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Health Services Research Department, Institute of Psychiatry, London, UK
Centre for Adolescent Health, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
Section of Epidemiology, Institute of Psychiatry, London, UK
Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, and Department of Paediatrics, University of Melbourne, Victoria, Australia
Centre for Adolescent Health, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
Correspondence: Dr Paul Moran, Health Services Research Department, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK. Tel: +44 (0)20 7 848 0568; fax: +44 (0)20 7 848 0333; e-mail: paul.moran{at}iop.kcl.ac.uk
Declaration of interest None. Funding detailed in Acknowledgements.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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Aims To examine the association between DSM–IV personality disorders and substance use disorders in a large representative sample of young community-dwelling participants.
Method Young Australian adults (n=1520, mean age=24.1 years) were interviewed to determine the prevalence of substance use disorders; 1145 also had an assessment for personality disorder.
Results The prevalence of personality disorder was 18.6% (95% CI 16.5–20.7). Personality disorder was associated with indices of social disadvantage and the likely presence of common mental disorders. Independent associations were found between cluster B personality disorders and substance use disorders. There was little evidence for strong confounding or mediating effects of these associations.
Conclusions In young adults, there are independent associations between cluster B personality disorders and substance use disorders.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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From a total sample of 2032 students, 1943 (96% of the sampling frame) participated at least once during the first six (adolescent) waves. In wave 8, 1520 young adults (78% of wave 1–6 participants) were interviewed between May 2001 and March 2003. Response rates are shown in Fig. 1. Reasons for non-participation at wave 8 were refusal (n=269), unable to contact person (n=147) and death (n=7).
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Personality disorder
The presence of DSM–IV personality disorder was assessed using the
ICD–10 version of the Standardised Assessment of Personality (SAP;
Pilgrim & Mann, 1990). The
instrument has good interrater (kappa=0.76) and test–retest reliability
(kappa=0.65; Pilgrim et al,
1993). The SAP is a semi-structured interview designed for use
with a person who has known the individual for at least 5 years. All wave 8
participants were asked to nominate a friend, sibling or partner, with whom we
could conduct an SAP interview. If the friend was unavailable or unable to be
contacted, cohort participants were asked to name an alternative person. Of
the 1520 participants at wave 8, 1145 interviews (75%) were conducted with
nominated interviewees. There were 304 participants that refused to nominate a
friend; 45 nominated people who refused to be interviewed or could not be
contacted and 26 nominated people who were located but did not respond to
requests for interviews. The majority of interviewees were female
(n=891, 78%); they had known the participant for a median 10 years
(interquartile range 5–18), had a median of 12 contacts per month
(interquartile range 4–30) and were predominantly under 35 years of age
(n=1115, 97%). The interviewees were friends or partners
(n=872, 76%), relations (n=253, 22%; e.g. sibling, cousin)
or spouses (n=20, 2%). Trained research psychologists carried out all
the SAP assessments as telephone interviews.
Behavioural/psychiatric measures
Common mental disorders. Depression and anxiety were assessed with
the 12-item General Health Questionnaire (GHQ–12;
Goldberg, 1972). The total
scores were dichotomised at the cut-off point of 3/4 to identify a mixed
depression–anxiety state at a lower threshold than syndromes of major
depression and anxiety disorder, but where clinical intervention would still
be appropriate.
Cannabis use. This was assessed by self-reported frequency of use in the previous 12 months. In the analysis, participants were dichotomised according to whether cannabis was used at least weekly.
Cannabis dependence (DSM–IV). This was assessed using the 12-month version of the Composite International Diagnostic Interview 2.1 (CIDI; World Health Organization, 1997). Only participants reporting weekly substance use were assessed.
Tobacco consumption. This was recorded using a 7-day retrospective diary. Daily smoking was defined as reported smoking on 6 or 7 days of the past week. Nicotine dependence was measured using the Fagerstrom Test for Nicotine Dependence (Heatherton et al, 1991) and was defined at a cut-off point of 3/4.
Alcohol use. This was assessed by self-reported frequency of use. Participants who reported drinking in the previous week were asked to record their consumption on each drinking day over Friday, Saturday and Sunday and the most recent drinking weekday. If appropriate, the weekday report was extrapolated to other drinking weekdays, enabling the estimation of total alcohol consumption for the week prior to the survey. Males consuming more than 430 g of alcohol per week were classified as hazardous drinkers (National Health and Medical Research Council, 2001); the corresponding figure for females was 280g.
Alcohol dependence (DSM–IV). This was assessed using the CIDI. Only participants reporting weekly alcohol consumption were assessed.
Amphetamine, ecstasy and cocaine use. Participants were classified as users if they reported using these substances in the past year.
Analysis
Data were collected from young people who were difficult to trace because
of the high mobility of the age-group. Although the response was high and
attrition low, a quarter of cohort members were not interviewed at wave 8 and
a quarter of those who were interviewed did not have an assessment of
personality disorder, leading to potential bias in summary measures at wave 8.
To address this, we used the method of multiple imputation, with five complete
data-sets created by imputation under a multivariate normal model
(Schafer, 1997). This model
incorporated all the outcome variables of interest measured at all waves of
data collection, along with the fixed covariates gender, age, rural or urban
residence, parental education and parental smoking (available for all
participants). Univariate and multivariate logistic regression analyses were
used to model associations, and Wald tests and related confidence intervals
were used to assess statistical significance and precision, combining
appropriately across the five imputed data-sets
(Carlin et al, 2003).
Analysis was performed with Stata version 8 for Windows.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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Prevalence and socio-demographic correlates of personality disorders
The overall prevalence of DSM–IV personality disorders was 18.6% (95%
CI 16.5–20.7). The prevalence of sub-categories and clusters of
DSM–IV personality disorders is shown in
Table 1.
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Cluster C personality disorders had the highest prevalence, although confidence intervals for the three clusters of personality disorder all overlapped. Almost a third of those diagnosed with a personality disorder met criteria for more than one cluster.
There was little evidence of association between participant age, gender, non-Australian birth or parental education and a diagnosis of any personality disorder. However, a diagnosis of cluster A personality disorder was more prevalent among females (OR=1.6, 95% CI 1.0–2.4) and less prevalent among those not born in Australia (OR=0.52, 95% CI 0.28–1.0).
Associations between personality disorders and substance use disorders
The prevalence of substance use and dependence is shown in
Table 2. Associations between
clusters of personality disorder and substance use disorders are displayed
with and without adjustment for the co-occurrence of other clusters.
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As gender and Australian birth were possible confounders, all estimates were also adjusted for these factors. No first-order interactions with gender were identified.
Adjustment for the presence of other clusters of personality disorder abolished any significant association between a cluster A or C diagnosis and any category of substance misuse. However, associations between all four categories of substance use and a cluster B diagnosis remained robust when adjusted for the other clusters of personality disorder.
We next examined the role of possible mediators of the association between cluster B personality disorders and substance use disorders. In order to identify which measures to include in the analysis, we first assessed univariate associations between any personality disorder (given the extent of cluster overlap) and common mental disorder (as measured by GHQ–12), relationship, educational and work status. All domains showed clear associations with the diagnosis of any personality disorder. Specifically, personality disorder was more prevalent in participants with common mental disorders (OR=1.9, 95% CI 1.4–2.7), in those not in a relationship (OR=1.4, 95% CI 1.1–1.9), with incomplete schooling (OR=1.8, 95% CI 1.4–2.5), without post-school qualifications (OR=1.6, 95% CI 1.1–2.4), in those receiving government benefits (OR=2.1, 95% CI 1.4–3.3) and in those currently not working (OR=1.8, 95% CI 1.2–2.6). There was a weak indication that those not living at home (OR=1.3, 95% CI 0.96–1.7) were more likely to be diagnosed with a personality disorder.
We assessed the confounding effects of gender and non-Australian birth and the potential mediating effects of common mental disorder, relationship, educational and work status, by adding each variable to the multivariate model sequentially. Confounding and mediating effects were inferred on the basis of change in the estimated association between each substance use measure and cluster B personality disorder (Table 3).
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The associations between cluster B personality disorders and substance use outcomes were only slightly reduced as additional covariates were added to the logistic regression model, indicating little evidence for strong confounding or mediating effects. The most consistent effects were a weakening of associations with tobacco use measures and cannabis dependence upon adjustment for educational and work status variables.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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Previous literature
This is the first epidemiological study of personality disorders and
substance use disorders in a sample of young community-dwelling adults.
Previous surveys have not focused on young adult populations and this is an
important gap in the literature, given that substance dependence is most
prevalent in the younger population. For example, in the Office for National
Statistics survey of psychiatric morbidity in England and Wales, 15% of
participants aged 16–24 years reported using a drug in the past year,
compared with 6% of those aged 25–34 and only 1% of those aged
45–55 (Farrell et al,
2001).
Earlier studies of adult clinical populations have indicated cross-sectional associations between cluster B personality disorders and alcohol, cocaine and cannabis use (Rounsaville et al, 1991; DeJong et al, 1993; Skodol et al, 1999; Grant et al, 2004). We have confirmed these findings in a young adult, non-clinical sample and have found strong independent associations between cluster B personality disorders and cigarette smoking. Despite the high community prevalence of personality disorders and the devastating impact of cigarette smoking on public health, surprisingly little research has examined whether there is an association between the two.
Only one other epidemiological survey of the full range of DSM–IV personality disorders and substance use disorders has previously been published (Grant et al, 2004). Despite the use of a large representative sample, that study had a number of methodological weaknesses. The authors did not use a recognised assessment of personality disorder, they failed to examine associations between personality disorders and specific categories of substance misuse and they did not control for potential confounders. In contrast, we used a reliable assessment of personality disorder based on an interview with a friend, partner or relative nominated by the participant (thereby reducing the risk of mental state biasing assessment). We explored associations with specific drugs, rigorously examined possible confounding and mediating effects using logistic regression, and handled the problem of missing data using multiple imputation.
Methodological considerations
The study relied on self-reported measures of substance use, leading to
possible underreporting. Nevertheless, this approach is standard in addictions
research (Del Boca & Noll,
2000) and our use of diaries minimised the problem of recall bias
for some measures. In addition, although we measured a range of indices of
social disadvantage, some aspects of this domain (family size, income and
housing tenure) were not captured. We used multiple imputation to adjust for
potential biases and loss of precision resulting from missing data. This is a
complex procedure, which relies on modelling assumptions about the reasons for
data being missing. The underlying statistical theory, as well as simulation
studies, provide assurance that the method works well even when these
assumptions are not met exactly (Schafer
& Graham, 2002). To optimise the performance of the method,
all variables that were used in the final analysis, as well as a number of
other variables potentially related to the missing data patterns, were
included in the imputation model.
Association between cluster B personality disorders and substance use disorders
Potential mediators of the association between cluster B personality
disorders and substance use included social disadvantage and the presence of
common mental disorders. However, in the logistic regression model, when we
sequentially adjusted for common mental disorders, relationship, educational
and work status, there was little change in the size of associations and hence
little evidence to support the occurrence of such mediating effects. The
GHQ–12 is a screening instrument and it is conceivable that if we had
used designated measures for detecting depression and anxiety, we would have
detected subtle mediating effects.
On balance, it seems likely that the characteristics of high novelty-seeking and low harm-avoidance present in those with cluster B personality disorders predispose them towards substance misuse (Cloninger et al, 1988; Caspi et al, 1997; Verheul, 2001). However, given the cross-sectional nature of these data, we cannot examine the direction of causality between personality disorders and substance use in this young adult population. We anticipate that longitudinal data from this cohort will help to further elucidate the causal pathways between personality disorders and substance misuse in young people.
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Clinical Implications and Limitations |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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Received for publication November 12, 2004. Revision received September 2, 2005. Accepted for publication September 2, 2005.
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
