Optimizing personalized management of depression: the importance of real‐world contexts and the need for a new convergence paradigm in mental health
Authors: Charles F. Reynolds
Journal: World Psychiatry (2020)

In this issue of the journal, Maj et al1 have revisited a fundamental tenet of psychiatric medicine, namely, that more precise clinical characterization of patients with depression will enhance the provision of personalized management – and the likelihood of optimal outcomes. The authors have conducted a comprehensive and balanced review of relevant domains, including clinical symptoms, severity of illness, depression subtypes, functional status, staging of illness, neurocognition, medical and psychiatric comorbidities, early life adversity, personality dysfunction, and environmental stressors. They have highlighted the importance of measurement-based assessment and care via the use of instruments both psychometrically sound and amenable to implementation in practice. Although not aiming to deal specifically with biomarkers, the authors suggest that progress in the identification and clinical use of biomarkers will be facilitated through multidimensional clinical assessment. It is indeed plausible that biomarkers will be found to correlate more closely with dimensions of psychopathology than with categorical diagnostic measures, which often hide important treatment-relevant aspects of illness. As such, biomarkers may become more useful as predictors, modifiers and mediators of response variability. An analogy with diabetes mellitus seems appropriate: finding an abnormal blood glucose (like a positive screen for depression) mandates a clinical workup across a number of dimensions to inform appropriate clinical management, aided by the use of laboratory tests that facilitate monitoring of progress in response to treatment and in prevention of adverse sequelae. Viewed from the perspective of someone living with depression, an optimal outcome entails both restoration of a sense of well-being and re-engagement in major social, vocational and family roles. As Maj et al note, these are among the outcomes that matter most to patients. Although reduction in symptom burden is clearly important (because residual symptoms indicate increased risk for a relapsing and chronic course), patients and their family carers hope for the return of pleasure and meaning in life, resumption of major roles, and mitigation of carer burden and its attendant demoralization. Answering the question "How well is well?" depends, therefore, upon taking both a patient-focused and family-centered approach. Depression does not occur monadically, but more often within a family context. Nor does it occur apart from myriad social, cultural and medical issues. Optimal care involves aiming at more than relief of anguish in the pursuit of personalized management. To say that depression does not occur "in pure culture" is thus to highlight several real-world contexts in which the more precise clinical characterization of depressed patients needs to occur. Relevant contexts for optimizing depression assessment and management include, among others, sociocultural, medical, and systems-based care-delivery issues. These contexts may be understood as a way of further grounding multidimensional clinical characterization in vivo. With respect to sociocultural context, for example, persons from different racial and ethnic groups vary in their understanding of what depression is, what constitutes acceptable treatment, and even whether treatment is needed at all. For some, "depression" is both stigmatized and stigmatizing. Furthermore, engaging persons living in low-resource settings, very different from high-income countries, may be quite challenging, particularly if family members do not "buy in" to the need for treatment. Using like-ethnic community health workers, as members of a treatment team, can be useful for gaining trust and for promoting engagement in treatment, treatment adherence, and access to community resources needed by impoverished or disadvantaged depressed adults in their journey to full recovery. Optimizing treatment outcomes, the goal of precise clinical characterization, begs the question of how best to close the world's treatment gap for depression2. The treatment gap arises especially from the dearth of mental-health specialty expertise in low- and middle-income countries (as well as in rural areas of high-income countries), where social determinants of ill-health, including depression, may be particularly powerful. Work-force issues further underscore the importance of early interventions to pre-empt or prevent depression in vulnerable people, as Maj et al emphasize in their discussion of staging. The implied analogy to cancer is especially compelling since, as with cancer, early preventive intervention may be curative or at least mitigate down-stream complications. In the case of depression, it may mitigate emergence of treatment resistance, chronicity, and adverse outcomes such as suicide and dementia. How to leverage mental health expertise broadly in the service of personalized prevention and treatment, therefore, becomes the central question. The use of task-shifting strategies in order to share tasks with primary medical personnel and with community health workers has increasingly found a place in team-based systems of depression prevention and treatment (see, for example, Dias et al3). Sometimes called "coordinated" care, such models facilitate improvements in evidence-based assessment and guideline-based delivery of care, informed by mental health specialists in the "hub" of the system. Models of coordinated and integrated behavioral and medical services, including the use of telemedicine and telepsychiatry, have enabled greater reach than is possible with traditional office-based treatment for depression and for reduction of suicidal behaviors. Shifts in reimbursement for telepsychiatry, where the psychiatrist does not actually have to see the patient face-to-face, is facilitating this change in practice – made even more important by the COVID-19 pandemic and its progeny of depression, anxiety, and prolonged grief disorder. Maj et al underscore how the heterogeneity of depression (in pathogenesis, clinical presentation, and response variability) often gives rise to difficult-to-treat illness (and hence the need for multidimensional evaluation to understand the origins of treatment resistance). A particularly important aspect of optimizing depression treatment is the need for guidelines that can inform shared decision-making with respect to augmenting, switching or combining treatment modalities to help people with difficult-to-treat or even treatment-resistant depression. In this context, since the goal of treatment is not only to avoid adverse effects and to get well, but also to stay well, understanding the long-term efficacy, effectiveness and tolerability of different strategies needs further attention. Different patient characteristics, such as neurocognitive function, the presence of suicidal ideation, and varying degrees of medical and/or psychiatric comorbidity will likely moderate, or influence, the strength of response to acute treatment and the durability of response and recovery in maintenance treatment. Personalizing management of depression depends upon identification of such variables, or moderators, as distinct from more general prognostic indicators. One can anticipate that biomarkers will be identified as response modifiers in depression treatment, as has been the case in oncology. In conclusion, multidimensional assessment, as reviewed by Maj et al, is clearly important for personalizing the care of persons at risk for, or already living with, depression. Optimizing short- and long-term outcomes through multidimensional, patient-centered clinical assessment seems more likely when carried out within the broader sociocultural, medical, and care-delivery contexts in which depression occurs in the real world. Needed now, I would suggest, is a new transdisciplinary, convergence paradigm to inform both research and practice in mental health4.