Centre for the Economics of Mental Health
Health Services Research Department
Centre for the Economics of Mental Health, Institute of Psychiatry, London
Centre for the Economics of Mental Health, Institute of Psychiatry; and PSSRU, London School of Economics, UK
Section of Epidemiology, Institute of Psychiatry, London
Correspondence: Paul Moran, Health Services Research Department, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK. Tel: 020 7848 0568; fax: 020 7848 0333; e-mail: paul.moran{at}iop.kcl.ac.uk
Declaration of interest This study was supported by grants from the Medical Research Council and the Department of Health.
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ABSTRACT |
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Aims To test the hypothesis that people with personality disorders have higher mean health and non-health costs compared with those without personality disorders.
Method Prospective cohort study design. A total of 303 general practice attenders were followed-up I year after they had been assessed for the presence of personality disorders. Costs were estimated in £ sterling at 1999 price levels.
Results The mean total cost for patients with personality disorders was £3094 (s.d.=5324) compared with £1633 (s.d.=3779) for those without personality disorders. Personality disorders were not independently associated with increased costs. Multivariate analyses identified the presence of a significant interaction between personality disorders and common mental disorders and increased total costs (coefficient=499, 95% CI 180.1-626.2, P=0.002).
Conclusions Personality disorders are not independently associated with increased costs. An interaction between personality disorders and common mental disorders significantly predicts increased total costs.
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INTRODUCTION |
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METHOD |
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One year follow-up
One year after recruitment all participants were sent a questionnaire by
post, containing the 12-item General Health Questionnaire (GHQ-12;
Goldberg, 1972), the physical
function sub-scale of the SF-36 (Jenkinson
et al, 1993), a life events questionnaire
(Brugha & Cragg, 1990) and
a use of services questionnaire. The GHQ-12 is a 12-item self-report
questionnaire that screens for the presence of psychiatric morbidity and
specifically common mental disorders, such as anxiety and
depression (an increase in GHQ-12 score indicates greater psychiatric
morbidity). The physical function sub-scale of the SF-36 is a self-report
measure of physical functioning (an increase in physical function score
indicates better physical functioning).
The use of services questionnaire was a variant of the Client Service Receipt Inventory (CSRI; Beecham & Knapp, 1992). This questionnaire was used to collect retrospective data on service utilisation in the last 6 months and covers the following domains: general practitioner (GP) consultations; practice nurse visits; hospital in-patient stays; hospital out-patient episodes; seeing a social worker; counselling and therapy contacts. Participants were asked about the duration of consultations and any costs incurred for private treatment. Information was also collected on employment status and time taken off work because of ill health.
Estimated unit costs were calculated for all services used, drugs prescribed and time taken off work because of illness. All costs were calculated in £ sterling at 1998/99 price levels. Service cost estimates were based on figures published in Netten et al (1999) and allowed for capital, overheads, travelling time, non-patient contact time, support services and London weighting. Where costs were not available in Netten et al, other sources and equivalent costs were used. Lost productivity costs, because of illness or unemployment, were calculated as the only non-health service costs. Figures were based on national average gross earnings by occupational status. The researcher responsible for costing the data (A.R.) was blind to the personality status of the participants.
Sample size
A pre-study power calculation showed that a sample of 300 participants
would be required to detect a 50 unit difference in mean costs between
participants with personality disorders and those without, with 80% power and
95% significance. This assumes a 30% prevalence of personality disorder
(Casey & Tyrer, 1990) and a
standard deviation of 125.
Statistical analysis
Analyses were performed using SPSS and Stata
(Norusis, 1993;
StataCorp, 1999). A strategy
for the statistical analysis of the association between baseline measures and
1-year cost was drawn up before inspecting the data. The main outcome measures
used were the arithmetic means for health service, non-health service and
total costs. Univariate associations between personality disorder and costs
were investigated using t-tests; the results were checked using non-parametric
bootstrap analyses (based on 2000 replications). Multiple linear regression
was then used to identify variables that predicted variations in costs. The
following variables are associated with health service use and were therefore
entered into the multiple regression models: age; gender; physical health and
psychiatric morbidity (Gill & Sharpe
1999; Dowrick et al,
2000). The following additional variables were entered into the
multiple regression models on a priori grounds: personality disorder status,
life events, marital status and age on finishing education (a proxy measure
for socio-economic status). Interactions between personality disorder and
SF36 physical function score and General Health Questionnaire score
were also included in the model.
Results from the multiple regression models were subject to two checks. First, they were compared with the results from non-parametric bootstrap regression to assess the robustness of confidence intervals and P values to non-normality in the cost distribution (Efron & Tibshirani, 1993). Confidence limits were obtained from bias-corrected estimates based on 2000 resamples; significance levels were obtained from percentiles of the distribution of bootstrapped re-samples. Second, the results were compared with those obtained from a generalised linear model, where a non-normal (gamma distribution) was assumed for costs.
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RESULTS |
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Univariate analyses
Mean costs were consistently higher for patients with personality disorders
compared with those without personality disorders, although the difference was
only statistically significant for total costs
(Table 1). Non-parametric
bootstrap analyses confirmed these findings.
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Multivariate analyses
Health service costs
A percentage (12%) of the variance of health service costs was explained by
the variables in the multivariate model
(Table 2). The SF36
physical function sub-scale score was the only variable that was significantly
associated with health service costs; a one-point increase in physical
function score was associated with a £46 decrease in health service
costs. Personality disorder was not significantly associated with health
service costs in this model. Bootstrap regression analysis and generalised
linear modelling confirmed these findings.
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Non-health service costs
A percentage (24%) of the variance of non-health service costs was
explained by the variables in the multivariate model
(Table 3). Three variables were
significantly associated with non-health service costs: GHQ-12 score, gender
and marital status. In addition, non-health service costs were significantly
associated with an interaction between personality disorder and GHQ12
score. For those participants without a personality disorder, a one-point
increase in GHQ12 score was associated with a £253 increase in
non-health service costs. For patients with personality disorders, a one-point
increase in GHQ12 score was associated with a £764 increase in
non-health service costs.
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Bootstrap regression analyses largely confirmed these results, although marital status failed to remain significantly associated with non-health service costs. A generalised linear model of non-health service costs did not find the interaction between GHQ12 score and personality disorder to be statistically significant.
Total costs
Some of the variance of total costs (28%) was explained by variables in the
multivariate model. Four variables were significantly associated with total
costs: GHQ12 score, gender, age on finishing education and marital
status (Table 4).
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In addition, an interaction between GHQ12 score and personality disorder was significantly associated with total costs. For participants without personality disorders, a one-point increase in GHQ12 score was associated with a £285 increase in total costs. For participants with personality disorders, a one-point increase in GHQ12 score was associated with a £784 increase in total service costs.
The non-parametric bootstrap analysis confirmed the presence of a statistically significant interaction between GHQ12 score and personality disorder in association with total costs. However, a generalised linear model of total costs failed to confirm the presence of this interaction.
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DISCUSSION |
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A significant interaction emerged between personality disorder and common mental disorders in the multivariate analyses of non-health and total costs; the costs for patients with personality disorder were significantly higher in the presence of common mental disorders. The presence of this interaction was confirmed by non-parametric bootstrap analyses (which assume an additive model of costs), although it was not confirmed by generalised linear modelling (which assumes a multiplicative model of costs). This makes us more cautious in attributing significance to the interaction. Nevertheless, it also raises methodological questions as to the need for multiple confirmatory analysis (Knapp et al, 2002).
Evaluation of the study design
This is the first published study of the economic impact of personality
disorder in the UK health service. The study is characterised by three
positive features. First, it is a prospective study in which the assessment of
costs was not influenced by the assessment of personality. Second, the
researcher costing the data was blind to the personality status of
participants, therefore minimising observer bias in the estimation of costs.
Third, the use of an informant-based assessment of personality minimised the
possibility of concurrent abnormal mental state biasing the assessment of
personality. Despite these features, the study has a number of limitations.
First, the patients in the study were primary care attenders and are likely to
be different from the patients seen by general psychiatrists. This therefore
limits the extent to which the findings can be generalised to psychiatric
populations. Second, a convenience and not a random sample of practices was
used and the findings might not therefore be generalisable to patients from
other UK practices. Third, our sample of consecutive attenders could have
included a greater number of heavy service users. This could have inflated the
costs for the whole sample, although it should not have affected the relative
costs of attenders with personality disorders compared with attenders without
personality disorders. Fourth, our power calculation was based on a prevalence
of 30% for personality disorder, although the detected prevalence was only
24%. Therefore, our study could have been underpowered to detect an
association between personality disorder and cost. Underpowering is a problem
common to health economic studies, especially in the mental health field
(Gray et al, 1997), although in
our case, we had used the best available prior data to try to avoid this
difficulty. Finally, the basis for our measure of cost, the CSRI, is a
self-report instrument and the estimated costs could, therefore, be
susceptible to recall bias.
Explained variation in cost
Health service costs were associated with physical function status. This is
consistent with the finding that heavy general practice service users are less
likely to report excellent and good health and more likely to have physical
disease, compared to controls (Gill &
Sharpe, 1999). However, our final regression model only explained
12% of the variance in total health service costs. Unmeasured variables of
relevance to the prediction of health service costs could include factors such
as cigarette and alcohol consumption, physical activity, access to services
and socio-economic status (Knapp,
1998). Our finding of no association between personality disorder
and health service costs is at odds with previous reports of an association
between personality disorder and high psychiatric service utilisation
(Seivewright et al, 1991;
Saarento et al, 1997).
However, as noted above, this could reflect the fact that, despite our best
efforts, the study was underpowered to detect such an association.
In our sample of GP attenders, the presence of common mental disorders predicted both non-health service costs (lost productivity) and total costs. This finding is consistent with other reports of a significant association between common mental disorders and disability (Ormel et al, 1993). However, our findings suggest that common mental disorders interacted with personality disorders in predicting both non-health service and total costs; patients with personality disorders were only more expensive if they also had common mental disorders. Personality disorders are often associated with a poor prognosis for the treatment of associated mental illness (Patience et al, 1995; Mennin & Heimberg, 2000). We therefore suggest that increased total costs for the patients with personality disorders could have occurred as a result of chronicity of associated mental illness (Seivewright et al, 1998).
Personality disorders are currently the subject of great debate. However, the current debate is focused on the small proportion of individuals who have a severe personality disorder and could pose a danger to the public. By examining the economic impact of the whole diagnostic group of patients with personality disorders, we have shown that personality disorders could have a subtle effect on non-health service and total costs through an interaction with psychiatric comorbidity. Clearly our findings need replication in a larger, more representative sample of patients. However, we believe that this study is an important step towards improving the evidence base in an area that is overburdened with opinion rather than fact.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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Received for publication September 26, 2001. Revision received March 14, 2002. Accepted for publication March 21, 2002.
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