(2) Design of a health-economic Markov model to assess cost-effectiveness and budget impact of the prevention and treatment of depressive disorder

SECTION: TITLE
Design of a health-economic Markov model to assess cost-effectiveness and budget impact of the prevention and treatment of depressive disorder

SECTION: ABSTRACT
ABSTRACT

Background/objective: To describe the design of 'DepMod,' a health-economic Markov model for assessing cost-effectiveness and budget impact of user-defined preventive interventions and treatments in depressive disorders.

Methods: DepMod has an epidemiological layer describing how a cohort of people can transition between health states (sub-threshold depression, first episode of mild, moderate or severe depression (partial) remission, recurrence, death). Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and alternative scenario comparing the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Results are presented in terms of quality-adjusted life years (QALYs) gained and healthcare expenditure. Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis.Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis.

Results: DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of $20,000 per QALY.

Conclusion: DepMod is a Markov model that assesses the cost-utility and budget impact of different healthcare packages aimed at preventing and treating depression and is freely available for academic purposes upon request at the authors.

SECTION: INTRO
Introduction

Depressive disorder has consistently been highlighted as a leading cause of disease burden, particularly in terms of years lived with disability (YLD). Depression is associated with substantial economic costs from a patient perspective, a healthcare perspective and a societal perspective. For example, the total annual cost of depression in Europe was estimated at $118 billion in 2004.

Extensive research has been done on the effectiveness of both the treatment of depression (e.g.), and the prevention of depression. Optimizing a healthcare system under restricted budgets requires that the cost-effectiveness of a package of interventions can be assessed along with associated impacts of implementing an intervention on the healthcare budget. Such information helps to inform policymakers about the implications of reforming the healthcare system. One way of addressing these issues is to develop a health-economic model which synthesizes all available evidence, while also considering preferences of patients and healthcare professionals.

The aim of this paper is to present a health-economic simulation model for depression, DepMod, and to describe DepMod's design features and how it can be put to use to inform policymakers about the health and cost implications of changing the healthcare system for depressive disorders. DepMod builds on previous health-economic models, such as the ACE (Assessing Cost Effectiveness) prevention models, and CHOICE (CHOosing Interventions that are Cost-Effective) models. To promote transparency regarding the model structure (its inputs, assumptions, strengths and limitations), this paper presents the building blocks of the model to enable future users of the model to use it for answering their own research questions and adapt the model to their own (geographical) context in terms of epidemiology and treatment mix for the prevention and treatment of major depressive disorder.

SECTION: METHODS
Methods

Problem definition and target population

The aim of the model is to compare the cost-effectiveness and budget impact of competing intervention packages targeting adults with, or at risk of developing, a depressive disorder in the Netherlands, such that the model contributes to optimizing health outcomes under budget constraints.

Model development

DepMod was developed as a decision-analytic and health-economic Markov model built-in Microsoft Excel.DepMod was developed as a decision-analytic and health-economic Markov model built-in Microsoft Excel. Such a model evaluates costs and health outcomes by simulating a cohort of hypothetical patients that transition across a series of health and disease states (healthy, at risk, depressed, relapsing, chronic (partially) recovered, death) until the end of the model's time horizon. The outcomes of the model (costs and quality-adjusted life years gained) are then synthesized into incremental cost-effectiveness ratios.

The development of the health-economic model DepMod was in line with the recommendations from the International Society for Pharmacoeconomics and Outcomes Research, starting off with scoping the types of research questions that DepMod should be able to answer, conceptualizing the model, gathering evidence from relevant sources, and testing the model with the model users and stakeholders. Figure 1 depicts the schematic overview of the development of the model.

Expert input was used throughout the process of developing the health-economic model. In drafting the starting point of the conceptual model, researchers in the field of depression were consulted.

The model was then refined in an iterative process where the model was updated multiple times based on feedback by healthcare users, healthcare professionals, and researchers involved in the development of a Standard of Care for treating depressive disorders in the Netherlands.The model was then refined in an iterative process where the model was updated multiple times based on feedback by healthcare users, healthcare professionals, and researchers involved in the development of a Standard of Care for treating depressive disorders in the Netherlands.

Population: Dutch adults (aged 18-65 years) with sub-threshold, mild, moderate, and severe major depression as defined by DSM-IV with the possibility of recurrences
Population: Dutch adults (aged 18-65 years) with sub-threshold, mild, moderate, and severe major depression as defined by DSM-IV with the possibility of recurrencesPopulation: Dutch adults (aged 18-65 years) with sub-threshold, mild, moderate, and severe major depression as defined by DSM-IV with the possibility of recurrences, which is in line with the major source of available epidemiological evidence (i.e. The Netherlands Mental Health Survey and Incidence Study-1 (NEMESIS-1), and NEMESIS-2).

Intervention: User-defined intervention(s) which will be compared to a reference scenario. The intervention in DepMod can either be a single intervention or a mix of interventions (e.g., scaling up existing evidence-based interventions or adding new preventive interventions).

Comparator: User-defined reference scenario, typically the treatments provided under care as usual, to which the intervention will be compared.

Outcome: Additional healthcare expenditure (in $) relative to quality-adjusted life years (QALYs) gained.

Time horizon: Costs and QALYs can be modeled out over 5 years (as there is a paucity of evidence in the literature on longer-term health effects induced by treatments).

During the iterative updating process, DepMod's scope was defined in terms of the following PICOT:

Taking a Markov model approach similar to ACE and CHOICE. A Markov model was preferred over a micro-simulation (or discrete event) approach, as our aim was to make a population-level model, which makes individual traits affecting the course of illness less relevant.

Taking a healthcare system approach, looking at packages of interventions (instead of individual interventions) to be compared to a 'reference scenario' (e.g., depression care concordant with the latest clinical guideline) with an 'alternative scenario' (e.g., an alternative user-defined package of interventions for depression).

Using an incidence-based model (e.g., of the depressive disorder in the Dutch adult population), to be able to examine the impact of preventive interventions.

In addition, the following a priori design criteria for the model were adopted:

A layered model structure

DepMod is a layered model. In the first layer, the epidemiology of depression in the Dutch population is simulated (incidence, disease duration, remission, recurrence, chronicity, and depression-related excess mortality). A second layer is superimposed on the epidemiological substratum. Here a set of interventions can be modeled along with their coverage and compliance rates, their effectiveness and the costs of offering the interventions. Both layers are described in more detail below.

Epidemiological layer

To apply DepMod to the Dutch situation, NEMESIS-1 and NEMESIS-2, both Dutch population-based cohort studies on the incidence and prevalence of mental disorders, served as the main sources to build DepMod's epidemiological substratum. NEMESIS-2 was used to obtain the most recent available data on the incidence and prevalence of depression. Furthermore, experts provided literature on chronicity (partial) recovery/duration, and recurrence of depression. These were modeled as transition rates between the health and disease states In DepMod. The model takes a population-incidence approach, where available evidence on the incidence of depression was used as direct input to the model, and transition parameters such as remission and recurrence rates were calibrated such that resulting prevalence-based outcomes matched the prevalence-based outcomes reported in NEMESIS-2.

Intervention layer

Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and an alternative scenario to compare the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Each intervention is described by four parameters: effectiveness (standardized mean difference in case of treatment, and risk difference or relative risk in case of prevention), per-patient intervention costs (in $), coverage (the fraction of the target population receiving the intervention) and adherence (the fraction of users compliant with that intervention).

End-users can modify the included interventions in DepMod according to their own preference. The effectiveness parameters for interventions are best extracted from the meta-analytical trial literature, while coverage and adherence rates can be elicited from focus groups of clinicians and patients, respectively.

Conceptual model

Figure 2 depicts the conceptual model formed by depression experts that was used as a starting point in developing the model. The conceptual model begins with the population at-risk of developing a depression (e.g., the estimated 456,600 people with subthreshold depression (warranting indicated prevention) in the Dutch adult population), which can develop into a mild, moderate or severe major depressive disorder. Depression can then either remit (fully or partially) or not, and -in case of (partial) remission-transit into a recurrent major depressive disorder or remain a single episode. People developing a second episode of depression reenter the loop, where they can again remit and develop a next episode of depression.

Perspective, outcomes and time horizon

DepMod takes a healthcare perspective, considering the costs and effects of interventions for preventing first episodes of depression, treatment of acute depressive episodes, and prevention of recurrent episodes of depression. Health outcomes are expressed as QALYs. QALYs are based on the health state valuations for mild, moderate, and severe depressive disorder of 0.86, 0.65, and 0.24, respectively, as reported by Stouthard et al., whereas changes in health-related quality of life are based on the standardized effect sizes of the interventions that are converted to utilities using Sanderson et al.'s (2004) conversion factor. Total QALYs are estimated by multiplying the time spend in a specific health state by the valuation (utility) of that health state. The model uses a time horizon of 5 years to capture the longer-term health effects of treatment and prevention of depression without extrapolating too far from the available evidence-base. Costs and QALYs occurring after 1 year are discounted at 4% and 1.5%, respectively, to account for differential timing, in accordance with the Dutch guideline for health economic evaluation.

In DepMod, the incremental cost-effectiveness ratio for the alternative scenario as compared to the reference scenario, is calculated by dividing the difference in total healthcare costs (between the alternative and reference scenario) by the difference in effects between the scenarios. Probabilistic sensitivity analyses are conducted by calculating the costs and effects in the reference scenario and alternative scenario by a user-defined number of times (e.g., 1,000 times) using Monte Carlo simulation, where each time a parameter value for each of the parameters is drawn at random from underlying cost, effect, and transition distributions.scenario and alternative scenario by a user-defined number of times (e.g., 1,000 times) using Monte Carlo simulation, where each time a parameter value for each of the parameters is drawn at random from underlying cost, effect, and transition distributions. We assume costs to be Gamma distributed, standardized effect sizes to be normally distributed, and transition probabilities to follow a beta-distribution, in line with recommendations. Results of probabilistic sensitivity analyses are depicted in a cost-effectiveness plane and a cost-effectiveness acceptability curve, presenting the probability that the alternative healthcare scenario is cost-effective compared to the reference scenario, given varying levels of willingness-to-pay for a QALY gained.

Epidemiology and the Markov model

Available evidence on epidemiology determined to which extent the level of detail as depicted in the conceptual model in Figure 2 could be modeled. As available evidence reported mostly in time intervals of 1 year, it was chosen to use a cycle length in our Markov-model of 1 year as well. A half-cycle correction was applied to account for the fact that transitions between states are expected to occur on average in the middle of each cycle. It was chosen to only model the states 'subthreshold depression' (without a history of depression), 'depressive episode' (first or recurrent episode), 'no depression,' a 'chronic state' (i.e., more than 2 years), 'cured' (absence of (subthreshold) depression, and 'death.' In line with Solomon et al. (2000), up to five depressive episodes were considered, which resulted in the Markov-model as depicted in Figure 3. Technically, to allow for increasing recurrence rates after each previous episode (i.e., to build memory into the Markov-model), we constructed a transition matrix containing tunnel states for the number of depressive episodes (at most five, i.e., one per year), representing the transition rates after no previous episode, one previous episode, etc.

SECTION: TABLE
Parameters in DepMod (point estimates, range, justification)

Input parameters Point estimate Sensitivity Source Incidence 1.28% NA De Graaf et al., 2013[23] Disability weights depression Mild (dw) 0.14 0.086 - 0.194 Stouthard et al., 1997[27] Moderate (dw) 0.35 0.272 - 0.425 Stouthard et al., 1997[27] Severe (dw) 0.76 0.556 - 0.971 Stouthard et al., 1997[27] Depression duration Mild 3 months NA based on Spijker et al., 2002[24] Moderate 6 months NA based on Spijker et al., 2002[24] Severe 9 months NA based on Spijker et al., 2002[24] Background mortality 0.001984 NA Based on life tables [36] Calibration parameters Point estimate Sensitivity To match outcome severity distribution at incidence Mild 30.0% NA distribution prevalence mild, moderate, severe depression as reported in Chisholm et al., 2004[35] Moderate 47.0% NA Severe 23.0% NA Recurrence rates after 1st episode 59.8% NA recurrence rates as reported in Solomon et al., 2000[32] after 2nd episode 77.2% NA after 3rd episode 84.2% NA after 4th episode 91.1% NA after 5th episode 92.9% NA Occurring after one year 40% NA recurrence rates as reported in Solomon et al., 2000[32] two years 30% NA three years 20% NA four years 7% NA five years 3% NA Excess mortality 1.65 NA De Graaf et al., 2012[34] Outcomes Model outcome Reference value Source Prevalence of depression 5.24% 5.21% De Graaf et al., 2012[34] Severity distribution Mild 30.4% 30% Chisholm et al., 2004[35] Moderate 47.0% 47% Chisholm et al., 2004[35] Severe 22.7% 23% Chisholm et al., 2004[35] Recurrence rates ranging from 24% (risk of recurrence after 1 year, given 1 previous episode) to 84% (risk of recurrence after 5 years given 4 previous episodes) ranging from 25% (20%-32%) (risk of recurrence after 1 year, given 1 previous episode) to 79% (35%-95%) (risk of recurrence after 5 years given 3 previous episodes) Solomon et al., 2000[32]

SECTION: METHODS
Based on NEMESIS-2, the first (one-year) incidence rate was set at 1.28%. Transition parameters that could not be taken from literature were calibrated (see Table 1) such that the resulting epidemiology matched the internally consistent epidemiological structure of depression as derived from the NEMESIS-studies (see Figure 4). Recurrence rates were based on Solomon et al. (2000), who reported that the risk of recurrence increases with on average 16% after each previous episode of major depression. In our model, recurrence rates ranged from a 21% risk of recurrence within 12 months given one previous episode, to an 88% risk of recurrence within 5 years given four previous episodes. Solomon et al. (2000) report 13 recurrence rates (for differing number of previous episodes and number of years until recurrence), along with their confidence intervals. DepMod defines 25 distinct recurrence rates (one to five recurrences that can each occur 1 to 5 years after the previous episode), which was simplified by estimating the average relative increase in recurrence rates after the first up to the fifth episode (so five increased recurrence rates) and five probabilities regarding the number of years after which the recurrence occurs. To allow for increasing recurrence rates, each of the depressive episodes (first to the fifth) was modeled as separate Markov tunnel states.

In line with NEMESIS-2, reporting a prevalence of major depression of 5.2%, the epidemiology as modeled by DepMod resulted in a 12-month prevalence rate of 5.18%. Chisholm et al. (2004) reported the proportion of people with a mild, moderate, and severe depression as 30%, 47%, and 23%, respectively. The epidemiology in DepMod was calibrated to match this distribution (see Table 1).

Background mortality was based on (weighted) average mortality rate of adults between 18 and 65 in the Netherlands. Depression-related excess mortality (e.g., due to unhealthy behavior or suicide) was calibrated to be in line with NEMESIS-2, resulting in a relative risk of 1.65.

An economic evaluation compares the costs and effects of two alternatives. In DepMod, the reference scenario can be compared to an alternative scenario, simply by copying in this reference scenario and adjusting some of its parameters. For example, increasing the coverage rate of prevention implies that more prevention is offered in the alternative scenario, which may then cascade into a range of health and economic impacts (see, e.g.).

Effectiveness of interventions

through a reduction in the transition from the at-risk status to a first episode of depression (primary prevention) and a reduction in the transition to a recurrent depression (prevention of recurrence). The effectiveness of prevention is expressed as either a risk difference (primary prevention) or a relative risk (prevention of recurrence).

through symptom improvement in people currently suffering from mild, moderate or severe depression. Symptom improvement follows from standardized effect sizes (Cohen's d; i.e. standardized mean difference), expressing improvement on a depression-related symptom scale in standard units, and should be based on meta-analyses where possible. The average symptom improvement can be downward adjusted for less than optimal coverage and impaired adherence (by multiplication with the coverage and adherence rates). The resulting adjusted effect sizes are then converted into QALYs using Sanderon et al.'s (2004) conversion factor and assuming that treatment effects would last as long as (but not longer than) the average episode duration of depression.

through a prophylactic effect, where receiving psychological intervention for the treatment of a depressive episode may increase the likelihood of staying in remission after successful treatment (rather than recurrence at end of treatment), because psychological intervention (unlike pharmaceutical intervention) is suggested to train patients to better self-manage emerging depressive symptoms should these re-occur in the future.

The effectiveness of interventions impacts positively on the epidemiology in three different ways:

The total QALY gain is then determined by difference in total QALYs between the reference and alternative scenarios.

SECTION: RESULTS
Results of model testing

SECTION: TABLE
Illustration of selected evidence-based interventions by depression severity level: target group reached by the intervention expressed as Coverage (%) and Compliance with therapy (%). Effect expressed as risk difference (RD) or Relative Risk (RR) when impacting on transitions or as standardized effect size (Cohen's d) when impacting on symptom severity, all representing average values

Intervention by depression severity level Coverage rate Compliance rate Effect Prevention of first incidence (targeting subclinical depression) % % RD E-health intervention (unsupported) 1 0 80 0.077 Treatment of mild depression % % d E-health intervention (supported) 2 2 43 0.33 Individual psychological intervention, primary care, 8 sessions 3 17 56 0.51 Treatment of moderate depression % % d E-health intervention (supported) 2 2 43 0.33 Individual psychological intervention, primary care, 8 sessions 3 16 56 0.51 % % d Individual psychological outpatient care, 8-24 sessions 3 18 68 0.51 Anti-depressants, 3-6 months via GP 4 20 44 0.30 Anti-depressants, 3-6 months, with psychological support 3 20 56 0.51 Prevention of recurrent depression % % RR Clinical management with maintenance medication, 12 months 5 0 42 0.75 Mindfulness-based CT 6 0 68 0.66 Cognitive (behavior) therapy 6 0 56 0.68

1Taken from van Zoonen et al. 2014; 2 Taken from Karyotaki et al. 2017; 3 Taken from Cuijpers et al. 2019; 4 Taken from Cipriani et al. 2018; 5 Taken from Vittengl et al. 2007; 6 Taken from Biesheuvel-Leliefeld et al. 2015.

SECTION: RESULTS
To apply DepMod to a real-world example, a list of evidence-based interventions needs to be constructed. In our application, we constructed a reference scenario by using a list of evidence-based interventions, using the Dutch multidisciplinary guideline for depression (see Table 2). This healthcare system served as a reference scenario against which alternative intervention mixes (i.e., alternative scenarios) could be compared in terms of cost-effectiveness and budget impact. The effectiveness of included interventions was based on meta-analyses (see Table 2).

With the interventions in the reference scenario defined, it is possible to conduct what-if analyses, where the user can assess the cost-effectiveness of hypothetical alternative healthcare systems. For example, currently, first-incidence prevention and prevention of depression is not systematically offered in the Netherlands although it has been recommended in clinical guidelines for some time. For illustrative purposes, we've assessed the cost-effectiveness of scaling up preventive interventions for people with subclinical depression (i.e., online CBT) and people at risk of developing a recurrent depression with a coverage of 15%. Hence, we simply copied the reference scenario as outlined in Table 2 into the alternative scenario and increased the coverage rates of the preventive interventions from 0% to 15%.

SECTION: TABLE
Intervention costs expressed in 2019 Euros

Intervention by depression severity level Prevention of first incidence (targeting subclinical depression) $* Resource use E-health intervention (unsupported) 160 1x GP; intake psychologist; 4-9 sessions unsupported online self-help; incl. hosting costs Treatment of mild depression $ Resource use E-health intervention (supported) 265 1x GP; intake psychologist; 4-9 sessions online self-help; 4-5 telephone support prevention worker; hosting cost of the e-health intervention Individual psychotherapy 962 1x GP; 8 sessions with a psychologist; coordination with GP Treatment of moderate depression $ Resource use E-health intervention (supported) 265 1x GP; intake psychologist; 4-9 sessions online self-help; 4-6 telephone support prevention worker; hosting Individual psychotherapy 962 1x GP; 8 sessions with a psychotherapist; coordination with GP Treatment of severe depression $ Resource use Individual psychotherapy, outpatient care 1,984 1x GP; 8-24 sessions by a psychotherapist Antidepressants 607 12 months of medication; contacts with GP or psychiatrist Antidepressants with additional psychological support 679 12 months of medication; contacts with GP or psychiatrist; 3-6 visits GP support Combination therapy (medication plus psychotherapy) 2,520 12 months of medication; contacts with GP or psychiatrist; 8-24 sessions with a psychotherapist Prevention of recurrent depression $ Resource use Clinical management with maintenance medication 1,002 1x psychiatrist; 8-14 visits GP; 12 months of medication Mindfulness-based CT 361 1x GP; 8 group sessions (11 participants on average) Cognitive (behavior) therapy 429 1x GP; 8 group sessions (8 participants on average)

* Intervention costs were estimated by multiplying the guideline concordant resource use associated with each intervention by their standard (unit) cost price, as listed in the Dutch guideline for health-economic evaluation.

SECTION: RESULTS
In this example, intervention costs were estimated by multiplying the guideline concordant resource use associated with each intervention by their standard (unit) cost price, as listed in the Dutch guideline for health-economic evaluation. Costs were expressed in 2019 Euros. The resulting costs per intervention are shown in Table 3.

Increasing the coverage rate of prevention from 0% to 15% and subsequently simulating the costs and effects of both healthcare systems, results in the cost-effectiveness plane and cost-effectiveness acceptability curve depicted in Figure 5. The output tells us that at the commonly accepted willingness-to-pay threshold of $20,000 per QALY, there is an 82% probability that scaling up prevention is cost-effective.

Furthermore, the expected 18,400 QALYs gained by offering the additional prevention over the considered time horizon of 5 years require an additional budget of $191.1 million euros (net present value). Tabulated results can be found in Appendix 1.

SECTION: DISCUSS
Discussion

We built a Markov model to evaluate the cost-effectiveness and budget impact of a healthcare system of psychological and pharmacological interventions for people with depressive disorders, as well as people at risk of developing a first or recurrent episode of depression. Taking the healthcare system perspective, the model combines available evidence on the epidemiology of depressive disorder, the effectiveness of interventions, and the costs of the interventions while considering the preferences of healthcare professionals and healthcare users for offering and receiving interventions. The model was refined using feedback by healthcare users, healthcare professionals, and researchers in the field of depression. DepMod adds to other health-economic models described in literature (e.g.) by (i) considering both the prevention and treatment of depression, (ii) considering both psychological and pharmacological interventions, (iii) modeling increasing recurrence rates for patients with a higher number of previous episodes, (iv) considering a five-year time horizon, and (v) allowing for assessing the cost-effectiveness of interventions aiming to increase adherence. The model can be adapted to other settings by adding or removing interventions, by altering their coverage rate, adherence rate, or healthcare costs and by adjusting the epidemiology.

Outcomes of the model were shown in an example for the Dutch situation where the cost-effectiveness and budget impact were assessed of increasing the coverage of preventive interventions. Results of that analysis showed favorable outcomes suggesting that, when assuming a willingness-to-pay threshold of $20,000 per QALY, scaling up prevention is likely to be cost-effective. This finding is in line with previous research in this field that demonstrated that depression prevention is associated with probabilities in the range of 68-90% of being cost-effective. This example demonstrates the ability of the model to assess the cost-effectiveness of alternative healthcare systems, while making use of an extensive evidence-base from epidemiology, clinical effectiveness and economic costs.

Strengths and limitations

The strength of our model is that it synthesizes information from different domains into a single model that can be used by policymakers as a decision-support tool for estimating the cost-effectiveness and budget impact of alternative intervention packages by incorporating fully tweakable scenarios. However, our model has a number of limitations that need to be acknowledged.

First of all, because information from different domains was combined, it was necessary to make assumptions, for example, on how effectiveness of interventions impacted on epidemiology, thereby introducing uncertainty in the model's outcomes. Whenever assumptions had to be made, we favored conservative assumptions that are more likely to underestimate the cost-effectiveness of interventions.

A second limitation of DepMod, common in health-economic models, is the presence of uncertainty regarding both input parameters like transition probabilities (parameter uncertainty) and structural model choices like the chosen health states or type of analytical framework (model uncertainty). Parameter uncertainty was addressed using probabilistic sensitivity analysis on the parameters of interest. Although we partially account for model uncertainty, the main value of our model lies in interpreting the comparison between the outcomes of two scenarios, rather than the absolute costs and effects associated with a single scenario.

A third limitation is that there is no unique solution when determining each of the calibration parameters of the Markov model. Given the episodic nature of depressive disorder, the epidemiology cannot be modeled using just incidence, prevalence, and excess mortality, as would be the case with chronic conditions. Instead, information is required on remission rates as well as recurrence rates, adding degrees of freedom which takes away the possibility of a unique set of parameters defining an internally consistent epidemiology. Calibration parameters were set to closely match available epidemiological evidence. The model's resulting epidemiology was in line with the epidemiological evidence-base, with the exception of two of 13 recurrence rates, which were below the confidence interval as reported by Solomon et al. (2000); thus indicating that the risk of recurrence in DepMod is slightly lower in these instances, thereby resulting in conservative estimates of the cost-effectiveness of preventive interventions. Final parameter choices were checked for face-validity with experts in the field of depression. Future research should aim to validate these findings.

Fourth, we chose to model epidemiology using a Markov-model rather than a discrete event model. Both models have their strengths and limitations. As our aim was to make a population-level model, we were less interested in the ability of our model to capture individual traits affecting the course of illness. Nevertheless, cost-effectiveness might be affected by clinicians adjusting their treatment decisions based on the patient's personal traits. We partly compensated for this by modeling two forms of heterogeneity in the epidemiology. First, the severity of depression (mild, moderate, and severe) was captured in the model. Second, the number of previous episodes was accounted for in the model using tunnel states. By adding these features, patient heterogeneity was considered to a limited extent. Moreover, as nuances such as medication switching were not specifically incorporated, ideally they should be captured within the parameters of each intervention (e.g., included in the average costs and average effect size of pharmacotherapy).

Fifth, we used one-year cycles in the model as available epidemiological evidence did not provide enough information for a shorter cycle-length. This allowed only one transition or episode of depression per year, even though changes in the course of depression can occur more frequently.

Sixth, ideally all healthcare costs, not only in the mental healthcare services, would be used for each health state, as depression could be associated with additional healthcare usage in general. Moreover, one would ideally also have data on the care patients receive when in intermediate (remissions and recurrences) states. These types of costs, but also other types of costs such as productivity losses, are not covered in the model. Moreover, although the model is unable to track how many treatments each patient has received (i.e., given it is a population model) and in theory patients could receive the same treatment multiple times during the five-year time horizon (each time with the same assumed effectiveness), this is likely only an issue when users would implement a 100% coverage rate for a specific intervention. In addition, the model is not optimally suited for treatments with a treatment duration longer than the time horizon (i.e., 1 year), given that treatments are offered every cycle.

Seventh, using NEMESIS-2 as our main source of epidemiological input, DepMod is restricted to the population aged 18-65 years. Using the model for other age-groups requires updating the input parameters to the setting and age group of interest.

Eighth, people in DepMod remain in their initial mild, moderate, or severe depression severity level throughout the entire period under consideration, whereas in reality people could transition between severity levels. Also, our model is limited to major depressive disorder and does not consider dysthymia.

Ninth, DepMod reports on the desirability of an alternative setup of the healthcare system from a perspective of cost-effectiveness, but does not take into account other perspectives, such as equity, feasibility, sustainability, and acceptability.

Tenth, QALYs were calculated based on disability weights instead of utility values based on Stouthard et al. (1997) and Sanderon et al.'s (2004) conversion factor. A disability weight is a weight factor that reflects the severity of the disease on a scale from 0 (perfect health) to 1 (equivalent to death). In contrast, a utility represents the quality of a health state on a scale from 0 (equivalent to death) to 1 (perfect health). In general, a utility value incorporates a broader spectrum of dimensions of health state, not merely focusing on disease related factors and may also lead to states worse than death. However, the disability weights derived by Stouthard et al. (1997) were based on the EuroQol-5D instrument, an instrument commonly used to obtain utility values and therefore captures this broader view.

Last, DepMod constitutes a relatively short time horizon, in avoid extrapolating too far from the available epidemiological evidence-base. However, the implementation of a life time horizon may enable decision makers to more adequately capture societal costs, for example, it is known that depression is frequently associated with low marital quality, low work performance, and low earnings, thereby increasing societal costs and decreasing quality of life. The episodic nature of depression underscores the importance of identifying individuals at ultra-high risk of relapse or recurrence, as enduring depression symptoms may require further treatment and continue to generate personal, financial, and societal costs. Future research should therefore focus on developing a health-economic model using patient-level simulations (e.g., a discrete-event simulation) to determine the impact of personalized preventive actions targeting patients at high risk of a recurrent depression.

SECTION: CONCL
Conclusion

SECTION: FIG
Schematic overview of the model development process

Conceptual model of the course of depression serving as a starting point in the process of model development

Markov-model

Internally consistent epidemiological structure of depression based on NEMESIS-studies (yearly number of people in each health state in parentheses)

(a) Cost-effectiveness plane (top) and (b) cost-effectiveness acceptability curve (bottom) associated with scaling up prevention. The cost-effectiveness acceptability curve expresses the probability that scaling up prevention is cost-effective (y-axis) and on the x-axis the willingness to pay for one QALY gained given various ceiling ratios

SECTION: CONCL
Overall, our model estimates the cost-effectiveness of changing the configuration of a healthcare system for depression. Outcomes of the model should always be considered in light of other equally important (societal) values such as equity, feasibility, sustainability, and acceptability (see for example [52]). Above all, our model aims to facilitate decision-making with respect to optimizing the healthcare system for major depressive disorder but should never be seen as an auto-pilot for decision-making.

Article highlights

DepMod is a methodologically sound model that can be used to examine the cost-effectiveness and budget impact of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression

DepMod can be used for various interventions (e.g., medication, cognitive behavior therapy, et cetera) in a relatively easy and accessible way.

The transition rates between health states were derived from NEMESIS-2, a large psychiatric cohort study of adults (18 - 65 years) in the Netherlands, but DepMod permits user-defined adaption of its epidemiology for use in other geographies or age groups.

For illustrative purposes, DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression in the Netherlands, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of $20,000 per QALY.

DepMod is freely available for academic purposes upon request by the authors.

SECTION: METHODS
Availability of data and materials

The model (DepMod) is freely available upon request at the authors.

SECTION: TABLE
Total costs (95%CI) Total QALYs (95%CI) Incremental costs Incremental QALYs ICER Reference scenario $632,198 ($566,008 - $700,627) 36,662 (27,079-48,500) NA NA NA Alternative scenario $823,304 ($748,926 - $898,681) (55,069 (42,569-71,031) $191,106 18,408 $10 per QALY gained