A comparison of cost-effectiveness between offering antidepressant-CBT combinations first or second, for moderate to severe depression in Japan

Patient-Informed Value Elements in Cost-Effectiveness Analyses of Major Depressive Disorder Treatment: A Literature Review and Synthesis

OBJECTIVES

Prior work identified 6 key value elements (attributes of treatment and desired outcomes) for individuals living with major depressive disorder (MDD) in managing their condition: mode of treatment, time to treatment helpfulness, MDD relief, quality of work, interaction with others, and affordability. The objective of our study was to identify whether previous cost-effectiveness analyses (CEAs) for MDD treatment addressed any of these value elements. A secondary objective was to identify whether any study engaged patients, family members, and caregivers in the model development process.

METHODS

We conducted a systematic literature review to identify published model-based CEAs. We compared the elements of the published studies with the MDD patient value elements elicited in prior work to identify gaps and areas for future research.

RESULTS

Of 86 published CEAs, we found that 7 included patient out-of-pocket costs, and 32 included measures of productivity, which were both priorities for individuals with MDD. We found that only 2 studies elicited measures from patients for their model, and 2 studies engaged patients in the modeling process.

CONCLUSIONS

Published CEA models for MDD treatment do not regularly include value elements that are a priority for this patient population nor do they include patients in their modeling process. Flexible models that can accommodate elements consistent with patient experience are needed, and a multistakeholder engagement approach would help accomplish this.

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.