJ, Peralta V, Gil P, Artamendi M. Psychopathological dimensions in first-episode
psychoses. From the trunk to the branches and leaves. Eur Arch Psychiatry Clin Neurosci. 2003;
253:73–79. [PubMed: 12799744]
23. Allardyce J, McCreadie RG, Morrison G, van Os J. Do symptom dimensions or categorical
diagnoses best discriminate between known risk factors for psychosis? Soc Psychiatry Psychiatr
Epidemiol. 2007; 42:429–437. [PubMed: 17502977]
24. Cardno AG, Sham PC, Murray RM, McGuffin P. Twin study of symptom dimensions in
psychoses. Br J Psychiatry. 2001; 179:39–45. [PubMed: 11435267]
25. Rosenman S, Korten A, Medway J, Evans M. Characterising psychosis in the Australian National
Survey of Mental Health and Wellbeing Study on Low Prevalence (psychotic) Disorders. Aust N
Z J Psychiatry. 2000; 34:792–800. [PubMed: 11037365]
26. Kitamura T, Okazaki Y, Fujinawa A, Yoshino M, Kasahara Y. Symptoms of psychoses. A factor-
analytic study. Br J Psychiatry. 1995; 166:236–240. [PubMed: 7728368]
27. Serretti A, Olgiati P. Dimensions of major psychoses: a confirmatory factor analysis of six
competing models. Psychiatry Res. 2004; 127:101–109. [PubMed: 15261709]
28. Peralta V, Cuesta MJ, Farre C. Factor structure of symptoms in functional psychoses. Biol
Psychiatry. 1997; 42:806–815. [PubMed: 9347129]
29. Demjaha A, Morgan K, Morgan C, Landau S, Dean K, Reichenberg A, et al. Combining
dimensional and categorical representation of psychosis: the way forward for DSM-V and
ICD-11? Psychol Med. 2009; 39:1943–1955. [PubMed: 19627645]
30. Dikeos DG, Wickham H, McDonald C, Walshe M, Sigmundsson T, Bramon E, et al. Distribution
of symptom dimensions across Kraepelinian divisions. Br J Psychiatry. 2006; 189:346–353.
[PubMed: 17012658]
31. McGrath JA, Nestadt G, Liang KY, Lasseter VK, Wolyniec PS, Fallin MD, et al. Five latent
factors underlying schizophrenia: analysis and relationship to illnesses in relatives. Schizophr Bull.
2004; 30:855–873. [PubMed: 15954195]
32. Van Os J, Gilvarry C, Bale R, Van Horn E, Tattan T, White I, et al. A comparison of the utility of
dimensional and categorical representations of psychosis. UK700 Group. Psychol Med. 1999;
29:595–606. [PubMed: 10405080]
33. Ehmann TS, Holliday SG, MacEwan GW, Smith GN. Multidimensional assessment of psychosis: a
factor-analytic validation study of the Routine Assessment of Patient Progress. Compr Psychiatry.
2001; 42:32–38. [PubMed: 11154713]
Potuzak et al. Page 11
Compr Psychiatry
. Author manuscript; available in PMC 2013 November 01.
$watermark-text $watermark-text $watermark-text
34. Toomey R, Faraone SV, Simpson JC, Tsuang MT. Negative, positive, and disorganized symptom
dimensions in schizophrenia, major depression, and bipolar disorder. J Nerv Ment Dis. 1998;
186:470–476. [PubMed: 9717864]
35. Ratakonda S, Gorman JM, Yale SA, Amador XF. Characterization of psychotic conditions. Use of
the domains of psychopathology model. Arch Gen Psychiatry. 1998; 55:75–81. [PubMed:
9435763]
36. Bunk D, Eggers C, Klapal M. Symptom dimensions in the course of childhood-onset
schizophrenia. Eur Child Adolesc Psychiatry. 1999; 8(Suppl 1):I29–I35. [PubMed: 10546981]
37. Brekke JS, DeBonis JA, Graham JW. A latent structure analysis of the positive and negative
symptoms in schizophrenia. Compr Psychiatry. 1994; 35:252–259. [PubMed: 7956180]
38. Bell MD, Lysaker PH, Beam-Goulet JL, Milstein RM, Lindenmayer JP. Five-component model of
schizophrenia: assessing the factorial invariance of the positive and negative syndrome scale.
Psychiatry Res. 1994; 52:295–303. [PubMed: 7991723]
39. Cardno AG, Jones LA, Murphy KC, Sanders RD, Asherson P, Owen MJ, et al. Dimensions of
psychosis in affected sibling pairs. Schizophr Bull. 1999; 25:841–850. [PubMed: 10667752]
40. Ventura J, Nuechterlein KH, Subotnik KL, Gutkind D, Gilbert EA. Symptom dimensions in recent-
onset schizophrenia and mania: a principal components analysis of the 24-item Brief Psychiatric
Rating Scale. Psychiatry Res. 2000; 97:129–135. [PubMed: 11166085]
41. Daneluzzo E, Arduini L, Rinaldi O, Di Domenico M, Petruzzi C, Kalyvoka A, et al. PANSS
factors and scores in schizophrenic and bipolar disorders during an index acute episode: a further
analysis of the cognitive component. Schizophr Res. 2002; 56:129–136. [PubMed: 12084427]
42. McClellan J, McCurry C, Speltz ML, Jones K. Symptom factors in early-onset psychotic disorders.
J Am Acad Child Adolesc Psychiatry. 2002; 41:791–798. [PubMed: 12108803]
43. Rapado-Castro M, Soutullo C, Fraguas D, Arango C, Paya B, Castro-Fornieles J, et al.
Predominance of symptoms over time in early-onset psychosis: a principal component factor
analysis of the Positive and Negative Syndrome Scale. J Clin Psychiatry. 2010; 71:327–337.
[PubMed: 20331934]
44. Boks MP, Leask S, Vermunt JK, Kahn RS. The structure of psychosis revisited: the role of mood
symptoms. Schizophr Res. 2007; 93:178–185. [PubMed: 17383856]
45. Kendler KS, Karkowski-Shuman L, O’Neill FA, Straub RE, MacLean CJ, Walsh D. Resemblance
of psychotic symptoms and syndromes in affected sibling pairs from the Irish Study of High-
Density Schizophrenia Families: evidence for possible etiologic heterogeneity. Am J Psychiatry.
1997; 154:191–198. [PubMed: 9016267]
46. Derks EM, Allardyce J, Boks MP, Vermunt JK, Hijman R, Ophoff RA. Kraepelin was right: a
latent class analysis of symptom dimensions in patients and controls. Schizophr Bull. 2012;
38:495–505. [PubMed: 20864620]
47. Peralta V, Cuesta MJ, Giraldo C, Cardenas A, Gonzalez F. Classifying psychotic disorders: issues
regarding categorical vs dimensional approaches and time frame to assess symptoms. Eur Arch
Psychiatry Clin Neurosci. 2002; 252:12–18. [PubMed: 12056576]
48. Wickham H, Walsh C, Asherson P, Taylor C, Sigmundson T, Gill M, et al. Familiality of symptom
dimensions in schizophrenia. Schizophr Res. 2001; 47:223–232. [PubMed: 11278139]
49. Serretti A, Macciardi F, Smeraldi E. Identification of symptomatologic patterns common to major
psychoses: proposal for a phenotype definition. Am J Med Genet. 1996; 67:393–400. [PubMed:
8837708]
50. Serretti A, Rietschel M, Lattuada E, Krauss H, Schulze TG, Muller DJ, et al. Major psychoses
symptomatology: factor analysis of 2241 psychotic subjects. Eur Arch Psychiatry Clin Neurosci.
2001; 251:193–198. [PubMed: 11697584]
51. Salvatore P, Khalsa HM, Hennen J, Tohen M, Yurgelun-Todd D, Casolari F, et al.
Psychopathology factors in first-episode affective and non-affective psychotic disorders. J
Psychiatr Res. 2007; 41:724–736. [PubMed: 16762370]
52. McGorry PD, Bell RC, Dudgeon PL, Jackson HJ. The dimensional structure of first episode
psychosis: an exploratory factor analysis. Psychol Med. 1998; 28:935–947. [PubMed: 9723148]
Potuzak et al. Page 12
Compr Psychiatry
. Author manuscript; available in PMC 2013 November 01.
$watermark-text $watermark-text $watermark-text
53. Murray V, McKee I, Miller PM, Young D, Muir WJ, Pelosi AJ, et al. Dimensions and classes of
psychosis in a population cohort: a four-class, four-dimension model of schizophrenia and
affective psychoses. Psychol Med. 2005; 35:499–510. [PubMed: 15856720]
54. McIntosh AM, Forrester A, Lawrie SM, Byrne M, Harper A, Kestelman JN, et al. A factor model
of the functional psychoses and the relationship of factors to clinical variables and brain
morphology. Psychol Med. 2001; 31:159–171. [PubMed: 11200955]
55. Blanchard JJ, Kring AM, Horan WP, Gur R. Toward the next generation of negative symptom
assessments: the collaboration to advance negative symptom assessment in schizophrenia.
Schizophr Bull. 2011; 37:291–299. [PubMed: 20861151]
56. Peralta V, Cuesta MJ. A dimensional and categorical architecture for the classification of psychotic
disorders. World Psychiatry. 2007; 6:100–101. [PubMed: 18235866]
57. Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor
analysis in psychological research. Psychol Methods. 1999; 4:272–299.
58. Byrne BM. Factor analytic models: viewing the structure of an assessment instrument from three
perspectives. J Pers Assess. 2005; 85:17–32. [PubMed: 16083381]
59. Kay SR, Sevy S. Pyramidical model of schizophrenia. Schizophr Bull. 1990; 16:537–545.
[PubMed: 2287938]
60. Grube BS, Bilder RM, Goldman RS. Meta-analysis of symptom factors in schizophrenia.
Schizophr Res. 1998; 31:113–120. [PubMed: 9689715]
61. Smith DA, Mar CM, Turoff BK. The structure of schizophrenic symptoms: a meta-analytic
confirmatory factor analysis. Schizophr Res. 1998; 31:57–70. [PubMed: 9633837]
62. World Health Organization. 10th revision. ed. Geneva: World Health Organization; 1992.
International statistical classification of diseases and related health problems.
63. Kendler KS, Karkowski LM, Walsh D. The structure of psychosis: latent class analysis of
probands from the Roscommon Family Study. Arch Gen Psychiatry. 1998; 55:492–499. [PubMed:
9633666]
64. Peralta V, Cuesta MJ. The nosology of psychotic disorders: a comparison among competing
classification systems. Schizophr Bull. 2003; 29:413–425. [PubMed: 14609237]
65. Spitzer RL, Endicott J, Robins E. Research diagnostic criteria. Psychopharmacol Bull. 1975;
11:22–25. [PubMed: 1153649]
66. Rosenman S, Korten A, Medway J, Evans M. Dimensional vs. categorical diagnosis in psychosis.
Acta Psychiatr Scand. 2003; 107:378–384. [PubMed: 12752034]
67. Muthen B. Should substance disorders be considered as categorical or dimensional? Addiction.
2006; 101(Suppl 1):6–16. [PubMed: 16930156]
68. Masyn KE, Henderson CE, Greenbaum PE. Exploring the latent structures of psychological
constructs in social development using the dimensional-categorical spectrum. Social Development.
2010; 19:470–493.
69. Gillespie NA, Kendler KS, Neale MC. Psychometric modeling of cannabis initiation and use and
the symptoms of cannabis abuse, dependence and withdrawal in a sample of male and female
twins. Drug Alcohol Depend. 2011; 118:166–172. [PubMed: 21507586]
70. McBride O, Teesson M, Baillie A, Slade T. Assessing the dimensionality of lifetime DSM-IV
alcohol use disorders and a quantity-frequency alcohol use criterion in the Australian population: a
factor mixture modelling approach. Alcohol Alcohol. 2011; 46:333–341. [PubMed: 21310744]
71. Lubke GH, Hudziak JJ, Derks EM, van Bijsterveldt TC, Boomsma DI. Maternal ratings of
attention problems in ADHD: evidence for the existence of a continuum. J Am Acad Child
Adolesc Psychiatry. 2009; 48:1085–1093. [PubMed: 19797980]
72. Naifeh JA, Richardson JD, Del Ben KS, Elhai JD. Heterogeneity in the latent structure of PTSD
symptoms among Canadian veterans. Psychol Assess. 2010; 22:666–674. [PubMed: 20822279]
Potuzak et al. Page 13
Compr Psychiatry
. Author manuscript; available in PMC 2013 November 01.
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Table
Summary of studies included in review
Source Sample Symptoms included Analysis Main results
Dimensional studies of psychosis
Allardyce et al.
[23] n = 464 psychosis
1st episode 28 OPCRIT items PFA 5 factors: mania, disorganization/
bizarre, depression, nonbizarre/
nonmood congruent delusions,
auditory hallucinations
Bassett et al.
[17] n = 72 members (5
families) with broad-
spectrum
psychopathology
8 PANSS items and
1 item created—inappropriate
affect
PCA 3 factors: (1) negative; (2) delusions/
hallucinations, thought disorder, and
inappropriate affect; (3) suspiciousness
and stereotyped thinking
Bell et al. [38] n = 146 SZ & SZA 30 PANSS items PCA then CFA of
present and Kay et
al. [59] samples
PCA: 5 factors: negative, positive,
cognitive, emotional discomfort,
hostility
CFA: Poor fit between 2 samples;
although similar dimensions
Brekke et al.
[37] n = 193 SZ & SZA 11 BPRS items,
2 CAF items, 4 QLS items CFA—goodness of
fit assessed for 6
models
3-factor model best fit: positive,
negative, disorganized
Bunk et al.
[36] n = 44 SZ, SZA,
schizophreniform,
affective illness
30 PANSS items at onset of
illness then 42 y later PCA at onset and
follow-up Onset: 5 factors: cognitive, social
withdrawal, antisocial behavior,
excitement, reality distortion
Follow-up: 5 factors: excitement,
cognitive/motor-restriction/rigidity,
positive, negative, anxiety/depression
Cardno et al.
[39] n = 109 SZ or SZA
sibling pairs 7 SANS/SAPS items
22 OPCRIT items PCA SANS/SAPS: 3 factors:
disorganization, negative, positive
OPCRIT: 4 factors: positive,
disorganization, negative, 1st-rank
delusions
Cardno et al.
[24] n = 224 psychosis twin
pairs 18 OPCRIT items analyzed
then
16 OPCRIT items
PCA of 18 items
PCA of 16 items 18 items (psychotic symptoms): 6
factors: disorganized, negative,
1st-rank delusions, paranoid, other
hallucinations,
1st-rank hallucinations
16 items (psychotic + affective
symptoms): 3 factors: manic, general
psychotic, depressive
Cuesta et al.
[21] n = 660 psychosis 64 AMDP items PCA 10 factors: pure paranoid, mania,
negative catatonia, depression,
dysphoria, disorganization,
Schneiderian, insight, psychomotor
poverty, positive catatonia
(hierarchical representation of factors)
Cuesta et al.
[22] n = 94 psychosis
1st episode 70 AMDP items PCA Hierarchical system with up to 10
factors: mania, disorganization/
dysphoria, insight, depression, anxiety/
guilt, psychomotor poverty,
Schneiderian hallucinations,
depersonalization/ derealization, other
disorders of ego integrity, paranoid
Daneluzzo et
al. [41] n = 234 BP & SZ 3 PANSS scales
6 PANSS cluster scores PCA of PANSS
scales + clusters PCA: SZ: 3 factors: positive, negative,
depressive
PCA: BP: 3 factors: positive, negative,
mixed
Demjaha et al.
[29] n = 536 psychosis
1st episode 28 SCAN items PAF 5 factors: mania, reality distortion,
negative, depression, disorganization
Dikeos et al.
[30] n = 191 psychosis 51 OPCRIT items PCA 5 factors: mania, reality distortion,
depression, disorganization, negative
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Potuzak et al. Page 15
Source Sample Symptoms included Analysis Main results
Ehmann et al.
[33] n = 165 psychosis 21 RAPP items PCA 5 factors: aggression, positive,
negative, organic/disorganization,
anxiety/somatization
Kitamura et al.
[26] n = 584 psychosis Semistructured interview Factor analysis 5 factors: (1) manic, (2) depressive, (3)
negative symptoms and formal thought
disorder, (4) positive, (5) catatonic
McClellan et
al. [42] n = 69 SZ, BP, psychosis
NOS (early-onset) 7 BPRS-C items
4 SAPS items
5 SANS items
PCA 4 factors: negative, positive,
behavioral problems, dysphoria
McGorry et al.
[52] n = 509 psychosis
1st episode 92 RPMIP items PAF 4 factors: mania, depression,
Bleulerian (negative-disorganization),
Schneiderian (positive)
McGrath et al.
[31] n = 1043 SZ, SZA 44 items from Diagnostic
Checklist for
DSM-IV
LCFA 5 factors: positive, affective,
disorganized, negative, early onset/
developmental
McIntosh et al.
[54] n = 204 psychosis 33 OPCRIT items PCA performed
separately at 4
consecutive
inpatient
admissions
4 factors: manic, depressive,
disorganization, reality distortion
(stable over time)
Minas et al.
[16] n = 114 psychosis 35 SAPS/SANS items PCA 3 factors: negative, thought disorder,
delusions/hallucinations
Peralta et al.
[28] n = 314 psychosis 11 AMDP items
8 SANS/SAPS global ratings PCA AMDP
PCA SANS/SAPS
CFA SANS/SAPS
AMDP items: 3 factors: catatonic,
manic, depressive
SANS/SAPS: 3 factors: psychosis
(positive), disorganization, negative
CFA results support PCA results
Peralta et al.
[20] n = 660 psychosis 50 SAPS/SANS items PCA
1st order then 2nd
order
11 1st-order factors: poverty of affect/
speech, thought disorder/inappropriate
affect, bizarre delusions, social
dysfunction, other delusions, paranoid
delusions, bizarre behavior, non-
auditory hallucinations, auditory
hallucinations, manic thought disorder,
attention
3 2nd-order factors: psychosis,
disorganization, negative
Rapado-Castro
et al. [43] n = 99 psychosis
1st episode, early onset 30 PANSS items baseline, 4
wk, 6 mo PCA at each time
point 5 factors: positive, negative,
depression, cognitive, hostility
Dimensions stable over time but
predominance differed: negative
predominant baseline/4 wk; depression
predominant at 6 mo
Ratakonda et
al. [35] n = 412 SZ & non-SZ 9 SAPS/SANS global ratings PCA performed
separately for SZ
and non-SZ
3 factors similar for both SZ and non-
SZ: positive, negative, disorganization
Rosenman et
al. [25] n = 978 psychosis 64 SCAN items PFA 5 factors: dysphoria, positive,
negative/incoherence, mania,
substance abuse
Salvatore et al.
[51] n = 377 psychosis
1st episode 78 AMDP items
34 BSABS items PCA 4 factors: pure mania with psychosis,
depressive-excited mixed state,
excited-hallucinatory-delusional state,
disorganized-catatonic-autistic state
Serretti et al.
[49] n = 1004 SZ spectrum &
mood disorder 38 OPCRIT items PCA on half of
sample
CFA on other half
4 factors: excitement, depression,
disorganization, delusion
CFA showed good fit of model
Serretti et al.
[50] n = 2241 psychosis 46 OPCRIT items PCA 4 factors: excitement, psychotic,
depression, disorganization
Serretti et al.
[27] n = 1294 SZ, BP,
delusional disorder 29 OPCRIT items CFA of 6 factor
models 5 factor best fit: positive, negative,
depressive, manic, disorganized
Compr Psychiatry
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Source Sample Symptoms included Analysis Main results
Toomey et al.
[19] n = 630 psychoses
global-level PCA n = 549
psychoses item-level
PCA
9 SAPS/SANS global ratings
50 SAPS/SANS items PCA on global
ratings
FA on global
ratings then again
separately on
individual items
PCA: replicated 3 factors found in
other studies: positive, negative,
disorganization
FA global ratings: 2 factors: positive
(SAPS), negative (SANS)
FA individual items: 5 factors:
diminished expression,
disorganization, disordered relating,
bizarre delusions, auditory
hallucinations
Toomey et al.
[34] n = 369 SZ, MDD, BP 9 SAPS/SANS global ratings PCA performed
separately for each
diagnosis
SZ: 3 factors: negative,
disorganization, positive
MDD: 4 factors: diminished
expression, diminished instrumental
behavior, positive, disorganization
BP: 2 factors: negative, positive
MDD + BP: negative, positive,
disorganization
Psychotic (SZ + MDD + BP): 3
factors: negative, disorganization,
positive
Nonpsychotic (MDD + BP): 3 factors:
(1) affective flattening/alogia/
attention; (2) apathy/anhedonia/
thought disorder; (3) disorganization
van Os et al.
[18] n = 166 psychosis recent
onset 20 OCCPI items PCA 7 factors: inappropriate-catatonia,
delusions-hallucinations, mania,
insidious-blunting, depression, lack of
insight, paranoid delusions
van Os et al.
[32] n = 706 psychosis 65 CPRS items
46 OPCRIT items PCA on CPRS then
OPCRIT items CPRS 4 factors: depressive, manic,
negative, positive
OPCRIT 5 factors: manic, depressive,
negative, positive, disorganization
Ventura et al.
[40] n = 141 SZ, SZA, bipolar
manic 18 BPRS items
24 BPRS items PCA on 18 items
then 24 items 18 item: 4 factors: negative,
depression-anxiety, hostile-
uncooperativeness, positive
24 item: 4 factors: manic-excitement,
negative, positive, depression-anxiety
Wickham et al.
[48] n = 155 SZ, SZA,
psychosis
NOS (61 families)
53 OPCRIT items PCA 5 factors: depressive, manic, reality
distortion, disorganization,
psychomotor poverty
Categorical studies of psychosis
Boks et al. [44] n = 1056 psychosis (after
examination, some
proved not to be
psychotic but left in
analysis)
52 CASH items EFA then CFA
LCA of factor
scores
5 factors: disorganization, negative,
positive, depression, mania
6 classes: bipolar-schizomania,
schizodepressive, hebephrenia, classic
schizophrenia, non-psychotic, major
depression
Derks et al.
[46] n = 4286 psychosis (SZ,
SZA, BPI, BPII, BP
NOS, MDD, healthy,
other)
79 CASH items EFA
LCA of factor
scores
EFA 5 factors: disorganization,
negative, mania, positive, depression
LCA 7 classes: schizophrenia,
affective psychosis, manic-depression,
deficit non-psychosis, depression,
healthy, no symptoms
Kendler et al.
[45] n = 256 siblings w/ SZ n
= 457 siblings with
nonaffective psychoses
11 MSSS items + 2 additional
variables: age at onset, sex Factor analysis 11
MSSS items
LCA of factor
scores using
11 MSSS items, age
at onset, and gender
3 factors SZ pairs: negative, positive,
affective/manic
3 factors nonaffective pairs: negative,
positive, affective/good prognosis
LCA 5 classes: (1) SZA, (2) negative
symptom SZ, (3) prominent delusions,
flat affect, thought disorder SZ, (4)
paranoid SZ, (5) remitting/relapsing
catatonic SZ
Kendler et al.
[63] n = 343 SZ and affective
disorders 21 items: 19 from
OPCRIT,
2 items from MSSS
LCA 6 classes: classic schizophrenia, major
depression, schizophreniform, bipolar-
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Source Sample Symptoms included Analysis Main results
schizomania, schizodepression,
hebephrenia
Peralta et al.
[64] n = 660 psychosis 16 MAS items LCA 5 classes index episode: schizophrenia,
psychosis, schizomania,
schizodepression, cycloid
5 classes lifetime: schizophrenia,
atypical schizophrenia, psychosis,
schizobipolar, schizodepression
Studies comparing categorical vs. dimensional classification
Murray et al.
[53] n = 387 psychosis 62 OPCRIT items PCA
LCA PCA: 4 factors: mania, reality
distortion, depression, disorganization
LCA: 4 classes: depression,
disorganization, bipolar, reality
distortion/depression
Peralta et al.
[47] n = 110 psychosis 12 subscale global ratings and
inappropriate affect from
CASH
3 time frames: index, lifetime,
interepisode
LCA then factor
analysis for each
time frame
Index 4 classes: psychotic, mixed
positive-negative, schizomanic,
schizodepressive
Lifetime 4 classes: mixed psychotic,
psychotic, schizobipolar,
schizodepressive
Interepisode 3 classes: remitting
psychosis, chronic psychosis, defect
psychosis
Index 4 factors: depression-motor
poverty, negative, disorganization,
psychosis
Lifetime 4 factors: negative, mania,
depression, psychosis
Interepisode 3 factors: negative-
disorganization, psychosis, depression-
motor poverty
SZ, schizophrenia; SZA, schizoaffective; BPRS, Brief Psychiatric Rating Scale; CAF, Community Adjustment Form; QLS, Quality of Life Scale;
AMDP, Manual for the Assessment and Documentation of Psychopathology; BP, bipolar; SCAN, Schedules for Clinical Assessment in
Neuropsychiatry; PAF, principal axis factoring; RAPP, Routine Assessment of Patient Progress; RPMIP, Royal Park Multidiagnostic Instrument
for Psychosis; PFA, principal factor analysis; LCFA, latent class factor analysis; BSABS, Bonn Scale for Assessment of Basic Symptoms; FA,
factor analysis; MDD, major depressive disorder; CPRS, Comprehensive Psychopathological Rating Scale; CASH, Comprehensive Assessment of
Symptoms and History; BPI, bipolar I; BPII, bipolar II; BP NOS, bipolar not otherwise specified; RMSEA, root mean square error of
approximation; MSSS, Major Symptoms of Schizophrenia Scale; MAS, Manual for the Assessment of Schizophrenia.
Compr Psychiatry
. Author manuscript; available in PMC 2013 November 01.
... In physics, matter itself is sometimes better conceived in terms of waves (a dimensional concept) and other times in terms of particles (a categorical one). Similarly, in psychiatry, a pluralist approach that allows the employ ment of a range of different dichotomous and continuous constructs seems appro priate 88,89 . ...
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Psychiatry has always been characterized by a range of different models of and approaches to mental disorder, which have sometimes brought progress in clinical practice, but have often also been accompanied by critique from within and without the field. Psychiatric nosology has been a particular focus of debate in recent decades; successive editions of the DSM and ICD have strongly influenced both psychiatric practice and research, but have also led to assertions that psychiatry is in crisis, and to advocacy for entirely new paradigms for diagnosis and assessment. When thinking about etiology, many researchers currently refer to a biopsychosocial model, but this approach has received significant critique, being considered by some observers overly eclectic and vague. Despite the development of a range of evidence‐based pharmacotherapies and psychotherapies, current evidence points to both a treatment gap and a research‐practice gap in mental health. In this paper, after considering current clinical practice, we discuss some proposed novel perspectives that have recently achieved particular prominence and may significantly impact psychiatric practice and research in the future: clinical neuroscience and personalized pharmacotherapy; novel statistical approaches to psychiatric nosology, assessment and research; deinstitutionalization and community mental health care; the scale‐up of evidence‐based psychotherapy; digital phenotyping and digital therapies; and global mental health and task‐sharing approaches. We consider the extent to which proposed transitions from current practices to novel approaches reflect hype or hope. Our review indicates that each of the novel perspectives contributes important insights that allow hope for the future, but also that each provides only a partial view, and that any promise of a paradigm shift for the field is not well grounded. We conclude that there have been crucial advances in psychiatric diagnosis and treatment in recent decades; that, despite this important progress, there is considerable need for further improvements in assessment and intervention; and that such improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.
... Psychosis is a multi-factorial mental disorder characterized by delusions, hallucination, incoherent speech, social withdrawal, sleep disturbances etc. More specifically, psychotic symptoms are categorized as positive (delusions, hallucinations), negative (anhedonia, avolition, poverty of thought and motivation, flat affect) and cognitive (impaired attention, concentration, judgment and cognition) [1,2]. It has long been established that elevation in dopamine levels in mesolimbic pathway with some presynaptic dysregulation accounts for psychosis-like symptoms [3]. ...
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Psychosis is a complex mental illness of behavioral, psychological, and emotional disturbances. Secondary psychosis can be elicited by prescription medications and literature is replete with examples of such drugs. Objectives: The goal of this article is to gather information from multiple published sources to highlight the culprit prescription medications that are linked with psychotic episodes and compose the findings into a simplified, standalone publication for readers to conveniently become aware of this phenomenon. It is also intended to reiterate the critical role and significance of the pharmacist as a vital player in a health care team. Methods: The scientific literature was searched on the PubMed database using the key search phrase “psychosis, medicines or drug or prescription.” The search was limited to the time period from 1960-2019 to ensure the inclusion of the vast majority of previously reported cases of currently available medicines and their related psychoses. Conclusion: Commonly prescribed medications can cause serious, albeit preventable, psychiatric issues. Pharmacists as vital players in patient care can avert the untoward psychotic episode by taking timely steps in notifying the prescribers or counseling the patient on measures to avoid serious and fatal psychotic issues.
... Reducing the complexity of diagnosis may contribute to promote the development of early intervention strategies that are still largely lacking for affective psychoses (Chia et al., 2019;Conus and McGorry, 2002). This simpler grouping may be a complement to a completely dimensional approach where treatment would be constructed on the presence of each psychopathological domain, which has its limitations (Potuzak et al., 2012). ...
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The concept of affective psychosis regroups psychotic disorders with mood syndrome. Previous studies provided evidence to support a dichotomy between affective and non-affective psychoses although questions remain regarding the utility and validity of such a category to develop clinical guidelines. The aim of this study is to explore similarities and differences within affective psychoses to question whether strategies would apply to all the diagnoses falling under this umbrella term. Using Bayesian model comparison methods, we explored the homogeneity of the characteristics of first-episode affective patients (N = 77) treated in a specialized 3-year early intervention in psychosis programme. Our analysis revealed affective psychoses display many similarities regarding socio-demographic variables, the course of positive and manic symptoms over three years, and outcome at discharge. Our results did not support the heterogeneous model. However, despite no significant differences in the course of symptoms with the major depressive disorder group, the schizoaffective disorder group displayed a more severe clinical picture at the beginning of the programme and a poorer functional outcome than the two other groups. Absence of clear boundaries and the several similarities within affective psychoses suggest they can usefully be grouped to define treatment strategies that are easily legible by clinicians.
... Secondly, no specific scale was used to assess negative symptomatology, due to constraints associated with the PANSS scale. Future studies making use of improved negative symptom scales----such as the Brief Negative Symptom Scale (BNSS) 72 or the Clinical Assessment Interview for Negative Symptoms (CAINS) 73 ----might differentiate between primary and secondary negative symptoms, 74 which is an unmet need of current research on FEP. However, it is a naturalistic and multicentric study with a representative sample of non-affective FEP in a stable clinical phase in Spain. ...
Article
Introduction: Sex differences in first episode of psychosis (FEP) have been widely studied. However, the existence of controversial results may be attributable to not considering relevant factors such as substance use. Cannabis use is associated with an earlier age of onset of psychosis and rates of cannabis use are consistently higher among men. The main objective of this study was to analyze and describe sex differences while considering the presence of substance use and its potential role when predicting age at onset of psychosis. Material and Methods: A cross-sectional study of 223 non-affective FEP patients was performed. Participants were divided into “current substance users”, defined as those who reported having used a substance in the past 30 days, and those who did not as “not current substance users”. Descriptive analyses, general linear modeling and multiple regression modeling were used. Results: In the current substance group, women were older, with an older age of onset, a better premorbid adjustment and a higher cognitive reserve while presenting less clinical severity, a better functioning and a better verbal memory performance in comparison with men. In males, but not in females, lifetime of cannabis use and accumulative lifetime substance use was associated with age of onset. Conclusions: Clinical presentation of FEP varies by sex and especially when considering substance use. Our results suggest that early interventions need to be tailored to the different clinical needs of males and females and according to substance consumption in FEP.
... Categorical diagnoses have survived because some individuals (specifically those with 356 chronic established illness) do indeed fit within these nosological entities and more valid357 solutions remain elusive to date(59). However, within the scope of affective and non-358 affective major psychiatric diseases, the Kraepelinian dichotomy of dementia praecox and 359 manic-depressive psychosis has long been challenged. ...
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... The Kraepelinian dichotomy draws a sharp boundary between BP and SZ; diagnoses that share characteristics of both disorders may be conceptualized as categorically separate disorders or may be considered to fall along a continuum in which someone may move toward one end or the other reflecting shifting symptom profiles (2,3). Nevertheless, clear evidence of the superiority of categorical vs. dimensional classification systems has not been demonstrated (7), and pre-defined categorization of diagnoses is commonly used in both clinical and research settings. ...
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Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed.
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With the revision of the DSM-IV underway, two important research issues currently dominate the addiction literature: (a) how can the dimensionality of DSM-IV alcohol use disorders (AUD) diagnostic criteria best be described? and (ii) should a quantity-frequency alcohol use (QF) criterion be added to the existing diagnostic criteria set in the DSM-V? The current study addressed these aims by analysing lifetime data from a recent Australian population survey. Data from adults screened for lifetime DSM-IV AUD in the 2007 National Survey on Mental Health and Wellbeing (NSMHWB) were analysed (n = 5409). A series of alternative factor analytic, latent class and factor mixture or 'hybrid' models were used to assess the dimensionality of lifetime DSM-IV AUD diagnostic criteria and a lifetime QF criterion. Examination of the goodness-of-fit indices revealed that a one-factor or a two-factor model, a three-class latent class model or a two-factor zero-class hybrid model, were all acceptable models for the data. A simple structure one-factor model was considered to be the most parsimonious and theoretically meaningful model, given the high correlation between the abuse and dependence factors (0.874) in the two-factor model. The inclusion of the QF criterion did not enhance the fit of the one-factor model. Incorporating both dimensional and categorical conceptions of lifetime AUD did not provide substantial gains over a simple structure unidimensional model of AUD severity. The utility of a QF use criterion in helping to diagnose AUD is questionable. These findings should be of relevance to the DSM-5 substance use disorder workgroup.
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Background Symptomatology in psychoses can be summarised as quantitative symptom dimensions, but their genetic basis is unknown. Aims To investigate whether genes make an important contribution to symptom dimensions. Method A total of 224 probandwise twin pairs (106 monozygotic, 118 same-gender dizygotic) where probands had psychosis were ascertained from the Maudsley Twin Register in London. Factor analysis was performed on lifetime symptoms rated on the Operational Checklist for Psychotic Disorders (OPCRIT). Correlations of dimension scores within monozygotic and dizygotic pairs concordant for Research Diagnostic Criteria psychoses were performed. Relationships between dimension scores and genetic loading for psychoses were assessed using logistic regression. Results Patterns of familial aggregation consistent with a genetic effect were found for the disorganised dimension and for some measures of the negative, manic and general psychotic dimensions. Disorganised dimension scores were related significantly to genetic loading for psychoses. Conclusions The disorganised dimension, and possibly other symptom dimensions, may be useful phenotypes for molecular genetic studies of psychoses.
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Background. The usefulness of any diagnostic scheme is directly related to its ability to provide clinically useful information on need for care. In this study, the clinical usefulness of dimensional and categorical representations of psychotic psychopathology were compared. Method. A total of 706 patients aged 16–65 years with chronic psychosis were recruited. Psychopathology was measured with the Comprehensive Psychopathological Rating Scale (CPRS). Lifetime RDC, DSM-III-R, and ICD-10 diagnoses and ratings of lifetime psychopathology were made using OPCRIT. Other clinical measures included: ( i ) need for care; ( ii ) quality of life; ( iii ) social disability; ( iv ) satisfaction with services; ( v ) abnormal movements; ( vi ) brief neuropsychological screen; and ( vii ) over the last 2 years – illness course, symptom severity, employment, medication use, self-harm, time in hospital and living independently. Results. Principal component factor analysis of the 65 CPRS items on cross-sectional psychopathology yielded four dimensions of positive, negative, depressive and manic symptoms. Regression models comparing the relative contributions of dimensional and categorical representations of psychopathology with clinical measures consistently indicated strong and significant effects of psychopathological dimensions over and above any effect of their categorical counterparts, whereas the reverse did not hold. The effect of psychopathological dimensions was mostly cumulative: high ratings on more than one dimension increased the contribution to the clinical measures in a dose-response fashion. Similar results were obtained with psychopathological dimensions derived from lifetime psychopathology ratings using the OCCPI. Conclusions. A dimensional approach towards classification of psychotic illness offers important clinical advantages.
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"This book describes the isolation and confirmation of 10 major behaviorally-defined psychotic syndromes and it presents a tentative scheme for classifying individuals into 6-syndrome-based psychotic types. It begins with a critical examination of current psychiatric classification and its basic concepts. The development of a measuring instrument for assessing syndromes is reported, and a review of factor analytic reports is then presented. Several novel conceptual schemes for relating the syndromes are proposed and finally, following a discussion of the theory of typing, a report is given of the evaluation of 6 psychotic types with the hope of establishing a more objective taxonomy for the so-called behaviour disorders." (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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This paper provides an introduction to a recently developed conceptual framework—the dimensional–categorical spectrum—for utilizing general factor mixture models to explore the latent structures of psychological constructs. This framework offers advantages over traditional latent variable models that usually employ either continuous latent factors or categorical latent class variables to characterize the latent structure and require an a priori assumption about the underlying nature of the construct as either purely dimension or purely categorical. The modeling process is discussed in detail and then illustrated with data on the delinquency items of Achenbach's child behavior checklist from a sample of children in the National Adolescent and Child Treatment Study.
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Although previous studies have suggested the inadequacy of the two-factor models of positive and negative symptoms in schizophrenia, confirmatory testing of the putative three-factor models is needed. Using a sample of 193 individuals diagnosed with schizophrenia, this study tested the relative goodness-of-fit of one-, two-, and three-factor models of the positive and negative symptoms. Using confirmatory factor analysis (CFA), the three-factor model of Addington et al., Arndt et al., and Liddle and Barnes that specifies positive, negative, and disorganized factors had the best fit with the data. Allowing the factors to co-vary and specifying dimensionality to the negative symptoms substantially improved the fit of the model. The study addressed several other issues. First, whereas the correlation between positive and negative symptoms was modest, the disorganized symptoms were significantly and more strongly related to both the positive and negative symptoms. Second, depression was not correlated with negative symptoms, but was significantly related to both the positive and disorganized symptoms. Third, the relationships between the three factors and levels of global, social, and work functioning in the sample supported the criterion-related validity of the three-factor model.