Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore
Department of Mental Hygiene, The Johns Hopkins School of Public Health, Baltimore
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore
Gerontology Research Center, National Institute of Ageing, Baltimore
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, USA
Correspondence: Dr Jack Samuels, Department of Psychiatry and Behavioral Sciences, Johns Hopkins Hospital, Meyer 4-181, 600 N. Wolfe Street, Baltimore, MD 21287, USA
Declaration of interest Supported by National Institutes of Health grants MH50616 and MH47447, and a Johns Hopkins University School of Medicine Clinical Scientist Award (to J.B.).
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ABSTRACT |
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Aims To estimate the prevalence of personality disorders in a community sample and to identify demographic subgroups with especially high prevalence.
Method Clinical psychologists used the International Personality Disorder Examination to assess DSM-IV and ICD-10 personality disorders in a sample of 742 subjects, ages 34-94 years, residing in Baltimore, Maryland. Logistic regression was used to evaluate the association between demographic characteristics and DSM-IV personality disorder clusters.
Results The estimated overall prevalence of DSM-IV personality disorders was 9%. Cluster A disorders were most prevalent in men who had never married. Cluster B disorders were most prevalent in young men without a high school degree, and cluster C disorders in high school graduates who had never married.
Conclusions Approximately 9% of this community sample has a DSM-IV personality disorder. Personality disorders are over-represented in certain demographic subgroups of the community.
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INTRODUCTION |
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METHOD |
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From these 1920 subjects, we selected all those who were examined by psychiatrists in 1981, as well as all subjects who were identified by the DIS as having a lifetime diagnosis of mania, depression, panic disorder, obsessivecompulsive disorder, alcohol use disorders, or drug use disorders at follow-up. In addition, a random sample (222/884; 25%) was selected from the remaining subjects. Of the 1258 subjects selected with these criteria, 516 could not be interviewed because they: (a) could not be traced (n=144); (b) refused participation (n=134); (c) were deceased (n=103); or (d) were too ill or elderly to participate (n=92); another 43 subjects had an interview pending when data collection was terminated for budgetary reasons at the end of 1999. A total of 742 subjects completed the personality examinations between 1997 and 1999. The gender and ethnic distributions of these subjects were similar to those of the 516 subjects who were not interviewed; however, the interviewed subjects were younger (mean age 47 years) than non-interviewed subjects (mean age 61 years) (t1256=15.6, P<0.001). The gender and ethnic distributions of the study subjects were also similar to those of the 3481 subjects examined in 1981, although the study subjects were younger.
Personality disorder assessment
The personality disorder assessments were conducted by four masters-level
clinical psychologists. The psychologists obtained informed consent prior to
the interview. They interviewed the subjects about family background,
childhood behaviours, academic performance, social activities and impairments;
they also administered the General Health Questionnaire
(Goldberg, 1972) and Parental
Bonding Instrument (Parker,
1979). The psychologists conducted a comprehensive assessment of
personality disorders using the International Personality Disorder Examination
(IPDE) (Loranger et al,
1994). The IPDE is a semi-structured instrument designed to be
administered by clinicians to detect all the relevant criteria for diagnosis
of all DSM-IV or ICD-10 personality disorders. The version used in this study
combined the DSM-IV and ICD-10 versions into a single instrument, under the
guidance of Dr Armand Loranger. The psychologists were directed to evaluate
abnormal personality traits manifest over the subject's entire adult life.
Each criterion was rated 0 (absent), 1
(accentuated or exaggerated), 2 (criterion level or
pathological) or 9 (missing or unknown). In 40 jointly rated
interviews, the intraclass correlation co-efficients for number of DSM-IV
personality disorder traits rated present (1 or 2)
were: schizoid (0.81); schizotypal (0.58); paranoid (0.63); antisocial (0.80);
borderline (0.76); histrionic (0.62); narcissistic (0.62); avoidant (0.89);
dependent (0.76); and obsessivecompulsive (0.70).
During the informed consent procedure, the subject was asked to provide the names of three individuals who knew him/her well over most of his/her adult life. Following the interview, the examiners interviewed at least one of these informants by telephone, using questions from the IPDE and scored as above. The examiners were required to evaluate more than half of the criteria for each of the personality disorders, and they were also encouraged to enquire from the informant about additional criteria. The required criteria were those that occurred most frequently, based on prior studies in eastern Baltimore (Samuels et al, 1994). The total personality assessment (subject and informant) took about 3 h, on average, to complete. After the interviews, the psychologists formulated a final rating for each criterion based on their clinical judgment of both subject and informant reports, and they completed a case summary describing the personality of each subject.
Statistical analysis
Diagnoses of the specific personality disorders were generated by
algorithms based on DSM-IV and ICD-10 criteria, as operationalised by Loranger
et al (1994). About
2% of the subjects were missing information on more than one criterion for
each of the personality disorders. If at least half of the criteria for
diagnosis were recorded, the diagnostic algorithm operated by assigning the
value of 0 to data items that were missing. If fewer than half
of the items were recorded, then the disorder was coded as missing.
To estimate the prevalence of personality disorders in the eastern Baltimore population, we used weights to account for the unequal selection probabilities. The weights are the product of the two selection probabilities from the baseline study and the subsequent screening at follow-up. Weighted results better represent the population, whereas unweighted results better represent the sample. The major advantage that these sample data have is that they do not suffer from either of the two major sources of bias in epidemiology the bias of the clinic (socalled Berkson's, or treatment-seeking, bias; Berkson, 1946) or the bias of chronicity (sometimes called Neyman bias, or the clinician's illusion; Cohen & Cohen, 1984). In the current research design, the major advantage of weighting the data is to remove a third bias that can result from selecting a sample with prevalence of axis I disorders higher than that in the general population. This logic leads us to compare the weighted and unweighted data, in early analyses, and then to follow with presentations of the more straightforward unweighted data.
Confidence intervals, and statistical inference in general, are important in understanding the results. This sample has been selected probabilistically from the population, but it is a multi-stage clustered sample which has suffered multiple sources of attrition over more than a decade, and multiple logics of selection. It is difficult, and misleading, to attempt to represent the multiple sources of selection and sources of attrition as aspects of sample design for use in typical statistical algorithms for complex sample variance estimation (Lehtonen & Pakkinen, 1995). In this situation, we have estimated sample variances with the bootstrap strategy (Efron & Tibshirani, 1986) as implemented in Stata (StataCorp, 1999). Ten thousand resamples of individuals were selected randomly with replacement from the original sample and were of the same sample size (n=742) as the original sample. Each standard error was calculated as the square-root of the sample variance of the corresponding weighted prevalence estimate across the 1000 re-samples. The boot-strapped variances have been compared with variances estimated as if the sample were a simple random sample, and the differences (presented below) are not great. The logic of selecting individuals with axis I psychopathology (as discussed above) presumably increases the variance in prevalence estimates of personality disorders, as higher axis I psychopathology is likely to be associated with higher axis II psychopathology, and the higher prevalence will be associated with larger variance. Therefore, we consider the bootstrap estimates presented below to be conservative.
DSM-IV groups the personality disorders into three clusters: cluster A or
oddeccentric disorders (paranoid, schizoid, schizotypal);
cluster B or dramaticemotional disorders (antisocial,
borderline, histrionic, narcissistic); and cluster C or
anxiousfearful disorders (avoidant, dependent,
obsessivecompulsive). The occurrence of each of these clusters was
compared across demographic characteristics using 2 tests.
Multivariate logistic regression (Hosmer
& Lemeshow, 1989) was used to evaluate the association between
demographic characteristics and each of the clusters. For each cluster, the
adjusted model presents the magnitude of the association between the cluster
and the demographic characteristics, controlling for the other characteristics
in the model.
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RESULTS |
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Prevalence of personality disorders
Overall, 10% of the subjects (9% weighted) had one or more DSM-IV
personality disorders (Table
2). The prevalence of most individual disorders was 1-2%.
Antisocial personality disorder was the most frequent, occurring in nearly 5%
of the sample; narcissistic and dependent personality disorders were uncommon.
For most disorders, there was little difference between unweighted and
weighted estimates. For schizotypal, borderline and narcissistic personality
disorders, however, the weighted estimates were less than half the unweighted
estimates. Nevertheless, for all disorders, the 95% confidence intervals
overlap for unweighted and weighted estimates.
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The overall prevalence of ICD-10 personality disorders was 7% (5% weighted) (Table 2). The prevalence of most individual disorders ranged from 1-2%. Dissocial personality disorder was the most frequent (3%), whereas emotionally unstable (borderline), histrionic and dependent personality disorders were uncommon. Although the weighted estimates are generally lower than the unweighted estimates, especially for histrionic personality disorder, the 95% confidence limits of unweighted and weighted estimates overlap.
Association with demographic characteristics
The overall prevalence of cluster A disorders was significantly greater in
men than women (Table 3). The
prevalence of this cluster also varied across categories of marital status;
subjects who were separated or divorced had a greater prevalence, and subjects
who had never married had an even greater prevalence of this cluster of
disorders, compared with subjects who were married or widowed.
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Cluster B disorders were significantly more prevalent in men than in women. The occurrence of this cluster was inversely related to age, being most prevalent in the youngest age group and least prevalent in the oldest age group. In addition, these disorders were most prevalent in subjects who had not graduated from high school and least prevalent in subjects who continued their education after high school (Table 3).
The occurrence of cluster C disorders was related to current marital status. This cluster was most prevalent in subjects who had never married (Table 3).
We investigated the relationship between each personality disorder cluster and demographic characteristics in multivariate logistic regression models. As shown in Table 4, the odds of having a cluster A disorder were four times greater in men than women, controlling for all other characteristics in the model. Compared with subjects who were married or widowed, those who were separated or divorced had almost an eightfold increase in the odds of having a cluster A disorder; subjects who were never married were more than 20 times as likely to have one of these disorders.
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The multivariate odds of having a cluster B personality disorder were nearly five times greater in men than women. The occurrence of cluster B disorders was inversely related to age; the odds of having one of these disorders declined about 6% for every year increase in age. Furthermore, compared with subjects who had not graduated from high school, those who graduated and those with post-high school education had one-third and one-half the odds of having a cluster B disorder, respectively (Table 4).
The odds of having a cluster C personality disorder were nearly seven times greater in subjects who had never married compared with those who were married or widowed. In addition, subjects who had graduated from high school, but had not continued their education, had a ninefold increase in the odds of having a cluster C disorder, compared with those who were not high school graduates (Table 4).
We repeated these analyses separately in men and women. In general, the magnitude of the relationships between demographic characteristics and personality disorder clusters were similar in men and women. There was, however, one major difference: the relationship between marital status and cluster A disorders was much greater in men than in women. In women, those who had never married did not have a significantly greater risk of having a cluster A disorder, compared to those who were married or widowed (odds ratio 4.3, 95% CI 0.3-71.7, P=0.32). In contrast, men who had never married were at much greater risk of a cluster A disorder (odds ratio 45.9, 95% CI 4.9-432.9, P<0.001).
Based on the results of the logistic regression analyses, we investigated whether specific demographic features would be useful in identifying subgroups having a large proportion of individuals with personality disorders. We found that 8 (22%) of the 37 men who had never married had a cluster A personality disorder: 5 schizotypal; 2 paranoid; and 1 schizoid. Also, 10 (23%) of the 43 males who were younger than 55 years of age and who had not graduated from high school had a cluster B disorder: 9 antisocial and 1 histrionic. In addition, 7 (11%) of the 61 never-married subjects who were high school graduates had a cluster C disorder; 5 avoidant; 2 obsessivecompulsive, and 1 dependent (1 subject had both avoidant and obsessivecompulsive personality disorders).
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DISCUSSION |
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In general, the weighted DSM-IV prevalence estimates were smaller than the unweighted ones. The biggest differences between unweighted and weighted estimates were found for borderline, histrionic and narcissistic personality disorders. These impulsive disorders are often comorbid with several of the most frequent axis I disorders on which the sample was overselected (e.g. major depression, drug and alcohol use disorders) (Samuels et al, 1994; Skodol et al, 1999), and the weighting procedure minimised this bias.
The estimated overall weighted prevalence of ICD-10 personality disorders in this sample is 5.1%, similar to the 6.5% reported from the Australian National Survey (Jackson & Burgess, 2000). The current study allowed comparison of the prevalence of ICD-10 and DSM-IV disorders in the same subjects evaluated by the same interviewers, using the same instrument, at the same time. We found that, for most of the disorders, ICD-10 prevalence estimates are lower than DSM-IV; the difference was most pronounced for histrionic, dissocial (antisocial) and anxious/avoidant disorders. It is noteworthy that for these disorders, ICD-10 has a greater threshold than does DSM-IV (i.e. a greater proportion of criteria must be present for the diagnosis to be made). These higher diagnostic thresholds, as well as fewer total number of disorders, help explain why the overall prevalence of ICD-10 personality disorders is lower than that of DSM-IV (5% v. 9%).
Demographic correlates
In general, the prevalence of personality disorders in this community
sample was higher in men than in women; higher in formerly married and highest
in never-married, compared with ever-married individuals; and higher in people
who dropped out of high school than in graduates. There also was a striking
decline in prevalence with age of the subject. This could reflect a decline in
the expression of these disorders with age, especially for the most common
cluster B disorders. In addition, individuals born in earlier generations
might be less likely to have the disorders (cohort effect). Alternatively,
individuals with personality disorders could be less likely to be included in
the sample because of attrition (e.g. mortality)
(Cohen et al,
1994).
Results from the multivariate analyses indicate that the patterns of demographic associations were different between the three DSM-IV personality disorder clusters. Cluster A disorders were extremely prevalent in men who had never married. Cluster B disorders were most prevalent in younger males who had not completed high school, and cluster C disorders were most prevalent in high school graduates who had never married. These demographic features are useful in identifying subgroups having personality disorders at rates much higher than in the sample at large. Although the cross-sectional nature of this study precludes discriminating precursors from consequences of personality disorders, it is plausible that the cognitive and social difficulties of individuals with cluster A disorders, the impulsive behaviours of individuals with cluster B disorders, and the social anxiety of individuals with cluster C disorders impede marital and educational achievements.
Limitations
Several limitations of the present study must be considered. We were unable
to interview subjects in the target sample who were deceased, unable or
unwilling to participate, or could not be traced. However, we did find that
the sample reflected fairly closely the demographic distribution of the
original Epidemiologic Catchment Area sample from 1981, except that the study
subjects were significantly younger than those who could not be interviewed;
this could have influenced the rates of specific disorders. We also found
that, among subjects who had a personality disorder assessment as part of the
clinical reappraisal in 1981, the rates of personality disorder clusters did
not differ between those who were and were not interviewed in the current
study (data not shown).
Although we examined nearly 750 subjects, the sample size precluded investigation of the demographic distribution of rare disorders. Our results for DSM-IV clusters reflect primarily the most prevalent disorders, namely antisocial, schizotypal, avoidant and obsessivecompulsive disorders. Progress in understanding the epidemiology of abnormal personality would benefit from studying greater numbers of subjects with specific personality disorders, either by sampling a larger number of subjects or by developing screening instruments to enrich the sample for specific disorders (Lenzenweger et al, 1997).
Some subjects may have under- or over-reported specific personality features, either deliberately or because they lacked insight. In addition, the assessment of personality features by subjects and their informants might have been influenced by current symptoms (Reich et al, 1986). However, the examining psychologist did not rely exclusively on a subject's self-report; in making a final assessment, the psychologist took into consideration information provided by an informant who knew the subject over a long period of time, as well as characteristics of the subject that emerged during the interview. Furthermore, the examiner attempted to elicit enduring personality features present over the subject's entire adult life.
In this paper, we have addressed personality disorders categorically, as required by the DSM and ICD and in conformity with clinical tradition. We recognise that a dimensional approach has merit for understanding personality pathology (Frances, 1982). In future studies, we will investigate the utility of assessing personality from these alternative perspectives.
Implications
Despite these limitations, the results provide estimates of the occurrence
and demographic distributions of DSM-IV personality disorders in this
community sample. Overall, we estimate that 9% of the subjects have
maladaptive patterns of personality traits that are severe enough to cause
them, and others around them, difficulties. The results also indicate that
there are demographic subgroups of the community in which personality
disorders are over-represented. These subgroups would be useful for
investigating the determinants and consequences of these disorders, and the
service needs of people suffering from them.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication April 30, 2001. Revision received January 24, 2002. Accepted for publication January 25, 2002.