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Institute for Medical Technology Assessment, Erasmus Medical Centre, Rotterdam
Department of Health Policy and Management, Erasmus Medical Centre, Rotterdam and Trimbos Institute, Utrecht
Institute for Medical Technology Assessment, Erasmus Medical Centre, Rotterdam
Department of Health Policy and Management, Erasmus Medical Centre, Rotterdam, The Netherlands
Correspondence: Dr Leona Hakkaart-van Roijen, Institute for Medical Technology Assessment, PO Box 1738, 3000 DR Rotterdam, The Netherlands. Tel: +31 10 4088567; fax: +31 10 4089081; e-mail: l.hakkaart-vanroijen{at}erasmusmc.nl
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 assess the cost-utility of brief therapy compared with CBT and care as usual.
Method A pragmatic randomised controlled trial involving 702 patients was conducted at 7 Dutch mental healthcare centres (MHCs). Patients were interviewed at baseline and then every 3 months over a period of 1.5 years, during which time data were collected on directcosts, indirect costs and quality of life.
Results The mean direct costs of treatment at the MHCs were significantly lower for brief therapy than for CBTand care as usual.However, after factoring in other healthcare costs and indirect costs, no significant differences between the treatment groups could be detected.We found no significant differences in quality-adjusted life-years between the groups.
Conclusions Cost-utility did not differ significantly between the three treatment groups.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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At these MHCs, an approach known as brief therapy, namely a short-term psychological treatment consisting of a maximum of seven sessions, is currently growing in popularity. The aim of this study was to assess the long-term cost-effectiveness of brief therapy compared with cognitive–behavioural therapy (CBT) and care as usual as a first-line treatment in an MHC out-patient population. We used a pragmatic trial design to enhance external validity.
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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A follow-up period of 1.5 years was chosen to establish the long-term effects on health status and costs. Measurements were recorded at baseline and then every 3 months. Patients were permitted to switch to an alternative treatment arm if their medical condition rendered this necessary. We applied intention-to-treat analyses.
Interventions
Brief therapy is a formalised ‘stepped-care’ approach. The
therapy focuses mainly on the present, and emphasises abilities rather than
disabilities. Brief therapy is expected to reduce costs and increase
efficiency in the short term. Its claim to long-term cost-effectiveness in the
Dutch mental healthcare setting for a broad range of psychiatric problems has
not yet been confirmed by scientific evidence. Problems may recur at a later
date, or patients may require additional treatment in mental healthcare
services or other parts of the healthcare sector and consequently generate
additional costs. In the present study, patients who were undergoing brief
therapy for depression or anxiety were compared with those who were receiving
CBT, which had been shown by earlier studies to be an effective approach
(Black et al, 1993;
Cloaguan et al, 1998; Dorrepaal
et al, 1998). In this study, CBT was formalised and the
main focus was on altering irrational cognitions by challenging them. The
maximum number of sessions was 15. In the same way as for brief therapy, a
‘stepped-care’ approach was adopted for CBT, in which all patients
initially received a first-line treatment, and they were only switched to
another therapy if the first therapy proved to be inadequate. In The
Netherlands, care as usual is not formalised, and a multidisciplinary team is
free to assign a therapy from a wide range of therapeutic options. The
treatment decision is based on professional experience, taking into account
the specific problems and characteristics of the individual patient. The
number of sessions depends on the therapy that is offered. Evidence on the
(cost-)effectiveness of care as usual is lacking. However, this type of
‘matched care’ is claimed to be more effective than a
‘stepped-care’ approach, in which one form of (relatively brief)
therapy is started for all patients indiscriminately, and patients are
switched to other options if there is no subsequent improvement or if
side-effects occur.
Measures
At baseline, several demographic characteristics (e.g. age, gender,
educational level and employment status) were assessed. In all interviews that
were conducted during treatment, we assessed health-related quality of life,
use of medical resources and productivity loss using the EuroQol Questionnaire
(EQ–5D) (Essink-Bot et al,
1993) and the Trimbos and iMTA Questionnaire on Costs Associated
with Psychiatric Illness (TiC–P;
Hakkaart-van Roijen,
2002).
Quality of life
Quality of life was assessed with the EQ–5D, which is a validated
tool for measuring general health-related quality of life. It consists of five
items (mobility, self-care, usual activities, pain/discomfort and
anxiety/depression), each of which is rated as causing ‘no
problems’, ‘some problems’ or ‘extreme
problems’. The health descriptions can be linked directly to empirical
values for health states of the general public, which allows utilities to be
computed (Essink-Bot et al,
1993). The patient mean utility scores were estimated by applying
the area-under-the-curve method, which involves summing the areas of the
geometrical shapes obtained by linearly interpolating between utility scores
over the study period (Matthews et
al, 1990).
Direct and indirect costs
The economic evaluation was undertaken from a societal perspective, and
included costs due to medical resource utilisation (direct medical costs) and
costs attributable to production losses (indirect costs).
We used the TiC–P to collect data on direct and indirect costs from the patients (Hakkaart-van Roijen, 2002). The first part of the Tic–P measures medical resource utilisation by asking for the number of contacts with different (medical and psychological) healthcare providers (e.g. GP, psychiatrist, medical specialist, physiotherapist, alternative health practitioner, day care/hospital length of stay, and medication) during the past 4 weeks. We assumed that the number of contacts and/or days in those 4 weeks were representative of the total period between assessment points (an average of 3 months). Data on the number of contacts at the MHCs were collected directly from the participating centres. Subsequently, the number of medical contacts was multiplied by the unit costs for 2002 (Oostenbrink et al, 2000; College for Health Insurance (CVZ), 2002). All costs were estimated for the year 2002 and are presented in euros.
The second part of the TiC–P measures productivity losses and includes a short version of the Health and Labour Questionnaire (van Roijen et al, 1996; Dam et al, 1998). Data on the number of days of absence from work were divided into short-term and long-term absence from work. Short-term absence referred to periods of less than 2 weeks. When calculating the indirect costs due to short-term absence from work, we assumed that the number of days lost over the past 2 weeks was representative of the total period between the measurement points (an average of 3 months). However, if respondents indicated that they had been absent for the whole of the past 2 weeks, we collected additional information concerning when this period of long-term absenteeism had begun, as the recall period for long-term absence from work was determined by the start of this period. This additional information was used to value the production losses according to the friction cost method (Koopmanschap et al, 1995; Koopmanschap & Rutten, 1996). The period of time needed to replace a worker (the so-called ‘friction period’) in 2002 is estimated to be 154 days. Absenteeism among workers was valued by the average production value by age and gender per day or per hour.
Patients may go to work despite being ill, which may impair job performance. Therefore all patients who had worked were asked if they had experienced ‘no impediment’ or ‘some/considerable impediments’.
Data analysis
Costs and quality-adjusted life-years
The results of the cost and quality-adjusted life-year (QALY) analyses are
presented as mean values with standard errors. Data on the number of contacts
at the MHC were collected directly at the centres, and therefore data were
available for nearly all patients (n=611; 87%). However, data on
healthcare utilisation and absence from work for estimating other health costs
and indirect costs and quality of life were collected by means of a
questionnaire. At baseline, data from the TiC–P and the EQ–5D were
available for 646 (92%) of the participants in all treatment groups. At the
1-year follow-up and 1.5-year follow-up, data from the TiC–P and the
EQ–5D were available for 423 (60%) and 394 (56%) of the respondents
respectively. Data with regard to individual resource-use items were
unavailable for a small proportion (3%) of patients.
To account for the missing data and the additional uncertainty that they introduce, we used the multiple imputation technique in which each missing value is replaced by m>1 simulated values (Rubin, 1987; Rubin & Schenker, 1991; Lavori et al, 1995). After the multiple imputations have been created, m plausible versions of the complete data exist, each of which is analysed by standard methods. The results of the multiple imputation analyses are then combined to produce a single result that includes uncertainty owing to the missing data (Rubin & Schenker, 1991; Rubin, 1996; Schafer, 1997). For the proportion of missing data in the present study, m=10 was found to be sufficiently large to stabilise the outcomes in terms of the standard errors for all analyses (Schafer, 1997). The overall mean costs are simply calculated as the mean of the mean costs in each data-set. The overall associated variance is determined by combining the variance within data-sets with the variance between data-sets (Schafer, 1997). We used the Monte Carlo Markov Chain approach to impute the missing values. This approach assumes that the underlying distribution is multivariate normal. However, it has been shown in a large simulation study that even with skewed data this method often performs well (Oostenbrink & Al, 2005).
Standard errors were derived both by the parametric approach as suggested by Rubin (1996) and by a (non-parametric) bootstrap procedure (Rubin & Schenker, 1991). However, because these two methods yielded equivalent results, only the parametric standard errors are presented here.
Cost-utility
Cost-utility was evaluated by relating the difference in direct medical
costs per patient who received either brief therapy or the control treatment
(CBT or care as usual) to the difference in terms of QALYs gained, which
yielded an estimate of cost per QALY. In addition, we estimated the cost per
QALY including the indirect costs.
Uncertainty was assessed by means of bootstrapping, and the results are presented as acceptability curves (van Hout et al, 1994). Since we were dealing here with three interventions instead of two, we used an adjusted version of the acceptability curve, which leads to a cost-effectiveness frontier that indicates which treatment is optimal for various threshold incremental cost-effectiveness ratios (Fenwick et al, 2001).
Sensitivity analyses
The way in which missing data are handled is vitally important when
assessing the results of economic evaluations
(Oostenbrink et al,
2003; Oostenbrink & Al,
2005). Therefore two alternative methods for imputing missing
data, namely linear extrapolation and complete case analysis, were applied in
the sensitivity analysis. Applying linear extrapolation, the other healthcare
costs and costs due to short-term absence from work were extrapolated to 1.5
years by dividing the observed costs of each patient by the number of observed
days for which the patient remained in the study, and multiplying the results
by 548.
The complete case analysis excluded the data for all patients who dropped out of the study before the 1.5-year follow-up.
Statistical analysis
Multiple imputation analysis was performed using PROC ‘MI’ in
SAS for Windows (available in version 8.02 and higher). All other statistical
analyses were performed using SPSS version 10.1 for Windows.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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At baseline, there were no statistically significant differences in demographic data, health status or costs between the group for which there was a complete data set and the respondents who were lost to follow-up.
Direct and indirect costs
Table 2 shows the estimated
total mean direct medical costs and indirect costs per patient over a period
of 1.5 years. As was expected, the mean number of contacts and the associated
costs in the MHCs were significantly lower in the brief therapy group than in
the CBT group (95% CI 
169 – 741) or the usual care group
(95% CI 14 – 464). However, no significant difference was
found between the three treatment groups with regard to the mean total direct
medical costs per patient (i.e. including the costs due to resource
utilisation in other parts of the healthcare service), nor was there a
significant difference in indirect costs between the three treatment
groups.
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Over time, for all treatment groups the percentage of patients in paid employment who had long-term absence from work declined, and conversely the percentage of patients who had no impediments increased (Table 3).
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Quality of life
The utility scores for the three treatment groups also showed significant
improvement during the study period and did not differ significantly between
the groups (Table 4). The
improvement in utility scores was moderate during the first year, but was low
during the final 6 months of follow-up
(Cohen, 1988). At the end of
the study period, the patients’ quality of life was still significantly
lower than the average utility score of the general population (0.88)
(Table 4).
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Cost utility
Table 5 shows the
incremental cost-effectiveness ratios both for direct medical costs per QALY
gained and for total costs per QALY gained. As we are comparing three
treatment options, we have reported the results in the format suggested by
Karlsson & Johannesson
(1996). The treatments are
ordered from least to most effective. Comparison of the direct medical costs
of care as usual and CBT shows that CBT is superior to usual care (with lower
costs and better outcomes), so an incremental cost-effectiveness ratio is only
calculated for brief therapy 
. CBT, yielding a value of 262 857 per
QALY gained. With regard to total costs the same relationship applies (CBT
could achieve the same number of QALYs at lower costs), so again only the
incremental cost-effectiveness ratio for brief therapy . CBT is
calculated, yielding a value of 222 956 per QALY gained.
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To assess the uncertainty, we constructed acceptability curves for each treatment by calculating the proportion of bootstrap replicates for which that treatment is optimal, for a number of threshold incremental cost-effectiveness ratios (Fig. 1) (Fenwick et al, 2001). Figure 1 relates to the direct medical costs per QALY, and it indicates that, taking uncertainty into account, CBT is optimal. However, the preference for CBT becomes less strong as the threshold incremental cost-effectiveness ratio increases. The same is true if all costs are considered (data not shown).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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Sample selection
We conducted a large-scale multicentre randomised trial in the setting of
general mental healthcare services in The Netherlands. External validity may
have been affected by selection bias resulting from patients either refusing
to enter the study or being lost to follow-up. Our data on refusal and our
missing data seem to be comparable to those of other important trials
(Lambert & Ogles, 2004).
Moreover, treatment drop-out in this study was comparable to that in the
general population of MHC patients (van
der Sande et al, 1992). We recruited patients with a wide
range of mood and anxiety disorders, comparable to a normal population of
out-patient mental healthcare patients. Furthermore, the patients in our trial
reported more (severe) psychological symptoms at baseline (mean score of 223
on the 90-item Symptom Checklist (SCL–90)
(Derogatis, 1977)) than did a
reference population of arbitrarily selected Dutch psychiatric out-patients
(mean SCL–90 score=204) (Arrindell
& Ettema, 2003). There was no indication of any selection bias
leading to the enrolment of only ‘mild’ cases in the study.
Furthermore, we found no significant differences in baseline characteristics
or mental health status between patients who completed the study and those who
did not. Therefore there is no compelling reason to believe that selection
bias affected the external validity of the study.
Study design
Unfortunately, this study did not provide an opportunity to evaluate the
cost-effectiveness of psychological treatment compared with natural recovery
of the patient. The potential effect of antidepressant medication, which was
allowed in addition to the treatment to which patients were randomised, is
also unclear. At baseline, 36% of the respondents used antidepressant
medication, and there were no significant differences between the treatment
groups.
Direct costs
It was not possible to distinguish between healthcare utilisation for
depression and/or anxiety disorders and that for eventual other general
healthcare problems which relied on other areas of the healthcare system.
However, this was equally true for all three treatment groups. Furthermore,
data were collected on healthcare utilisation, which it was expected would be
relevant to the treatment of psychological problems. Data on healthcare
utilisation other than sessions at the MHCs were collected by self-report. A
previous study has indicated that such patients’ self-reports are a
valid source of data on days of hospitalisation and out-patient visits.
However, costs of medication may be underestimated
(van den Brink et al,
2004).
Indirect costs
Our study indicated that inclusion of the indirect costs for patients with
depression and/or anxiety was highly relevant. We did not assess productivity
losses resulting from reduced efficiency at work and from unpaid work (e.g.
household work), because of the practical and methodological difficulties
involved in measuring these losses. Consequently, the actual productivity
losses to society were probably underestimated.
In a population that has social insurance it is unlikely that respondents include the societal impact of ill health in quality-of-life measures, because they do not bear the full consequences of their reduced productivity (Brouwer et al, 1997; Meltzer & Johannesson, 1999). Recently, an empirical study by Sendi & Brouwer (2005) indicated that respondents do not include the effect of ill health on income if the instrument used does not explicitly ask about this effect (as is the case for the EQ–5D).
Cost-effectiveness
The results of our study are consistent with the findings of a systematic
review on the effectiveness and cost-effectiveness of brief psychological
treatments for depression (Churchill et
al, 2001). The review suggested that some forms of brief
psychological treatment, particularly those derived from
cognitive–behavioural models, are beneficial in the treatment of people
with depression who are being managed outside the hospital setting
(Churchill et al,
2001).
A cost-effectiveness study by Pyne et al (2003) estimated the cost per QALY of a primary care intervention for women with depression. Its findings suggested that enhanced care for women with depression was more expensive and more effective than usual primary care, the additional cost being US$5244 per QALY.
Overall, we found no cost savings of brief therapy over CBT or care as usual. However, in terms of the MHCs, brief therapy was a cost-effective treatment and may help to reduce waiting lists. In routine practice, ‘stepped care’ is characterised not only by fewer sessions, but also by an earlier start after intake. Subsequently, the MHCs may increase the quality of care that they provide by a formalised approach, and be more successful in meeting the preferences of patients. This might allow redistribution of some resources to the group of patients who are not effectively treated. This treatment policy should be supported by a monitoring system for detecting inadequately treated patients to ensure that there is a timely switch to a more appropriate treatment option.
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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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This study was funded by ZonMw (the main provider of financial support for independent research on health and healthcare in The Netherlands) and SBWOGG (an independent foundation).
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONClinical Implications and...ACKNOWLEDGMENTSREFERENCES |
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Received for publication September 13, 2004. Revision received June 1, 2005. Accepted for publication June 8, 2005.
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