The cost-effectiveness of changes to the care pathway used to identify depression and provide treatment amongst people with diabetes in England: a model-based economic evaluation

The patient-level mathematical model used discrete-event simulation, and consisted of two inter-linked disease-specific models for diabetes and depression. A simplified schematic of the model is presented in Fig. 1.

The diabetes sub-model, which included mortality, was a modification of the United Kingdom Prospective Diabetes Study Outcomes Model version 2 (UKPDS OMv2), which has been described in detail elsewhere [14]. Briefly, this used patient characteristics (demographics and clinical measurements) to assess the progression of diabetes risk factors over time, and the development of diabetes-related complications. Diabetes-related complications included were either microvascular (ischaemic heart disease [IHD], blindness or renal failure) or macrovascular (heart failure, myocardial infarction [MI], stroke, diabetes ulcer or amputation). Both the risk of developing a diabetes-related complication and the probability of dying were taken from published regression models from the UKPDS OMv2, based on an individual with T2DM’s diabetes-related risk factors for complications and history of other complications. Progression rates for diabetes-related risk factors for complications included in the UKPDS OMv2 have not been published and hence were assumed to be constant. This is similar to the approach used in other diabetes models [15].

The depression sub-model was based on an existing model of depression care pathways, which was adapted to include minor (sub-threshold) depression as a separate health state [16]. This sub-model included the natural history of depression in the absence of treatment (in the form of incidence, remission and recurrence), and the impact of depression screening and treatment on this natural history.

The two disease-specific models were linked by modelling a bi-directional relationship between depression and diabetes-related complications such that individuals with T2DM and diabetes-related complications were more likely to experience depression; whilst patients with depression were more likely to develop diabetes-related complications. Successful depression treatment had the potential to decrease the time to remission. Conversely, the development of a diabetes-related complication had the potential to increase the hazard of depression incidence or recurrence.

Model parameters were informed by literature reviews, as described elsewhere [1]. Where possible, evidence was taken from studies that considered patient populations with both diabetes and depression. Otherwise, data from patients with depression alone or patients with diabetes irrespective of depression status were used supplemented by expert opinion. The evidence for depression progression, screening and case-finding used in the economic model is summarised in Table 1, whilst the evidence for depression treatment is summarised in Additional file 1. Evidence used to inform the progression of diabetes risk factors and the development of diabetes related complications were taken from the UKPDS OMv2 [14].

Baseline characteristics were taken mainly from the National Diabetes Audit [17] and the UKPDS OMv2 [14]. Neither of these contained evidence on the prevalence of depression amongst individuals with T2DM; evidence for major depression was taken from a cross-sectional English study [18]. Further details about the baseline characteristics are provided in Additional file 1. The rate of progression from minor to major depression was based on the randomised-controlled trial of Bot et al. [19]. Evidence about the incidence of major depression and relapse rates amongst individuals with diabetes were taken from a Dutch cohort study [20] and a randomised-controlled trial [21] respectively. In the absence of suitable evidence, the incidence of, prevalence of, and relapse rates for minor depression were assumed to be the same as those for major depression.

The average annual number of routine primary care appointments for individuals with T2DM but no depression was taken to be 12.5, based on published evidence [22]. It was assumed that individuals with both T2DM and depression would be less likely to engage with healthcare services and so have an average of eight primary care appointments per annum (excluding any appointments relating to their depression treatment). It was presumed that under the current care pathways, individuals with T2DM and a history of depression had a 20% probability of receiving an opportunistic screen during a primary care appointment, and a 5% probability for individuals with T2DM and no history of depression. It was assumed that the Whooley questionnaire was used for depression screening, with sensitivity and specificity taken from an existing meta-analysis [11]. The choice of the Whooley questionnaire for depression screening was motivated by national guidance in the UK Quality and Outcomes Framework [23]. Advisors concurred with this approach, although they noted that in practice the Whooley questions may be asked formally or informally. Patients with a positive screen underwent depression case-finding in the form of a structured interview, which was assumed to either confirm cases of depression (amongst people with depression) or correct cases of incorrectly identified depression (amongst people without depression).

Evidence on the effectiveness of depression treatment was taken from meta-analyses for both pharmacotherapy [24] and low-intensity psychotherapy [25]. Evidence for high-intensity psychotherapy was taken from an existing economic evaluation [16], who considered depression amongst the general population.

Based on the available evidence [1] and discussions with advisors, it was assumed that there was a bi-directional association between depression and diabetes-related complications, such that:

This association was incorporated within the model via a set of hazard ratios. Individuals with T2DM had an annual probability of developing a diabetes-related complication based on the UKPDS risk equations [14]. If an individual with T2DM had depression this risk was elevated in the model, based on the amount of time spent with depression in the preceding year (for example, if an individual spent half of the year with depression, then the elevated risk due to having depression was halved). Further, if an individual with T2DM and depression was receiving depression treatment, and they ultimately responded to it, it was assumed that their time spent with depression did not contribute towards an elevated risk. The hazard ratios for developing a diabetes-related complication due to having depression were taken from Lin et al. [26], for microvascular disease they were 1.31 for minor depression and 1.36 for major depression, whilst for macrovascular disease they were 1.00 for minor depression and 1.25 for major depression (values greater than one indicate an increased risk). If an individual with T2DM had a diabetes-related complication, the hazard for developing depression was increased. It was assumed that the hazard of developing depression was the same if individuals had one or several complications. No studies were identified which estimated the effect of having a diabetes complication on developing depression, so a hazard ratio of 1.5 was assumed for the effect of any diabetes complication on either minor or major depression. Further details on how this association was modelled are provided in Additional file 1. The robustness of model results to this assumed value was tested in sensitivity analyses.

The baseline utility values (measured using the EQ-5D) for patients with diabetes and the decrements in health-related quality of life (HRQoL) associated with MI, IHD, stroke, heart failure, amputation and blindness were taken from the most recent analysis of the UKPDS [27]. Utility decrements associated with foot ulcers, severe hypoglycaemia and renal failure were not reported in this study and were sourced from the literature [28‐30]. No studies were identified which reported on the impact of depression on HRQoL for patients with diabetes. These utility decrements were derived from a study amongst patients with depression [31]. It was assumed that the utility decrement due to depression was halved for the duration of treatment if the individual ultimately responded to treatment, reflecting the concept that HRQoL improved whilst on successful treatment. Estimates of resource use and unit costs were taken from national sources and from the literature [16, 32]. An overview of the costs and utilities is provided in Table 2, with details about resource use for depression treatment available in Additional file 1 (section B). In general, resource use and costs for depression treatment came from the economic evaluation of Tosh et al. [16]. The costs of treating diabetes and its complications were taken from Clarke et a.l [32]. Remaining costs were primarily based on national sources or assumptions where evidence was lacking. Based on discussions with advisors, it was unclear if implementing an opportunistic screen for depression would lead to an increase in the costs of a GP appointment. For the base-case an additional cost of £2 was used; two sensitivity analyses considered having no additional cost, and an additional cost of £4. Further detail on how the costs were derived is available elsewhere [1].

The policy of implementing collaborative care was modelled as an enhancement to the existing care pathway for individuals with depression and diabetes. The additional resource use associated with collaborative care was based on resource use patterns described in NICE clinical guideline 91 [11]. Implementing collaborative care was assumed to decrease the probability of drop-out (relative risk 1.33) and increase the probability of responding to depression treatment (relative risk 1.79), with evidence taken from a meta-analysis by Huang et al. [33]. Further details on the implementation of collaborative care are available in Additional file 1 (section C).

Under the policy of improved opportunistic screening, it was assumed that every primary care appointment for individuals with diabetes included an opportunistic screen for depression (with costs as detailed in Table 2), unless the individual had identified depression.

The combined policy included the costs and effects for both collaborative care and improved opportunistic screening, with no further changes.

Health economic outcomes were summarised using the incremental cost effectiveness ratio (ICER), defined as incremental costs divided by incremental quality adjusted life years (QALYs). The clinical outcomes considered were the number of diabetes-related complications and the number of depressive episodes (identified and unidentified) experienced by an individual with T2DM. The analysis took the perspective of the NHS and social services. Wider societal benefits were also explored which considered the impact on productivity (days off work due to ill-health) and informal care (days received any unpaid care provided by family or friends). This was based on methodology developed to inform the Department of Health’s proposed approach to Value Based Pricing [34].

Costs were reported in 2013 British Pound Sterling. A lifetime horizon was used. Both costs and QALYs were discounted at a rate of 3 · 5 per cent per year [35]. All results were estimated by running 600,000 patients through the mathematical model, and multiplying-up the results to reflect those for a cohort of 2,000,000 adults diagnosed with T2DM in England [17].

A Probabilistic sensitivity analysis was not performed due to computational restrictions. Thirty univariate sensitivity analyses were conducted by changing the values of specified model inputs and assessing the impact on the health economic results. These sensitivity analyses included changes to the natural history of depression, the bi-directional association between having a diabetes-related complication and depression, the magnitude of disutility due to having depression, and the costs of screening and collaborative care. Further details are available in Additional file 1.

Predicted lifetime clinical outcomes experienced by the cohort for current practice and each of the policy changes are displayed in Table 3. Under current practice the cohort would experience approximately 1.5 million diabetes-related complications. All three policy changes produced a reduction in both microvascular and macrovascular diabetes-related complications. The combined policy (collaborative care and improved opportunistic screening) avoided the greatest proportion of complications; about 1.5%, whilst the other two policies both avoided about 1%.

It was predicted that the cohort would experience about 15.5 million depressive episodes under current clinical practice over their lifetime, of which just over half (51%) would be identified. Implementing collaborative care did not affect the proportion of identified depression episodes, as this service change only affects depression treatment. However, implementing improved opportunistic screening, either on its own or in combination with collaborative care was estimated to increase the proportion of identified cases to approximately 87%.

Results of the cost-effectiveness analysis are provided in Table 4. Of the three service changes considered, the combined policy was estimated to have the largest impact on both life years and QALYs compared with current practice, with increases of 0.6 and 1.8% respectively. These increases were driven by a reduction in the number of diabetes related complications. The largest reductions were observed for renal failure and amputation, which also have the largest impact on HRQoL. However, this policy was also associated with the largest increases in lifetime healthcare costs, with an increase compared to current practice of £9.6 billion (23.2%) from £41.6 billion. Increases for the other policies were: collaborative care £6.7 billion (16.2%) and improved opportunistic screening £1.6 billion (3.9%). The main drivers for increased costs were an increase in the costs of treating depression, and an increase in the costs of the diagnostic interview (for the two policies that included changes in opportunistic screening).

All three policy changes led to a reduction in both the number of informal care days received and the number of days off paid employment due to ill health. Reductions were largest for the combined policy and smallest for the policy of improved opportunistic screening on its own. The estimated QALY loss due to depression was 1,746 for current practice. Each of the policies was associated with a reduction in lost QALYs, with the largest reduction for the combined policy (1,631; 6.6% of the value for current practice).

Under an incremental analysis, improved opportunistic screening was dominated by collaborative care being both more expensive and less effective. The ICER for collaborative care compared to current practice was estimated at £10,798 per QALY gained, whilst the ICER for the combined policy compared to collaborative care alone was estimated at £68,017 per QALY. Based on these results, the use of collaborative care would be recommended if using the threshold for cost-effectiveness of £13,000 per QALY, which has been estimated to be the current threshold in the NHS [36].

The results of one-way sensitivity analyses indicated that the cost-effectiveness results were most sensitive to the estimated time until relapse, and the hazard ratio for depression affecting diabetes-related complications. Results were robust to the majority of the other parameters varied. Full results of the sensitivity analyses are available in Additional file 1.

The results presented in Table 4 demonstrate that the key drivers for differences in cost between the policies were related to depression diagnosis, screening and treatment. In contrast, differences in QALYs gained were due to improvements in both diabetes and depression outcomes. If only depression outcomes had been considered, then the ICERs compared with usual practice (£17,000, £91,000 and £50,000 for policies 1, 2 and 3 respectively) would have been higher than when considering both diabetes and depression. Hence, if only outcomes relating to depression were considered then the cost-effectiveness of each of the policies would have been under-estimated.