A Canadian Simulation Model for Major Depressive Disorder: Study Protocol

Shahzad Ghanbarian (), Gavin W. K. Wong, Mary Bunka, Louisa Edwards, Sonya Cressman, Tania Conte, Sandra Peterson, Rohit Vijh, Morgan Price, Christian Schuetz, David Erickson, Linda Riches, Ginny Landry, Kim McGrail, Jehannine Austin and Stirling Bryan
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Shahzad Ghanbarian: University of British Columbia
Gavin W. K. Wong: University of British Columbia
Mary Bunka: University of British Columbia
Louisa Edwards: University of British Columbia
Sonya Cressman: University of British Columbia
Tania Conte: University of British Columbia
Sandra Peterson: University of British Columbia
Rohit Vijh: University of British Columbia
Morgan Price: University of British Columbia
Christian Schuetz: University of British Columbia
David Erickson: University of British Columbia
Linda Riches: Patient Partner
Ginny Landry: Patient Partner
Kim McGrail: University of British Columbia
Jehannine Austin: University of British Columbia
Stirling Bryan: University of British Columbia

PharmacoEconomics - Open, 2024, vol. 8, issue 3, No 12, 493-505

Abstract: Abstract Background Major depressive disorder (MDD) is a common, often recurrent condition and a significant driver of healthcare costs. People with MDD often receive pharmacological therapy as the first-line treatment, but the majority of people require more than one medication trial to find one that relieves symptoms without causing intolerable side effects. There is an acute need for more effective interventions to improve patients’ remission and quality of life and reduce the condition’s economic burden on the healthcare system. Pharmacogenomic (PGx) testing could deliver these objectives, using genomic information to guide prescribing decisions. With an already complex and multifaceted care pathway for MDD, future evaluations of new treatment options require a flexible analytic infrastructure encompassing the entire care pathway. Individual-level simulation models are ideally suited for this purpose. We sought to develop an economic simulation model to assess the effectiveness and cost effectiveness of PGx testing for individuals with major depression. Additionally, the model serves as an analytic infrastructure, simulating the entire patient pathway for those with MDD. Methods and Analysis Key stakeholders, including patient partners, clinical experts, researchers, and modelers, designed and developed a discrete-time microsimulation model of the clinical pathways of adults with MDD in British Columbia (BC), including all publicly-funded treatment options and multiple treatment steps. The Simulation Model of Major Depression (SiMMDep) was coded with a modular approach to enhance flexibility. The model was populated using multiple original data analyses conducted with BC administrative data, a systematic review, and an expert panel. The model accommodates newly diagnosed and prevalent adult patients with MDD in BC, with and without PGx-guided treatment. SiMMDep comprises over 1500 parameters in eight modules: entry cohort, demographics, disease progression, treatment, adverse events, hospitalization, costs and quality-adjusted life-years (payoff), and mortality. The model predicts health outcomes and estimates costs from a health system perspective. In addition, the model can incorporate interactive decision nodes to address different implementation strategies for PGx testing (or other interventions) along the clinical pathway. We conducted various forms of model validation (face, internal, and cross-validity) to ensure the correct functioning and expected results of SiMMDep. Conclusion SiMMDep is Canada’s first medication-specific, discrete-time microsimulation model for the treatment of MDD. With patient partner collaboration guiding its development, it incorporates realistic care journeys. SiMMDep synthesizes existing information and incorporates provincially-specific data to predict the benefits and costs associated with PGx testing. These predictions estimate the effectiveness, cost-effectiveness, resource utilization, and health gains of PGx testing compared with the current standard of care. However, the flexible analytic infrastructure can be adapted to support other policy questions and facilitate the rapid synthesis of new data for a broader search for efficiency improvements in the clinical field of depression.

Date: 2024
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DOI: 10.1007/s41669-024-00481-y

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