A proposed model for economic evaluations of major depressive disorder

Development and validation of an individual-based state-transition model for the prediction of frailty and frailty-related events

Frailty is a biological syndrome that is associated with increased risks of morbidity and mortality. To assess the value of interventions to prevent or manage frailty, all important impacts on costs and outcomes should be estimated. The aim of this study is to describe the development and validation of an individual-based state transition model that predicts the incidence and progression of frailty and frailty-related events over the remaining lifetime of older Australians. An individual-based state transition simulation model comprising integrated sub models that represent the occurrence of seven events (mortality, hip fracture, falls, admission to hospital, delirium, physical disability, and transitioning to residential care) was developed. The initial parameterisation used data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The model was then calibrated for an Australian population using data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The simulation model established internal validity with respect to predicting outcomes at 24 months for the SHARE population. Calibration was required to predict longer terms outcomes at 48 months in the SHARE and HILDA data. Using probabilistic calibration methods, over 1,000 sampled sets of input parameter met the convergence criteria across six external calibration targets. The developed model provides a tool for predicting frailty and frailty-related events in a representative community dwelling Australian population aged over 65 years and provides the basis for economic evaluation of frailty-focussed interventions. Calibration to outcomes observed over an extended time horizon would improve model validity.

Cost-Utility of Internet-Based Cognitive Behavioral Therapy in Unipolar Depression: A Markov Model Simulation

BACKGROUND AND OBJECTIVE

Unipolar depression is the most common form of depression and demand for treatment, such as psychotherapy, is high. However, waiting times for psychotherapy often considerably exceed their recommended maximum. As a potentially less costly alternative treatment, internet-based cognitive behavior therapy (ICBT) might help reduce waiting times. We therefore analyzed the cost-utility of ICBT compared to face-to-face CBT (FCBT) as an active control treatment, taking differences in waiting time into account.

METHODS

We constructed a Markov model to simulate costs and health outcomes measured in quality-adjusted life years (QALYs) for ICBT and FCBT in Germany. We modeled a time horizon of 3 years using six states (remission, depressed, spontaneous remission, undergoing treatment, treatment finished, death). The societal perspective was adopted. We obtained parameters for transition probabilities, depression-specific QoL, and cost data from the literature. Deterministic and probabilistic sensitivity analyses were conducted. Within a scenario analysis, we simulated different time-to-treatment combinations. Half-cycle correction was applied.

RESULTS

In our simulation, ICBT generated 0.260 QALYs and saved €2536 per patient compared to FCBT. Our deterministic sensitivity analysis suggests that the base-case results were largely unaffected by parameter uncertainty and are therefore robust. Our probabilistic sensitivity analysis suggests that ICBT is highly likely to be more effective (91.5%), less costly (76.0%), and the dominant strategy (69.7%) compared to FCBT. The scenario analysis revealed that the base-case results are robust to variations in time-to-treatment differences.

CONCLUSION

ICBT has a strong potential to balance demand and supply of CBT in unipolar depression by reducing therapist time per patient. It is highly likely to generate more QALYs and reduce health care expenditure. In addition, ICBT may have further positive external effects, such as freeing up capacities for the most severely depressed patients.

Model Structuring for Economic Evaluations of New Health Technologies

In countries such as Australia, the UK and Canada, decisions on whether to fund new health technologies are commonly informed by decision analytic models. While the impact of making inappropriate structural choices/assumptions on model predictions is well noted, there is a lack of clarity about the definition of key structural aspects, the process of developing model structure (including the development of conceptual models) and uncertainty associated with the structuring process (structural uncertainty) in guidelines developed by national funding bodies. This forms the focus of this article. Building on the reports of good modelling practice, and recognising the fundamental role of model structuring within the model development process, we specified key structural choices and provided ideas about model structuring for the future direction. This will help to further standardise guidelines developed by national funding bodies, with potential impact on transparency, comprehensiveness and consistency of model structuring. We argue that the process of model structuring and structural sensitivity analysis should be documented in a more systematic and transparent way in submissions to national funding bodies. Within the decision-making process, the development of conceptual models and presentation of all key structural choices would mean that national funding bodies could be more confident of maximising value for money when making public funding decisions.

Setting the Boundaries for Economic Evaluation: Investigating Time Horizon and Family Effects in the Case of Postnatal Depression

OBJECTIVES

This study investigates the impact of varying the boundaries of economic evaluation: time horizon and inclusion of family effects. The context is postnatal mental health, where although advocates for investment often include longer-term and family problems in describing the burden of postnatal depression, economic evaluations are usually limited to mothers' effects with a relatively short time horizon. This discrepancy may lead to suboptimal allocation of healthcare resources.

METHODS

The question of whether such boundary extensions could make a difference to decision-making is explored using decision analytic models, populated with data from the literature, to estimate the cost-effectiveness of a hypothetical preventive intervention under alternate boundary-setting approaches.

RESULTS

The results suggest that broader boundaries, particularly extension of the time horizon, could make substantial differences to estimated cost-effectiveness. Inclusion of family effects without extension of the time horizon had little impact, but where a longer time horizon was used, family effects could make a significant difference to the conclusions drawn from cost-effectiveness analysis.

CONCLUSIONS

Considerations in applying broader boundaries include the substantial resource requirements for evaluation, potential equity implications, relevance to decision-makers, methods for inclusion, and the interpretation and use of such results in decision-making. However, this context underscores the importance of considering not only caregiving but also family health effects, and illustrates the need for consistency between the arguments presented to decision-makers and the analytical approach taken in economic evaluation.

Better guidelines for better care: accounting for multimorbidity in clinical guidelines – structured examination of exemplar guidelines and health economic modelling

Background

Multimorbidity is common but most clinical guidelines focus on single diseases.

Aim

To test the feasibility of new approaches to developing single-disease guidelines to better account for multimorbidity.

Design

Literature-based and economic modelling project focused on areas where multimorbidity makes guideline application problematic.

Methods

(1) Examination of accounting for multimorbidity in three exemplar National Institute for Health and Care Excellence guidelines (type 2 diabetes, depression, heart failure); (2) examination of the applicability of evidence in multimorbidity for the exemplar conditions; (3) exploration of methods for comparing absolute benefit of treatment; (4) incorporation of treatment pay-off time and competing risk of death in an exemplar economic model for long-term preventative treatments with slowly accruing benefit; and (5) development of a discrete event simulation model-based cost-effectiveness analysis for people with both depression and coronary heart disease.

Results

(1) Comorbidity was rarely accounted for in the clinical research questions that framed the development of the exemplar guidelines, and was rarely accounted for in treatment recommendations. Drug–disease interactions were common only for comorbid chronic kidney disease, but potentially serious drug–drug interactions between recommended drugs were common and rarely accounted for in guidelines. (2) For all three conditions, the trials underpinning treatment recommendations largely excluded older, more comorbid and more coprescribed patients. The implications of low applicability varied by condition, with type 2 diabetes having large differences in comorbidity, whereas potentially serious drug–drug interactions were more important for depression. (3) Comparing absolute benefit of treatments for different conditions was shown to be technically feasible, but only if guideline developers are willing to make a number of significant assumptions. (4) The lifetime absolute benefit of statins for primary prevention is highly sensitive to the presence of both the direct treatment disutility of taking a daily tablet and competing risk of death. (5) It was feasible to use a discrete event simulation-based model to represent the relevant care pathways to estimate the relative cost-effectiveness of pharmacological treatments of major depressive disorder in primary care for patients who are also likely to go on and receive treatment for coronary heart disease but the analysis was reliant on eliciting some parameter values from experts, which increases the inherent uncertainty in the results. The key limitation was that real-life use in guideline development was not examined.

Conclusions

Guideline developers could feasibly (1) use epidemiological data characterising the guideline population to inform consideration of applicability and interactions; (2) systematically compare the absolute benefit of long-term preventative treatments to inform decision-making in people with multimorbidity and high treatment burden; and (3) modify the output from economic models used in guideline development to examine time to benefit in terms of the pay-off time and varying competing risk of death from other conditions.

Future work

Further research is needed to optimise presentation of comparative absolute benefit information to clinicians and patients, to evaluate the use of epidemiological and time-to-benefit data in guideline development, to better quantify direct treatment disutility and to better quantify benefit and harm in people with multimorbidity.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

A conceptual framework for a long-term economic model for the treatment of attention-deficit/hyperactivity disorder

BACKGROUND

Models incorporating long-term outcomes (LTOs) are not available to assess the health economic impact of attention-deficit/hyperactivity disorder (ADHD).

OBJECTIVE

Develop a conceptual modelling framework capable of assessing long-term economic impact of ADHD therapies.

METHODS

Literature was reviewed; a conceptual structure for the long-term model was outlined with attention to disease characteristics and potential impact of treatment strategies.

RESULTS

The proposed model has four layers: i) multi-state short-term framework to differentiate between ADHD treatments; ii) multiple states being merged into three core health states associated with LTOs; iii) series of sub-models in which particular LTOs are depicted; iv) outcomes collected to be either used directly for economic analyses or translated into other relevant measures.

CONCLUSIONS

This conceptual model provides a framework to assess relationships between short- and long-term outcomes of the disease and its treatment, and to estimate the economic impact of ADHD treatments throughout the course of the disease.

Modelling the cost-effectiveness of pharmacotherapy compared with cognitive-behavioural therapy and combination therapy for the treatment of moderate to severe depression in the UK

BACKGROUND

The National Institute of Health and Care Excellence (NICE) in England and Wales recommends the combination of pharmacotherapy and psychotherapy for the treatment of moderate to severe depression. However, the cost-effectiveness analysis on which these recommendations are based has not included psychotherapy as monotherapy as a potential option. For this reason, we aimed to update, augment and refine the existing economic evaluation.

METHOD

We constructed a decision analytic model with a 27-month time horizon. We compared pharmacotherapy with cognitive-behavioural therapy (CBT) and combination treatment for moderate to severe depression in secondary care from a healthcare service perspective. We reviewed the literature to identify relevant evidence and, where possible, synthesized evidence from clinical trials in a meta-analysis to inform model parameters.

RESULTS

The model suggested that CBT as monotherapy was most likely to be the most cost-effective treatment option above a threshold of £ 22,000 per quality-adjusted life year (QALY). It dominated combination treatment and had an incremental cost-effectiveness ratio of £ 20,039 per QALY compared with pharmacotherapy. There was significant decision uncertainty in the probabilistic and deterministic sensitivity analyses.

CONCLUSIONS

Contrary to previous NICE guidance, the results indicated that even for those patients for whom pharmacotherapy is acceptable, CBT as monotherapy may be a cost-effective treatment option. However, this conclusion was based on a limited evidence base, particularly for combination treatment. In addition, this evidence cannot easily be transferred to a primary care setting.

Exploring structural uncertainty in model-based economic evaluations

Given the inherent uncertainty in estimates produced by decision analytic models, the assessment of uncertainty in model-based evaluations is an essential part of the decision-making process. Although the impact of uncertainty around the choice of model structure and making incorrect structural assumptions on model predictions is noted, relatively little attention has been paid to characterising this type of uncertainty in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC). The absence of a detailed description and evaluation of structural uncertainty can add further uncertainty to the decision-making process, with potential impact on the quality of funding decisions. This paper provides a summary of key elements of structural uncertainty describing why it matters and how it could be characterised. Five alternative approaches to characterising structural uncertainty are discussed, including scenario analysis, model selection, model averaging, parameterization and discrepancy. We argue that the potential effect of structural uncertainty on model predictions should be considered in submissions to national funding bodies; however, the characterisation of structural uncertainty is not well defined within the guidelines of these bodies. There has been little consideration of the forms of structural sensitivity analysis that might best inform applied decision-making processes, and empirical research in this area is required.

Core discrete event simulation model for the evaluation of health care technologies in major depressive disorder

OBJECTIVE

A review of existing economic models in major depressive disorder (MDD) highlighted the need for models with longer time horizons that also account for heterogeneity in treatment pathways between patients. A core discrete event simulation model was developed to estimate health and cost outcomes associated with alternative treatment strategies.

METHODS

This model simulated short- and long-term clinical events (partial response, remission, relapse, recovery, and recurrence), adverse events, and treatment changes (titration, switch, addition, and discontinuation) over up to 5 years. Several treatment pathways were defined on the basis of fictitious antidepressants with three levels of efficacy, tolerability, and price (low, medium, and high) from first line to third line. The model was populated with input data from the literature for the UK setting. Model outputs include time in different health states, quality-adjusted life-years (QALYs), and costs from National Health Service and societal perspectives. The codes are open source.

RESULTS

Predicted costs and QALYs from this model are within the range of results from previous economic evaluations. The largest cost components from the payer perspective were physician visits and hospitalizations. Key parameters driving the predicted costs and QALYs were utility values, effectiveness, and frequency of physician visits. Differences in QALYs and costs between two strategies with different effectiveness increased approximately twofold when the time horizon increased from 1 to 5 years.

CONCLUSION

The discrete event simulation model can provide a more comprehensive evaluation of different therapeutic options in MDD, compared with existing Markov models, and can be used to compare a wide range of health care technologies in various groups of patients with MDD.

Practice nurse involvement in primary care depression management: an observational cost-effectiveness analysis

Background

Most evidence on the effect of collaborative care for depression is derived in the selective environment of randomised controlled trials. In collaborative care, practice nurses may act as case managers. The Primary Care Services Improvement Project (PCSIP) aimed to assess the cost-effectiveness of alternative models of practice nurse involvement in a real world Australian setting. Previous analyses have demonstrated the value of high level practice nurse involvement in the management of diabetes and obesity. This paper reports on their value in the management of depression.

Methods

General practices were assigned to a low or high model of care based on observed levels of practice nurse involvement in clinical-based activities for the management of depression (i.e. percentage of depression patients seen, percentage of consultation time spent on clinical-based activities). Linked, routinely collected data was used to determine patient level depression outcomes (proportion of depression-free days) and health service usage costs. Standardised depression assessment tools were not routinely used, therefore a classification framework to determine the patient’s depressive state was developed using proxy measures (e.g. symptoms, medications, referrals, hospitalisations and suicide attempts). Regression analyses of costs and depression outcomes were conducted, using propensity weighting to control for potential confounders.

Results

Capacity to determine depressive state using the classification framework was dependent upon the level of detail provided in medical records. While antidepressant medication prescriptions were a strong indicator of depressive state, they could not be relied upon as the sole measure. Propensity score weighted analyses of total depression-related costs and depression outcomes, found that the high level model of care cost more (95% CI: -$314.76 to $584) and resulted in 5% less depression-free days (95% CI: -0.15 to 0.05), compared to the low level model. However, this result was highly uncertain, as shown by the confidence intervals.

Conclusions

Classification of patients’ depressive state was feasible, but time consuming, using the classification framework proposed. Further validation of the framework is required. Unlike the analyses of diabetes and obesity management, no significant differences in the proportion of depression-free days or health service costs were found between the alternative levels of practice nurse involvement.

Improving the Accuracy and Comparability of Model-Based Economic Evaluations of Health Technologies for Reimbursement Decisions

Increasingly, decision analytic models are used within economic evaluations of health technologies (e.g., pharmaceuticals) submitted to national reimbursement bodies in countries like Australia and UK, where such models play a fundamental role in informing public funding decisions. Concerns regarding the accuracy of model outputs and hence the credibility of national reimbursement decisions are frequently raised. We propose a framework for developing reference models for specific diseases to inform economic evaluations of health technologies and their appraisal. The structure of a reference model reflects the natural history of the condition under study and defines the clinical events to be represented, the relationships between the events, and the effect of patient characteristics on the probability and timing of events. We contend that the use of reference models will improve the accuracy and comparability of public funding decisions. This can lead to the more efficient allocation of public funds.