Plain Language Summary

This study looked at whether past research evaluating the costs and benefits of treatments for major depressive disorder considered aspects of care that matter most to patients. Earlier studies had already identified 6 important treatment features valued by people living with major depressive disorder: (1) how treatment is delivered, (2) how quickly it begins to help, (3) relief of depression symptoms, (4) quality of work life, (5) relationships with others, and (6) affordability.

The main goal of this research was to see if existing cost-effectiveness analyses included any of these 6 value elements. A second goal was to find out whether any of the studies had involved patients, family members, or caregivers when designing their evaluation models. The researchers conducted a systematic review of published cost-effectiveness analyses of major depressive disorder treatments. They compared each study’s content against the 6 patient-informed value elements to identify which aspects were covered and which were overlooked.

Out of 86 cost-effectiveness analyses reviewed, only 7 studies considered patients’ out-of-pocket costs, and 32 included productivity-related outcomes. Just 2 studies directly collected information from patients for their models, and another 2 involved patients in the modeling process. Most cost-effectiveness analyses mainly focused on clinical trial results, such as remission and relapse, rather than broader impacts like work quality or social connections.

The findings show that current cost-effectiveness analyses often miss important patient priorities and rarely engage patients in shaping the models. To better reflect the real experiences and needs of people living with major depressive disorder, future evaluations should use flexible approaches that incorporate patient perspectives. Involving patients and other stakeholders could help create more meaningful and relevant assessments to inform healthcare decisions.

^{Note: This content was created with assistance from artificial intelligence (AI) and has been reviewed and edited by ISPOR staff. For more information or for inquiries on ISPOR’s AI policy, click here or contact us at info@ispor.org.}