Institute of Psychiatry, Kings College London
Institute of Psychiatry, Kings College London, and Personal Social Services Research Unit, London School of Economics, London
University of New South Wales, Sydney, Australia
Institute of Psychiatry, Kings College London
Ultrasis UK Ltd, London
Universities of Leeds and Sheffield
Institute of Psychiatry, Kings College London
Imperial College, London
Institute of Psychiatry, Kings College London, UK
Correspondence: Dr Paul McCrone, Centre for the Economics of Mental Health, Box PO24, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK. Tel: +44(0)20 7848 0198
Declaration of interest J.P. and J.A.G. are minority partners in the commercial exploitation of Beating the Blues, the computerised therapy program used in the study, and D.G. and D.A.S. are occasional consultants to Ultrasis plc; K.C. works for Ultrasis plc.
See pp. 4654,
this issue.
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ABSTRACT |
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Aims To assess the cost-effectiveness of computer-delivered CBT.
Method A sample of people with depression or anxiety were randomised to usual care (n=128) or computer-delivered CBT (n=146). Costs were available for 123 and 138 participants, respectively. Costs and depression scores were combined using the net benefit approach.
Results Service costs were £40 (90% CI £28 to £148) higher over 8 months for computer-delivered CBT. Lost-employment costs were £407 (90% CI £196 to £586) less for this group. Valuing a 1-unit improvement on the Beck Depression Inventory at £40, there is an 81% chance that computer-delivered CBT is cost-effective, and it revealed a highly competitive cost per quality-adjusted life year.
Conclusions Computer-delivered CBT has a high probability of being cost-effective, even if a modest value is placed on unit improvements in depression.
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INTRODUCTION |
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METHOD |
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Intervention
The computerised therapy program used (Beating the Blues)
consisted of a 15 min introductory video followed by eight 50 min sessions of
cognitivebehavioural therapy (further details available from the
authors upon request). General practitioners and practice nurses were kept
informed about the patients progress by means of automatically
generated computer printouts following each session. Treatment as usual
consisted of a variety of interventions, including discussions with the
general practitioner, referral to a counsellor, practice nurse or mental
health professional, and treatment of physical conditions.
Outcome measures
Clinical measures were recorded at baseline and at a number of follow-up
points. This was a cost-effectiveness analysis, and it was therefore
appropriate to use the primary clinical outcome measure in the evaluation.
Further analyses used an economic measure, the quality-adjusted life year
(QALY), to compare the cost-utility of the interventions. The primary clinical
outcome measure was the change in the level of depression, rated using the
Beck Depression Inventory (BDI; Beck et
al, 1996), between randomisation and 6 months following the
end of treatment (which was around 8 months following randomisation). Other
clinical outcome measures used were the Beck Anxiety Inventory (BAI;
Beck & Steer, 1990) and the
Work and Social Adjustment (WSA) scale
(Marks, 1986).
Where BDI scores were missing, values were imputed using best subset regression analysis in Stata (StataCorp, 2002). The independent variables were the available BDI scores (pre-treatment, post-treatment, and at 1 month, 3 months and 6 months following treatment), as well as BAI and WSA scores and a number of socio-demographic characteristics (age, gender, ethnicity, employment status, marital status, length of illness and whether antidepressants were being taken).
A secondary outcome measure was an estimate of the number of depression-free days in the 8 months following randomisation, on the basis of BDI scores at four assessment points (immediately post-treatment, and 1 month, 3 months and 6 months following treatment), adapting an algorithm developed by Lave et al (1998). The calculation did not use the imputed values described above; if BDI data were missing, then it was conservatively assumed that the participant was in a state of depression at that time. The number of days in a state of depression between time points was estimated using a straight line interpolation. Therefore, if someone was not depressed at the post-treatment assessment but was depressed at the 1-month follow-up (or the score was missing), it was assumed this person had had 15 depression-free days during the period. A further measure, the number of QALYs gained, was also used; these were estimated using the method described by Lave et al (1998). On a utility scale of 0 to 1, a depression-free day was assumed to score 1 and a day with depression was assumed to score 0.59. These values were averages, calculated by Lave et al (1998), of those reported in the literature. Costs were formally linked to the above two outcome measures in the form of cost-effectiveness and cost-utility analyses (see below).
Service use
Service use data were collected from general practitioners notes and
other primary care sources by nurses for patients in each arm of the trial for
two periods: the 6 months prior to randomisation and the 8 months following
randomisation these periods were considered sufficiently long to
capture the use of rarily accessed (but often expensive) services. The
collection of baseline data allowed differences that might exist even within
randomised controlled trials to be controlled for. The reliability of this
method of data collection clearly depends on the reliability of the
record-keeping of primary care staff. The aim was to be comprehensive, so that
the effects on all health care services of providing the intervention or usual
care could be observed. Because data were collected from primary care sources
it was not possible to measure use of social service care other than that of
home helps. Other studies too have focused on health care costs
(Bower et al,
2000).
Services measured included actual contacts with mental health care staff (psychiatrists, psychologists, community mental health nurses, counsellors and other therapists); with primary care staff (general practitioners, practice nurses, district nurses and health visitors); with hospital services (in-patient care for psychiatric and physical health reasons, out-patient care, day surgery, and accident and emergency attendances); with home helps; medication (all medication was recorded, but only data on antidepressants, anxiolytics and sedatives were used in the analyses); and contacts with other services (chiropodists, physiotherapists and dieticians). The number of contacts with each service was recorded or, in the case of medication, the length of the course and the dosage.
Service costs
Unit costs (which aim to reflect the long-run marginal costs) for most
services were obtained from a recognised national source
(Netten & Curtis, 2000),
which calculated staff costs by dividing the total cost (salary, oncosts,
overheads, capital, land and training) of the service over 1 year by an
appropriate unit of activity. Hospital costs (accident and emergency care, day
surgery, generic in-patient and out-patient services and psychiatric
in-patient care) were also obtained from this source. Medication costs were
taken from the British National Formulary
(British Medical Association & Royal
Pharmaceutical Society of Great Britain, 2001). Unit costs were
multiplied by the service use data to generate service costs per patient.
Although general practitioners were not charged for the use of the computerised therapy program in this study, in routine practice they would need to purchase a licence to use Beating the Blues. The average price per patient using this program was estimated by the manufacturer to be £100, taking into account the expected throughput of patients. To this was added £16 to cover the overhead and capital costs of the primary care setting where the application would be used, a figure derived from costs reported by Netten & Curtis (2000). The total cost was then divided by 8 to calculate the cost per session (£14.50). The system is designed to be used independently by patients; however, primary care staff would be on hand to offer assistance if necessary, and they would also retrieve from the system reports on patients progress.
Lost production
We recorded the number of days of absence from work during the baseline and
follow-up periods on the basis of the issue of a certificate by the general
practitioner. Work days lost that did not require such a certificate were not
recorded, and this measure of lost work is therefore an underestimate. We used
the human capital approach of assuming that the cost of each day
of lost employment is equal to the age- and gender-specific national average
daily wage; our rationale was that depression and anxiety may be less likely
than other illnesses to result in long-term work absence and, therefore,
replacement would not be as probable. Given the methodological debate
concerning such costs, and because changes in production costs are more
correctly seen as a consequence of treatment, we present service costs and
total costs (including lost employment) separately.
The baseline and follow-up service use and cost periods differed in length. In order to make meaningful comparisons, the baseline 6-month costs were all multiplied by 1.33 in order to generate 8-month cost figures.
Statistical analysis
The analyses were conducted on an intention-to-treat basis, with the main
focus on comparing the intervention and control groups. Significance tests for
the difference in mean total costs at follow-up were conducted by generating
bootstrapped 90% confidence intervals (with 5000 repetitions), because of the
expected non-normality of the cost data. We controlled for baseline cost
differences and phase of data collection (which was an indicator variable)
using multiple regression analysis. (The rationale for using 90% confidence
intervals rather than those at the more conventional level of 95% was that we
are less risk-averse when making financial decisions than we are when making
clinical decisions. It could, of course, be argued that financial decisions in
health care have potentially serious implications, but these implications are
likely to be clinical.) There might have been differences in costs between the
practices, and therefore we used the cluster option in the
bootstrapped regression analysis. Clustered regression generally results in
larger standard errors than standard regression.
Cost-effectiveness
The cost-effectiveness of the intervention compared with usual care was
determined using the net benefit approach
(Briggs, 2001). There is a
theoretical, but unknown, value (represented by the term below) that
society would place on a 1-unit reduction in depression score as measured by
the BDI. The net benefit (NB) to society of the intervention can be defined as
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We estimated net benefits for all patients in the sample by assuming
different values for ranging from £0 to £100 in £10
increments. A regression model was then used to determine the mean difference
in net benefit between the intervention (Beating the Blues
BtB) and treatment as usual (TAU) groups for every value of
,
controlling for baseline costs and the phase of data collection. For each
model, 5000 regression coefficients for the BtB/TAU variable were generated
using bootstrapping, and the proportion of these that were greater than zero
indicated the probability that the intervention was cost-effective (i.e. it
resulted in a mean incremental net benefit greater than zero). These
probabilities were subsequently used to generate a cost-effectiveness
acceptability curve. (There was no information available to inform the range
and increments of
; we therefore chose a range that would show what
the value of
had to be to achieve a probability value of around 0.8
for BtB being cost-effective.)
The net benefit approach was also used to analyse the link between costs
and depression-free days. In this analysis, ranged between £0
and £50 and increased in £5 increments. (These increments were
different from those used above, as it became clear that the likelihood of BtB
being more cost-effective than TAU was sensitive to smaller changes in
.) The same approach was used to assess the cost-utility of BtB
relative to TAU. However, here alternative societal values for 1 QALY gained
were used to generate the cost-utility acceptability curve. The alternative
values ranged between £0 and £50 000 and increased in increments
of £5000. Again, these values were chosen for pragmatic reasons, i.e. to
show the point at which BtB becomes clearly cost-effective.
The clinical trial also used the BAI and the WSA scale. These measures were not formally linked with the cost data, although the cost findings were viewed alongside changes in these scores in the form of a costconsequences analysis in order to draw broad conclusions about the efficiency of BtB v. TAU.
Sensitivity analyses
Uncertainty often exists around some of the parameters in economic
evaluations. In this study the unit cost of BtB was originally assumed to be
£14.50 per session, but this was dependent on the costs to general
practices of the system and the expected throughput of patients. We therefore
examined the impact of different unit costs on service and total costs and
also on the cost-effectiveness of BtB relative to TAU. The alternative values
were £5 and £30 per session.
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RESULTS |
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Service use
During the 6 months prior to randomisation (baseline), most of the patients
had contact with their general practitioner
(Table 2). Slightly under half
of the participants in each group had contact with practice nurses and
approximately a quarter had out-patient appointments; the latter were
predominantly for physical health reasons, as were all in-patient episodes.
Participants in the usual treatment group were more likely to use services in
the other category (these services, identified from the case
notes, were dietician, midwife, support worker, chiropodist, complementary
health care and a medical check with a private health insurer).
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The high level of contact with general practitioners, practice nurses and out-patient services continued into the 8-month follow-up period. Large differences were observed for the proportion of patients attending accident and emergency or out-patient departments, and having contacts with community psychiatric nurses, counsellors and other therapists. Greater use was made by the TAU group for all these services. For psychotherapy services, including counselling, this may reflect the suppression of such services to the BtB group during the treatment period imposed by the study design. By follow-up, the proportion of patients who had had to stop working at some time was reduced slightly in the BtB group.
At follow-up, two people in the BtB sample had had psychiatric in-patient treatment, for 20 days and 30 days respectively. None of the available information suggested that the study intervention had precipitated the need for in-patient psychiatric care. In both cases the patient either did not want the treatments offered in primary care or did not respond to them, and therefore more specialist mental health care was one option open to them. At the baseline assessment over a third of patients had been taking antidepressant medication, and this proportion increased slightly at follow-up.
Service costs
The mean costs of individual services were generally quite low for the two
groups at baseline (Table 3),
and the overall mean total service cost was £33 lower for the BtB group
at this assessment point. Few substantial differences between the groups had
emerged by follow-up; however, the mean costs of both counsellors and other
therapists over the 8-month period were substantially higher for the TAU
group. The mean service cost was £40 higher for the BtB group at
follow-up; with baseline costs and phase of data collection controlled for,
this difference was not statistically significant (90% CI £28 to
£148).
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Total costs
Lost employment costs were on average £267 greater for the TAU group
at baseline. With the inclusion of lost employment, the TAU group was shown to
have mean baseline costs that were £299 higher than those for the BtB
group. The mean cost of lost employment at follow-up was £407 less for
the BtB patients and this was statistically significant (90% CI
£196£586). The TAU group was £367 more expensive with
the inclusion of the costs of lost employment, and controlling for baseline
costs this was seen to be statistically significant (90% CI
£123£589).
Outcomes
Computer-delivered CBT resulted in improved scores on the BDI, BAI and WSA
scale (full details of the main clinical outcomes are reported by
Proudfoot et al,
2004, this issue). With imputation for missing values (which was
particular to the economic analysis), this therapy resulted in a mean
reduction in BDI score, relative to the usual treatment group, of 3.5 (95% CI
0.66.4). Based on the BDI scores over time, the TAU group was estimated
to have a mean of 61.0 (s.d.=67.1) depression-free days, v. 89.7
(s.d.=74.2) depression-free days for the BtB group. This estimate is limited
by the small number of measurement points and the uncertainty created by
missing data. Complete BDI follow-up scores were available for 148 (57%) of
the 261 participants: 65 (53%) of the TAU group and 83 (60%) of the BtB group.
For 34 (13%) one of the scores was missing, for 21 (8%) two scores were
missing, for 16 (6%) three scores were missing and for 42 (16%) all four were
missing. The difference in number of depression-free days between the groups
was 28.4, after controlling for phase of data collection, and this was highly
significant (95% CI 10.745.5). These figures are equivalent to an
incremental QALY gain of 0.032 for BtB over TAU. This equates to 3% of 1 QALY,
which is relatively small, but the follow-up period was also relatively
short.
Cost-effectiveness and cost-utility analysis
The intervention was both more expensive and more effective than treatment
as usual (although only the effectiveness difference was statistically
significant); it was therefore uncertain whether it was more cost-effective.
Figure 2 shows that if society
places a zero value on a unit reduction in BDI score then there is only a 14%
chance that BtB is more cost-effective than TAU. However, the probability of
the intervention being more cost-effective soon increases with positive values
placed on such a reduction, and at a value of £40 and above per unit
reduction, the probability of BtB being cost-effective is in excess of
0.8.
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The effects of using unit costs of £5 and £30 for the computer-delivered therapy sessions on cost-effectiveness are also shown. In the former case it can be seen that even with a zero value placed on a unit reduction in BDI score there is a 45% chance that BtB is more cost-effective than TAU; for the more expensive sessions, slightly higher values are required before BtB is clearly more cost-effective.
Figure 3 shows that if society places a zero value on a depression-free day then there is only a 14.5% chance that BtB is more cost-effective than TAU. However, if a value of £5 is placed on a depression-free day there is an 80% chance of BtB being more cost-effective, and this soon approaches 100% for higher values.
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The results of the cost-utility analysis are shown in Fig. 4. If society places a £5000 value on 1 QALY there is an 85% chance that BtB is more cost-effective. The figure becomes over 99% with QALYs valued at £15 000.
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DISCUSSION |
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Service costs
Service costs were comparable to those reported, also in the UK, by Bower
et al (2000), who
found similar costs before and after psychological treatments for patients
with depression receiving face-to-face psychological therapies or treatment as
usual in primary care. It might be considered disappointing that neither study
found any direct cost savings from the provision of clinically effective
psychological therapies, whether delivered face-to-face or by computer.
However, a treatment yielding substantial clinical benefit, without incurring
significantly or commensurately greater costs, may reasonably be deemed
cost-effective.
Acceptability
Formally linking cost and outcomes data added further strength to the claim
that computer-delivered CBT is cost-effective. Even if the value placed by
society on a unit reduction in BDI score is considered to be only £40,
this therapy attains an 81% probability of cost-effectiveness. Similarly,
assigning a value of £5 to a depression-free day would result in an 80%
chance of the therapy being cost-effective. With regard to the cost-utility
analysis, there is a 99% probability that it is cost-effective if QALYs are
valued at £15 000, which appears to be well below the decision-making
threshold used by the National Institute for Clinical Excellence (NICE)
often assumed to be between £30 000 and £50 000, although
such a range has not been officially defined. Finally, the fact that the
therapy produced significantly better outcomes as measured by the BAI and the
WSA scale indicates that it is cost-effective in a broader sense than shown by
the analyses reported here.
Lost employment
Mean certificated lost-employment costs were lower following the
intervention. This result resembles that reported by Simon et al
(2000), who found that
previously depressed patients in remission are most likely to remain in paid
employment and report fewer missed days from work owing to illness. Our
findings suggest that the employment benefits of increasing access to
effective treatments for anxiety and depression are likely to outweigh the
direct savings in health care costs.
Implications
Computerised CBT offers a worthwhile contribution to the provision of
greater and more equitable access to psychological treatment for common mental
health problems encountered in primary care, as called for by the National
Service Framework for Mental Health
(Department of Health, 1999).
It could have a role in a stepped care model that would enable trained
cognitivebehavioural therapists to conserve their limited resources for
more complex and challenging cases. However, we have no evidence yet to
indicate whether computerised therapy is more or less cost-effective than
face-to-face therapy for patients with different levels of symptom severity.
Computerised therapy might also have a place in the management of patients who
refuse psychotropic medication, as well as of those whose medication
compliance is impaired by side-effects. The cost-utility analysis allows NHS
commissioners and others to compare the benefits of computer-delivered CBT
with those of interventions in other health care domains, with which
treatments of depression and anxiety such as BtB may be in competition for
scarce resources. Our cost-utility findings suggest that clinically effective
treatment of anxiety and depression yields good value for money relative to
many other areas of NHS expenditure. However, although computerised therapy
was cost-effective in securing better clinical and lost-employment outcomes
than usual treatment at costs that were not significantly greater, it must be
acknowledged that it did not appear to reduce health care costs either.
The effect of computer-delivered CBT on lost employment has wide implications for the value of effective mental health interventions for many stakeholders. Lost employment has adverse consequences for both individuals and their employers. Since both workers and employers stand to gain from computerised CBT, our findings support its provision within employee assistance programmes.
Limitations
This study has some limitations. The number of days of lost employment is
likely to be an underestimate, as lost days for which a doctors
certificate was not obtained were not included. Depression and anxiety
typically result in many shorter, uncertificated episodes of absence, which
are also likely to be curtailed by the provision of effective treatment
programmes. Their exclusion is likely to bias a comparison against an
effective treatment such as computer-delivered CBT.
A second limitation is that the service utilisation focus was on health care costs, although people with depression or anxiety may also make increased use of services provided by other agencies. In their comparison of short-term counselling with standard primary care for patients with depression, Simpson et al (2000) found that social care services accounted for 14% of total costs and criminal justice services 3% of total costs.
Third, the QALYs used in the cost-utility analysis were not directly measured as part of the study. Utility values were obtained from another study simply for days with and days without depression, and clearly there should be a more graduated spectrum of values between these two extremes. In addition, crucial assumptions had to be made with regard to missing BDI scores on which the depression-free days were based. The cost-utility analysis should thus be seen as more tentative than the cost-effectiveness analysis. Future studies should either measure utility directly, or should use an instrument from which QALYs can be more readily derived.
Fourth, the cost-effectiveness analysis required us to assume societal values for unit improvements in outcome. There are, however, no recognised benchmarks as to what are acceptable values for making decisions. A high probability of cost-effectiveness when a QALY gain is valued at £15 000 suggests good value for money, but this is only based on previous decision-making by bodies such as NICE. It is less clear what constitutes an appropriate value for a unit change in depression. The use of cost-effectiveness acceptability curves addresses this problem in part, but value judgements still need to be made when a treatment is both more expensive and more effective.
Finally, the costs of general practitioner consultations by patients in the intervention group may be slightly inflated because the protocol called for general practitioners to review patients proactively. Although such consultations might also have met clinical needs that would have occasioned the visits in any event, it is possible that we over-estimated the number of consultations that would be required by patients using computerised therapy in a non-research context.
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Clinical Implications and Limitations |
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LIMITATIONS
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Received for publication March 3, 2003. Revision received November 28, 2003. Accepted for publication December 15, 2003.
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