The Prevalence of Personality DisorderAmong UK Primary Care Attenders

PAUL MORAN, RACHEL JENKINS, ANDRE TYLEE, ROBERT BLIZARD

AND ANTHONY MANN

PAUL MORAN*, MSc MRCPsych, Section of Epidemiology and

General Practice,

RACHEL JENKINS, MD, FRCPsych, FRIPHH, Director, WHO

Collaborating Centre, Institute of Psychiatry, London;

ANDRE TYLEE, MD, MRCPsych, FRCGP, Director, RCGP Unit

for Mental Health Education, Institute of Psychiatry,

London

ROBERT BLIZARD, MSc, Medical Statistician, Section of

Epidemiology and General Practice, Institute of

Psychiatry

1

ANTHONY MANN, MD, MPhil, FRCP, FRCPsych, Head of Section,

Section of Epidemiology and General Practice, Institute

of Psychiatry

*Correspondence: Dr Paul Moran, Section of Epidemiology

and General Practice, Institute of Psychiatry, De

Crespigny Park, London SE5 8AF, UK.

Tel. 0171. 919 3125/3150. Fax. 0171. 277. 0283. E-

mail: spjupam@iop.kcl.ac.uk

2

The Prevalence of Personality Disorder

Among UK Primary Care Attenders

3

ABSTRACT

Objective. To determine the prevalence rate of

personality disorder amongst a consecutive sample of UK

primary care attenders. Associations between a diagnosis

of personality disorder, sociodemographic background, and

common mental disorder were examined.

Method. 303 consecutive primary care attenders were

examined for the presence of ICD-10 and DSM-4 personality

disorders using an informant-based interview.

Results. Personality disorder was diagnosed in 24% (95%

CI: 19-29) of the sample. Personality-disordered

subjects were more likely to have psychiatric morbidity

as indicated by GHQ-12, to report previous psychological

morbidity, to be single and to attend the surgery on an

emergency basis. ‘Cluster B’ personality disorders were

particularly associated with psychiatric morbidity.

Conclusions. There is a high prevalence rate of

personality disorders amongst primary care attenders.

4

These disorders are associated with the presence of

common mental disorder and unplanned surgery attendance.

Personality disorders may represent a significant source

of burden in primary care.

Keywords: Personality Disorders, Primary Health Care,

Prevalence

INTRODUCTION

Personality disorders are associated with impaired social

functioning and high rates of mental disorder. They also

significantly effect the course and outcome of mental

illness and predict poorer response to treatment (1). In

the United Kingdom, there is increasing emphasis on a

primary-care-led National Health Service. The

delineation of the epidemiology of mental disorder in

this setting is, therefore, vital in order to inform

service development. Although there is a large

literature on the form and frequency of mental disorders

5

in primary care (2), comparatively few studies have

examined the epidemiology of personality disorders in

this setting. Previous research has shown that the

prevalence rate of personality disorder in primary care

lies between 13-30% (3,4,5,6). This wide range of

estimated prevalence is derived from studies which have

used samples with conspicuous psychiatric morbidity (3)

or samples drawn from practice lists (4). To the best of

our knowledge, there have been no published UK studies of

the prevalence of personality disorder in consecutive

samples of primary care attenders. This group must be

studied in order to understand the burden for a disorder

in primary care.

This report is on the prevalence rate of personality

disorder amongst a UK sample of consecutive primary care

attenders. Associations between a diagnosis of

personality disorder, use of primary care services,

sociodemographic factors and the presence of common

mental disorder were explored.

6

METHOD

Site of study

The study was carried out between December 1997 and July

1998. Subjects were recruited from four general

practices, three of which were in inner London and the

fourth of which was in a suburb. (The practices are

referred to as practices 1-4 in the results; practice 4

was the suburban practice). The practices had either 3

and 4 partners per practice and the average list size per

GP lay between 1530 and 2100. These characteristics are

comparable with those of the practices included in the

most recent UK National Survey of Morbidity in General

Practice (7) (GPs per practice: 4; average number of

patients per GP: 1917).

Selection of patients

A consecutive sample of patients was studied in each

practice. Alternate patients on the surgery list,

between the ages of 18 - 75 years were approached as they

sat in the waiting area. This 1-in-2 sampling frame was

chosen in order to minimise any disruptive effect that

7

recruitment would have upon the flow of patients. The

usual GP consultation time was 10 minutes and recruitment

and completion of questionnaires took approximately 10

minutes. Each of the surgeries operated ‘emergency’ and

‘prebooked’ appointment systems and patients were sampled

from both of these groups, during morning and evening

surgeries. Specialist clinics, such as antenatal and

diabetic clinics were not included in the study. During

the recruitment process, no substitution occurred for

those patients who refused to take part or who failed to

keep their appointment.

Initial assessment of the attenders

Written consent was obtained. Each subject was then

asked to complete a demographic schedule (which included

details of the presenting complaint), a GHQ-12 (8), and

the physical functioning subscale of the SF-36 (9). (The

physical subscale was selected in order to measure a

dimension of interest not covered by the rest of the

assessment). A cut-off score of 2/3 on the GHQ-12 was

used to define psychiatric caseness (10). Subjects were

8

also asked to give details of any persistent physical or

psychological problems which they may have experienced

over the preceding year.

Personality assessment

Personality was assessed using a revised ICD-10 and DSM-

IV version of the Standardised Assessment of Personality

(SAP) (11). The SAP is a semi-structured interview

designed for use with an informant (usually a relative or

close friend) who has known the subject for at least 5

years. It takes about 10-15 minutes to administer and

may be carried out by telephone. The first part of the

interview is open-ended, where the informant describes

the subject’s usual self. The informant is then asked a

series of probes relating to key features of personality

disorder categories. If any of these features emerge,

the rater proceeds to ask a set of questions relating to

that category of disorder. The SAP has been shown to

have good inter-rater (kappa = 0.76) and test-retest

reliability (kappa = 0.65) (11). The version of the SAP

used in the study was able to make an ICD-10 clinical and

9

research diagnosis of personality disorder and a DSM-IV

diagnosis of personality disorder. Mann et al (12) have

compared the level of agreement between the SAP and the

patient-based International Personality Disorder

Examination (IPDE). Using the IPDE as a ‘gold standard’,

they found that the SAP had a high negative predictive

value, thus making it a suitable screening instrument for

the IPDE.

Subjects were asked to nominate an informant for the SAP,

who was initially contacted in writing and then later

followed-up with a telephone interview. The main

investigator (PM) conducted all the SAP interviews.

Analysis

The study was primarily descriptive. All analyses were

performed using SPSS for Windows (13). In the case of

some variables, namely, ethnicity, marital status and

social class, the data was grouped in order to carry out

the analysis. Associations between a diagnosis of

personality disorder (according to either ICD-10 or DSM

10

taxonomies) and clinical and service variables were

examined using Chi-square tests and where appropriate

Fishers Exact Test. Statistically significant

associations (p= 0.05) were explored further with the

calculation of relative risks (with 95% confidence

intervals) for these associations.

RESULTS

Sample characteristics

451 consecutive attenders were approached, of whom 374

(83%) agreed to participate in the study. The non-

participants consisted of 50 refusers (11%), 12 people

who failed to attend for their appointment, 9 people who

did not speak English well enough to participate, and 6

people who were missed during recruitment, cancelled

their appointment, or who were too unwell to participate.

69% (n = 209) of subjects had a pre-booked appointment

and 31% (n = 94) had an emergency appointment. These

figures closely reflected the proportions of the two

types of appointment seen at the four practices.

11

66% (n= 246) of the sample were female and 34% (n= 128)

were male. There was no significant difference in the

gender (X2= 0.3, 1 df, p= 0.6) of non-participants. The

registrar general’s scheme was used to classify social

class of the subjects (14). (This scheme provides a

classification of social class based on ‘best ever’

occupation). Using this scheme, the social class

characteristics of the sample were as follows:

professional (denoted, I): 5.3% (n= 20); intermediate

(II): 31.8% (n= 119); skilled, non-manual (III-n): 29.4%

(n= 110); skilled, manual (III-m): 13.9% (n= 52); partly-

skilled (IV): 11.8% (n=44); unskilled (V): 3.2% (n= 12)

and uncodable: 4.5% (n= 17). 77% (n=287) of the sample

were white, 18% (n=67) were black and 5% (n= 20) belonged

to another ethnic group. 52% (n= 196) were married or

cohabiting and 48% (n=178) were single. 57% (n= 215)

were working and 43% (n= 159) were not working.

The mean age of participants was 41.8 years (range: 18-74

yrs) (Males: 43.7 yrs; range: 20-74 yrs. Females: 40.8

yrs; range: 18-74 yrs). There was no significant

12

difference in the mean age of non-participants compared

to participants (Mann-Whitney U test, 2-tailed p= 0.4).

45.5% of subjects were GHQ cases at a cut-off score of

2/3 and the mean SF-36 physical functioning subscale

score was 80.8 (SD 24.6). (UK population mean for SF-36

physical functioning score: 88.4, SD: 18.0) (15).

Informants

329 of those subjects who agreed to participate nominated

an informant and 303 of these informants agreed to take

part in an SAP. Personality ratings were therefore

obtained on 67% of all attenders initially approached.

The majority of informants were female (73%; n= 240) and

informant characteristics were as follows: 38% (n= 115)

were friends, 25% (n= 76) were spouses or partners, 14%

(n= 42) were parents, 12 % (n= 38) were siblings, and 11%

(n= 32) were children of the subject. 90% of informants

had known their subjects for greater than 5 years and the

mean duration of contact between informants and subjects

was 23.0 years (range: 1-67 years).

13

Personality status

72 of the subjects were classed as having a personality

disorder according to either ICD-10 research criteria or

DSM-4 criteria, giving an overall prevalence rate of 23.8

% (95% CI: 19.0 – 28.6). 68 subjects met criteria for

one or more ICD-10 (research criteria) personality

disorders, giving a prevalence rate of 22.4% (95%CI:

17.7-27.1) and 64 subjects met criteria for one or more

DSM-4 personality disorders, giving a prevalence rate of

21.1% (95%CI: 16.5 - 25.7). The relative frequencies,

prevalence rates and 95% confidence intervals for

individual categories of personality disorder from ICD-10

and DSM-4 taxonomies are given in Table 1.

Table 1 here

Many of the subjects satisfied criteria for more than one

category of personality disorder and therefore the sum of

the numbers of the individual diagnoses exceeds the

number of patients with personality disorder. Of the 68

subjects who were personality disordered according to

ICD-10 criteria, 62% (n=42) met criteria for two or more

14

personality disorder diagnoses. Of the 64 subjects who

were personality disordered according to DSM-4 criteria,

47% (n=30) met criteria for two or more personality

disorder diagnoses. The most prevalent categories of

ICD-10 (research criteria) personality disorder were

anankastic (7.9%) and impulsive disorders (7.6%). For

DSM-4, the most prevalent categories were paranoid and

anxious, each of which had a prevalence rate of 8.3%.

There was no significant difference in the prevalence

rates of personality disorder between the four practices

(X2= 0.9, 3 df, p= 0.8).

There were no statistically significant differences in

the prevalence rate of personality disorder between

gender or age groups, social class, ethnicity, or

employment status. However, personality-disordered

subjects were more likely to be living as single (this

category includes subjects who were divorced or

separated) (X2= 4.4, 1 df, p= 0.04).

15

The clinical and service characteristics of the 72

subjects with either an ICD-10 or DSM-4 personality

disorder were compared to those of non-disordered

subjects and these data are given in Table 2.

Table 2 here

In terms of service utilisation, personality-disordered

subjects were more likely to attend for an emergency

appointment compared to non-disordered subjects.

Personality-disordered subjects were more likely to be a

GHQ-12 case and to report having experienced

psychological problems in the year preceding recruitment

to the study. There were no significant differences in

the physical functioning scores of the personality

disordered and non-disordered subjects (Mann Whitney U

test, 2-tailed p = 0.5). When the characteristics of the

64 subjects with only a DSM-4 diagnosis were compared to

those of non-disordered subjects, all statistically

significant differences were lost with the exception of

type of visit. DSM-4 personality-disordered subjects

were more likely to attend the surgery on an emergency

basis (relative risk: 1.5; 1.1 - 2.1).

16

By convention, DSM-4 divides the personality disorder

categories into three clusters: A (paranoid, schizoid

and schizotypal), B (histrionic, narcissistic, antisocial

and borderline) and C (avoidant, dependent, obsessive-

compulsive and passive aggressive). 64 subjects had a

DSM-4 diagnosis and these subjects were allocated to one

of the 3 clusters, according to which cluster they scored

the highest number of traits on. 47% (n= 30) were

allocated to cluster C, 33% (n= 21) to cluster A, and 20%

(n= 13) to cluster B. Median ages (with interquartile

ranges) for clusters A, B and C were 49 yrs (37-55), 32

yrs (25.5-46) and 33 years (28.8-48) respectively.

Significant group heterogeneity was found with respect to

age (Kruskall-Wallis 1 way ANOVA; X2=8.6, 2df, p=0.01),

although pairwise testing showed that no two groups

differed significantly at the 5% level of significance.

Social class was the only sociodemographic characteristic

which distinguished the clusters from one another.

Cluster B subjects were significantly less likely to

belong to social classes I and II (Fishers exact test, p=

17

0.02). Although 85% of cluster B subjects were GHQ-12

cases and reported psychological difficulties in the

preceding year, this difference failed to reach the 5%

level of statistical significance. When two clusters

combined were compared against the third cluster (e.g. A

vs B+C), the excess in GHQ-12 cases and previous

psychological morbidity amongst cluster B subjects became

statistically significant (GHQ-12 caseness: X2= 5.6, p=

0.02; persistent psychological problems: X2= 4.3, p=0.04).

No other differences emerged.

DISCUSSION

To the best of our knowledge, this is the first published

study of the prevalence of personality disorder in a

representative sample of primary care attenders. By

focusing on attenders, the study fills a gap in the

knowledge about the distribution of the condition from

the perspective of the general practitioner. The

combined ICD-10/DSM-4 prevalence rate of 24% for

personality disorder among the attenders was perhaps

higher than anticipated, but is consistent with

previously published literature on the prevalence of the

18

condition among GP patients with conspicuous psychiatric

morbidity (5).

There are several possible reasons why the prevalence

rate was high in this study. First, the assessment of

personality disorder is notoriously problematic and

researchers remain divided as to what constitutes the

‘best’ method. This study used an informant-based

approach to personality assessment and there is some

evidence to suggest that this sort of approach may elicit

more personality pathology than an approach which relies

on self-report (16). Mann et al (12) found that when the

SAP was compared to the ‘gold standard’ of the IPDE, the

SAP has a high negative predictive value (97%), making it

a suitable screening instrument for the IPDE, but the

positive predictive value of the instrument was poor

(47%). This was explained by apparent over-

identification of cluster B personality disorders by the

informant compared to self-report. There was however,

better agreement between the instruments with regard to

cluster A and cluster C personality disorders.

19

Second, the presence of psychiatric illness has been

shown to have a powerful effect on the assessment of

personality (17,18). In this study personality-

disordered subjects were more likely to report previous

psychological difficulties and were more likely to be

GHQ-12 cases. Some subjects might therefore have been

wrongly assigned to the category of personality disorder

due to the effects of psychiatric state bias. However,

the merit of this standardised informant-based approach

to personality assessment is that it aims to separate the

subject’s current mental state from usual self in the

interview.

Third, a non-random sample of practices was used in this

study. Although the characteristics of the practices

compare favourably with the national characteristics of

general practices in the UK (7), it remains possible that

these London area practices were not representative of

general practices as a whole.

20

Fourth, it is possible that consecutive attenders at

general practice comprise a large population of those

with personality disorder, as these individuals might be

attending attending more frequently (19). This would

explain why the results are higher than studies drawn

from practice lists, but does not invalidate the rate.

With the exception of social class, subjects belonging to

DSM-4 clusters A, B and C failed to show any significant

pairwise differences in clinical or demographic

characteristics. However, the analysis was based on

small numbers and this finding could be attributed to a

type II statistical error. When compared to clusters A

and C combined, cluster B subjects were more likely to be

a GHQ-12 case and to report previous psychological

morbidity. This finding is consistent with the high

rates of axis I disorder known to occur in patients with

cluster B personality disorders (20).

From the perspective of service utilisation, the

personality-disordered subjects were more likely to

21

attend their general practitioner on an emergency basis.

The prior appointment system has been shown to reduce

patient waiting time (21) and more likely to lead to

increased patient satisfaction. The excess of unplanned

visits among personality-disordered subjects in this

study may therefore contribute to an added source of

stress and dissatisfaction for the GPs and the

consulters. This finding provides, in advance of

prospective data, some independent behavioural

confirmation of traits associated with personality

disorders.

A limitation of this type of cross-sectional study is

that we are only able to report prevalence data and

associations. Longitudinal data, now being collected,

will be more informative and test the validity of the

diagnosis of personality disorder, by discovering its

power to predict the outcome of associated psychiatric

and physical morbidity and patterns of health service

use. These data will in turn contribute to the

refinement of categories of personality disorder and will

22

discriminate which of the two classification systems

(either ICD-10 or DSM-4) is the more useful. They may

also help determine whether indeed the informant-based

approach to assessment over-diagnoses personality

disorder.

23

Table 1. Frequencies of personality disorder subcategories according to both ICD-10 and DSM-4 taxonomies in the study population (n= 303)(ICD-10 DCR= diagnostic criteria for research) Shaded boxes indicate categories of disorder which do not appear in thatparticular taxonomy

PersonalityDisorder

ICD-10 DCR DSM-4

No.

% 95%CI

No.

% 95% CI

Paranoid 22 7.3 4.6-10.8

25 8.3 5.4-11.9

Schizoid 17 5.6 3.3-8.8

12 4.0 2.1-6.8

Schizotypal 7 2.3 0.9-4.7

Dissocial/Antisocial

11 3.6 1.8-6.4

5 1.7 0.5-3.8

Impulsive 23 7.6 4.9-11.2

Borderline 14 4.6 2.6-7.6

13 4.3 2.3-7.2

Histrionic 7 2.3 0.9-4.7

7 2.3 0.9-4.7

Narcissistic 5 1.7 0.5-3.8

Anankastic/Obsessive-compulsive

24 7.9 5.2-11.6

19 6.3 3.8-9.6

Anxious 23 7.6 4.9-11.2

25 8.3 5.4-11.9

Dependent 8 2.6 1.1-5.1

5 1.7 0.5-3.8

24

Table 2. Distribution of personality status by clinical andservice variables

VARIABLE PD(either ICD-10

or DSM-4)

Non-PD X2

(p)Relati

veRisk

95% CI

n (%) n (%) Practice1 21 (29.2

)55 (23.8

)0.9 - -

2 16 (22.2)

54 (23.4)

(0.8)

3 20 (27.8)

66 (28.6)

4 15 (20.8)

56 (24.2)

Type of visitEmergency 31 (43.1

)63 (27.3

)6.4 1.7 1.1-2.5

Booked 41 (56.9)

168 (72.7)

(0.01)

GHQ statuscase 39 (56.5

)96 (42.3

)4.3 1.6 1.0-2.4

non-case 30 (43.5)

131 (57.7)

(0.04)

Persistent physicalproblemyes 38 (52.8

)124 (53.7

)0.02 - -

no 34 (47.2)

107 (46.3)

(0.9)

Persistent psychologicalProblemYes 44 (61.1

)104 (45.0

)5.7 1.7 1.1-2.5

No 28 (38.9)

127 (55.0)

(0.02)

25

26

ACKNOWLEDGEMENTS

This work was supported by a grant from the High Security

Psychiatric Services Commissioning Board. We should

particularly like to thank Dr Dilys Jones of the

Commissioning Board for her support. The following

general practitioners kindly allowed us to recruit

patients from their surgeries: Dr S. Shepherd, Dr C.

Glasson, Dr E. Williams, Dr A. Blake, Dr A Parvez, Dr S

Ledingham, Dr G Varughese, Dr D Abraham, Dr C Gostling,

Dr C Roe, Dr S Gibbs, Dr K John, Dr M Free and Dr C

Davies. Finally, we should like to thank the patients

and reception staff who generously gave of their time and

without whom this study would not have been possible.

27

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