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The Structure and Stability of Common Mental Disorders
The NEMESIS Study
Wilma A. M. Vollebergh, PhD;
Jurjen Iedema, PhD;
Rob V. Bijl, PhD;
Ron de Graaf, PhD;
Filip Smit, MSc;
Johan Ormel, PhD
Arch Gen Psychiatry. 2001;58:597-603.
ABSTRACT
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Background We analyzed the underlying latent structure of 12-month DSM-III-R diagnoses of 9 common disorders for the general population
in the Netherlands. In addition, we sought to establish (1) the stability
of the latent structure underlying mental disorders across a 1-year period
(structural stability) and (2) the stability of individual differences in
mental disorders at the level of the latent dimensions (differential stability).
Methods Data were obtained from the first and second measurement of the Netherlands
Mental Health Survey and Incidence Study (NEMESIS) (response rate at baseline:
69.7%, n = 7076; 1 year later, 79.4%, n = 5618). Nine common DSM-III-R diagnoses were assessed twice with the Composite International
Diagnostic Interview with a time lapse of 1 year. Using structural equation
modeling, the number of latent dimensions underlying these diagnoses was determined,
and the structural and differential stability were assessed.
Results A 3-dimensional model was established as having the best fit: a first
dimension underlying substance use disorders (alcohol dependence, drug dependence);
a second dimension for mood disorders (major depression, dysthymia), including
generalized anxiety disorder; and a third dimension underlying anxiety disorders
(simple phobia, social phobia, agoraphobia, and panic disorder). The structural
stability of this model during a 1-year period was substantial, and the differential
stability of the 3 latent dimensions was considerable.
Conclusions Our results confirm the 3-dimensional model for 12-month prevalence
of mental disorders. Results underline the argument for focusing on core psychopathological
processes rather than on their manifestation as distinguished disorders in
future population studies on common mental disorders.
INTRODUCTION
EPIDEMIOLOGICAL studies on DSM-III-R diagnoses
in the general population have often revealed strikingly high levels of comorbidity;
substantial proportions of respondents meet the criteria for more than 1 psychiatric
disorder.1, 2, 3, 4, 5, 6
This comorbidity represents a serious challenge to those seeking to understand
the specificity of distinguished DSM-III-R disorders.7, 8, 9, 10, 11
A recent analysis of comorbidity in the ARCHIVES identified latent dimensions
underlying the different diagnoses in the National Comorbidity Survey.10 Results of this study suggest that a 2-dimensional
(D) structure of internalizing and externalizing disorders is suited to explain
the comorbidity between common mental disorders in the general population.
Thereby, the internalizing dimension appeared to distinguish 2 subdimensions—one
referring to the combination of anxious-depressive diagnoses (depression,
dysthymia, and generalized anxiety disorder [GAD]) and one referring to diagnoses
of more specific anxiety disorders (simple phobia, social phobia, agoraphobia,
and panic disorder). It was concluded that these latent dimensions should
be interpreted in terms of core psychopathological processes underlying the
different diagnoses of common mental disorders.
This analysis met some serious criticism.12
The exclusive reliance on lifetime diagnoses, which allowed no distinction
between subjects with current comorbid disorders and those with temporally
separate disorders over the life span, was mentioned as a chief limitation
of the study. However, Krueger10 did report
replicating his model using 12-month diagnoses, which was in accordance with
his analysis on common mental disorders in an earlier article.11
The restricted range of 10 DSM-III-R diagnoses and
the fact that these were studied without considering subthreshold diagnostic
information would further limit the interpretation of the findings. It was
concluded that future studies would have to substantiate and replicate the
findings of Krueger before the implications of these findings could be fully
evaluated. In addition, more conclusive evidence would have to be provided
by, for example, prospective longitudinal or age-cohort studies that allow
for more systematic evaluation of the system and its properties.12
In this article we want to replicate and extend the findings of Krueger
for the general population in the Netherlands, using data from a survey that
resembles the National Comorbidity Study, the Netherlands Mental Health and
Incidence Study (NEMESIS). Because NEMESIS was organized as a longitudinal
population study, we were able to substantiate and extend the findings in
2 ways. First, by assessing the stability of the underlying latent structure
of mental disorders across a 1-year period by testing whether the structural
model found at baseline was the same as that found 1 year later (we denote
this as structural stability). Second, by assessing the stability of individuals'
scores on the latent dimensions in the structural model over time (we denote
this as differential stability).
SUBJECTS AND METHODS
SAMPLE
First Wave
NEMESIS is based on a multistage, stratified, random sampling procedure.5, 6 Our first step was to draw a sample
of 90 Dutch municipalities. The stratification criteria were urbanization
and adequate dispersion over the 12 provinces. The second step was to draw
a sample of private households (addresses) from post office registers. The
number of households selected in each municipality was determined by the size
of its population. The selected households were sent a letter of introduction
and shortly thereafter were contacted by telephone by the interviewers. Households
with no telephone or with unlisted numbers (18%) were visited in person. One
respondent was randomly selected in each household, the member with the most
recent birthday, on the condition that he or she was between 18 and 64 years
of age and sufficiently fluent in Dutch to be interviewed. Persons who were
not immediately available (owing to circumstances such as hospitalization,
travel, or imprisonment) were contacted later in the year. To establish contact,
the interviewers made a minimum of 10 telephone calls or visits to a given
address at different times of the day and week, if necessary. Respondents
were interviewed in person. They received a small gift at the end of the interview.
To optimize response and to compensate for possible seasonal influences,
we spread the initial data collection phase over the entire period from February
through December 1996. A total of 7076 subjects were interviewed in person
in the first wave. The response rate was 69.7% (of the adult persons eligible
for interviewing). Compared with the Dutch population (figures of Statistics
Netherlands), the participants in the survey are representative of the population
in terms of sex, civil status, and degree of urbanization of place of residence.
Only the 18- to 24-year-old age group was underrepresented. Poststratification
weighting factors were used to approximate the distribution of major demographic
variables in the Dutch population.5, 6
Second Wave
All persons who took part in the first interview were approached for
follow-up. As in the first wave, to make contact the interviewers made a minimum
of 10 telephone calls or visits at various times of the day and week. A tracing
process involving mail, telephone, field tracing, and municipality records
was used to locate the original sample. The fieldwork in the second wave took
place from February 1997 through January 1998. The mean (SD) interval between
the first and second interview was 379 (35) days. Of the 7076 subjects who
participated in the first interview, 5618 were reinterviewed in the second
wave (79.4%). Psychopathology did not have a strong impact on attrition. Of
all investigated disorders presented here, only 12-month agoraphobia (odds
ratio [OR], 1.96) and social phobia (OR, 1.37), adjusted for demographic factors,
was associated with an increased likelihood to be lost to follow-up.13
ASSESSMENT OF DSM-III-R DIAGNOSES
The analyses in this article were based on 12-month diagnoses in NEMESIS.
Diagnoses were made using the Composite International Diagnostic Interview
(CIDI).14 The CIDI is a structured interview
developed by the World Health Organization15, 16
on the basis of the Diagnostic Interview Schedule and the Present State Examination.
It was designed for use by trained interviewers who are not clinicians. The
CIDI version 1.1 has 2 diagnostic programs to compute diagnoses according
to the criteria and definitions of both DSM-III-R
and International Classification of Diseases, 10th Revision. The CIDI is now being used worldwide, and World Health Organization
research has found high interrater reliability,17, 18
high test-retest reliability,19, 20
and acceptable validity for practically all diagnoses.21, 22
Diagnoses examined were made without the imposition of hierarchical exclusion
rules.
STATISTICAL ANALYSIS
Our analysis focused on a subset of DSM-III-R
disorders assessed in NEMESIS. Because we wanted to ensure reliable, stable
estimates of covariation between disorders, we excluded disorders with a very
low base rate (schizophrenia, mania, obsessive-compulsive disorder, eating
disorders). In addition, alcohol abuse and drug abuse were excluded because
more severe variants (alcohol dependence, drug dependence) were included.
For 9 DSM-III-R disorders, the prevalence rates were
high enough to be used in further analysis: major depression, dysthymia, agoraphobia,
social phobia, simple phobia, GAD, panic disorder, alcohol dependence, and
drug dependence. The Prelis computer program23
was used to create a tetrachoric correlation matrix and an asymptotic covariance
matrix from the 12-month diagnostic variables, which was used as input for
the confirmatory factor analysis. The following 4 models were put to the test:
(1) the 1-D model, in which comorbidity between diagnoses is assumed to reflect
one common factor of vulnerability, (2) the 2-D model that is most in accordance
with child psychiatric epidemiology and assumes that 2 underlying dimensions
of internalizing and externalizing pathological processes are able to explain
the comorbidity between diagnoses,24, 25
and (3) a 3-D model in which patterns of comorbidity are assumed to be in
accordance with higher order categories of the DSM-III-R spectrum (mood disorders, anxiety disorders, and substance use disorders).26 (4) In addition, we put the alternative 3-D model
of Krueger10 to the test, in which GAD is assumed
to belong to the mood disorders (the "anxious-misery" dimension), while the
specific phobias are assumed to be united in a separate dimension (the "fear"
dimension).12 Owing to the absence of antisocial
personality disorder in our data set, we were not able to test a 4-D model.
The confirmatory factor analysis models were tested using the Lisrel
computer program,27 version 827, with the weighted
least squares procedure. The fit of the models was evaluated with the Z2 and
corresponding P value (owing to large sample size, 
= .01), the goodness of fit index, the adjusted goodness of fit index, the
root mean square residual (RMR), and the Bayesian information criterion (BIC).27 Compared with the other indexes, the BIC emphasizes
the selection of parsimonious models.9 Differences
in BIC greater than 10 represent strong evidence in favor of the model with
the smaller BIC value.28 In addition, as most
of the factor models are nested, the models can be tested directly against
each other to find the superior model. A model can be considered a significant
improvement in comparison to another model if the resulting  2
differs significantly from the 2 of the other model (with
= .01, this is the case when the change in 2 exceeds the critical
value of 21 = 6.635, and 22 = 9.210).
In our analysis, we first tested the 4 models for all respondents in
the first wave (n = 7076) and repeated this for the second wave (n = 5618).
To determine the differential stability of the first order latent dimensions
during a 1-year period, we estimated the confirmatory factor analysis models
simultaneously, thereby positing paths linking the latent factors measured
at both times of measurement. To determine the structural stability of the
latent structure underlying mental disorders during a 1-year period, we tested
the fit of the last 3-factor model, thereby making the restriction that the
indicator paths were the same for both waves, and assessed whether the fit
of this model was not worse than the fit of the unrestricted model (in which
indicator paths were set free, and thus could diverge between baseline and
follow-up). If the fit of the restricted model is not worse, this indicates
that a comparable structure is found at both times of measurement.
RESULTS
Tetrachoric correlations among these disorders were computed for the
entire sample at T0, the entire sample at T1, and between T0 and T1 in the
sample that participated twice (the entire sample at T1). (The resulting correlation
matrices are available from W.A.M.V.) Inspection of these matrices revealed
a comparable pattern of comorbidity between 12-month diagnoses and that found
in the National Comorbidity Survey on lifetime diagnoses, with relatively
high correlations between the disorders represented within the same factors,
while correlations between disorders in different factors were lower. In addition,
correlations between disorders at T0 and T1 were substantial, ranging from
0.48 (agoraphobia) to 0.84 (drug dependence).
LATENT DIMENSIONS AT T0
Fit indices were computed for each of the tested models in the entire
sample at T0 (Table 1). Results
revealed the best fit for the alternative 3-factor model, which achieved the
only nonsignificant 2 value, the lowest BIC value, and was
superior to the other models at reproducing the observed sample correlations
(smallest RMR). Direct factor model comparisons (Table 1, last 4 columns) showed that the alternative 3-factor model
fitted significantly better than the other factor models. Although the 3-factor
model could not directly be compared with the 3DSM-factor model (these models are not nested), it fitted significantly
better than the 2-factor model, while the 3DSM-factor
model did not. Furthermore, it can be noted that the 2-factor model fitted
significantly better than the 1-factor model and did not fit significantly
worse than the 3DSM-factor model.
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Fit Indices for and Model Comparisons Between 1-, 2-, and 3-Factor
Models of 9 DSM-III-R Diagnoses in 2 Waves of NEMESIS*
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Because of the high correlation between the anxious-misery and fear
factors, like Krueger10 we reparameterized
the alternative 3-factor model by defining those 2 factors as latent indicators
of a higher-order internalizing factor (Figure
1). The large loadings of the higher-order internalizing factor
on these subfactors (0.96 and 0.85) reveal that this is a good way of expressing
the 3-factor model. All factor loadings in Figure 1 are very large, with the exception of alcohol dependence
(0.47 vs loadings  0.69 for all other disorders). Thus, comorbidity between
disorders belonging to the other 2 (sub)dimensions is stronger than that between
the substance use disorders.
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Figure 1. Best-fitting 3-dimensional model
displaying underlying structure for common mental disorders at first measurement
of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). All
parameter estimates are standardized and significant at P<.01.
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LATENT DIMENSIONS AT T1
Fit indices were computed for each of the tested models for the entire
sample at T1. Again, the alternative 3-factor model achieved the best fit.
As in the first measurement, this model achieved the only nonsignificant 2 value, achieved the lowest BIC value, the lowest RMR, and beat all
competing models in direct model comparisons (Table 1). Again the second-order factor of internalizing disorders
seems to be a reasonable way of expressing the underlying processes with loadings
of 0.92 and 0.94 of the internalizing dimension on its subfactors (Figure 2). In contrast to the first measurement,
all loadings in Figure 2 are uniformly
large (lowest loading, 0.71), indicating that all disorders can be interpreted
as a good indicator of the corresponding factor, including substance use disorders.
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Figure 2. Best-fitting 3-dimensional model
displaying underlying structure for common mental disorders at second measurement
of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). All
parameter estimates are standardized and significant at P<.01.
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DIFFERENTIAL STABILITY OF LATENT DIMENSIONS ACROSS A 1-YEAR INTERVAL
Fit indices were computed for each of the tested models and the longitudinal
sample that took part in both measurements. In line with results at T0 and
T1, the 3-factor model achieved the best fit for the longitudinal data, revealed
by the lowest 2 value, the lowest BIC value, and the lowest
RMR (Figure 3) and again beat all
competing models in direct model comparisons. The differential stability of
the 3 factors proved to be substantial (0.85 for the anxious-misery factor,
0.89 for the fear factor, and 0.96 for the addiction factor), confirming the
structural character of the underlying processes revealed in the latent dimensions.
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Figure 3. Best-fitting 3-dimensional model
displaying underlying structure for common mental disorders at first and second
measurement of the Netherlands Mental Health Survey and Incidence Study (NEMESIS).
All parameter estimates are standardized and significant at P<.01. Errors in indicators with italicized names were allowed to
covary across the 2 waves.
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To determine whether the differential stability was the same for the
anxious-misery factor as for the fear factor, we reran this analysis, forcing
both paths to be equal. Results showed that this model fitted significantly
worse (21 = 11.73, P<.001).
Therefore, the differential stability of the fear factor is slightly higher
than that of the anxious-misery factor. Similar tests showed that the stability
of the externalizing factor was higher than that of either the anxious-misery
factor (21 = 28.91, P<.001)
or the fear factor (21 = 13.10, P<.001).
STRUCTURAL STABILITY ACROSS A 1-YEAR INTERVAL
Next we checked whether the underlying latent structure of mental disorders
was comparable at both waves. We reran the 3-factor model for the longitudinal
data, thereby making the restriction that the indicator paths were the same
for both waves. The fit of this model was 2131
= 263.37; P<.001; RMR, 0.09; and BIC, -868.
The restricted model did not fit worse than the unrestricted model (26 = 5.34, P>.05). According to
the lower BIC, the restricted model is even preferable as it is more parsimonious.
In conclusion, the indicator paths of wave 1 are similar to those of wave
2 or, in other words, the underlying latent structure of mental disorders
is the same at both waves.
COMMENT
This article examined the factor structure of 9 mental disorders in
the general population in the Netherlands and sought to establish the model
that provided the best fit for comorbidity between 12-month prevalences of DSM-III-R diagnoses. The alternative 3-D model achieved
the best fit, revealing a dimension of substance use disorders resembling
the externalizing factor found by Krueger10
and a broad internalizing factor that further distinguished between an anxious-misery
subdimension and a fear subdimension. The structural stability of this model
was adequate, and the differential stability of the latent dimensions was
substantial.
The strengths and weaknesses of our study should be kept in mind when
interpreting these results. Like the National Comorbidity Survey, NEMESIS
is a strong survey because of its large sample size and its representativeness.
Results from NEMESIS can therefore be generalized to the population of the
Netherlands. An additional strength of the NEMESIS survey is its longitudinal
design, which enabled us to study the structural and differential stability
of latent dimensions in the general population. Weaknesses of the NEMESIS
study resemble those in the National Comorbidity Survey. Diagnoses were based
on interviews by trained nonclinicians and are likely to be less accurate
as diagnoses made by professional clinicians. Furthermore, we had to restrict
ourselves to 9 common mental disorders (antisocial personality disorder was
not included in NEMESIS), whereas in the National Comorbidity Survey, data
on 10 common disorders were available. The absence of antisocial personality
disorder has implications for the interpretation of the externalizing factor
found in our study. In NEMESIS this factor was restricted to the substance
dependence disorders and it should therefore be interpreted as such. In general,
restricting ourselves to the most common mental disorders is a weakness in
the sense that the generalization of our findings cannot be extended beyond
these disorders. On the other hand, population studies may not be the best
tool for studying less common disorders, as the number of respondents meeting
criteria for other diagnoses is too small to enable reliable and valid results.
The main extension of Krueger's findings presented in this article lies
in the fact that our analysis was based on longitudinal data from 2 measurements
with a 1-year interval. We were able to analyze whether the main findings
of the first measurement were replicated for the data of the second measurement
and thus asses the structural stability of the models, and we were also able
to model the stability of underlying factors during this 1-year interval.
These extended analyses led to results that were strikingly in accordance
with those of Krueger. We replicated the fit of the alternative 3-D model
for 12-month prevalence in common mental disorders at both times of measurement
with substantial longitudinal stability of underlying dimensions throughout
a 1-year interval, stability coefficients being greater than or equal to 0.85
for all factors.
However, there are some difficulties in the interpretation of these
findings. Analyses were based on a relatively small number of common DSM-III-R diagnoses. Criteria for diagnosis of these disorders
are quite frequently met in population studies, and the clinical relevance
of this fact has often been questioned.29, 30
We should consider the possibility that meeting criteria for these DSM-III-R diagnoses in a population study, without consideration of
severity of disorders or of related functional impairments, need not indicate
the presence of a complete, clinically relevant psychiatric syndrome.30 If so, comorbidity of diagnoses in our study may
reflect co-occurrence of symptoms at subthreshold level that—as Wittchen
et al12 have rightly pointed out—are
shared to a substantial degree by the disorders studied here. As we had to
rely on categorical classifications, our approach is furthermore not able
to address the different diagnoses in more detail on the symptom level. In
future research we therefore plan to make more detailed analysis of patterns
of co-occurrence between symptoms to address subthreshold manifestations of
different psychiatric syndromes. In doing so, focusing on underlying common
factors in the manifestation of psychopathological symptoms and syndromes
seems to be a promising approach.11, 31
Notwithstanding these shortcomings, we consider the dimensional approach
an important tool for analyzing comorbidity between common mental disorders
for the following reasons. First, our final dimensional model illustrates
in a simple and elegant way that within dimensions there is probably no strong
line to be drawn between different disorders, as comorbidity between diagnoses
is the rule rather than the exception. Future research could benefit from
searching for core psychological features of these disorders rather than searching
to further differentiate between subtypes of disorders. This comorbidity between
common DSM-III-R diagnoses tends to follow the classification
of disorders in the DSM-III-R with one major exception:
GAD seems to co-occur more frequently with mood disorders and not with other
anxiety disorders. This finding is in accordance with prior studies and seriously
questions the conventional distinction between mood and anxiety disorders
in DSM-III-R.11, 31, 32, 33, 34, 35
The suggestion that depressive disorders and GAD may be mused by the same
genetic factors and can be interpreted as different manifestations of the
same vulnerability10, 31, 32
is clearly enhanced by our results.
In addition, using structural equation modeling or dimensional models
offers considerable methodological and analytical advantages in the analysis
of comorbidity between common mental disorders10
as it is no longer necessary to resort to common but problematic research
strategies like analyzing atypical samples (eg, analyzing "pure cases") to
avoid confounding, or disregarding, comorbidity when analyzing particular
disorders. We think that further research may greatly benefit from using a
dimensional approach. We hope that our work will be regarded as a further
stimulation of such efforts.
AUTHOR INFORMATION
Accepted for publication January 23, 2001.
NEMESIS was supported by the Netherlands Ministry of Health, Welfare
and Sports (VWS), the Medical Sciences Department of the Netherlands Organisation
of Scientific Research (NOW-MW), and the National Institute for Public Health
and Environment (RIVM).
From the Trimbos-Institute, Netherlands Institute of Mental Health
and Addiction, Utrecht (Drs Vollebergh, Bijl, and de Graaf and Mr Smit); the
Social and Cultural Planning Office, The Hague (Dr Iedema); and the Department
of Social Psychiatry, University of Groningen, Groningen (Dr Ormal), the Netherlands.
Corresponding author and reprints: Wilma A. M. Vollebergh, PhD, Trimbos-Institute,
PO Box 725, 3500 AS Utrecht, The Netherlands.
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