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Background

The economic burden of obsessive–compulsive disorder

The total economic burden of OCD for the NHS and society in the UK is difficult to estimate and is not accurately known.²³⁷Work conducted in the USA during the 1990s suggests that the total costs of OCD equated to 5.7% of the estimated US$147.8B cost of all mental illness, and 18.0% of the costs of all anxiety disorders.²³⁸The direct costs to health services and patients of medical care represents only one aspect of the total burden. Indirect costs to patients and society as a result of lost productivity and wider impacts on informal care from friends and family members are also substantial.²³⁹Very few studies have estimated the per-patient health-care costs of OCD or the incremental costs compared with the general population or patients with other mental health problems.^240,241The limited evidence available suggests that OCD has a similar health-care burden to depression, but with a relatively higher use of psychotropic medications.²⁴⁰The high cost of care for patients with OCD raises the possibility that therapies with a substantial and sustained effect on symptoms may reduce health-care costs in the long run.

Existing evidence on the cost-effectiveness of treatment for obsessive–compulsive disorder: primary studies

There are very few primary economic studies of interventions for patients with OCD, particularly economic evaluations conducted alongside RCTs likely to provide the most internally valid data. Tolin et al.²⁴² collected cost and outcome data alongside a trial comparing stepped with standard ERP therapy in

30 adults with moderate OCD symptoms (YBOCS score of ≥ 16) of at least 12 months’ duration. This study reported no statistically significant differences in efficacy between interventions, measured by mean improvement in YBOCS scores or response rates (defined as YBOCS score of ≤ 12) at 3 months’ follow-up.

Total costs, including direct and indirect costs to patients, those who pay for health care (e.g. regional health-care authorities) and health-care providers (e.g. hospitals), were lower in the stepped care arm (US$2480 vs. US$4280; p < 0.05). An incremental cost-effectiveness ratio was not calculated and the small sample size limits interpretation. However, the authors conclude that their results suggest that stepped ERP care can significantly reduce treatment costs. McCrone et al.²⁴³report an economic evaluation of a three-arm RCT¹⁷⁸comparing computer-guided BT, clinician-guided BT and a relaxation control therapy in 218 adults with DSM-IV-defined OCD. In incremental analyses, the authors report that the cost per one point improvement in YBOCS score of computer-guided therapy (£64, 95% CI £36 to £249) and clinician-guided therapy (£90, 95% CI £61 to £167) was modest compared with relaxation control.

A Cochrane review of psychological treatments for OCD, noting the lack of evidence on efficiency, called for future trials to include an economic evaluation.²⁴⁴Such trials are under way, including the Obsessive Compulsive Treatment Efficacy Trial,²⁴⁵which compares the cost-effectiveness of computerised CBT with guided self-help, and a Dutch trial comparing schema therapy versus clarification-oriented psychotherapy versus treatment as usual.²⁴⁶When published, these trials will improve the evidence base on cost-effective care for OCD. However, they will not answer many of the questions facing clinicians, policy-makers and health-care funders. A single trial cannot compare the large number of pharmacological and behavioural therapies available for OCD and typically will not have sufficient follow-up to determine whether or not initially expensive therapies are justified by better long-term outcomes.²⁴⁷A decision analysis based on a NMA of RCTs, estimating costs and outcomes beyond the end of trial follow-up is likely to provide the best evidence to inform this complex decision.

Existing evidence on the cost-effectiveness of treatment for obsessive–compulsive disorder: models

Previous work²⁴⁸has developed decision-analytic models to evaluate the cost-effectiveness of therapy for patients with OCD underpinning the NICE appraisal of computerised CBT. These authors developed a decision tree comparing three interventions (computer-guided BT, clinician-led BT or relaxation) based predominantly on one RCT in 218 adults with DSM-IV-defined OCD. The decision model tracked

compliance with BT, response among compliers and relapse among responders during 6-month cycles over an 18-month time horizon. The authors concluded that, subject to substantial uncertainties, therapist-led CBT is effective compared with relaxation and that computerised CBT has the potential to be cost-effective, depending on the licence fees for health-care commissioners.²⁴⁸The authors acknowledged significant limitations in their model, particularly relating to the indirect method of estimating quality-of-life (utility) scores for calculating quality-adjusted life-years (QALYs) because data on this parameter are scarce.

In developing their clinical guidelines for the treatment of OCD, NICE¹¹⁸also describe a crude model for comparing the cost-per-responder of usual care, SSRIs, CBT and combination therapy. Pooled effect sizes for each therapy were estimated based on separate pairwise meta-analyses. NICE concluded that CBT alone is dominated by SSRIs and combination therapy and, therefore, that CBT alone is unlikely to be cost-effective.

However, this conclusion does not appear to be supported by the data (see table 3, p. 214¹¹⁸); furthermore, no probabilistic sensitivity analyses were conducted to estimate statistical uncertainty about this conclusion.

Our model addresses a broader question than the previous cost per QALY gained model²⁴⁸by comparing behavioural and pharmacological interventions. We used a more comprehensive range of evidence, based on a NMA of RCTs, to inform model estimates of effect size and allowing a full probabilistic assessment of the relative cost-effectiveness of treatment strategies.

Cost-effectiveness model methods

Overview

The model evaluates the cost-effectiveness (cost per QALY gained) of pharmacotherapies, psychological interventions and combinations of both from a NHS perspective. In the final section of this chapter, we discuss the likely implications of a broader societal perspective. The primary model time horizon is 5 years.

The interventions evaluated in trials are relatively inexpensive, meaning that therapies with a sustained effect on OCD symptoms would be expected to become cost-effective over a relatively short time horizon.

Furthermore, as longitudinal cohort studies of patients with OCD over protracted periods of time are rare, any extrapolation of trial results over the lifetime of patients would be very speculative. Therefore, we elected to evaluate cost-effectiveness over a 5-year time horizon. The model uses probabilistic analysis to quantify the stochastic uncertainty around estimates of cost-effectiveness. The importance of parameter and structural uncertainty is also tested through sensitivity analyses.

Patient populations and interventions compared

The model evaluates the cost-effectiveness of interventions in two patient populations; children and

adolescents, and adults. This reflects our NMA, which is also stratified by age. The weighted average age of patients recruited to adult trials is approximately 36 years, compared with 12 years in trials conducted in children or adolescents. The model structure is identical for the two populations; however, the parameter values vary to reflect differing treatment effects and long-term probabilities of response and relapse in these patient populations. All active interventions that were included in the NMA for both outcomes (dropout and YBOCS/CYBOCS scores) were compared in the cost-effectiveness model. We did not evaluate pharmacological and psychological placebos and the herbal remedy hypericum, as they are not directly relevant to NHS decision-makers. In total, there were 13 interventions compared in adult trials, including six SSRIs (see Table 20), and seven interventions evaluated in trials of children and adolescents, including three SSRIs (see Table 37). As the NMA revealed no clear difference within SSRIs in effect on symptoms or dropout rates, we elected to evaluate SSRIs at the class level in the cost-effectiveness analysis. Therefore, the

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cost-effectiveness of eight interventions in the adult model and five interventions in the child and adolescent model is compared. In sensitivity analyses, we reran the model restricting the evidence on treatment response and dropout rates to those RCTs considered to have (1) low attrition; (2) low risk of bias on ‘incomplete outcome assessment’; or (3) low risk of bias on ‘blinding of the outcome assessor’, to evaluate the potential impact of RCT bias on our findings and mirror the NMA. In the adult model, we also conducted a sensitivity analysis excluding RCTs that used a waitlist control group for psychological therapies. Blinding of participants is not possible in these trials and, therefore, they may be more prone to bias.

Model structure

The model comprises a decision tree covering the initial response to treatment at 12 weeks and a Markov model to simulate the course, costs and outcomes (utilities) of OCD from 12 weeks to 5 years. The initial 12-week period is chosen, as this represents the median follow-up period used in the trials summarised in our meta-analysis. The structure of the decision tree is the same for all interventions in both adult and child and adolescent models (Figure 12). Patients are assigned to treatment and will either continue to receive the prescribed course of treatment during the 12-week period or prematurely discontinue treatment (drop out). In our primary analysis, we assumed that if a patient drops out of treatment they get no benefit from treatment (‘no response’). Patients who continue treatment (comply) during the 12-week period are categorised in accordance with the degree to which their symptoms improve after treatment (‘full response’, ‘partial response’, ‘no response’). The appropriateness of the assumption that patients who drop out of treatment have no response depends on the statistical methods used in RCTs when analysing CYBOCS/YBOCS scores. It would be appropriate in trials reporting ‘per-protocol’ analyses, where mean CYBOCS/YBOCS scores exclude those who drop out. However, in trials reporting ‘intention-to-treat’ analyses where dropouts are already included in the CYBOCS/YBOCS effect estimate, it would effectively double-weight poorer outcomes in patients who drop out. It was often difficult to ascertain whether trials had conducted a pure ‘per-protocol’

analysis or a pure ‘intention-to-treat’ analysis. Therefore, in sensitivity analysis, we test this structural assumption.

After the initial 12 weeks, the course of patients’ OCD symptoms is tracked using a Markov model with four health states (Figure 13). The Markov model includes a ‘dead’ state; however, in a young cohort of patients with OCD over a 5-year time horizon this will be a very rare event. The remaining three health states are connected by bidirectional arrows, meaning that patients in the model can relapse to a more symptomatic state or achieve partial or full symptom response at any point during the 5 years. In order to estimate the pathway of a patient cohort through this Markov model, we need information on nine transition probabilities at each time point (cycle) of the model. The Markov model uses a 12-week (3-month) cycle length to track OCD symptom response at intervals from 12 weeks to 5 years.

Markov

Complied

Dropout: no response

Full response

Partial response

No response

Markov

Markov

Markov

Model parameters: dropouts and responses during the initial 12 weeks

The results of the NMA are used to estimate the probability that patients will drop out of treatment before 12 weeks. In the meta-analysis, the ORs for dropout, compared with drug placebo, were typically close to 1 and, with the exception of clomipramine in adults, had wide CrIs spanning unity (see Tables 27 and 44).

The equivalent probability of dropout and associated CrIs for each intervention are provided in Table 49 (adults) and Table 50 (children/adolescents).

We also used the results of the NMA to estimate the initial probability of full, partial and no response to therapy. One challenge in using this modelling approach is that there is no consistent definition in the literature of how response should be measured or categorised.²⁴⁹Response may be defined based on the CYBOCS/YBOCS, using absolute (e.g. YBOCS score of ≤ 12) or relative (e.g. YBOCS score improves by ≥ 25 or 30% or 35% from baseline) thresholds,^90,250,251or using additional measures such as the Clinical Global

Full response

Partial response

No

response Dead

FIGURE 13 Markov model structure for disease course from 12 weeks to 5 years.

TABLE 49 Adult dropout probabilities

Intervention Source Probability 95% CrI Probabilistic analysis

SSRIs NMA 0.21 0.15 to 0.28 5000 MCMC posterior distribution

Venlafaxine NMA 0.10 0.03 to 0.22 5000 MCMC posterior distribution

Clomipramine NMA 0.27 0.20 to 0.36 5000 MCMC posterior distribution

BT NMA 0.21 0.10 to 0.35 5000 MCMC posterior distribution

CBT NMA 0.17 0.08 to 0.29 5000 MCMC posterior distribution

CT NMA 0.21 0.09 to 0.37 5000 MCMC posterior distribution

Fluvoxamine + CBT NMA 0.41 0.01 to 0.96 5000 MCMC posterior distribution

Clomipramine + BT NMA 0.25 0.11 to 0.44 5000 MCMC posterior distribution

MCMC, Markov chain Monte Carlo.

TABLE 50 Children and adolescents dropout probabilities

Intervention Source Probability 95% CrI Probabilistic analysis

SSRIs NMA 0.20 0.04 to 0.48 5000 MCMC posterior distribution

Clomipramine NMA 0.46 0.11 to 0.87 5000 MCMC posterior distribution

BT NMA 0.62 0.09 to 0.99 5000 MCMC posterior distribution

CBT NMA 0.14 0.02 to 0.42 5000 MCMC posterior distribution

CBT + sertraline NMA 0.16 0.02 to 0.49 5000 MCMC posterior distribution

MCMC, Markov chain Monte Carlo.

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Impressions²⁵²scale assessment of overall illness improvement or psychiatric status ratings (PSRs).^91,97Not all RCTs in the NMA reported response rates, and definitions of response varied among those that did. Therefore, it is not possible to directly estimate response rates from the meta-analysis. Instead, we indirectly estimate the initial response based on CYBOCS/YBOCS scores. In our primary analysis, we used a CYBOCS/YBOCS score threshold of < 16 to define full response and a CYBOCS/YBOCS score of ≥ 16 and < 20 to define partial response.

The < 20 threshold corresponds to an approximately 25% improvement or 1 SD improvement upon the mean baseline YBOCS scores observed in trials. We tested a range of other values in sensitivity analysis.

We estimate a normal distribution for individual CYBOCS/YBOCS scores on placebo (reference). The mean of this distribution is estimated by fitting a standard normal random-effects meta-analysis model to all reference (placebo) arms of trials (included in the NMA) that recorded a mean score and standard error at follow-up. The mean score was estimated using a standard meta-analysis model in which each study provides an estimate of the mean with associated standard error. The SD of the distribution is estimated by fitting a normal random-effects distribution to the SDs at follow-up for all treatments that report this. Note that this assumes that the spread of CYBOCS/YBOCS scores does not depend on treatment. A prediction from these two random-effects distributions (i.e. predictive distribution for mean and SD response) is used to describe our uncertainty in the estimated normal distribution parameters. Relative treatment effects obtained from the NMA were added to the mean reference (placebo) CYBOCS/YBOCS scores, to obtain a predicted mean CYBOCS/YBOCS score for each intervention, and the SD in absolute scores is assumed to be equal for all interventions [and equal to that predicted for the reference (placebo)]. This gives us a prediction for the distribution of absolute CYBOCS/YBOCS scores across individuals for each intervention at follow-up. Assuming these scores follow a normal distribution, the proportion of patients achieving a CYBOCS/YBOCS score of < 16 (full response), between 16 and 20 (partial response), and > 20 (no response) were estimated using appropriate evaluations of the cumulative distribution function for the normal distribution. All of the above is computed at each iteration of a Bayesian Markov chain Monte Carlo simulation, so that we fully reflect uncertainty and correlations in our estimates of the proportions in each category for each intervention. The resulting probabilities for response at 12 weeks, stratified by intervention class and age, are provided in Tables 51 and 52.

TABLE 51 Probability of full, partial and no response at 12 weeks, based on a NMA; adult population stratified by intervention

Intervention Source

Probability of full

response 95% CrI

Probability of partial

response 95% CrI Probabilistic analysis

SSRIs NMA 0.32 0.02 to 0.71 0.22 0.09 to 0.42 5000 MCMC posterior

distribution

Venlafaxine NMA 0.32 0.01 to 0.78 0.20 0.05 to 0.40 5000 MCMC posterior

distribution

Clomipramine NMA 0.39 0.04 to 0.79 0.22 0.10 to 0.43 5000 MCMC posterior

distribution

BT NMA 0.84 0.50 to > 0.99 0.09 <0.01 to 0.23 5000 MCMC posterior

distribution

CBT NMA 0.42 0.05 to 0.86 0.21 0.08 to 0.42 5000 MCMC posterior

distribution

CT NMA 0.80 0.39 to > 0.99 0.11 <0.01 to 0.27 5000 MCMC posterior

distribution

Fluvoxamine + CBT NMA 0.54 0.07 to 0.97 0.19 0.02 to 0.38 5000 MCMC posterior distribution

Clomipramine + BT NMA 0.78 0.33 to > 0.99 0.11 <0.01 to 0.29 5000 MCMC posterior distribution

Model parameters: initial pharmacological and psychological therapy costs

The mean daily dose of pharmacological interventions varied between and within trials (see Appendix 6).

In order to estimate the initial costs of pharmacotherapy, we selected a daily dose close to the mean of the mean daily doses reported in RCTs, stratified by adults and children and adolescents populations

(Table 53). This dose was rounded to the nearest multiple of a tablet/capsule size available. We also used data on mean daily dose reported in RCTs to define the plausible maximum and minimum daily dose, and tested the impact of these daily doses on incremental costs and cost-effectiveness in deterministic sensitivity analyses.

TABLE 52 Probability of full, partial and no response at 12 weeks, based on a NMA; child and adolescent population stratified by intervention

response 95% CrI Probabilistic analysis

SSRIs NMA 0.53 0.05 to 0.97 0.16 0.02 to 0.31 5000 MCMC posterior

distribution

Clomipramine NMA 0.62 0.08 to 0.99 0.14 0.01 to 0.29 5000 MCMC posterior

distribution

BT NMA 0.71 0.13 to > 0.99 0.12 <0.01 to 0.26 5000 MCMC posterior

distribution

CBT NMA 0.73 0.19 to > 0.99 0.11 <0.01 to 0.26 5000 MCMC posterior

distribution

CBT + sertraline NMA 0.78 0.25 to > 0.99 0.10 <0.01 to 0.24 5000 MCMC posterior distribution

MCMC, Markov chain Monte Carlo.

TABLE 53 Mean daily dose, cost and minimum and maximum value of pharmacotherapy stratified by drug and age group

Fluoxetine 49.46 20 80 60 8.48 1.01

Fluvoxamine 252.32 50 300 250 117.81 33.66

Paroxetine 45.95 20 60 50 9.21 3.29

Sertraline 154.25 50 200 150 12.48 4.16

Citalopram 42.73 20 60 40 3.51 1.17

Escitalopram 15 10 20 15 71.10 23.70

Venlafaxine 282.5 225 350 300 14.46 2.41

Clomipramine 196.48 50 300 200 25.80 2.15

Children/adolescents

SSRIs 22.92 9.56

Fluoxetine 32.35 20 80 40 5.66 1.01

Fluvoxamine 165 50 200 150 70.69 33.66

Sertraline 154.36 25 200 150 12.48 4.16

Clomipramine 190 75 200 200 25.80 2.15

a Mean of the mean dose in RCT arms where reported.

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The unit costs for pharmaceuticals were based on the British National Formulary estimates.²⁵³The cheapest combination of pack sizes was used to derive the cost of pharmacotherapy for 12 weeks (Table 54). The cost of the SSRI class was estimated by taking an average cost of the SSRIs used in the RCTs, weighted by the number of participants randomised to each SSRI. We assumed that patients who complied with therapy would incur pharmaceutical costs throughout the initial 12-week period. We assumed that patients who dropped out of pharmacotherapy incurred only the cost of one prescription (see Table 53).

The number of psychological therapy sessions showed little consistency within or between BT, CT and CBT trials (see Appendix 6), ranging from a maximum of 40 to fewer than 10 sessions. Session duration, where reported, ranged from < 1 hour to 2.5 hours per session. We estimated typical therapist contact hours of psychological therapy, stratified by therapy type (BT, CT and CBT) and patient group (adults, children/

adolescents) based on the mean number of contact hours estimated from trial reports (Table 55). We used the contact hours reported in RCTs to define the plausible maximum and minimum contact hours for use in sensitivity analyses. We used Personal Social Services Research Unit (PSSRU)²⁵⁴unit costs to value initial psychological therapy. The estimated hourly face-to-face cost of conducting all types of psychological therapy (BT, CT and CBT) was assumed to be equal to the CBT hourly cost (£99; 2013 prices) estimated by the PSSRU. We assumed that patients who dropped out of psychological therapy did so after attending, on average, one-quarter of sessions, thereby incurring one-quarter of therapy costs. The cost of combinations of pharmacological and psychological therapies were estimated to be the sum of the components.

TABLE 55 Mean contact hours, cost and minimum and maximum value of psychological therapy, stratified by therapy type and age group

Intervention

BT 17.17 10 46.5 1699.83 424.96

CBT 20.78 10 60 2057.22 514.31

CT 15.25 8 30 1509.75 377.44

Children/adolescents

BT 22.5 15 30 2227.50 556.88

CBT 15 10 21 1485.00 371.25

TABLE 54 British National Formulary drug costs stratified by pack size and dose

Intervention Units I

Fluoxetine 30 20 1.01 30 60 28.79

Fluvoxamine 60 50 16.83 30 100 16.83

Paroxetine 30 20 1.52 30 30 1.77

Sertraline 28 50 1.92 28 100 2.24

Citalopram 28 10 0.91 28 20 1.00 28 40 1.17

Citalopram 28 10 0.91 28 20 1.00 28 40 1.17

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