On the homogeneity of personality disorder clusters

Comprehensive Psychiatry (Official Journal of the American Psychopathological

VOL. 32, NO. 5

On the Homogeneity

Association)

SEPTEMBER/OCTOBER

of Personality Clusters

1991

Disorder

Horatio Fabrega, Jr., Richard Ulrich, Paul Pilkonis, and Juan Mezzich This study was conducted using as subjects persons seeking psychiatric care in a public intake facility. Patients who received a diagnosis of a personality disorder (PD) on axis II constituted the study sample. The study is unique in that it attempts to analyze in a treatment population the relationships between all of the personality disorders (PD) stipulated in DSM-III with emphasis on their relationships to the associated features recorded in the remaining axes of DSM-III. The specific aim of the study was to determine the variation that exists with respect to clinical and demographic variables acrossthe PD clusters categorized in DSM-RI and acrossthe PDtypes of each of the DSM-III PD clusters. In general, considerable inhomogeneity was shown within and across clusters in both demographic and clinical variables, although differences among clusters were noted as well. Cluster B differs prominently from its cohorts with respect to demographic and clinical characteristics. Some of the limitations of these results are emphasized. Copyright 0 1991 by W.B. Saunders Company

T

HE SYSTEM of diagnosis incorporated in DSM-III has been influential in producing knowledge pertaining to traditional psychiatric disorders comprising axis I. During the last two decades, there has taken place an increasing focus on the clinical validity, epidemiology, and therapeutic requirements of such disorders.‘,2 This must be seen as a result of both an independent movement toward empirically established operational criteria of diagnosis, and as a result of the transformation in diagnosis wrought by the DSM-III system itself. If the nature of traditional psychiatric disorders remains problematic today, this cannot be ascribed to a lack of interest or focus on the empirical problems pertaining to their diagnosis. Whereas researchers who use DSM-III have largely capitalized on its potential vis a vis axis I disorders, there has been less interest shown on the personality disorders that comprise axis II. It is fair to say that the value of the catalogue of axis II categories and the manner in which these are assembled and classified have been accepted and applied in psychiatric practice. Although the descriptive validity and theoretical warrant for this classification scheme have been the object

From the UniversiQ of PittsburghSchool of Medicine, Western Psychiatric Institute and Clinic, Pittsburgh, PA. Address reprint requests to Horatio Fabrega, Jr., M.D., lJniversi@ of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O’Hara St, Pittsburgh, PA 15213. Copyright 0 1991 by W.B. Saunders Company 0010~440X/91/3205-0009$03.OOfO

Comprehensive

Psychiatry, Vol. 32, No. 5 (September/October),

1991: pp 373-386

373

374

FABREGA

ET AL

of much discussion and analysis,3-Lothe empirical basis for its support has been less commonly studied. However, investigators have recently attacked the problems of reliability and accuracy of personality disorder (PD) diagnosis,“-‘3 and that of their grouping in the DSM-III catalogue has received preliminary attention.11-‘3 A particularly neglected topic in PD research is that involving the empirical bases for the classification schema provided by DSM-III. Types of PD have been brought together into clusters in terms of similarity of psychopathology using general semantic, as well as psychodynamic, considerations. Although some general attention has been given to the content and descriptive features of PD types and clusters,14315little is known about how the clusters compare with each other or, indeed, about how PD types comprising any one cluster compare with their analogues in terms of such things as prevalence or clinical and demographic parameters. In this light, the potential of the multiaxial system incorporated in DSM-III for clarifying the clinical properties of axis II disorders has not been assessed. To obtain a clear picture of the descriptive and clinical validity of PD clusters is, of course, an accomplishment that requires a great deal of clinical and epidemiologic research. However, provided a suitable and large sample of PD patients can be assembled and one sets modest initial goals, it should be possible to provide a preliminary descriptive picture if not validity profile of PD clusters using the multiaxial system provided by DSM-III. RATIONALE

OF THE STUDY

All patients above the age of 19 who were assigned a PD diagnosis on axis II following an intake evaluation in a large public University and metropolitan-based psychiatric facility comprised the sample of this study. Each patient is held to incorporate what we have termed a clinical condition to draw attention to the complex picture of comorbidity and associated clinical factors.16,17A clinician conducting the intake evaluation codifies information using the multiaxial resources of DSM-III and any related data (clinical and demographic) otherwise routinely collected. The study aims to clarify the clinical and demographic characteristics of the clusters of PD assembled together in DSM III. Two sets of analysis of variance (ANOVA) procedure were used in order to clarify properties of PD clusters: (1) a comparison of clinical and demographic variables across four PD clusters (i.e., the three of DSM-III plus an atypical PD type/cluster); and (2) a comparison of clinical and demographic variables across PD types comprising each of the DSM-III stipulated clusters: cluster A, paranoid, schizoid, and schizotypal; Cluster B, histrionic, narcissistic, antisocial, and borderline; Cluster C, avoidant, dependent, compulsive, and passive-aggressive. METHOD Clinical Setting and Evaluation Process This study was conducted at the Western Psychiatric Institute and Clinic (WPIC) of the University of Pittsburgh, a large, comprehensive, urban-based university psychiatric facility that also serves as a community mental health center, It admits approximately 2,000 inpatients and has over 100,000 outpatient visits per year. This psychiatric population encompasses all age groups, and it experiences a wide variety of psychopathologic symptoms and levels of dysfunction; it is served through specialized child, adolescent, adult, and geriatric clinical programs. The immediate setting of the study was the Diagnostic and Evaluation Center, a 24-hour, seven-day-a-week, walk-in clinic and the main entry

HOMOGENEITY

OF PERSONALITY

375

DISORDER CLUSTERS

point for inpatient and outpatient care at the WPIC. Its major functions are to provide psychiatric evaluations, emergency care, and clinical dispositions. Patient evaluations are typically conducted by an initial interviewer (a psychiatric resident or a nurse clinician specially trained in psychiatric assessment) and a supervising faculty psychiatrist. The initial interviewer reviews all pertinent records and interviews the patient and accompanying persons according to the Initial Evaluation Form (IEF), a semistructured assessment procedure having mutual complementary narrative and standardized components. The initial interviewer then presents the case to the faculty psychiatrist, who conducts a brief complementary interview of the patient and accompanying persons to clarify any pending issues. The process culminates with a multiaxial diagnostic formulation along the lines stipulated in DSM-III and a decision regarding disposition jointly reached by the evaluation team. The initial interviewer later completes the IEF and information in it reflects all of the deliberations bearing on the patient. The subjects of this study were drawn from the population of new referrals 19 years of age or older who came to WPIC for evaluation between 1983 and 1989. The IEF contains 64 symptoms that are rated on a 4-point scale (0, absence of a symptom; 1, slight; 2, moderate; three, severe). These symptoms are drawn from various clinical rating scales. The level of symptoms is evaluated for each patient separately and, although obviously playing a role in diagnosis, is not an operational component of diagnosis (like with structured diagnostic instruments). Two measures of symptom level were obtained: total number of symptoms present and number of symptoms whose severity ratings exceeded “slight.” Two types of variables and statistical tests were used in comparing patients with as versus those without a PD. The demographic variables, which are handled as nominal in nature, are tested with chi-square measures, and include education, occupation, social class, race, gender, and age. The nominal clinical variables also tested by chi-square include presence versus absence of axis III diagnosis and type of axis I diagnosis. The clinical variables measured on continuous scales and evaluated by means oft tests and F tests, include number of definite axis I diagnoses: number of axis I rule out diagnoses; number of stressors; level of stressor severity; highest level of adaptive functioning during last year; current level of social functioning at work, with family, and at leisure; and, finally, the two symptom scores, number of symptoms present, and total level of severe symptom ratings.

RESULTS Characteristics of the Sample

A total of 2,344 patients constituted the study sample. This represented 12.9% of the patients evaluated during the time interval chosen. Complete data were not available for all patients and thus the following analyses do not always reach this total figure. Demographic characteristics of the sample patients are listed in Table 1, which shows that 52.5% were female, 20.3% black, and that most patients fell into the youngest age category. The sample showed a high level of education: whereas only 17% failed to complete high school, a total of 48.9% advanced beyond the high school level. Most patients fell into the lowest social class category (42.1%), but more than one third (34.1%) were in the highest social class (Hollingshead I-III). The axis I characteristics of the sample are listed in Table 2. In general, the sample showed a low overall measure of comorbidity on axis I, with patients Table 1. Demographic

Characteristics

of Patients With Personality Disorders Education

Age W

Sex Group PD

Race

Male

Female

20.35

36-50

51+

White

Black

1,113 47.5%

1,231 52.5%

1,572 87.1%

571 24.4%

201 8.6%

1,889 79.7%

475 20.3%

Social Class 1 710 34.2%

2 475 22.9%

3 893 43.0%

Missing

Less Than HS

HS Grad

Above HS

71 3.0%

399 17.0%

728 31.1%

1,148 48.9%

l.lf

.59*

Dial;;yes

yu$l:;.

1

1,219 52.0%

0

503 21.5%

106 4.5%

3

I DX

4 .2%

24 41 1.8%

327 14.2%

Substance

in axis I.

Organic 116 5.0%

Psychoses 36 1.6%

Sipolars 159 6.9%

Maj Dep Single 275 11.9%

Maj Dep Recur

Type of Primary Axis

I Characteristics of Patients With Personality Disorder

and rule out diagnoses

512 21.8%

2

No. of Definite Axis

*Mean number of positive (definite) diagnoses

2,344

Total Total No. Positive No. wp$h Diai;;es

Table 2. Axis

292 12.7%

Dysthymia Other Affect

I DX

189 8.2%

Anxiety

339 14.7%

Adjust Disorder

249 10.8%

Other

282 12.2%

Defer

HOMOGENEITY

OF PERSONALITY

DISORDER CLUSTERS

377

averaging 1.1 definite axis I diagnoses. Over one fifth (21.5%) failed to receive a definite diagnosis on axis I. On the other hand, more than one fourth (26.5%) received at least two definite diagnoses on axis I. Among types of axis I disorders, substance use and adjustment disorders were the most common primary axis I diagnoses, whereas organic (including dementia) and bipolar disorders were relatively infrequent (1.8% and 1.6%, respectively). The frequencies of types of PD in the sample are listed in Table 3. The category “atypical” was the most frequently encountered (30.2%) followed by the antisocial (17.4%) and borderline (16.6%) categories. The categories “passiveaggressive” and “paranoid” were the least frequently encountered (1.0% and 1.3%, respectively). Comparison Across PD Clusters

The chi-square and ANOVA procedures were used to compare measures of central tendency across the four clusters. The data pertaining to demographic background are listed in Table 4. There were statistically significant variations across the clusters with respect to sex (x2 = 45.9, P = .OOOOO),age (x’ = 127.1, P = .OOOOO),race (x2 = 76.6, P = .OOOOO),education (x2 = 71.89, P = .OOOOO), and social class (x2 = 78.76, P = .OOOOO). The following observations can be made: (1) cluster A and, to a slight extent, cluster B contain proportionately more males; (2) cluster B contains comparatively more persons in the youngest age, more blacks, lower levels of education, and more persons in the lowest social class; (3) cluster C has the highest proportion of females, persons in the oldest category, and persons in the highest social class. In general, PD clusters are shown to possess a highly significant degree of variation with respect to demographic characteristics. Table 5 lists the frequencies pertaining to type of primary axis I diagnoses, number of definite axis I diagnoses, and presence versus absence of axis III. Number of positive diagnoses in axis I yielded a statistically significant chi-square (x’ = 75.08, P = .OOOOO),with cluster B tending to show a comparatively larger number of definite axis I diagnoses and cluster A comparatively fewer. The frequency of type of axis I diagnosis differed significantly across the four cluster (x2 = 382.62, P = .OOOOO).Inspection of the frequencies in Table 5 shows that cluster B has a high association with substance use disorders and cluster A with psychotic disorders, while cluster C and the large group of atypical PD all have a high association with mood disorders (major depression single and recurrent, dysthymic other affective), adjustment disorders, and (especially cluster C) anxiety disorders. Table 6 lists measures on the clinical variables that were rated on continuous scales. All of the variables produced F values that were highly significant statistically, underscoring the significant degree of across-cluster variability with respect to stressors, adaptive functioning, and symptom levels. Inspection of the magnitude of the F statistics shows that axis IV and symptom levels produced a comparatively smaller degree of variation, whereas number of axis I rule out diagnoses, and especially axis V, produced the highest degree. Cluster C showed the lowest level of social impairment; cluster B tended to show the highest level of social impairment and also showed the highest level of symptoms.

PD clusters Cluster A (n = 160) Cluster B (n = 996) Cluster C (n = 481) Atypical (n = 707) PD types Paranoid (n = 31) Schizoid (n = 39) Schizotypal (n = 90) Histrionic (n = 150) Narcissistic (n = 57) Antisocial (n = 409) Borderline (n = 390) Avoidant (n = 107) Dependent (n = 233) Compulsive (n = 118) Passiveaggressive (n = 23) Atypical (n = 707)

Group

8 5.1% 236 24.3% 18 3.8% 65 9.3%

3 10.3% 1 2.6% 4 4.4% 7 4.7% 4 8.5% 164 40.1% 61 16.5% 5 4.7% IO 4.4% 2 1.8%

1 4.5% 65 9.3%

4 2.5% 19 1.9% IO 2.1% 8 1 .2%

1 3.4% 2 5.1% 1 1.1% 1 0.7% 1 2.1% 15 3.7% 2 0.5% 0 0.0% 8 3.5% 2 1.8%

0 0.0% 8 1.2%

Organic Disorders

Substance Use Disorder

2 1.2% 16 1.6% 4 0.8% 14 2.0% 0 0.0% 1 2.6% 1 1.1% 0 0.0% 0 0.0% 7 1.7% 9 2.4% 1 0.9% 1 0.4% 1 0.9% 1 4.5% 14 2.0%

4 13.7% 11 28.2% 11 12.2% 2 1.3% 0 0.0% 48 11.8% 7 1.9% 0 0.0% 6 2.6% 0 0.0% 3 13.6% 24 3.4%

Bipolar Disorders

0 0.0% 37 8.1%

31i% 7 6.5% 24 10.4% 14 12.3%

2 6.9% 4 10.3% 2 2.2% 12 8.0% 9 19.1% 17 4.2%

5.0% 45 9.5% 57 8,1%

49

Maj Dep Single

Types of Primary Axis

4 18.2% 81 11.6%

3 10.3% 2 5.1% 7 7.8% 25 16.7% 5 10.6% 13 3.2% 51 13.8% 14 13.1% 51 22.2% 19 16.7%

12 7.6% 94 9.7% 88 18.6% 81 11.6%

4 18.2% 118 16.9%

0 0.0% 6 15.4% 8 8.9% 18 12.0% 8 17.1% 12 2.9% 53 14.3% 22 20.5% 32 13.9% 11 9.7%

14 8.9% 91 9.4% 69 14.5% 118 16.9%

Dysthymic and Other Affective

in Total Sample

Maj Dep Recurrent

I Diagnoses

Between Axis II and Primary Diagnoses in Axis

26 16.5% 57 5.9% 9 1.9% 24 3.4%

PsyacnhdOtic Schizophrenic

Table 3. Association

I

0 0.0% 53 7.5%

2 6.9% 2 5.1% 4 4.4% 21 14.0% 1 2.1% 4 1.O% 10 2.7% 35 33.7% 21 9.0% 35 20.4%

8 5.5% 36 3.7% 92 19.5% 53 7.5%

Anxiety and Phobia

3 13.6% 106 15.1%

8 27.6% 0 0.0% 6 6.7% 21 14.0% 7 14.9% 71 17.4% 51 13.8% 6 5.6% 51 22.2% 9 7.9%

14 8.9% 150 15.4% 69 14.6% 106 15.1%

Adjust Disorder

1 4.5% 18 2.5%

1 3.4% 1 2.6% 3 3.3% 5 3.3% 1 2.1% 6 1.5% 0 0.0% 1 0.9% 3 1.3% 4 3.6%

5 3.1% 16 1.6% 9 1.6% 18 2.5%

Other

5 22.7% 96 13.7%

3 10.3% 5 12.8% 26 28.9% 20 13.3% 4 8.5% 22 5.4% 74 20.1% 8 7.5% 10 4.3% 9 7.9%

34 21.5% 10 12.3% 32 6.8% 96 13.7%

Defer

7 D

E

g

B

Y ca

HOMOGENEITY

OF PERSONALITY

379

DISORDER CLUSTERS

Table 4. Demographic

Features of Personality Disorder Clusters Education

Age Iv)

Sex Male

Female

20-35

Cluster A (n = 160) Cluster6

115 71.9% 480

45 28.1% 516

65.6% 760

25.6% 193

(n = 996) ClusterC (n = 481) ClusterD (n = 707)

48.2% 205 42.6% 313 44.3%

51.8% 276 57.4% 394 55.7%

76.3% 264

19.4% 126

105

Race

36.50

51+

White

Black

14 8.8% 43 4.3%

131 81.9% 711 71.4%

29 18.1% 285 28.6%

91 18.9%

414 86.1%

67 13.9%

53 7.5%

613 86.7%

94 13.3%

41

54.9% 443

26.2% 211

62.7%

Chi-square Significance

45.86 .ooooo

29.8% 127.10 .ooooo

Contingency coefficient

.13853

.22679

76.63 .ooooo .17992

Missing 5

Less Than HS 26

HS Grad 42

Above HS Grad 87

3.1% 38

16.3% 232

26.3% 320

54.4% 406

3.8% 13

23.3% 55

32.1% 156

2.7% 15 2.1%

11.4% 32.4% 86 210 12.2% 29.7% 71.89 .ooooo .17250

Social Class 1 45

2 31

3 63

32.4% 214

22.3% 195

45.3% 455

40.8% 257

24.8% 191

22.6% 102

52.7% 142

53.4% 396 56.0%

43.9% 200 40.6%

23.4% 147

32.6% 233

23.0% 36.4% 76.76 .ooooo .19109

Comparisons Within PD Clusters

Each of the PD clusters composed of more than one type of PD (cluster A, B, and C) was evaluated with respect to its degree of variation on the dependent variables. The chi-square test was used to evaluate proportions across the types of PD comprising a cluster. The data pertaining to demographic variables are listed in Table 7. Besides x2 values and probability measures, we have listed the contingency coefficients, since the latter offers a measure of the degree of variation that is less affected by the size of the sample. Cluster A is relatively homogeneous: no significant variation was noted with respect to sex, race, age, education, and social class. Cluster B was the most heterogenous, for it alone produced significant variation on all demographic variables: sex (x2 = 463.1, P = .OOOOO), age (x2 = 75.76, P = .OOOOO), race (x2 = 202.69, P = .OOOOO), education (x’ = 183.57, P = .OOOOO),and social class (x2 = 144.7, P = .OOOOO).Finally, the degree of variation observed in cluster C was intermediate between that of A and B. Significant variation obtained with respect to sex (x” = 33.60, P = .OOOOO), age (x2 = 16.73, P = .01031), education (x2 = 36.37, P = .00003), and social class (x’ = 43.9, P = .OOOOO), but not on race. The comparisons involving axis I and III are shown in Tables 8 and 9. Table 8 shows that cluster A and B produced a significant degree of variation with respect to number of axis I diagnoses and presence versus absence of axis III. Data pertaining to the comparisons involving the frequency of types of axis I diagnoses in each of the three clusters containing more than one PD type are summarized in Table 9. All of the chi-square values were highly significant and the contingency coefficients well above 0.4, indicating a high degree of heterogeneity within clusters with respect to type of primary axis I diagnosis. Table 10 summarizes the data involving the comparison within PD clusters by means of ANOVA of the clinical variables measured on continuous scales. Cluster B shows the highest level of variation with respect to clinical variables: nine of 10 ANOVA yielded statistically significant F values and in each of the nine instances cluster B produced the highest F statistics of the three clusters (stressor severity the exception). Cluster A showed the lowest degree of variation, only two variables producing significant F values (number of positive axis I diagnoses, P = .0304 and highest level of adaptive functioning last year, P = .0470). Cluster

Chi-square Significance Contingency coefficient

Cluster D

Cluster C

Cluster 6

Cluster A

57 35.6% 220 22.1% 67 13.9% 159 22.5%

0

74 46.3% 463 46.5% 307 63.8% 375 53.0%

1

.17617

26 16.3% 247 24.8% 93 19.3% 146 20.7% 75.08 .ooooo

2

No. of Positive Axis

3 1.9% 64 6.4% 12 2.5% 27 3.8%

3

2 0.2% 2 0.4%

24

I Diagnoses

8 5.1% 236 24.2% 18 3.8% 65 9.3%

Organic 4 2.5% 19 2.0% 10 2.1% 8 1.1%

Bipolars 2 1.3% 16 1.6% 4 0.8% 14 2.0%

Psychoses 26 16.5% 57 5.9% 9 1.9% 24 3.4%

8 5.1% 49 5.0% 45 9.5% 57 8.1%

.37731

14 8.9% 91 9.3% 69 14.6% 118 16.9%

Dysthymia Other Aff

I Diagnosis

Maj Dep Recur

12 7.6% 94 9.7% 88 18.6% 81 11.6% 382.62 .ooooo

Maj Dep Single

Type of Axis

a 5.1% 36 3.7% 92 19.5% 53 7.6%

Anxiety

I and Axis III Characteristics of Personality Disorder Clusters

Substance Use

Table 5. Axis

14 8.9% 150 15.4% 69 14.6% 106 15.1%

Adjust Disorder

28 17.7% 106 10.9% 37 7.8% 78 11.1%

Other

34 21.5% 120 12.3% 32 6.8% 96 13.7%

Defer

.07297

110 50 68.8% 31.3% 591 405 59.3% 40.7% 257 224 53.4% 46.6% 421 286 59.5% 40.5% 12.55 .00593

Yes

Axis III No

HOMOGENEITY

OF PERSONALITY

381

DISORDER CLUSTERS

Table 6. Clinical Differences Across Personalty Disorder Clusters Axis No. of Positive Diagnoses

I

Axis V

No. of Rule out Diagnoses

Axis IV No. of Stressors

Stressor Severity

Last Year Adaptive Function

Current Function Work

Current Function Family

current Function Group

Symptom Total

Symptom Severity

Cluster A Cluster 6

.64 1.16

.76 .53

.67 .77

4.1 4.3

4.5 4.2

4.2 3.9

3.7 3.9

4.3 3.9

10.66 11.19

7.96 9.03

Cluster c Cluster D F test

1.11 1.06 8.5

.46 .68 12.6

.60 .73 5.2

4.3 4.4 4.1

3.9 3.9 24.2

3.6 3.7 12.2

3.6 3.6 13.1

3.8 3.8 12.4

10.24 11.12 5.64

8.09 7.99 6.27

.oooo

.oooo

.0014

.0058

.oooo

.oooo

.oooo

.oooo

.0006

.0003

Significance

C was intermediate (six of 10 F values were significant). Highest level of adaptive functioning during the previous year was the only variable that yielded significant F values in all three clusters. Four of the variables produced significant F values in only one instance: stressor severity on cluster C and number of rule out axis I diagnoses, number of stressors, and symptom total in the case of cluster B. The rest of the variables produced significant F values in two of the three clusters. DISCUSSION

The PD types and clusters vary in their frequency in this intake setting. Cluster A patients, as an example, are comparatively infrequent, whereas those in cluster B show the highest frequency. These frequency figures reflect the influence of several factors; in particular, community incidence and prevalence, attitudes toward psychiatric care, and the level of morbidity (particularly with respect to axis I), which prompts the need to seek evaluation. It is difficult to untangle the interplay of these factors as they pertain to the frequency measures observed in our study, although we judge that morbidity level is an important consideration. Fortunately, our sample is sufficiently large and allows concentrating on the similarities and differences among the clusters and among the PD types within each of them. A highly significant level of demographic variation or heterogeneity was manifest across PD clusters. All of the x2 values resulting from analyses of the demographic variables were high and significant. DSM-III clusters thus incorporate persons of widely varying demographic background. As an example, inspection of our data shows that even though PD’s are more commonly diagnosed in younger patients, age still appears to show a high level of variation across PD clusters. In general, cluster B displayed the most striking degree of deviation with respect to demography, and one can conclude that patients placed in this cluster may be more discrepant demographicahy from those of other PD clusters. PD types are defined in terms of salient and abstract clinical features of a descriptive and especially psychodynamic nature. A rationale geared to partly preserving the clinical salience presumably motivated the assemblage of PD types into clusters and by implication was used to separate PD clusters one from another. The results of this study bearing on demographic differences across clusters suggest that parallel to this clinical descriptive contrast among PD clusters there exists a striking demographic contrast as well. Although there are points of similarity between clusters with respect to selected demographic

coefficient

Contingency

Test

Total

coefficient

Passive-aggressive

Compulsive

Dependent

ClusterC Avoidant

Contingency coefficient

Test

Total

Borderline

Antisocial

Narcissistic

ClusterB Histrionic

Contingency

Test

Total

Schizotypal

Schizoid

ClusterA Paranoid

43.2% 11 47.2%

55.8% 12 52.2%

.25554

205 275 42.6% x2 = 33.5 P = .ooooo

49 45.8% 165 10.8% 51

58 54.2% 58 29.2% 57

P = .ooooo .56353

310 79.5% 515 51.8% 48.2% x* = 453.9

29.8% 55 13.4%

70.2% 354 85.6%

80 20.5% 480

137 91.3% 14

0.7% 33

13

P = .71854 .05413

54.9%

59.6% 254

54 45.8% 15

58.2% 121 51.9%

73

79.2% 325 83.3% 750 73.3%

57.3% 25 53.2% 324

85

55.6%

71.9% 28.1% x2 = .66

51.5% 24 51.5%

15

20.35

55 72.2% 105

9 29.0% 9 23.1%

Female

27 30.0% 45

75.9% 53 70.0% 115

22 71.0% 30

Male

Sex

.18336

25.2% x* = 15.7 P = .01031

35 30.5% 3 13.0% 125

53 27.0%

24 22.4%

P = .ooooo .26587

59 15.1% 193.1 19.4% x' = 75.0

15 31.9% 74 18.1%

45 30.0%

25.5% x2 = 8.0 P = .09005 .21876

21 23.3% 41

29.0% 11 28.2%

9

35-50

91 18.9%

28 23.7% 4 17.4%

49 21.0%

10 9.3%

43 4.3%

5 1.5%

19 12.7% 7 14.9% 11 2.7%

14 8.8%

10.3% 4 4.4%

5 19.4% 4

51+

19 21.1%

4 10.3%

6 19.4%

Black

75.5% 193 47.2%

131 87.3% 35

6 26.1%

10 8.5%

38 15.3%

13 12.1%

.12100

414 57 85.1% 13.9% x2 = 7.1 P = .05735

83.7% 108 91.5% 17 73.9%

94 87.9% 195

P = .ooooo .41121

285 71.4% 28.5% x2 = 205.7

45.5% 191 43.9%

55.7% 10

32.7% 71

43 43.0% 67

24.8%

33.5% 214

22 5.4% 114

215 52.8% 39 10.0%

55 41.0% 23 50.0%

30.8% 45 32.4%

32.4% 24

37.0% 11

10

1

19 12.7% 11 23.4%

P = .33295 .11545

29 81.9% 18.1% xz = 2.2

89.7% 71 78.9% 131

80.5% 35

25

White

Race

35 44.9%

P = .ooooo .30275

102 23.4% x2 = 43.9

9 40.9%

3 13.6%

142 32.5%

91 44.4% 18 16.7%

24 24.0%

33 33.0% 47 22.9% 19 17.5%

P = .ooooo .37873

52.7%

42.6% 455

12 26.1% 257 74.7% 145

11 23.9% 65 18.9%

41 30.5%

38 28.4%

.53413 .13445

81 23.8% 195 22.5% x2 = 144.7

P=

53 45.3%

14 41.2%

19 24.4% 31 22.3% x1 = 2.5

14 51.9%

9 26.5%

3

3 11.1%

2

Social Class

4.3% 13 2.7%

1

.8%

3.0% 1

4 3.7% 7

2.8% 38 3.8%

6.4% 11

26

.7%

1

3.3% 5 3.1%

1 2.5% 3

1 3.2%

Missing

Variables Within Personalty Disorder Clusters

351 90.0% 711

of Demographic

Age(w)

Table 7. Comparison

23 25.5% 42

8 25.8% 11 28.2%

21.3% 162 39.5%

40 25.7% 10

8

21.2% 5 21.7%

29.0% 95 40.8% 25

31

56 155 11.4% 32.4% x2 = 36.8 P= .00003 .25545

1 4.3%

7.5% 37 15.9% 9 7.5%

P = .ooooo .39449

54 108 27.7% 13.8% 232 320 23.3% 32.1% x2 = 183.5

152 37.2%

22 14.7% 4 8.5%

.19238

16.3% 25.3% x1 = 6.1 P = .40558

15 17.8% 25

8 25.8% 2 5.1%

G;:d

Education Less Than HS

53.4%

83 70.3% 15 59.5% 257

59.8% 94 40.3%

54

40.8%

55.5% 405

33 70.2% 59 15.9% 217

87 58.2%

48 53.3% 87 54.4%

45.2% 25 54.1%

14

AbHDSve Grad

$

F

1

$

g

HOMOGENEITY

OF PERSONALITY

Table 8. Comparison

383

DISORDER CLUSTERS

of Axis

I and Axis ill Within Personality Disorder Clusters Axis I No. of Positive Diagnoses

0 Cluster A Paranoid Schizoid Schizotypal Total

Antisocial Borderline Total

Compulsive Passive-aggressive Total Test Contingency coefficient

Yes

78 29 19.3% 52.0% 27 9 19.1% 57.4% 180 138 44.0% 33.7% 178 71 18.2% 45.6% 463 247 46.5% 24.8% x2 = 79.9 P = .ooooo .27249

5 3.4% 1 2.1% 42 10.3% 18 4.7% 66 6.6%

76 74 49.3% 50.7% 32 15 68.1% 31.9% 263 146 64.3% 35.7% 220 170 43.6% 56.4% 591 405 40.7% 59.3% x2 = 11.7 P = .00837 .10789

15 14.0% 24 10.3% 22 18.6% 6 26.1% 67 13.9%

61 23 57.0% 21.5% 158 48 20.6% 67.8% 76 17 14.4% 64.4% 12 5 21.7% 52.2% 307 93 63.8% 19.3% x2 = 21.8 P = .04024 .20806

8 7.4% 3 1.3% 3 2.5%

62 45 42.1% 57.9% 114 119 48.9% 51.1% 69 49 58.5% 41.5% 12 11 47.8% 52.2% 257 224 53.4% 46.6% x2 = 4.0 P = .26186 .09077

Contingency coefficient

Dependent

No

38 25.3% 10 21.3% 49 12.0% 123 31.5% 220 22.1%

Test

Cluster C Avoidant

>3

15 10 32.3% 48.4% 24 4 61.5% 10.3% 35 12 13.3% 38.9% 74 26 16.3% 46.3% x* = 15.9 P = .01429 .30067

Contingency coefficient

Narcissistic

2

6 19.4% 10 25.6% 41 45.6% 57 35.6%

Test

Cluster B Histrionic

1

Axis Ill Diagnosis

1 2.6% 2 2.2% 3 1.9%

14 2.9%

16 15 48.4% 51.6% 26 13 33.3% 66.7% 68 22 24.4% 75.6% 110 50 68.8% 31.3% x2 = 6.3 P = .04379 .19399

variables, each cluster may be construed as embodying a group of patients showing a more or less distinctive mix of demographic characteristics. A high level of variation with respect to clinical features was also shown across PD clusters. Virtually all of the clinical parameters that are codified in the DSM-III multiaxial protocol, as well as the two symptom variables, yielded scores and ratings that showed statistically significant variation across the four clusters. To be sure, the comparatively large sample size ensures that moderate differences in variation of measures will produce statistics that will exceed the level required for significance. In many instances, then, one is here dealing with statistical as versus clinical significance. However, inspection of the size of the F statistics and

384

FABREGA ET AL Table 9. Summary of Comparison of Types of Primary Axis Within Personality Disorder Clusters

Cluster A Cluster B Cluster C

X2

Significance P Value

61.5 322.7 109.9

.00322 .ooooo .ooooo

I Diagnoses Contingency Coefficient .45604 .49886 .43416

of the average scores within the clinical categories reveals interesting facts that require further explanation. As an example, patients with a PD located in cluster A are far more likely to present for evaluation without an associated axis I or axis III diagnosis, whereas the obverse is the case of patients with a cluster C PD who usually present for evaluation with an associated axis I disorder. Cluster C patients are far more frequent in the sample. Taken together, these findings suggest that whereas cluster A disorders may show a lower level of co-occurrence with major psychiatric syndromes, in their own right they can produce high enough levels of morbidity to prompt an evaluation. Continuing with comparisons across clusters, it was noted that each PD cluster tended to show a pattern of axis I comorbidity that was different from that of another. Some of the associations between axis I diagnoses and axis II clusters were anticipated. As an example, substance use disorders are far more likely to be associated contingently with cluster B, which includes antisocial PD, a disorder whose manifestations can implicate the resort to substance use. Similarly, psychotic disorders are associated with cluster A, which consists of schizoid, schizotypal, and paranoid PD, all of which are thought to relate contingently and analytically to psychotic conditions.‘s*19 Some of the associations were not predictable on purely analytical grounds, but constitute empirical findings that confirm prior research; for example, major depression recurrent and anxiety disorders with cluster C.*’ The implications of the association between cluster A and the heterogenous collection of axis I disorders included in the “other” category require further research. Cluster B showed the highest number of symptoms and the highest level of symptom severity, a finding that parallels its high level of comorbidity on axis I. This cluster also showed a tendency toward a higher level of impairment on axis V Table 10. Summary of Comparison of Clinical Variables Within Personality Disorder Clusters Axis No. of Positive Diagnoses

I

Axis V Axis IV

No. of Rule Out Diagnoses

No. of Stressors

Stressor Severity

Cluster A F statistic Significance

3.57 .0304

0.436 .6475

1.82 .1643

.61 .54

Cluster a F statistic Significance

23.68 .oooo

4.58 .0034

3.6 .0131

Cluster c F statistic Significance

2.58 .0533

.54 .6554

1.a0 .1474

Last Year Adaptive Function

Current Function Work

3.12 .0470

66 .5196

.25 .6610

52.67 .oooo

6.6 .0002

5.89 .0006

Current Function Family

Current Function Group

Symptom Total

SYmptom Severity

.06 .94

.63 .5314

0.54 .5841

.88 .4187

34.84 .oooo

14.23 .oooo

7.10 .OOOl

17.34 .oooo

3.2 -0220

6.21 .0004

5.19 .0016

4.7 .0028

1.0 .3937

2.9 -0342

HOMOGENEITY

OF PERSONALITY

DISORDER CLUSTERS

385

(during the preceding year and currently). The lowest level of social impairment on axis V was demonstrated by cluster A, which suggests that the manifestations of its component types may prompt early evaluation and/or do not markedly impact on social functioning. In general, among the axes of DSM-III, axis V social impairment (current social functioning and especially highest level of functioning during the preceding year) were associated with a particularly high level of variation. With respect to intra-cluster analyses, comparison of the contingency coefficients suggested that cluster A is associated with a particularly high level of variation with respect to number of definite axis I diagnoses and with respect to axis III. Compared with other clusters, then, its component PD types appear to show a more variable qualitative pattern of comorbidity regarding number of associated disorders. Each of the clusters produced a high level of variation with respect to type of axis I diagnosis, with cluster B tending to show a larger amount. With regard to comorbidity viewed qualitatively or descriptively, then, it is the types of axis I diagnoses as versus the number that distinguish cluster B. Cluster B displayed the highest level of variation with respect to clinical variables measured on continuous scales, whereas cluster A showed the least amount, a pattern that parallels the variation related to demography. In brief, disregarding the matter of quantitative comorbidity, it seems clear that cluster B is comprised of PD types that are relatively heterogenous clinically and demographically, whereas the converse appears to hold for cluster A. An important consideration bearing on the generalizability of our results involving the homogeneity of PD clusters devolves from the special character of clinical conditions seen in a public intake setting such as ours. First, from the point of view of the consumer, persons seeking psychiatric care are characterized by relatively high levels of morbidity consisting of psychological symptoms and social and interpersonal problems. As an example, a large percentage of these patients present with crises and require hospitalization. Furthermore, almost all of the remaining patients are in need of prompt outpatient care. Second, from the standpoint of the dispensers of care, intake clinicians operate under relatively exigent psychiatric conditions: the kinds of clinical conditions prompting evaluation and the seeking of care create for clinicians requirements of rapid assessment, diagnosis and prudent disposition. The clinician’s focus is of necessity geared to the striking and morbidly more salient aspects of the clinical condition of the patient. Thus, special consumer and provider related factors constrain the way clinical conditions are categorized and evaluated in intake settings. In summary, it needs to be appreciated that the demographic and clinical characteristics of PD types and clusters reported here partly reflect the exigencies of the way clinical conditions are diagnosed in an intake setting like ours. Our picture of general inhomogeneity across clusters and differences among selected ones must be seen as conditioned by the nature of the setting and its mode of operation. Studies involving other types of clinical populations which are evaluated in settings governed by other types of constraints are needed in order to arrive at a more definitive and balanced account of the homogeneity of PD clusters.

386

FABREGA

ET AL

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