A Canadian Simulation Model for Major Depressive Disorder: Study Protocol.

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.

Costs of major depression covered / not covered in British Columbia, Canada.

BACKGROUND: Major depressive disorder (MDD) is one of the world's leading causes of disability. Our purpose was to characterize the total costs of MDD and evaluate the degree to which the British Columbia provincial health system meets its objective to protect people from the financial impact of illness. METHODS: We performed a population-based cohort study of adults newly diagnosed with MDD between 2015 and 2020 and followed their health system costs over two years. The expenditure proportion of MDD-related, patient paid costs relative to non-subsistence income was estimated, incidences of financial hardship were identified and the slope index of inequality (SII) between the highest and lowest income groups compared across regions. RESULTS: There were 250,855 individuals diagnosed with MDD in British Columbia over the observation period. Costs to the health system totalled >$1.5 billion (2020 CDN), averaging $138/week for the first 12 weeks following a new diagnosis and $65/week to week 52 and $55/week for weeks 53-104 unless MDD was refractory to treatment ($125/week between week 12-52 and $101/week over weeks 53-104). The proportion of MDD-attributable costs not covered by the health system was 2-15x greater than costs covered by the health system, exceeding $700/week for patients with severe MDD or MDD that was refractory to treatment. Population members in lower-income groups and urban homeowners had disadvantages in the distribution of financial protection received by the health system (SII reached - 8.47 and 15.25, respectively); however, financial hardship and inequities were mitigated province-wide if MDD went into remission (SII - 0.07 to 0.6). CONCLUSIONS: MDD-attributable costs to health systems and patients are highest in the first 12 weeks after a new diagnosis. During this time, lower income groups and homeowners in urban areas run the risk of financial hardship.

Cost-effectiveness of pharmacogenomic-guided treatment for major depression.

BACKGROUND: Pharmacogenomic testing to identify variations in genes that influence metabolism of antidepressant medications can enhance efficacy and reduce adverse effects of pharmacotherapy for major depressive disorder. We sought to establish the cost-effectiveness of implementing pharmacogenomic testing to guide prescription of antidepressants. METHODS: We developed a discrete-time microsimulation model of care pathways for major depressive disorder in British Columbia, Canada, to evaluate the effectiveness and cost-effectiveness of pharmacogenomic testing from the public payer's perspective over 20 years. The model included unique patient characteristics (e.g., metabolizer phenotypes) and used estimates derived from systematic reviews, analyses of administrative data (2015-2020) and expert judgment. We estimated incremental costs, life-years and quality-adjusted life-years (QALYs) for a representative cohort of patients with major depressive disorder in BC. RESULTS: Pharmacogenomic testing, if implemented in BC for adult patients with moderate-severe major depressive disorder, was predicted to save the health system $956 million ($4926 per patient) and bring health gains of 0.064 life-years and 0.381 QALYs per patient (12 436 life-years and 74 023 QALYs overall over 20 yr). These savings were mainly driven by slowing or avoiding the transition to refractory (treatment-resistant) depression. Pharmacogenomic-guided care was associated with 37% fewer patients with refractory depression over 20 years. Sensitivity analyses estimated that costs of pharmacogenomic testing would be offset within about 2 years of implementation. INTERPRETATION: Pharmacogenomic testing to guide antidepressant use was estimated to yield population health gains while substantially reducing health system costs. These findings suggest that pharmacogenomic testing offers health systems an opportunity for a major value-promoting investment.

Evaluating treatment outcomes in pharmacogenomic-guided care for major depression: A rapid review and meta-analysis.

Pharmacogenomic (PGx) testing may increase the probability of remission and response in patients with major depressive disorder (MDD) undergoing pharmacotherapy. Given the potential implications of these outcomes and recent proliferation of PGx studies, we conducted a systematic review to evaluate the effectiveness of PGx testing on clinical outcomes in patients with MDD as compared to treatment as usual (TAU). MEDLINE, Embase, PsycInfo, and CENTRAL were searched for English-language articles from 2000 to 2021 for randomized controlled trials (RCTs) comparing PGx-guided treatment vs. TAU in patients with MDD. Meta-analyses were conducted in R. Ten RCTs were included: eight reported remission and seven reported response. The best available evidence suggests that PGx-guided care for moderate-to-severe adult depression is more likely to result in remission and response than TAU (both risk ratios significant). However, there are limitations in the evidence base, including high risk of bias and inconsistency between trials. Despite the consequent very low certainty in the magnitude of effect, there is confidence in the direction. Though modest, the beneficial effects of PGx for adults with moderate-severe MDD could - as a result of the scope and scale of the condition and its impacts - have important ramifications for patients and the health system.

Collaborating with Patient Partners to Model Clinical Care Pathways in Major Depressive Disorder: The Benefits of Mixing Evidence and Lived Experience.

BACKGROUND: Partnering with patients can enrich the design and development of models of clinical care pathways, yet the practice is not commonplace. Guidelines or "best practices" for patient involvement in modeling are scarce. OBJECTIVES: In this paper, we outline the steps we took to form an effective partnership with patients to design a robust microsimulation Markov model of major depressive disorder care pathways in British Columbia, Canada, with the aim of encouraging other teams to partner with patients in healthcare modeling endeavors. METHODS: We describe three unique phases of our collaborative process: uncertainty, mapping, and structured collaboration. We then explore the unique contributions the patient partners made, not only to the model itself, but to our process. Key perspectives are shared from both the modeler and the patient partners in their own words. RESULTS: The patient partners made distinct contributions by challenging and verifying modeling assumptions, noting limitations of the model, and suggesting areas for future research. Both the patient partners and the modelers saw great value in the partnership and agreed that the model was strengthened by the diversity of the team. CONCLUSIONS: We present our learning and key recommendations for future modeling teams in the absence of tested frameworks. We encourage more widespread adoption of patient involvement in modeling and the development of guidelines for such work to increase the democracy of scientific decision making.

The Risk of Relapse of Depression During Pregnancy After Discontinuation of Antidepressants: A Systematic Review and Meta-Analysis.

OBJECTIVE: The aim of this systematic review and meta-analysis was to determine the effect of antidepressant discontinuation on the risk of relapse of depression during pregnancy. DATA SOURCES: MEDLINE, EMBASE, CINAHL, and PsycInfo were searched from the inception of each database through March 2019 using keywords such as antidepressants, pregnancy, preconception, discontinuation, stop, recurrence, reintroduction, and relapse. STUDY SELECTION: Original studies that involved pregnant women who discontinued antidepressants during preconception (ie, 3 months prior to pregnancy) or pregnancy and examined the relapse of depression during pregnancy (ie, the reemergence of depression or reintroduction of medication) and published in English were included. A total of 2,172 records were identified, and the full texts of 37 articles were reviewed. Eight studies met the inclusion criteria, 6 of which fulfilled the quality criteria, with 4 studies providing data for the meta-analysis. DATA EXTRACTION: Data were extracted using a data extraction form developed for the purpose of this study. The Cochrane Collaboration Review Manager software version 5.3 was used to conduct the meta-analysis. RESULTS: Pooled data did not show higher risk of relapse of depression during pregnancy for women who discontinued antidepressants than for those who continued antidepressants (risk ratio [RR] = 1.74; 95% CI, 0.97 to 3.10; P = .06). In the subanalysis based on the severity and recurrence of depression in the study populations, the risk of relapse was significantly higher for populations suggestive of severe or recurrent depression (RR = 2.30; 95% CI, 1.58 to 3.35) but not for populations suggestive of mild or moderate depression severity (RR = 1.59; 95% CI, 0.83 to 3.04). CONCLUSIONS: Women with severe or recurrent depression should be informed about the increased risk of relapse following antidepressant discontinuation, and those who discontinue antidepressants should be monitored for relapse.