The Massachusetts General Hospital Guide to Depression

Current Clinical Psychiatry Series Editor: Jerrold F. Rosenbaum

Benjamin G. Shapero David Mischoulon Cristina Cusin Editors

The Massachusetts General Hospital Guide to Depression New Treatment Insights and Options

Current Clinical Psychiatry Series Editor Jerrold F. Rosenbaum Department of Psychiatry Massachusetts General Hospital Boston, MA, USA

Current Clinical Psychiatry offers concise, practical resources for clinical psychiatrists and other practitioners interested in mental health. Covering the full range of psychiatric disorders commonly presented in the clinical setting, the Current Clinical Psychiatry series encompasses such topics as cognitive behavioral therapy, anxiety disorders, psychotherapy, ratings and assessment scales, mental health in special populations, psychiatric uses of nonpsychiatric drugs, and others. Series editor Jerrold F. Rosenbaum, MD, is Chief of Psychiatry, Massachusetts General Hospital, and Stanley Cobb Professor of Psychiatry, Harvard Medical School. More information about this series at http://www.springer.com/series/7634

Benjamin G. Shapero · David Mischoulon Cristina Cusin Editors

The Massachusetts General Hospital Guide to Depression New Treatment Insights and Options

Editors Benjamin G. Shapero Depression Clinical and Research Program Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston, MA USA

David Mischoulon Depression Clinical and Research Program Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston, MA USA

Cristina Cusin Depression Clinical and Research Program Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston, MA USA

Current Clinical Psychiatry ISBN 978-3-319-97240-4    ISBN 978-3-319-97241-1 (eBook) https://doi.org/10.1007/978-3-319-97241-1 Library of Congress Control Number: 2018957086 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana Press imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To all those suffering with depression and those who have given their time and patience to participate in research, this book and its contents could not have been written without you. Although not every treatment works for everyone, the intent of this book is to provide patients, practitioners, and loved ones with options and alternatives. There is always hope. –B.G.S. In memory of Joyce R. Tedlow, M.D. –D.M. To my patients and my teachers, for inspiring me in continuing this work. To my mentors and my family for their enduring support. –C.C.

Foreword: Introduction and Historical Perspective

I have to confess that I am very proud of this terrific MGH Guide to Depression, showcasing the important work of the faculty of the Depression Clinical and Research Program (DCRP) at Massachusetts General Hospital (MGH). As the founder of this program and as the person with the greatest longitudinal knowledge of its evolution over the years, I will try to provide a brief overview of its history and a commentary about some of the critical decisions that were made and the important studies that were conducted over the years. When I founded the DCRP in 1990, there was a lot of enthusiasm in the field about what a new class of antidepressants, the selective serotonin reuptake inhibitors (SSRIs), could do for the treatment of depression. After decades of use of tricyclic antidepressants and monoamine oxidase inhibitors, which were certainly effective, yet burdened by bothersome side effects and the risk of lethality in overdose, the safer SSRIs appeared to clinicians as much more acceptable to patients from a risk/ benefit ratio. The introduction of these new compounds into the market was generating a great deal of interest and focus on the biology of major depressive disorder (MDD), perhaps with an oversimplification of the role of the serotonergic system in this disorder. My mentor Jerry Rosenbaum and I obtained a fairly large industry contract from Eli Lilly and Company to have MGH serve as one of the five academic sites implementing a multicenter study called “Fluoxetine vs Placebo: Long-Term Treatment of MDD,” utilizing a fairly novel design with staggered randomized withdrawal. The industry grant provided us funding for the recruitment of two psychiatrists, Drs. Mary McCarthy and Ron Steingard, and a psychologist, Dr. Joel Pava, as well as four research assistants. Our team worked very hard to recruit and enroll at MGH about one fifth of 839 medication-free outpatients with MDD undergoing open-label treatment with fluoxetine 20 mg/day for 12 weeks, followed by randomization of the patients whose MDD was in remission to either stay on fluoxetine on go on placebo at different time points [1]. Since the grant paid for the recruitment and for all the procedures related to the randomized withdrawal study, it became clear to me that I could leverage the study not only to create at MGH a nucleus of researchers in depression, but also to support multiple ancillary studies that would enhance the value of the study itself. This principle of “using all the parts of the buffalo” has always appealed to me, as it maximizes efficiency and reduces overall costs, and was clearly adopted in the first large study of the DCRP. In particular, I was able to obtain an additional grant from the sponsor to fund an ancillary study called “Biological and Psychological Predictors of Response and Relapse in Major Depressive Disorder.” This study allowed us in the DCRP to assess our patients before and after open-label treatment with fluoxetine with a battery of psychological tests and biomarker studies. Through this ancillary study alone, we were able to conduct a number of analyses, which led to the publication of approximately 20 original articles. For example, one of the psychological assessments was a questionnaire I had developed, the Anger Attacks Questionnaire [2], which was meant to capture those MDD patients with irritability and episodes of anger outbursts, called anger attacks. We were able to administer this questionnaire to our patients before and after treatment, demonstrating a distinct clinical and biomarker profile of MDD with anger attacks compared to MDD without such attacks, and also that treatment with the SSRI fluoxetine led to the disappearance of anger attacks in the majority of the patients. These findings were later replicated by our group in a different cohort of patients. vii

viii

Foreword: Introduction and Historical Perspective

Another interesting ancillary project to the “Fluoxetine vs Placebo: Long-Term Treatment of MDD” study was the one assessing the outcome of a dose increase among patients who relapsed or had a recurrence of their MDD while on fluoxetine 20  mg/day [3]. The study, which was open-label and uncontrolled, showed a dose increase benefit, which was later confirmed in a double-blind study of the fluoxetine weekly formulation. The “Fluoxetine vs Placebo: Long-Term Treatment of MDD” study itself generated a large cohort of fluoxetine nonresponders, which were discontinued from the study as per protocol. I had the idea that those patients could enter a relatively inexpensive randomized study of next-­ step strategies, and I persuaded the PIs of two other sites to join in the effort. This led to a three-site study, published in 1994  in the American Journal of Psychiatry [4], showing the superiority of a fluoxetine dose increase compared to augmentation of fluoxetine with low-­ dose lithium or low-dose desipramine. Of interest, this particular study design, with a prospective identification of MDD patients nonresponding to antidepressant therapies, followed by randomization to next-step strategies, was later on adopted by the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study [5] and by phase three programs that led to the approval of aripiprazole and brexpiprazole as augmentation strategies among MDD patients with inadequate response to antidepressant therapies. The prospective lead-in design that I had developed and the pilot data generated from this collaborative three-site ancillary study led to the first R01 grant from the National Institute of Mental Health (NIMH) to the DCRP in 1992. That 5-year grant, in addition to other industry studies, allowed me to recruit 2 psychiatrists from McLean Hospital as Associate Directors, Andrew Nierenberg and Jonathan Alpert. This recruitment turned out to be a critical one, as they are both immensely talented and went on to become my partners in fostering the growth and evolution of the DCRP over the years. Jonathan served as Associate Director of the DCRP from 1992 until April 2014, when I stepped down from my role of Director of the DCRP to take on the role of Director of the Division of Clinical Research of the MGH Research Institute and Jonathan became Director of the DCRP.  In March 2017, Jonathan was subsequently recruited to become the Dorothy and Marty Silverman University Chair of the Department of Psychiatry and Behavioral Sciences and Professor of Psychiatry, Neuroscience, and Pediatrics at Montefiore Health System and Albert Einstein College of Medicine in New York. Jonathan, during his 24 years as a DCRP leader, played a crucial role as a mentor and advisor to countless fellows and junior faculty, and he is greatly missed. While at MGH, he also served as Associate Chief of Psychiatry responsible for outpatient, inpatient, and emergency services at MGH and was the first incumbent of the Joyce R.  Tedlow Chair in the field of depression studies at Harvard Medical School (Dr. Tedlow, I wish to note, was an outstanding research fellow and later part-time research and clinical staff member with the DCRP from 1994 until her death in 2003). Andrew Nierenberg also went on to take on new responsibilities in addition to those concerning the leadership of the DCRP, by becoming the Director of the Bipolar Clinic and Research Program in 2008, and he currently serves as Director of the Dauten Family Center for Bipolar Treatment Innovation and is the first incumbent of the Thomas P. Hackett Chair of Psychiatry at MGH. Andrew continues to serve as Associate Director for the DCRP and as a mentor and advisor to many DCRP fellows and faculty. In 1997, I was able to obtain another R01 from NIMH for a study called “Prediction of Outcome during Fluoxetine Continuation,” allowing me to recruit as junior faculty the graduating Psychopharmacology Chief Resident at MGH, David Mischoulon. This also turned out to be a critical recruitment for the DCRP, as David showed outstanding leadership skills and served first for 10  years as Director of Research of the DCRP until March 2017, when he became Director of the DCRP and the second incumbent of the Joyce R. Tedlow Professor of Psychiatry at Harvard Medical School. It is not a coincidence that he is the coeditor of this terrific guide. In 1999, I became the Co-principal Investigator with Drs. A.  John Rush and Madhukar Trivedi of the largest clinical trial ever conducted in depression, the STAR*D, whose findings

Foreword: Introduction and Historical Perspective

ix

were published in journals such as The New England Journal of Medicine (NEJM) [6, 7] and JAMA [8]. The DCRP was one of the sites of the study, and the DCRP participation in this study allowed many of our faculty to take the lead in countless publications derived from it. The DCRP has also conducted innovative work on MDD patients with anger attacks, showing that these individuals present a blunted prolactin response to fenfluramine challenge, are more likely to have brain white matter hyperintensities, and may selectively respond to serotonergic compounds [9]. In addition, the DCRP has conducted important investigations on the role of folate deficiency in depression [10, 11] and on the efficacy in depression of one carbon cycle elements with putative antidepressant effects such as S-adenosylmethionine (SAMe) [11–13] and methylfolate [12]. This pioneering work in this area led to an R01 grant on the efficacy of SAMe in major depressive disorder (MDD) [13] and to the successful trial of SAMe augmentation in resistant depression, published by George Papakostas in the American Journal of Psychiatry [14]. The DCRP has conducted and published the first prospective, placebo-­ controlled study of discontinuation-emergent adverse events of the newer antidepressants and has designed and developed a protocol for the first, large multicenter study on the effects of abrupt interruption of SSRI treatment. The DCRP has completed a large single-site study of bupropion augmentation of the nicotine patch in depressed smokers (funded by an R01) [15] and has conducted the first studies utilizing a novel study design, the sequential parallel comparison design or SPCD, aimed at reducing the placebo response and the sample size needs [16]. I had developed this design with David Schoenfeld in 2002 [17] and had obtained six US patents, but we needed the practical proof of its utility, which was demonstrated in several DCRP studies. The DCRP has also been very active in developing new instruments to measure the effects of antidepressant treatments and several validated instruments (such as the Antidepressant Treatment Response Questionnaire [ATRQ] instrument [18] to capture the degree of treatment resistance in depression, the Discontinuation-Emergent Signs and Symptoms [DESS] scale [19] to identify signs and symptoms emerging in the context of stopping antidepressants, the Sexual Functioning Questionnaire [SFQ] scale to assess sexual functioning on and off antidepressants [20], the Cognitive and Physical Functioning Questionnaire [CPFQ] scale to measure cognitive and physical functioning in depression [21], and the Symptoms of Depression Questionnaire [SDQ] scale [22] to measure severity of depression in a very comprehensive way, instruments that are being used by clinical investigators all over the world. With respect to publications, the DCRP faculty has published over 800 original articles in refereed medical journals with international circulation, cited over 60,000 times in the literature. Over the years, the DCRP has recruited a number of extremely talented junior faculty who had trained at MGH in psychiatry or psychology. In 1997, Albert Yeung, who had served as Chief Resident in the Primary Care Psychiatry at MGH, was recruited to the DCRP and has since served as Associate Professor of Psychiatry, Harvard Medical School, and Director of Primary Care Studies of the MGH DCRP. In 2000, the DCRP recruited Dan Iosifescu, who went on to become Director of Translational Neuroscience of the DCRP until 2010, when he became Director of the Mood and Anxiety Disorders Program at the Icahn School of Medicine at Mount Sinai and is now Director of Clinical Research of the Nathan Kline Institute for Psychiatric Research at New  York University. In the same year (2000), psychologist Amy Farabaugh was recruited to expand the breadth of research activities of the program to include cognitive-behavioral studies. She has served for many years as Assistant Professor in Psychology at Harvard Medical School and Director of Psychotherapy Research of the MGH DCRP. In 2001, a former Chief Resident in Psychopharmacology, Roy Perlis, was recruited to the DCRP where he served as Director of Pharmacogenomic Research until he became Medical Director of the Bipolar Clinic and Research Program and now serves as Professor of Psychiatry at Harvard Medical School, Director of the Center for Quantitative Health in the Division of Clinical Research of the MGH Research Institute, and Director of the Center for Experimental Drugs and Diagnostics at MGH. In 2002, psychiatrist George Papakostas was also recruited as junior faculty, going on to serve as Director of Treatment-Resistant Depression Studies of the

x

Foreword: Introduction and Historical Perspective

DCRP and now serving as Associate Professor of Psychiatry at Harvard Medical School and as Scientific Director of the MGH Clinical Trials Network and Institute (CTNI), an academic contract research organization (CRO) that I founded in 2007. In the same year (2007), psychiatrist Nhi-Ha Trinh joined the DCRP as junior faculty and has since served as Director of Clinical Services and Multicultural Studies of the DCRP, Director of the Department of Psychiatry Center for Diversity, and Assistant Professor of Psychiatry at Harvard Medical School. In 2008, psychiatrist Trina Chang joined the DCRP and now serves as Director of Community-Based Studies of the DCRP and Assistant Professor of Psychiatry at Harvard Medical School. In the past 5  years, 2 psychiatrists (Simmie Foster and Felipe Jain) and 3 psychologists (Lauren Fisher; Ben Shapero, the lead editor of this textbook; and Kate Bentley) were recruited as junior faculty. Other notable former trainees and junior faculty of the DCRP include psychologist Timothy Petersen who joined our group in 1999 and is currently an Assistant Professor and Program Director with the MGH Bulfinch Program; psychiatrist John Denninger who joined the DCRP in 2003 and now is Director of Research at the BensonHenry Institute for Mind Body Medicine; psychiatrist Janet Witte, who joined us in 2006 and is now Director for Quality Assurance at the MGH CTNI; and psychiatrist Justin Chen, who joined the DCRP in 2013 and recently became Medical Director of Ambulatory Psychiatry and Co-director of the Primary Care Psychiatry program in the MGH Psychiatry Department. The accomplishments of these individuals showcase the vitality of the program and the wonderful talent of this group. Many national and international DCRP former fellows have gone on to become leaders in mood disorders research and have stayed on at MGH. Nassir Ghaemi, DCRP Research Fellow in 1994, went on to become Professor of Psychiatry at Tufts University School of Medicine and is now Director of Translational Medicine, Neuroscience, at Novartis. Ari Zaretsky, DCRP Research Fellow in 1994, is now Psychiatrist-in-Chief, Sunnybrook Health Sciences Centre, and Professor of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Shamsah Sonawalla began as a Research Fellow in 1997 and remained with the DCRP for 10 years, becoming an Assistant Professor, before returning to India, where she is now Associate Director for Psychiatry Research at Jaslok Hospital, and runs a transcranial magnetic stimulation (TMS) program in Mumbai. In 1999, Christina Dording was recruited as a Research Fellow of the DCRP from the University of Massachusetts Medical Center and now serves as Director of Sexual Behavior Studies of the DCRP and Assistant Professor of Psychiatry at Harvard Medical School. Two international psychiatrists were recruited from Italy as fellows: Paolo Cassano in 2001 and Cristina Cusin in 2004. They both subsequently ended up completing a residency training program in adult psychiatry at MGH and being recruited into the DCRP as junior faculty. Paolo Cassano now serves as Director of Photobiomodulation of the DCRP and Assistant Professor in Psychiatry at Harvard Medical School. Cristina Cusin – the senior editor of this textbook  – is now Director of Translational Studies of the DCRP and is Assistant Professor in Psychiatry at Harvard Medical School and runs the DCRP Ketamine Program. In 2005, Paola Pedrelli, a psychologist from Italy who had trained in the USA, was recruited as a Fellow. She now serves as Director of Dual Diagnosis Studies of the DCRP and Assistant Professor of Psychology at Harvard Medical School. In 2008, the DCRP recruited psychiatrist Nadia Iovieno as Research Fellow, and she now serves as Medical Director of the MGH Clinical Trials Network and Institute (Europe). In 2009, the Israeli psychiatrist Yechiel Levkovitz came to MGH as a DCRP Research Fellow and is now Associate Professor of Psychiatry at the Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod HaSharon, Israel. In 2010, we recruited three new fellows, including Ottavio Vitolo, an Italian psychiatrist who had trained at Washington University and who now serves as Senior Vice President, Head of R&D, and Chief Medical Officer at Relmada Therapeutics; psychologist Maren Nyer, who now serves as Director of Yoga Studies of the DCRP and Assistant Professor of Psychology at Harvard Medical School; and Belgian psychiatrist Martin Desseilles, who now serves as Professor at the University of Namur (UNamur) and University of Liège, Belgium. In 2011, Australian

Foreword: Introduction and Historical Perspective

xi

naturopath and acupuncturist Jerome Sarris pursued a mini-fellowship in the DCRP and is now Professor of Integrative Mental Health and Deputy Director of the NICM Integrative Medicine Research Institute at Western Sydney University. Also in 2011, Dutch psychiatrist and DCRP Research Fellow Marasha De Jong carried out a study of mindfulness-based cognitive therapy for depression and chronic pain, the results of which served as the basis for her doctoral dissertation that she defended in 2018 at Maastricht University. In 2012, the South Korean psychiatrist Hong Jin Jeon came as a DCRP Fellow for 2 years and is now Associate Professor of Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea, and Executive Director of Depression Center of Samsung Medical Center. Finally, in 2013, the Danish physician Soren Ostergaard spent a year as a Fellow in the DCRP and is now Professor at Aarhus University and Aarhus University Hospital, Risskov. These successful and accomplished academic psychiatrists and psychologists from all over the world are a reflection of the international impact and contribution to training that the DCRP has provided since its early years. Finally, over the last three decades, there have been countless research assistants who have obtained their initial training in our program who have then gone on to medical school, clinical psychology doctoral programs, or other advanced degrees in related fields, which continue to make an impact on the field and are a mark of the richness and talent of this program. With all the talent, skills, and clinical expertise of the DCRP faculty, it is not surprising to me that this MGH Guide to Depression provides such a superb overview of this highly prevalent and disabling condition and its treatments. The ability to connect the bedside with clinical and translational research has always been a distinctive feature of the DCRP, and this book demonstrates it once again.

References 1. Fava M, Rosenbaum JF, Cohen L, Reiter S, McCarthy M, Steingard R, Clancy K. High-­ dose fluoxetine in the treatment of depressed patients not responsive to a standard dose of fluoxetine. J Affect Disord. 1992;25:229–34. 2. Fava M, Rosenbaum JF, McCarthy M, Pava J, Steingard R, Bless E.  Anger attacks in depressed outpatients and their response to fluoxetine. Psychopharmacol Bull. 1991;27:275–9. 3. Fava M, Rappe SM, Pava JA, Nierenberg AA, Alpert JE, Rosenbaum JF.  Relapse in patients on long-term fluoxetine treatment: response to increased fluoxetine dose. J Clin Psychiatry. 1995;56:52–5. 4. Fava M, Rosenbaum JF, McGrath PJ, Stewart JW, Amsterdam JD, Quitkin FM. Lithium and tricyclic augmentation of fluoxetine treatment for resistant major depression: a double-­ blind, controlled study. Am J Psychiatry. 1994;151:1372–4. 5. Rush AJ, Trivedi M, Fava M. Depression, IV: STAR*D treatment trial for depression. Am J Psychiatry. 2003;160:237. 6. Rush AJ, Trivedi MH, Wisniewski SR, et al. Bupropion-SR, sertraline, or venlafaxine-XR after failure of SSRIs for depression. N Engl J Med. 2006;354:1231–42. 7. Trivedi MH, Fava M, Wisniewski SR, et al. Medication augmentation after the failure of SSRIs for depression. N Engl J Med. 2006;354:1243–52. 8. Weissman MM, Pilowsky DJ, Wickramaratne PJ, et al. Remissions in maternal depression and child psychopathology: a STAR*D-child report. JAMA. 2006;295:1389–98. 9. Iosifescu DV, Renshaw PF, Dougherty DD, Lyoo IK, Lee HK, Fraguas R, et al. Major depressive disorder with anger attacks and subcortical MRI white matter hyperintensities. J Nerv Ment Dis. 2007;195:175–8. 10. Mischoulon D, Burger JK, Spillmann MK, Worthington JJ, Fava M, Alpert JE. Anemia and macrocytosis in the prediction of serum folate and vitamin B12 status, and treatment outcome in major depression. J Psychosom Res. 2000;49:183–7.

xii

Foreword: Introduction and Historical Perspective

11. Fava M, Mischoulon D Folate in depression: efficacy, safety, differences in formulations, and clinical issues. J Clin Psychiatry. 2009;70 Suppl 5:12–7. 12. Alpert JE, Mischoulon D, Rubenstein GEF, Bottonari K, Nierenberg AA, Fava M. Folinic acid (Leucovorin) as an adjunctive treatment for SSRI-refractory depression. Ann Clin Psychiatry. 2002;14:33–8. 13. Mischoulon D, Price LH, Carpenter LL, et  al. A double-blind, randomized, placebo-­ controlled clinical trial of S-adenosyl-L-methionine (SAMe) versus escitalopram in major depressive disorder. J Clin Psychiatry. 2014;75:370–6. 14. Papakostas GI, Mischoulon D, Shyu I, Alpert JE, Fava M. S-adenosyl methionine (SAMe) augmentation of serotonin reuptake inhibitors for antidepressant nonresponders with major depressive disorder: a double-blind, randomized clinical trial. Am J Psychiatry. 2010;167:942–8. 15. Evins AE, Culhane MA, Alpert JE, Pava J, Liese BS, Farabaugh A, Fava M. A controlled trial of bupropion added to nicotine patch and behavioral therapy for smoking cessation in adults with unipolar depressive disorders. J Clin Psychopharmacol. 2008;28:660–6. 16. Fava M, Mischoulon D, Iosifescu D, Witte J, Pencina M, Flynn M, et al. A double-blind, placebo-controlled study of aripiprazole adjunctive to antidepressant therapy among depressed outpatients with inadequate response to prior antidepressant therapy (ADAPT-­A Study). Psychother Psychosom. 2012;81:87–97. 17. Fava M, Evins AE, Dorer DJ, Schoenfeld DA. The problem of the placebo response in clinical trials for psychiatric disorders: culprits, possible remedies, and a novel study design approach. Psychother Psychosom. 2003;72:115–27. 18. Desseilles M, Witte J, Chang TE, Iovieno N, Dording CM, Ashih H, et al. Assessing the adequacy of past antidepressant trials: a clinician’s guide to the antidepressant treatment response questionnaire. J Clin Psychiatry. 2011;72:1152–4. 19. Rosenbaum JF, Fava M, Hoog SL, Ascroft RC, Krebs WB. Selective serotonin reuptake inhibitor discontinuation syndrome: a randomized clinical trial. Biol Psychiatry. 1998;44:77–87. 20. Fava M, Rankin MA, Alpert JE, Nierenberg AA, Worthington JJ. An open trial of oral sildenafilin antidepressant-induced sexual dysfunction. Psychother Psychosom. 1998;67:328–31. 21. Fava M, Graves LM, Benazzi F, Scalia MJ, Iosifescu DV, Alpert JE, Papakostas GI. A cross-sectional study of the prevalence of cognitive and physical symptoms during long-­ term antidepressant treatment. J Clin Psychiatry. 2006;67:1754–9. 22. Pedrelli P, Blais MA, Alpert JE, Shelton RC, Walker RSW, Fava M. Reliability and validity of the Symptoms of Depression Questionnaire (SDQ). CNS Spectr. 2014;19:535–46.

Boston, MA, USA May 2018

Maurizio Fava

Foreword: MGH Guide to Depression

Depression represents an inescapable form of suffering with emotional, cognitive, and behavioral torment that reverberates through families and friends and renders pleasure unappealing and ordinary life insufferable, a state of anguish with loss of interest and motivation and associated with disability and mortal danger. Depression is a brain based but total body state of distress that has far-reaching impacts on physical health and well-being, emotional state, cognition, and motivated behavior. It is universally prevalent. In 2015, in the United States, 6.7% of the population (16.1 million people) had a major depressive episode. Over the course of lifetime, one in four women and one in ten men will be afflicted. Depression is not, however, one thing; it is not one disease but is very heterogeneous. There are a set of symptoms that are used to make a diagnosis of depression, or as it is diagnostically termed major depressive disorder (MDD), including nine symptoms of which it is necessary to have five and one of two core symptoms to have an official diagnosis. That convention is inadequate to capture the complexity and heterogeneity of the condition. Those criteria do not necessarily capture some of the most painful features of depression – the cognitive impairment in attention, irritability and anger, hopelessness, helplessness and worthlessness, bodily pain, GI dysfunction, anxiety, and the so common and often crippling feature of rumination – that torments and often incompletely resolves even with treatment. There are people who have one episode and never another; those who can never remember feeling well; those with multiple recurrent episodes over the years; those who start in childhood and those who have onset in late life; those whose disorder is linked to a loss or trauma, or an illness, or with cancer or a stroke; those whose illness seems to come out of the blue in the sunniest of lives; those with loaded family histories; and some with no apparent genetic influence. Some are complicated with substance use and others may feature a psychosis. And, there are those who respond robustly to first-line treatments and others who struggle to find any relief. One in three people with depression thinks about suicide, but a smaller number intend or plan an attempt. Between 2% and 9% of those who have had a diagnosis of depression eventually die of suicide. Suicide is the second leading cause of death for college students and those aged 25–34. And, 70% of the 14,000 Americans a year who die of suicide have been depressed. Depression kills in other ways. It is associated with a flurry of inflammatory markers including cytokines like IL6 and TNFa. It is not clear whether the bodily inflammation seen in depression is cause or effect; but it is no surprise that flu feels like depression, and depression feels like flu. There is some evidence that addressing inflammation directly helps some with depression. But the association between inflammation and depression likely accounts for how depression shortens the life span. The death rate in otherwise matched elders in nursing homes is fourfold in those depressed. Diabetic control is worse in the depressed. And, mortality from heart disease is fourfold higher in those after a myocardial infarction with depression. Beyond feeling ill, the changes in brain biology sap you of the resources you need to fight it, like motivation and cognitive tools. It is no surprise either that a key factor in treating depression that has resisted responding to treatment, is the addition of therapeutics that augment the brain’s use of dopamine, the essential neurotransmitter in the brain’s reward system.

xiii

xiv

Foreword: MGH Guide to Depression

It is not clear why depression is so common, as it implies that the condition has evolved for some purpose in our existence. Some evolutionary biologists have speculated that it reflects a defensive state designed to cause us to retreat, hide, and withdraw, “conservation withdrawal,” and that it is more common in those prepared for a world of defeat and threat. And, inflammation is designed to fight off wound infections as is the stress response. And the disorder definitely is more likely in those recently defeated by job loss, or loss of a loved one, or those in a state of chronic defeat like poverty, or in particular those whose brains have been wired to expect a world of trauma and defeat, as in those who were victims of abuse early in life. The stress system is more reactive, and the brain is tuned to expect threat and loss in those so victimized. When a person is chronically stressed, the brain changes. There is a region of the brain called the hippocampus, which is critically important for your memories and cognition, literally your place in the world. Brain health requires the ongoing incorporation of new neurons, new born neurons, or what we call neurogenesis. When one is stressed over time, this activity shuts down, and neurons, axons, and dendrites look like branches in winter, rather than spring: withered, thin, and without new buds, growth, and connections. The same change is seen in depression. Successful treatment of depression reverses these effects, and neurogenesis returns, along with budding, arborization, and new synapse formation. This observation makes one wonder if antidepressants are actually “antidepressant” or rather just more generally neuroprotective in allowing the normal resilience enhancement of neurogenesis to allow the brain to recover from the depressed state as it can at times on its own. Some people’s genetic portfolio contains more risk than others’. A third of the variance accounting for who gets depressed is genetic, the largest risk factor. A first-degree relative of someone with depression has a risk roughly three times that of the general population. Other established risk factors are early parental loss, neglect, or abuse; persistent stress; loss, including loss of social support; a number of illness conditions; endocrine changes; and substance use. So, how to keep from getting depressed or to prevent it from recurring? –– –– –– –– –– –– –– –– –– ––

Pick your parents wisely. Have a nurturing and enriched early developmental experience. Avoid early trauma and loss. Manage stress: cognitive tools, meditation, mindfulness, and attention to nutrition (avoid a pro-inflammatory diet). Sleep. Exercise: Exercise in animal models is as pro-neurogenesis as antidepressants. Maintain a social network that is supportive. Maintain a schedule of activities of things you enjoy: “well-being therapy.” If the pattern is seasonal, use full spectrum lights regularly in the morning. Treat substance use.

• What if I become depressed? –– First, do all the abovementioned prevention. –– Seek evaluation and treatment early. –– The longer the depression, the harder it is to reverse. –– Determine the correct diagnosis: see a primary care physician, psychologist, or psychiatrist. –– Treatment options are vast, but the science of predicting which treatment to assign to which patient in which order is lacking. –– One third will respond well to the first standard treatment, but treatment is a journey that requires persistence because there are so many ways to treat depression that can work. –– For some, they are discovered easily, but for others it takes time and persistence. –– Consider medications, psychotherapies, neurotherapeutics and other devices, alternative medications, medication combinations, and light.

Foreword: MGH Guide to Depression

xv

–– I tell my patients that 60% will recover, 90% will be improved, but relapse is common, and treatment changes are typically necessary. –– 10% are treatment nonresponders. As is evident in this exciting and inspiring new volume on cutting-edge strategies to treat depression, we are searching for new treatments and introducing new strategies, finding new mechanisms of action and arriving on the cusp of a new generation of targets and treatments. Challenges that make this journey harder include the fact that diagnostic boundaries of our current diseases do not align with biological boundaries. Even with this limitation, there are exciting new tools involving deep phenotyping, big data, and artificial intelligence and methodologies like network analysis that will guide us forward. Just as experienced clinicians have learned from unrelenting pursuit of treatments that work for their patients, the field knows to never give up. This volume is a milepost on this journey. Boston, MA, USA Jerrold F. Rosenbaum December 2017

Preface: Introduction to the Book

In writing this monograph on the treatment of depression, it has been our intention to provide an academically oriented yet clinically relevant synthesis of the newest approaches to the management of this complex illness. Given the high number of FDA-approved antidepressants (more than 40 as of this writing), somatic therapies, and psychotherapies, with encouraging evidence to support their efficacy, this is in many ways an excellent time to be a psychiatrist or psychologist in the field of depression. Yet managing depression remains a challenging and difficult task. The available treatments can only do so much, and new meta-analyses and reanalyses have in the past decade cast some doubt as to the efficacy of established antidepressant therapies. While those analyses have been controversial, with limitations of their own, clinicians cannot deny the reality that they see every day: many patients remain depressed, despite aggressive treatment, and among those who do respond to treatment, many will have depressive recurrences. The well-known, long-established therapies for depression have been covered in many excellent textbooks and treatment manuals, and it is not our goal to compete with any of these books. It is likewise not our goal to critique the many accepted guidelines for frontline treatments of depression such as the American Psychiatric Association (APA) practice guidelines (1), the Canadian Network for Mood and Anxiety Treatments (CANMAT) guidelines (2), or the UK National Institute for Health and Care Excellence (NICE) guidelines (3), to name a few. Rather, we seek to present our own vision as to what the potential for treating depressive disorders can be when you work in a specialized clinical and research program such as ours. The Massachusetts General Hospital’s Depression Clinical and Research Program (DCRP), founded in 1990 by Dr. Maurizio Fava, has grown into a world-class center for research on treatments for depression as well as management of these conditions. Our group, consisting of about 15 psychiatrists and psychologists, as well as 8–10 research coordinators and clinical staff who facilitate our work with patients, has always had the philosophy of “if it has to do with depression, we want to study it, and if it works, use it.” This conviction is reflected in the tremendous breadth of areas that our group has researched and applied to clinical care over the past three decades. Hence this book seeks to present a snapshot of where we are now in terms of our clinical and research endeavors and offerings. We have organized the book into five parts, covering severe depression and comorbidities, depression in special populations, psychopharmacology, psychotherapy, and alternative therapies. Each chapter is written by members of the DCRP and some long-standing collaborators, all of whom have contributed, and are actively contributing, to the expanding body of knowledge in our field. The chapters seek to present syntheses of the research evidence for efficacy and safety of various cutting-edge and experimental treatments for depression and include clinical vignettes to illustrate their applicability in the treatment setting. Finally, each chapter has a “frequently asked questions” section at the end to help consolidate the knowledge presented into a question-and-answer format that clinicians may keep in mind when discussing these therapies with patients in the office setting. It is our hope that this volume will appeal to the researcher who wishes to learn more about the state of the art in depression management, as well as to the clinician who seeks practical approaches to be easily implemented in practice, and provide a framework for communicating xvii

xviii

this complex body of knowledge to patients in a clear and accessible manner. In addition, we hope that this book will be of interest to psychiatry and psychology trainees who are considering careers in the field of depression and wish to educate themselves about the finer points of depression management. Again, this book is not intended to be a comprehensive guide to depression, nor will it tell you everything there is to know about the management of this complex disorder. The Massachusetts General Hospital Guide to Depression – New Treatment Insights and Options is the way we practice psychiatry.

References 1. Gelenberg AJ, Freeman MP, Markowitz JC, Rosenbaum JF, Thase ME, Trivedi MH, Van Rhoads RS. Practice guidelines for the treatment of patients with major depressive disorder. 2010. https://psychiatryonline.org/pb/assets/raw/sitewide/practice_guidelines/guidelines/ mdd.pdf. 2. Lam RW, Kennedy SH, Parikh SV, MacQueen GM, Milev RV, Ravindran AV; CANMAT Depression Work Group. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder. 2016. http://canmat.org/canmatpub.html#Guidelines. 3. National Institute for Health and Care Excellence. Depression in adults: recognition and management. Clinical guidelines. 2009. https://www.nice.org.uk/guidance/cg90.

Benjamin G. Shapero David Mischoulon Cristina Cusin 

Boston MA, USA 

Preface: Introduction to the Book

Acknowledgments

On behalf of ourselves and our team of authors, we wish to thank Drs. Maurizio Fava, Jonathan E.  Alpert, and Andrew A.  Nierenberg, for their guidance, mentorship, and tireless support during our careers. The foundation that they laid with the MGH Depression Clinical and Research Program has contributed enormously to the understanding and treatment of depression, as well as to the education and training of current and future generations of psychiatrists and psychologists. They paved the road for us. We carry their legacy. Benjamin G. Shapero, PhD David Mischoulon, MD, PhD Cristina Cusin, MD

xix

Contents

Part I Severe and Comorbid Conditions 1 Treatment-Resistant Depression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cristina Cusin and Stefan Peyda 2 Co-occurring MDD and Problematic Alcohol Use. . . . . . . . . . . . . . . . . . . . . . . 21 Paola Pedrelli and Kate H. Bentley Part II Special Population Considerations 3 Depression and Chronic Medical Illness: New Treatment Approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Trina E. Chang and Sean D. Boyden 4 Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Nhi-Ha Trinh and Taquesha Dean 5 Early Onset of Depression During Childhood and Adolescence . . . . . . . . . . . 59 Benjamin G. Shapero and Erica Mazzone 6 Cross-Cultural Approaches to Mental Health Challenges Among Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Xiaoqiao Zhang, Tat Shing Yeung, Yi Yang, Rohit M. Chandra, Cindy H. Liu, Dana Wang, Sukhmani K. Bal, Yun Zhu, Rebecca Nika W. Tsai, Zhenyu Zhang, Lusha Liu, and Justin A. Chen 7 Depression After Traumatic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Lauren B. Fisher, Garrett Thomas, Ryan A. Mace, and Ross Zafonte Part III Medication Approaches 8 Personalized Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Simmie L. Foster, Samuel R. Petrie, David Mischoulon, and Maurizio Fava 9 Depression, Antidepressants, and Sexual Functioning . . . . . . . . . . . . . . . . . . . 123 Christina M. Dording and Sean D. Boyden 10 Ketamine as a Rapid Antidepressant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Cristina Cusin 11 Neuroactive Steroids and Depression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Karen K. Miller

xxi

xxii

Part IV Psychotherapy Approaches 12 Unified Protocol for Treatment of Depression. . . . . . . . . . . . . . . . . . . . . . . . . . 155 Kate H. Bentley, Laren R. Conklin, James F. Boswell, Benjamin G. Shapero, and Olenka S. Olesnycky 13 Mindfulness-Based Cognitive Therapy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Benjamin G. Shapero, Jonathan Greenberg, Paola Pedrelli, Gaelle Desbordes, and Sara W. Lazar 14 The Role of Technology in the Treatment of Depression. . . . . . . . . . . . . . . . . . 179 Paola Pedrelli, Kate H. Bentley, Esther Howe, and Benjamin G. Shapero Part V Alternative Treatment Approaches 15 Supplements and Natural Remedies for Depression. . . . . . . . . . . . . . . . . . . . . 195 David Mischoulon and Nadia Iovieno 16 The Effects of Tai Chi and Qigong on Anxiety and Depression. . . . . . . . . . . . 211 Albert Yeung, Benjamin Campbell, and Jessie S. M. Chan 17 Yoga as a Treatment for Depression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Maren Nyer, Regina Roberg, Maya Nauphal, and Chris C. Streeter 18 Photobiomodulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Marco Antonio Caldieraro and Paolo Cassano Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  247

Contents

Contributors

Sukhmani  K.  Bal, BS  Massachusetts General Hospital, Center for Cross-Cultural Student Emotional Wellness, Boston, MA, USA Kate H. Bentley, PhD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA James F. Boswell, PhD  Department of Psychology, University at Albany, State University of New York, Center for Elimination of Minority Health Disparities, Albany, NY, USA Sean D. Boyden, BS  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Marco Antonio Caldieraro, MD, PhD  Hospital de Clínicas de Porto Alegre, Department of Psychiatry, Porto Alegre, Brazil Benjamin  Campbell, BA Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Paolo Cassano, MD, PhD  Depression Clinical and Research Program and Center for Anxiety and Traumatic Stress Disorders, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Jessie S. M. Chan, PhD, MPH  Department of Psychology, The University of Hong Kong, Laboratory of Neuropsychology, Laboratory of Social Cognitive Affective Neuroscience, Hong Kong, China Rohit M. Chandra, MD  Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA Trina  E.  Chang, MD, MPH Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Justin  A.  Chen, MD, MPH Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Laren  R.  Conklin, PhD Chalmers P.  Wylie VA Ambulatory Care Center, Columbus, OH, USA Cristina Cusin, MD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Taquesha Dean, BA  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Gaelle  Desbordes, PhD Harvard Medical School, Massachusetts General Hospital, Department of Radiology, Charlestown, MA, USA Christina  M.  Dording, MD Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA xxiii

xxiv

Maurizio Fava, MD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Lauren B. Fisher, PhD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Simmie  L.  Foster, MD, PhD Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Jonathan  Greenberg, PhD Harvard Medical School, Massachusetts General Hospital, Department of Psychiatry, Charlestown, MA, USA Esther  Howe, BA Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Nadia  Iovieno, MD, PhD Clinical Trials Network and Institute (CTNI), Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA Sara W. Lazar, PhD  Harvard Medical School, Massachusetts General Hospital, Department of Psychiatry, Charlestown, MA, USA Cindy H. Liu, PhD  Departments of Pediatric Newborn Medicine and Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Lusha Liu, MD  Private Practice, Adult Psychiatry, Boston, MA, USA Department of Psychiatry, Northpoint Health and Wellness Center, Emotional Wellness and Behavior Health, Minneapolis, MN, USA Ryan A. Mace, MS  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Department of Psychology, Suffolk University, Boston, MA, USA Erica  Mazzone, BA Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Karen K. Miller, MD  Neuroendocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA David  Mischoulon, MD, PhD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Maya Nauphal, BA  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Maren  Nyer, PhD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Olenka  S.  Olesnycky, BS Center for Anxiety and Related Disorders, Boston University, Department of Psychological and Brain Sciences, Boston, MA, USA Paola Pedrelli, PhD  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Samuel R. Petrie, BS  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Stefan Peyda, MSc  Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

Contributors

Contributors

xxv

Regina Roberg, BA  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Benjamin  G.  Shapero, PhD Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Chris C. Streeter, MD  Harvard Medical School, Boston, MA, USA Departments of Psychiatry and Neurology, Boston University School of Medicine, Boston, MA, USA Department of Psychiatry, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, USA Department of Psychiatry, Boston Medical Center, Boston, MA, USA Garrett Thomas, BA  Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Nhi-Ha  Trinh, MD, MPH Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Rebecca  Nika  W.  Tsai, BS Massachusetts General Hospital, Center for Cross-Cultural Student Emotional Wellness, Boston, MA, USA Dana Wang, MD  Rivia Medical PLLC, New York, NY, USA Yi Yang, PhD  Private Practice, Clinical Psychology, Arlington, MA, USA Albert  Yeung, MD, ScD Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Tat  Shing  Yeung, MA, MS Northeastern University, Department of Applied Psychology, Boston, MA, USA Ross  Zafonte, DO Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Red Sox Foundation and Massachusetts General Hospital Home Base Program, Boston, MA, USA Xiaoqiao Zhang, MS, PhD  The Pennsylvania State University, College of Education, State College, PA, USA Zhenyu Zhang, BL  Boston University School of Medicine, Allston, MA, USA Yun Zhu, MS  Harvard Chan School of Public Health, Cambridge, MA, USA

Part I Severe and Comorbid Conditions

1

Treatment-Resistant Depression Cristina Cusin and Stefan Peyda

Case Vignette

Jenny is a 26-year-old graduate student referred to the depression clinic by her prescriber for “treatment-­ resistant depression.” She reports feeling hopeless because her prescriber has told her that “she has already tried everything” and she is looking for another avenue for treatment. During the thorough diagnostic evaluation, she reported symptoms of a major depressive episode persisting for the prior 7 years and severe PTSD from childhood abuse, which she had not yet disclosed to the current prescriber. At the consultation visit, she was asked to bring all the records from the pharmacy documenting the doses and the duration of each medication trial. From the records and the prescriber’s notes, she had received adequate trials (regarding dose and duration) of two SSRIs, sertraline and fluoxetine, while one SNRI, venlafaxine, was not tolerated above 75 mg, and bupropion, started at 300  mg, was also not tolerated and was discontinued after a few days. When she was prescribed aripiprazole as augmentation of an SSRI at a dose of 5 mg, she did notice a slight improvement in mood but experienced akathisia and therefore discontinued it. The patient was relieved when she was told there were numerous other options for treatment, including other classes of medications, antidepressant combinations, and non-pharmacologic treatments like repetitive

C. Cusin (*) Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected] S. Peyda Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT). She agreed to start a new antidepressant at very low dose and to engage in individual therapy focusing on both depression and PTSD.

Introduction Major depressive disorder (MDD) is a common illness, with an estimated lifetime prevalence as high as 16.6% [1]. Almost one third of patients suffering from MDD do not improve after adequate treatments and will be considered as having treatment-resistant depression (TRD) [2], a condition associated with chronic disability and an increased risk of suicide. Diagnosing and managing TRD can be challenging, even for experienced psychiatrists [3]. In the following chapter, we will review the definition, diagnosis, and epidemiology of TRD, followed by an overview of different management strategies.

Definition Major depressive disorder (MDD) is defined as either the presence of depressed mood or loss of pleasure or interest in daily activities, together with a total of at least five out of nine symptoms, persisting for at least two consecutive weeks and leading to a significantly impaired function in a social, occupational, or educational context [4]. There is yet no consensus on how to define a patient with MDD as “treatment-resistant” [5], but one of the most commonly accepted definitions is “lack of response to at least two different antidepressant treatments of an adequate dose for an adequate duration in the current episode.” However, this definition is not clinically helpful, because it leads to grouping together heterogeneous patients with a wide range

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_1

3

4

of severity of illness, such as patients who have not improved with two drugs of the same class (i.e., two selective serotonin reuptake inhibitors, or SSRIs) and patients who have failed multiple trials with medications from different classes and have failed electroconvulsive therapy (ECT) as well. Later in this chapter, we will review the strengths and weaknesses of several staging models that have been proposed to more accurately classify “lack of response” in patients treated with antidepressants [6].

C. Cusin and S. Peyda

severity in terms of risk of death or residual impairment, and possess clinical significance for prognosis and choice of therapeutic modality” [24]. Staging has been applied successfully to a variety of medical diseases, especially in cancer, where it has been crucial to devise specific treatments for specific stages of illness. Ideally, a staging model for TRD should be able to classify patients according to their level of treatment resistance and should allow the clinician to predict chances of future remission, hence guiding treatment selection. For MDD, different staging models have been developed Epidemiology for different purposes, such as staging of disease progression [25–27] and staging of treatment resistance [6, 28]. The worldwide prevalence of major depressive disorder is TRD is often assessed retrospectively (based on patient’s estimated between 216 million [7] and 322 million cases [8]. recall), with occasional corroboration of previous nonreIn the USA, 6.6–7.6% of Americans aged 12 and over have sponse by validated rating scales and pharmacy records. It is been reported to suffer from MDD [9, 10], and around 3% noteworthy that most reported definitions of TRD focused have treatment-resistant depression [11]. A systematic exclusively on previous pharmacological treatment and did review of inpatients and outpatients followed for medium-to-­ not include psychological treatments such as cognitive long-term periods (6 months to 10 years) found that less than behavioral therapy (CBT) or interpersonal therapy (IPT). half of patients with TRD recovered or avoided relapse Thus, a more apt term of commonly used definitions of TRD beyond a mild degree, while between 28% and 68% had a should be antidepressant treatment-resistant. More imporrelapse requiring readmission or suffered from premature tantly, none of these staging models of TRD have been indedeath from all causes [12]. Patients with TRD are at least pendently examined for reliability and predictive utility. twice as likely to be hospitalized (for both psychiatric and Five different staging models for TRD were reviewed medical reasons), have more outpatient visits, and have thoroughly in Ruhé et al. [6] and will not be discussed here depression-related costs 20 times higher than nontreatment-­ in detail. Over time the attempt at staging evolved from resistant depressed patients. Most importantly, TRD patients considering a single antidepressant at the time, to a multihave a higher overall mortality from all causes and are at dimensional and more continuously scored staging model much greater risk for suicide attempts [13–17]. In terms of which also introduced TRD characteristics (severity and economic burden, the estimated total cost for TRD in duration of illness). Moreover the operationalization criteAmerica is over 20,000 USD per patient-year [18], nearly ria improved, and the scoring of different treatment stratetwice as high as costs for nontreatment-resistant MDD [18, gies (between/within class switching, augmentation/ 19]. Total annual direct (medical) and indirect (loss of pro- combination) was refined by including results from clinical ductivity) costs of TRD may be as high as 48 billion USD trials. Overall only some of the classifications address dif[18], lower than the 130 billion USD for heart disease [20], ferent types of MDD (with or without psychotic features); or 245 billion USD for diabetes [21], but having a significant in general they (a) define treatment nonresponse as “less social and economic impact nonetheless. than 50% reduction in depression scores on a validated scale”, (b) evaluate each treatment separately (ignoring synergistic effects of combinations), and (c) do not assume Staging Models for Treatment-Resistant a hierarchy of antidepressant treatments (i.e., failing one SSRI is considered equivalent to failing a monoamine oxiDepression dase inhibitor MAOI). Although many believe the term “major depression” to repWe will discuss in a separate section how clinical factors resent a single entity, it more likely includes a group of such as comorbid anxiety, history of trauma, abuse or neglect, highly heterogeneous disorders, with different courses of ill- personality disorder diagnosis, depression severity, melanness and treatment response. Studies of first-episode MDD cholic features, early age at onset, and nonresponse to the patients showed that their prognosis varied from recovering first lifetime antidepressant may be independently associated within 3  months (50%) to remaining depressed for longer with worse outcomes and nonresponse to antidepressants than 2 years, and that 60% of subjects who remitted eventu- [29]. To date, there are no biological markers for MDD corally developed a subsequent episode [22, 23]. According to related with different stages or level of treatment resistance Gonnella et al., “Staging defines discrete points in the course to specific treatments (see also Chap. 8 on “Personalized of individual diseases that are clinically detectable, reflects Medicine”).

1  Treatment-Resistant Depression

 linical Characteristics Associated C with Treatment-Resistant Depression While numerous studies have attempted to identify which patients have a higher likelihood of responding or not responding to treatment, these investigations are mostly retrospective association studies which have not yet been replicated in independent or prospective samples. Most of those patient characteristics are proxy measures for severity and chronicity and include long duration of illness, number of depressive episodes, number of hospitalizations, and lack of response to previous treatments. These cannot “technically” be considered predictors because they manifest late in the course of the disease. A recent review that included 51 published papers on antidepressant treatment in adults investigated possible predictive factors such as age of onset, current severity of illness, subtypes of depression, early improvement, menopausal status, fatigue, hopelessness, presence of comorbidities (both medical and psychiatric), personality disorder diagnosis, psychosocial and environmental predictors (such as employment, educational status, marital status, family and social support, place of residence, life events, financial status, and quality of life), global functioning, family history of psychiatric comorbidity, and suicidal behavior [30]. They found that older age has been consistently associated with poorer response, while gender does not seem to affect the likelihood of improvement with treatment overall. As mentioned before, higher baseline depression severity, early age of onset, and long duration of illness are considered poor predictors of treatment response. Comorbid Axis I disorders, particularly anxiety disorders, all seem to decrease the likelihood of response to antidepressants, similarly to family history of depression and other psychiatric disorders. In contrast, the presence of a personality disorder diagnosis has been inconsistently associated with treatment outcome. The presence of medical comorbidity or chronic pain seems to negatively affect the chance of response, as can adverse life circumstances such as unemployment, being single, and having a lower level of education or socioeconomic status. The level of evidence for other variables is quite limited, and their consideration, while helpful in formulating a comprehensive plan of treatment in clinical practice, is not helpful in predicting who will or will not respond to a specific antidepressant medication, and consequently their value as treatment guiders are limited.

Recommendations for Practitioners  valuation and Diagnostics in TRD (See also E APA, NICE, and CANMAT Guidelines) The lack of consensus on what is defined as TRD [5] has probably slowed down the development of new drugs, because it has allowed clinical trials to enroll patients with

5

very different a priori probabilities of response to a novel intervention. Clinicians and researchers alike agree that a minimum of two failed antidepressant trials would qualify the depression as being treatment-resistant. The likelihood of responding to a new treatment seems to decrease with the number of previous failed trials [31], although it is not clear whether the probability continues to decrease further after the fourth failed antidepressant trial. From a clinical point of view, when a patient does not respond to treatment, the first step is to confirm that the diagnosis of MDD is correct [32] by obtaining a detailed history of the current and past depressive episodes and by reviewing environmental stressors and life events. It is important to conduct a semi-structured interview to detect bipolar depression and to identify possible comorbidities [1, 33, 34] and to distinguish MDD from other depressive disorders, such as depression secondary to substance use disorder or PTSD. Moreover, a detailed medical history would allow the detection of disorders with symptoms in part overlapping with MDD, such as obstructive sleep apnea (causing fatigue and cognitive difficulties), hypothyroidism (causing low mood, irritability, weight gain, and lethargy), and early manifestations of Parkinson’s disease (anhedonia, apathy, low mood, lack of energy), among others. A thorough physical and neurological examination, as well as basic laboratory testing (TSH, CBC, electrolytes, glucose, liver function tests, and vitamin B12 and folate if vitamin deficiency is suspected), should precede the diagnosis of treatment-resistant depression. It is necessary to review the patient’s list of medications and supplements to detect possible drugs with adverse effects on mood or that may cause interactions with antidepressants. Finally, it is important to inquire about the patient’s compliance with the medication itself.

Optimization “Pseudo-resistance” is a term coined to describe lack of clinical improvement resulting from a treatment trial of subtherapeutic duration or dose [35]. Indeed, undertreatment in MDD is often observed in clinical practice [36], and the first recommended step in treating TRD is optimization [37]. “Optimizing” the treatment means to increase the dose of the current antidepressant drug [38], to the upper limit of the standard dose interval or as tolerated by the individual patient, while carefully monitoring for side effects [39]. Once an adequate dose is reached, it should be continued for 6–12  weeks [40]. With this approach, response, or 50% improvement from baseline, was observed within 5–8 weeks for roughly one-fifth of previously nonresponsive patients [41]. Improving adherence (for instance, by collaborative care interventions) is important and clinically beneficial

6

[42], because patients may spontaneously discontinue treatment, often without informing their doctor [43], and approximately 25% of primary care patients who discontinue prematurely will need to restart treatment within 9 months [44]. It is important to evaluate changes in symptoms, utilizing either clinician-administered or self-rated depression scales (such as the HAM-D [45], MADRS [46], or QIDS [47]) at each visit because individual biases may lead patients with MDD to inaccurately report their symptoms. When a treatment trial has been optimized with regard to dose, time, and ascertained compliance, it may then be labeled as unsuccessful in eliciting a response [48].

Augmentation The term “augmentation” refers to adding a psychotropic medication (with or without a specific FDA indication for MDD) to an antidepressant, in order to enhance its effect [49]. In patients where the initial treatment has been well tolerated but only partially effective, augmentation could be an efficient strategy [50], because combining agents with different mechanisms of action broadens the therapeutic effect. Several augmenting agents have been studied [51], the most common and effective ones being atypical antipsychotics, lithium, and thyroid hormones [52].

Atypical Antipsychotics Second-generation, or atypical, antipsychotic drugs (AAPs) have strong scientific evidence for use as augmentation in TRD [53]. Compared to placebo, AAPs are significantly more effective [54] with a higher remission rate [55–57], and a number needed to treat (NNT; the average number of patients who need to be treated with AAP for one of them to benefit compared with a control) variable between 4 and 19, where an NNT of 6 or less is desirable [57, 58]. Four AAPs are currently approved by the US FDA for augmentation or as adjunctive therapy in TRD: aripiprazole, quetiapine extended release, olanzapine in combination with fluoxetine (OFC), and brexpiprazole [59, 60]. The odds ratio for remission is increased for all of these drugs (in combination with an antidepressant) compared to antidepressant plus placebo, with aripiprazole having the highest odds ratio, followed by quetiapine and OFC [57]. However, there is a substantial risk of adverse effects when treating a patient with AAPs. Long-term treatment with AAPs has been associated with an increased risk of cardiovascular disease, metabolic abnormalities (such as weight gain, dyslipidemia, hyperglycemia, diabetes), hyperprolactinemia (causing gynecomastia in men, oligo- or amenorrhea in women, or sexual dysfunction in both), extrapyramidal side effects (such as akathisia and tardive dyskinesia), and neurocognitive symptoms [58, 60, 61]. Thus, augmentation

C. Cusin and S. Peyda

with AAPs should be considered for patients with more severe depressive symptoms or when there is an urgent need for rapid clinical improvement. There are no clear guidelines in regard to the ideal duration of treatment for AAP augmentation; in general AAPs should not be continued beyond 6  weeks in cases of nonresponse, and, if successful, they should eventually be tapered gradually to prevent a relapse [60]. Daily doses of adjunctive AAPs in TRD are generally lower compared to the doses used in the treatment of primary psychotic disorders [53, 62], but the dose should be adjusted based on the observed efficacy and side effects. Aripiprazole was the first AAP to be approved by the FDA for augmentation in TRD [51], and its mechanisms of action are quite complex, including effects at dopaminergic, serotonergic, and alpha adrenergic receptors. Several studies support the use of aripiprazole in TRD [54, 63–69], and this drug is broadly regarded as an effective augmentation strategy. One of its most common side effects is akathisia, which often leads to early discontinuation; weight gain seems to be less common compared to other AAP [60]. A number of randomized clinical trials (RCTs) have found quetiapine to be an effective adjunctive treatment in TRD [54], and this drug has received FDA approval. One RCT of nearly 500 patients found augmentation with the daily addition of 300 mg of extended-release quetiapine to significantly increase the odds ratio of remission compared to placebo [70]. Quetiapine also has a complex mechanism of action, with effects on serotonergic, dopaminergic, noradrenergic, and muscarinic receptors. While the risk for extrapyramidal side effects is relatively low for quetiapine compared to older antipsychotics, major drawbacks include sedation, dry mouth, and increased risk for metabolic syndrome [60]. Olanzapine has mainly been studied as an adjunctive in TRD in combination with fluoxetine [54, 56]. This olanzapine-­ fluoxetine combination (OFC) appears to be more effective than monotherapy in inducing remission [71], although evidence is limited [72]. Furthermore, meta-­analyses suggest that olanzapine might not be as effective as augmentation with aripiprazole or quetiapine [56], and head-­to-­head comparison studies are necessary to determine superiority of an AAP over another. Thase and colleagues showed in an RCT study that OFC is superior to fluoxetine monotherapy in reducing depression severity in TRD patients after only 1 week of treatment, and this effect was sustained at 8 weeks [73]. Another large RCT did not find a dose-response pattern; in fact, a low dose of olanzapine and fluoxetine was significantly more effective than a higher dose [74]. Like other AAPs, olanzapine has a broad receptor profile with affinity for serotonergic, dopaminergic, adrenergic, muscarinic, and histaminergic receptors. One animal study of the mechanism of action of OFC involved the inhibition of GABAergic neurons and a consequent increase of serotonin levels in the

1  Treatment-Resistant Depression

synaptic cleft [75]. Weight gain and increased appetite are two side effects more commonly reported with OFC than with other AAP augmentations [57, 72, 73]. Discontinuation due to adverse effects in patients treated with OFC relative to aripiprazole is pronounced [56, 76], but compared to monotherapy with fluoxetine, OFC treatment does not appear to increase the risk of extrapyramidal symptoms [77]. Two recent meta-analyses evaluated the efficacy of brexpiprazole in TRD and found augmentation to be superior to placebo in inducing remission [78, 79], albeit with a modest improvement in depression score (using both the MADRS and 17-item HAM-D) [78]. Patients who fail to respond to a first augmentation with AAPs might benefit from switching to adjunctive brexpiprazole therapy, as demonstrated in a 6-week open-label trial of brexpiprazole added to the current antidepressant treatment in which over half of the patients remitted by the end of the study [80]. A daily dose of 3 mg of adjunctive brexpiprazole was found to produce a greater reduction in MADRS score than 1 mg in another study, suggesting that a higher dose produces greater improvement [61]. Weight gain, akathisia, and restlessness are the most common adverse effects associated with brexpiprazole [60, 81]. Other second-generation antipsychotics have also been studied, including ziprasidone [82, 83] and risperidone [84], but not to the same extent as the AAPs approved by the FDA. Overall, the data supporting the efficacy of AAP augmentation is strong [51], and those drugs are commonly prescribed as first- and second-line augmentation agents, despite the dearth of long-term studies on effectiveness for preventing depressive relapses and risk of complications such as diabetes, cardiovascular disease, and tardive dyskinesia [52, 85, 86].

Lithium Lithium augmentation has been widely used in psychiatry for half a century [87] in the treatment of TRD [88–90] with nearly half of patients responding within 2–6 weeks [91] and a pooled odds ratio of response of lithium augmentation of 3.31 compared to placebo [92]. Moreover, lithium has been associated with a significantly reduced risk of suicide and overall risk for death (from all causes) in MDD [93]. Clinical factors that may predict response in lithium augmentation include a family history of depression or bipolar disorder, severe depressive symptoms, or a history of four or more episodes of MDD, although the level of evidence is low [89]. Empirical titration is the most widely used method for lithium initiation [94], with target serum levels of lithium of 0.5–0.8 mmol/L [95], although these values are derived from studies in bipolar disorder and it is likely that a lower level could be efficacious and better tolerated in MDD. Lithium augmentation should be evaluated after a minimum of 2  weeks (although it may take longer) and, if response is seen, should be maintained for at least 12 months [95]. The narrow therapeutic window of lithium and varia-

7

tions of renal clearance of lithium between individuals [96] require regular monitoring of the blood levels of lithium, since it may quickly reach toxic levels. Symptoms of lithium intoxication can range from mild, including tremor [97], nausea or diarrhea [98], or polyuria and polydipsia, to more severe, including cognitive impairment and death [99]. Furthermore, long-term treatment with lithium carries an increased risk of weight gain and is associated with a higher prevalence of hypothyroidism, hyperparathyroidism, and a reduction of renal function. Thus, serum thyroid levels, calcium concentrations, creatinine, and body mass index (BMI) should be checked at least every 12 months [100]. A systematic review found no statistically significant difference in terms of efficacy of augmentation of SSRIs with lithium when compared to augmentation with AAPs, but the former was reported to have a preferable cost-effectiveness profile [90].

Thyroid Hormones Hypothyroidism and hyperthyroidism have been correlated with mood symptoms [101]. Yet, hypo- and hyperthyroidism in outpatients with depression seems to be uncommon, and neither the diagnosis of thyroid disorder or thyroid hormone levels per se seem to have significant impact on the outcome of treatment with regard to response or remission rates [101, 102]. However, adding thyroid hormones – such as triiodothyronine (T3) or levothyroxine (T4) – as an adjunctive to antidepressants is another well-studied strategy for TRD.  T3 augmentation more than doubles the likelihood of response compared to controls, with an absolute improvement of 23% in response rate [103]. Moreover, when prescribed at initiation of treatment, T3 augmentation appears to accelerate the clinical response in depressed patients treated with some antidepressants, such as tricyclics, while not in patients treated with others, such as paroxetine or sertraline [104–107]. Concurrent treatment with 40–50  μg of T3 versus placebo added to sertraline for 8 weeks has yielded diverging results in double-blind randomized clinical trials (RCT); one large RCT resulted in response in 70% of cases (OR: 2.9, 95% CI 1.23–7.35) [108], while another study found no difference between treatment groups [109]. An open-label study found that T3 augmentation of SSRIs may be effective in TRD inpatients with atypical MDD (characterized by hypersomnia and hyperphagia, instead of insomnia and lack of appetite seen in melancholic MDD) [110]. In another study, which compared T3 to lithium augmentation in TRD, the rates of clinical remission with T3 were modest and not statistically significant compared to lithium [111]. Augmentation with T4 has been studied to a lesser extent. High-dose T4, at an average dose of 400–550 μg/day, was given adjunctively in severely depressed patients taking antidepressant medications. Although this was a very small study, response was observed within 8 weeks in three of five patients [112].

8

 ther Agents for Augmentation O Several agents have been studied for off-label use as potential adjunctive treatment [113]. Pramipexole [114], buspirone [115, 116], modafinil [113, 117], and pindolol [113, 118] have all shown promising outcomes in studies, but additional trials are necessary to increase the evidence grade. Studies of lamotrigine [119–121], memantine [113, 122, 123], and methylphenidate [113] have reported unclear or negative results.

Switching Switching between antidepressant drugs is another common therapeutic approach in TRD [124]. Given that over 30 different antidepressants from several distinct pharmacological classes are available, there are many switching permutations. The most commonly employed strategies include: 1. A direct switch, i.e., the current antidepressant is stopped abruptly and the new antidepressant is started the next day. 2. A taper followed by an immediate switch, that is, gradually withdrawing the first antidepressant and then starting the new antidepressant immediately after discontinuation. 3. Tapering the antidepressant, allowing a washout period, and then performing the switch. 4. Cross-titration, in which the dose of the first antidepressant is tapered down gradually and the dose of the new medication is simultaneously increased. The decision to switch could be based on patient tolerability, safety, comorbidities, and drug-drug interactions and may be a possible strategy for patients with nonresponse, partial response, or poor tolerability to one type of drug. The most appropriate method is dictated by the antidepressant being taken prior to the switch and/or the antidepressant that is to be taken following the switch. For instance, the rapid switch between an irreversible MAOI to another antidepressant, or from fluoxetine to a MAOI, would expose the patient to a risk of serotonin syndrome; therefore, a washout period between drugs is necessary. Other factors to consider are overall duration of the previous medication trial, pharmacokinetic properties of the drug (particularly half-life), severity of the current symptoms, patient susceptibility to side effects, and patient preference. As an example, in a severely depressed patient, a long taper prior to starting the new antidepressant would delay treatment significantly, though a rapid taper could induce sudden worsening of depression. Conversely, in an elderly or medically ill patient, it may be reasonable to use a longer taper to prevent complications such as withdrawal and/or interactions. The clinician must be prudent in

C. Cusin and S. Peyda

balancing the length of taper – and subsequent risk of discontinuation symptoms – with any delay in treatment. Other factors may also contribute to the success of a switch, for example, the timing of improvement; one study, for example, found that switching within 2  weeks may be rapidly beneficial in TRD [125]. Furthermore, in certain subtypes of depression, a between-class switch may be more efficacious, thus eliciting a more pronounced response to the treatment [126]. However a study reported that switching was inferior in terms of remission rates, or time-to-­remission, compared to augmentation [127], in conflict with the results of another 12-week augmentation trials, indicating that augmentation of aripiprazole modestly increased the rate of remission compared to switching to bupropion [128]. As for tolerability, sertraline [129, 130], citalopram, escitalopram, fluoxetine, and vortioxetine [130] are among the drugs associated with fewer dropouts. Switching across or within antidepressant classes both seem to be effective strategies after a first failed trial [131]. Whether a between-class switch should be preferred over a within-class switch has been debated. Switching to antidepressants of the same class could be just as efficient as switching to a different class [132] because they might not be entirely similar with regard to receptor profile and one may be better tolerated [133]. In addition, an advantage of withinclass switching is that it can be done more rapidly. Furthermore, a switch between classes brings no clear benefit over within-class switching according to recent metaanalyses [131, 134]. However, the ARGOS study [135] – the largest one to examine switching in over 3000 outpatients who failed to respond to 4  weeks of optimized treatment, compared switching to a serotonin-­norepinephrine reuptake inhibitor (SNRI; in this case venlafaxine extended release) to switching to another SSRI (most commonly fluoxetine, paroxetine, sertraline, or citalopram)  – found between-class switching to generate slightly higher, but statistically significant, remission rates. Nevertheless, the difference was modest, 59% vs. 52%, respectively. Another study, by Papakostas and colleagues, also found that an interclass switch may produce modest improvements in remission rates, with a number needed to treat of 22 per additional remitter, when switching from an SSRI to a non-SSRI – such as bupropion, mirtazapine, or venlafaxine [136]. Any type of switching was found to be similarly effective in TRD in the STAR*D study, with one in four patients remitting after switching from the SSRI citalopram to either another SSRI (sertraline), an SNRI (extended-release venlafaxine), or a norepinephrine-dopamine reuptake inhibitor (NDRI, in this case sustained-release bupropion) [137]. Moreover, in a large randomized controlled trial in children and adolescents not responding to 8 weeks of SSRI, switching to another SSRI proved to be as efficacious as and better tolerated than switching to an SNRI [138].

1  Treatment-Resistant Depression

Mirtazapine, an atypical antidepressant [139], may act faster [140] and has a different profile of adverse effects – including lower rates of gastrointestinal side effects or sexual dysfunction and higher rates of weight gain, increased appetite, or drowsiness  – [141] than SSRIs or SNRIs, while its long-term efficacy seems to be comparable to that of typical antidepressants [142]. Thus, mirtazapine could be an efficacious treatment option in patients with poor tolerability or incomplete response to SSRIs [143]. Tricyclic antidepressant drugs (TCAs) were developed as antidepressants in the mid-1950s [144], and they are still used in clinical practice. TCAs likely inhibit the reuptake of both serotonin (5-HT) and norepinephrine (NA) by acting on transporters in the plasma membrane [145]. Although effective, TCAs do not seem to provide additional efficacy compared to more modern antidepressants, such as SSRIs [146] or SNRIs [147], while being less tolerable [148–151] than MAOIs in inpatients suffering from MDD [152]. Interestingly, one study reported men to be more likely than women to respond to TCAs [153]. The mechanism of action of monoamine oxidase inhibitors (MAOIs) is believed to involve increased signaling in the serotonergic and noradrenergic systems, secondary to enzymatic breakdown of these neurotransmitters [145]. MAOIs hold a potent antidepressant effect, and an additional anxiolytic effect, but are less frequently prescribed [154, 155] because of safety concerns and dietary restrictions. It is important to be aware that MAOIs should not be combined with antidepressant drugs that inhibit serotonin reuptake (such as SSRIs, SNRIs, NRIs, NDRI, and TCAs), because it increases the risk of serotonin syndrome. Likewise, MAOIs should not be combined with other drugs (such as tramadol, central stimulants, appetite suppressants, or decongestants) that can result in dangerous hypertension [154]. Thus, when switching is considered for MAOIs, a 2-week washout is necessary at both initiation and discontinuation to avoid those adverse effects. Furthermore, patients on MAOIs need to be careful not to ingest certain tyramine-rich foods, such as aged cheeses, soy products, fava beans, and tap beers to name a few, in order to prevent serious or even lethal side effects from the so-called tyramine reaction [154]. Transdermal administration of the MAOI selegiline may be better tolerated than oral forms [156], but it is expensive, and remission rates with selegiline have not been directly compared with other antidepressants. Vortioxetine is a more recently developed antidepressant with modulating effects on different 5-HT receptors, acting as both an agonist and antagonist [157]. Vortioxetine has been found superior to placebo in the treatment of MDD [158, 159] and might be as effective as some SSRIs (such as fluoxetine [160]) but not SNRIs (such as duloxetine or venlafaxine [159, 161, 162]) and seems to have better tolerability than SNRIs [130, 162]. Nausea [159, 162], vomiting

9

[162], and headache [163] are the most common adverse effects in patients treated with vortioxetine. Vortioxetine is also thought to have a positive effect on cognitive functioning in patients with MDD [164]. In terms of balancing side effects and efficacy, a dose of 10 mg/day might be optimal [165]. However, the number of head-to-head studies comparing vortioxetine to other second-generation antidepressants is currently low, and the topic is not sufficiently investigated. Studies on switching to vortioxetine are also few; the REVIVE study [163], a double-blind randomized controlled study (RCT) (n = 252), found depression to improve significantly in both in- and outpatients who did not respond to SSRIs or SNRIs, and remission was achieved in about half of patients at 12 weeks [166]. Another antidepressant, vilazodone, has been comparable in response rates to SSRIs [160, 167] and SNRIs [160], with diarrhea, nausea, dizziness, and insomnia being common side effects [168]. There are many remaining questions regarding options to switch between medications in TRD.  A large, multicenter effectiveness study (“ASCERTAIN”) is currently in progress to address the differential benefits for patients who are not responsive to first-line antidepressants between switching to venlafaxine and augmenting with aripiprazole or rTMS (clinical trial number NCT02977299).

Combination Treatments Combining two antidepressant drugs with different pharmacodynamic profiles, once considered indicative of bad psychopharmacologic practice, is now a commonly used strategy in treatment-resistant depression with the intention of creating a synergistic effect [169], yet its comparative effectiveness versus other strategies has not been well established in the literature [170–172]. A recent meta-analysis identified a possible benefit of adding a presynaptic adrenergic α2-autoreceptor antagonist [173], such as mirtazapine [139, 174], to a monoamine reuptake inhibitor. Currently, only mirtazapine has been approved by the FDA for combination treatment. The CO-MED study [175] found no difference in remission and response rates in 665 outpatients with moderate-to-­ severe depression following a 12-week treatment period with either bupropion plus escitalopram or venlafaxine plus mirtazapine compared to escitalopram plus placebo. Albeit small, other studies have identified increased remission rates in depressed patients treated with fluoxetine plus trazodone [176, 177]. Drug-drug interactions and adverse effects should always be kept in mind when polypharmacy is considered [178, 179], especially since combinations of SSRIs with MAOIs may cause severe serotonin syndrome [180] or death. However, most combinations using new antidepressant drugs are considered safe. A recent study comparing antidepressant combination to AAP augmentation in TRD

10

found the latter strategy to produce a greater decrease in depression severity, while the former had a higher percentage of patients reaching remission, but these differences were not statistically significant. Both strategies significantly decreased depressive symptoms at the 3-month follow-up [181]. In summary, although switching, augmentation, and combination are commonly used strategies, the scientific knowledge is currently lacking strength to recommend one over the others in treatment-resistant patients on antidepressant monotherapy [182]. Another promising drug to treat patients with TRD is ketamine, and we will discuss this treatment in a separate chapter (see Chap. 10).

Somatic Therapies Neurostimulation, or neuromodulation, is an expanding area of research and clinical interest, driven in part by the growing understanding of the neurocircuitry of depression [183– 185]. Neurostimulation treatments overall use electrical currents or magnetic stimulation targeting specific brain regions with either noninvasive techniques, such as transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), electroconvulsive therapy (ECT), and magnetic seizure therapy (MST), or invasive surgical techniques, such as vagus nerve stimulation (VNS) and deep brain stimulation (DBS). Most of these device-based interventions have been studied and are typically used in patients with TRD who have failed to respond to five or more standard treatments [186]. While an exhaustive review of neurotherapeutics [187, 188] is beyond the scope of this chapter, we believe that the clinician must keep those options in mind and weigh pros and cons in recommending another medication trial versus a device-based treatment at any given time of the course of MDD. A brief review of each of these treatments is offered here.

Electroconvulsive Treatment Electroconvulsive treatment (ECT) consists of the application of a current delivered through two electrodes placed in contact with the cranium, after the induction of general anesthesia and application of a muscle relaxant. ECT is considered the “gold standard” for treatment of TRD because of its track record of effectiveness [189] and safety [190] over decades. The ECT technique has been gradually refined over the past three decades, preserving efficacy while decreasing cognitive side effects [191]. The objective is to deliver an electric stimulus to the brain, strong enough to induce a brief seizure. The parameters needed to achieve this result are

C. Cusin and S. Peyda

individualized for each patient and can be adjusted by different combinations of frequency, pulse-width, amplitude, and duration of the stimulus [187, 192]. ECT is administered two or three times a week [193, 194]. Because of (mostly transient) side effects such as cognitive impairment, antero- or retrograde amnesia, headache, arrhythmia, or myalgia [195, 196], ECT is reserved for patients with severe treatment-­ resistant depression or who present with psychotic features, acute suicidal ideation, or catatonia [187]. Tolerability can be increased by utilizing ultra-brief pulses with right unilateral electrode placement initially, although the clinical response might be inferior or delayed [195, 197]. ECT is considered superior to all other treatments of depression [189, 198], with remission rates of 60–90% reported in clinical trials, depending on the patient population and type of stimulus used [189, 195, 199, 200]. However, the chance of response might be lower in patients with longer depressive episodes and/or who have failed to respond to numerous medications [201]. Predicting which patients will respond is difficult; preliminary results have suggested that using functional magnetic resonance imaging (fMRI) before ECT could be useful, because the identification of a certain resting-state network level of function (including the dorsolateral prefrontal cortex, orbitofrontal cortex, and posterior cingulate cortex) may predict whether a patient would remit from depression [202]. To increase the success rate, optimizing the conditions for ECT can be done by having the patient refrain from any anticonvulsant drugs or benzodiazepines and to make sure the patient is properly hydrated [197] prior to treatment. Among patients who respond successfully to ECT, more than 50% will relapse within 12  months [203]. In a controlled study comparing a nortriptyline-lithium combination versus nortriptyline alone, versus placebo to sustain the response after ECT, the first group had a marked advantage in time to relapse, superior to both placebo and nortriptyline alone. Over the 24-week trial, relapse rates were 84% for placebo, 60% for nortriptyline, and 39% for nortriptyline-lithium [204]. Thus, continuing active, adequate treatment following ECT is important to prevent relapse. There are no absolute contraindications for ECT [189], although preexisting serious medical conditions should be screened for and managed prior to ECT [200]. Reviews of the workup prior to ECT can be found elsewhere [190, 194, 195]. The focus of the assessment should be on detecting cardiovascular, pulmonary, or neurological conditions  – such as recent cerebral hemorrhage, stroke or myocardial infarction, ischemia, cardiac arrhythmias or atrial fibrillation, unstable angina, aortic stenosis, uncontrolled hypertension or pheochromocytoma, suboptimal anti-coagulation, uncontrolled diabetes, asthma or COPD, brain aneurysms, increased intracranial pressure, or space-occupying cerebral lesions  – which may carry an increased risk for adverse events if left untreated [186, 190].

1  Treatment-Resistant Depression

11

Repetitive Transcranial Magnetic Stimulation

Transcranial Direct Current Stimulation

Repetitive transcranial magnetic stimulation (rTMS) is now FDA-approved and a first-line treatment for patients with MDD who have failed at least one antidepressant [186]. rTMS uses focused magnetic field pulses to induce electrical currents in a small area on the surface of the brain which in turn can affect deeper areas. rTMS is usually delivered by a trained technician or nurse, under physician supervision, and, unlike ECT, it does not require anesthesia and there are no expected cognitive side effects. Standard protocols deliver rTMS once daily, 5 days/week. The recommendation is to administer 20 sessions before declaring treatment failure, with extension to 25–30 sessions if improvement occurs. More than 30 systematic reviews and meta-analyses have been conducted on rTMS in depression, with most studies involving patients with some degree of treatment resistance (i.e., having failed at least 1 or 2 ­antidepressant trials). rTMS has been found to have a better effect in younger adult patients and in those with lower levels of treatment refractoriness [205]. Compared to ECT, rTMS is less effective, especially as maintenance treatment, and patients who do not respond to ECT are not as likely to respond to rTMS [186]. Nonetheless, rTMS is considered a first-line treatment for MDD in patients who have failed at least one antidepressant treatment [186], with bilateral and low-frequency rTMS appearing to be the most efficacious and best tolerated strategies [206].

Transcranial direct current stimulation (tDCS) is a form of brain stimulation that delivers a continuous low-amplitude electrical current to a specified cortical region using scalp electrodes. This device is easy to use, inexpensive, and safe and has low potential for adverse effects. Studies evaluating the efficacy of tDCS have demonstrated mixed results. One meta-analysis (six trials, N = 200) found no significant differences between tDCS and sham treatments [210], while a subsequent meta-analysis (seven trials, N  =  269) showed modest differences between active and sham conditions with a small effect size of 0.37 for tDCS [211]. A more recent meta-analysis (ten trials, N  =  393) also found tDCS to be superior to sham with a small but significant effect size (g = 0.30) [212]. There are no controlled studies of tDCS for maintenance treatment or relapse prevention. Given the small number of studies with heterogeneous methodologies and the inconsistent results from meta-analyses, further research is needed to establish the efficacy of tDCS as monotherapy or combination therapy for acute treatment of MDD.

Vagal Nerve Stimulation The vagal nerve stimulator (VNS) is an implantable neurostimulation device consisting of a pulse generator (IPG) that is surgically inserted underneath the skin of the chest and connected to a wire placed around one of the vagus nerves in the neck. Originally approved in 1997 for the treatment of drugresistant epilepsy, VNS was approved by the US Food and Drug Administration (FDA) in 2005 for the adjunct long-term treatment of chronic or recurrent depression for adult patients with MDD and failure to respond to four or more adequate antidepressant treatments. A meta-analysis of seven openlabel studies (N = 426) found an overall response rate of 31.8% [207]. However, only one RCT (N = 235) has evaluated the efficacy of VNS versus a sham-control condition, with no significant differences in efficacy between the conditions at 12 weeks [208]. A long-term follow-up study of 795 patients receiving adjunctive VNS compared to treatment as usual (TAU) showed that the VNS group had better clinical outcomes than the TAU group, including a significantly higher 5-year cumulative response rate (67.6% compared with 40.9%) and a significantly higher remission rate (cumulative first-time remitters, 43.3% compared with 25.7%) [209].

Deep Brain Stimulation Deep brain stimulation (DBS) involves a reversible neurosurgical procedure, in which electrodes are implanted at specific anatomical locations, where they deliver an electrical impulse of variable intensity and frequency. DBS is thought to induce an electrical field that alters the firing patterns of the surrounding neurons and thus modifies activity in the neuronal circuits. DBS has been utilized for treatment refractory tremor, and it is approved for Parkinson’s disease and dystonia. In 2009, DBS was approved for treatment of intractable obsessive-compulsive disorder (OCD) in Europe and in the USA. The implantation of DBS electrodes and batteries is a complex neurosurgical procedure involving stereotactical localization of the cerebral target and implantation of the batteries in the chest area, under general anesthesia. Systematic outpatient adjustment of stimulation parameters (active contacts, amplitude, duration, and frequency) and frequent follow-ups are necessary, especially during the first 6–12 months after implantation. The rates of surgical complications are quite variable and include intracranial hemorrhage, infections, and rarely stroke, lead erosion, and lead migration. For the treatment of depression, a number of targets have been investigated, including subcingulate area 25, ventral anterior internal capsule/ventral striatum, medial forebrain bundle, and to a lesser degree nucleus accumbens, lateral habenula, and inferior thalamic peduncle. Preliminary studies in the treatment of TRD have suggested safety and efficacy of DBS, but the majority of the studies are small and open-label [213].

12

Two sham-controlled studies of a DBS target (VC/VS) [214] and one targeting subcingulate Brodmann area 25 [215] showed no separation between active and sham stimulation regarding antidepressant efficacy. It has to be emphasized that DBS remains an investigational and experimental procedure, not available for clinical care of patients with TRD. The preclinical and clinical studies on DBS for MDD were recently reviewed in detail elsewhere and are beyond the scope of the present chapter [216].

Conclusion This chapter provides a summary of the current thoughts in the field of TRD. As a first step, a clinician faced with a case of “suspected TRD” should begin with a thorough assessment, both from the psychiatric and the medical point of view. This is important to rule out some of the most common factors that may contribute to the burden of illness, such as comorbidity with severe anxiety or substance use disorders, traumatic brain injury (TBI), or obstructive sleep apnea. Then the clinician needs to ascertain patients’ compliance with prescribed treatments, and history of medication trials, obtaining as much detail as possible about the doses, duration, efficacy, and side effects. Only at this point it is possible to identify strategies not yet tried, for example, combination of different antidepressants or augmentation with other psychotropic drugs. The clinician must keep in mind not only pharmacologic therapies but other types of interventions with proven efficacy for MDD such as device-based treatments, psychotherapy, and combination with natural or alternative treatments with the goal of improving patient’s symptoms and, whenever possible, of restoring an adequate level of functioning.

FAQs: Common Questions and Answers Q1.  When should TRD be diagnosed in a patient with depression? A1. From clinical trials, the most common definition of TRD requires the lack of response to at least two adequate treatments for depression, including medications, talk therapy, or ECT, in the current episode. However this definition is of limited value in the clinical setting because it does not consider lack of response to antidepressant treatments in previous episodes, or the phenomenon of tachyphylaxis; the loss of effect of a medication after the initial benefit was sustained for a period of time, which probably has different underlying mechanisms. Q2. What are the best steps when a depressed patient does not respond to a first-line treatment?

C. Cusin and S. Peyda

A2. The first recommendation is always to reevaluate the main diagnosis and all comorbidities, both from the medical and psychiatric point of view, in addition to the presence of concomitant medications that may affect mood or change the metabolism of the antidepressant. Bipolar depression, comorbid alcohol or substance use disorder, severe anxiety disorder, PTSD or OCD, and autoimmune disorders may significantly affect the likelihood of response to treatment. Psychiatry is lagging behind other disciplines in the habit of quantifying the severity of symptoms of depression over time, for example, with a self-administered or clinician-administered scale, to help with clinical decision making. Q3. For how long should an antidepressant be tried before implementing changes? A3. Most clinical trials to determine efficacy of a drug last 8 weeks, although if a patient does not show any clinical improvement after 4–6 weeks at an adequate dose, it is reasonable to modify the treatment with augmentation or combination. Q4.  When should ECT be considered in a patient with treatment-­resistant depression? A4. A clinician should consider multiple factors, including the severity and duration of depressive episode, the presence of suicidal ideation or behaviors, the number of past treatments failed, the presence of intolerable side effects from medications, potential medical contraindications, and patient wishes. ECT should be considered earlier in the algorithm in severe cases where the patient is at significant risk of harming self or others, is requiring physical restraint, and is acutely psychotic or catatonic or when the symptoms are life-threatening (e.g., refusal of food and water). Q5. Could the label “TRD” have implications for subsequent response? Should we tell patients they are diagnosed with TRD? A5. We know from research studies and from clinical practice that expectations on treatment outcome may directly influence the likelihood of the outcome itself (e.g., an enthusiastic endorsement from the clinician can increase the response rate to a treatment – even to a placebo – and conversely a tepid support may decrease it); however no study has specifically investigated whether the label “TRD” has a possible negative impact on subsequent outcome. Given the fact that patients with TRD are often experiencing hopelessness, it is important for the clinician to be aware of the existence of multiple modalities of treatment, to express positive expectations in the context of prescribing an intervention, and finally to consider expert consultation as another tool to help care for a patient. Q6. How should we consider non-pharmacological and non-­ device-­based therapies in the classification of TRD?

1  Treatment-Resistant Depression

A6. The currently available classifications of TRD do not include other efficacious treatments. Part of the reason for excluding non-pharmacological and non-devicebased interventions in the definitional criteria may be due to the difficulty in defining a failed trial. For example, how would a “failure to respond to an adequate course of CBT” be defined (a course of adequate duration and with adequate attendance and participation of the patient)? This is an important consideration because in controlled studies no antidepressant treatment has been shown to be clearly superior to another, even though different patients may respond to one medication and not to another. Similarly, a patient may experience poor response to medications but excellent response to individual therapy. The goal for the clinician is to match the best possible treatment to the patient. The chances of success are increased with the number of different options available for patients who do not improve with a first-line approach.

References 1. Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617–27. 2. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163(1):28–40. 3. Simon GE, Von Korff M, Rutter CM, Peterson DA.  Treatment process and outcomes for managed care patients receiving new antidepressant prescriptions from psychiatrists and primary care physicians. Arch Gen Psychiatry. 2001;58(4):395–401. 4. American Psychiatric Association, American Psychiatric Association, DSM-5 Task Force. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: American Psychiatric Association; 2013. 5. Trevino K, McClintock SM, McDonald Fischer N, Vora A, Husain MM.  Defining treatment-resistant depression: a comprehensive review of the literature. Ann Clin Psychiatry. 2014;26(3):222–32. 6. Ruhé HG, van Rooijen G, Spijker J, Peeters FPML, Schene AH.  Staging methods for treatment resistant depression. A systematic review. J Affect Disord. 2012;137(1–3):35–45. 7. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Lond Engl. 2016;388(10053):1545–602. 8. WHO|Depression and Other Common Mental Disorders [Internet]. WHO. [Cited 2018 Mar 14]. Available from: http://www.who.int/mental_health/management/depression/ prevalence_global_health_estimates/en/. 9. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095–105. 10. Pratt LA, Brody DJ. Depression in the U.S. household population, 2009–2012. NCHS Data Brief. 2014;(172):1–8. 11. Nemeroff CB. Prevalence and management of treatment-resistant depression. J Clin Psychiatry. 2007;68(Suppl 8):17–25.

13 12. Fekadu A, Wooderson SC, Markopoulo K, Donaldson C, Papadopoulos A, Cleare AJ.  What happens to patients with treatment-­resistant depression? A systematic review of medium to long term outcome studies. J Affect Disord. 2009;116(1–2):4–11. 13. Osby U, Brandt L, Correia N, Ekbom A, Sparén P. Excess mortality in bipolar and unipolar disorder in Sweden. Arch Gen Psychiatry. 2001;58(9):844–50. 14. Crown WH, Finkelstein S, Berndt ER, Ling D, Poret AW, Rush AJ, et al. The impact of treatment-resistant depression on health care utilization and costs. J Clin Psychiatry. 2002;63(11):963–71. 15. Olchanski N, McInnis Myers M, Halseth M, Cyr PL, Bockstedt L, Goss TF, et al. The economic burden of treatment-resistant depression. Clin Ther. 2013;35(4):512–22. 16. McCrone P, Rost F, Koeser L, Koutoufa I, Stephanou S, Knapp M, et al. The economic cost of treatment-resistant depression in patients referred to a specialist service. J Ment Health Abingdon Engl. 2017;23:1–7. 17. Amos TB, Tandon N, Lefebvre P, Pilon D, Kamstra R, Pivneva I, et  al. Direct and indirect cost burden and change of employment status in treatment-resistant depression: a matched-cohort study using a US commercial claims database. J Clin Psychiatry. 2018;79(2):17m11725. 18. Mrazek DA, Hornberger JC, Altar CA, Degtiar I.  A review of the clinical, economic, and societal burden of treatment-­ resistant depression: 1996–2013. Psychiatr Serv Wash DC. 2014;65(8):977–87. 19. Greenberg PE, Fournier A-A, Sisitsky T, Pike CT, Kessler RC.  The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 2015;76(2):155–62. 20. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123(8):933–44. 21. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033–46. 22. Solomon DA, Keller MB, Leon AC, Mueller TI, Lavori PW, Shea MT, et al. Multiple recurrences of major depressive disorder. Am J Psychiatry. 2000;157(2):229–33. 23. Spijker J, de Graaf R, Bijl RV, Beekman ATF, Ormel J, Nolen WA.  Duration of major depressive episodes in the general population: results from The Netherlands Mental Health Survey and Incidence Study (NEMESIS). Br J Psychiatry J Ment Sci. 2002;181:208–13. 24. Gonnella JS, Hornbrook MC, Louis DZ.  Staging of disease. A case-mix measurement. JAMA. 1984;251(5):637–44. 25. Fava GA, Tossani E. Prodromal stage of major depression. Early Interv Psychiatry. 2007;1(1):9–18. 26. Hetrick SE, Parker AG, Hickie IB, Purcell R, Yung AR, McGorry PD.  Early identification and intervention in depressive disorders: towards a clinical staging model. Psychother Psychosom. 2008;77(5):263–70. 27. Cosci F, Fava GA. Staging of mental disorders: systematic review. Psychother Psychosom. 2013;82(1):20–34. 28. Fekadu A, Wooderson S, Donaldson C, Markopoulou K, Masterson B, Poon L, et al. A multidimensional tool to quantify treatment resistance in depression: the Maudsley staging method. J Clin Psychiatry. 2009;70(2):177–84. 29. Souery D, Oswald P, Massat I, Bailer U, Bollen J, Demyttenaere K, et  al. Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry. 2007;68(7):1062–70. 30. De Carlo V, Calati R, Serretti A.  Socio-demographic and clinical predictors of non-response/non-remission in treatment resistant depressed patients: a systematic review. Psychiatry Res. 2016;30(240):421–30.

14 31. McGrath PJ, Stewart JW, Fava M, Trivedi MH, Wisniewski SR, Nierenberg AA, et  al. Tranylcypromine versus venlafaxine plus mirtazapine following three failed antidepressant medication trials for depression: a STAR*D report. Am J Psychiatry. 2006;163(9):1531–41. quiz 1666. 32. Mitchell AJ, Vaze A, Rao S.  Clinical diagnosis of depression in primary care: a meta-analysis. Lancet Lond Engl. 2009;374(9690):609–19. 33. Melartin TK, Rytsälä HJ, Leskelä US, Lestelä-Mielonen PS, Sokero TP, Isometsä ET.  Current comorbidity of psychiatric ­disorders among DSM-IV major depressive disorder patients in psychiatric care in the Vantaa Depression Study. J Clin Psychiatry. 2002;63(2):126–34. 34. Schmitt A, Falkai P.  Differential diagnosis of major depression and bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2013;263(2):83–4. 35. Nierenberg AA, Amsterdam JD.  Treatment-resistant depression: definition and treatment approaches. J Clin Psychiatry. 1990;51(Suppl):39–47. discussion 48–50. 36. Hirschfeld RM, Keller MB, Panico S, Arons BS, Barlow D, Davidoff F, et al. The National Depressive and Manic-Depressive Association consensus statement on the undertreatment of depression. JAMA. 1997;277(4):333–40. 37. Ionescu DF, Rosenbaum JF, Alpert JE.  Pharmacological approaches to the challenge of treatment-resistant depression. Dialogues Clin Neurosci. 2015;17(2):111–26. 38. Cipriani A, Furukawa TA, Salanti G, Geddes JR, Higgins JP, Churchill R, et  al. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-­ analysis. Lancet Lond Engl. 2009;373(9665):746–58. 39. Kelly K, Posternak M, Alpert JE.  Toward achieving optimal response: understanding and managing antidepressant side effects. Dialogues Clin Neurosci. 2008;10(4):409–18. 40. Rush AJ.  STAR*D: what have we learned? Am J Psychiatry. 2007;164(2):201–4. 41. Henssler J, Kurschus M, Franklin J, Bschor T, Baethge C. Trajectories of acute antidepressant efficacy: how long to wait for response? A systematic review and meta-analysis of long-­ term, placebo-controlled acute treatment trials. J Clin Psychiatry. 2018;79(3):0–0. 42. Vergouwen ACM, Bakker A, Katon WJ, Verheij TJ, Koerselman F. Improving adherence to antidepressants: a systematic review of interventions. J Clin Psychiatry. 2003;64(12):1415–20. 43. Maddox JC, Levi M, Thompson C.  The compliance with antidepressants in general practice. J Psychopharmacol Oxf Engl. 1994;8(1):48–52. 44. Burton C, Cochran AJ, Cameron IM.  Restarting antidepressant treatment following early discontinuation – a primary care database study. Fam Pract. 2015;32(5):520–4. 45. Hamilton M.  A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. 46. Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry J Ment Sci. 1979;134:382–9. 47. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, et  al. The 16-item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-­ report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54(5):573–83. 48. Fava M.  Diagnosis and definition of treatment-resistant depression. Biol Psychiatry. 2003;53(8):649–59. 49. Trivedi MH, Fava M, Wisniewski SR, Thase ME, Quitkin F, Warden D, et  al. Medication augmentation after the failure of SSRIs for depression. N Engl J Med. 2006;354(12):1243–52. 50. Thase ME.  Treatment-resistant depression: prevalence, risk factors, and treatment strategies. J Clin Psychiatry. 2011;72(5):e18. 51. Connolly KR, Thase ME. If at first you don’t succeed: a review of the evidence for antidepressant augmentation, combination and switching strategies. Drugs. 2011;71(1):43–64.

C. Cusin and S. Peyda 52. Zhou X, Ravindran AV, Qin B, Del Giovane C, Li Q, Bauer M, et  al. Comparative efficacy, acceptability, and tolerability of augmentation agents in treatment-resistant depression: systematic review and network meta-analysis. J Clin Psychiatry. 2015;76(4):e487–98. 53. Cowen PJ. Backing into the future: pharmacological approaches to the management of resistant depression. Psychol Med. 2017;47(15):2569–77. 54. Zhou X, Keitner GI, Qin B, Ravindran AV, Bauer M, Del Giovane C, et  al. Atypical antipsychotic augmentation for treatment-­ resistant depression: a systematic review and network meta-­ analysis. Int J Neuropsychopharmacol. 2015;18(11):pyv060. 55. Papakostas GI, Shelton RC, Smith J, Fava M.  Augmentation of antidepressants with atypical antipsychotic medications for treatment-­resistant major depressive disorder: a meta-analysis. J Clin Psychiatry. 2007;68(6):826–31. 56. Nelson JC, Papakostas GI. Atypical antipsychotic augmentation in major depressive disorder: a meta-analysis of placebo-controlled randomized trials. Am J Psychiatry. 2009;166(9):980–91. 57. Spielmans GI, Berman MI, Linardatos E, Rosenlicht NZ, Perry A, Tsai AC. Adjunctive atypical antipsychotic treatment for major depressive disorder: a meta-analysis of depression, quality of life, and safety outcomes. PLoS Med. 2013;10(3):e1001403. 58. Goodwin G, Fleischhacker W, Arango C, Baumann P, Davidson M, de Hert M, et al. Advantages and disadvantages of combination treatment with antipsychotics ECNP Consensus Meeting, March 2008, Nice. Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol. 2009;19(7):520–32. 59. Wang S-M, Han C, Lee S-J, Jun T-Y, Patkar AA, Masand PS, et al. Second generation antipsychotics in the treatment of major depressive disorder: an update. Chonnam Med J. 2016;52(3):159–72. 60. Thase ME.  Adverse effects of second-generation antipsychotics as adjuncts to antidepressants: are the risks worth the benefits? Psychiatr Clin N Am. 2016;39(3):477–86. 61. Thase ME, Youakim JM, Skuban A, Hobart M, Zhang P, McQuade RD, et al. Adjunctive brexpiprazole 1 and 3 mg for patients with major depressive disorder following inadequate response to antidepressants: a phase 3, randomized, double-blind study. J Clin Psychiatry. 2015;76(9):1232–40. 62. McIntyre RS, Filteau M-J, Martin L, Patry S, Carvalho A, Cha DS, et  al. Treatment-resistant depression: definitions, review of the evidence, and algorithmic approach. J Affect Disord. 2014;156: 1–7. 63. Berman RM, Marcus RN, Swanink R, McQuade RD, Carson WH, Corey-Lisle PK, et  al. The efficacy and safety of aripiprazole as adjunctive therapy in major depressive disorder: a multicenter, randomized, double-blind, placebo-controlled study. J Clin Psychiatry. 2007;68(6):843–53. 64. Marcus RN, McQuade RD, Carson WH, Hennicken D, Fava M, Simon JS, et al. The efficacy and safety of aripiprazole as adjunctive therapy in major depressive disorder: a second multicenter, randomized, double-blind, placebo-controlled study. J Clin Psychopharmacol. 2008;28(2):156–65. 65. Thase ME, Trivedi MH, Nelson JC, Fava M, Swanink R, Tran Q-V, et  al. Examining the efficacy of adjunctive aripiprazole in major depressive disorder: a pooled analysis of 2 studies. Prim Care Companion J Clin Psychiatry. 2008;10(6):440–7. 66. Nelson JC, Pikalov A, Berman RM.  Augmentation treatment in major depressive disorder: focus on aripiprazole. Neuropsychiatr Dis Treat. 2008;4(5):937–48. 67. Kamijima K, Higuchi T, Ishigooka J, Ohmori T, Ozaki N, Kanba S, et  al. Aripiprazole augmentation to antidepressant therapy in Japanese patients with major depressive disorder: a randomized, double-blind, placebo-controlled study (ADMIRE study). J Affect Disord. 2013;151(3):899–905. 68. Kaneriya SH, Robbins-Welty GA, Smagula SF, Karp JF, Butters MA, Lenze EJ, et  al. Predictors and moderators of remission with aripiprazole augmentation in treatment-resistant late-life

1  Treatment-Resistant Depression depression: an analysis of the IRL-GRey randomized clinical trial. JAMA Psychiat. 2016;73(4):329–36. 69. Han C, Wang S-M, Kwak K-P, Won W-Y, Lee H, Chang CM, et  al. Aripiprazole augmentation versus antidepressant switching for patients with major depressive disorder: a 6-week, randomized, rater-blinded, prospective study. J Psychiatr Res. 2015;66–67:84–94. 70. Bauer M, Pretorius HW, Constant EL, Earley WR, Szamosi J, Brecher M.  Extended-release quetiapine as adjunct to an antidepressant in patients with major depressive disorder: results of a randomized, placebo-controlled, double-blind study. J Clin Psychiatry. 2009;70(4):540–9. 71. Shelton RC, Tollefson GD, Tohen M, Stahl S, Gannon KS, Jacobs TG, et  al. A novel augmentation strategy for treating resistant major depression. Am J Psychiatry. 2001;158(1):131–4. 72. Komossa K, Depping AM, Gaudchau A, Kissling W, Leucht S.  Second-generation antipsychotics for major depressive disorder and dysthymia. Cochrane Database Syst Rev. 2010;(12):CD008121. 73. Thase ME, Corya SA, Osuntokun O, Case M, Henley DB, Sanger TM, et  al. A randomized, double-blind comparison of olanzapine/fluoxetine combination, olanzapine, and fluoxetine in treatment-resistant major depressive disorder. J Clin Psychiatry. 2007;68(2):224–36. 74. Corya SA, Williamson D, Sanger TM, Briggs SD, Case M, Tollefson G.  A randomized, double-blind comparison of olanzapine/fluoxetine combination, olanzapine, fluoxetine, and venlafaxine in treatment-resistant depression. Depress Anxiety. 2006;23(6):364–72. 75. Asaoka N, Nagayasu K, Nishitani N, Yamashiro M, Shirakawa H, Nakagawa T, et  al. Olanzapine augments the effect of selective serotonin reuptake inhibitors by suppressing GABAergic inhibition via antagonism of 5-HT6 receptors in the dorsal raphe nucleus. Neuropharmacology. 2015;95:261–8. 76. Gao K, Kemp DE, Fein E, Wang Z, Fang Y, Ganocy SJ, et  al. Number needed to treat to harm for discontinuation due to adverse events in the treatment of bipolar depression, major depressive disorder, and generalized anxiety disorder with atypical antipsychotics. J Clin Psychiatry. 2011;72(8):1063–71. 77. Brunner E, Tohen M, Osuntokun O, Landry J, Thase ME.  Efficacy and safety of olanzapine/fluoxetine combination vs fluoxetine monotherapy following successful combination therapy of treatment-resistant major depressive disorder. Neuropsychopharmacology. 2014;39(11):2549–59. 78. Yoon S, Jeon SW, Ko Y-H, Patkar AA, Masand PS, Pae C-U, et al. Adjunctive brexpiprazole as a novel effective strategy for treating major depressive disorder: a systematic review and meta-analysis. J Clin Psychopharmacol. 2017;37(1):46–53. 79. Romeo B, Blecha L, Locatelli K, Benyamina A, Martelli C. Meta-­ analysis and review of dopamine agonists in acute episodes of mood disorder: efficacy and safety. J Psychopharmacol Oxf Engl. 2018;32(4):385–96. 80. Fava M, Okame T, Matsushima Y, Perry P, Weiller E, Baker RA. Switching from inadequate adjunctive or combination treatment options to brexpiprazole adjunctive to antidepressant: an open-label study on the effects on depressive symptoms and cognitive and physical functioning. Int J Neuropsychopharmacol. 2017;20(1):22–30. 81. Thase ME, Youakim JM, Skuban A, Hobart M, Augustine C, Zhang P, et  al. Efficacy and safety of adjunctive brexpiprazole 2 mg in major depressive disorder: a phase 3, randomized, placebo-­controlled study in patients with inadequate response to antidepressants. J Clin Psychiatry. 2015;76(9):1224–31. 82. Papakostas GI, Fava M, Baer L, Swee MB, Jaeger A, Bobo WV, et  al. Ziprasidone augmentation of escitalopram for major depressive disorder: efficacy results from a randomized, doubleblind, placebo-controlled study. Am J Psychiatry. 2015;172(12): 1251–8.

15 83. Iovieno N, Shelton RC, Petrie SR, Cusin C, Fava M, Papakostas GI. Efficacy of ziprasidone augmentation of escitalopram for cognitive symptoms of major depressive disorder. J Clin Psychiatry. 2018;79(1):16m10920. 84. Keitner GI, Garlow SJ, Ryan CE, Ninan PT, Solomon DA, Nemeroff CB, et  al. A randomized, placebo-controlled trial of risperidone augmentation for patients with difficult-to-treat unipolar, non-psychotic major depression. J Psychiatr Res. 2009;43(3):205–14. 85. Berman RM, Thase ME, Trivedi MH, Hazel JA, Marler SV, McQuade RD, et  al. Long-term safety and tolerability of open-­ label aripiprazole augmentation of antidepressant therapy in major depressive disorder. Neuropsychiatr Dis Treat. 2011;7:303–12. 86. Lenze EJ, Mulsant BH, Blumberger DM, Karp JF, Newcomer JW, Anderson SJ, et  al. Efficacy, safety, and tolerability of augmentation pharmacotherapy with aripiprazole for treatment-resistant depression in late life: a randomised, double-blind, placebo-­ controlled trial. Lancet Lond Engl. 2015;386(10011):2404–12. 87. Zall H, Therman PG, Myers JM.  Lithium carbonate: a clinical study. Am J Psychiatry. 1968;125(4):549–55. 88. Nelson JC, Baumann P, Delucchi K, Joffe R, Katona C. A systematic review and meta-analysis of lithium augmentation of tricyclic and second generation antidepressants in major depression. J Affect Disord. 2014;168:269–75. 89. Bauer M, Adli M, Ricken R, Severus E, Pilhatsch M.  Role of lithium augmentation in the management of major depressive disorder. CNS Drugs. 2014;28(4):331–42. 90. Edwards SJ, Hamilton V, Nherera L, Trevor N. Lithium or an atypical antipsychotic drug in the management of treatment-resistant depression: a systematic review and economic evaluation. Health Technol Assess Winch Engl. 2013;17(54):1–190. 91. Bauer M, Adli M, Baethge C, Berghöfer A, Sasse J, Heinz A, et al. Lithium augmentation therapy in refractory depression: clinical evidence and neurobiological mechanisms. Can J Psychiatry Rev Can Psychiatr. 2003;48(7):440–8. 92. Bauer M, Dopfmer S. Lithium augmentation in treatment-­resistant depression: meta-analysis of placebo-controlled studies. J Clin Psychopharmacol. 1999;19(5):427–34. 93. Cipriani A, Hawton K, Stockton S, Geddes JR. Lithium in the prevention of suicide in mood disorders: updated systematic review and meta-analysis. BMJ. 2013;346:f3646. 94. Sienaert P, Geeraerts I, Wyckaert S. How to initiate lithium therapy: a systematic review of dose estimation and level prediction methods. J Affect Disord. 2013;146(1):15–33. 95. Bschor T, Lewitzka U, Sasse J, Adli M, Köberle U, Bauer M.  Lithium augmentation in treatment-resistant depression: clinical evidence, serotonergic and endocrine mechanisms. Pharmacopsychiatry. 2003;36(Suppl 3):S230–4. 96. Grandjean EM, Aubry J-M. Lithium: updated human knowledge using an evidence-based approach. Part II: clinical pharmacology and therapeutic monitoring. CNS Drugs. 2009;23(4):331–49. 97. Baek JH, Kinrys G, Nierenberg AA.  Lithium tremor revisited: pathophysiology and treatment. Acta Psychiatr Scand. 2014;129(1):17–23. 98. Dols A, Sienaert P, van Gerven H, Schouws S, Stevens A, Kupka R, et al. The prevalence and management of side effects of lithium and anticonvulsants as mood stabilizers in bipolar disorder from a clinical perspective: a review. Int Clin Psychopharmacol. 2013;28(6):287–96. 99. Gitlin M. Lithium side effects and toxicity: prevalence and management strategies. Int J Bipolar Disord. 2016;4(1):27. 100. McKnight RF, Adida M, Budge K, Stockton S, Goodwin GM, Geddes JR.  Lithium toxicity profile: a systematic review and meta-analysis. Lancet Lond Engl. 2012;379(9817):721–8. 101. Ittermann T, Völzke H, Baumeister SE, Appel K, Grabe HJ.  Diagnosed thyroid disorders are associated with depression and anxiety. Soc Psychiatry Psychiatr Epidemiol. 2015;50(9):1417–25.

16 102. Fava M, Labbate LA, Abraham ME, Rosenbaum JF.  Hypothyroidism and hyperthyroidism in major depression revisited. J Clin Psychiatry. 1995;56(5):186–92. 103. Aronson R, Offman HJ, Joffe RT, Naylor CD.  Triiodothyronine augmentation in the treatment of refractory depression. A meta-­ analysis. Arch Gen Psychiatry. 1996;53(9):842–8. 104. Appelhof BC, Brouwer JP, van Dyck R, Fliers E, Hoogendijk WJG, Huyser J, et  al. Triiodothyronine addition to paroxetine in the treatment of major depressive disorder. J Clin Endocrinol Metab. 2004;89(12):6271–6. 105. Posternak M, Novak S, Stern R, Hennessey J, Joffe R, Prange A, et  al. A pilot effectiveness study: placebo-controlled trial of adjunctive L-triiodothyronine (T3) used to accelerate and potentiate the antidepressant response. Int J Neuropsychopharmacol. 2008;11(1):15–25. 106. Altshuler LL, Bauer M, Frye MA, Gitlin MJ, Mintz J, Szuba MP, et  al. Does thyroid supplementation accelerate tricyclic antidepressant response? A review and meta-analysis of the literature. Am J Psychiatry. 2001;158(10):1617–22. 107. Rosenthal LJ, Goldner WS, O’Reardon JP.  T3 augmentation in major depressive disorder: safety considerations. Am J Psychiatry. 2011;168(10):1035–40. 108. Cooper-Kazaz R, Apter JT, Cohen R, Karagichev L, Muhammed-­ Moussa S, Grupper D, et al. Combined treatment with sertraline and liothyronine in major depression: a randomized, double-blind, placebo-controlled trial. Arch Gen Psychiatry. 2007;64(6):679–88. 109. Garlow SJ, Dunlop BW, Ninan PT, Nemeroff CB. The combination of triiodothyronine (T3) and sertraline is not superior to sertraline monotherapy in the treatment of major depressive disorder. J Psychiatr Res. 2012;46(11):1406–13. 110. Iosifescu DV, Nierenberg AA, Mischoulon D, Perlis RH, Papakostas GI, Ryan JL, et  al. An open study of triiodothyronine augmentation of selective serotonin reuptake inhibitors in treatment-resistant major depressive disorder. J Clin Psychiatry. 2005;66(8):1038–42. 111. Nierenberg AA, Fava M, Trivedi MH, Wisniewski SR, Thase ME, McGrath PJ, et al. A comparison of lithium and T(3) augmentation following two failed medication treatments for depression: a STAR*D report. Am J Psychiatry. 2006;163(9):1519–30. quiz 1665. 112. Bauer M, Hellweg R, Gräf KJ, Baumgartner A.  Treatment of refractory depression with high-dose thyroxine. Neuropsychopharmacology. 1998;18(6):444–55. 113. Kleeblatt J, Betzler F, Kilarski LL, Bschor T, Köhler S. Efficacy of off-label augmentation in unipolar depression: a systematic review of the evidence. Eur Neuropsychopharmacol. 2017;27(5): 423–41. 114. Cusin C, Iovieno N, Iosifescu DV, Nierenberg AA, Fava M, Rush AJ, et al. A randomized, double-blind, placebo-controlled trial of pramipexole augmentation in treatment-resistant major depressive disorder. J Clin Psychiatry. 2013;74(7):e636–41. 115. Fava M, Targum SD, Nierenberg AA, Bleicher LS, Carter TA, Wedel PC, et al. An exploratory study of combination buspirone and melatonin SR in major depressive disorder (MDD): a possible role for neurogenesis in drug discovery. J Psychiatr Res. 2012;46(12):1553–63. 116. Appelberg BG, Syvälahti EK, Koskinen TE, Mehtonen OP, Muhonen TT, Naukkarinen HH. Patients with severe depression may benefit from buspirone augmentation of selective serotonin reuptake inhibitors: results from a placebo-controlled, randomized, double-blind, placebo wash-in study. J Clin Psychiatry. 2001;62(6):448–52. 117. Goss AJ, Kaser M, Costafreda SG, Sahakian BJ, Fu CHY. Modafinil augmentation therapy in unipolar and bipolar depression: a systematic review and meta-analysis of randomized controlled trials. J Clin Psychiatry. 2013;74(11):1101–7.

C. Cusin and S. Peyda 118. Liu Y, Zhou X, Zhu D, Chen J, Qin B, Zhang Y, et al. Is pindolol augmentation effective in depressed patients resistant to selective serotonin reuptake inhibitors? A systematic review and meta-­ analysis. Hum Psychopharmacol. 2015;30(3):132–42. 119. Barbee JG, Thompson TR, Jamhour NJ, Stewart JW, Conrad EJ, Reimherr FW, et  al. A double-blind placebo-controlled trial of lamotrigine as an antidepressant augmentation agent in treatment-refractory unipolar depression. J Clin Psychiatry. 2011;72(10):1405–12. 120. Barbosa L, Berk M, Vorster M.  A double-blind, randomized, placebo-controlled trial of augmentation with lamotrigine or placebo in patients concomitantly treated with fluoxetine for resistant major depressive episodes. J Clin Psychiatry. 2003;64(4):403–7. 121. Kagawa S, Mihara K, Nakamura A, Nemoto K, Suzuki T, Nagai G, et al. Relationship between plasma concentrations of lamotrigine and its early therapeutic effect of lamotrigine augmentation therapy in treatment-resistant depressive disorder. Ther Drug Monit. 2014;36(6):730–3. 122. Smith EG, Deligiannidis KM, Ulbricht CM, Landolin CS, Patel JK, Rothschild AJ. Antidepressant augmentation using the N-methyl-­ D-aspartate antagonist memantine: a randomized, double-blind, placebo-controlled trial. J Clin Psychiatry. 2013;74(10):966–73. 123. Kishi T, Matsunaga S, Iwata N. A meta-analysis of memantine for depression. J Alzheimers Dis JAD. 2017;57(1):113–21. 124. Garcia-Toro M, Medina E, Galan JL, Gonzalez MA, Maurino J. Treatment patterns in major depressive disorder after an inadequate response to first-line antidepressant treatment. BMC Psychiatry. 2012;12:143. 125. Nakajima S, Uchida H, Suzuki T, Watanabe K, Hirano J, Yagihashi T, et al. Is switching antidepressants following early nonresponse more beneficial in acute-phase treatment of depression?: A randomized open-label trial. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35(8):1983–9. 126. Poirier MF, Boyer P.  Venlafaxine and paroxetine in treatment-­ resistant depression. Double-blind, randomised comparison. Br J Psychiatry J Ment Sci. 1999;175:12–6. 127. Gaynes BN, Dusetzina SB, Ellis AR, Hansen RA, Farley JF, Miller WC, et al. Treating depression after initial treatment failure: directly comparing switch and augmenting strategies in STAR*D. J Clin Psychopharmacol. 2012;32(1):114–9. 128. Mohamed S, Johnson GR, Chen P, Hicks PB, Davis LL, Yoon J, et al. Effect of antidepressant switching vs augmentation on remission among patients with major depressive disorder unresponsive to antidepressant treatment: the VAST-D randomized clinical trial. JAMA. 2017;318(2):132–45. 129. Cipriani A, La Ferla T, Furukawa TA, Signoretti A, Nakagawa A, Churchill R, et al. Sertraline versus other antidepressive agents for depression. Cochrane Database Syst Rev. 2010;(1):CD006117. 130. Mohamed S, Johnson GR, Chen P, Hicks PB, Davis LL, Yoon J, et al. Effect of antidepressant switching vs augmentation on remission among patients with major depressive disorder unresponsive to antidepressant treatment: the VAST-D randomized clinical trial. JAMA. 2017;318(2):132–45. 131. Ruhé HG, Huyser J, Swinkels JA, Schene AH.  Switching antidepressants after a first selective serotonin reuptake inhibitor in major depressive disorder: a systematic review. J Clin Psychiatry. 2006;67(12):1836–55. 132. Souery D, Serretti A, Calati R, Oswald P, Massat I, Konstantinidis A, et  al. Switching antidepressant class does not improve response or remission in treatment-resistant depression. J Clin Psychopharmacol. 2011;31(4):512–6. 133. Brown WA, Harrison W.  Are patients who are intolerant to one serotonin selective reuptake inhibitor intolerant to another? J Clin Psychiatry. 1995;56(1):30–4. 134. Fava M, Rush AJ, Wisniewski SR, Nierenberg AA, Alpert JE, McGrath PJ, et  al. A comparison of mirtazapine and nortripty-

1  Treatment-Resistant Depression line following two consecutive failed medication treatments for depressed outpatients: a STAR*D report. Am J Psychiatry. 2006;163(7):1161–72. 135. Baldomero EB, Ubago JG, Cercós CL, Ruiloba JV, Calvo CG, López RP.  Venlafaxine extended release versus conventional antidepressants in the remission of depressive disorders after previous antidepressant failure: ARGOS study. Depress Anxiety. 2005;22(2):68–76. 136. Papakostas GI, Fava M, Thase ME. Treatment of SSRI-resistant depression: a meta-analysis comparing within-versus across-class switches. Biol Psychiatry. 2008;63(7):699–704. 137. Rush AJ, Trivedi MH, Wisniewski SR, Stewart JW, Nierenberg AA, Thase ME, et  al. Bupropion-SR, sertraline, or venlafaxine-­XR after failure of SSRIs for depression. N Engl J Med. 2006;354(12):1231–42. 138. Brent D, Emslie G, Clarke G, Wagner KD, Asarnow JR, Keller M, et al. Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. JAMA. 2008;299(8):901–13. 139. Anttila SA, Leinonen EV. A review of the pharmacological and clinical profile of mirtazapine. CNS Drug Rev. 2001;7(3):249–64. 140. Behnke K, Søgaard J, Martin S, Bäuml J, Ravindran AV, Agren H, et  al. Mirtazapine orally disintegrating tablet versus sertraline: a prospective onset of action study. J Clin Psychopharmacol. 2003;23(4):358–64. 141. Watanabe N, Omori IM, Nakagawa A, Cipriani A, Barbui C, Churchill R, et al. Mirtazapine versus other antidepressive agents for depression. Cochrane Database Syst Rev. 2011;(12):CD006528. 142. Watanabe N, Omori IM, Nakagawa A, Cipriani A, Barbui C, McGuire H, et al. Mirtazapine versus other antidepressants in the acute-phase treatment of adults with major depression: systematic review and meta-analysis. J Clin Psychiatry. 2008;69(9):1404–15. 143. Fava M, Dunner DL, Greist JH, Preskorn SH, Trivedi MH, Zajecka J, et al. Efficacy and safety of mirtazapine in major depressive disorder patients after SSRI treatment failure: an open-label trial. J Clin Psychiatry. 2001;62(6):413–20. 144. Steinberg H, Himmerich H.  Roland Kuhn-100th birthday of an innovator of clinical psychopharmacology. Psychopharmacol Bull. 2012;45(1):48–50. 145. Berton O, Nestler EJ.  New approaches to antidepressant drug discovery: beyond monoamines. Nat Rev Neurosci. 2006;7(2):137–51. 146. Anderson IM. Selective serotonin reuptake inhibitors versus tricyclic antidepressants: a meta-analysis of efficacy and tolerability. J Affect Disord. 2000;58(1):19–36. 147. van den Broek WW, Mulder PGH, van Os E, Birkenhäger TK, Pluijms E, Bruijn JA. Efficacy of venlafaxine compared with tricyclic antidepressants in depressive disorder: a meta-analysis. J Psychopharmacol Oxf Engl. 2009;23(6):708–13. 148. Arroll B, Macgillivray S, Ogston S, Reid I, Sullivan F, Williams B, et al. Efficacy and tolerability of tricyclic antidepressants and SSRIs compared with placebo for treatment of depression in primary care: a meta-analysis. Ann Fam Med. 2005;3(5):449–56. 149. Furukawa TA, McGuire H, Barbui C. Meta-analysis of effects and side effects of low dosage tricyclic antidepressants in depression: systematic review. BMJ. 2002;325(7371):991. 150. Rief W, Nestoriuc Y, von Lilienfeld-Toal A, Dogan I, Schreiber F, Hofmann SG, et  al. Differences in adverse effect reporting in placebo groups in SSRI and tricyclic antidepressant trials: a systematic review and meta-analysis. Drug Saf. 2009;32(11):1041–56. 151. von Wolff A, Hölzel LP, Westphal A, Härter M, Kriston L. Selective serotonin reuptake inhibitors and tricyclic antidepressants in the acute treatment of chronic depression and dysthymia: a systematic review and meta-analysis. J Affect Disord. 2013;144(1–2):7–15.

17 152. Thase ME, Trivedi MH, Rush AJ.  MAOIs in the contemporary treatment of depression. Neuropsychopharmacology. 1995;12(3):185–219. 153. Kornstein SG, Schatzberg AF, Thase ME, Yonkers KA, McCullough JP, Keitner GI, et al. Gender differences in treatment response to sertraline versus imipramine in chronic depression. Am J Psychiatry. 2000;157(9):1445–52. 154. Stahl SM, Felker A.  Monoamine oxidase inhibitors: a modern guide to an unrequited class of antidepressants. CNS Spectr. 2008;13(10):855–70. 155. López-Muñoz F, Alamo C, Juckel G, Assion H-J. Half a century of antidepressant drugs: on the clinical introduction of monoamine oxidase inhibitors, tricyclics, and tetracyclics. Part I: monoamine oxidase inhibitors. J Clin Psychopharmacol. 2007;27(6):555–9. 156. Amsterdam JD.  A double-blind, placebo-controlled trial of the safety and efficacy of selegiline transdermal system without dietary restrictions in patients with major depressive disorder. J Clin Psychiatry. 2003;64(2):208–14. 157. Sowa-Kućma M, Pańczyszyn-Trzewik P, Misztak P, Jaeschke RR, Sendek K, Styczeń K, et al. Vortioxetine: a review of the pharmacology and clinical profile of the novel antidepressant. Pharmacol Rep PR. 2017;69(4):595–601. 158. Thase ME, Mahableshwarkar AR, Dragheim M, Loft H, Vieta E. A meta-analysis of randomized, placebo-controlled trials of vortioxetine for the treatment of major depressive disorder in adults. Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol. 2016;26(6):979–93. 159. Koesters M, Ostuzzi G, Guaiana G, Breilmann J, Barbui C. Vortioxetine for depression in adults. Cochrane Database Syst Rev. 2017;7:CD011520. 160. Wagner G, Schultes M-T, Titscher V, Teufer B, Klerings I, Gartlehner G.  Efficacy and safety of levomilnacipran, vilazodone and vortioxetine compared with other second-generation antidepressants for major depressive disorder in adults: a systematic review and network meta-analysis. J Affect Disord. 2018;228:1–12. 161. Pae C-U, Wang S-M, Han C, Lee S-J, Patkar AA, Masand PS, et  al. Vortioxetine: a meta-analysis of 12 short-term, randomized, placebo-controlled clinical trials for the treatment of major depressive disorder. J Psychiatry Neurosci JPN. 2015;40(3): 174–86. 162. Meeker AS, Herink MC, Haxby DG, Hartung DM.  The safety and efficacy of vortioxetine for acute treatment of major depressive disorder: a systematic review and meta-analysis. Syst Rev. 2015;4:21. 163. Montgomery SA, Nielsen RZ, Poulsen LH, Häggström L. A randomised, double-blind study in adults with major depressive disorder with an inadequate response to a single course of selective serotonin reuptake inhibitor or serotonin-noradrenaline reuptake inhibitor treatment switched to vortioxetine or agomelatine. Hum Psychopharmacol. 2014;29(5):470–82. 164. Rosenblat JD, Kakar R, McIntyre RS.  The cognitive effects of antidepressants in major depressive disorder: a systematic review and meta-analysis of randomized clinical trials. Int J Neuropsychopharmacol. 2015;19(2). pii: pyv082. 165. He H, Wang W, Lyu J, Zheng J, Guo L, An X, et al. Efficacy and tolerability of different doses of three new antidepressants for treating major depressive disorder: a PRISMA-compliant meta-­ analysis. J Psychiatr Res. 2018;96:247–59. 166. Papakostas GI, Nielsen RZ, Dragheim M, Tonnoir B.  Efficacy and tolerability of vortioxetine versus agomelatine, categorized by previous treatment, in patients with major depressive disorder switched after an inadequate response. J Psychiatr Res. 2018;101:72–9. 167. Mathews M, Gommoll C, Chen D, Nunez R, Khan A.  Efficacy and safety of vilazodone 20 and 40  mg in major depressive

18 disorder: a randomized, double-blind, placebo-controlled trial. Int Clin Psychopharmacol. 2015;30(2):67–74. 168. Croft HA, Pomara N, Gommoll C, Chen D, Nunez R, Mathews M. Efficacy and safety of vilazodone in major depressive disorder: a randomized, double-blind, placebo-controlled trial. J Clin Psychiatry. 2014;75(11):e1291–8. 169. Stahl SM.  Enhancing outcomes from major depression: using antidepressant combination therapies with multifunctional pharmacologic mechanisms from the initiation of treatment. CNS Spectr. 2010;15(2):79–94. 170. Lam RW, Wan DDC, Cohen NL, Kennedy SH. Combining antidepressants for treatment-resistant depression: a review. J Clin Psychiatry. 2002;63(8):685–93. 171. Thase ME.  Antidepressant combinations: widely used, but far from empirically validated. Can J Psychiatry Rev Can Psychiatr. 2011;56(6):317–23. 172. Lopes Rocha F, Fuzikawa C, Riera R, Ramos MG, Hara C.  Antidepressant combination for major depression in incomplete responders  – a systematic review. J Affect Disord. 2013;144(1–2):1–6. 173. Henssler J, Bschor T, Baethge C.  Combining antidepressants in acute treatment of depression: a meta-analysis of 38 studies including 4511 patients. Can J Psychiatry Rev Can Psychiatr. 2016;61(1):29–43. 174. Blier P, Ward HE, Tremblay P, Laberge L, Hébert C, Bergeron R. Combination of antidepressant medications from treatment initiation for major depressive disorder: a double-blind randomized study. Am J Psychiatry. 2010;167(3):281–8. 175. Rush AJ, Trivedi MH, Stewart JW, Nierenberg AA, Fava M, Kurian BT, et  al. Combining medications to enhance depression outcomes (CO-MED): acute and long-term outcomes of a single-­blind randomized study. Am J Psychiatry. 2011;168(7): 689–701. 176. Maes M, Vandoolaeghe E, Desnyder R. Efficacy of treatment with trazodone in combination with pindolol or fluoxetine in major depression. J Affect Disord. 1996;41(3):201–10. 177. Nierenberg AA, Cole JO, Glass L. Possible trazodone potentiation of fluoxetine: a case series. J Clin Psychiatry. 1992;53(3):83–5. 178. Thase ME. Antidepressant combinations: cutting edge psychopharmacology or passing fad? Curr Psychiatry Rep. 2013;15(10):403. 179. Spina E, Trifirò G, Caraci F.  Clinically significant drug interactions with newer antidepressants. CNS Drugs. 2012;26(1): 39–67. 180. Mitchell PB. Drug interactions of clinical significance with selective serotonin reuptake inhibitors. Drug Saf. 1997;17(6):390–406. 181. Gobbi G, Ghabrash MF, Nuñez N, Tabaka J, Di Sante J, Saint-­ Laurent M, et al. Antidepressant combination versus antidepressants plus second-generation antipsychotic augmentation in treatment-resistant unipolar depression. Int Clin Psychopharmacol. 2018;33(1):34–43. 182. Bschor T, Kern H, Henssler J, Baethge C.  Switching the antidepressant after nonresponse in adults with major depression: a systematic literature search and meta-analysis. J Clin Psychiatry. 2018;79(1):16r10749. 183. Drevets WC, Price JL, Furey ML. Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct Funct. 2008;213(1–2):93–118. 184. Price JL, Drevets WC.  Neurocircuitry of mood disorders. Neuropsychopharmacology. 2010;35(1):192–216. 185. Murrough JW, Iacoviello B, Neumeister A, Charney DS, Iosifescu DV.  Cognitive dysfunction in depression: neurocircuitry and new therapeutic strategies. Neurobiol Learn Mem. 2011;96(4): 553–63. 186. Milev RV, Giacobbe P, Kennedy SH, Blumberger DM, Daskalakis ZJ, Downar J, et  al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the

C. Cusin and S. Peyda management of adults with major depressive disorder: section 4. Neurostimulation treatments. Can J Psychiatry Rev Can Psychiatr. 2016;61(9):561–75. 187. Cusin C, Dougherty DD. Somatic therapies for treatment-resistant depression: ECT, TMS, VNS, DBS. Biol Mood Anxiety Disord. 2012;2:14. 188. Farah WH, Alsawas M, Mainou M, Alahdab F, Farah MH, Ahmed AT, et  al. Non-pharmacological treatment of depression: a systematic review and evidence map. Evid Based Med. 2016;21(6):214–21. 189. UK ECT Review Group. Efficacy and safety of electroconvulsive therapy in depressive disorders: a systematic review and meta-­ analysis. Lancet Lond Engl. 2003;361(9360):799–808. 190. Tess AV, Smetana GW.  Medical evaluation of patients undergoing electroconvulsive therapy. N Engl J Med. 2009;360(14): 1437–44. 191. Endler NS.  The origins of electroconvulsive therapy (ECT). Convuls Ther. 1988;4(1):5–23. 192. Tor P-C, Bautovich A, Wang M-J, Martin D, Harvey SB, Loo C. A systematic review and meta-analysis of brief versus ultrabrief right unilateral electroconvulsive therapy for depression. J Clin Psychiatry. 2015;76(9):e1092–8. 193. Charlson F, Siskind D, Doi SAR, McCallum E, Broome A, Lie DC.  ECT efficacy and treatment course: a systematic review and meta-analysis of twice vs thrice weekly schedules. J Affect Disord. 2012;138(1–2):1–8. 194. Kellner CH, Greenberg RM, Murrough JW, Bryson EO, Briggs MC, Pasculli RM.  ECT in treatment-resistant depression. Am J Psychiatry. 2012;169(12):1238–44. 195. Lisanby SH. Electroconvulsive therapy for depression. N Engl J Med. 2007;357(19):1939–45. 196. Semkovska M, McLoughlin DM.  Objective cognitive performance associated with electroconvulsive therapy for depression: a systematic review and meta-analysis. Biol Psychiatry. 2010;68(6):568–77. 197. Reti IM, Walker M, Pulia K, Gallegos J, Jayaram G, Vaidya P. Safety considerations for outpatient electroconvulsive therapy. J Psychiatr Pract. 2012;18(2):130–6. 198. Pagnin D, de Queiroz V, Pini S, Cassano GB. Efficacy of ECT in depression: a meta-analytic review. J ECT. 2004;20(1):13–20. 199. Kho KH, van Vreeswijk MF, Simpson S, Zwinderman AH.  A meta-analysis of electroconvulsive therapy efficacy in depression. J ECT. 2003;19(3):139–47. 200. Petrides G, Fink M, Husain MM, Knapp RG, Rush AJ, Mueller M, et  al. ECT remission rates in psychotic versus nonpsychotic depressed patients: a report from CORE.  J ECT. 2001;17(4):244–53. 201. Haq AU, Sitzmann AF, Goldman ML, Maixner DF, Mickey BJ.  Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. J Clin Psychiatry. 2015;76(10):1374–84. 202. van Waarde JA, Scholte HS, van Oudheusden LJB, Verwey B, Denys D, van Wingen GA. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-­ resistant depression. Mol Psychiatry. 2015;20(5):609–14. 203. Jelovac A, Kolshus E, McLoughlin DM. Relapse following successful electroconvulsive therapy for major depression: a meta-­ analysis. Neuropsychopharmacology. 2013;38(12):2467–74. 204. Sackeim HA, Haskett RF, Mulsant BH, Thase ME, Mann JJ, Pettinati HM, et al. Continuation pharmacotherapy in the prevention of relapse following electroconvulsive therapy: a randomized controlled trial. JAMA. 2001;285(10):1299–307. 205. Fregni F, Marcolin MA, Myczkowski M, Amiaz R, Hasey G, Rumi DO, et al. Predictors of antidepressant response in clinical trials of transcranial magnetic stimulation. Int J Neuropsychopharmacol. 2006;9(6):641–54.

1  Treatment-Resistant Depression 206. Brunoni AR, Chaimani A, Moffa AH, Razza LB, Gattaz WF, Daskalakis ZJ, et  al. Repetitive transcranial magnetic stimulation for the acute treatment of major depressive episodes: a systematic review with network meta-analysis. JAMA Psychiat. 2017;74(2):143–52. 207. Martin JLR, Martín-Sánchez E.  Systematic review and meta-­ analysis of vagus nerve stimulation in the treatment of depression: variable results based on study designs. Eur Psychiatry. 2012;27(3):147–55. 208. Rush AJ, George MS, Sackeim HA, Marangell LB, Husain MM, Giller C, et  al. Vagus nerve stimulation (VNS) for treatment-­ resistant depressions: a multicenter study. Biol Psychiatry. 2000;47(4):276–86. 209. Aaronson ST, Sears P, Ruvuna F, Bunker M, Conway CR, Dougherty DD, et al. A 5-year observational study of patients with treatment-resistant depression treated with vagus nerve stimulation or treatment as usual: comparison of response, remission, and suicidality. Am J Psychiatry. 2017;174(7):640–8. 210. Berlim MT, Van den Eynde F, Daskalakis ZJ.  Clinical utility of transcranial direct current stimulation (tDCS) for treating major depression: a systematic review and meta-analysis of randomized, double-blind and sham-controlled trials. J Psychiatr Res. 2013;47(1):1–7. 211. Shiozawa P, Fregni F, Benseñor IM, Lotufo PA, Berlim MT, Daskalakis JZ, et  al. Transcranial direct current stimulation

19 for major depression: an updated systematic review and meta-­ analysis. Int J Neuropsychopharmacol. 2014;17(9):1443–52. 212. Meron D, Hedger N, Garner M, Baldwin DS. Transcranial direct current stimulation (tDCS) in the treatment of depression: systematic review and meta-analysis of efficacy and tolerability. Neurosci Biobehav Rev. 2015;57:46–62. 213. Holtzheimer PE, Kelley ME, Gross RE, Filkowski MM, Garlow SJ, Barrocas A, et al. Subcallosal cingulate deep brain stimulation for treatment-resistant unipolar and bipolar depression. Arch Gen Psychiatry. 2012;69(2):150–8. 214. Dougherty DD, Rezai AR, Carpenter LL, Howland RH, Bhati MT, O’Reardon JP, et  al. A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression. Biol Psychiatry. 2015;78(4):240–8. 215. Holtzheimer PE, Husain MM, Lisanby SH, Taylor SF, Whitworth LA, McClintock S, et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry. 2017;4(11):839–49. 216. Dandekar MP, Fenoy AJ, Carvalho AF, Soares JC, Quevedo J.  Deep brain stimulation for treatment-resistant depression: an integrative review of preclinical and clinical findings and translational implications. Mol Psychiatry. 2018;23(5):1094–112.

2

Co-occurring MDD and Problematic Alcohol Use Paola Pedrelli and Kate H. Bentley

Case Vignette

Mark had been a strong student in high school and was admitted to a good college. During his freshman year, he joined a fraternity and started drinking heavily at least weekly, at times several times a week, with his fraternity brothers. Although he often blacked out from drinking, he was not concerned about it because most of his friends had similar experiences. Despite continuing to engage in heavy drinking throughout his 4 years of college, he was able to reduce his alcohol use during midterms and finals and ultimately graduate with a 3.0 GPA. During junior year, he had his first major depressive episode after his girlfriend broke up with him because of his excessive arguing and “embarrassing” behavior while intoxicated. Mark grew to rely on heavy drinking as a strategy to cope with his low mood. Upon graduating from college, Mark started his own business in sales, which was moderately successful. After college, he experienced several bouts of depression, especially during the winter, which would last several months and typically improve in the later spring or summer. Mark’s alcohol consumption pattern remained consistent after college as he continued to drink moderately a few nights during the week and more heavily on weekends. His alcohol consumption did not immediately cause problems at work because he was self-employed and therefore was able to start working later on the days he was hung over. In addition, his job involved seeing clients and going to social events, which often involved alcohol and served to support his continued heavy

P. Pedrelli (*) · K. H. Bentley Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]; [email protected]

drinking. Mark continued to affiliate with friends who had similar drinking patterns to his, so he did not consider his alcohol consumption excessive. At age 24, after being arrested for driving under the influence (DUI), he reduced his drinking for a short period of time but resumed his old pattern within 6 months. Mark first presented to therapy at age 27 to seek treatment for depression, reporting moderate to high levels of distress regarding his decreased productivity. Additionally, he reported low mood, low motivation, difficulty sleeping, fatigue, and irritability. He noted that the only thing that would give him pleasure was to go out with his friends “partying.” Mark also reported developing tolerance to alcohol, continuing to drink despite complaints from his girlfriend, and not going to work a few mornings each month because of hangovers. In the context of the intake evaluation, Mark was told that he met criteria for both major depressive disorder (MDD) and mild, recurrent alcohol use disorder (AUD). At first, Mark minimized the consequences of his alcohol consumption saying that it was “normal.” Mark’s clinician was trained in treating co-occurring disorders with an integrated approach and began to address his alcohol use by using techniques consistent with motivational interviewing [1]. Accordingly, during the initial treatment sessions, through the use of MI techniques, the clinician engaged Mark in a discussion about the role of alcohol use in his life and its impact on his goals and mood. Through daily mood and behavior monitoring, it emerged that heavy drinking on a weekday was often followed by lower productivity at work, which would then trigger negative thoughts about himself and low self-esteem. The clinician was then able to highlight the discrepancy between Mark’s goal of being productive and successful at work and his drinking

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_2

21

22

P. Pedrelli and K. H. Bentley

behavior. Furthermore, an association between Mark’s pattern of heavy drinking on Saturdays and experiencing low mood and feelings of worthlessness on Sundays was acknowledged. Progressively, Mark agreed to reduce his drinking and, through cognitive-behavioral therapy (CBT) techniques, was able to also experience an improvement in depressive symptoms.

Introduction MDD often co-occurs with substance use disorders (SUDs) [2], as well as AUD specifically [3]. Whereas patients with MDD and other substance use disorders (SUDs; e.g., opioid use disorder, stimulant use disorder) often present and may be better served by clinics specializing in addiction, individuals with MDD and AUD, or problematic alcohol use, often seek treatment for MDD but not for their drinking behavior [4, 5]. Hence, in the current chapter, we will describe problematic alcohol use, review prevalence and consequences of co-occurring depressive symptoms and problematic alcohol use, and provide treaters of patients with these co-occurring conditions therapeutic options and an example of an evidence-based treatment protocol for MDD and AUD examined in several research studies. Problematic alcohol use is a term that describes several drinking behaviors including alcohol misuse, heavy drinking, and binge drinking. Alcohol misuse is often used synonymously with the Diagnostic and Statistical Manual of Mental Disorders-IV-TR (DSM-IV-TR) term alcohol abuse [6] and with the DSM-5 term of AUD, mild [7]. The DSMIV-TR defined alcohol abuse as a “maladaptive pattern of alcohol use leading to clinically significant impairment or distress” [6]. In the DSM-5, the authors defined a spectrum approach based on the number of criteria fulfilled, with AUD, mild being assigned to individuals who meet two to three of the AUD criteria, generally corresponding to the DSM-IV TR diagnosis of alcohol abuse [7]. This is in contrast to AUD, moderate which is assigned in the presence of four to five AUD symptoms and AUD, severe which is assigned in the presence of six or more AUD symptoms [7]. SAMHSA (2017) [2], which conducts yearly national surveys on mental health and substance use behaviors, defines binge drinking as consuming four standard drinks for females and five standard drinks for males in one sitting and heavy drinking as binge drinking on five or more days in the past month. The National Institute of Alcohol Abuse and Alcoholism (NIAAA) [8] has further specified this definition by adding that binge drinking consists of consuming the 4/5 drinks for women/men in 2  h, a pattern of alcohol use that typically raises blood alcohol concentration to 0.08 g/dl [8].

Problematic drinking is common among all ages in the United States. In the 2016 annual SAMHSA survey, an estimated 65.3 million people aged 12 or older reported binge drinking in the past 30  days [2]. Binge drinking is most common among younger adults aged 18–34  years but is ­ reported across the lifespan [9]. Specifically, past month binge drinking was endorsed by 4.9% of adolescents and 38.4% of young adults 18–25 years old [2]. Approximately one quarter (24.2%) of adults aged 26 or older were current binge alcohol users in 2016 in the United States. Many individuals engage in binge drinking multiple times a week, which increases the risk of problems. In 2016, heavy drinking was reported by approximately 1 out of 125 (0.8%) adolescents, 1 out of every 10 young adults 18–25 years old (10.1%), and approximately 6% of adults 26 years or older [2]. Excessive alcohol use cost the United States an estimated $249 billion in 2010 [10], and binge drinking was responsible for $191 billion, or 77% of these costs [10]. These figures resulted from losses in workplace productivity, health-care expenditures, criminal justice costs, and other expenses [10]. Excessive alcohol use accounts for one in ten deaths among working-age adults in the United States [11]. Binge drinking among college students is associated with numerous adverse consequences, including motor vehicle accidents, accidental injuries, sexual transmitted diseases, sexual assaults, and suicide, as well as impairments in prefrontal cortex functions (i.e., memory and attention problems) [12–14]. Furthermore, many young adults who are heavy drinkers continue to engage in problematic drinking in adulthood. Specifically, a longitudinal study documented that one third of heavily drinking youth in college continued to drink heavily 7 years later [15]. Moreover, heavy drinking in college increases the risk for an AUD 10 years later by ninefold in men and sevenfold in women [16]. As such, problematic drinking represents a major public health problem [17, 18].

 omorbidity of MDD and Problematic C Drinking The National Survey on Drug Use and Health (NSDUH) indicates that an estimated 13.9% of individuals in the United States (US) have a past-year AUD, and one third has a lifetime AUD [3]. AUD increases the risk of developing MDD [3], and individuals with MDD are also at higher risk of having an AUD [19]. Data on prevalence of comorbid AUD and MDD in the United States are scarce, but a national Canadian survey showed that the 12-month prevalence of MDD in persons with DSM-IV alcohol abuse was 6.9% [20]. Conversely, the 12-month prevalence of alcohol misuse in persons with a 12-month diagnosis of MDD was 12.3% (95% CI, 9.4–15.2) [20]. Others have reported prevalence of any AUD (alcohol dependence and alcohol abuse) among people with

2  Co-occurring MDD and Problematic Alcohol Use

depression being 16% (range 5–67%) for current and 30% (range 10–60%), for lifetime AUD [21]. This is approximately double the rate of lifetime alcohol problems in the general population (7% for current and 16–24% for lifetime) [21]. Although no national surveys have examined the cooccurrence of MDD and problematic alcohol use specifically, evidence suggests that MDD has high co-occurrence rates not only with AUD but also with alcohol misuse (including binge drinking). An association between problematic alcohol use and depression has been shown among youth receiving emergency department (ED) services [22, 23], among college students [24], and among adults [25]. Approximately half (46.9%) of college students with MDD report binge drinking in the previous 2 weeks [5], and one out of ten college students meets both criteria for MDD and has engaged in binge drinking in the past 2 weeks [24]. It has been consistently shown that the co-occurrence of alcohol dependence and depressive symptoms are associated with poor long-term outcomes, including higher recurrence and persistence of alcohol problems [26], as well as increased risk for alcohol relapse after treatment [27, 28]. Moreover, patients with MDD and AUD exhibit more chronic and persistent symptoms compared to patients with only MDD [29]. The co-occurrence of heavy drinking and depression is also problematic because it is associated with more severe alcohol-related consequences [30–33]. Specifically, individuals with both heavy drinking and depressive symptoms report a higher number of alcohol-related consequences than heavy drinking individuals without depressive symptoms. In addition, individuals with MDD tend to consume alcohol to cope with their depressive symptoms, and it has been consistently shown that drinking to cope is associated with more severe alcohol-related problems than drinking for other reasons [32–34]. Importantly, patients with co-occurring depressive symptoms and problematic alcohol use tend to experience poorer treatment outcomes, because depression may prevent sustained reduction of alcohol use and alcohol consumption may dampen response to pharmacological treatment [27, 35, 36].

Motivation/Importance of Screening Overall, a low number of individuals with AUD seek treatment. National data indicate that only 19.8% of individuals with lifetime AUD are ever treated [2]. Treatment seeking increases as symptoms severity increases. For example, among individuals with past 12 months AUD mild, moderate, and severe, treatment seeking is 2.7%, 4.9%, and 21.3%, respectively, and, among those with lifetime AUD mild, moderate, and severe, is 4.4%, 8.7%, and 34.7%, respectively [3]. Individuals with lifetime and past-year AUD receive help from 12-step groups, health-care practitioners, and out-

23

patient facilities and rehabilitation programs [3]. Among respondents with lifetime AUD, a similar percentage of them sought treatment through rehabilitation programs (9.0%) and from health-care practitioners (8.7%) [3]. Similarly, very few young adults with binge drinking are interested in treatment for their hazardous alcohol use [37], and between 5% and 13% are receiving treatment for it [4, 5]. This is in contrast with the fact that approximately 65% of individuals with depression treatment are in treatment [2]. Given the high prevalence of heavy episodic drinking (HED), many individuals seeking treatment for MDD also engage in HED. Consistently, a recent study reported that almost half of students with MDD also reported HED [24]. Thus, treating HED among those presenting for MDD seems an ideal opportunity to treat problematic alcohol use among those with low insight [24, 37]. Although many patients presenting for treatment of MDD may engage in problematic alcohol use, they may not report it, making systematic screening of alcohol use critical. A common and well-validated instrument to assess hazardous alcohol use is the Alcohol Use Disorders Identification Test (AUDIT) [38, 39]. A score of 8 or higher is considered at-risk alcohol consumption and warrants further assessment [38]. Similarly, there is reason to integrate depressive screenings into settings such as emergency departments and primary care clinics, where brief interventions including the screening, brief intervention, and referral to treatment (SBIRT) [40, 41] approach to reduce risky alcohol use are used [42–44]. Thus, acknowledgment and proper screening of coexisting disorders in high-risk dual-diagnosis populations may allow to maximize success of brief interventions [45].

History Treatment Treatments for co-occurring MDD and AUD can be singlediagnosis focused (i.e., treating only one condition), integrated (i.e., treating both conditions at the same time), or sequential (i.e., treatment is administered for one disorder at a time, and mood symptoms are not addressed until a period of abstinence from alcohol is achieved). For patients with MDD and co-occurring AUD, sequential treatment was formerly the standard of care [46]. However, sequential treatment or single-focused treatment may be associated with poorer outcomes, because patients with MDD and untreated co-occurring alcohol problems may not experience improvement due to continued alcohol use, and those with AUD and untreated depression may have a higher risk to relapse due to their mood symptoms [27, 28, 47]. Over the past decade, the notion of treating co-occurring disorders together has become more established, and integrated treatment

24

approaches are increasingly common and widely considered the more accepted approach [46, 48]. Single evidence-based treatments for unipolar MDD include cognitive-behavioral therapy (CBT) [49] and other approaches such as mindfulness-based cognitive therapy (MBCT) [50] that have been described in other chapters. The effectiveness of CBT for MDD has been supported by several meta-analyses and is one of the most common evidence-based treatments for the treatment of MDD [51– 53]. Briefly, CBT is based on the conceptualization of thoughts, feelings, and behaviors as connected, and that depression is associated with inaccurate and dysfunctional thinking and unhelpful behavioral patterns. Thus, CBT includes two primary techniques: cognitive restructuring, consisting of teaching patients to identify and change inaccurate thinking and self-views, and behavioral activation— consisting of motivating depressed individuals to engage in pleasant activities [49]. Single-diagnosis treatments for problematic alcohol use have drawn primarily from motivational interviewing (MI) [1]. MI has been defined as a “client-centered, directive method for enhancing intrinsic motivation to change by exploring and resolving ambivalence” [54]. MI consists of a type of communication that combines both style (e.g., empathy) and technique (e.g., reflective listening) to create an atmosphere of collaboration during the session [1]. MI is used to enhance motivations to change high-risk drinking behaviors and reduce alcoholrelated consequences. Treatment programs for binge drinking among young adults consist of brief motivational interventions (BMI), typically involving one session drawing largely from MI principles. Sessions usually comprise personalized feedback on alcohol consumption and problems experienced in the context of heavy drinking and personalized normative feedback (e.g., comparison of individual’s drinking to national drinking norms) [55–57]. Most integrated treatments for MDD and problematic alcohol use combine these evidence-based psychological treatments (CBT and MI); at the end of this chapter, we present an example of such a protocol. Few trials to date have examined treatments for co-occurring alcohol misuse and depression. One group compared four different interventions of different lengths and including different treatment modalities. One group included brief intervention (BI) alone that was comprised of rapport building, case formulation, feedback from assessment, MI, brief advice to reduce alcohol, and self-help material for depression and alcohol use problems. The other three groups included BI combined with longer interventions including single treatment focused on MDD, single treatment focused on alcohol, and integrated MI/CBT among people with co-occurring MDD and alcohol misuse. Notably, all patients in the study received the BI, and hence, all conditions in the study included elements of MI.  Short-term and long-term outcomes were examined in

P. Pedrelli and K. H. Bentley

two separate publications [58, 59]. At the first post-treatment assessment, the longer interventions were associated with higher reduction of weekly drinking relative to BI, and ­integrated MI/CBT treatment was more potent than singlefocused interventions in lowering alcohol consumption [58]. Similarly, integrated treatment was associated with greater reduction of depressive symptoms compared to singlefocused treatments [58]. Long-term outcome data showed that the longer interventions were more effective than the shorter intervention in reducing both depression and alcohol use. However, benefits on alcohol reduction in the longer intervention were not maintained at the 3-year follow-up. Integrated treatment was better than single-focused treatment for MDD at the 6-month follow-up, while the alcoholfocused intervention had superior outcomes than the depression-focused treatment in reducing alcohol use. Given their findings, the authors proposed a stepped-care approach in which all patients may receive brief integrated treatment, and non-responders would then receive a treatment of higher intensity or for a longer time [58]. The same group extended Baker and colleagues’ [58] study in individuals with MDD and comorbid alcohol and/or cannabis misuse, of which half met criteria for alcohol misuse. The investigators compared (1) one session of brief intervention (BI) alone (including MI techniques), (2) BI plus nine sessions of MI/CBT delivered by a therapist, and (3) BI plus nine additional sessions of MI+CBT delivered by a computer (plus brief weekly input from a clinician). All conditions were associated with improvement of depressive symptoms and reduction of alcohol consumption, with higher benefit in the longer conditions at the 12-month follow-up [60]. Similarly, a brief Internet-based intervention for co-occurring depression and problematic alcohol use in young people showed that MI/CBT was associated with higher reduction in depression and alcohol use at the posttreatment assessment relative to an attention-control condition [61]. However, while patients in the MI/CBT maintained their improvement at the 6-month follow-up assessment, the difference between groups was no longer significant [61]. There is also evidence that adding MI to standard treatment is associated with benefits. For example, a trial in a large outpatient psychiatry program, where participants were randomized to receive either (1) three sessions of MI, one in person and two by phone, or (2) printed literature about alcohol and use risk as an adjunct to standard treatment for depression (control condition), showed at the 6-month assessment that MI was more effective than the control condition in reducing hazardous drinking [62]. The report did not specify what standard treatment was provided for depression. Taken together, these studies offer evidence that MI plus CBT may be associated with better outcomes than short interventions and single-focused interventions. It appears

2  Co-occurring MDD and Problematic Alcohol Use

that brief interventions lead to some improvements in both mental health and alcohol use, and longer interventions produced even better outcomes. Although further studies are needed for clear guidelines for MDD and alcohol misuse, it is important to note that a meta-analysis on studies examining CBT+MI for MDD and AUD (alcohol abuse and alcohol dependence) found that this combined treatment was associated with a small reduction of alcohol consumption and a small but significant improvement of depression relative to control conditions [63]. Furthermore, the authors found that a greater number of CBT/MI sessions were significantly associated with better alcohol outcomes, but not with the depression outcomes [63]. Thus, integrated interventions incorporating MI and CBT may be an appropriate treatment option for individuals with co-occurring depressive symptoms and problematic alcohol use. Notably, it has been suggested that whereas integrated treatment is also more effective for individuals with personality disorders, longer interventions may provide more lasting benefits (e.g., at 12-month follow-up) for this population [64].

Pharmacological Interventions Thus far, we have focused primarily on psychological approaches to treating patients with comorbid MDD and heavy drinking. Combining evidence-based behavioral interventions with medications to treat substance use disorders (i.e., medication-assisted treatment or MAT) is also a wellestablished approach to managing problematic alcohol use. Though a thorough review of all pharmacological interventions for heavy drinking is beyond the scope of this chapter, we will briefly note several of the most widely used options. Currently, the US Food and Drug Administration (FDA) has approved three oral medications to treat alcohol use. These consist of disulfiram (i.e., Antabuse), acamprosate (i.e., Campral), and naltrexone (i.e., ReVia), the latter of which is also available as an extended-release injectable (i.e., Vivitrol). Briefly, disulfiram inhibits enzymes that metabolize alcohol, resulting in unpleasant physical reactions (e.g., nausea, palpitations) when alcohol is consumed [65]. Acamprosate is thought to reduce symptoms of protracted withdrawal (e.g., insomnia, anxiety, dysphoria) by normalizing brain systems disrupted by alcohol consumption. Naltrexone blocks opioid receptors associated with cravings and rewarding effects from drinking. Overall, these medications have generally been shown to help individuals reduce their alcohol consumption, avoid relapse to heavy drinking, and/or achieve and maintain sobriety [66]. It is important to note, however, that these medications have been tested primarily among patients who have already undergone a period of sobriety before beginning the medication. Though not FDA-approved, topiramate, an anticonvulsant medication used for bipolar disorder, has also shown promising effects in reducing heavy drinking and is also often prescribed off-label for alcohol use [67].

25

There remains a need for more studies to identify effective medications to simultaneously treat MDD with ­co-occurring heavy drinking, given the limited and, in some cases, contradictory data to guide specific pharmacological recommendations [67]. Moreover, since abstinence is often not the goal (at least initially) for patients with MDD who also present with heavy drinking, it is unclear whether these results can be generalized to these patients. Overall, findings from the available research in this area suggest that antidepressants effectively treat depressive symptoms among patients with unipolar depression and comorbid alcohol use [68, 69]. Findings are mixed, however, with regard to whether antidepressants (e.g., SSRIs) can also decrease alcohol use [67]. There is some support for using antidepressants early in these “dual diagnosis” patients, because patients with depression and alcohol use disorder who do not take an antidepressant may be at higher risk for relapse [70]. In terms of combining pharmacotherapies for both conditions, studies suggest that using sertraline with naltrexone may be more effective in improving depression and alcohol use than antidepressant or AUD medication alone [65, 67, 71]. More research characterizing the impact of combining antidepressants with other approved medications for problematic alcohol use is needed. It is important to emphasize that the most commonly used medications for MDD (e.g., SSRIs, SSNRIs, bupropion) can be associated with harmful drug-drug interactions among patients who engage in heavy drinking (e.g., worsened side effects, sedation, increased depression, and liver damage). Importantly, monoamine oxidase inhibitors (MAOIs) can result in critical cardiac side effects when combined with alcohol [36]. Further, drinking while taking antidepressants can counter antidepressant benefits [36], and thus these patients may require higher doses of antidepressant medications to reach the therapeutic range. Thus, it is particularly important to carefully examine any potential adverse effects associated with combining an antidepressant with alcohol and to discuss the risks of drinking while on an antidepressant. Overall, more rigorous and controlled research on integrated treatments (both psychosocial and pharmacological) for problematic alcohol use and MDD are needed to provide clinical guidelines, given that providers who regularly treat individuals with depression are highly likely to encounter heavy drinking in their practice.

Recommendations for Practitioners Problematic alcohol use is common among patients presenting for MDD treatment; therefore it is critically important to assess alcohol consumption when treating this population. As described by DeVido and Weiss [67], evidence suggest-

26

ing that heavy alcohol use can lead to depression and vice versa makes it difficult to assign accurate diagnoses. It can be a challenge to determine whether depressive symptoms are a result of a primary depressive disorder or due to alcohol misuse and whether they would persist if the alcohol consumption were to change. DeVido and Weiss [67] provide the following recommendations to address these challenges: (1) conduct a thorough diagnostic interview that carefully assesses the chronological order of the onset of both conditions, and (2) obtain collateral information from family members on temporal presentation of symptoms. It is also important to assess for the presence of depressive symptoms during period of abstinence and inquire about family history of MDD and AUD. A review of treatment history can also aid with the assessment of diagnoses. It is important to note that several large studies that have carefully examined the chorological order of MDD and AUD have shown that presence of primary, independent MDD is more common than substance induced disorders [72, 73]. When problematic alcohol use and MDD co-occur, an integrated psychotherapeutic approach combining MI and CBT strategies may be the most beneficial. A computerbased program entitled Self-Help for Alcohol and Other Drug use and Depression (SHADE) is an example of how to integrate CBT and MI strategies. SHADE is based on a harm reduction approach to decrease alcohol use, allowing participants to choose their therapy goals [74, 75]. SHADE has been used in several studies with patients with co-occurring alcohol misuse and MDD [58–60] that could be used as a model for outpatient treatment [75]. SHADE includes CBT and MI strategies and is based on a harm reduction approach to decrease alcohol use, allowing participants to choose their therapy goals. CBT strategies allow recognition and exploration of the relationship between depressive symptoms and alcohol use problems, including how each condition may be exacerbated by the other. MI is used throughout treatment, with early sessions integrating specific techniques with CBT strategies (e.g., decisional balance, developing change plans) and in later sessions as a general nonconfrontational approach to discussions regarding making and maintaining changes. Specifically, at Session 1, all participants receive comprised assessment feedback, case formulation (covering the development and maintenance of coexisting depression and alcohol problems), MI, planning of behavior change, and education about depression and hazardous alcohol use. Session 2 includes a description of the rationale for CBT and the beginning of mood and/or craving monitoring, activity scheduling, and mindful walking. During Session 3, there is an introduction to thought monitoring and assessment of change and mindful listening. In Session 4, participants develop an activity list, clarify their change plan, receive information about coping with impulsive thoughts or cravings, and undertake mindfulness of pleasant activities.

P. Pedrelli and K. H. Bentley

Session 5 teaches how to identify and change unhelpful automatic thoughts and reviews mindful breathing. Session 6 reviews problem-solving strategies and mindful visual experiences. Session 7 includes examination of evidence supporting problematic core beliefs and practice using a 3-min breathing space. Session 8 includes a practice of cognitive therapy techniques and assertiveness or alcohol refusal skills and the development of an emergency plan. Session 9 includes relapse prevention techniques and further mindfulness practice. During Session 10, participants outline a relapse prevention plan and write a management plan for relapse risk [58–60, 75]. As noted, it is important to consider pharmacological options when treating co-occurring depression and alcohol misuse. Evidence-based behavioral interventions may be effectively combined with medications designed to treat depression and/or AUD; however, clinicians must attend to the potential drug-drug interactions of prescribing certain medications to individuals engaging in heavy drinking behavior.

FAQs: Common Questions and Answers Q1. What is the best approach with a patient reporting drinking over the recommended guidelines? A1. Evidence suggests that many patients seeking treatment for depression may also engage in heavy alcohol use. Clinicians should therefore systematically inquire about alcohol consumption during intake and throughout the course of treatment. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends brief intervention with individuals with “at risk” drinking or “heavy drinking,” defined as drinking 5 drinks on any day or 14 per week for men and drinking more than 3 drinks on any day and 7 per week for women [76]. Patients may not be aware that their drinking is considered “at risk,” and education on NIAAA-recommended guidelines and on the risk associated with higher alcohol use is an important first step of treatment. Clinicians should carefully review all possible consequences that patients may have already experienced due to their alcohol consumption, including health-related problems, relationship problems (i.e., arguments with friends or significant others), legal problems (e.g., DUI), and employment problems (e.g., missing days at work). Given the low insight often associated with problematic drinking, clinicians may use MI techniques [1], which follow five general principles: express empathy through reflective listening, develop discrepancy between clients’ goals or values and their current behavior, avoid argument and direct confrontation, adjust to client resistance rather than opposing it directly, and

2  Co-occurring MDD and Problematic Alcohol Use

support self-efficacy and optimism. Clinicians may also adopt communication techniques consistent with the spirit of MI, such as reflective statements, affirmation, evocation, and use statements that highlight discrepancies between the patient current behaviors and his/her goals. These principles are characterized by the brief intervention structure of FRAMES which refers to the use of Feedback, Responsibility for change lying with the individual, Advice-giving, providing a Menu of change options, Empathic counseling style, and the enhancement of Self-efficacy [77]. Thus, clinicians may focus on enhancing discrepancies between alcohol use and personal goals and collaboratively identify drinking goals that fall within the recommended guidelines and may be consistent with patients’ goals. Progressively, depression and alcohol use would be addressed in conjunction, highlighting their association and strengthening the patient’s motivation to not engage in heavy drinking. Q2. What is the best setting for treating co-occurring depression and alcohol misuse? A2. One important clinical consideration to be mindful of when treating patients with co-occurring MDD and problematic alcohol use is that these patients often have low insight into the problems associated with their drinking behavior [78] and thus may have low motivation to change. Often their goal for treatment is to only reduce their alcohol consumption. Hence, patients with problematic alcohol use may not be willing to attend specialty clinics serving patients with co-occurring MDD and SUD because they are not interested in abstinence (often required in these programs) and because they may not want to interface with patients with severe addictive behaviors with whom they may not identify. Patients with problematic alcohol use may be more open to receiving treatment in general outpatient psychiatry clinics focusing on depression and related conditions (e.g., anxiety). Moreover, whereas patients with SUD often attend more intensive outpatient (e.g., intensive outpatient, partial hospitalization) programs, onceweekly treatment sessions may be an appropriate level of care for individuals with MDD and co-occurring problematic alcohol use (i.e., milder forms of AUD) [62, 63]. As such, it has been argued that treating problematic alcohol use among those with MDD may be an ideal strategy to address this high-risk behavior because these patients do not seek treatment for their alcohol use but are interested in treatment for MDD. Q3. When is it better to adopt a sequential versus integrated treatment approach? A3. Research has generally shown that integrated treatment is associated with better outcomes for patients with co-occurring depression and alcohol problems. However, if clinicians

27

do not have experience in treating problematic drinking and/ or the treatment setting lacks necessary resources, it is advisable to refer the patient for a short course of MI by a clinician who is well versed in this method. As noted above, neglecting to address the problematic alcohol use may also prevent improvement of mood symptoms. Q4. What is the best way to assess for problematic alcohol use? A4. The Task Force on Recommended Alcohol Questions, a task force of NIAAA’s Council, developed minimum sets of downward compatible alcohol consumption questions, and they recommended sets of 3, 4, 5, and 6 items presented here which resulted from the work of that task force. They recommend asking a minimum of three questions including frequency of drinking in the past year (e.g., “During the last 12 months, how often did you usually have any kind of drink containing alcohol?”), number of drinks consumed on a typical drinking day in the past 12  months (e.g., “During the last 12 months, how many alcoholic drinks did you have on a typical day when you drank alcohol?”), and frequency of heavy drinking in the past 12 months (e.g., “During the last 12 months, how often did you have five or more drinks [males] or four or more drinks [females] containing any kind of alcohol in within a 2-hour period?”). The four-item set adds a question about the maximum number of drinks consumed in a 24-h period in the past year (e.g., “During your lifetime, what is the maximum number of drinks containing alcohol that you drank within a 24-hour period?”), and the five-item set adds a question about maximum drinks in a 24 h period in the respondent’s lifetime (e.g., “During the last 12 months what is the maximum number of drinks containing alcohol that you drank within a 24-hour period?”). The sixitem set adds a question, immediately following the item about maximum drinks in a 24-h period in the past 12 months, which asks about the frequency of consuming this maximum number of drinks in the past 12 months (e.g., “During the last 12 months, how often did you drink this largest number of drinks?”) [79]. Similarly, the Alcohol Use Disorders Identification Test (AUDIT) [38] is a self-report screening tool extensively used to identify individuals with problematic drinking. The AUDIT is a ten-item screening questionnaire with three questions inquiring about amount and frequency of drinking, three questions on alcohol dependence, and four on problems caused by alcohol. All the items are scored using a Likert scale from 1 to 5, and total scores range from 0 to 40. Norms have been developed for this instrument, but overall the authors advise to provide alcohol education in the case of scores between 0 and 7, simple advice with scores between 8 and 15, and simple advice plus brief counseling and continued monitoring in the case of scores between 16 and 19 [38].

28

Q5. What is the difference between having treatment goals of abstinence and moderate drinking? A5. In the presence of heavy episodic drinking, the common approach is a harm reduction strategy in which the goal consists of engaging in alcohol consumption within recommended guidelines. Most individuals with problematic alcohol use initially are not open to abstain from alcohol use, making alcohol reduction the most feasible goal. Some patients may be unable to reduce drinking even though they do not meet criteria for AUD because of inability to control themselves. In those cases, it is recommended clinicians review the pros and cons of abstinence with the patient. There is debate as to whether a harm reduction strategy would be appropriate for severe AUD, but that is outside the scope of the current chapter focused on MDD and less severe forms of AUD [80, 81].

References 1. Miller WR, Rollnick S. Motivational interviewing: preparing people to change addictive behavior. New York: Guilford Press; 1991. 2. Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: results from the 2016 National Survey on Drug Use and Health (HHS Publication No. SMA 17-5044, NSDUH Series H-52). Rockville: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2017. Retrieved from https://www.samhsa.gov/data/. Accessed 5/12/2018. 3. Grant BF, Saha TD, Ruan WJ, Goldstein RB, Chou SP, Jung J, et al. Epidemiology of DSM-5 drug use disorder: results from the National Epidemiologic Survey on Alcohol and Related ConditionsIII. JAMA Psychiat. 2016;73(1):39–47. 4. Blanco C, Okuda M, Wright C, Hasin DS, Grant BF, Liu SM, Olfson M. Mental health of college students and their noncollege-attending peers: results from the National Epidemiologic Study on Alcohol and Related Conditions. Arch Gen Psychiatry. 2008;65:1429–37. 5. Cranford JA, Eisenberg D, Serras AM.  Substance use behaviors, mental health problems, and use of mental health services in a probability sample of college students. Addict Behav. 2009;34(2):134–45. 6. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington DC 2000. 7. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington: American Psychiatric Publishing; 2013. 8. National Institute of Alcohol Abuse and Alcoholism. NIAAA council approves definition of binge drinking. NIAAA Newslett. 2004;3:3. 9. Kanny D, Liu Y, Brewer RD, Lu H. Binge drinking—United States, 2011. MMWR Suppl. 2013;62(suppl 3):77–80. 10. Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. 2010 national and state costs of excessive alcohol consumption. Am J Prev Med. 2015;49(5):e73–9. 11. Stahre M, Roeber J, Kanny D, Brewer RD, Zhang X. Contribution of excessive alcohol consumption to deaths and years of potential life lost in the United States. Prev Chronic Dis. 2014;11:130293. 12. Hingson RW, Edwards EM, Heeren T, Rosenbloom D.  Age of drinking onset and injuries, motor vehicle crashes, and physical

P. Pedrelli and K. H. Bentley fights after drinking and when not drinking. Alcohol Clin Exp Res. 2009;33(5):783–90. 13. Glasheen C, Pemberton MR, Lipari R, Copello EA, Mattson ME.  Binge drinking and the risk of suicidal thoughts, plans, and attempts. Addict Behav. 2015;43:42–9. 14. Baliunas D, Rehm J, Irving H, Shuper P. Alcohol consumption and risk of incident human immunodeficiency virus infection: a metaanalysis. Int J Public Health. 2010;55(3):159–66. 15. Jackson KM, Sher KJ, Gotham HJ, Wood PK.  Transitioning into and out of large-effect drinking in young adulthood. J Abnorm Psychol. 2001;110(3):378–91. 16. Jennison KM.  The short-term effects and unintended long-term consequences of binge drinking in college: a 10-year follow-up study. Am J Drug Alcohol Abuse. 2004;30(3):659–84. 17. Ham LS, Hope DA.  College students and problematic drinking: a review of the literature. Clin Psychol Rev. 2003;23(5):719–59. Review. 18. Hingson R, Heeren T, Winter M, Wechsler H. Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18–24: changes from 1998 to 2001. Annu Rev Public Health. 2005;26:259–79. Review. 19. Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64:830–42. 20. Currie SR, Patten SB, Williams JV, Wang J, Beck CA, El-Guebaly N, Maxwell C.  Comorbidity of major depression with substance use disorders. Can J Psychiatr. 2005;50(10):660–6. 21. Sullivan LE, Fiellin DA, O’Connor PG. The prevalence and impact of alcohol problems in major depression: a systematic review. Am J Med. 2005;118(4):330–41. 22. Becker SJ, Spirito A, Hernandez L, Barnett NP, Eaton CA, Lewander W, et al. Trajectories of adolescent alcohol use after brief treatment in an emergency department. Drug Alcohol Depend. 2012;125:103–9. 23. Patton R, Lau CH, Blow FC, Ranney ML, Cunningham RM, Walton MA. Prevalence and correlates of depression and drinking behaviors among adolescents and emerging adults in a suburban emergency department. Subst Use Misuse. 2016;51(1):34–40. 24. Pedrelli P, Borsari B, Lipson SK, Heinze JE, Eisenberg D. Gender differences in the relationships among major depressive disorder, heavy alcohol use, and mental health treatment engagement among college students. J Stud Alcohol Drugs. 2016;77(4):620–8. 25. Dixit AR, Crum RM. Prospective study of depression and the risk of heavy alcohol use in women. Am J Psychiatry. 2000;157(5): 751–8. 26. Boschloo L, Vogelzangs N, van den Brink W, Smit JH, Veltman DJ, Beekman AT, Penninx BW.  Alcohol use disorders and the course of depressive and anxiety disorders. Br J Psychiatry. 2012;200(6):476–84. 27. Greenfield SF, Weiss RD, Muenz LR, Vagge LM, Kelly JF, Bello LR, Michael J.  The effect of depression on return to drinking: a prospective study. Arch Gen Psychiatry. 1998;55(3):259–65. 28. Suter M, Strik W, Moggi F. Depressive symptoms as a predictor of alcohol relapse after residential treatment programs for alcohol use disorder. J Subst Abus Treat. 2011;41(3):225–32. 29. Boschloo L, Vogelzangs N, van den Brink W, Smit JH, Beekman AT, Penninx BW.  Predictors of the 2-year recurrence and persistence of alcohol dependence. Addiction. 2012;107(9):1639–40. 30. Camatta CD, Nagoshi CT. Stress, depression, irrational beliefs, and alcohol use and problems in a college student sample. Alcohol Clin Exp Res. 1995;19(1):142–6. 31. Marmorstein NR.  Longitudinal associations between depressive symptoms and alcohol problems: the influence of comorbid delinquent behavior. Addict Behav. 2010;35(6):564–71.

2  Co-occurring MDD and Problematic Alcohol Use 32. Kenney S, Jones RN, Barnett NP. Gender differences in the effect of depressive symptoms on prospective alcohol expectancies, coping motives, and alcohol outcomes in the first year of college. J Youth Adolesc. 2015;44(10):1884–97. 33. Pedrelli P, Collado A, Shapero BG, Brill C, MacPherson L. Different pathways explain alcohol-related problems in female and male college students. J Am Coll Heal. 2016;64(7):535–44. 34. Bravo AJ, Pearson MR. In the process of drinking to cope among college students: an examination of specific vs. global coping motives for depression and anxiety symptoms. Addict Behav. 2017;73:94–8. 35. Marlatt GA, Gordon JR, editors. Relapse prevention: maintenance strategies in the treatment of addictive behaviors. New  York: Guilford Press; 1985. 36. National Institute of Alcohol Abuse and Alcoholism, NIH Publication No. 13–5329 Published 2003, Revised 2014. 37. Capron DW, Bauer BW, Madson MB, Schmidt NB.  Treatment seeking among college students with comorbid hazardous drinking and elevated mood/anxiety symptoms. Subst Use Misuse. 2017;27:1–10. 38. Babor TF, de la Fuente JR, Saunders J, Grant M. AUDIT: the alcohol use disorders identification test. Guidelines for use in primary health care. Geneva: World Health Organization; 1992. 39. Sauders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M.  Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption  – II.  Addiction. 1993;88(6):791–804. 40. Babor T, Higgins-Biddle J, Higgins P, Gassman R, Gould B.  Training medical providers to conduct alcohol screening and brief interventions. Subst Abus. 2004;25:17–26. 41. Babor T, McRee B, Kassebaum P, Grimaldi P, Ahmed K, Bray J. Screening, brief intervention, and referral to treatment (SBIRT). Subst Abus. 2007;28(3):7–30. 42. Barata IA, Shandro JR, Montgomery M, Polansky R, Sachs CJ, Duber HC, et al. Effectiveness of SBIRT for alcohol use disorders in the emergency department: a systematic review. West J Emerg Med. 2017;18(6):1143–52. 43. Taggart IH, Ranney ML, Howland J, Mello MJ.  A systematic review of emergency department interventions for college drinkers. J Emerg Med. 2013;45(6):962–8. 44. Singh M, Gmyrek A, Hernandez A, Damon D, Hayashi S. Sustaining screening, brief intervention and referral to treatment (SBIRT) services in health-care settings. Addiction. 2017;112(Suppl 2):92–100. 45. Clark DB, Bukstein OG.  Psychopathology in adolescent alcohol abuse and dependence. Alcohol Health Res World. 1998;22(2):117– 21. 126. Review. 46. Drake RE, Mueser KT, Clark RE, Wallach MA. The course, treatment, and outcome of substance disorder in persons with severe mental illness. Am J Orthopsychiatry. 1996;66(1):42–51. 47. Cornelius JR, Bukstein OG, Salloum IM, Kelly TM, Wood DS, Duncan B.  Fluoxetine in depressed AUD adolescents: a 1-year follow-up evaluation. J Child Adolesc Psychopharmacol. 2004;14(1):33–8. 48. Timko C, Dixon K, Moos RH. Treatment for dual diagnosis patients in the psychiatric and substance abuse systems. Ment Health Serv Res. 2005;7(4):229–42. 49. Beck AT.  Depression: causes and treatment. Philadelphia: University of Pennsylvania Press; 1967. 50. Teasdale JD, Segal ZV, Williams JM, Ridgeway VA, Soulsby JM, Lau MA.  Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. J Consult Clin Psychol. 2000;68(4):615–23. 51. Butler AC, Chapman JE, Forman EM, Beck AT. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin Psychol Rev. 2006;26(1):17–31.

29 52. Dobson KS.  Evaluation of the adequacy of cognitive/behavioural theories for understanding depression in women: a commentary. Can Psychol J. 1989;30(1):56–8. 53. Gloaguen V, Cottraux J, Cucherat M, Blackburn IM. A meta-analysis of the effects of cognitive therapy in depressed patients. J Affect Disord. 1998;49(1):59–72. 54. Miller BE, Miller MN, Verhegge R, Linville HH, Pumariega AJ.  Alcohol misuse among college athletes: self-medication for psychiatric symptoms? J Drug Educ. 2002;32(1):41–52. 55. Dimeff LA, Baer JS, Kivlaha DR, Marlatt GA.  Brief alcohol screening and intervention for college students (BASICS): a harm reduction approach. New York: Guilford Press; 1999. 56. Larimer ME, Cronce JM. Identification, prevention and treatment: a review of individual-focused strategies to reduce problematic alcohol consumption by college students. J Stud Alcohol Suppl. 2002;14:148–63. 57. Larimer ME, Cronce JM. Identification, prevention, and treatment revisited: individual-focused college drinking prevention strategies 1999–2006. Addict Behav. 2007;32(11):2439–68. 58. Baker AL, Kavanagh DJ, Kay-Lambkin FJ, Hunt SA, Lewin TJ, Carr VJ, Connolly J.  Randomized controlled trial of cognitive behavioural therapy for coexisting depression and alcohol problems: short-term outcome. Addiction. 2010;105(1):87–99. 59. Baker AL, Kavanagh DJ, Kay-Lambkin FJ, Hunt SA, Lewin TJ, Carr VJ, McElduff P.  Randomized controlled trial of MICBT for co-existing alcohol misuse and depression: outcomes to 36-months. J Subst Abus Treat. 2014;46(3):281–90. 60. Kay-Lambkin FJ, Baker A, Lewin TJ.  Computer-based psychological treatment for comorbid depression and substance use problems: a randomised controlled trial of clinical efficacy. Addiction. 2009;104:378–88. 61. Deady M, Mills KL, Teesson M, Kay-Lambkin F. An online intervention for co-occurring depression and problematic alcohol use in young people: primary outcomes from a randomized controlled trial. J Med Internet Res. 2016;18(3):e71. 62. Satre DD, Leibowitz A, Sterling SA, Lu Y, Travis A, Weisner C. A randomized clinical trial of Motivational Interviewing to reduce alcohol and drug use among patients with depression. J Consult Clin Psychol. 2016;84(7):571–9. 63. Riper H, Andersson G, Hunter SB, de Wit J, Berking M, Cuijpers P.  Treatment of comorbid alcohol use disorders and depression with cognitive-behavioural therapy and motivational interviewing: a meta-analysis. Addiction. 2014;109:394–406. 64. McCarter KL, Halpin SA, Baker AL, Kay-Lambkin FJ, Lewin TJ, Thornton LK, et al. Associations between personality disorder characteristics and treatment outcomes in people with co-occurring alcohol misuse and depression. BMC Psychiatry. 2016;16:210. 65. Helton SG, Lohoff FW.  Pharmacogenetics of alcohol use dis orders and comorbid psychiatric disorders. Psychiatry Res. 2015;230:121–9. 66. Gianoli MO, Petrakis IL. Pharmacotherapy for comorbid depression and alcohol dependence. Curr Psychiatr Ther. 2013;12(1):24–33. 67. DeVido JJ, Weiss RD. Treatment of the depressed alcoholic patient. Curr Psychiatry Rep. 2012;14(6):610–8. 68. Iovieno N, Tedeschini E, Bentley KH, Evins AE, Papakostas GI.  Antidepressants for major depressive disorder and dysthymic disorder in patients with comorbid alcohol use disorders: a metaanalysis of placebo-controlled randomized trials. J Clin Psychiatry. 2011;72(8):1144–51. 69. Nunes EV, Levin FR.  Treatment of depression in patients with alcohol or other drug dependence: a meta-analysis. JAMA. 2004;291(15):1887–96. 70. Greenfield BL, Venner KL, Kelly JF, Slaymaker V, Bryan AD. The impact of depression on abstinence self-efficacy and substance use outcomes among emerging adults in residential treatment. Psychol Addict Behav. 2012;26(2):246–54.

30 71. Pettinati HM, Oslin DW, Kampman KM, Dundon WD, Xie H, Gallis TL, et al. A double blind, placebo-controlled trial that combines sertraline and naltrexone for treating co-occurring depression and alcohol dependence. Am J Psychiatry. 2010;167(6): 668–57. 72. Grant BF, Stinson FS, Hasin DS, et  al. Immigration and lifetime prevalence of DSM-IV psychiatric disorders among Mexican Americans and non-Hispanic whites in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61:1226–33. 73. Reis RK, Yuodelis-Flores C, Comtois KA, et al. Substance-induced suicidal admissions to an acute psychiatric service: characteristics and outcomes. J Subst Abus Treat. 2008;34:72–9. 74. Shade Program. Retrieved at www.shadetreatment.com.au/. Accessed 4 May 2018. 75. Kay-Lambkin F, Baker A, Bucci S.  Treatment manual for the SHADE project (self-help for alcohol/other drug use and depression). Callahan: University of Newcastle; 2002.

P. Pedrelli and K. H. Bentley 76. The National Institute on Alcohol and Alcoholism (NIAAA). Rethinking drinking. NIH Publication No. 15-3770. 2016. Revised May 2016. 77. Hester RK, Miller WR.  Handbook of alcoholism treatment approaches. 2nd ed. Boston: Allyn and Bacon; 1995. 78. McLennan JD, Shaw E, Shema SJ, Gardner WP, Pope SK, Kelleher KJ.  Adolescents’ insight in heavy drinking. J Adolesc Health. 1998;22(5):409–16. 79. National Institute of Alcohol Abuse and Alcoholism. Recommended alcohol questions. Retrieved from https://www.niaaa.nih.gov/ research/guidelines-and-resources/recommended-alcohol-questions. Accessed 5/12/2018. 80. Miller WR, Leckman AL, Delaney HD, Tinkom M.  Long-term follow-up of behavioral self-control training. J Stud Alcohol. 1992;53(3):249–61. 81. Walters G.  Behavioral self-control training for problem drink ers: a meta-analysis of randomized control studies. Behav Ther. 2000;31(1):135–49.

Part II Special Population Considerations

3

Depression and Chronic Medical Illness: New Treatment Approaches Trina E. Chang and Sean D. Boyden

Case Vignette

Patient NE was a 59-year-old separated Puerto Rican woman with a past psychiatric history of depression and post-traumatic stress disorder and a past medical history notable for diabetes, high cholesterol, hypertension, and gastroesophageal reflux disease. On evaluation for a behavior change group, she endorsed all nine neurovegetative symptoms of depression; her longtime psychiatrist noted that NE had never experienced significant improvement in mood even with antidepressants. In addition, her hemoglobin A1C (a measure of blood glucose) prior to starting the group was 9.1%, signifying poorly controlled diabetes, and her total cholesterol level was 194 mg/dL. Over the course of the group, which included elements of support, motivational interviewing, problem-­solving treatment, and information, NE discussed both psychiatric and medical issues. She noted a connection between her mood and her diabetes control, with her blood glucose levels being higher on days when she felt stressed, down, or anxious. She also reported issues with maintaining her motivation to eat healthfully and exercise, particularly when stressed about caring for her grandchildren with autism spectrum disorders. In one session, she also

T. E. Chang (*) ∙ S. D. Boyden Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]; [email protected]

discussed her childhood traumas around growing up with physically and emotionally abusive parents. After 10 weeks of participating in the group, as well as some improvement in stressors related to home and family, both NE’s mood and health symptoms had improved significantly. She reported feeling more hopeful and more motivated to take care of herself; likewise, her outpatient psychiatrist felt that this was the first time NE had seemed to improve in more than 20  years of treatment. Her A1C had decreased to 7.9%, which while still poorly controlled represented a clinically significant improvement, and her total cholesterol to 140 mg/dL.

Introduction/Definition Depression is frequently comorbid with medical illnesses, but mental health treaters often do not take these comorbidities or their effect on illness course into account in treatment planning, beyond adjustment of medications to avoid interactions and manage side effects (either limiting unwanted effects or taking advantage of helpful ones). This could represent a missed opportunity in several ways. First, because the comorbid physical condition may be a stressor or a cause of depressive symptoms for the patient, addressing the physical issues through medical treatment or psychotherapy may alleviate the severity of depression. Second, since certain interventions that may alleviate depression (such as exercise) are frequently beneficial for the comorbid illness as well, there could be synergy from choosing those interventions. Finally, because depressive symptoms could worsen the course of the medical illness (e.g., poor motivation, energy, and concentration that contribute to worse adherence to treatment for the comorbid illness), targeting those symptoms could lead to improvement in medical outcomes.

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_3

33

34

For these reasons, interest in interventions specifically designed for depression in the medically ill is rising. In this section, we will describe emerging treatments that target both the depression and the medical illness in this population.

History The bidirectional relationship between depression and many medical illnesses has been well established. Higher rates of depression are seen with many medical illnesses, from cancer to stroke to HIV. For example, as many as one-third of people who suffer a myocardial infarction [1] will subsequently develop depression. The prevalence of depression is twice as high in those with diabetes as in those without [2], not including subthreshold depressive symptoms secondary to the additional burden of diabetes-related distress [3]. In HIV-infected individuals, depression is three times as prevalent [4], and among cancer patients, the prevalence of depression is up to five times higher than in the general population [5]. Conversely, depression appears to be associated with a higher risk of developing certain medical illnesses. A 2011 meta-analysis found that depression was associated with a 45% increased risk for stroke, a 25% increased risk for ischemic stroke, and a 55% increased risk for fatal stroke [6]. Another 2016 meta-analysis calculated a pooled adjusted hazard ratio of 1.31 for having a myocardial infarction in those with depression compared to those without [7]. To complicate the picture, some depression treatments may raise the risk of chronic medical illness as well by contributing to weight gain or metabolic syndrome (as in the case of second-generation antipsychotics) [8, 9]. Some of this comorbidity might be explained by how depression interferes with self-care and treatment adherence, presumably through neurovegetative symptoms such as diminished motivation, energy, and concentration. Medication nonadherence is one example: A meta-analysis of 31 studies including 18,245 participants with depression and a chronic medical illness (e.g., coronary heart disease, diabetes, hyperlipidemia, and hypertension) found that the odds of medication nonadherence in patients with depression were almost double those of nonadherence in nondepressed patients [10]. Attendance at medical appointments is another aspect of treatment adherence that may be affected; in an observational study of adults with HIV, spanning nearly a decade, the more time that a person with HIV spent depressed, the more likely the individual was to miss scheduled appointments (as well as to have a detectable viral load in the blood—or to die) [11]. Unsurprisingly, self-care also suffers in people with depression and a comorbid medical illness—with a decrease in healthy eating and exercise in particular [12, 13]. There is also speculation that common pathophysiological processes may underlie the comorbidity between depression and certain chronic medical illnesses. One example

T. E. Chang and S. D. Boyden

involves the hypothalamic-pituitary-adrenal axis, which is involved in the body’s response to stress. It shows evidence of dysregulation in as many as half of people with major depressive disorder [14, 15]; HPA disruptions also have been seen in a number of chronic medical conditions, including diabetes, increased metabolic risk factors [16], chronic fatigue, and possibly even obesity [17]. Another example is inflammation. People with depression exhibit many biological markers of an inflammatory response, including elevated levels of C-reactive protein, tumor necrosis factor, and several others. Some studies have found that administering certain pro-inflammatory agents can lead to depressive symptoms, while other research indicates that anti-­ inflammatory agents may help reduce depressive symptoms in patients with certain medical illnesses. Furthermore, elevated levels of inflammation have been associated with poorer response to antidepressant treatments [18] (see Chaps. 8 and 15). Similarly, inflammation is a feature of several chronic medical illnesses, such as cardiovascular diseases and diabetes, leading some scientists to hypothesize that there are common mechanisms linking stress, depression, and these chronic medical illnesses [19]. When depression co-occurs with a chronic medical illness, higher medical utilization and higher costs result. For example, research has found that people who have a chronic medical illness plus comorbid depression make more visits to primary care, specialty care, and emergency departments and are more likely to be hospitalized medically [20, 21]. Several analyses have found higher ambulatory and inpatient costs in those with depression, only a small portion of which were due to increased mental health service utilization [22– 24]. Among Medicaid beneficiaries, those with a chronic physical condition plus a mental health issue other than substance use disorder incurred total medical costs 60–75% higher than the costs of those with similar medical illnesses but no mental health comorbidity [25]. For these reasons, finding ways to reduce the disease burden in people with a chronic medical illness plus comorbid depression has special public health importance. Rather than treat the depression and medical illness(es) separately, some newer treatment approaches are focusing expressly on some aspect of the comorbidity, with the ultimate goal of promoting better outcomes. Here, we discuss recent research on psychotherapy-based approaches and systems-based approaches to depression comorbid with medical illness.

New Advances and Research Support  ognitive Behavior Therapy for Adherence C and Depression (CBT-AD) One approach with strong research evidence involves combining cognitive behavior therapy with additional support

3  Depression and Chronic Medical Illness: New Treatment Approaches

35

for medication adherence (Cognitive Behavior Therapy for Adherence and Depression (CBT-AD)). This approach was originally developed for a patient population with HIV and depression and has been extended to diabetes plus depression as well. It consists of 6 modules that are intended to take about 10–12 sessions to complete: medication adherence, psychoeducation/motivational interviewing, activity scheduling, adaptive thinking (using CBT skills to address depressive thoughts, some of which may relate to the person’s medical illness), problem-solving, and relaxation training. The content of each of the modules is delineated below [26].

A substantial body of evidence supports the efficacy of CBT-AD for HIV and diabetes. In patients with HIV, for whom the intervention was originally developed, initial research found that intervention patients showed a significant improvement in depression and treatment adherence at 3 months compared to those assigned to enhanced treatment as usual; these gains were generally maintained at 6- and 12-month follow-ups. In this crossover study, control subjects also showed an improvement after receiving the intervention. Additionally, patients who were originally assigned to the intervention showed a decrease in plasma HIV RNA levels at the final follow-up [27]. In a subsequent three-arm study comparing CBT-AD with adherence counseling plus Life Steps (or Medication Adherence)  In this single-­ supportive therapy (ISP-AD) and enhanced treatment as session intervention, the therapist first provides the patient usual (ETAU), patients receiving CBT-AD showed improvewith information on the importance of medication adher- ment in depression and adherence compared to ence in this illness. The patient then articulates goals around ETAU.  Patients receiving ISP-AD also showed improvemedication adherence, identifies potential barriers, and ment, leading the authors to speculate that combining their makes a plan and a backup plan for improving adherence CBT-based adherence intervention (Life Steps) with what(the AIM framework). The therapist may use techniques ever psychotherapy a patient is receiving for depression from cognitive behavior therapy and problem-solving treat- could be effective [28]. ment to help the patient think about the goals, consider how Similarly, a study of patients with uncontrolled type 2 diato overcome barriers, and maximize the likelihood of suc- betes found that the intervention was associated with cess with the plan. improved adherence to medications and self-monitored blood glucose testing, decreased depressive symptoms, and Psychoeducation and Motivational Interviewing  In this better measures of blood glucose control (hemoglobin A1C) module, the therapist introduces the patient to the CBT levels compared to enhanced treatment as usual [29]. A pilot model, particularly as it applies to the depression and the suggested feasibility, acceptability, and effectiveness for patient’s medical illness, and uses motivational interviewing psychological and physical measures in patients with type 1 techniques to enhance the person’s motivation to change and diabetes as well [30]. practice the skills learned in treatment. Activity Scheduling  This module teaches behavioral activation skills, with particular attention to the intersection between depression and the comorbid medical illness—ways in which complications might affect behavioral activation plans, for example, or activities that could be beneficial for both illnesses. Adaptive Thinking  Over the course of five or so sessions, the patient and therapist apply CBT principles and skills to address the depression and the comorbid medical illness. Problem-Solving  This module teaches basic skills of problem-­solving, such as defining the problem, setting a goal, brainstorming options, evaluating those ideas, creating an action plan, and reviewing progress after putting the plan into place. It can be applied to the depression, the comorbid medical illness, or other related issues in the patient’s life. Relaxation Training  Techniques such as progressive muscle relaxation and deep breathing are used to help patients cope with stress and manage physical symptoms of their illness.

Problem-Solving Treatment + Motivational Interviewing Another possible strategy is to use psychotherapeutic techniques to improve overall illness self-management, drawing upon psychotherapies that also have evidence for treating depression. Our work has focused on combining elements of problem-solving treatment, information, support, and motivation (PRISM) in a group format to facilitate behavior change including better treatment adherence in patients with chronic medical illness, drawing on the information-­ motivation-­behavioral skills model of behavior change [31]. Problem-solving treatment (PST) has a solid evidence base for its effectiveness for treating depression [32], as does group psychotherapy [33]. Given that problem-solving treatment and motivational interviewing share some common features, such as a preponderance of “change” talk and an emphasis on patient (or client)-driven solutions, it is not a large conceptual leap to integrate the two. In fact, a study in Holland did attempt to use problem-solving treatment plus motivational interviewing in a prevention trial aimed at reducing the risk of developing type 2 diabetes or dying from

36

cardiovascular disease in patients at risk for those outcomes. It did not find a positive effect on risk, unfortunately, but whether it could have affected mood is unknown because the study did not target depressed patients or report on depressive symptoms [34]. Our PRISM-D intervention for diabetes consists of six 2-week modules, each covering a topic from the AADE7 self-care behaviors for managing diabetes [35]: eating healthfully, being active, monitoring, taking medication, reducing risk of complications, and coping. Problem-solving, another one of the AADE7 behaviors, is woven into the framework of the intervention, as each module goes through the steps of PST as adapted for use in primary care settings [36]. The first session of each module includes a general discussion of the topic, using motivational interviewing techniques to elicit participants’ experience and change talk, with the goal of helping participants define a key problem in that area by the end of the session and set a goal (two of the first steps of PST). The second session of each module takes participants through the remaining problem-solving steps of brainstorming potential solutions, analyzing pros and cons, deciding on a solution to try, and creating an action plan. Group co-leaders use techniques from group psychotherapy to manage group dynamics and discussion; while they do not review the diabetes self-management education curriculum, they may provide information if participants ask or if participants express misconceptions about diabetes and its management. In pilot studies, we have provided this intervention to disadvantaged populations in a community setting, such as Spanish-speaking patients with poorly controlled diabetes. Subjects who received the intervention experienced a significant decrease in a measure of diabetes-related distress (the Problem Areas in Diabetes scale) compared to the control patients.

Lifestyle Interventions Another category of interventions combines a psychosocial component with a lifestyle component, such as weight loss or exercise. Depression can include some somatic symptoms, such as changes in weight and energy level, and some of its treatments may have adverse side effects on weight and other metabolic measures. Thus addressing these symptoms can target mental and physical health simultaneously. Exercise alone has been associated with improvement in mood. For example, in the HF-ACTION trial, subjects with heart failure who were randomized to supervised aerobic exercise experienced a modest improvement in depressive symptoms and were also less likely to die or be hospitalized during the study follow-up, compared to patients receiving education and usual care [37]. In another study comparing exercise to an antidepressant (sertraline) or placebo, depres-

T. E. Chang and S. D. Boyden

sion remission rates were comparable in the groups receiving the antidepressant or supervised exercise [38]. Findings like these led the Canadian Network for Mood and Anxiety Treatments to include exercise as one of its first-line treatments for mild to moderate depression and as a second-line adjunctive treatment for moderate to severe depression [39]. That said, a Cochrane review on the effect of exercise on depression noted that the effect was smaller in the high-­ quality trials it reviewed [40], suggesting that more rigorous research is needed. Along similar lines, some research has investigated whether weight loss interventions could improve mood. The Look AHEAD trial of an intensive lifestyle intervention for weight loss in more than 5000 patients with obesity and type 2 diabetes found a lower incidence of depressive symptoms of at least mild severity over the course of the study (median follow-up = 9.6 years) in the patients receiving the lifestyle intervention compared to those receiving diabetes support and education [41]. Whether a lifestyle intervention combined with a psychological treatment is more helpful than either one alone has been less well studied [42]. In one 12-week study of patients with NYHA Class II or III heart failure plus depression, subjects were randomized to exercise alone, CBT alone, exercise plus CBT (EX/CBT), or usual care. While results did not reach statistical significance, the greatest improvement in depression scores was seen in the EX/CBT group. Among the patients with moderate to severe depression, only those in the EX/CBT arm showed improvement in their performance on a measure of physical function or in depression scores at 12 and 24 weeks; they also showed the greatest improvement in health-related quality of life [43]. On the other hand, in a recent study of patients with diabetes, preliminary data indicated that CBT alone, exercise alone, and a combination of CBT plus exercise were each associated with statistically significant improvement in depressive symptoms and diabetes distress (“significant negative emotional reactions to the diagnosis of diabetes, threat of complications, self-management demands, unresponsive providers, and/or unsupportive interpersonal relationships” [3]) compared to usual care, among other findings. Exercise alone was also associated with clinically significant improvements in glycemic control among subjects with poorly controlled diabetes (A1C >= 7.0%). Patients undergoing only CBT or exercise treatment alone experienced higher rates of depression remission compared to patients receiving usual care, but the improvement did not reach statistical significance in the patients undergoing the combination treatment. That said, combination treatment and exercise alone were associated with a significant improvement in diabetes-­ specific quality of life [44]. Schneider et  al. have piloted an intervention that combines exercise with behavioral activation (a technique from cognitive behavior therapy that has been found to have

3  Depression and Chronic Medical Illness: New Treatment Approaches

benefits for depression) in a group format for patients with diabetes. Subjects found the intervention acceptable, and intervention subjects did report greater exercise enjoyment, but there was no significant difference between intervention and control arms in depressive symptoms, glycemic control or physical activity [45]. In the area of obesity comorbid with moderate to severe depression, one study in the Pacific Northwest compared a behavioral weight loss intervention alone with a combination of the same weight loss intervention plus cognitive behavior treatment. Both study groups experienced modest weight loss and depression improvement, but there was no significant difference by treatment arm [46]. Another trial named “Be Active” took a serial approach, evaluating the effect of providing a brief behavioral treatment for depression followed by a lifestyle weight loss intervention, with the rationale that it was necessary to treat the depression first before trying to promote weight loss. While the two study groups did not differ significantly in terms of weight loss at 6 months, the group that received the depression treatment did show greater improvement in depression symptoms. Furthermore, those whose depression remitted lost more weight than those whose depression did not remit [47]. In summary, these studies have generally suggested some benefit for depression, though it is not clear that combination treatment with a psychological intervention plus a lifestyle intervention is superior to either one alone. Additional studies in this area are in progress, such as a study combining problem-solving treatment for depression plus behavioral interventions for weight control for patients with obesity and depression (the RAINBOW intervention (“Research aimed at improving both mood and weight”)) [48] and another study of a Nutrition, Exercise, and Wellness Treatment (NEW Tx) for decreasing cardiovascular disease risk factors in patients with bipolar disorder [49, 50].

Collaborative Care Interventions Building upon the success of care redesign efforts that have led to clinical improvement and cost savings in depressed primary care patients, a number of researchers have begun to study the use of similar models in the medically ill. In the collaborative care model for depression, primary care patients with depression can be managed in primary care settings with the help of a behavioral health care manager and a psychiatric consultant. The psychiatric consultant does not see the patient directly but rather reviews the patient’s case with the care manager and provides guidance to the primary care provider. The primary care provider retains responsibility for writing prescriptions for antidepressant medications and making appropriate referrals. The behavioral health care manager plays a crucial role, not only in acting as a liaison between the patient, psychiatric consul-

37

tant, and primary care provider but also in working directly with the patient to assess symptom severity, treatment adherence and response, and other needs and to coach the patient in self-management interventions such as behavioral activation. The care manager also facilitates population health management by tracking who is not responding and therefore might benefit from a treatment change as well as who has been lost to follow-up. Patients who do not improve with primary care management may be “stepped up” to specialty management as indicated. The seminal model for this kind of care is the IMPACT model (from the trial Improving Mood— Promoting Access to Care and Treatment [51]), though there are a number of variations that fall under the general rubric of “collaborative care.” This model of care has amassed a significant body of evidence of clinical and cost-effectiveness. A Cochrane review of 79 studies established that this intervention is associated with clinically significant improvement in depression remission and response rates [52]. Furthermore, when total medical expenditures including all inpatient and outpatient visits and pharmacotherapy are considered, collaborative care appears to save money compared to usual care; in one of the original studies, patients who received the intervention over 1 year incurred $3363 less in total medical expenditures over 4 years compared to patients in usual care [53]. These positive results have led both to the widespread dissemination of this model and the expansion of research on it. Organizations across the country have implemented this model on a wide scale, and Medicare began reimbursing for this care starting in January 2017. Furthermore, other investigators have tested the applicability of similar care models for the management of other psychiatric illnesses, such as PTSD and bipolar disorder. Perhaps not surprisingly, researchers have begun to apply this care model for the management of people with a behavioral health condition and a comorbid medical condition, such as prediabetes or diabetes, hypertension, or cancer. Some models address depression in patients with a specific medical illness; others take a serial approach of first addressing the depression and then adding components to manage the medical illness. Regardless, evidence suggests that collaborative care is equally effective for depression in patients with or without a medical comorbidity [54].

Depression and Cancer Subgroup analyses from the original IMPACT trial suggested that the intervention led to improvement in depression in its subjects with cancer [55], raising interest in trials targeted specifically to this population. Some of the subsequent trials included Depression Care for People with Cancer from the Symptom Management Research Trials group (SMaRT) [56] and ADAPt-C for patients at oncology

38

clinics treating low-income populations [57, 58]. A 2017 meta-analysis of psychological, pharmacological, or collaborative care interventions for depression in patients with cancer found that collaborative care (the focus of four trials in the review) outperformed usual care in terms of reducing depression, and benefits were maintained at 12 months. By contrast, the studies of psychological or pharmacological treatment that were included in the meta-analysis found depressive improvement only in the short term but not in the long term [59]. As for medical outcomes, the SMaRT trial found greater improvement in depression, anxiety, and fatigue [60–62] in its intervention patients and showed evidence of cost-effectiveness [63].

Depression and Cardiovascular Disease A number of studies have concentrated on patients with cardiovascular disease at the inpatient or outpatient level: Bypassing the Blues following coronary artery bypass surgery; the Coronary Psychosocial Evaluation Study (COPES) and the Comparison of Depression Interventions after Acute Coronary Syndrome (CODIACS), both for patients after acute coronary syndrome; and the Screening Utilization and Collaborative Care for more Effective and Efficient treatment of Depression study (SUCCEED) and the Management of Sadness and Anxiety in Cardiology (MOSAIC) trial for cardiac inpatients [64]. A meta-analysis of these trials plus another one that focused on coronary heart disease plus diabetes (described later in this text) found significant improvement in depression severity, depression remission rates, and anxiety symptoms in patients receiving a collaborative care intervention. There were short term benefits in terms of reductions in major adverse cardiac events (MACE), but these benefits were not sustained in the long term [65]. In addition, when one site in the original IMPACT trial revisited its patients some 8–10  years after their original study participation, it found a significantly lower rate of MACE in the patients who did not have cardiovascular disease at baseline [66]. That said, the authors cautioned that interpreting the results of this post hoc analysis should be done with caution and noted that the study did not collect data that would allow an examination of reasons for the apparent cardioprotective effect of the intervention, e.g., whether the effect was depression-dependent, whether it might have been related to antidepressant exposure, etc.

Depression and Diabetes Two meta-analyses of collaborative care interventions for patients with depression though came to slightly different conclusions about blood sugar control. One meta-analysis

T. E. Chang and S. D. Boyden

of seven randomized controlled trials found evidence of improved depression scores and hemoglobin A1C levels (average decrease of 0.33%) [67]. The other meta-analysis, which included eight randomized controlled trials, showed improvement in depression severity, depression remission rates, and adherence to antidepressant medication and oral hypoglycemic agents; however, the improvement in hemoglobin A1C did not reach statistical significance in their analysis [68]. A number of additional studies of collaborative care interventions for this population have been published since those meta-analyses, including several for disadvantaged populations, and have reported similar findings [69, 70]. Cost-effectiveness studies have lent further support to the utility of collaborative care for depression and diabetes. In a cost-effectiveness analysis of the original IMPACT trial, researchers found that the collaborative care for depression was cost-effective within 2 years for its subjects with diabetes [71], compared to 4  years for its sample overall [53]. Over 2  years, total health care costs (inpatient and outpatient) were $896 lower in the intervention group. Furthermore, the incremental cost of each depression-free day was 25 cents, while the incremental cost per quality-adjusted life-­ year gained ranged from $198 to $397. In the Pathways study, which utilized a 12-month collaborative care intervention based on the IMPACT model for patients with depression and diabetes, subjects in the intervention arm incurred nearly $4000 less in total medical costs over 5  years than subjects in the usual care arm [72]. In a more recent trial, a collaborative care intervention for low-income Hispanic patients with diabetes cost an average of $4053 per quality-­ adjusted life-year [73].

 epression Comorbid with Diabetes and/or D Cardiovascular Disease Expanding on the previous research, several groups have applied the depression collaborative care model to patients who have either diabetes or cardiovascular disease or both— a logical extension, given the overlap between risk factors, lifestyle modifications required for each, and biological measures monitored. For example, the researchers behind the Pathways study for diabetes took the serial approach when adapting their intervention for diabetes/cardiovascular disease; they started with a treatment phase targeting depression, then a treatment phase oriented toward controlling disease targets such as controlling blood sugar and cholesterol, and then a wellness phase promoting healthy habits (such as exercise) [74]. A 12-month randomized controlled trial of this program, also known as the TEAMcare model, in patients with depression and comorbid diabetes and/or cardiovascular disease found greater improvement in

3  Depression and Chronic Medical Illness: New Treatment Approaches

depression scores, A1C scores, and other health measures. Additionally, disease management appeared to be more intense in the intervention group, with higher rates of initiation and adjustment of antidepressants, insulin, and antihypertensive medications, as well as more frequent monitoring of blood glucose levels and blood pressure [75]. Although only the improvement in depression was sustained at 18 and 24  months (i.e., 6 and 12  months after the intervention ended), 2-year cost-effectiveness analyses nevertheless found an overall decrease of $594  in total outpatient costs compared to usual care and an estimated $3297 cost per quality-adjusted life-year [76]. On the other side of the Atlantic, the COINCIDE study in England compared a collaborative care intervention vs. usual care for patients with depression and diabetes/coronary heart disease. Intervention subjects were offered case management plus a choice of low-intensity psychological treatments based on cognitive and behavioral therapies [77]. Compared to usual care, the intervention was associated with somewhat greater improvement in depression scores and better disease self-management on a self-reported questionnaire, but not with changes in disability scores or disease-related quality of life [78]. Cost-effectiveness analyses estimated that the cost per quality-adjusted life-year gained over a 24-month period was £16,123, which the authors noted was within the £20,000 threshold recommended by English decision-makers when considering whether an intervention is a good use of National Health Service resources [79, 80]. Finally, the COMPASS (Care of Mental, Physical, and Substance Use Syndromes) initiative applied a similar approach in a large-scale, multistate implementation project using collaborative care for patients with depression and diabetes or cardiovascular disease [81]. While it was a dissemination and implementation project rather than a randomized controlled trial, it did collect data on disease-related outcomes such as HbA1C as well as patient and provider satisfaction. Over a mean of 11  months of follow-up, they reported, 40% of patients achieved depression remission, 23% glucose control, and 58% blood pressure control; both patients and providers reported high satisfaction with the intervention. They did note large site-by-site variation in outcomes, however, possibly indicating differences in implementation [82].

 linical Applications and Recommendations C for Practitioners The most important conclusion from this review is that medical illnesses that co-occur with depression are more than something to record in the history section of the chart and consider mainly when selecting medications—they can be an important treatment target for mental/behavioral health

39

providers. Thus, if you are working with a patient with depression and a comorbid medical illness, inquire about how your patient’s depression affects and in turn is affected by his or her medical comorbidities, keeping the following treatment considerations in mind: • Could the prospect of addressing the comorbidity be useful for engaging the patient in depression treatment and increasing motivation to adhere to the treatment plan? For example, a patient who believes the depression is a subsidiary concern to other health issues might be more motivated to address it if informed that some research supports a serial approach of treating the depression first and then addressing other disease control targets. Patients also might be more interested in depression treatment if they learn that some of these treatments may improve psychiatric and medical outcomes simultaneously or if they understand how the depression might interfere with their ability to follow through on their medical treatment plan. • If the depression is affecting management of the medical illness, how might this affect depression treatment priorities in turn? Knowing whether a patient’s ability to adhere to medical treatments is being affected by poor energy vs. forgetfulness vs. disrupted sleep vs. low motivation or pessimism about treatment might change your evaluation of which symptoms to target first or medication side effects to avoid. • Are there common treatment goals for the medical and mental illnesses? For example, goals around healthy eating or increased activity could be the focus of a problem-­ solving exercise. Or medical self-care goals such as monitoring blood sugar or blood pressure could be incorporated into behavioral activation plans. • Can psychotherapeutic techniques be applied to improve the management of the medical illness and/or promote coping skills? Motivational interviewing is one obvious example, but as discussed earlier, techniques from cognitive behavioral therapy could be used to increase treatment adherence, while problem-solving therapy could be applied to overcoming barriers to disease management. • Could it be useful to address any distress due to the medical condition? Patients may not think to bring up concerns related to the medical illness in a psychiatric visit—for example, feeling scared when they think about living with diabetes or feeling that diabetes takes up too much of their mental or physical energy. Both of these problems appear in questionnaires about diabetes-related distress but would not be part of a standard depression assessment. While diabetes-related distress is indeed recognized as a separate phenomenon from major depression [3], there is evidence that greater reduction of diabetes-related distress predicts greater reduction of depressive symptoms [83]. So certainly for diabetes, and likely for other chronic

40

T. E. Chang and S. D. Boyden

medical illnesses, it may be worthwhile to ask specifically about distress related to the medical comorbidity and to intervene accordingly. There are well-validated scales available online that can provide ideas for questions to ask, such as the Diabetes Distress Scale [84] and the Problem Areas in Diabetes Survey [85] and the Distress Thermometer [86] for cancer. Trying such approaches to incorporate this dual focus into depression treatment could bring benefits for patients, providers and systems alike through better treatment alliance and satisfaction, increased treatment adherence, and overall improved clinical and cost outcomes.

FAQs: Common Questions and Answers Q1. No one in my referral network provides these psychotherapies. How can my patients take advantage of these approaches? A1. While many community providers are unlikely to be using these exact interventions or combinations of psychotherapies, most of which are still predominantly used in research, many therapists have some training in the elements or approaches that make up these treatments. For example, cognitive behavior therapists are skilled in teaching problem-solving, activity planning, and relaxation exercises. For them, delivering an intervention based on CBT-AD might mean devoting one session specifically to medication adherence, perhaps incorporating the elements of the Life Steps module from CBT-AD [26]. An increasing number of psychiatrists have training in CBT, which is now a required component of residency training, and they can incorporate brief CBT to enhance medication adherence into medication visits [87]. Providers can also seek out opportunities to learn other techniques that were taught only briefly or not at all in their training. The Motivational Interviewing Network of Trainers maintains a listing of MI trainers and a calendar of training events (http://www.motivationalinterviewing.org/motivational-interviewing-training) [88] as well as a library of resources on its website. Problem-solving treatment has been taught effectively to health care workers who are not trained psychotherapists, from family medicine residents to registered nurses to medical assistants. The AIMS Center at the University of Washington offers information about becoming certified in problem-solving treatment [89]. They estimate that the training takes about 17–22  h (including didactics and case review), much of which can be done by telephone, in addition to time spent working with an active caseload of PST patients. The

National Network of PST Clinicians, Trainers, & Researchers has developed a manual on PST, available online at http://pstnetwork.ucsf.edu/sites/pstnetwork. ucsf.edu/files/documents/Pst-PC%20Manual.pdf [90]. Q2.  Collaborative care sounds like something that takes place entirely in the primary care or medical specialty world. What does it mean for me as a mental health specialist? A2. While collaborative care is indeed delivered in nonpsychiatric settings, there are several points of interface with specialty mental health. First, mental health specialists should be aware that this model exists and that some of their patients could be seen in practices where this kind of care is available. For therapists, it could mean that if you have a patient who you think should be considering psychotropic medications, you can work with the primary care office and collaborative care team to consider this possibility with the patient and perhaps prescribe the medications, rather than automatically refer to a psychiatric prescriber. In addition, your patient may have access to a care manager who is trained to deliver some basic interventions such as behavioral activation. In this case, you’ll want to coordinate treatments so that you do not repeat each other’s efforts, and you may be able to concentrate on other aspects of the therapy while the care manager helps the patient with treatment adherence and sleep hygiene. If you are a prescriber, you could interface with the collaborative care team in several ways. You may see patients who were originally treated by the collaborative care team, in which case you will know that treatment was reviewed by a psychiatric consultant and can talk with the care manager to get collateral information. The care manager can act as a liaison with primary care, which may be particularly useful when you are treating a patient with complex medical issues and want to make sure medical and psychiatric treatments are coordinated. When the patient is stable from a psychiatric perspective, you may be able to work with the collaborative care team to transition the patient’s care and maintenance medications back to primary care management. Some mental health specialists may be interested in taking on a role within integrated care settings. One possibility is to become the psychiatric consultant for collaborative care teams. The American Psychiatric Association offers free live or online trainings in serving as a collaborative care psychiatric consultant (https://www.psychiatry.org/ psychiatrists/practice/professional-interests/integratedcare/get-trained) [91]. Another way to become involved is to work as the embedded behavioral health specialist in clinics that have adopted an integrated care model. Yet a third possibility is to serve more indirectly as trainers or supervisors for care managers who are learning to deliver basic behavioral interventions as part of their role.

3  Depression and Chronic Medical Illness: New Treatment Approaches

Some psychiatric settings are flipping the model on its head and implementing so-called reverse integration. In this model, patients whose mental health issues are their most significant health problem and who feel more closely linked with their psychiatric providers may stay primarily within their mental health clinics and receive primary care from internists or family practitioners who are embedded in the psychiatric clinic. Regardless of your direct involvement or overlap with collaborative care teams, it is important to be aware of this movement within psychiatry. It mirrors the growing shift toward chronic disease management within medicine as a whole. It is intended to help the health care system by improving access of all patients to specialist expertise, growing primary care capacity for managing basic behavioral health issues, and thereby freeing up the limited pool of mental health specialists to manage the more severe, complex, and/or chronic cases. It reflects a greater awareness that better mental health contributes to better overall health and therefore should be prioritized and funded by the health care system. Q3. Although I am not working in an integrated care setting, are there any lessons from collaborative care that could apply to my patients? A3. Patel and colleagues identified the key elements for successful collaborative care of depression, some of which could be adopted in outpatient behavioral health treatment [92]: • Support of self-care activities. This includes education about the illness and treatment, self-monitoring, and adherence. • Care management to monitor adherence, side effects, treatment response, and the course of care following evidence-based guidelines. • Treatment to target. This includes systematic monitoring of symptom severity (generally using validated depression symptom scales such as the nine-item Patient Health Questionnaire) and treatment adjustment as needed, following evidence-based treatment algorithms. • Systematic caseload review with a specialist. • Use of a registry to track cases and clinical outcomes and facilitate information sharing across the team. • Use of evidence-based intervention strategies appropriate to the team members’ skill levels. Support of self-care and treatment to target are the two elements that could most easily be incorporated into any specialty mental health practice. Clinics may be able to develop a registry, with or without a collaborative care team. Q4. My health care system is interested in adopting the collaborative care model. How would we go about implementing it?

41

A4. The AIMS (Advancing Integrated Mental Health Solutions) Center at the University of Washington (https://aims.uw. edu/) [93] offers an implementation guide and many other resources to support collaborative care implementation. Q5. How can we finance such a model? A5. While supporting the salaries for care managers and specialists for caseload review generally requires a larger system-wide commitment, the good news is that now that collaborative care is a billable service for Medicare, it is closer to being financially self-sustainable. In addition, health care systems that participate in risk contracts with insurers may have a reason to invest in starting collaborative care models: They are typically incentivized to keep the growth in health care costs down, and the research on collaborative care indicates they could save on overall health care costs with such a program. Some insurers, foundations, or even state governments may offer grants or seed funding specifically for collaborative care or more generally for programs that help health care delivery systems become accountable care organizations and/or improve behavioral health management. Acknowledgments Research reported in this publication was supported by the National Institute of Diabetes And Digestive and Kidney Diseases of the National Institutes of Health under Award Number K23DK097356. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References 1. Strik JJ, Lousberg R, Cheriex EC, Honig A.  One year cumulative incidence of depression following myocardial infarction and impact on cardiac outcome. J Psychosom Res. 2004;56(1):59–66. 2. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-­ analysis. Diabetes Care. 2001;24(6):1069–78. 3. Gonzalez JS, Fisher L, Polonsky WH. Depression in diabetes: have we been missing something important? Diabetes Care. 2011;34(1):236–9. 4. Do AN, Rosenberg ES, Sullivan PS, Beer L, Strine TW, Schulden JD, et al. Excess burden of depression among HIV-infected persons receiving medical care in the United States: data from the medical monitoring project and the behavioral risk factor surveillance system. PLoS One. 2014;9(3):e92842. 5. Hartung TJ, Brähler E, Faller H, Härter M, Hinz A, Johansen C, et  al. The risk of being depressed is significantly higher in cancer patients than in the general population: prevalence and severity of depressive symptoms across major cancer types. Eur J Cancer. 2017;72:46–53. 6. Pan A, Sun Q, Okereke OI, Rexrode KM, Hu FB. Depression and risk of stroke morbidity and mortality: a meta-analysis and systematic review. JAMA. 2011;306(11):1241–9. 7. Wu Q, Kling JM. Depression and the risk of myocardial infarction and coronary death: a meta-analysis of prospective cohort studies. Medicine. 2016;95(6):e2815. Xie W, ed. 8. Newcomer JW.  Second-generation (atypical) antipsychotics and metabolic effects: a comprehensive literature review. CNS Drugs. 2005;19(Suppl 1):1–93.

42 9. Rummel-Kluge C, Komossa K, Schwarz S, et  al. Head-to-head comparisons of metabolic side effects of second generation antipsychotics in the treatment of schizophrenia: a systematic review and meta-analysis. Schizophr Res. 2010;123(2–3):225–33. 10. Grenard JL, Munjas BA, Adams JL, et al. Depression and medication adherence in the treatment of chronic diseases in the United States: a meta-analysis. J Gen Int Med. 2011;26(10):1175–82. 11. Pence BW, Mills JC, Bengtson AM, et al. Association of increased chronicity of depression with HIV appointment attendance, treatment failure, and mortality among HIV-infected adults in the United States. JAMA Psychiatry published online February 21, 2018. https://doi.org/10.1001/jamapsychiatry.2017.4726. 12. Lin EH, Katon W, Von Korff M, Rutter C, Simon GE, Oliver M, et  al. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care. 2004;27(9): 2154–60. 13. Prugger C, Wellmann J, Heidrich J, De Bacquer D, De Smedt D, De Backer G, EUROASPIRE Study Group, et al. Regular exercise behaviour and intention and symptoms of anxiety and depression in coronary heart disease patients across Europe: results from the EUROASPIRE III survey. Eur J Prev Cardiol. 2017;24(1):84–91. 14. Varghese FP, Brown ES.  The hypothalamic-pituitary-adrenal axis in major depressive disorder: a brief primer for primary care physicians. Prim Care Companion J Clin Psychiatry. 2001;3(4):151–5. 15. Naughton M, Dinan TG, Scott LV.  Corticotropin-releasing hormone and the hypothalamic-pituitary-adrenal axis in psychiatric disease. Handb Clin Neurol. 2014;124:69–91. 16. Champaneri S, Wand GS, Malhotra SS, Casagrande SS, Golden SH.  Biological basis of depression in adults with diabetes. Curr Diab Rep. 2010;10(6):396–405. 17. Incollingo Rodriguez AC, Epel ES, White ML, Standen EC, Seckl JR, Tomiyama AJ.  Hypothalamic-pituitary-adrenal axis dysregulation and cortisol activity in obesity: a systematic review. Psychoneuroendocrinology. 2015;62:301–18. 18. Miller AH, Raison CL.  The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16(1):22–34. 19. Joseph JJ, Golden SH. Cortisol dysregulation: the bidirectional link between stress, depression, and type 2 diabetes mellitus. Ann N Y Acad Sci. 2017;1391(1):20–34. 20. Yoon J, Yano EM, Altman L, Cordasco KM, Stockdale SE, Chow A, et al. Reducing costs of acute care for ambulatory care-sensitive medical conditions: the central roles of comorbid mental illness. Med Care. 2012;50(8):705–13. 21. Himelhoch S, Weller WE, Wu AW, Anderson GF, Cooper LA. Chronic medical illness, depression, and use of acute medical services among Medicare beneficiaries. Med Care. 2004;42(6):512–21. 22. Katon WJ, Lin E, Russo J, Unutzer J. Increased medical costs of a population-based sample of depressed elderly patients. Arch Gen Psychiatry. 2003;60(9):897–903. 23. Luppa M, Sikorski C, Motzek T, Konnopka A, König HH, Riedel-­ Heller SG.  Health service utilization and costs of depressive symptoms in late life  – a systematic review. Curr Pharm Des. 2012;18(36):5936–57. 24. Crown WH, Finkelstein S, Berndt ER, Ling D, Poret AW, Rush AJ, Russell JM. The impact of treatment-resistant depression on health care utilization and costs. J Clin Psychiatry. 2002;63(11):963–71. 25. Boyd C, Leff B, Weiss C, Wolff J, Hamblin A, Martin L. Data brief: clarifying multimorbidity patterns to improve targeting and delivery of clinical services for Medicaid populations. Center for Health Care Strategies; 2010. https://www.chcs.org/media/clarifying_multimorbidity_patterns.pdf. 26. Gonzalez JS, Kane NS, Chang TE. Cognitive behavioral therapy for adherence and depression in diabetes. In: Vranceanu AM, Greer JA, Safren SA, editors. The Massachusetts general hospital handbook of behavioral medicine. New York: Springer; 2016. p. 115–37.

T. E. Chang and S. D. Boyden 27. Safren SA, O’Cleirigh C, Tan JY, Raminani SR, Reilly LC, Otto MW, Mayer KH. A randomized controlled trial of cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-­ infected individuals. Health Psychol. 2009;28(1):1–10. 28. Safren SA, Bedoya CA, O’Cleirigh C, Biello KB, Pinkston MM, Stein MD, et al. Cognitive behavioural therapy for adherence and depression in patients with HIV: a three-arm randomised controlled trial. Lancet HIV. 2016;3(11):e529–38. 29. Safren SA, Gonzalez JS, Wexler DJ, Psaros C, Delahanty LM, Blashill AJ, et  al. A randomized controlled trial of cognitive behavioral therapy for adherence and depression (CBT-AD) in patients with uncontrolled type 2 diabetes. Diabetes Care. 2014;37(3):625–33. 30. Markowitz SM, Carper MM, Gonzalez JS, Delahanty LM, Safren SA.  Cognitive-behavioral therapy for the treatment of depression and adherence in patients with type 1 diabetes: pilot data and feasibility. Prim Care Companion CNS Disord. 2012;14(2). pii: PCC.11m01220. 31. Fisher JD, Fisher WA. Changing AIDS-risk behavior. Psychol Bull. 1992;111(3):455–74. 32. Bell AC, D’Zurilla TJ. Problem-solving therapy for depression: a meta-analysis. Clin Psychol Rev. 2009;29(4):348–53. 33. McDermut W, Miller IW, Brown RA. The efficacy of group psychotherapy for depression: a meta-analysis and review of the empirical research. Clin Psychol Sci Pract. 2001;8(1):98–116. 34. Lakerveld J, Bot SD, Chinapaw MJ, et al. Motivational interviewing and problem solving treatment to reduce type 2 diabetes and cardiovascular disease risk in real life: a randomized controlled trial. Int J Behav Nutr Phys Act. 2013;10:47. 35. AADE7 Self-Care Behaviors. Diabetes Educ. 2008;34:445–9. 36. Arean P, Hegel M, Vannoy S, Fan MY, Unuzter J.  Effectiveness of problem-solving therapy for older, primary care patients with depression: results from the IMPACT project. Gerontologist. 2008;48(3):311–23. 37. Blumenthal JA, Babyak MA, O'Connor C, Keteyian S, Landzberg J, et  al. Effects of exercise training on depressive symptoms in patients with chronic heart failure: the HF-ACTION randomized trial. JAMA. 2012;308(5):465–74. 38. Blumenthal JA, Babyak MA, Doraiswamy PM, Watkins L, Hoffman BM, Barbour KA, et  al. Exercise and pharmacotherapy in the treatment of major depressive disorder. Psychosom Med. 2007;69(7):587. 39. Ravindran AV, Balneaves LG, Faulkner G, Ortiz A, McIntosh D, Morehouse RL, CANMAT Depression Work Group, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: section 5. Complementary and alternative medicine treatments. Can J Psychiatr. 2016;61(9):576–87. 40. Cooney GM, Dwan K, Greig CA, Lawlor DA, Rimer J, Waugh FR, et  al. Exercise for depression. Cochrane Database Syst Rev. 2013;12(9):CD004366. 41. Rubin RR, Wadden TA, Bahnson JL, Blackburn GL, Brancati FL, Bray GA, Look AHEAD Research Group, et al. Impact of intensive lifestyle intervention on depression and health-related quality of life in type 2 diabetes: the look AHEAD trial. Diabetes Care. 2014;37(6):1544–53. 42. Hearing CM, Chang WC, Szuhany KL, Deckersbach T, Nierenberg AA, Sylvia LG. Physical exercise for treatment of mood disorders: a critical review. Curr Behav Neurosci Rep. 2016;3(4):350–9. 43. Gary RA, Dunbar SB, Higgins MK, Musselman DL, Smith AL. Combined exercise and cognitive behavioral therapy improves outcomes in patients with heart failure. J Psychosom Res. 2010;69(2):119–31. 44. De Groot M, Hornsby G, Saha C, Yang Z, Pillay Y, Fitzpatrick K, Mather KJ, Shubrook J.  Program ACTIVE II: a comparative effectiveness trial to treat major depression in T2DM.  Presented

3  Depression and Chronic Medical Illness: New Treatment Approaches at the American Diabetes Association 77th Scientific Session, June 13, 2017, San Diego, CA. http://www.abstractsonline.com/ pp8/#!/4297/presentation/12748. 45. Schneider KL, Panza E, Handschin B, Ma Y, Busch AM, Waring ME, et al. Feasibility of pairing behavioral activation with exercise for women with type 2 diabetes and depression: the Get It study pilot randomized controlled trial. Behav Ther. 2016;47(2):198–212. 46. Linde JA, Simon GE, Ludman EJ, Ichikawa LE, Operskalski BH, Arterburn D, et  al. A randomized controlled trial of behavioral weight loss treatment versus combined weight loss/depression treatment among women with comorbid obesity and depression. Ann Behav Med. 2011;41(1):119–30. 47. Pagoto S, Schneider KL, Whited MC, Oleski JL, Merriam P, Appelhans B, et al. Randomized controlled trial of behavioral treatment for comorbid obesity and depression in women: the Be Active Trial. Int J Obes. 2013;37(11):1427–34. 48. Ma J, Yank V, Lv N, Goldhaber-Fiebert JD, Lewis MA, Kramer MK, et  al. Research aimed at improving both mood and weight (RAINBOW) in primary care: a type 1 hybrid design randomized controlled trial. Contemp Clin Trials. 2015;43:260–78. 49. Sylvia LG, Salcedo S, Bernstein EE, Baek JH, Nierenberg AA, Deckersbach T. Nutrition, exercise, and wellness treatment in bipolar disorder: proof of concept for a consolidated intervention. Int J Bipolar Disord. 2013;1:24. 50. https://clinicaltrials.gov/ct2/show/record/NCT01615367. 51. Unützer J, Katon W, Callahan CM, Williams JW Jr, Hunkeler E, Harpole L, et al. Improving mood-promoting access to collaborative treatment. Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA. 2002;288(22):2836–45. 52. Archer J, Bower P, Gilbody S, Lovell K, Richards D, Gask L, Dickens C, Coventry P. Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev. 2012;10:CD006525. 53. Unutzer J, Katon WJ, Fan MY, Schoenbaum MC, Lin EH, Della Penna RD, Powers D. Long-term cost effects of collaborative care for late-life depression. Am J Manag Care. 2008;14(2):95–100. 54. Panagioti M, Bower P, Kontopantelis E, Lovell K, Gilbody S, Waheed W, et  al. Association between chronic physical conditions and the effectiveness of collaborative care for depression: an individual participant data meta-analysis. JAMA Psychiat. 2016;73(9):978–89. 55. Fann JR, Fan MY, Unutzer J. Improving primary care for older adults with cancer and depression. J Gen Int Med. 2009;24(Suppl. 2): S417–24. 56. Walker J, Sharpe M. Integrated management of major depression for people with cancer. Int Rev Psychiatry. 2014;26(6):657–68. 57. Ell K, Quon B, Quinn DI, Dwight-Johnson M, Wells A, Lee P-J, Xie B.  Improving treatment of depression among low-income patients with cancer: the design of the ADAPt-C study. Gen Hosp Psychiatry. 2007;29(3):223–31. 58. Ell K, Xie B, Quon B, Quinn DI, Dwight-Johnson M, Lee PJ.  Randomized controlled trial of collaborative care management of depression among low-income patients with cancer. J Clin Oncol. 2008;26(27):4488–96. 59. Li M, Kennedy EB, Byrne N, Gérin-Lajoie C, Katz MR, Keshavarz H, et  al. Systematic review and meta-analysis of collaborative care interventions for depression in patients with cancer. Psychooncology. 2017;26(5):573–87. 60. Strong V, Waters R, Hibberd C, Murray G, Wall L, Walker J, et al. Management of depression for people with cancer (SMaRT Oncology 1): a randomised trial. Lancet. 2008;372(9632):40–8. 61. Sharpe M, Walker J, Holm Hansen C, Martin P, Symeonides S, Gourley C, SMaRT (Symptom Management Research Trials) Oncology-2 Team, et  al. Integrated collaborative care for comorbid major depression in patients with cancer (SMaRT Oncology-2):

43

a multicentre randomised controlled effectiveness trial. Lancet. 2014;384(9948):1099–108. 62. Walker J, Hansen CH, Martin P, Symeonides S, Gourley C, Wall L, SMaRT (Symptom Management Research Trials) Oncology-3 Team, et  al. Integrated collaborative care for major depression comorbid with a poor prognosis cancer (SMaRT Oncology-3): a multicentre randomised controlled trial in patients with lung cancer. Lancet Oncol. 2014;15(10):1168–76. 63. Walker J, Cassidy J, Sharpe M, SMaRT Oncology-2 Trialists. The second Symptom Management Research Trial in Oncology (SMaRT Oncology-2): a randomised trial to determine the effectiveness and cost-effectiveness of adding a complex intervention for major depressive disorder to usual care for cancer patients. Trials. 2009;10:18. 64. Huffman JC, Adams CN, Celano CM.  Collaborative care and related interventions in patients with heart disease: an update and new directions. Psychosomatics. 2018;59(1):1–18. 65. Tully PJ, Baumeister H. Collaborative care for comorbid depression and coronary heart disease: a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2015;5(12):e009128. 66. Stewart JC, Perkins AJ, Callahan CM.  Effect of collaborative care for depression on risk of cardiovascular events: data from the IMPACT randomized controlled trial. Psychosom Med. 2014;76(1):29–37. 67. Atlantis E, Fahey P, Foster J.  Collaborative care for comorbid depression and diabetes: a systematic review and meta-analysis. BMJ Open. 2016;6(10):e012514. 68. Huang Y, Wei X, Wu T, Chen R, Guo A.  Collaborative care for patients with depression and diabetes mellitus: a systematic review and meta-analysis. BMC Psychiatry. 2013;13:260. 69. Wu B, Jin H, Vidyanti I, Lee PJ, Ell K, Wu S. Collaborative depression care among Latino patients in diabetes disease management, Los Angeles, 2011–2013. Prev Chronic Dis. 2014;11:E148. 70. Chwastiak LA, Jackson SL, Russo J, DeKeyser P, Kiefer M, Belyeu B, et  al. A collaborative care team to integrate behavioral health care and treatment of poorly-controlled type 2 diabetes in an urban safety net primary care clinic. Gen Hosp Psychiatry. 2017;44:10–5. 71. Katon W, Unützer J, Fan MY, Williams JW Jr, Schoenbaum M, Lin EH, Hunkeler EM. Cost-effectiveness and net benefit of enhanced treatment of depression for older adults with diabetes and depression. Diabetes Care. 2006;29(2):265–70. 72. Katon WJ, Russo JE, Von Korff M, Lin EH, Ludman E, Ciechanowski PS. Long-term effects on medical costs of improving depression outcomes in patients with depression and diabetes. Diabetes Care. 2008;31(6):1155–9. 73. Hay JW, Katon WJ, Ell K, Lee PJ, Guterman JJ. Cost-effectiveness analysis of collaborative care management of major depression among low-income, predominantly Hispanics with diabetes. Value Health. 2012;15(2):249–54. 74. Katon W, Lin EH, Von Korff M, Ciechanowski P, Ludman E, Young B, et  al. Integrating depression and chronic disease care among patients with diabetes and/or coronary heart disease: the design of the TEAMcare study. Contemp Clin Trials. 2010;31(4):312–22. 75. Katon WJ, Lin EH, Von Korff M, Ciechanowski P, Ludman EJ, Young B, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med. 2010;363(27):2611–20. 76. Katon W, Russo J, Lin EH, Schmittdiel J, Ciechanowski P, Ludman E, et  al. Cost-effectiveness of a multicondition collaborative care intervention: a randomized controlled trial. Arch Gen Psychiatry. 2012;69(5):506–14. 77. Coventry PA, Lovell K, Dickens C, Bower P, Chew-Graham C, Cherrington A, et al. Collaborative Interventions for Circulation and Depression (COINCIDE): study protocol for a cluster randomized controlled trial of collaborative care for depression in people with diabetes and/or coronary heart disease. Trials. 2012;13:139.

44 78. Coventry P, Lovell K, Dickens C, Bower P, Chew-Graham C, McElvenny D, et al. Integrated primary care for patients with mental and physical multimorbidity: cluster randomised controlled trial of collaborative care for patients with depression comorbid with diabetes or cardiovascular disease. BMJ. 2015;h638:350. 79. Camacho EM, Ntais D, Coventry P, Bower P, Lovell K, Chew-­ Graham C, et  al. Long-term cost-effectiveness of collaborative care (vs usual care) for people with depression and comorbid diabetes or cardiovascular disease: a Markov model informed by the COINCIDE randomised controlled trial. BMJ Open. 2016;6(10):e012514. 80. National Institute for Clinical Excellence. Guide to the methods of technology appraisal. National Institute for Clinical Excellence; 2013. https://www.nice.org.uk/process/pmg9/resources/guidetothe-methods-of-technology-appraisal-2013-pdf-2007975843781. 81. Coleman KJ, Magnan S, Neely C, Solberg L, Beck A, Trevis J, et al. The COMPASS initiative: description of a nationwide collaborative approach to the care of patients with depression and diabetes and/or cardiovascular disease. Gen Hosp Psychiatry. 2017;44:69–76. 82. Rossom RC, Solberg LI, Magnan S, Crain AL, Beck A, Coleman KJ, et  al. Impact of a national collaborative care initiative for patients with depression and diabetes or cardiovascular disease. Gen Hosp Psychiatry. 2017;44:77–85. 83. Reimer A, Schmitt A, Ehrmann D, Kulzer B, Hermanns N. Reduction of diabetes-related distress predicts improved depressive symptoms: a secondary analysis of the DIAMOS study. PLoS One. 2017;12(7):e0181218.

T. E. Chang and S. D. Boyden 84. Polonsky WH, Fisher L, Earles J, et  al. Assessing psychosocial stress in diabetes: development of the diabetes distress scale. Diabetes Care. 2005;28:626–31. 85. Polonsky WH, Anderson BJ, Lohrer PA, Welch G, Jacobson AM, Aponte JE, Schwartz CE. Assessment of diabetes-related distress. Diabetes Care. 1995;18(6):754–60. 86. Gessler S, Low J, Daniells E, Williams R, Brough V, Tookman A, Jones L.  Screening for distress in cancer patients: is the distress thermometer a valid measure in the UK and does it measure change over time? A prospective validation study. Psychooncology. 2008;17(6):538–47. 87. Wright JH, Sudak DM, Turkington D, Thase ME.  High-yield cognitive-behavior therapy for brief sessions: an illustrated guide. Arlington: American Psychiatric Publishing Inc; 2010. 88. The Motivational Interviewing Network of Trainers. http://www. motivationalinterviewing.org/motivational-interviewing-training. 89. https://aims.uw.edu/collaborative-care/behavioral-interventions/ problem-solving-treatment/training. 90. The National Network of PST Clinicians. Trainers and researchers manual. http://pstnetwork.ucsf.edu/sites/pstnetwork.ucsf.edu/files/ documents/Pst-PC%20Manual.pdf. 91. American Psychiatric Association. https://www.psychiatry.org/ psychiatrists/practice/professional-interests/integrated-care/get-trained. 92. Patel V, Belkin GS, Chockalingam A, Cooper J, Saxena S, Unützer J. Grand challenges: integrating mental health services into priority health care platforms. PLoS Med. 2013;10(5):e1001448. 93. https://aims.uw.edu/collaborative-care/implementation-guide.

4

Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities Nhi-Ha Trinh and Taquesha Dean

Case Vignette

The daughter of a 55-year-old widowed self-identified Black woman calls her mother’s primary care provider (PCP) office for help. Her mother has a history of sleeping more than usual, “not doing anything anymore…not even going to church,” and will not make her bed or take a bath without prompting. Even though her mother’s primary care physician has previously recommended a psychiatric consultation, her mother is reluctant to seek counseling because “I’m not crazy!” She will only go to clinic visits to her primary care physician’s office with a ride from her daughter. Although the nurse from the PCP’s office encourages the daughter and the patient to come into the office, the daughter is unable to take time off of work to make an appointment during the workday. Over the next few weeks, however, the daughter calls her mother’s PCP’s office several times with similar concerns— her mother is not eating, caring for her activities of daily living, or going to church. Finally, the mother becomes very disoriented, and she walks outside during the day in her nightgown and

N.-H. Trinh (*) ∙ T. Dean Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]; [email protected]

knocks on a neighbor’s house confused, saying: “I think our house is on fire.” The neighbor calls the daughter from work; the daughter takes off work urgently and brings her mother into the emergency department for urgent evaluation. The mother is hospitalized to the medical service for delirium secondary to dehydration and seen by the psychiatry consult/liaison service first for delirium, visual hallucinations, and “sundowning.” The geriatric psychiatrist on the service is a selfidentified Black man whom the patient immediately takes to—“I’ve never been taken care of by such a handsome young physician before, even when I lived in the South!” Once the delirium has been stabilized, the inpatient medical team is concerned because the patient appears withdrawn, is not eating, nor is she able to sleep at night. Asking the patient about what she enjoys, the patient reveals that she loves going to church but has missed being able to do this because “I’ve been so weak and lonely.” The psychiatrist is able to build rapport with the patient and her daughter because he knows the patient’s community and church pastor. Although the patient is very reluctant to start medications, the psychiatrist uses a symptom-focused approach and asks her what is currently troubling her. The patient reveals, “I’ve been so lonely at home.” When she and her daughter confirm that the “not being able to sleep is the worst,” and the daughter expresses fears that her mother is losing too much weight, the psychiatrist recommends a low dose of mirtazapine to help with those symptoms. With his guidance and patient’s acceptance, the medical team starts mirtazapine 7.5 mg to help the patient with insomnia and appetite. After initiation of the medication over several days, the patient’s sleep quality improves, as does her appetite.

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_4

45

46

N.-H. Trinh and T. Dean

It is time for discharge, and inpatient team would like the patient to go to a rehabilitation facility prior to going home. The daughter is very upset about this plan, expressing concerns that she will have to put her mother in a long-term nursing home, since the d­ aughter works full-time and has a young family. “My mother doesn’t want that, and besides, those places aren’t welcoming to folks like us.” At the family meeting, the psychiatrist is able to talk with the family about their wishes to have the mother come home after a short stay in “rehab” (emphasizing the “short-term” nature of the stay), and case management is able to set up a day program at the community senior center affiliated with the patient’s church community. The patient and family request to see the psychiatrist in his outpatient practice after the mother’s discharge.

Introduction Depression is a common mental health condition that leads to significant distress and impaired functioning. In fact, as of 2017, depression is the leading cause of disability worldwide [1–3]. In the United States, racial and ethnic minorities have increased from 19% to 27% of the population in the past 20 years [4, 5]. The US Census estimates that by 2044, the United States is projected to become a “majority minority” nation, with no race or ethnic group projected to have a greater than a 50% share of the nation’s total population [6]. Given these demographic shifts, an increased focus is needed to understand the phenomenology and treatment of depression in racial and ethnic minority populations (including Black, Latino American, Native American, and Asian Americans). Though evidence-based interventions for treating depression are widely available, the condition is often undertreated among racial and ethnic minorities in particular. Indeed, significant disparities exist in the diagnosis and treatment of depression for these underserved groups as compared to whites [7, 8]. Disparities are defined as differences in healthcare services received by two groups that are not due to differences in underlying healthcare needs or preferences of members of the groups but rather due to the structure of the healthcare system, provider or patient biases, or clinical uncertainty [9]. The groundbreaking Surgeon General’s Report [10] “Culture, Race, and Ethnicity” in 2001 implicated systemic, clinical, and individual patient factors contributing to disparities in mental health access to care for racial and ethnic minorities, as compared to white populations in the United States. In sum, the report found that racial and ethnic minorities are less likely to receive needed care, and when they do receive

care, it is often found to be lacking in quality as compared to their white counterparts. The supplement to this report suggested multiple reasons for these disparities at different levels, including financial-, structural-, and patient-level barriers [11]. Financial barriers are related to the inability of patients to access healthcare given their insurance status (due to being uninsured or underinsured). Structural factors refer to the overall system’s availability to provide care, regardless of individual patient financial status, and can also include factors external to the process of seeking care, including availability and proximity of appropriate facilities for care in a geographical location. Finally, patient-level factors include patients’ beliefs and/or knowledge about the healthcare system, as well as communication barriers, which can be influenced by patient personal biases or cultural differences [11]. Rather than working independently, these barriers may intersect, leading to racial and ethnic minorities not receiving adequate screening and care for depression, and thus present to treatment at a later stage of disorder. In 2006, the Federal Collaborative for Health Disparities Research (FCHDR) has listed mental health disparities as one of the top four priorities for immediate research attention, conveying the gravity of this problem [12]. In this chapter, we will review specific risk factors for depression in racial and ethnic minorities and explore the challenges of diagnosis and treatment for these underserved populations. Taking these challenges into account, we will provide treatment recommendations to consider when caring for these populations at risk.

 istorical Perspective: Depression H and Minority Status  ey Definitions: Race, Ethnicity, Culture, K and Minority Status Prior to starting a discussion of how the field of mental health research and practice has evolved, key concepts are reviewed here. Race is defined as a category of humankind that shares certain distinctive physical traits [13], such as skin color, facial features, and stature. Most people think of race in biological terms; for more than 300  years, or since the era of white European colonialization of populations of color in the world, race has indeed served as the “premier source of human identity” [14]. Anthropologists, sociologists, and many biologists now question the value of these categories and thus the value of race as a helpful biological concept [15, 16]. Indeed, DNA studies have debunked race as a biological construct and more of a social construct, as less than 0.1% of all our DNA accounts for physical differences among people associated with racial differences [17]. However, because society has valued these physical differences, the classifica-

4  Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities

tion of individuals based on physical characteristics has led societies to treat them differently—and unequally. In contrast, ethnicity refers to a particular ethnic affiliation or group [18], with the term ethnic relating to large groups of people classed according to common racial, national, tribal, religious, linguistic, or cultural origin or background ethnic minorities [19]. Thus, ethnic groups also are a social construct and have shared social, cultural, and historical experiences, stemming from common national or regional backgrounds. The term culture has multiple meanings: culture can describe the beliefs, customs, arts, etc. of a particular society, group, place, or time; culture can be used as a synonym for a particular society that has its own beliefs, ways of life, art, etc.; finally culture can refer to a specific way of thinking, behaving, or working that exists in a place or organization [20]. Clearly certain ethnic or racial groups can have their own cultures, but the association is not always one to one, and groups or organizations made up of multiple races or ethnicities can create their own culture, such as, for example, “American culture” or the culture of medicine. A minority group is a part of a population differing from others in some characteristics and often subjected to differential treatment [21]. Minority groups are differentiated from the social majority, defined as those who hold on to major positions of social power in a society. The differentiation can be based on one or more observable human characteristics, including ethnicity, race, religion, disability, gender, wealth, health, or sexual orientation, and may be enforced by law. Usage of the term is applied to various situations and civilizations within history despite its association with a numerical, statistical minority [22].

Early Work on Race, Ethnicity, and Depression Early research in depression risk did not focus on the connection of race and depression, but rather on the effects of social status on risk for developing psychological disorders [23, 24]. In the 1980s, the focus turned to disentangling the effects of race, ethnicity, and socioeconomic status with depressive symptoms, with the recognition that certain sociodemographic factors such as poverty have differentially affected racial and ethnic minorities, particularly Black populations in the United States, throughout its history [25]. Although early studies found that the prevalence of depressive symptoms generally did not differ between whites, Blacks, and Latinos (both English and Spanish speaking) [26, 27], Jones-Webb and Snowden [28] later demonstrated that Black populations had specific sociodemographic risk factors as compared to whites, putting this population at risk for developing depression. For both Black and white ­populations, female gender, separated or widowed marital

47

status, lower social class, and lack of employment conferred increased risk for depression; however, the researchers found additional risk factors for the Black population in their sample: those who were 30–39 years of age, belonged to nonWestern religious groups, and lived in the West were at greater risk for depression as comparable whites [28]. This finding has since been expanded for the Latino American population, as both Blacks and Latinos have been found to be at risk for developing depression due to having fewer economic resources and a lack of wealth in comparison to whites [29]. Indeed, current epidemiological surveys indicate that prevalence rates of depression can vary considerably among racial and ethnic minority populations, not only in the United States, but also in Europe [30]. Socioeconomic conditions and (unconscious and conscious) bias against racial and ethnic minority groups have consistently been found to be important predictors of these differences [31].

 urrent Advances in Depression Research C in Racial and Ethnic Minorities Despite increased research on the mental health of racial and ethnic minority populations, disparities in diagnosis, treatment access, and outcomes continue to persist [32]. As defined above, a disparity is a difference in diagnosis, access to treatment, and treatment outcomes between two groups that is not based on clinical appropriateness and need. For example, researchers have found that minority populations generally utilize mental healthcare less than their white counterparts, even though they are more likely to have persistent mental disorders throughout their lifetime [33, 34]. In this section, we will explore potential causes for these disparities in mental health diagnosis, treatment, and outcomes in racial and ethnic minority populations.

 hallenges in Diagnosis of Depression in Racial C and Ethnic Minorities In order to receive any kind of mental health treatment or care, the first step for many racial and ethnic minorities is to see their primary care clinician for evaluation and to receive a correct diagnosis. However, the presentation of depression symptoms may be different for racial and ethnic minority groups compared to whites. This may be partly due to their cultural or ethnic backgrounds, but also the structure of the healthcare system they seek help in, and their past exposure of the meaning of having depression through media and interpersonal interactions [35]. Racial and ethnic minorities with depression may present more often with symptoms of insomnia and feelings of restlessness [36], increased distress and somatic symptoms [10, 37],

48

and increased cognitive impairment [38], in comparison to whites. Clinicians who search only for physiologic explanations for somatic complaints such as back pain, tinnitus, headaches, palpitations, and dizziness may miss depression or anxiety as the cause. Because clinicians may not be aware of these differences in symptom presentation, decreased detection of these symptoms often results in the underdiagnosis or misdiagnosis of depression in these populations [39]. Inaccurate initial diagnoses result in delays in treatment or suboptimal treatment, which may lead to a prolonged course of depression. Recent reviews have explored racial disparities in the diagnosis of depression. Simpson and colleagues [40] reviewed four empirical articles from a combined representative sample of over 75,000 participants in the United States. They assert that two out of the four articles reported significantly lower diagnostic rates for depression in both Blacks and Latinos as compared to whites. Furthermore, in the National Ambulatory Medical Care Study of over 96,000 patients, Stockdale and colleagues [41] found that in both psychiatric and primary care settings, both Black and Latino patients were less likely to be diagnosed with depression or anxiety, and thus less likely to receive subsequent treatment, as compared to whites. Additionally, researchers have also found that even when there is equal reporting of symptoms, primary care physicians are still less likely to detect depression in racial and ethnic minorities as compared to whites [39]. Finally, Coleman and colleagues [42] found that across 11 nationwide large not-for-profit healthcare systems, all racial and ethnic minority groups (Asian American, Native Hawaiian/Pacific Islander, Blacks, and Latino Americans) were less likely to be diagnosed with depression as compared to whites. This pattern of underdiagnosis of depression in racial and ethnic minorities is then compounded by barriers to treatment access for depression, which is discussed in more detail below.

Challenges with Access to Treatment As outlined previously, there are many barriers that exist in treatment access, including financial-, structural-, and patientlevel barriers, preventing racial and ethnic minorities with depression from seeking and receiving quality care [43]. For example, research has shown that Blacks, Latino Americans, and Asian Americans are all less likely to seek treatment for depression in comparison to whites [42, 44, 45]. In addition, even after adjusting for factors such as poverty, insurance, and education, race still plays an i­ndependent role in limiting access to treatment for depression [42, 44, 45].

Financial Barriers Financial barriers are related to the inability of patients to access healthcare given their insurance status due to being

N.-H. Trinh and T. Dean

uninsured or underinsured. In addition, in racial and ethnic minority populations, there are higher rates of poverty and less complete health insurance coverage as compared to nonLatino whites [44]. This trend in insurance coverage continues in spite of recent efforts by the Affordable Care Act in 2010 to expand insurance coverage and mandate coverage for mental health services for all [32, 46].

Structural Barriers Structural barriers refer to factors that limit the overall availability and accessibility of the healthcare system to patients and can include factors relating to the process of seeking care, as well as the characteristics of the healthcare system and providers themselves [11]. In other words, structural barriers to care are based on the healthcare system‘s specific policies, institutions, and internal belief systems including how our society’s unconscious and conscious biases regarding race may influence a healthcare system’s organization and attitudes [47]. In a review conducted by the Substance Abuse and Mental Health Services Administration (SAMHSA) [32], researchers proposed that limited mental health referrals were important structural barriers for a lack of access to mental health treatment for racial and ethnic minorities. As compared to whites, racial and ethnic minorities who endorse depressive symptoms may be less likely to be referred for appropriate counseling by their primary care providers. Similarly, Stockdale et al. [41] found that both Blacks and Latinos were less likely to receive referrals for counseling in both primary care and psychiatric settings as compared to white populations. This issue is significant, particularly in primary care settings, because racial and ethnic minorities are more likely to seek mental health treatment from their primary care physicians than other settings [32, 46]. The lack of availability of language capacity for non-English speakers is also a structural barrier. This illustrates a lack of capacity of healthcare institutions to provide services for the populations for which they are responsible. In a study of white, Black, Asian, and Latino populations, among subjects who spoke no English and specified a need for mental healthcare, only 8% of them actually received such care, versus 51% of subjects who spoke English only and 42% of subjects who were bilingual [48]. Furthermore, physician biases also play a role, not only in underdiagnosis of depression, but also in undertreatment. Racial and ethnic minorities who do receive care are less likely to receive antidepressants for depression diagnosis as compared to whites. Simpson et al. [40] found that Blacks and Latinos were less likely than whites to receive antidepressant treatment for depression. In addition, while both groups were less likely to receive antidepressants, Blacks were the least likely, in comparison to Latinos, to receive them [40].

4  Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities

These structural barriers, including the lack of referrals and adequate treatment, exist for racial and ethnic minorities then lead to structural vulnerability in these populations, defined as “an individual’s or a population group’s condition of being at risk for negative health outcomes through their interface with socioeconomic, political, and cultural/normative hierarchies” [49, 50]. Patients are structurally vulnerable when their location in their society’s multiple overlapping and mutually reinforcing power hierarchies (e.g., socioeconomic, racial, cultural) and institutional and policy-level statuses (e.g., immigration status, labor force participation) constrain their ability to access healthcare and pursue healthy lifestyles [47]. However, in addition to these significant external barriers, there are also patient-level barriers that challenge these underprivileged communities with depression.

Patient-Level Barriers Patient attitudes regarding mental illness and the stigma surrounding mental illness can prevent racial and ethnic minorities from seeking treatment. Brown et  al. [45] found that Blacks had more negative attitudes toward depression treatment as compared to whites. These personal attitudes toward mental health in turn influence whether people seek treatment. Gary [51] explored the role that racial prejudice and discrimination play for racial and ethnic minorities seeking treatment for mental health. She argued that individuals from historically disadvantaged groups experience both public stigma and self-stigma which, when experiencing any form of mental illness, has a compounding effect that creates a double stigma: of being part of a minority group as well as having a mental illness. When experiencing any form of a mood disorder, including depression, Gary also argued that a level of denial is an additional part of the resistance to seek treatment when experiencing any mood disorder, including depression. The combination of negative perceptions and mistrust toward mental healthcare systems, as well as negative feelings toward mental health providers, may lead racial and ethnic minorities to avoid the stigma of mental health systems altogether. Ward and colleagues [52] directly assessed coping behaviors and beliefs toward seeking mental health treatment in Black women. They found that participants believed that having mental illness leads to severe consequences, such as hospitalization or jail [52]. If participants were to seek treatment, they preferred counseling over medication due to reservations about potential side effects. Researchers found that most of the barriers preventing ­participants from seeking treatment included lack of knowledge about seeking mental healthcare, embarrassment related to mental illness, and fear of the stigma associated with mental illness [52]. Finally, practical barriers exist—which can be a result of patient-level barriers and beliefs based on prior experience

49

with structural barriers. For example, personal worries regarding transportation or scheduling of mental health visits, concerns regarding costs, and the fear that one may be treated unfairly due to race or ethnic background, have all been found to be significantly more present for Latino patients in the United States compared to European Latino patients [53]. Given the complexity of these barriers to care for racial and ethnic minorities, more work needs to focus on a multilevel approach to improve screening and diagnosis of illness, treatment access, and quality care for these underserved populations with depression.

Challenges with Treatment Retention Once racial and ethnic minorities access care, they may have difficulty continuing mental health treatment. In a study of a nationally representative sample of racial and ethnic minorities (Blacks, Latino Americans, and Asian Americans), Blacks and Asian Americans with history of depression within the past 12  months were less likely than whites to remain in treatment despite their continued clinical need [54]. In addition, Blacks were less likely to continue psychiatric treatment compared to other minority groups. Some of the variation in these differences may also be based on negative patient perceptions regarding antidepressant use. For example, one study within the review demonstrated that Latinos and Black patients were less likely to be taking antidepressant medications than whites, even though no differences were found in patient reports of their primary care providers’ recommendations for treatment [55]. Researchers have found that Blacks, Latino Americans, and Asian Americans were all less likely to receive standard depression care in comparison to whites. For example, Alegria et al. [44] found that compared to whites, racial and ethnic minorities received less standard depression care; in Alegria’s study, standard depression care was defined as receiving either (1) antidepressant use for the past month combined with four or more treatment visits in the past year or, (2) eight or more treatment visits that were at least 30 min, but without antidepressant use, in the past year. Indeed, Fortuna et al. [54] found that when Black, Latino American, and Asian American patients were seen by a mental health specialist (versus a primary provider) and were prescribed medication (versus therapy alone), they were significantly more likely to remain in treatment. In fact, Fortuna and colleagues found that the type of provider had the greatest impact on treatment retention; patients in treatment with a mental health specialist (versus a primary care provider) were more likely to remain in treatment. Underlying reasons for disparities in retention include the lack of diversity of mental health clinicians, lack of training in cultural sensitivity, low treatment effectiveness, and clinician bias and stereotyping, all of which have an impact

50

on clinician treatment alliance with patients [32]. Alegria and her colleagues concluded that having increased access to mental health professionals trained in cultural sensitivity could potentially improve treatment retention for racial and ethnic minorities [44].

Clinical Applications and Recommendations To elucidate the appropriate diagnosis and treatment for racial and ethnic minorities with depression, providers need to spend sufficient time and attention to cultural factors that affect an individual patient’s distress. In this section, we highlight trends in clinician diversity, education, and training, including the advances in the DSM-5 regarding culture. We conclude by giving specific recommendations regarding therapy and pharmacotherapy.

Cultural Competency Debates While the demographic profile of patients is changing rapidly, the demographic profile of mental health professionals is changing more slowly [56]. A 2013 survey found that a majority of psychologists were white and female (83% and 68.3%). Between 2005 and 2013, the percentage of racial and ethnic minority groups within the psychology workforce grew from 8.9% to 16.4%, compared to 39.6% for the overall workforce and 25.8% for the general doctoral/professional workforce [57]. Despite these advances, rates of Asian American, Black, and Latino American psychologists have continued to remain low (4.3%, 5.3% and 5%, respectively in 2016) [58]. Similarly, in 2013, the racial and ethnic breakdown of American physicians in practice was: non-Hispanic white (43.0%), Latino (4.0%), Black or African American (3.7%), Asian (10.9%), Native American/Alaskan Native (0.3%), other (0.4%), and unknown (37.7%) [59]. For US-trained psychiatrists, the numbers of minorities are even lower than those in the overall US physician workforce [60, 61]. For social workers, the situation is slightly less dire; in 2015, 67.3% of social workers were white, followed by 23.3% Black social workers and 5.3% Asian Americans [62]. In a survey of 689 psychologists, the majority of whom were white, more than 80% reported discussing racial or ethnic differences in at least one cross-racial therapeutic encounter in the previous 2 years [63]. Yet, the psychologists surveyed also reported that racial or ethnic differences were discussed in less than half of all cross-racial clinical sessions, a finding that is particularly surprising, as racial and ethnic identity is central to an individual’s experience in the world, similar to sexual or gender identity. Understanding a racial or ethnic minority individual’s cultural identity may be crucial to developing a therapeutic alliance and treatment

N.-H. Trinh and T. Dean

plan. Indeed, with the demographic composition of the United States rapidly evolving, ongoing efforts should be made to increase both the diversity of the mental health provider workforce and the capacity and skill of providers to deliver quality healthcare for diverse patient populations. Training focused on fostering an attitude of cultural respect will help equip providers for this challenge. Efforts to create and recruit a diverse mental health provider workforce should proceed in tandem with efforts to cultivate a culturally respectful mental health provider workforce [61]. Because of this, the term “cultural competence” has become au courant, as a clinical solution to bridge the disparities in depression care access and treatment for racial and ethnic minorities specifically, given how centrally racial and ethnic minority status may inform one’s cultural identity. Culture itself is a broad term, encompassing “the customary beliefs, social forms, and material traits of a racial, religious, or social group,” and individuals can belong to several cultural groups based on their racial, ethnic, religious, or family backgrounds [20]. Thus cultural competency can be defined as, “a set of congruent behaviors, attitudes, and policies that come together in a system, agency or among professionals and enable that system, agency or those professions to work effectively in cross-cultural situations” [64]. Cultural competency encompasses systems as well as individual therapeutic encounters. Betancourt and colleagues [11] define three levels for cultural competence interventions: organizational (leadership/workforce), structural (processes of care), and clinical (provider-patient encounter). They note that at the clinical level, training has often focused on a categorical approach which involves ascribing attitudes, values, beliefs, and behaviors to broad cultural groups  – which may lead to stereotyping. Combining knowledgebased training with training in the process of cross-cultural communication allows for a more nuanced understanding of how cultural content may or may not be relevant to individuals. There is some evidence that cultural competence training can lead to increased knowledge and awareness among providers, but it is unclear at this point whether training also improves patient outcomes, and more research is needed in this area. Cultural competence is not uniformly accepted as a core competency in therapy. Sue and colleagues [65] summarize debates on the utility of cultural competence through a series of questions. These include whether cultural competence stereotypes minorities, discriminates against other types of diverse identities such as social class or sexual orientation, overemphasizes external factors such as discrimination at the expense of intrapsychic factors, and creates pressure on therapists to ascribe to cultural competency in order to be viewed as non-racist. The authors respond by noting that the debates tend to oversimplify the concept of cultural competence and ignore a more nuanced perspective, which includes a focus

4  Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities

on multiple intersecting identities and an acknowledgment of intrapersonal, interpersonal, and societal influences on the lives of our patients. Ultimately, the authors argue that ­cultural competence is necessary as a response to a historical context resulting in systematic bias against including ­culturally-specific experiences in therapy. At the same time, however, research has been limited on how such interventions improve patient outcomes in racial and ethnic minority groups. In one review article evaluating the effect of cultural competence trainings on both patient, professional, and organizational outcomes, researchers found no evidence of improved treatment outcomes or evaluations of care based on cultural competence interventions [66]. In addition, they found that none of the studies evaluated potential adverse events of such interventions. Therefore, while there have been initiatives to address these issues of clinician bias and discriminatory behavior within the healthcare system through education, whether these interventions are effective based on the data provided is still unknown. Future research must focus on how to both better design and evaluate cultural competence interventions. Qureshi and colleagues [67] note that the term “cultural competence” itself may obscure important distinctions in the types of barriers faced by racial and ethnic minority patients. A focus on culture may pertain to differences in understanding and expressing symptoms, as well as how preferences for treatment are developed and communicated. However, the authors also argue that racial and ethnic bias, discrimination, financial or structural barriers presented by poverty, immigrant status, and other experiences linked to minority status are not “cultural”; rather, these are structural challenges disproportionately experienced by members of non-white racial and ethnic groups. Clinicians must therefore be prepared to address a wide range of possible experiences impacting their patients; however, many current training models focus primarily on acquisition of knowledge rather than on development of skills or examining attitudes that ultimately may prove to be more useful. Thus “cultural humility” is the “ability to maintain an interpersonal stance that is open in relation to aspects of cultural identity that are most important to the patient” [68]. The culturally-humble clinician is able to express respect and a lack of superiority with regard to the patient’s culture; they do not assume competence in terms of working with a particular patient simply based on prior experience with other patients from similar backgrounds. Finally, there are emerging efforts in medical education to focus on “structural competency” of clinicians, as a means to address disparities in clinical care. Structural competency is defined as “the ability for health professionals to recognize and respond with self-reflexive humility and community engagement to the ways negative health outcomes and lifestyle practices are shaped by larger socioeconomic, cultural, political, and economic forces” [47].

51

This represents a recent shift in medical education “toward attention to forces that influence health outcomes at levels above individual interactions” [69]. More work will be needed to fully develop curricula to address structural competency for trainees, as well as continuing education for clinicians in practice.

Culture and the DSM-5 Taking the debates regarding cultural competency into account, the Outline for Cultural Formulation (OCF) was first developed in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Revision (DSM-IV), to help clinicians gather and organize data as they care for their patients of diverse backgrounds. This includes information-gathering regarding patient explanatory models of illness, for diagnostic clarification and for treatment planning, taking the larger familial, community, and structural factors into account. The OCF includes five sections: (1) cultural identity of the individual, (2) cultural conceptualizations of distress (cultural explanations of the individual’s illness), (3) psychosocial stressors and cultural features of vulnerability and resilience (cultural factors related to psychosocial environment and functioning), (4) cultural features (elements) of the relationship between the individual and the clinician, and (5) overall cultural assessment (for diagnosis and care); the OCF is reviewed in detail below [70, 71]. Despite wide interest in and use of the OCF, substantial barriers to its adoption and implementation have also been reported; these include the format being too vague and unstructured and a lack of clarity about how the OCF fits into standard clinical practice [72]. More recently, the culture workgroup of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [73] proposed a number of significant changes to the way culture is conceived and utilized by mental health clinicians and others interested in psychiatric diagnosis. DSM-5 explicitly states that “all forms of distress are locally shaped, including the DSM disorders.” As such, the discussion of each disorder contains multicultural explanations for similar symptoms for direct use by clinicians as a cross-reference. For example, panic disorder contains a discussion of ataque de nervios, a well-known condition similar to panic attacks primarily seen in Latino individuals, though with some notable differences. Section III of DSM-5 contains two important updated tools for clinicians: an updated Outline for Cultural Formulation and the Cultural Formulation Interview (CFI), which operationalizes the OCF. The current CFI is a standardized, manualized interview based on 16 stem questions and probes, which has been tested for feasibility, acceptability, and clinical utility in a DSM-5 field trial [74].

52

The DSM-5 Outline for Cultural Formulation The DSM-5 emphasizes that a clinician must take into account an individual’s ethnic and cultural context in the evaluation of each DSM-5 disorder. This process, called “cultural formulation,” includes five distinct components.

 ultural Identity of the Individual C It is important to consider racial, ethnic, and cultural references, as well as the degree to which an individual is involved with his or her culture of origin (versus the culture in which he or she lives). It is crucial to listen for clues and to ask specific questions concerning a patient’s cultural identity. For instance, an Asian American individual who grew up in the Southern United States may exhibit patterns, behaviors, and views of the world more consistent with a Caucasian American southerner. Language abilities, preference, and pattern of use must also be considered to address difficulties accessing care and to identify the need for an interpreter. In addition, attention to religious affiliation, socioeconomic background, country of origin, migrant status, and sexual orientation may be considered important aspects of cultural identity.  ultural Conceptualizations of Distress C How an individual understands and experiences his or her symptoms is often communicated through cultural syndromes and idioms of distress (e.g., “nerves,” possession by spirits, somatic complaints, or misfortune). Thus, the meaning and severity of the illness in relation to one’s culture, family, and community should be determined. This “explanatory model” may be helpful when developing an interpretation, diagnosis, and treatment plan.  sychosocial Stressors and Cultural Features P of Vulnerability and Resilience It is important to identify psychosocial stressors and supports within a patient’s environment (e.g., religion, family, or social circle). Cultural interpretations of social stress and support, and the individual’s level of disability and function, must also be addressed. It is the physician’s responsibility to determine a patient’s level of functioning, resilience, and disability in the context of his or her cultural reference groups.  ultural Features of the Relationship Between C the Individual and the Clinician Cultural aspects of the relationship between the individual and the clinician, as well as of treatment, should be considered. Common barriers for clinicians include difficulties with language, establishing rapport, and eliciting symptoms or understanding their cultural significance.

N.-H. Trinh and T. Dean

 verall Cultural Assessment for Diagnosis O and Care The formulation concludes with a summary of the implications of each component outlined above for psychiatric diagnosis, treatment, and other clinically relevant issues. This step directly acknowledges the fact that each society establishes its own criteria regarding which forms of behavior are acceptable or abnormal, and which behaviors represent a medical problem—all of which bears on the way mental healthcare is conceived of and delivered.

The DSM-5 Cultural Formulation Interview DSM-5 also includes the Cultural Formulation Interview, a semi-structured interview composed of 16 questions that physicians can use to assess the influence of culture on a patient’s clinical presentation and care. The CFI focuses on four domains of assessment: (1) cultural definition of the problem; (2) cultural perceptions of the cause, context, and support of the problem; (3) cultural factors affecting selfcoping and past help-seeking; and (4) cultural factors affecting current help-seeking. The interview aims to avoid stereotyping as it centers on the individual and incorporates the cultural knowledge of the patient, as well as the social context of his or her illness experience. The CFI may be utilized when physicians experience difficulties in diagnostic assessment due to cultural differences, difficulties in determining illness severity or impairment, disagreements with patients regarding course of treatment, or difficulties engaging patients in treatment. Qualitative interviews with patients and clinicians suggest that use of the CFI enhances rapport through satisfaction with the interview, elicits both information and perspectives from the patient, and facilitates perceiving data at multiple levels of awareness [74]. Others have noted that, despite the presence of the CFI in the DSM-V, the overall manual still relies primarily on a conceptualization of mental health that is individually-focused and not embedded in a social context including exacerbating factors such as racism and discrimination [75]. Critiques aside, the CFI represents an important step forward in institutionalizing and standardizing the work of cultural psychiatrists, anthropologists, and others in a format accessible to all clinicians.

Treatment Recommendations Understanding the impact of race and ethnicity on treatment—including therapeutic considerations, psychopharmacology, and psychobiology—is necessary to ensure that high-quality care is provided for racial and ethnic minorities. Both therapeutic and biological issues can affect clinical practice.

4  Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities

Therapeutic Issues Affecting Clinical Practice As discussed above, culturally-shaped beliefs play a major role in determining whether an explanation and treatment plan will make sense to, and be accepted by, an individual patient. While evidence-based psychosocial therapies for depression (e.g., cognitive behavioral therapy and interpersonal therapy) were initially developed and tested on white middle-income populations [76], the scientific literature available clearly demonstrates that evidence-based care for depression improves outcomes for African Americans and Latinos, and that results are at least equal to or greater than for white Americans. Much fewer data are available for Asian populations, but the literature that is available suggests that established psychosocial care may well be effective for this population [77]. A recent meta-regression in 2017 of 56 randomized controlled trials of psychotherapy found a moderate effect size (g = 0.50) in favor of psychotherapy, with no significant moderating effect of race or ethnicity in bivariate and multivariate analyses [31]. Psychotherapy was defined in this study as (1) an intervention in which verbal communication between a therapist and a client was the core therapeutic element or (2) in which a systematic psychological method was conveyed in print or on a Web site (bibliotherapy) for the client to work through more or less independently but with some kind of personal support from a therapist (by telephone, e-mail, or otherwise). Findings from this metaregression suggest that multiple psychotherapy modalities are equally effective, regardless of the care seekers’ race or ethnicity; thus the authors of this study suggested that future research should focus on “filling the gap between effective mental health care and the delivery of these services” [31]. Regarding the extent to which interventions need to be culturally adapted to be effective for minority populations, Miranda and colleagues argue “that all psychosocial interventions are tailored to the individual being served. If we were treating medically ill patients for depression, we would address the impact of illness on mood. If we were treating impoverished patients for depression, we would develop lists of pleasant activities that include many opportunities that are either free of charge or have minimal costs attached. Similarly, when treating Latina women, we would be aware that we may need to encourage them to take care of themselves in order to care for their families, as we know that they may not feel focusing on themselves is appropriate” [77]. Thus, a perspective of cultural humility and structural competency gained from training in these areas may be most helpful as clinicians adapt treatments for culturally-sensitive care.

Therapeutic Issues Affecting Pharmacotherapy Patient adherence may be affected by incorrect dosing, by medication side effects, and by polypharmacy. Some racial

53

and ethnic minorities might expect rapid relief with treatment and are wary of potential side effects (including addiction) induced by Western medicine. Traditional and/or alternative methods are often utilized by minority populations. For example, some Asian Americans, Latinos, and Blacks frequently use herbal medicines, which may interact with psychotropic medications. The Japanese herbs Swertia japonica and kamikihi-to and the Cuban Datura candida have anticholinergic properties that may interact with tricyclic antidepressants (TCAs) or with low-potency antipsychotics. South American holly, Ilex guayusa, has a high caffeine content. The Nigerian root extract of Schumanniophyton problematicum (which is used to treat psychosis) is sedating and may interact with antipsychotics and benzodiazepines. The Chinese herbs Fructus schisandrae, Corydalis bungeana, Kopsia officinalis, Clausena lansium, muscone, ginseng, and Glycyrrhiza increase the clearance of many psychotropic medications by inducing cytochrome P450 (CYP) enzymes which metabolize certain psychotropic medications. Oleanolic acid in Swertia mileensis and Ligustrum lucidum also inhibit CYP enzymes. An herbal weight loss supplement containing Ephedra sinica (Ma-Huang), which is the main plant source of ephedrine, can induce mania and psychosis [78]. Other patient-level and structural factors affecting pharmacotherapy can include a poor therapeutic alliance, a lack of community support, money, or transportation, as well as substance abuse or concerns about a medication’s addictive potential. Communication difficulties and gap between a patient’s “explanatory model” and that of his or her treater (including the cause of distress, the reason for use of medications, and their anticipated side effects) can play an important role in why a person from a racial or ethnic minority background is significantly more likely to be nonadherent to prescribed medications or to drop out of treatment entirely.

Biological Aspects of Psychopharmacology Understanding how pharmacokinetics and environmental factors relate to different racial and ethnic populations can help clinicians predict side effects, blood levels, and potential drug-drug interactions, with the strong caveat that though enzymatic patterns may exist in certain racial and ethnic groups, biology of course is only one part of the clinical equation. Pharmacokinetics depends on absorption, distribution, metabolism, and excretion, and the activity of liver enzymes is controlled genetically, although environmental factors can also alter activity. Pharmacokinetics may be influenced by genetics, age, gender, total body weight, environment, diet, toxins, drugs, alcohol, and disease states. Environmental factors include medications, drugs, herbal medicines, steroids, sex hormones, caffeine, alcohol, constituents of tobacco, and dietary factors.

54

The CYP 2D6 isoenzyme metabolizes many antidepressants, including the tricyclic and heterocyclic antidepressants, and the selective serotonin reuptake inhibitors (SSRls). CYP 2D6 also plays a role in metabolizing antipsychotics, including clozapine, haloperidol, perphenazine, risperidone, and thioridazine. The incidence of poor metabolizers at the CYP 2D6 ranges from 3 to 10% in Caucasians, 1.9 to 7.3% in African Americans, 2.2 to 6.6% in Latinos, and approximately 0 to 4.8% in Asians [79]. Another genetic variation of the metabolizer gene leads to “intermediate metabolizers” or individuals who exhibit CYP 2D6 activity that is between that of poor (little or no CYP 2D6 function) and extensive metabolizers (normal CYP 2D6 function). Approximately 18% of Mexican Americans and 33% of Asian Americans and African Americans have this gene variation [79]. This may explain some of the ethnic differences in the pharmacokinetics of antipsychotics and antidepressants. The CYP 2C19 isoenzyme is involved in the metabolism of diazepam, clomipramine, imipramine, and propranolol; it is inhibited by fluoxetine and sertraline. The rates of poor metabolizers of this enzyme are approximately 3–6% in Caucasians, 4–18% in African Americans, and 18–23% in Asian Americans [79]. With the above in mind, some of the following clinical observations can be made concerning the use of psychotropic medications in different racial and ethnic minority groups, though clearly intragroup variability can exist. Asian Americans tend to require lower doses of tricyclic antidepressants (TCAs), whereas African Americans may respond faster to TCAs and at lower doses, but with a greater risk of neurotoxicity. Latino Americans may respond to lower doses of TCAs and experience greater side effects. Asian Americans may experience extrapyramidal symptoms (EPS) at a greater rate than African Americans, Latinos, and whites. Asian Americans appear to respond better to clozapine as well as to have greater side effects at lower doses and also appear to be more sensitive to benzodiazepines, compared with whites. Asian Americans appear to respond to lower levels of lithium (with literature suggesting they can be successfully maintained at serum levels of 0.4– 0.8 mEq/L), while some African Americans appear to have a greater risk of neurotoxicity, likely related to a slower lithium-sodium pathway and a higher propensity for hypertension [80]. Yet, even with these “rules of thumb” in mind, a culturallyhumble clinician must realize that biological variations are only a part of the clinical equation. Not only are significant interindividual variations common, apart from a patient’s biology and metabolism of medications, as we have explored, significant macrolevel, social factors (financial, structural) and individual patient-level factors can all intersect in the clinical encounter.

N.-H. Trinh and T. Dean

Case Vignette: Conclusion

After the patient’s discharge from the hospital, there is a several-week wait for the patient to see the psychiatrist in his clinic; however, the patient’s daughter leaves messages that her mother is doing well, going to the senior center daily, and taking the medication as prescribed. Prior to meeting the patient for the first outpatient appointment with the patient and her daughter, the psychiatrist reviews in advance the case’s “cultural formulation” to identify parts of the patient’s presentation to explore more with the patient and her daughter. 1. Cultural identity of the individual: 55-year-old widowed self-identified Black woman originally from Georgia. She moved to be closer to her daughter’s family 6  months ago and was able to join her daughter’s church. 2. Cultural conceptualizations of distress: She describes her challenges as stemming from being “lonely,” causing her to not feel like eating, sleeping, or doing much of anything. She was concerned about seeking mental healthcare initially, worried about the stigma as being seen as “crazy” if she were to talk with a mental health clinician. 3. Psychosocial stressors and cultural features of vulnerability and resilience: She describes her daughter’s family and her daughter’s church and church community as a source of support; her daughter believes the senior center is becoming a source of support as well, which has served to combat her sense of isolation. 4. Cultural features of the relationship between the individual and the clinician: The patient and her family are encouraged that the psychiatrist knows their church’s pastor and is part of the same broader church community. Also, the psychiatrist’s having cared for the patient and her family in the hospital deepens the connection between the psychiatrist and the patient. 5. Overall cultural assessment for diagnosis and care: This is a widowed woman who presents with depressive symptoms in the context of social isolation, though she does have family and church support. She has been able to develop an alliance with the psychiatrist based on a feeling of shared racial and faithbased background, which will help foster trust as they navigate together pharmacotherapy treatment focused on her symptoms, as well as psychosocial support to address the patient’s psychosocial trigger (social isolation) for her depression.

4  Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities

Reviewing this case, the psychiatrist realizes it would be helpful to explore more in depth with the patient 1) her racial and cultural identity, and how it informs her explanatory model of illness, 2) flesh out the meanings behind her stressors and supports, and 3) understand more explicitly her expectations for the treatment relationship. With these goals in mind, the psychiatrist refers to the relevant sections of the CFI to prepare his questions for the first visit.

Conclusion Disparities in diagnosis, access, and treatment of depression in racial and ethnic minorities in the United States are a persistent and pressing clinical, public health, and public policy challenge. Ultimately, multipronged interventions at the financial, structural, and patient level will be needed to reduce disparities and ensure equitable access to treatment for all racial and ethnic minority groups. Clinicians can be part of this effort by educating themselves on how to best approach the interview, diagnosis, and treatment of racial and ethnic minorities with depression. Future efforts should not only address how to better train mental health clinicians to care for racial and ethnic minorities, but should also seek innovative ways to improve the structure barriers of our healthcare system and inform local communities about the benefits of such mental healthcare options.

FAQs: Common Questions and Answers Q1. What’s the best way to approach a patient from a racial or ethnic minority background different from my own? A1. Realize that patients from different cultural backgrounds may have unique needs and issues that require additional time and resources. The clinical relationship will be more complex, and it will likely take longer to develop trust and alliance. Be respectful to all patients and address them formally (e.g., Mr., Ms., Mrs.), particularly early in the treatment relationship, before an alliance is well established. Q2. What is the best initial approach for engaging racial and ethnic minorities in psychiatric treatment? A2. Understand how personal biases and stereotyping may affect treatment. Beginning the initial therapeutic encounter with a minority patient, as with any patient, requires that the clinician put the patient at ease in order to develop rapport and a willingness to work together over time. Many depressed minority patients may have somatic complaints;

55

therefore, beginning the discussion with the medical aspects of their health often is a good way to “break the ice”. That being said, racial and ethnic groups are highly heterogeneous and may include a diverse mix of individuals with different cultures, overlapping identities, languages, practices, and experiences. Be careful not to make assumptions about a patient’s values or behavior based on race, ethnicity, or culture, as such generalizations can be misleading and have harmful effects on a patient. Q3. How can I overcome obstacles to clinician-patient communication when treating populations of ethnic backgrounds different from my own? A3.  Confronting challenges with communication: Assure patients about confidentiality, as it may be important due to shame, fear, or paranoia related to prior traumatic experiences. Pay attention to communication (e.g., nonverbal communication, expressive styles, and the connotations of words). Anticipate that the patient may have mistrust or fear of treatment due to prior poor experiences with healthcare systems. Q4. How can I address diagnostic considerations in these populations? A4.  Considering diagnostic dilemmas: If a diagnosis is unclear or might be impacted by ethnicity or culture, consider employing a structured diagnostic interview tool (such as the Cultural Formulation Interview) to reduce the possibility of misdiagnosis. Consider interviewing patients with a bilingual, bicultural interpreter, who can facilitate the education of patients and families to reduce stigma surrounding mental illness. Consider obtaining a curbside consultation from a clinician of the relevant ethnic background (if available). Q5. What particular medication considerations should I keep in mind for these populations? A5.  Considering medication choices: When encountering a patient of a particular ethnic background, one should avoid assuming every such patient will tolerate the same doses of medications. Be prepared to start with lower medication dosages and increase the dose slowly (as tolerated and as clinically indicated). In addition, be sure to ask about the use of herbal medicines, since use of these agents has increased dramatically in the United States in the past few decades. Remember that drug-herbal medicine interactions exist and should be carefully considered. Q6. What about including family members in treatment? A6. Involving family: As for all patients, providers should request consent prior to talking to and engaging family members in treatment. Both the way family members interact with one another and the family functions as a whole have a significant impact on psychiatric treatment. Many racial and ethnic minorities have a “closed network” that consists of multiple family members, kin, and intimate friends. Some may rely on interactions with

56

relatives for social support, and some become more demoralized when such interactions do not increase with treatment. Racial and ethnic minorities may request to have members of their extended family involved in their treatment, including discussions with their providers. Family members may be able to provide collateral information and be a source of emotional and practical support for patients. In some cases, family consensus may be desired by the patient prior to engaging in a particular course of treatment, and the clinician should be attuned to such familial dynamics. Q7. What can I do to increase retention of racial and ethnic minorities in my practice? A7. Improving adherence: In addition to the tips above, focusing on practical barriers faced by patients and their families (challenges with making and keeping appointments, insurance issues, etc.) may be helpful for racial and ethnic minorities. Providers can set the tone by engaging patients and their families in conversations regarding using their preferred vocabulary (rather than medical jargon) and by focusing on the patient’s goals—which may not always overlap with our clinical focus. The use of well-trained interpreters who possess knowledge of the patient’s understanding of treatment recommendations can also significantly impact adherence. Other factors to consider regarding adherence include misdiagnosis of a psychiatric condition, a placebo response, mistrust of the healthcare system, attention seeking at a later stage of illness, and cultural beliefs and expectations regarding treatment.

References 1. World Health Organization. Depression [Internet]. World Health Organization. 2018 [cited 2018 Mar 5]. Available from: http:// www.who.int/mediacentre/factsheets/fs369/en/. 2. World Health Organization. “Depression: let’s talk” says WHO, as depression tops list of causes of ill health [Internet]. World Health Organization. 2017 [cited 2018 Mar 26]. Available from: http://www. who.int/mediacentre/news/releases/2017/world-health-day/en/. 3. Marcus M, Yasamy MT, van Ommeren M van Chisholm D, Saxena S.  Depression: a global public health concern [Internet]. WHO Department of Mental Health and Substance Abuse; 2012 [cited 2018 Mar 26]. p.  6–8. Available from: https://doi.org/10.1037/ e517532013-004. 4. Bureau UC. Section 1. Population [Internet]. [cited 2018 Jan 10]. Available from: https://www.census.gov/library/publications/2011/ compendia/statab/131ed/population.html. 5. U.S.  Census Bureau QuickFacts: UNITED STATES [Internet]. [cited 2018 Jan 10]. Available from: https://www.census.gov/ quickfacts/fact/table/US/PST045217. 6. Colby SL, Ortman JM. Projections of the size and composition of the U.S. population: 2014 to 2060. US Census Bur. 2015 Mar;13. 7. Ebmeier KP, Donaghey C, Steele JD.  Recent developments and current controversies in depression. Lancet Lond Engl. 2006;367(9505):153–67. 8. Van Voorhees BW, Walters AE, Prochaska M, Quinn MT. Reducing health disparities in depressive disorders outcomes between

N.-H. Trinh and T. Dean non-Hispanic Whites and ethnic minorities: a call for pragmatic strategies over the life course. Med Care Res Rev MCRR. 2007;64(5 Suppl):157S–94S. 9. Institute of Medicine (US) Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care. Unequal treatment: confronting racial and ethnic disparities in health care [Internet]. In: Smedley BD, Stith AY, Nelson AR, editors. Washington, DC: National Academies Press (US); 2003 [cited 2018 Mar 26]. Available from: http://www.ncbi.nlm.nih.gov/books/ NBK220358/. 10. Mental Health: culture, race, and ethnicity. A supplement t. 2001 [cited 2018 Jan 10]. Available from: https://eric. ed.gov/?id=ED464308. 11. Betancourt JR, Green AR, Carrillo JE, Ananeh-Firempong O.  Defining cultural competence: a practical framework for addressing racial/ethnic disparities in health and health care. Public Health Rep. 2003;118(4):293–302. 12. Safran MA, Mays RA, Huang LN, McCuan R, Pham PK, Fisher SK, et  al. Mental health disparities. Am J Public Health. 2009;99(11):1962–6. 13. Race | Definition of Race by Merriam-Webster [Internet]. [cited 2018 Mar 26]. Available from: https://www.merriam-webster.com/ dictionary/race. 14. Audrey S. “Race” and the construction of human identity. Am Anthropol. 2008;100(3):690–702. 15. Smedley A. Race in North America: origin and evolution of a worldview [Internet]. 4th ed. Boulder: Westview Press; 2012. p.  386 Available from: https://catalyst.library.jhu.edu/catalog/bib_4051740. 16. 10.2 The Meaning of Race and Ethnicity|Sociology: under standing and changing the social world [Internet]. [cited 2018 Mar 26]. Available from: http://open.lib.umn.edu/sociology/ chapter/10-2-the-meaning-of-race-and-ethnicity/. 17. Begley S.  Race and DNA [Internet]. Newsweek. 2008 [cited 2018 Mar 26]. Available from: http://www.newsweek.com/ race-and-dna-221706. 18. Definition of Ethnicity [Internet]. [cited 2018 Mar 26]. Available from: https://www.merriam-webster.com/dictionary/ethnicity. 19. Definition of ETHNIC [Internet]. [cited 2018 Mar 26]. Available from: https://www.merriam-webster.com/dictionary/ethnic. 20. Definition of CULTURE [Internet]. [cited 2018 Mar 26]. Available from: https://www.merriam-webster.com/dictionary/culture. 21. Minority|Definition of Minority by Merriam-Webster [Internet]. [cited 2018 Mar 26]. Available from: https://www.merriam-webster.com/dictionary/minority. 22. Barzilai G.  Communities and law: politics and cultures of legal identities. Michigan, USA: University of Michigan Press; 2010. p. 381. 23. Dohrenwend BP, Dohrenwend BS.  Social status and psychological disorder: a causal inquiry. Michigan, USA: Wiley-Interscience; 1969. p. 232. 24. Tucker EM.  Social status and psychological disorder: a causal inquiry. Arch Gen Psychiatry. 1970;22(2):191–2. 25. United States. Current population reports. Series P-60, consumer income. [Internet]. Washington, DC: U.S.  Dept. of Commerce, Bureau of the Census: For sale by the Supt. of Docs, U.S. G.P.O.; 1948. v. (Consumer income.). Available from: //catalog.hathitrust. org/Record/000502248. 26. Aneshensel CS, Clark VA, Frerichs RR.  Race, ethnicity, and depression: a confirmatory analysis. J Pers Soc Psychol. 1983;44(2):385–98. 27. Somervell PD, Leaf PJ, Weissman MM, Blazer DG, Bruce ML. The prevalence of major depression in Black and white adults in five United States communities. Am J Epidemiol. 1989;130(4): 725–35. 28. Jones-Webb RJ, Snowden LR.  Symptoms of depression among blacks and whites. Am J Public Health. 1993;83(2):240–4.

4  Culture and Depression: Clinical Considerations for Racial and Ethnic Minorities 29. Dunlop DD, Song J, Lyons JS, Manheim LM, Chang RW. Racial/ ethnic differences in rates of depression among preretirement adults. Am J Public Health. 2003;93(11):1945–52. 30. Missinne S, Bracke P.  Depressive symptoms among immigrants and ethnic minorities: a population based study in 23 European countries. Soc Psychiatry Psychiatr Epidemiol. 2012;47(1):97–109. 31. Ünlü Ince B, Riper H, van ’tHof E, Cuijpers P.  The effects of psychotherapy on depression among racial-ethnic minority groups: a metaregression analysis. Psychiatr Serv Wash DC. 2014;65(5):612–7. 32. NREPP Learning center literature review: mental health disparities. 2016. 33. Lê Cook B, McGuire TG, Lock K, Zaslavsky AM.  Comparing methods of racial and ethnic disparities measurement across different settings of mental health care. Health Serv Res. 2010;45(3): 825–47. 34. Breslau J, Kendler KS, Su M, Gaxiola-Aguilar S, Kessler RC.  Lifetime risk and persistence of psychiatric disorders across ethnic groups in the United States. Psychol Med. 2005;35(3): 317–27. 35. Kirmayer LJ.  Cultural variations in the clinical presentation of depression and anxiety: implications for diagnosis and treatment. J Clin Psychiatry. 2001;22(13):22–8. 36. Hankerson SH, Fenton MC, Geier TJ, Keyes KM, Weissman MM, Hasin DS. Racial differences in symptoms, comorbidity, and treatment for major depressive disorder among Black and white adults. J Natl Med Assoc. 2011;103(7):576–84. 37. Myers HF, Lesser I, Rodriguez N, Mira CB, Hwang W-C, Camp C, et al. Ethnic differences in clinical presentation of depression in adult women. Cultur Divers Ethnic Minor Psychol. 2002;8(2):138–56. 38. Grant JE, Harries M, Chamberlain SR. Differences in the cognitive profile of depression between racial groups. Ann Clin Psychiatry. 2018;30(1):32–7. 39. Borowsky SJ, Rubenstein LV, Meredith LS, Camp P, JacksonTriche M, Wells KB. Who is at risk of nondetection of mental health problems in primary care? J Gen Intern Med. 2000;15(6):381–8. 40. Simpson SM, Krishnan LL, Kunik ME, Ruiz P.  Racial disparities in diagnosis and treatment of depression: a literature review. Psychiatry Q. 2007;78(1):3–14. 41. Stockdale SE, Lagomasino IT, Siddique J, McGuire T, Miranda J. Racial and ethnic disparities in detection and treatment of depression and anxiety among psychiatric and primary health care visits, 1995–2005. Med Care. 2008;46(7):668–77. 42. Coleman KJ, Stewart C, Waitzfelder BE, Zeber JE, Morales LS, Ahmed AT, et al. Racial/ethnic differences in diagnoses and treatment of mental health conditions across healthcare systems participating in the mental health research network. Psychiatr Serv Wash DC. 2016;67(7):749–57. 43. Carrillo JE, Carrillo VA, Perez HR, Salas-Lopez D, Natale-Pereira A, Byron AT. Defining and targeting health care access barriers. J Health Care Poor Underserved. 2011;22(2):562–75. 44. Alegría M, Chatterji P, Wells K, Cao Z, Chen C, Takeuchi D, et al. Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv Wash DC. 2008;59(11):1264–72. 45. Brown C, Conner KO, Copeland VC, Grote N, Beach S, Battista D, et al. Depression stigma, race, and treatment seeking behavior and attitudes. J Community Psychol. 2010;38(3):350–68. 46. Goodell S.  Health policy brief: mental health parity. Health Aff (Millwood). 2014; 47. Bourgois P, Holmes SM, Sue K, Quesada J. Structural vulnerability: operationalizing the concept to address health disparities in clinical care. Acad Med J Assoc Am Med Coll. 2017;92(3):299–307. 48. Sentell T, Shumway M, Snowden L. Access to mental health treatment by English language proficiency and race/ethnicity. J Gen Intern Med. 2007;22(2):289–93.

57

49. Bourgios P, Hart LK. Commentary on Genberg et al.: the structural vulnerability imposed by hypersegregated U.S.  Inner city neighborhoods: a theoretical and practical challenge for substance abuse research. Addict Abingdon Engl. 2011;106(11):1975–7. 50. Holmes SM.  Structural vulnerability and hierarchies of ethnicity and citizenship on the farm. Med Anthropol. 2011;30(4):425–49. 51. Gary FA. Stigma: barrier to mental health care among ethnic minorities. Issues Ment Health Nurs. 2005;26(10):979–99. 52. Ward EC, Clark LO, Heidrich S.  African American Women’s beliefs, coping behaviors, and barriers to seeking mental health services. Qual Health Res. 2009;19(11):1589–601. 53. Falgas I, Ramos Z, Herrera L, Qureshi A, Chavez L, Bonal C, et al. Barriers to and correlates of retention in behavioral health treatment among Latinos in 2 different host countries: the United States and Spain. J Public Health Manag Pract JPHMP. 2017;23(1):e20–7. 54. Fortuna LR, Alegria M, Gao S. Retention in depression treatment among ethnic and racial minority groups in the United States. Depress Anxiety. 2010;27(5):485–94. 55. Miranda J, Cooper LA.  Disparities in care for depression among primary care patients. J Gen Intern Med. 2004;19(2):120–6. 56. Bureau UC. Census 2010 News – U.S. Census Bureau [Internet]. [cited 2018 Jan 12]. Available from: https://www.census. gov/2010census/news/press-kits/demographic-profiles.html. 57. 2005–13: Demographics of the U.  S. Psychology Workforce [Internet]. http://www.apa.org. [cited 2018 Jan 11]. Available from: http://www.apa.org/workforce/publications/13-demographics/ index.aspx. 58. CWS Data Tool: Demographics of the U.S. Psychology Workforce [Internet]. http://www.apa.org. [cited 2018 Mar 26]. Available from: http://www.apa.org/workforce/data-tools/demographics. aspx. 59. Nivet MA, Castillo-Page L, Diversity in the physician workforce: facts & figures 2014 [Internet]. The Association of American Medical Colleges (AAMC); 2014 [cited 2018 Mar 26]. (Facts & figures data series). Report No.: 18. Available from: http://aamcdiversityfactsandfigures.org/. 60. Brotherton SE, Etzel SI. Graduate medical education, 2012–2013. JAMA. 2013;310(21):2328–46. 61. Lokko HN, Chen JA, Parekh RI, Stern TA. Racial and ethnic diversity in the US psychiatric workforce: a perspective and recommendations. Acad Psychiatry J Am Assoc Dir Psychiatr Resid Train Assoc Acad Psychiatry. 2016;40(6):898–904. 62. Social workers [Internet]. Data USA. [cited 2018 Mar 26]. Available from: https://datausa.io/profile/soc/211020/#demographics. 63. Maxie AC, Arnold DH, Stephenson M.  Do therapists address ethnic and racial differences in cross-cultural psychotherapy? Psychotherapy. 2006;43(1):85–98. 64. NCCC: Curricula enhancement module series [Internet]. [cited 2018 Mar 26]. Available from: https://nccc.georgetown.edu/curricula/culturalcompetence.html. 65. Sue DW. Asian-American mental health and help seeking behavior: comments on Solberg et al. (1994), Tata and Leong (1994), and Lin (1994). J Couns Psychol. 1994;41:280–7. 66. Horvat L, Horey D, Romios P, Kis-Rigo J.  Cultural competence education for health professionals. Cochrane Database Syst Rev. 2014;5:CD009405. 67. Qureshi A, Collazos F, Ramos M, Casas M. Cultural competency training in psychiatry. Eur Psychiatry J Assoc Eur Psychiatr. 2008;23(Suppl 1):49–58. 68. Hook JN, Davis DE, Owen J, Worthington EL, Utsey SO. Cultural humility: measuring openness to culturally diverse clients. J Couns Psychol. 2013;60(3):353–66. 69. Neff J, Knight KR, Satterwhite S, Nelson N, Matthews J, Holmes SM.  Teaching structure: a qualitative evaluation of a structural competency training for resident physicians. J Gen Intern Med. 2017;32(4):430–3.

58 70. Lewis-Fernández R, Aggarwal NK, Bäärnhielm S, Rohlof H, Kirmayer LJ, Weiss MG, et  al. Culture and psychiatric evaluation: operationalizing cultural formulation for DSM-5. Psychiatry. 2014;77(2):130–54. 71. Association AP.  Diagnostic and statistical manual of mental disorders: DSM-IV-TR. American Psychiatric Association; 2000. p. 1002. 72. Aggarwal NK, Nicasio AV, DeSilva R, Boiler M, Lewis-Fernández R. Barriers to implementing the DSM-5 cultural formulation interview: a qualitative study. Cult Med Psychiatry. 2013;37(3):505–33. 73. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub; 2013. p. 1952. 74. Aggarwal NK, Desilva R, Nicasio AV, Boiler M, Lewis-Fernández R. Does the cultural formulation interview for the fifth revision of the diagnostic and statistical manual of mental disorders (DSM5) affect medical communication? A qualitative exploratory study from the New York site. Ethn Health. 2015;20(1):1–28. 75. La Roche MJ, Fuentes MA, Hinton D. A cultural examination of the DSM-5: research and clinical implications for cultural minorities. Prof Psychol Res Pract. 2015;46(3):183.

N.-H. Trinh and T. Dean 76. Miranda J, Nakamura R, Bernal G. Including ethnic minorities in mental health intervention research: a practical approach to a longstanding problem. Cult Med Psychiatry. 2003;27(4):467–86. 77. Miranda J, Bernal G, Lau A, Kohn L, Hwang W-C, LaFromboise T.  State of the science on psychosocial interventions for ethnic minorities. Annu Rev Clin Psychol. 2005;1:113–42. 78. Chen JA, Durham MP, Madu A, Trinh N, Fricchione GL, Henderson DC. Culture and psychiatry. In: Stern TA, Freudenreich O, Smith FA, Fricchione GL, Rosenbaum JF, editors. Massachusetts General Hospital handbook of general hospital psychiatry. 7th ed. Philadelphia: Elsevier; 2018. p. 559–68. 79. Bertilsson L, Dahl M-L, Dalén P, Al-Shurbaji A. Molecular genetics of CYP2D6: clinical relevance with focus on psychotropic drugs. Br J Clin Pharmacol. 2002;53(2):111–22. 80. Pi EH, Zhu W. Cross-cultural psychopharmacology: a review. Ann General Psychiatry. 2010;9(Suppl 1):S82.

5

Early Onset of Depression During Childhood and Adolescence Benjamin G. Shapero and Erica Mazzone

Case Vignette

John, an 18-year-old Caucasian male, came in for his first appointment at the college counseling center appearing sad and down and reporting that his life was “a mess.” During the intake, he described his early childhood years, living at home with both his parents and younger sister. He indicated that he wasn’t the smartest or most athletic child but wasn’t the worst either. However, he described frequent insulting and belittling comments by his father and yelling matches whenever he wasn’t “the best” or didn’t get A’s in school. In middle school, he began to feel down some of the time and generally kept to himself, saying that he had fewer friends than most other kids his age. He noticed that when his friends didn’t say ‘hi’ to him in the hallways, he got upset and was hard on himself. In high school, he described being picked on by his peers and feeling more alone and hopeless. He was able to get by academically because he had a couple of teachers that provided considerable help. Now, he is a college freshman at a local university. He had high hopes that his life would “turn around” now that he was in a new city and had a new start. However,

B. G. Shapero (*) ∙ E. Mazzone Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]

he is having a hard time making friends, and school is a lot more difficult than he anticipated. Although he experienced a down mood in middle school that got worse in high school, he now feels sad almost all the time. He also describes sleeping all the time, procrastinating on his school work because he can’t concentrate or understand the material, feeling guilty and worthless about having no friends, and isolating himself in his room. He talks and moves in a slow, methodical manner and reports that he has started to think that it “wouldn’t be so bad if he was no longer around.” The clinician conducts a thorough semi-structured clinical evaluation and safety plan. John reports a family history of depression in his mother, who took antidepressants, and substance abuse in his father, who never sought help. He seems to recall a cousin having “manic depression” but doesn’t know much about it. John denies any periods of mania or hypomania and any psychotic symptoms. He describes being hesitant in new social situations, slow to warm, and intensely anxious when he gives presentations. He indicates that he avoids raising his hand in class or asking for extra help for fear that he will be negatively judged. He denies feeling generally anxious about other aspects of his life and describes his life as hopeless at this point. Although John reports some passive suicidal ideation, he denies wanting to hurt or kill himself, denies having a plan or intent to commit suicide, and indicates that he is able to put these thoughts out of his mind and that his mother and sister are significant protective factors. At the intake, the clinician diagnosed John with major depressive disorder and social anxiety disorder as a secondary, co-occurring diagnosis. The clinician described two empirically supported treatment options that included psychotherapy and the possibility of medications.

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_5

59

60

Introduction Major depressive disorder (MDD) is one of the most common psychiatric disorders and frequently presents in adulthood [1]. The rates of depression in childhood are relatively low with estimates below 1% [2] but dramatically, as much as sixfold, from mid-adolescence to young adulthood [3, 4]. The most comprehensive epidemiological study conducted in the United States indicated that 14% of adolescents have experienced MDD [5]. In addition, community epidemiological surveys indicate a higher percent of youth (20–50%) who self-report significant levels of depressive symptoms, above the established cut points for clinically significant depression [2]. Adolescence is also the time when the gender gap in the rates of depression begins. In childhood, the rates of depression between boys and girls are similar. During adolescence, the rates begin to diverge where by young adulthood, women are twice as likely to become depressed than men [3]. The early onset of depression during childhood and adolescence is associated with many psychosocial problems, including an increased risk for academic and social difficulties, substance abuse, and suicide [6, 7]. Furthermore, depression is a highly recurrent disorder [8, 9], and depression that arises during adolescence is likely to continue into adulthood [10]. In addition, subsyndromal depression symptoms that may not meet the level for diagnosis of MDD are still important to note because the presence of elevated depression symptoms during adolescence is associated with concurrent impairment in functioning [7, 11] and is predictive of the onset of depression in adulthood [10–13]. Additionally, depression and hopelessness during this time are associated with suicide. Indeed, suicide is now the second leading cause of death in late adolescence and emerging adulthood according to the Centers for Disease Control [14]. The goal of this chapter is to provide an overview of depression during these important developmental periods. We will review important factors to consider when treating depression during childhood and adolescence: first, the presentation and differential diagnosis during this age range; second, historical factors associated with early identification and treatment; and finally, empirical evidence in preventative efforts and several front-line treatment approaches will be presented as options for mental health practitioners.

Presentation The diagnostic criteria for MDD during childhood and adolescence are similar to those in adulthood. In order to diagnose major depression or MDD in youth, at least five of nine symptoms (depressed mood, anhedonia/apathy, weight/ appetite change, sleep change, psychomotor agitation/retar-

B. G. Shapero and E. Mazzone

dation, fatigue, worthlessness/guilt, concentration impairment, thoughts of death/suicide) need to be present most days for at least a 2-week period, and these symptoms must impact functioning or lead to impairment. However, the essential features of MDD in adulthood, i.e., depressed mood or the loss of interest or pleasure, may present as consistent or intense irritability in children [15]. Irritability in children and adolescents can therefore count as a diagnostic symptom not necessarily seen in adulthood. Similarly, for the diagnosis of persistent depressive disorder (dysthymia), irritability can replace depressed mood as the diagnostic symptom. Further, in persistent depressive disorder, the duration of the mood episode is reduced to at least 1 year instead of 2 years. Diagnosing depression during childhood and adolescence can be difficult. The need to differentiate between what is or is not normative sadness or irritability is particularly difficult. Feelings of sadness and irritability are common, particularly during the transitional adolescent years. Adolescence is a period with numerous changes, including puberty, shifting support from parents to peers, increased academic pressures, and social stressors. However, it is helpful and necessary to account for the number (5 or more), pervasiveness (across situations), persistence (over time), and severity (intensity) of the symptom presentation. For the most part, children and adolescents have similar depressive symptomatology, duration of illness, and rates of recovery [16]. Frequent irritability associated with depression is often difficult to differentiate from other related diagnoses during childhood. For example, oppositional defiant disorder (ODD) is characterized by angry or irritable mood and argumentativeness or defiant behavior that last for at least 6  months [15]. In contrast to MDD, ODD can occur in only one situation (school or home) and may not have a negative emotional component like irritability or dysphoric mood. Another diagnosis that can be difficult to diagnose in youth is bipolar disorder particularly without the presence of a hypo/manic episode. A new diagnosis in the DSM-5, disruptive mood dysregulation disorder (DMDD), captures the presentation of children with persistent irritability and frequent episodes of emotional and behavioral dysregulation [15] that may be a precursor to bipolar disorder. This is a disorder that can only be diagnosed during childhood and is characterized by recurrent emotional outbursts that are inconsistent with developmental level and out of proportion from the situation or precipitating event. In addition, the child’s temper between outbursts needs to be persistently irritable or angry for most of the day nearly every day. One differentiating point is the intensity and frequency of these outbursts in comparison to the irritability that may be associated with MDD. DMDD is relatively uncommon and presents in roughly 0.8–3.3% of youth [17]; however those who are diagnosed with DMDD, are four times more likely to develop a psychiatric disorder in adulthood than youth without a childhood diagnosis [18].

5  Early Onset of Depression During Childhood and Adolescence

Depression has high rates of comorbidity with anxiety disorders during childhood and adolescence, similar to the high rates of comorbidity in adulthood [19]. Behaviors and thoughts related to depression and anxiety may look similar but be distinct in content. For example, reduced time spent with friends or decreased engagement in activities can be due to avoidance, which may be more consistent with anxiety, or to withdrawal, which may be due to anhedonia or apathy as well as depression. Similarly, perseverative negative thinking is common among both diagnostic presentations, but ruminative thinking (e.g., thinking about sad feelings or negative events) tends to focus on the past, whereas worry tends to be about future activities or events. In addition, the rates of anxiety disorders tend to occur at a younger age [1], with research supporting anxiety disorders during childhood as predictors for depression during adolescence and in adulthood (e.g., [20, 21]). In addition, another group that is at high risk of developing depression besides those with childhood anxiety are youth with attention-deficit/hyperactivity disorder (ADHD). ADHD is one of the most commonly diagnosed disorders in childhood, with prevalence estimates ranging from 4% to 9% [22–24]. The co-occurrence of ADHD and MDD is well documented [25], and evidence suggests that children with ADHD are at significantly higher risk of developing depression in their lifetime [26]. For example, Biederman and colleagues [27] followed children and adolescents with and without ADHD for 5 years and found that youth with ADHD were 2.5 times more likely to develop MDD by adolescence compared to those without ADHD.  Similarly, Chronis-­ Tuscano and colleagues [28] conducted a longitudinal study of 4–6 years old children with and without ADHD and followed them until they turned 18 years old. They found that youth with ADHD were four times more likely to develop depression or dysthymia and three times more likely to attempt suicide [28].

History There is a long history of diagnosing and treating depression during childhood and adolescence. Although the rates of depression during these ages have remained relatively similar over the past few decades [29], there is some evidence that individuals may become depressed earlier [30] and an increasing public health concern about the care of youth who develop depression. This is particularly the case because the rates of suicide have increased [13]. Indeed, suicide is now the second leading cause of death in this age range [31]. During these pivotal developmental years, suicidal ideation is common, presenting in roughly one out of five youth [32]. Fortunately, the rates of youth who transition from thinking about suicide (without a plan) to making suicide attempts are

61

relatively low (roughly 15%) [33]. Depression and hopelessness are two of the most consistent risk factors for suicidal ideation, attempts, and completed suicides [34, 35] highlighting the importance of identifying, potentially preventing, and treating depression early. Historically, treatments for children and adolescents were initially developed and found to be efficacious for adult depression and then applied to younger people. Significant adaptations to psychotherapies such as cognitive-behavioral therapy (CBT) and interpersonal psychotherapy (IPT) were needed to account for developmental and social differences in children and adolescents. For example, psychotherapy with younger children tends to focus on behavioral interventions and includes parents more, due to limitations with abstract thinking, perspective-taking, and other cognitive abilities. There is much concern about the use of psychotropic medications in youth. Although several medication options have been proven efficacious, as will be discussed below, parents and clinicians are at times hesitant to choose this treatment approach as a first option. Part of this hesitation is due to difficulties with diagnosing depression. It can be difficult to differentiate depression from the predictable, yet often disruptive, psycho-behavioral patterns of irritability and sadness that are typical during these transitional developmental years. There is also concern about an underlying, or yet to emerge, diagnosis of bipolar disorder, particularly given the presence of a family history of this disorder. Indeed, for many, the first mood episode in bipolar disorder tends to be a depressive episode [36, 37]. Another part of this hesitation is due to the black-box warnings of the increase in suicide risk for youth taking antidepressant medications. This is a widely debated and controversial topic (e.g., [38–41]). The evidence tends to support an increased risk of suicidality with the use of antidepressants in children and adolescents [42, 43]; however, the association may be modest and may apply more to suicidal ideation rather than suicide attempts per se [44]. There should be caution in using antidepressant medications in adults and youth. However, the benefits and risks of taking versus not taking antidepressants, particularly in severe depression, should be weighed carefully [45]. In the following sections, we review the empirical support for preventative efforts, as well as evidence for different therapeutic options for clinicians treating depression in youth, and convey this information to patients and families.

Empirical Evidence Interventions for childhood and adolescent depression can occur at multiple times. Primary prevention efforts attempt to prevent depression before the initial onset, whereas secondary prevention, or treatment, aims to reduce the impact of

62

depression by alleviating symptoms and reducing its impact on overall function. Numerous intervention options are available to treat child and adolescent depression. We review the empirical evidence for the various prevention programs that attempt to identify youth at risk for depression and prevent the initial onset of the disorder. We will then review treatment options that include psychotherapy, medications, their combination, and alternative forms of treatment.

B. G. Shapero and E. Mazzone

in a large-scale implementation study and was found to significantly prevent depression after 9  months [52], ­ 33 months [53], and 75 months later [54]. The most widely studied school-based prevention program has been the Penn Resiliency Program [55]. This is also a group-based CBT intervention that was designed for late childhood and early adolescent years. Initial studies of this intervention found that those who participated in this group had fewer depressive symptoms after 2  years and reductions in other clinically important symptoms [56]. Prevention Brunwasser and colleagues [57] conducted a meta-­analysis to investigate the Penn Resiliency Program’s (PRP) effecDue to the long-term impact of depression that occurs during tiveness in targeting depressive symptoms in children and childhood and adolescence, researchers and clinicians have adolescents. The results of 17 studies involving 2498 particistrived to prevent the initial onset of this disorder. These pre- pants revealed that, compared to those that received no interventative efforts attempt to identify risk factors associated vention, those in the PRP group reported lower levels of with the onset of depression and to intervene early. There are depressive symptoms through the follow-up period of numerous risk factors for depression, including contextual 12 months [57]. However, there was no statistical difference factors such as a family history of depression, experiences of between PRP and active control conditions in reducing significant early-life stress or maltreatment, or low socioeco- depression diagnoses, with the authors citing inadequate nomic status; individual characteristics, such as being power to detect statistical differences [57]. A recent meta-­ female, temperament/personality (neuroticism), negative analysis of the PRP and other school-based prevention procognitive styles, and reactions to stress; and prodromal fac- grams in youth suggests that, overall, these programs have tors such as anxiety or behavioral disorders or subclinical significant but small effects at reducing depression sympdepressive symptoms [46]. toms immediately after the program and also after 1  year Historically, preventative interventions have focused on [58]. It further indicates that programs that focus on a tartwo of the above factors: a family history of depression and geted sample (e.g., selected because of symptoms or parental prodromal depression symptoms. Youth with a parent with depression) have a larger effect than when conducted univerdepression are two to four times more likely to develop sally (e.g., all children in a school/grade). In addition, these depression than those without such a history [47, 48]. In programs were more effective when provided by external addition, elevated youth depressive symptoms that do not yet mental health practitioners compared to when they were permeet the clinical threshold are likely to go on to transition to formed by school staff [58]. a full major depressive episode in adulthood [13, 49]. Once Family-based preventative interventions in offspring of identified as being high risk, prevention programs have been parents with a history of depression have also been shown to employed in several manners. The three most studied pre- be effective. For example, Compas and colleagues found that ventative interventions have been group-based, school-­ a family-based cognitive-behavioral preventative intervenbased, and family-based [46]. tion reduced symptoms of depression in youth and lowered Group-based cognitive-behavioral therapy (CBT) pro- depressive episodes in parents and children after 2  years, grams have been shown to be effective at reducing depres- compared to the control condition which consisted of written sion symptoms and improving overall functioning in at-risk psychoeducational material [59, 60]. In addition, other youth. Clarke and colleagues developed the first of these family-­based interventions delivered by clinicians not only interventions called the Coping with Stress (CWS) course reduce depressive symptoms but also produce positive fam[50]. This manualized psychoeducational program was ini- ily changes such as increasing parental understanding of tially tested in youth with a family history of depression that their child and improving family communication [61, 62]. also had elevated symptoms of depression and consisted of Overall, prevention programs appear beneficial for chil15 group sessions focused on negative cognitions associated dren and adolescents. Numerous reviews indicate that these with depression. In a randomized, controlled trial, this inter- programs reduce depression symptoms and may prevent the vention was shown to reduce depression symptoms, increase onset of depressive episodes. Based on this literature, prooverall functioning, and reduce the likelihood of developing grams that specifically target high-risk individuals (selected depression after 1  year, with the control group being five samples based on either known risk factors such as parental times more likely to develop depression compared to the depression or indicated samples based on subclinical sympintervention group [51]. This group-based CBT has been toms) produce the largest effects compared to universal studtested across settings and with different investigator groups ies that include youth regardless of level of risk [63, 64].

5  Early Onset of Depression During Childhood and Adolescence

However, further work is needed to establish the effectiveness of these programs under real-world conditions in order to disseminate them widely [65].

Treatment There are a number of treatment options that have been shown to be effective for depression during childhood and adolescence. We review the psychotherapy and medication options that have been shown to be efficacious and should be considered front-line treatments. We will also review promising alternative forms of treatment. While we intend to emphasize the most well-validated treatments, this review is not meant to provide an exhaustive review of all available treatment options.

Psychotherapy Several psychosocial treatments have received considerable research support for efficacy in children and adolescents with depression. Studies on treatments for adolescents present stronger positive evidence than those examining treatments for children. For adolescents, cognitive-behavioral therapy (CBT) and interpersonal psychotherapy (IPT) have strong research supporting efficacy for adolescent depression [66]. Cognitive-behavioral therapy (CBT) currently has the most support for treating child and adolescent depression. CBT is a skills-oriented, present-focused, time-limited approach that focuses on the connections between thoughts, emotions, and behaviors [67]. In a recent meta-analysis of 11 randomized controlled trials for depression, CBT demonstrated strong effects of reducing depressive symptoms during adolescence and provided support for its effectiveness as a treatment option [68]. In addition, in a review of 52 ­randomized trials of evidenced-based psychotherapies, CBT was the most common intervention and outperformed usual care in treating depression in youth [69]. Although there are more studies for the treatment of adolescent depression, partly due to the increase in base rates during this developmental period, there is also support for CBT in treating childhood depression. David-Ferdon and Kaslow [70] reviewed a decade of research in which ten studies investigated psychosocial interventions for depressive disorders or elevated depressive symptoms in children, including CBT. This analysis bolstered previous findings that group-based CBT was effective in treating depressed children and suggested that it is one of the most empirically supported intervention types for this age group [70]. Interpersonal psychotherapy (IPT) has also been shown to be efficacious for adolescent depression. This adapted ado-

63

lescent version of IPT is designed to decrease depressive symptoms, improve interpersonal skills, and strengthen social support networks [71]. This is especially important because interpersonal stress is associated with depression, particularly during childhood and adolescence [72–74]. This is a collaborative, individual treatment that engages adolescents, develops emotional and relationship skills, provides psychoeducation about depression, enhances social skills, and develops communication abilities. A network meta-­ analysis of 52 studies and 3805 patients established that IPT and CBT were the only two psychotherapies examined that were more significantly effective than control conditions in treating pediatric depression. In this review, it was suggested that IPT was more acceptable than CBT, based on fewer discontinuations with the former [75]. Additionally, the authors suggested that IPT may be more beneficial than CBT over long-term follow-up in terms of reducing symptoms. Though CBT and IPT were found to be the two superior methods, these results, similarly to previous studies, were less robust when applied specifically to children [75]. In a recent update of the state of the evidence base of psychosocial treatment for child and adolescent depression, Weersing and colleagues [66] applied stringent criteria for defining empirically supported therapies. They suggest that the evidence base for treating childhood depression is considerably weaker than for adolescents. Although they suggest that there is no “well-established” treatment for children, CBT appears to be the most efficacious [66]. For adolescents, CBT and IPT both demonstrate superiority over control conditions and were considered “well-established.” Taken together, both CBT and IPT show the most support for treating depression in youth and should be considered front-­ line treatment options.

Medications Numerous pharmacological interventions have been studied to treat pediatric depression including selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), and tricyclic antidepressants (TCAs). Several reviews and meta-analyses have been conducted of studies that have tested various medication options. In a review of 38 clinical trials, Wallace and colleagues [76] performed a meta-analysis of seven RCTs to investigate SSRIs as a treatment for pediatric depression. The results demonstrated that depressed children and adolescents were more likely to respond to SSRIs than to placebo [76]. However, the medication group was also more likely to experience serious adverse events, such as manic symptoms and harm-related events, than the placebo group [76]. Another meta-analysis of data from five randomized trials investigated the efficacy

64

of SSRIs compared to placebo in the treatment of child and adolescent depression and reported that fluoxetine alone presents a favorable risk-benefit profile [77]. Fluoxetine led to significantly higher response rates and remission rates than placebo, and it was also associated with few adverse events. Among the other medications examined, paroxetine and sertraline showed some efficacy, but the authors concluded that the benefit did not outweigh the risks of having a significant adverse event and of suicidality. The risk-benefit profiles for citalopram and venlafaxine were also unfavorable [77]. A more recent network meta-analysis of published and unpublished double-blind RCTs revealed that out of 14 antidepressant medications, fluoxetine was the only one that was significantly more effective than placebo, with a medium effect size [78]. The authors suggest that fluoxetine was also the best tolerated drug, while imipramine, venlafaxine, and duloxetine were less tolerable, having more adverse eventrelated discontinuations than placebo [78]. The review also investigated a link between these antidepressants and suicide-related outcomes (ideation or behavior) and found that venlafaxine was associated with significantly increased risk, while fluoxetine, paroxetine, citalopram, and sertraline were associated with a nonsignificant increase in risk [78]. However, this study did not distinguish between suicidal ideation versus behavior. Qin and colleagues [79] also performed a meta-analysis comparing the efficacy and acceptability of SSRIs versus tricyclic antidepressants in depressed children, adolescents, and young adults, concluding that SSRIs had both superior efficacy and tolerability. Additionally, of the two SSRIs investigated, fluoxetine was more effective than paroxetine, and of the three TCAs (clomipramine, nortriptyline, and imipramine), imipramine was most effective [79]. The patients taking SSRIs reported more suicidal behavior or ideation than patients taking TCAs, but this difference was not significant [79].

Comparative Effectiveness and Combination As described above, both medication and psychotherapeutic treatments are effective in treating child and adolescent depression. Researchers have conducted comparative effectiveness studies to try to identify whether one form of treatment is more advantageous than another. In addition, others have attempted to understand whether the combination of medication and therapy is more beneficial than either treatment alone. The Treatment for Adolescents with Depression Study (TADS) is one of the largest randomized controlled comparative effectiveness studies conducted to date. This multisite trial with over 400 adolescents with depression compared the efficacy of fluoxetine alone, CBT alone, the combination of fluoxetine and CBT, and placebo [80]. The primary results

B. G. Shapero and E. Mazzone

from this landmark study indicated that the combination of fluoxetine and CBT was the most effective in reducing depressive symptoms and suicidal thinking, and this treatment group was the only one to reach a statistically significant advantage over placebo [80]. In their follow-up of the long-term effectiveness and safety outcomes, they found that all three active treatments converged on most end-points by 36 weeks, suggesting that CBT alone, fluoxetine alone, and their combination were all effective [81]. They concluded that treatment with fluoxetine alone or in combination with CBT produced more rapid improvements in depressive symptoms than CBT alone [81]. However, clinically significant suicidal ideation persisted in a minority of patients and was more common in patients treated with fluoxetine alone than with combination therapy or CBT [81]. Since this seminal study, numerous other papers have examined the comparative effectiveness of medications and CBT with mixed findings. For example, a review of eight studies of treatment-resistant depression suggested that the combination of SSRI medication and CBT was more effective than an SSRI alone [82]. However, this meta-analysis included RCTs and non-RCTs which limits the conclusions. In contrast, Dubicka et al. [83] examined five RCTS and did not find a significant benefit in reducing depressive symptoms, suicidality, or global improvement when adding CBT to antidepressant medications versus antidepressants alone [83]. It is possible that depression severity may influence the relative effectiveness of medication versus CBT or their combination. For example, the Treatment of SSRI Resistant Depression in Adolescents (TORDIA) randomized controlled trial determined that the combination of CBT and switching to a new antidepressant medication was more effective than the new medication alone in adolescents with treatment-resistant depression [84]. For the switch in medication component, the study compared venlafaxine, an SNRI, with several SSRIs, and it was noted that a switch to either type of drug was equally beneficial [84].

Additional Treatment Options As the prior review indicates, both front-line psychotherapies (CBT and IPT) and medication (primarily fluoxetine) are well-established treatments for depression during adolescence and likely effective during childhood although the data supporting their use is less established. However, not everyone benefits from these treatments alone or in combination. Therefore, it is important to provide additional options. There are several other forms of treatment that have been shown to be helpful for depression during childhood and adolescence. Psychotherapy that focuses on behaviors can be a helpful intervention for children and adolescents with depression.

5  Early Onset of Depression During Childhood and Adolescence

There are various such therapies that focus on aspects of one’s behavior or environment that reinforces or contributes to undesirable emotions, thoughts, or furthering those negative behaviors. Behavioral activation is an example of a behavioral therapy that has strong support in adulthood that may be particularly helpful during childhood and adolescence. This intervention is focused on treating the underlying consequences and reinforcers associated with apathy and anhedonia. Individuals with depression tend to withdraw or disengage from their environment; this may result over time in the loss of positive and rewarding experiences, which in turn maintains and exacerbates one’s depression. Behavioral activation seeks to increase the patient’s contact with rewarding experience by focusing on increasing activities and social contact and reducing factors that may inhibit engagement or lead to avoidance. Several recent meta-analyses indicate that behavioral activation demonstrates large effects at reducing depression symptoms in adults [85, 86]. Behavioral activation has also been shown to be comparable to antidepressant medication and CBT in reducing acute depression in adults [87]. Although relatively less studied in youth, behavioral activation is a promising intervention to treat depression [88] with several recent studies indicating significant improvement in symptoms after treatment in youth [89, 90]. Behavioral activation and other behavior therapies may be particularly helpful in younger individuals who are less able to engage with cognitive therapies that rely upon a certain level of introspection, perspective-taking, and abstract thinking. Therefore, the focus on problem solving and behavior change, along with enlisting parental and other environmental supports, may be a good option. However, more research is needed to provide better evidence that this form of treatment is effective with children and adolescents. Another alternative treatment option that has considerable support is exercise. In adults, exercise has been shown to be effective at treating mild to moderate depression. A recent meta-analysis that included roughly 1000 participants determined that, compared to a control group (including wait-list or usual care), exercise demonstrated significant reductions in depressive symptoms with a moderate to large effect size [91]. Further, there was no significant difference found between exercise and a comparison psychosocial treatment or antidepressant medication [91]. A meta-analysis of exercise to treat adolescent depression showed similar results. A review of 11 trials of group-based, supervised aerobic exercise indicated a significant improvement in depressive symptoms after the treatment with a moderate effect size [92]. The number of trials reviewed by this meta-analysis was not large enough to provide specific frequency, duration, or intensity recommendations, but most programs utilized low- to moderate-­ intensity exercise [92]. Similar positive results were found in childhood depression, with a meta-analysis indicating improvement in depression symptoms, but no

65

increase in remission of MDD diagnosis [93]. Not surprisingly, children and adolescents with psychiatric disorders, including depression, report significantly less exercise and team or individual sport participation [94], thus increasing these activities has many benefits. Exercise may be particularly beneficial if talk therapy or medication is not effective. In addition, consistent exercise increases energy and reduces fatigue [95], which are impactful symptoms of depression during this age. Therefore, translating the adult public health dose of exercise to youth, 3–5  days a week of moderate-­ intensity exercise, may be helpful in treating depression symptoms [96]. Other psychotherapy and medication approaches may be helpful as well. One such psychotherapy treatment is family therapy focused on attachment. Several reviews suggest that attachment-based family therapy is effective in treating acute depression symptoms and show treatment responses sustained over time [69, 97–99]. Researchers and clinicians are beginning to test mindfulness-based interventions, nutritional supplements, and other nutraceuticals that have been shown to be effective in treating adult depression with youth, but these investigations are at a more preliminary stage.

 linical Application and Recommendation C for Practitioners There are several factors that a clinician should consider prior to planning or recommending treatment approaches. These considerations include, but are not limited to, age of the youth, severity of the depressive episode, presence of suicidal ideation, and family history or clinical suspicion of bipolar disorder. As described, there are several empirically supported psychotherapeutic approaches, medications, and alternative treatments available. A stepped care model and ideographic approach, weighing the benefits and costs, is helpful when considering treatment options. In discussing possible interventions for treating child and adolescent depression, Sakolsky and Birmaher [100] first note that cases of mild or brief depression can be treated with education or supportive therapy, followed by specific psychotherapies such as CBT, IPT, and other therapies if symptoms persist. In cases of severe depression (or depression with psychosis or severe suicidality), medications should be considered, especially in combination with psychotherapy [100]. In addition, medications may also be needed for those who do not respond to front-­line psychotherapies. A first-choice medication for clinicians is often fluoxetine, due to its well-established efficacy for child and adolescent depression. In addition, fluoxetine is the only medication approved by the FDA to treat depression in both children and adolescents, and escitalopram is the only other medication approved for adoles-

66

cent depression [101]. Sakolsky and Birhmaher also suggest that dosing strategies may be different for children and adolescents and that SSRIs should be started at lower doses for preadolescents [100]. The choice of psychotherapy or the use of medications should also take into account several individual factors. As previously described, children who may be less able to engage in cognitively demanding therapies may benefit more from behavioral interventions such as behavioral activation or from family-based interventions. In addition, group forms of CBT are more established for treating depression in children than in other age groups. For severe depression at young ages, patients, parents, and clinicians should weigh the benefits and drawbacks of medication, with the consideration that untreated or undertreated depression may have detrimental effects on academic, family, and social functioning and increased risk for recurrences or suicidal behaviors [16]. If medications are chosen, children and adolescents should be consistently monitored for suicidal ideation and behaviors. Finally, a family history of bipolar disorder should be taken into account when selecting or monitoring medications, in view of the risk of cycling associated with antidepressants in bipolar individuals.

Conclusion Taken together, mental health practitioners can best help their depressed child and adolescent patients by identifying emergence of the disorder or elevated symptoms of depression early and by utilizing the above recommended psychotherapies and/or medications. When making diagnostic determination, clinicians should keep in mind that although pediatric depression presents similarly to adult depression, irritability could display in youths as a criterial symptom. Other challenges in diagnosing child and adolescent depression include differentiating between MDD symptoms and typical irritability and sadness found in these age groups, as well as from other related disorders. As in the case vignette, John presented with elevated symptoms of depression during early adolescence that were persistent. Unfortunately, like many other youth, John’s symptoms were not identified and he did not receive treatment. He continued to struggle as he progressed through school and had his first onset of MDD in college. As with many, John had a comorbid condition with his MDD. Given the numerous characteristics and other possible disorders to assess for, clinicians should look for the presence of numerous key symptoms and determine their pervasiveness, persistence, and severity. In preventing the onset of MDD, clinicians and parents should also look for established risk factors and, in particular, family history of depression or prodromal depression. The three most empirically supported prevention interven-

B. G. Shapero and E. Mazzone

tion types are group-based, school-based, and family-based. Both the Cognitive-Behavioral “Coping With Stress” group-­ based course and the school-based Penn Resiliency Program have been shown to reduce depressive symptoms. Additionally, family-based interventions show promise in preventing onset of depression in children and in helping parents with their own depression as well. For children and adolescents that are experiencing a depressive episode, we recommend that practitioners turn to a clinically supported psychotherapy as a front-line treatment, followed by an antidepressant treatment (alone or in combination with a psychotherapy) if the depression persists or becomes more severe. For psychotherapies, evidence from several meta-analyses supports the efficacy of CBT and IPT in reducing depressive symptoms and remaining effective over follow-up. Clinicians may want to consider alternative therapies such as behavioral activation, for younger, less cognitively developed children. Additionally, despite the reasonable hesitancy in prescribing antidepressants to young people, medications can also be recommended, especially when psychotherapy fails to provide sufficient improvement on its own. SSRIs, SNRIs, and TCAs have all been used to treat pediatric depression. SSRIs, however, outperform both placebo and other medication groups in randomized trials. Of the SSRIs, fluoxetine has demonstrated the greatest efficacy in numerous studies, creating a favorable benefit-to-risk profile with higher response rates, lower remission rates, and fewer adverse events than other medications. It has also been suggested that the combination of fluoxetine and CBT is more effective than either monotherapy alone, but further research is needed. The same is true of alternative forms of therapy, such as exercise and attachment-based family therapy. As black-box warnings state, there are risks in treating child and adolescent depression, especially with medications. However, it is important to note that untreated depression can lead to psychosocial problems, impaired functioning, recurrent episodes in adulthood, and suicidal thoughts or behaviors. Therefore, the treatment options recommended present benefits that far outweigh the risks, provided clinicians are adept and flexible in making treatment regimen modifications with respect to individual differences and patient safety concerns.

FAQs: Common Questions and Answers Q1.  Does antidepressant use in children and adolescents increase their risk of suicidal behaviors? A1. There is some evidence suggesting that antidepressant use is associated with an increased risk for suiciderelated events. However, child and adolescent patients

5  Early Onset of Depression During Childhood and Adolescence

that do not receive clinically sufficient care for their depression are also at risk for these thoughts and behaviors. Many studies have demonstrated that the benefit of antidepressants in moderate to severe cases of pediatric depression outweighs the risks, particularly for fluoxetine, which is well tolerated and approved for children as young as 8. Clinicians should closely monitor patients who begin antidepressant regimens and start them on lower doses, especially in cases of preadolescent children. If suicide-related adverse events (or other sideeffects such as mania or non-suicidal injurious behavior) result, psychotherapy and other kinds of interventions should be considered after careful reassessment. Q2. Why are certain psychotherapies and medications more effective in treating adolescent depression than childhood depression? A2. It is important to keep in mind that incidence rates tend to be higher in older children (with prevalence increasing throughout mid-to-late adolescence); therefore, the sample population is larger and easier to target for research purposes. This is not to say that childhood depression rates are not significant, and many studies acknowledge the need to effectively adapt treatment approaches that work for adolescents to younger patients as well. One reason that current evidence-based psychotherapies are less effective for young children is that they utilize cognitive skills that may still be developing. For example, while CBT has shown to be effective for younger children, there is less evidence for IPT, perhaps due to the level of self-­reflection and self-awareness that is needed to complete this treatment. The available research also demonstrates that medications are also less effective in treating this age group, and due to a possible increased risk of suicide behavior, only one antidepressant (fluoxetine) is currently approved for children under the age of 12. It has been suggested that this lower efficacy is due to higher placebo response in children and/or their less developed noradrenergic systems. Q3.  What settings are appropriate for administering psychotherapies? A3. The most empirically supported interventions for treating depression in children are group-based CBT and behavioral interventions. For adolescents, CBT and IPT were shown to be effective when applied individually or in a group. Additionally, it is suggested that delivering mental health care to adolescents in alternative settings, such as school, home, and in community-based care (especially for interventions such as CBT), shows promise for reducing depression and suicidality [97]. Q4. What are some individual differences between youth that may account for varying treatment effectiveness? A4. Some differences in treatment responses can be explained by biological differences. As with adults, certain antide-

67

pressants may work better in some children and adolescents as opposed to others. Age is a factor, since fluoxetine has been shown to be the most effective medication for preadolescents and effective for adolescents as well. For medications and psychotherapies alike, better outcomes are associated with earlier responses to treatment. Notable modifiers to treatment courses include the severity of the depression, the level of conflict with parents, lifestyle differences such as exercise habits, the presence of comorbid anxiety, and the individual’s own ability to cope and regulate mood. Q5. It has been mentioned that suicidal ideation and behaviors are possible risks in prescribing antidepressants to young people. Are there any other adverse events that have resulted in previous studies? A5. Yes, other adverse events have been reported, such as headaches, nausea or vomiting, agitation, sedation, sexual dysfunction, increases in blood pressure, and skin problems. Serious adverse events, such as manic symptoms and harm-related events, have also been reported. Q6. How have various psychotherapies been adapted from treating adult populations to children and adolescents? A6. CBT and IPT can be delivered to children and adolescents in a variety of settings. One difference from adult treatment is that they can be administered in schools. While both interventions maintain similar treatment strategies as compared to adults, clinicians utilize language and examples that are relatable to youth, are more active in describing concepts, and apply more helpful reminders and strategies to increase retention [70]. Additionally, many psychotherapies involve parents in the treatment. Q7. How can mental health practitioners work to prevent relapse and recurrence of depression in children and adolescents? A7. Practitioners should consistently monitor their patients, especially when medication is being used continuously, to check for adverse events that may emerge. Medications that are well tolerated can be continued to maintain treatment benefits. With psychotherapies, maintenance can be delivered in the form of booster sessions, which have been shown to effectively prevent relapses [67].

References 1. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry. 2005;62:593–602. 2. Kessler RC, Avenevoli S, Merikangas KR.  Mood disorders in children and adolescents: an epidemiologic perspective. Biol Psychiatry. 2001;49:1002–14. 3. Hankin BL, Abramson LY, Moffitt TE, Silva PA, McGee R, Angell KE.  Development of depression from preadolescence to young

68 adulthood: emerging gender differences in a 10-year longitudinal study. J Abnorm Psychol. 1998;107:128. 4. Costello EJ, Egger H, Angold A. 10-year research update review: the epidemiology of child and adolescent psychiatric disorders: I. Methods and public health burden. J Am Acad Child Psychiatry. 2005;44:972–86. 5. Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, et  al. Lifetime and 12-month prevalence of DSM-­ III-­ R psychiatric disorders in the United States: results from the national comorbidity. Surv Arch Gen Psychiatry. 1994;51: 8–19. 6. Birmaher B, Ryan ND, Williamson DE, Brent DA, Kaufman J, Dahl RE, et  al. Childhood and adolescent depression: a review of the past 10 years. Part I. J Am Acad Child Adolesc Psychiatry. 1996;35:1427–39. 7. Hammen C, Brennan PA, Keenan-Miller D, Herr NR. Early onset recurrent subtype of adolescent depression: clinical and psychosocial correlates. J Child Psychol Psychiatry. 2008;49:433–40. 8. Monroe SM, Harkness KL. Life stress, the “kindling” hypothesis, and the recurrence of depression: considerations from a life stress perspective. Psychol Rev. 2005;112:417. 9. Judd LL. The clinical course of unipolar major depressive disorders. Arch Gen Psychiatry. 1997;54:989–91. 10. Lewinsohn PM, Rohde P, Seeley JR, Klein DN, Gotlib IH. Natural course of adolescent major depressive disorder in a community sample: predictors of recurrence in young adults. Am J Psychiatry. 2000;157:1584–91. 11. Fergusson DM, Horwood LJ, Ridder EM, Beautrais AL.  Subthreshold depression in adolescence and mental health outcomes in adulthood. Arch Gen Psychiatry. 2005;62:66–72. 12. Klein DN, Shankman SA, Lewinsohn PM, Seeley JR. Subthreshold depressive disorder in adolescents: predictors of escalation to full-syndrome depressive disorders. J Am Acad Child Adolesc Psychiatry. 2009;48:703–10. 13. Pine DS, Cohen E, Cohen P, Brook J.  Adolescent depressive symptoms as predictors of adult depression: moodiness or mood disorder? Am J Psychiatry. 1999;156:133–5. 14. Heron M.  Deaths: leading causes for 2014. Natl Vital Stat Rep. 2016;65:1–96. 15. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington: American Psychiatric Pub; 2013. 16. Birmaher B, Williamson DE, Dahl RE, Axelson DA, Kaufman J, et al. Clinical presentation and course of depression in youth: does onset in childhood differ from onset in adolescence? J Am Acad Child Psychiatry. 2004;43:63–70. 17. Copeland WE, Angold A, Costello EJ, Egger H.  Prevalence, comorbidity, and correlates of DSM-5 proposed disruptive mood dysregulation disorder. Am J Psychiatry. 2013;170:173–9. 18. Copeland WE, Shanahan L, Egger H, Angold A, Costello EG. Adult diagnostic and functional outcomes of DSM-5 disruptive mood dysregulation disorder. Am J Psychiatry. 2014;171:668–74. 19. Brady EU, Kendall PC. Comorbidity of anxiety and depression in children and adolescents. Psychol Bull. 1992;111:244–55. 20. Stein MB, Fuetsch M, Müller N, Höfler M, Lieb R, Wittchen HU. Social anxiety disorder and the risk of depression: a prospective community study of adolescents and young adults. Arch Gen Psychiatry. 2001;58:251–6. 21. Woodward LJ, Fergusson DM.  Life course outcomes of young people with anxiety disorders in adolescence. J Am Acad Child Adolesc Psychiatry. 2001;40:1086–93. 22. Centers for Disease Control. Attention-deficit/hyperactivity disorder: data and statistics from. 2010. http://www.cdc.gov/ncbddd/ adhd/data.html. 23. Kessler RC, Adler L, Barkley R, Biederman J, Conners CK, Demler O, et  al. The prevalence and correlates of adult ADHD

B. G. Shapero and E. Mazzone in the United States: results from the national comorbidity survey replication. Am J Psychiatry. 2006;163:716–23. 24. Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA. The worldwide prevalence of ADHD: a systematic review and meta-regression analysis. Am J Psychiatry. 2007;164:942–8. 25. Meinzer MC, Pettit JW, Viswesvaran C.  The co-occurrence of attention-deficit/hyperactivity disorder and unipolar depression in children and adolescents: a meta-analytic review. Clin Psychol Rev. 2014;34:595–607. 26. Meinzer MC, Lewinsohn PM, Pettit JW, et  al. Attention-deficit/ hyperactivity disorder in adolescence predicts onset of major depressive disorder through early adulthood. Depress Anxiety. 2013;30:546–53. 27. Biederman J, Ball SW, Monuteaux MC, et al. New insights into the comorbidity between ADHD and major depression in adolescent and young adult females. J Am Acad Child Adolesc Psychiatry. 2008;47:426–34. 28. Chronis-Tuscano A, Molina BSG, Pehlahm WE, et al. Very early predictors of adolescent depression and suicide attempts in children with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2010;10:1044–51. 29. Jane Costello E, Erkanli A, Angold A.  Is there an epidemic of child or adolescent depression? J Child Psychol Psychiatry. 2006;47:1263–71. 30. Kessler RC, Birnbaum H, Bromet E, Hwang I, Sampson N, Shahly V. Age differences in major depression: results from the national comorbidity survey replication (ncs-r). Psychol Med. 2010;40:225–37. 31. Center for Disease Control. Trends in the prevalence of suicide-­ related behavior national YRBS: 1991–2015. 2015. http://www. cdc.gov/healthyyouth/data/yrbs/index.htm. 32. Nock MK, Green JG, Hwang I, McLaughlin KA, Sampson NA, et  al. Prevalence, correlates, and treatment of lifetime suicidal behavior among adolescents: results from the National Comorbidity Survey Replication Adolescent Supplement. JAMA Psychiatry. 2013;70:300–10. 33. Nock MK, Borges G, Bromet EJ, Alonso J, Angermeyer M, Beautrais A, et  al. Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Brit J Psychiatry. 2008;192:98–105. 34. Beck AT, Steer RA, Kovacs M, Garrison B. Hopelessness and eventual suicide: a 10-year prospective study of patients hospitalized with suicidal ideation. Am J Psychiatry. 1985;142: 559–63. 35. May AM, Klonsky ED.  What distinguishes suicide attempters from suicide ideators? A meta-analysis of potential factors. Clin Psychol-Sci Pr. 2016;23:5–20. 36. Baldessarini RJ, Tondo L, Visioli C. First-episode types in bipolar disorder: predictive associations with later illness. Acta Psychiat Scand. 2014;129:383–92. 37. Lish JD, Dime-Meenan S, Whybrow PC, Price RA, Hirschfeld RM. The National Depressive and Manic-depressive Association (DMDA) survey of bipolar members. J Affect Disord. 1994;31:281–94. 38. Brent DA.  Antidepressants and pediatric depression-the risk of doing nothing. N Engl J Med. 2004;251:1598–9. 39. Brent DA. Antidepressants and suicidal behavior: cause or cure? Am J Psychiatry. 2007;164:989–91. 40. Newman TB.  A black-box warning for antidepressants in children. N Engl J Med. 2004;351:1595–7. 41. Simon GE. How can we know whether antidepressants increase suicide risk? Am J Psychiatry. 2006;163:1861–2. 42. Olfson M, Marcus SC, Shaffer D.  Antidepressant drug therapy and suicide in severely depressed children and adults: a case-­ control study. Arch Gen Psychiatry. 2006;63:865–72. 43. Wohlfarth TD, van Zwieten BJ, Lekkerkerker FJ, Gispen-de Wied CC, Ruis JR, et  al. Antidepressants use in children and

5  Early Onset of Depression During Childhood and Adolescence adolescents and the risk of suicide. Eur Neuropsychopharmacol. 2006;16:79–83. 44. Hammad TA, Laughren T, Racoosin J.  Suicidality in pediatric patients treated with antidepressant drugs. Arch Gen Psychiatry. 2006;63:332–9. 45. Bridge JA, Iyengar S, Salary CB, Barbe RP, Birmaher B, Pincus HA, et  al. Clinical response and risk for reported suicidal ideation and suicide attempts in pediatric antidepressant treatment: a meta-analysis of randomized controlled trials. J Am Med Assoc. 2007;297:1683–96. 46. Beardslee WR, Gladstone TR.  Prevention of childhood depression: recent findings and future prospects. Biol Psychiatry. 2001;49:1101–10. 47. Beardslee WR, Gladstone TR, O'Connor EE.  Transmission and prevention of mood disorders among children of affectively ill parents: a review. J Am Acad Child Psychiatry. 2011;50:1098–109. 48. Weissman MM, Wickramaratne P, Nomura Y, Warner V, Pilowsky D, Verdeli H. Offspring of depressed parents: 20 years later. Am J Psychiatry. 2006;163:1001–8. 49. Lewinsohn PM, Rohde P, Klein DN, Seeley JR.  Natural course of adolescent major depressive disorder: I. Continuity into young adulthood. J Am Acad Child Psychiatry. 1999;38:56–63. 50. Clarke GN, Hawkins W, Murphy M, Sheeber LB, Lewinsohn PM, Seeley JR. Targeted prevention of unipolar depressive disorder in an at-risk sample of high school adolescents: a randomized trial of a group cognitive intervention. J Am Acad Child Psychiatry. 1995;34:312–21. 51. Clarke GN, Hornbrook M, Lynch F, Polen M, Gale J, Beardslee W, et al. A randomized trial of a group cognitive intervention for preventing depression in adolescent offspring of depressed parents. Arch Gen Psychiatry. 2001;58:1127–34. 52. Garber J, Clarke GN, Weersing VR, Beardslee WR, Brent DA, Gladstone TR, et  al. Prevention of depression in at-risk adolescents: a randomized controlled trial. J Am Med Assoc. 2009;301:2215–24. 53. Beardslee WR, Brent DA, Weersing VR, Clarke GN, Porta G, Hollon SD, et al. Prevention of depression in at-risk adolescents: longer-term effects. JAMA Psychiat. 2013;70:1161–70. 54. Brent DA, Brunwasser SM, Hollon SD, Weersing VR, Clarke GN, Dickerson JF, et al. Effect of a cognitive-behavioral prevention program on depression 6 years after implementation among at-risk adolescents: a randomized clinical trial. JAMA Psychiat. 2015;72:1110–8. 55. Gillham JE, Reivich KJ, Jaycox LH.  The Penn Resiliency Program. Unpublished manuscript, University of Pennsylvania. 2008. 56. Gillham JE, Reivich KJ, Jaycox LH, Seligman ME.  Prevention of depressive symptoms in schoolchildren: two-year follow-up. Psychol Sci. 1995;6:343–51. 57. Brunwasser SM, Gillham JE, Kim ES. A meta-analytic review of the Penn Resiliency Program’s effect on depressive symptoms. J Consult Clin Psych. 2009;77:1042. 58. Werner-Seidler A, Perry Y, Calear AL, Newby JM, Christensen H.  School-based depression and anxiety prevention programs for young people: a systematic review and meta-analysis. Clin Psychol Rev. 2017;51:30–47. 59. Compas BE, Forehand R, Keller G, Champion JE, Rakow A, Reeslund KL, et  al. Randomized controlled trial of a family cognitive-­behavioral preventive intervention for children of depressed parents. J Consult Clin Psych. 2009;77:1007–20. 60. Compas BE, Forehand R, Thigpen JC, Keller G, Hardcastle EJ, Cole DA, et  al. Family group cognitive–behavioral preventive intervention for families of depressed parents: 18-and 24-month outcomes. J Consult Clin Psych. 2011;79:488. 61. Beardslee WR, Gladstone TR, Wright EJ, Cooper AB. A family-­ based approach to the prevention of depressive symptoms in chil-

69 dren at risk: evidence of parental and child change. Pediatrics. 2003;112:99–111. 62. Beardslee WR, Wright EJ, Gladstone TR, Forbes P.  Long-­ term effects from a randomized trial of two public health preventive interventions for parental depression. J Fam Psychol. 2007;21:703–13. 63. Horowitz JL, Garber J. The prevention of depressive symptoms in children and adolescents: a meta-analytic review. J Consult Clin Psych. 2006;74:401–15. 64. Stice E, Shaw H, Bohon C, Marti CN, Rohde P. A meta-analytic review of depression prevention programs for children and adolescents: factors that predict magnitude of intervention effects. J Consult Clin Psych. 2009;77:486–503. 65. Brunwasser SM, Garber J. Programs for the prevention of youth depression: evaluation of efficacy, effectiveness, and readiness for dissemination. J Clin Child Adolesc Psychol. 2016;45:763–83. 66. Weersing VR, Jeffreys M, Do MC, Schwartz KT, Bolano C.  Evidence base update of psychosocial treatments for child and adolescent depression. J Clin Child Adolesc Psychol. 2017;46:11–43. 67. Lewinsohn PM, Clarke GN. Psychosocial treatments for adolescent depression. Clin Psychol Rev. 1999;19:329–42. 68. Klein JB, Jacobs RH, Reinecke MA.  Cognitive-behavioral therapy for adolescent depression: a meta-analytic investigation of changes in effect-size estimates. J Am Acad Child Psychiatry. 2007;46:1403–13. 69. Weisz JR, Kuppens S, Eckshtain D, Ugueto AM, Hawley KM, Jensen-Doss A.  Performance of evidence-based youth psychotherapies compared with usual clinical care: a multilevel meta-­ analysis. JAMA Psychiat. 2013;70:750–61. 70. David-Ferdon C, Kaslow NJ. Evidence-based psychosocial treatments for child and adolescent depression. J Clin Child Adolesc Psychol. 2008;37:62–104. 71. Mufson L, Weissman MM, Moreau D, Garfinkel R.  Efficacy of interpersonal psychotherapy for depressed adolescents. Arch Gen Psychiatry. 1999;56:573–9. 72. Rudolph KD, Hammen C, Burge D, Lindberg N, Herzberg D, Daley SE. Toward an interpersonal life-stress model of depression: the developmental context of stress generation. Dev Psychopathol. 2000;12:215–34. 73. Shih JH, Eberhart NK, Hammen CL, Brennan PA.  Differential exposure and reactivity to interpersonal stress predict sex differences in adolescent depression. J Clin Child Adolesc Psychol. 2006;35:103–15. 74. Vrshek-Schallhorn S, Stroud CB, Mineka S, Hammen C, Zinbarg RE, et  al. Chronic and episodic interpersonal stress as statistically unique predictors of depression in two samples of emerging adults. J Abnorm Psychol. 2015;124:918. 75. Zhou X, Hetrick SE, Cuijpers P, Qin B, Barth J, Whittington CJ, et al. Comparative efficacy and acceptability of psychotherapies for depression in children and adolescents: a systematic review and network meta-analysis. World Psychiatry. 2015;14:207–22. 76. Wallace AE, Neily J, Weeks WB, Friedman MJ.  A cumulative meta-analysis of selective serotonin reuptake inhibitors in pediatric depression: did unpublished studies influence the efficacy/ safety debate? J Child Adol Psychopharmacol. 2006;16:37–58. 77. Whittington CJ, Kendall T, Fonagy P, Cottrell D, Cotgrove A, Boddington E.  Selective serotonin reuptake inhibitors in childhood depression: systematic review of published versus unpublished data. Lancet. 2004;363:1341–5. 78. Cipriani A, Zhou X, Del Giovane C, Hetrick SE, Qin B, Whittington C, et al. Comparative efficacy and tolerability of antidepressants for major depressive disorder in children and adolescents: a network meta-analysis. Lancet. 2016;388:881–90. 79. Qin B, Zhang Y, Zhou X, Cheng P, Liu Y, Chen J, et al. Selective serotonin reuptake inhibitors versus tricyclic antidepressants in

70 young patients: a meta-analysis of efficacy and acceptability. Clin Ther. 2014;36:1087–95. 80. March J, Silva S, Petrycki S, Curry J, Wells K, Fairbank J, et al. Fluoxetine, cognitive-behavioral therapy, and their combination for adolescents with depression: Treatment for Adolescents With Depression Study (TADS) randomized controlled trial. J Am Med Assoc. 2004;292:807–20. 81. March JS, Silva S, Petrycki S, Curry J, Wells K, Fairbank J, et  al. The Treatment for Adolescents With Depression Study (TADS): long-term effectiveness and safety outcomes. Arch Gen Psychiatry. 2007;64:1132–43. 82. Zhou X, Michael KD, Liu Y, Del Giovane C, Qin B, Cohen D, et  al. Systematic review of management for treatmentresistant depression in adolescents. BMC Psychiatry. 2014; 14:340. 83. Dubicka B, Elvins R, Roberts C, Chick G, Wilkinson P, Goodyer IM.  Combined treatment with cognitive–behavioural therapy in adolescent depression: meta-analysis. Brit J Psychiatry. 2010;197:433–40. 84. Brent D, Emslie G, Clarke G, Wagner KD, Asarnow JR, Keller M, et al. Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. J Am Med Assoc. 2008;299:901–13. 85. Mazzucchelli T, Kane R, Rees C. Behavioral activation treatments for depression in adults: a meta-analysis and review. Clin Psychol-­ Sci Pr. 2009;16:383–411. 86. Cuijpers P, Van Straten A, Warmerdam L.  Behavioral activation treatments of depression: a meta-analysis. Clin Psychol Rev. 2007;27:318–26. 87. Dimidjian S, Hollon SD, Dobson KS, Schmaling KB, Kohlenberg RJ, Addis ME, et  al. Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the acute treatment of adults with major depression. J Consult Clin Psych. 2006;74:658–70. 88. Tindall L, Mikocka-Walus A, McMillan D, Wright B, Hewitt C, Gascoyne S.  Is behavioural activation effective in the treatment of depression in young people? A systematic review and meta-­ analysis. Psychol Psychother. 2017;90(4):770–96.

B. G. Shapero and E. Mazzone 89. McCauley E, Gudmundsen G, Schloredt K, Martell C, Rhew I, et  al. The adolescent behavioral activation program: adapting behavioral activation as a treatment for depression in adolescence. J Clin Child Adolesc Psychol. 2016;45:291–304. 90. Ritschel LA, Ramirez CL, Cooley JL, Craighead EW. Behavioral activation for major depression in adolescents: results from a pilot study. Clin Psychol-Sci Pr. 2016;23:39–57. 91. Kvam S, Kleppe CL, Nordhus IH, Hovland A.  Exercise as a treatment for depression: a meta-analysis. J Affect Disord. 2016;202:67–86. 92. Carter T, Morres ID, Meade O, Callaghan P. The effect of exercise on depressive symptoms in adolescents: a systematic review and meta-analysis. J Am Acad Child Psychiatry. 2016;55:580–90. 93. Korczak DJ, Madigan S, Colasanto M. Children’s physical activity and depression: a meta-analysis. Pediatrics. 2017;139:2016–266. 94. Mangerud WL, Bjerkeset O, Lydersen S, Indredavik MS. Physical activity in adolescents with psychiatric disorders and in the general population. Child Adol Psych Men. 2014;8:2. 95. Puetz TW, O’connor PJ, Dishman RK. Effects of chronic exercise on feelings of energy and fatigue: a quantitative synthesis. Psychol Bull. 2006;132:866. 96. Dunn AL, Trivedi MH, Kampert JB, Clark CG, Chambliss HO. Exercise treatment for depression: efficacy and dose response. Am J Prev Med. 2005;28:1–8. 97. Asarnow JR, Miranda J. Improving care for depression and suicide risk in adolescents: innovative strategies for bringing treatments to community settings. Ann Rev Clin Psychol. 2014;10:275–303. 98. Curry JF. Specific psychotherapies for childhood and adolescent depression. Biol Psychiatry. 2001;49:1091–100. 99. Curry JF. Future directions in research on psychotherapy for adolescent depression. J Clin Child Adolesc Psychol. 2014;43:510–26. 100. Sakolsky D, Birmaher B.  Developmentally informed pharmacotherapy for child and adolescent depressive disorders. Child Adol Psych Cl. 2012;21:313–25. 101. Maalouf FT, Brent DA. Child and adolescent depression intervention overview: what works, for whom and how well? Child Adol Psychiatr Clin. 2012;21:299–312.

6

Cross-Cultural Approaches to Mental Health Challenges Among Students Xiaoqiao Zhang, Tat Shing Yeung, Yi Yang, Rohit M. Chandra, Cindy H. Liu, Dana Wang, Sukhmani K. Bal, Yun Zhu, Rebecca Nika W. Tsai, Zhenyu Zhang, Lusha Liu, and Justin A. Chen

Case Vignette

Teresa is the 18-year-old American-born daughter of Asian immigrant parents who both work in a scientific field. An only child, she is a perfectionist who shares her parents’ high standards and somewhat rigid thinking style. She began to exhibit symptoms of anxiety and depression as a junior at an academically rigorous high school and experienced frequent suicidal ideation. However, she and her parents were skeptical of mental illness and did not seek treatment at that time. Despite her symptoms, she continued to perform at a high level academically and took on leadership roles in several intensive extracurricular activities, but outside of school she was socially isolated and exhibited significant anxiety and avoidance.

X. Zhang The Pennsylvania State University, College of Education, State College, PA, USA e-mail: [email protected]

The summer before matriculating at an elite New England university, her anxiety became so paralyzing that she could not complete basic tasks, including sending emails and completing required paperwork for the fall semester. She became increasingly isolated, spending most of her time in her room watching TV or surfing the web. She could not imagine starting school in the fall, but feared if she didn’t, she would end up working at a fast food restaurant. Her suicidal thoughts increased, and her parents brought her to a psychiatrist for evaluation. The psychiatrist was alarmed by the severity and duration of Teresa’s untreated symptoms and recommended regular psychotherapy and initiation of a selective serotonin reuptake inhibitor to

D. Wang Rivia Medical PLLC, New York, NY, USA e-mail: [email protected] S. K. Bal · R. N. W. Tsai Massachusetts General Hospital, Center for Cross-Cultural Student Emotional Wellness, Boston, MA, USA e-mail: [email protected]; [email protected]

T. S. Yeung Northeastern University, Department of Applied Psychology, Boston, MA, USA e-mail: [email protected]

Y. Zhu Harvard Chan School of Public Health, Cambridge, MA, USA e-mail: [email protected]

Y. Yang Private Practice, Clinical Psychology, Arlington, MA, USA

Z. Zhang Boston University School of Medicine, Allston, MA, USA e-mail: [email protected]

R. M. Chandra Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA e-mail: [email protected]

L. Liu Private Practice, Adult Psychiatry, Boston, MA, USA

C. H. Liu Departments of Pediatric Newborn Medicine and Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]

Department of Psychiatry, Northpoint Health and Wellness Center, Emotional Wellness and Behavior Health, Minneapolis, MN, USA J. A. Chen (*) Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_6

71

72

X. Zhang et al.

treat depression and anxiety, with possible escalation to a higher level of care if her suicidal thoughts worsened. Both the student and her family were reluctant to conceptualize her symptoms in terms of a mental illness that might benefit from psychiatric treatment despite being provided with medical literature by the psychiatrist, who had hoped that this approach might appeal to their scientific backgrounds. Ultimately, the family declined all recommendations for intervention and expressed a belief that once she started school she would improve on her own. Midway through her first semester, she presented to the university’s student health service complaining of an asthma exacerbation and abdominal discomfort. During her evaluation by the medical nurse practitioner, she also acknowledged heightened anxiety and suicidal thoughts. She was referred back to the same psychiatrist, and at that time, she and her parents agreed to psychiatric hospitalization.

Introduction The Asian American population has increased by 72% since 2000 [1], and demographic studies predict that by 2055, Asians will surpass Hispanics as the nation’s largest immigrant group [2]. Applauded as the “model minority” by the mainstream media, Asian Americans in fact represent an extremely heterogeneous group hailing from more than 20 countries of origin and representing a wide range of languages, religions, cultural values, and immigration patterns, with a correspondingly wide range of trajectories, successes, and challenges within the United States [3]. As a diverse but relatively small group representing less than 6% of the total US population [4, 5], the stresses experienced by Asian families in the process of immigration, adaptation, and acculturation, and subsequent effects on mental health, have not been well characterized. Given the cultural stigma surrounding mental illness and treatment, as well as an emphasis on “­ saving face,” Asian Americans are less likely to seek professional help for their emotional distress and are less likely to self-disclose mental health problems [6]. As “hidden ideators”—i.e., those whose suicidal ideation only becomes evident after explicit assessment by a mental health provider [7]—Asian Americans’ psychological struggles are therefore frequently invisible to the outside world.

Recently, Asian American student mental health has become an increasing focus of national concern and media attention. Suicide clusters involving high proportions of Asian American students in Palo Alto, CA, and Newton, MA, prompted an epidemiological study by the US Centers for Disease Control and Prevention, which concluded that common mental health problems such as depression and anxiety were major factors in the suicides of these youths [8]. Meanwhile, high-profile articles published in lay press outlets have increasingly highlighted the role of parental factors in affecting mental health of students in school districts with large proportions of Asian immigrant parents [9, 10]. Anecdotal reports have suggested that Asian American college students have an elevated rate of suicide, including those at elite institutions. For example, of the completed suicides at Cornell University from 1996 to 2006, 62% involved Asian American students [11], and of the 19 documented suicides on the MIT campus since 2000, 42% involved Asian Americans [12]—both disproportionate to the percentage of students on campus who are Asian. These media accounts are supported by the research literature, which suggests that Asian American adolescents and young adults are at higher risk for depression, suicidal ideation, and suicide attempts compared to their peers [13]. Asian American students are also known to struggle with high levels of social anxiety and stress [14]. In light of the increased attention to mental health, parents, schools, community members, and even celebrities have raised concerns about the role of parental pressures and academic stress in contributing to psychiatric problems in Asian American youth. Along with Asian Americans, another fast-growing and understudied group is international students coming to the United States to pursue education at the university, graduate, postdoctoral, and increasingly even the high school levels. The number of international students on US university campuses exceeded one million for the first time in 2016—double the number in 1996—and continues to rise [15]. Of these one million students, well over half are from Asia, including 32.5% from China, 17.3% from India, and 5.4% from South Korea [15]. In particular, the Chinese international student population has increased more than sixfold during the past 15 years [16]. Despite increased attention to depression and other mental health issues among international students, research and basic epidemiologic data remain limited. The few studies on Chinese international student mental health suggest that these students face a greater burden of depressive symptoms than their counterparts who remained in China for their education. However, there remains a lack of large-scale, high-quality epidemiologic studies about the prevalence and trajectory of mental health problems in this population and among international students from countries other than China [16].

6  Cross-Cultural Approaches to Mental Health Challenges Among Students

The factors that contribute to the onset of psychological distress in Asian American populations vary considerably for these two groups. First-generation Asian Americans (i.e., new immigrants or international students) often experience significant levels of stress from daily school activities and peer interactions due to language barriers, unfamiliarity with the host culture, lack of supports, etc. Meanwhile, secondgeneration Asian Americans (i.e., those born in the United States to immigrant parents) may experience stress related to the cross-cultural gap with their family of origin, parenting factors [17], and challenges of forming a self-identity, all of which will be discussed in greater detail below [18]. However, there are certain factors common to both groups, including culturally based stigma and variation in illness beliefs regarding mental health problems and their treatment, which affect awareness and help-seeking. This chapter focuses on depression in Asian American students and Asian international students as one particularly striking example of the mental health challenges facing diverse populations in the United States. We have chosen to focus our efforts on students because education is considered a top priority in many Asian cultures as a pathway for socioeconomic advancement, and therefore students often represent the main focus of Asian immigrant families’ hopes, dreams, and anxieties for the future, at times resulting in heightened emotional and psychological stresses during this critical social-emotional developmental period. Because such a large proportion of Asians in the United States are either immigrants themselves (“first generation”) or the children of immigrants (“second generation”), understanding cross-cultural challenges is key when considering Asian American mental health. In this chapter, we briefly survey the available literature regarding prevalence of symptoms and diagnoses of depression among Asian American and Asian international students. Next, we summarize some of the key factors that have been proposed to contribute to increased rates of depression and poor outcomes in these populations, including lack of psychoeducation and awareness, family dynamics, mental health stigma, stereotyping, and culturally influenced illness beliefs and health behaviors. The MGH Center for Cross-Cultural Student Emotional Wellness is described as an innovative approach to tackle the challenges outlined in this chapter. Finally, we provide clinical suggestions and discuss frequently asked questions regarding management of depression in Asian American and international student populations.

Who Are Asian Americans? Asian Americans are an extremely diverse group of individuals originating from the more than 20 countries that make up the largest continent in the world, sometimes categorized or

73

clustered into geographic regions such as East Asia (e.g., China, Japan, Korea), Southeast Asia (e.g., the Philippines, Thailand, Vietnam, Cambodia), and South Asia (e.g., India, Bangladesh, Pakistan). Among Asians in the United States, individuals of Chinese descent represent the largest ethnic group (Five million, or 24% of all Asian Americans), followed by those of Indian (Four million, or 19.5%) and Filipino (3.9 million, or 19%) descent [1]. Asian countries possess distinct cultures, customs, languages, and habits that are influenced by differing religious, historical, and other factors. Some of these countries, such as China, Japan, and Korea, or Pakistan and India, bear historical enmities and grudges dating back decades, if not centuries, and yet when they move to the United States they are often encountered as one indistinguishable “Asian” race. Indeed, some cultural commentators have gone so far as to state that “‘AsianAmerican’ is mostly a meaningless term” and that the one thing that most Asians in America have in common is stereotypes [19]. The lumping together of Asian Americans into a single, wildly heterogeneous group is a legacy of the European racial anthropology tradition of the mid-nineteenth century, which sought to categorize humans based on biological differences with the goal of explaining and predicting individual and group behavior, moral character, and even intelligence [20]. Since that time, race has been shown to be primarily socially rather than biologically constructed [21]. Nonetheless, race exerts a clear and demonstrable impact on a wide range of outcomes, including health and disease, through social mechanisms. Therefore, modern medical and public health studies continue to utilize race as a useful construct to better understand health conditions and disparities. In addition to these scientific considerations, preserving the construct of “Asian Americans” serves pragmatic ends as well; despite their rapid demographic growth, the fact that individuals of Asian origin comprise less than 6% of the total US population means that their numbers are dwarfed by other major minority groups in this country, including African Americans (12.6%) and Hispanics (17%), another group that is often lumped together into a single group despite significant underlying heterogeneity. Attempting to study each of the individual Asian American cultures might yield more specific insights, but is not feasible or generalizable given the demographic realities in this country. Although they clearly differ from one another in numerous ways, Asian cultures have been described as sharing certain common characteristics. These include collectivistic and family-oriented value systems that emphasize education and respect for elders, more hierarchical and group-oriented ways of relating in society, and high-context “indirect” communication styles, in which an emphasis is placed on nonverbal communication strategies such as contextual factors and meaning is not always conveyed verbally [22, 23]. Some members of

74

X. Zhang et al.

these groups may also share similar patterns of and motivations for migration to the United States, including a desire for greater financial, educational, and occupational opportunities. Insofar as these shared attributes may exert an impact on issues that we are interested in better understanding, including mental health, it is both reasonable and prudent to utilize the umbrella term “Asian American,” while recognizing and acknowledging the considerable limitations of this term.

 revalence of Depression in Asian American P and Asian International Students Case Vignette

Ping, a 16-year-old Chinese student, was seemingly doing well at school until 2 years ago, when an end-ofsemester project led to a nervous breakdown, self-injurious behavior, and a trip to the emergency room. She had worked long and hard to achieve perfect grades and live up to high academic expectations, and the possibility of a less-than-perfect final project was the last straw. Now she barely eats, hallucinates under stress, and has difficulty concentrating and sleeping. She has taken medical leaves, transferred schools, and is aiming only for online schooling. “How could she suddenly have depression?” asked Ping’s mother, looking worried and clueless. For Ping, however, “[Depression] has always been part of my life. I feel such a hole in me.” Since Ping was a toddler, her father had multiple mistresses along with his business success in booming China. One of these women bore him a son, considered more favorable among the older generation. Ping’s father chose not to get divorced and has been living with this other child and mother. Not knowing about this history as a child, Ping was confused that her father’s side of the family turned their back on her and her mother. To add more confusion and pain, she did not understand why her mom stopped singing to her or kissing her and instead began to be extremely critical of her every action. She also began to exercise corporal punishment on Ping. After she finally learned the truth about her father’s other family at age 12, Ping felt she and her mother were “dumped out” to the United States—fitting a trend among wealthy Chinese families of sending their children for Western education and saving the father the trouble of juggling two households in close proximity. Away from family and friends, and overwhelmed by language and cultural barriers, she cried herself to

sleep, had no luck in making friends, yet still got fine grades until her breakdown. In therapy, Ping’s individual “hole” is viewed against a larger backdrop of the lust and losses, vibrancy, and confusion of mainland China as a whole, such as the lust for power, wealth, and privilege, the loss of certain traditional values and practices, the vibrant economic and social development, as well as the questioning and reflection for every individual regarding what is worth pursuing beyond material goods. In the therapeutic space, Ping’s sorrows are held, her anger is validated, and her fears are explored. The therapist strives to see, appreciate, and draw upon Ping’s strengths. The therapist asks questions on a variety of levels regarding life choices and values to broaden Ping’s perspectives and self-definition. Psychoeducation on depression and developmental psychology is provided to Ping’s mother, but significant work remains for this family in terms of reaching an understanding of adolescent mental health in a cross-cultural and cross-generational context, as well as practicing positive parenting.

The prevalence of mental health problems among Asian American students has been characterized to a limited extent within the academic literature. Young and colleagues compared depression severity in Asian American and White students at a single university campus using the 9-item Patient Health Questionnaire (PHQ-9), an instrument commonly used to screen for depression in the primary care setting and that can produce a numerical score that indicates symptom severity [24]. These researchers found that Asian Americans exhibited significantly elevated levels of depressive symptoms compared to Whites, with Korean Americans having the highest rates. These findings were replicated in a later study by Hunt and colleagues using data from the Healthy Minds Study (HMS), a multicampus survey of students’ mental health with 13,028 participants. Also using the PHQ9, this study found that Asian American college students had a higher rate of depressive symptoms compared to their White counterparts [25]. Other studies, while not directly reporting on depression rates, have repeatedly uncovered concerning disparities in Asian American mental health, including suicidality. Kisch and colleagues, analyzing data from the American College Health Association’s 2000 National College Health Assessment, found that Asian Americans had 1.6 times the odds of seriously considering attempting suicide compared to White students [13]. Indeed, Asian American women aged 15–24 have the second-highest rate of completed suicide of any race/ethnicity in that age group, second only to Native American women [26].

6  Cross-Cultural Approaches to Mental Health Challenges Among Students

Fewer studies have attempted to examine the prevalence of mental health problems in younger Asian American students, including at the secondary school level. Such research is limited by methodological challenges of gathering data in a younger age group as there is often less access to the populations of interest within larger-scale epidemiologic studies that can accurately assess for incidence and prevalence of disorders. As the number of Chinese international high school students increases, in part to increase the odds of being selected to attend a prestigious US university [16], much more research and improved validation of translated measures will be needed to characterize the mental health of this new cohort of students. Regarding the mental health of Asian international students, no large-scale epidemiological studies specifically focused on this population have been conducted to date. Most studies investigating depression rates among Asian international students have adopted a cross-sectional design to assess different risk factors and correlates of depression based on questionnaire data without formal diagnoses [16]. For instance, Asian international students from a private Northeastern university and a public Midwestern university in the United States reported more depressive symptoms on average compared to college students back in China [16, 27, 28]. Specifically, two to four times more Asian international students scored above cutoff points for their respective measures of depression when compared with university students in China, indicating that the students who had come to the United States for their studies were more likely to be at risk for depression than their peers who remained in China. Asian international students also exhibit more depressive symptoms than White American and Asian American college students [29, 30]. Wang and colleagues reported that Asian international students exhibit significantly stronger suicidal ideation than Asian American students [31]. It should be noted that data from these studies were collected cross-sectionally from one or two universities in different regions of the United States and therefore may not be generalizable to the larger Asian international student population in the United States. Nonetheless, the limited research in this area suggests there is cause for concern regarding international student mental health and should be followed up with largerscale and nationally representative studies.

 ontributing Factors and Correlates C of Depression in Asian American and Asian International Students When Asian families move to the United States, whether as part of a process of immigration or education, many issues arising from both within the family system and the external environment can affect students’ emotional well-being. Most

75

parents understand that their children must integrate with the host culture to a certain degree to be successful, yet these same parents may desire for their children to retain aspects of their home culture. Clashes between parents’ expectations for their children and the cultural expectations those same children receive from the Western society in which they are being educated and socialized may contribute to significant cross-cultural and intergenerational conflict [32]. Simultaneously, Asians in America face stresses familiar to other minorities, including stereotyping, discrimination, and a lack of culturally appropriate services. Elucidating and understanding these various risk factors can help to explain the increased rates of depression and suicidality in these populations as well as identify potential targets for focused intervention. In this section, we provide a brief overview and summary of the existing literature in this area. We take a bioecological systems approach beginning with factors arising from within the individual family unit and then expanding out to larger societal contributors. We conclude this section with a discussion tailored toward specific factors affecting Asian international students.

Family Expectations In many families from collectivistic Asian cultures, conformity and obedience are valued, and deviations from the cultural norm, including displays of strong emotion in public, are often met with disapproval [33]. Group-oriented values like conformity, humility, respect for authority, and family duty are prioritized [34]. For example, the role of the child is often understood as obeying the wishes of parents and dutifully pursuing education and work until the time of marriage. Meanwhile, parents are expected to provide for both their children and their parents and have the primary responsibility for day-to-day operations and decisions within the household. Elderly family members often possess significant power within the family hierarchy. For example, elderly members of Asian families may expect to provide childcare and other useful services as part of their accepted social roles, but also for their adult children to care for them in their old age. In line with Asian values of collectivism, conformity to norms, emotional self-control, and humility [35], group harmony within the family unit is privileged over the pursuit of individual desires [34]. Among East Asian cultures, these values are highly influenced by Confucian philosophy, in which harmony, respect, and avoidance of direct conflict are highly valued and respected [36]. While this model possesses many strengths, it may present challenges when an individual family member’s needs or wishes conflict with the groups’. Due to the elevated importance of role expectations in Asian families, each stage of the life cycle presents unique

76

conflicts that can contribute to the development of psychological problems or mental illness [33]. From a young age, Asian youths are expected to suppress their emotions, with the result that they may struggle with emotional management later in life [33]. Adolescents are encouraged to stay within the group, and seeking definition of oneself outside the family as well as sharing private information with others is discouraged [33]. Meanwhile, in America, late adolescence and early adulthood (including the college period) are considered a time for exploration. During this stage in the life cycle, students may be living away from home for the first time and experimenting with autonomy and forging a unique personal identity. Asian Americans in this age group may feel significant peer pressure to conform to societal norms and fit in with their American counterparts. However, the parents’ focus at this stage remains on pursuing educational and financial achievements. Therefore, students frequently experience conflict between family expectations and peer group values [33].

Parental Factors and Acculturative Stress There is often a gap between the values of second-generation Asian American youths who were born and raised in the United States and their parents, especially with regard to academic and career expectations [37]. Traditional Asian parenting is often characterized by strictness, inflexibility, setting high expectations, and choosing punishment over discipline, also described as an “authoritarian” or “high control, low warmth” parenting style by the development psychologist Diana Baumrind [38]. The Confucian concept of filial piety (孝. xiao), which refers to a virtue of respect for one’s parents, elders, and ancestors, helps to explain the authoritarian parenting style seen among many parents from Confucian cultures—e.g., China, Korea, and Vietnam [39]. Under this model, parents’ goal is to train their children to be hardworking, self-disciplined, and obedient, and this is often ­accomplished through strictness, adherence to a rigid family hierarchy, and conformity to parental and societal expectations. Thus, Asian parents with traditional values may be inclined to emphasize the value of filial piety and convey an expectation that their children will prioritize family values over personal interests. Meanwhile, Asian American children often feel that their parents are clinging to a simplistic hard line on education and career and have little understanding of the American school system. Consequently, they believe their parents have little capacity to assist them in navigating the reality of their educational journey [40]. As these youths become further acculturated to Western cultural values that emphasize autonomy and self-expression, they may experience increasing conflict with their parents’ demand for obedience [41].

X. Zhang et al.

When children feel unable to meet the expectations of both cultures, they risk maladjustment and worse mental health outcomes. In Baumrind’s model, the authoritarian parenting style is seen as less adaptive than authoritative parenting, which is characterized by a combination of high control and high warmth in which parents balance a high degree of demandingness with responsiveness to and support for the child’s needs [38]. The high school and college years are also the period when Asian American children are encouraged to adopt certain aspects of their host culture [33]. One obvious aspect that must be adopted is language, needed for both educational and career success. However, Asian immigrant parents may continue to utilize their native language at home and promote traditional values, leading to a confusing hybrid of languages and cultures for the child. The process of reconciling these different cultural influences can result in significant strife and intergenerational conflict, sometimes also known as “acculturative stress” [42]. This construct has been a particular focus of research in depression among Asian American students. In a study conducted by Park, immigrant Asian Americans as well as those born in the United States but who spoke a language other than English at home had higher levels of depression compared to Asian American youths who were born in the United States and whose families spoke English [43]. The fourfold model of acculturation can be helpful for characterizing an individual’s approach toward acculturation and for identifying possible vulnerabilities for developing mental health problems [44]. This model categorizes acculturation strategies along two dimensions: retention or rejection of an individual’s native culture versus adoption or rejection of the host culture. The four acculturation strategies that have been described are assimilation (the individual rejects his or her native culture in favor of the host culture), separation (the individual maintains his or her native culture and rejects the host culture), integration (biculturalism, or the development and acceptance of a hybrid between the native and host cultures), and marginalization (the individual rejects both native and host cultures) [45]. Individuals navigating conflicting expectations of Asian and American cultures through assimilation experience “bicultural stress” arising from the desire to remove themselves from their own family and find their own sense of self more in line with American values. Iwamoto and Liu found that the conflict that arises when children attempt to separate from their family’s Asian culture while retaining a perceived personal need to adhere to certain traditional values leads to a greater incidence of interpersonal stress and, consequently, higher risk of depression [46]. Similarly, Rhee and colleagues discuss how acculturative stress can exert a significant impact on self-esteem and life satisfaction, leading to a variety of psychosocial adjustment problems [47]. Integration

6  Cross-Cultural Approaches to Mental Health Challenges Among Students

has been found to result in the most positive adaptation, but the successful adoption of integration depends on the specific individual’s views regarding the dominant culture and the minority cultures represented by his or her native family, community, and society [44].

Stereotypes The “model minority” stereotype, promoted in part by the mainstream media [48], suggests that Asian Americans do not have issues normally attributed to other minorities. Liu and colleagues go further to describe a stereotype that “Asian Americans have successfully overcome discrimination to become a uniformly successful minority group worthy of admiration by other minorities” [34]. This may seem like a compliment to Asian Americans, but this stereotype, like all others, can have negative consequences. Stereotypes mask the wide diversity within the Asian American population with respect to educational needs, financial needs, and mental well-being [34]. For example, according to the Congressional Asian Pacific American Caucus, approximately 40% of Hmong, Laotian, and Cambodian populations do not finish high school, and 32% of Korean Americans and 25% of Native Hawaiians and Pacific Islanders do not have health insurance [49]. Meanwhile, according to the Economic Policy Institute, between 2007 and 2010 Asian Americans and Pacific Islanders had the highest share of long-term unemployment of any racial group [49]. The stereotype that Asian Americans are uniformly successful and have no issues contributes to their mental health problems going unnoticed and untreated [34]. Asian Americans may be teased, harassed, or socially excluded by classmates because they perceive them to only be interested in schoolwork and to receive preferential treatment from teachers, which may in turn lead to poor social skills and poor mental health outcomes in Asian American children [34]. Asian Americans may feel significant pressure to fulfill public expectations of the model minority stereotype, which adds to the preexisting psychological stress about academic achievement and ironically can further harm their academic performance [34].

Illness Beliefs, Face, Shame, and Stigma Research has shown that conceptualizations of mental health and mental illness vary across cultures [22]. In EuroAmerican culture, self-identity is treasured, and the psychological self is central to one’s well-being [50]. However, many other cultures including Asian and Hispanic emphasize group identity and have a holistic view of physical and mental health [51]. The collectivistic Asian emphasis on

77

shame as a foundational and useful regulatory emotion may discourage the sort of individualism that is so prized in American culture [52]. Shame is closely linked to the concept of “face,” which has been described in Confucian cultures as representing a person’s moral standing within the community [53]. Individuals from collectivist cultures have been shown to believe that losing face is more painful than physical abuse [54]. The interdependent nature of Asian culture can leave those with more traditional values feeling like a burden in the face of failure to maintain harmony. An emphasis on group identity means that disclosing personal problems to mental health professionals may be viewed as not only representing weakness as an individual but also a potential source of disgrace for the family. This sense of shame in turn affects many Asian Americans’ perceived productivity and sense of selfworth, eliciting further feelings of guilt, shame, and burdensomeness that can give rise to suicidal ideation and behaviors. The Interpersonal Theory of Suicide describes thwarted belongingness (unmet need to belong) and perceived burdensomeness (being a liability to others because of personal flaws) as two interpersonal constructs that contribute to suicidal behaviors [55, 56]. Chu and colleagues found that perceptions of being a burden can predict suicidal behaviors in Asian American college students and older adults, a finding that echoes the important role of perceived burdensomeness in contributing to self-harm thoughts in Joiner’s Interpersonal Theory of Suicide [55, 57]. Face and shame are also linked to another important mental health-related issue, namely, stigmatization of mental illnesses and their treatment. This stigma prevents people from seeking and accessing mental health care and may contribute to the exacerbation of mental health problems among Asian American and international students [58–60]. In relation to mental health, researchers have identified a two-factor structure of stigma: public stigma (i.e., the public endorsement of prejudice about a stigmatized group) and personal stigma (i.e., the internalization of public stigma among members of a stigmatized group) [61]. The differences in conceptualizations in turn affect mental health-care seeking behavior. Consequently, in many Asian cultures, there is a negative perception of people seeking mental health care, and parents are also reluctant to admit that their children have mental health issues and/or need to see a mental health provider [62]. Indeed, Asian American parents frequently decline receiving help for their children even when they show signs of depression. This is especially true among Asian Americans with low levels of acculturation and/or strong traditional East Asian values, as these individuals endorse less positive attitudes about seeking help for mental health problems [34]. These findings mirror attitudes toward help-seeking among Asian Americans in general, who are among the least likely of any racial/ethnic group in the United States to seek mental health

78

services [6].There is a lack of basic psychoeducation and awareness of mental health problems in the Asian American community, and some mental health conditions may not be easily described in certain cultures or languages. Additionally, culture and illness conceptualizations may influence the experience and expression of mental disorders; for example, Asians have been found to place a greater emphasis on somatic rather than psychological symptoms of depression [63]. Thus, practitioners using Western diagnostic tools to work with Asian American population may find it difficult for patients to understand and express themselves with Westernbased terminology and concepts [64]. In addition, although cultural conceptualizations of distress (CCD) are identified in the DSM-5, these constructs may not be considered “mental illnesses” per se in the country of origin [65]. Some cultures may consider a mental illness the result of spirit possession, and patients may seek help from a folk healer or spiritual guru rather than a medical professional [66]. Some Asian cultures also believe that mental illnesses may just be a personal problem and can be solved by willpower and avoidance of negative thoughts [67, 68]. Students may then not be aware of the seriousness of the mental problems that they face or recognize the need to seek treatment [69].

 isk Factors and Correlates of Depression R in Asian International Students Asian American and Asian international students share certain risk factors for mental health problems, including cultural stigma, family factors, and stereotyping. However, as individuals who have had to leave their home country and language to study in the United States, Asian international students face unique challenges that bear special mention here. The process of moving to a new country may involve the international student alone, or in the case of “astronaut families,” one parent may immigrate to the United States with the student while the other parent stays in the country of origin to make money. Common examples of the stressors encountered by international students in the United States include language barriers, homesickness, academic challenges, culture shock, loneliness, and perceived discrimination [70, 71]. In this section, we will present some of the issues that may specifically contribute to depression among Asian international students.

Language Barriers and Communication Issues Language difficulties are a common issue among international students, even though most are required to pass an English language proficiency test before admission [72, 73]. In academic settings, some international students struggle

X. Zhang et al.

with accents, reading comprehension, listening comprehension, pronunciation, and writing [74, 75]. These linguistic challenges are directly related to academic performance and may cause significant distress in international students [76]. In daily life, the cultural aspect of communication may be more pronounced. References and jokes are culturally dependent, and for some international students, a lack of cultural knowledge can negatively affect their ability to develop relationships with Americans [77]. As a result of these difficulties, some Asian international students may choose to only interact with students of the same cultural background and to avoid social interaction with American students [78]. In clinical settings, international students may not possess the language to express themselves properly and may exhibit verbal and nonverbal communication styles incongruous with the mainstream American culture, risking misinterpretation by the clinician [22]. For example, traditional Asian culture values subtlety and indirectness in verbal communication instead of the expressiveness that is often promoted in American culture [79], and some Asian patients may avoid directly stating their beliefs or disagreeing with their provider about a given treatment approach. Sue and Sue outline four dimensions of nonverbal communication clinicians or counselors should pay attention to when providing therapy to the culturally and linguistically diverse including proxemics (personal space), kinesics (bodily movements such as facial expressions, gestures, and eye contact), paralanguage (vocal cues such as loudness, pauses, and silences), and high-low context communication (a high-context culture relies on context in communication, whereas a low-context culture focuses on the explicit content of a message) [22]. If the clinician or counselor is not culturally sensitive, misunderstanding may occur and the patient is more likely to terminate therapy early [80, 81].

Family Expectations Demanding family expectations influenced by traditional cultural values and collectivistic worldviews can exacerbate maladaptive perfectionism among Asian international students who were raised with an emphasis on the value of diligence and a view of academic achievement as an honor to the family [22, 82]. Whereas some international students receive financial assistance from their university and external organizations, the majority rely on their family’s monetary support to study in the United States [15]. The economists McNeal and Yeh coined the term “compensation syndrome” to describe a phenomenon in which parents who were deprived of opportunities in their youth push their children to achieve academic and career success [83]. At the same time that many international students identify family support as an important criterion for them to succeed,

6  Cross-Cultural Approaches to Mental Health Challenges Among Students

they feel burdened by the need to please their family by meeting their high expectations [74, 84]. Because of all these factors, many international students devote the great majority of their time to their studies. The pressure to achieve makes these students more reluctant to admit even serious problems or to seek help from family and may also lead to a relative underemphasis on other aspects of normal development including socialization, nonacademic activities, and healthy habits such as exercise, diet, and sleep [85]. Over time, this dynamic contributes to a gradual accumulation of stress.

Social Support International students in the United States are separated from the social support systems in their home countries [86]. This issue is particularly important for international students because they often prefer to seek help for psychological problems from people within their social network rather than from mental health professionals [87]. Existing literature has found that lower levels of social support are correlated with an increase in depressive symptoms [78, 88]. Yang and Clum reported that Asian international students with less social support express more hopelessness, a symptom of depression and risk factor for suicide [88, 89]. Sümer and colleagues found that international students who have greater English language proficiency and have stayed in the United States longer tend to report stronger social support [78]. This finding is consistent with the negative effect of language barriers as a risk factor for depression discussed above, since students with better English language skills are able to communicate more effectively with peers.

Meeting the Need: The Formation of the MGH Center for Cross-Cultural Student Emotional Wellness To respond to the challenges detailed in the previous sections, the MGH Center for Cross-Cultural Student Emotional Wellness (CCCSEW), an academic consortium based within a research hospital, was founded in 2014 by three Bostonbased physicians who recognized a tremendous need to support the emotional health of students from non-Western cultural backgrounds. As practicingmental health clinicians, they noticed increasing referrals of Asian American students for problems like anxiety, depression, and suicidal thoughts. As bicultural individuals themselves, with perspectives informed by diverse experiences including clinical work with Asian and Western patients, public health training, and parenthood, they agreed the solution to these problems lay not in increased mental health treatment but rather in early recognition, education, and primary prevention.

79

New England is home to some of the most prestigious secondary schools and universities in the country. The stress faced by students in these competitive environments is enormous. This is magnified among individuals whose cultural backgrounds make it difficult to integrate easily into US educational and social systems, who struggle to establish social networks with their Western peers, and who bring different cultural values and practices to campus. Meanwhile, educational institutions often struggle with the challenge of providing appropriate support to these students. All of the above contributing factors can create a “perfect storm” for students caught between parental expectations, a developmentally appropriate search for personal identity, a lack of awareness and understanding of the impact of cultural factors on mental health among educators and clinicians, and the many unique challenges of immigration and acculturation. To tackle these complex challenges, CCCSEW has partnered with a diverse group of stakeholders involved in student mental health, including clinicians, researchers, educators, parents, and students, to improve communication between the different parties and to open a dialogue about mental health in diverse student populations. CCCSEW’s mission is to understand and promote the emotional health and psychological resilience of students and scholars from diverse cultural backgrounds. To achieve this goal, CCCSEW focuses on three main areas: (1) education and primary prevention, (2) research, and (3) consultation, treatment, and referral. In the following section, we will describe some of the current programs and practices of the Center that serve to advance these various aspects of our mission.

Current Programs and Practices of the Center To help better organize, prioritize, and execute the range of programs and initiatives that could potentially benefit cross-cultural student emotional wellness, CCCSEW has developed three committees, each roughly corresponding to one of the Center’s primary focuses: a Research Committee, a Clinical Committee, and a Programming and Content Committee. Additionally, CCCSEW’s core faculty and staff are routinely invited to give talks and trainings to various groups around the country as described above. CCCSEW strives to be a clearinghouse of high-quality, evidence-based information about cross-cultural student emotional wellness. Regarding CCCSEW’s first aim of education and primary prevention: Since its inception, CCCSEW has provided numerous educational workshops to a wide range of stakeholders in 12 states and Canada, including parent and community organizations, high schools (public and private), universities, and national educational and mental health organizations about the importance of emotional wellness

80

across cultures. CCCSEW faculty have also provided trainings to educators and clinicians at both local and national levels on specific issues affecting mental health of international students and how to provide culturally appropriate services. Regarding the second aim of research and investigation: The Research Committee is responsible for generating research ideas of potential interest, prioritizing and carrying out the most pressing and/or most feasible of these, and exploring and cataloguing potential sources of research funding. All of the Center’s affiliated faculty members and staff are committed to providing high-quality, evidencebased guidance to the public and mental health practitioners and to expanding the empirical research base available to the field at large. Unfortunately, CCCSEW came into being at a time when traditional funding sources for mental health research have shown relatively less interest in clinical intervention development and testing relative to more mechanistic, biologically based investigations. Therefore, the Research Committee has focused on identifying foundation grants and philanthropic donations to support the mission of the Center. Of particular interest for the Research Committee from the beginning has been the development and testing of parent-focused mental health interventions that focus on improving intergenerational and cross-cultural communication in Asian American families and communities. As of this writing, CCCSEW has obtained Institutional Review Board (IRB) approval for such a project in conjunction with Asian American parent organizations in Brookline and Lexington, two suburbs in the greater Boston area with large populations of immigrant Asian families. Focus groups and interviews will be conducted with parents, students at both the high school and college levels, and clinicians who work with these populations, to better understand the unique stressors facing these groups. This information will subsequently be utilized to develop and pilot test a series of innovative parent guidance workshops that will help empower these communities to better support the mental health of their students. The Clinical Committee provides a space for discussing cases with a cross-cultural component and therefore serves both training and patient care functions. In this way, this committee provides peer support and supervision to the Center’s practitioners working with culturally diverse populations and also creates an opportunity to better understand and characterize the unique distinguishing features of this work through sharing and exploration of clinical dilemmas and identification of patterns that may be relevant to other providers. Finally, the Clinical Committee has pioneered a multilingual consultation service and referral network for students and families who are seeking further treatment by culturally sensitive mental health clinicians in the Boston and New England area.

X. Zhang et al.

The Programming and Content Committee focuses on generating tangible resources that will benefit the Center’s target audiences and are available in multiple languages. To date, the focus for these resources has included materials for download from the website and digital media content. The Committee is in the process of generating a growing library of evidence-based fact sheets on a wide range of topics related to mental health, cultural adjustment, and intergenerational and cross-cultural challenges; translated into languages such as Chinese, Korean, and Vietnamese; and available as resources for students, families, and schools. Additionally, the Programming and Content Committee has spearheaded the production of easy-to-understand “concept videos” on some of these topics to again bring the message regarding the importance of emotional wellness to diverse audiences.

Clinical Suggestions and Implications As the mental health issues of students from non-Western backgrounds have drawn increasing attention, clinical guidelines are urgently needed to guide treaters in the community. Our Center has begun to publish some of our experiences, for example, the “SWEET Life” approach to mental health treatment among Asian international students [90]. This model is intended to equip clinicians and educators with a skillful, culturally sensitive strategy for addressing the difficult combination of stigma, low mental health literacy, and reluctance to seek treatment often seen in students from diverse cultural backgrounds. “SWEET” is an acronym for Sleep, Wake up on time, Eat healthily, Exercise, and Task engagement and provides a framework that patients often find helpful for discussing mental health challenges. This model relies on three core principles: grounding in solid psychiatric and neuroscientific research, being careful to avoid stigmatizing language in favor of culturally relatable concepts such as success and wellness, and focusing on those outcomes that matter most to patients. At the heart of SWEET Life is the concept of stress, which is familiar and acceptable across most cultures. The idea that chronically high levels of stress can exert negative effects on both physical and mental health is intuitive and universal and can be used to explain the emergence of a wide array of symptoms. This discussion leads naturally to an introduction of the diathesis-stress model of mental illness. Students may benefit from analogies to other, less stigmatized medical conditions that similarly result from a combination of genetic and environmental triggers, such as diabetes or cancer. Next, the Yerkes-Dodson Law is introduced, which describes an “inverted U” relationship between the degree of stress and challenge faced by an individual or organism and his or her level of performance, health, and happiness.

6  Cross-Cultural Approaches to Mental Health Challenges Among Students

Students learn that not all stress is bad, but on the contrary stress is often necessary for triggering challenge and growth. However, too much stress without appropriate supports can lead to a feeling of helplessness and ultimately adverse health effects. Finally, the specific components of SWEET Life are introduced—all well-accepted concepts from empirically validated treatments such as cognitive behavioral therapy— but presented in an accessible and non-stigmatizing way. For individuals with more severe symptoms or diagnosable mental illness, the model can be used to explain that medication can help “rescue” the brain when it is severely dysregulated by stress to manage symptoms that interfere with the SWEET Life, with the ultimate goal of restoring balance in the brain—a neuroscientifically based model that accords well with traditional Asian medical and Ayurvedic conceptualizations of health. Even with the benefit of this model, many students and their families may remain reluctant to conceptualize their symptoms as a medical illness and to accept treatments, including medications. Nonetheless, clinical applications of approaches such as the SWEET Life or the Engagement Interview Protocol pioneered by Yeung and colleagues [91], which focuses on bridging the patient’s illness explanatory model and modern biomedical understandings of illness, represent a significant improvement in the ability of clinicians and educators to engage with diverse clinical populations. Individuals ascribing to more traditional Asian beliefs tend to view Western medical approaches such as pharmacotherapy as overly invasive or powerful, possessing significant side effects, and potentially addictive. Clinicians may benefit from not immediately raising the possibility of medications or focusing too much on a specific diagnosis or ­psychopathology but instead utilizing the language of balance and homeostasis as described above to negotiate with patients regarding potential solutions to their distress. Pharmacokinetic differences mean that many Asians metabolize psychotropic medications more slowly than Whites and often require lower doses of common medications including selective serotonin reuptake inhibitors, mood stabilizers, and antipsychotic medications. The maxim “start low, go slow” certainly applies in this population. Further challenging the treatment, many Asian students may be reluctant to engage in individual psychotherapy due to concerns about “airing dirty laundry,” conflicted feelings about speaking ill of their parents or families of origin (who are often one of the sources of their psychological distress), and fears regarding privacy or the impact of speaking out on their academic standing. The latter concern is particularly prevalent among Asian international students, who may fear that engaging in mental health treatment will jeopardize their visa status or be reported to their academic advisors and/or parents. Anticipating and proactively addressing

81

these issues can help significantly with the process of treatment engagement. Even when students have agreed to individual treatment, providers should be aware of cultural differences that may present in sessions. For many Asian students, a culturally influenced tendency to respect age and authority may contribute to less active participation and a more deferential style of interaction. Students may specifically ask providers to tell them their impressions or give advice, which runs counter to more insight-oriented and exploratory styles of psychotherapy. This does not mean that the students cannot engage in this type of therapy, but rather, providers may need to provide significant early psychoeducation about the process of mental health treatment itself and/or be somewhat more directive and skills-based in the early stages of therapy. The therapist may consider adopting distress tolerance and emotional regulation training tools from both Dialectical Behavioral Therapy and Cognitive Behavioral Therapy. For example, for a student whose perfectionism paralyzes him/ her in front of a project deadline, skills like radical acceptance and opposite action have proven effective. Providers should remain mindful of the pervasiveness of underdiagnosed mental disturbances. Many different factors can serve to obscure the severity and urgency of an individual student’s suffering, including the abundance of resources of some students or the high performance of others. Relatedly, providers should be attuned to many students’ desire to prove themselves as successful, worthy of love, or confident. These students may have developed an extreme sensitivity to and fear of failure and may also exhibit lower self-esteem than students raised in mainstream White American cultures. It may be helpful to understand certain unconstructive behaviors from the perspective of self-protection—e.g., not trying as a strategy for avoiding the risk of failure. Additionally, providers should pay attention to students’ academic performance and be mindful of their somatic complaints. Because many Asian students, particularly more recent immigrants, are more hesitant to express their emotions and mental health issues, they may instead tend to emphasize somatic symptoms such as pains in the head or stomach, dizziness, insomnia, or a lack of energy. If students spend inordinate amounts of time on studying, or complain about physical health problems without signs of a physical illness, clinicians, educators, and parents should consider a psychiatric diagnosis. It is important to listen for evidence of racism and bullying at school or in the neighborhood and to be mindful about racial trauma. As a general principle, it is crucial to truly see and appreciate anything that is good in the student. Strength-focused techniques and validation can be used to identify a positive ability, strength, value, behavior, or attitude. This may be even truer for students raised by authoritarian, low-warmth parents. This approach will help the student develop a sense

82

of groundedness and belonging, promote self-appreciation, and solidify self-worth. One of the therapeutic goals of working with these students is to balance common conflicting needs such as the need for autonomy but also care/safety, or the need for personal space but also intimacy. The task of working through these conflicting needs is developmentally normative for all students in adolescence to early adulthood. However, due to the many cultural factors described earlier in this chapter, Asian students may have greater unresolved issues and conflicts around autonomy, intimacy, and individual identity than their White peers. Non-leading openended questions that aim to broaden possibilities, rather than closed-ended ones, are usually appropriate when the therapist navigates between conflicting needs. Following a crisis such as self-injury, a suicide attempt, or hospitalization, it is crucial to work with the student’s family to help contextualize the mental health problem and support both student and parents alike. A mental health crisis often starts to endow a sense of legitimacy to these students’ longlasting mental issues, provides a language (e.g., depression) to communicate their struggles with their family and school, and invites the long overdue conversation. The whole family is likely in shock after a crisis, and the therapist needs to be skillful in terms of avoiding the pitfalls of blaming and scapegoating. Instead, seize the timing to unite the family and energize the support system; when a crisis creates a threat to a family, the family will be more motivated to learn and change than ever. Parents may need to be coached to understand that their advice—despite coming from a caring place—may do more harm than good, widening the emotional distance between them and their child. On the other hand, even with motivation, change takes time. The family usually has a long way to go in terms of understanding what a mental disturbance means in general and for their child specifically, how to support their child, and how to recognize and change the old problematic patterns and dynamics. The therapist may work with parents on mindful parenting and positive parenting. Individual therapy for the parent(s) may be recommended in addition to family therapy. It should be stressed that every individual is unique and complex. These clinical suggestions only attempt to highlight some important and common aspects that may be helpful to clinicians who work with these populations.

Conclusion As the Asian American and international student populations continue to grow, their unique mental health issues have gradually attracted more attention from mental health professionals, researchers, and the public. Despite common stereotypes of Asian Americans as the “model minority,” a review of existing literature reveals significant mental health

X. Zhang et al.

problems and disparities among Asian American students, especially with regard to common problems such as depression and suicidality. Similarly, mental health professionals and school personnel have also raised growing concerns about mental health in the burgeoning population of international students from Asian countries coming to the United States to study. Based on the limited literature, common and distinct risk factors and correlates of depression in each population have been identified by researchers in hopes of understanding their mental health problems and informing culturally sensitive intervention and treatment for these student populations. The mental health challenges facing Asian American and Asian international students are in many ways representative of those faced by other diverse groups in the United States. Insights gained from our work and experience can and should be modified and adapted for other minorities or others not well-served by the current mental health-care system to increase access and engagement. Clinicians should be wary of the within-group and individual differences in the manifestations, diagnoses, and treatments of depression, as the vast majority of extant research on mental health problems and their treatment are based primarily on White populations using a Western perspective regarding mental health. Recognizing a dire need to expand this conversation, the MGH Center for Cross-Cultural Student Emotional Wellness has developed an innovative approach to addressing the mental health needs of diverse student populations through a multipronged mission of education and primary prevention, research, and consultation, treatment, and referral. We hope the initiatives and guidelines in the chapter can help support the work of other clinicians, educators, and researchers who share our goal of promoting emotional wellness among all students.

FAQs: Common Questions and Answers Q1. Do Asian Americans even get depressed? A1. Yes. The model minority myth obscures problems faced by Asian Americans, including higher rates of depression and suicidal ideation and attempts than their White counterparts. Q2. How does Asian American depression present? A2. The standard symptoms of depression are common and should be screened for, including low mood, anhedonia, disturbances in sleep and appetite, low energy, excessive feelings of guilt or worthlessness, psychomotor agitation or retarding, and suicidal ideation. Additionally, many Asian students will preferentially emphasize somatic rather than psychological symptoms, including pains in the head or stomach, dizziness, insomnia, or a lack of

6  Cross-Cultural Approaches to Mental Health Challenges Among Students

energy. Depressed Asian international students may simply stop attending classes and/or cease responding to any forms of communication, including emails from teachers or academic advisors. If students spend inordinate amounts of time on studying, complain about physical health problems without signs of a physical illness, or exhibit other signs of disengagement or disappearance, clinicians, educators, and parents should consider a psychiatric diagnosis and consider raising this with the student in a culturally sensitive way as described above. Q3. What factors can prevent Asian Americans with depression from seeking clinical care? A3. Lack of psychoeducation and awareness regarding symptoms of depression and treatment options, stigma regarding anything viewed to be related to mental illness, a desire to achieve academic success coupled with reluctance to disclose problems, fear of parents’ disappointment or disapproval, and lack of representation and role modeling of other Asian Americans seeking help or speaking out about their own struggles with mental health. Q4.  The international student I am treating has limited English proficiency. Should I just refer him/her to a bilingual therapist? A4. Language and communication is an important component in mental health treatment, which focuses on deeply personal and complex thoughts and feelings. How a language barrier is overcome may require more understanding about resources available and the needs of the particular client, including referral concerns, transportation, his/her rapport with you, desire to engage in treatment in another language, etc. Bilingual therapists may not always be available in different parts of the country, and sometimes they may not have adequate training in the areas of concern. Therefore, the answer to this question depends on the resources available and the patient and provider’s comfort in proceeding with the work of mental health treatment in English. Q5. Should we match clients and therapists based on their cultural backgrounds? A5. Given the tremendous and growing racial/ethnic diversity of the US population (including the number of individuals identifying as biracial or mixed race), it is impossible that the mental health workforce demographic will ever perfectly match that of the general population. If the concept of culture is further expanded beyond race to other deeply held and important aspects of personal identity, including gender, sexual orientation, religious affiliation, etc., it is clear that patients cannot expect their providers to be similar to them in every respect. Training in cultural humility and respect is therefore vital for equipping the modern mental health workforce with the knowledge, skills, and attitudes required to care for diverse populations.

83

Nonetheless, there are times when a shared cultural background within the treatment dyad can be therapeutically useful. Some patients may assume that someone from a very different culture cannot understand their issues or may have had a negative prior experience with the general health-care system. It is important to inform clients of their options from the outset so they can choose who they are most comfortable with. At the same time, providers should be careful not to make assumptions about a patient’s preference, as acculturation levels vary and some minority clients may actually prefer a therapist from another culture for a range of reasons, including fear that word of their struggles may leak back to their family or other members of their community. Also, a shared cultural background may lead to assumptions and therapeutic missteps in the opposite direction, including a mistaken wish that the provider will automatically understand everything about the patient’s experience.

References 1. Lopez G, Ruiz NG, Patten E.  Key facts about Asian Americans, a diverse and growing population [Internet]. Fact Tank, Pew Research Center. 2017. Available from: http://www.pewresearch. org/fact-tank/2017/09/08/key-facts-about-asian-americans/. 2. Suárez-Orozco C, Yoshikawa H, Tseng V. Intersecting inequalities: research to reduce inequality for immigrant-origin children and youth [Internet]. New  York: William T.  Grant Foundation; 2015. Available from: https://files.eric.ed.gov/fulltext/ED568399.pdf 3. Shimpi PM, Zirkel S. One hundred and fifty years of “the Chinese question”: an intergroup relations perspective on immigration and globalization: immigration as interracial interaction. J Soc Issues. 2012;68(3):534–58. 4. Lee RG.  Orientals: Asian Americans in popular culture. Philadelphia: Temple University Press; 1999. 5. Min PG. Asian Americans: contemporary trends and issues. 2nd ed. Thousand Oaks: Pine Forge Press; 2006. 6. Abe-Kim J, Takeuchi DT, Hong S, Zane N, Sue S, Spencer MS, et al. Use of mental health–related services among immigrant and US-born Asian Americans: results from the National Latino and Asian American Study. Am J Public Health. 2007;97(1):91–8. 7. Morrison LL, Downey DL. Racial differences in self-disclosure of suicidal ideation and reasons for living: implications for training. Cult Divers Ethn Minor Psychol. 2000;6(4):374–86. 8. Garcia-Williams A, O’Donnell J, Spies E, Zhang X, Young R, Azofeifa A, et  al. Epi-Aid 2016–018: undetermined risk factors for suicide among youth, ages 10–24 — Santa Clara County, 2016 [Internet]. Internet: Centers for Disease Control and Prevention; 2017. Available from: https://www.sccgov.org/sites/phd/hi/hd/epiaid/Documents/epi-aid-report.pdf. 9. Spencer K. New Jersey school district eases pressure on students, baring an ethnic divide. New York Times [Internet]. 2015. Available from: http://www.nytimes.com/2015/12/26/nyregion/reforms-toease-students-stress-divide-a-new-jersey-school-district.html. 10. Spencer K. It takes a suburb: a town struggles to ease student stress. New  York Times [Internet]. 2017. Available from: https://www. nytimes.com/2017/04/05/education/edlife/overachievers-studentstress-in-high-school-.html. 11. Ramanujan K.  Health expert explains Asian and Asian-American students’ unique pressures to succeed. Cornell Chronicle [Internet].

84 2006. Available from: http://news.cornell.edu/stories/2006/04/ health-expert-explains-asian-students-unique-pressures-succeed. 12. Fang J. Asian American student suicide rate at MIT is quadruple the national average [Internet]. Reappropriate. 2015. Available from: http://reappropriate.co/2015/05/asian-american-student-suiciderate-at-mit-is-quadruple-the-national-average/. 13. Kisch J, Leino EV, Silverman MM. Aspects of suicidal behavior, depression, and treatment in college students: results from the spring 2000 National College Health Assessment Survey. Suicide Life Threat Behav. 2005;35(1):3–13. 14. Jacob J, Gray B, Johnson A. The Asian American family and mental health: implications for child health professionals. J Pediatr Health Care. 2013;27(3):180–8. 15. Open Doors. Report on international educational exchange 2017. New York: Institute of International Education; 2017. 16. Chen JA, Liu L, Zhao X, Yeung AS.  Chinese international students: an emerging mental health crisis. J Am Acad Child Adolesc Psychiatry. 2015;54(11):879–80. 17. Hahm HC, Gonyea JG, Chiao C, Koritsanszky LA.  Fractured identity: a framework for understanding young Asian American women’s self-harm and suicidal behaviors. Race Soc Probl. 2014;6(1):56–68. 18. Kiang L, Buchanan CM.  Daily stress and emotional well-being among Asian American adolescents: same-day, lagged, and chronic associations. Dev Psychol. 2014;50(2):611–21. 19. Kang JC. What a fraternity hazing death revealed about the painful search for an Asian-American identity. New York Times [Internet]. 2017. Available from: https://www.nytimes.com/2017/08/09/magazine/what-a-fraternity-hazing-death-revealed-about-the-painfulsearch-for-an-asian-american-identity.html?_r=0. 20. Sarich V, Miele F. Race: the reality of human differences. Boulder: Westview Press; 2004. 21. Witzig R. The medicalization of race: scientific legitimization of a flawed social construct. Ann Intern Med. 1996;125(8):675–9. 22. Sue DW, Sue D. Counseling the culturally diverse: theory and practice. 7th ed. Hoboken: Wiley; 2016. 23. Sue S, Okazaki S.  Asian-American educational achievements: a phenomenon in search of an explanation. Am Psychol. 1990;45(8):913–20. 24. Young CB, Fang DZ, Zisook S.  Depression in Asian–American and Caucasian undergraduate students. J Affect Disord. 2010;125(1–3):379–82. 25. Hunt JB, Eisenberg D, Lu L, Gathright M.  Racial/ethnic disparities in mental health care utilization among U.S. college students: applying the institution of medicine definition of health care disparities. Acad Psychiatry. 2015;39(5):520–6. 26. Suicide Among Asian Americans [Internet]. American Psychological Association; 2012. Available from: http://www.apa. org/pi/oema/resources/ethnicity-health/asian-american/suicidefact-sheet.pdf. 27. Han X, Han X, Luo Q, Jacobs S, Jean-Baptiste M.  Report of a mental health survey among Chinese international students at Yale University. J Am Coll Heal. 2013;61(1):1–8. 28. Wei M, Ku T-Y, Russell DW, Mallinckrodt B, Liao KY-H. Moderating effects of three coping strategies and self-esteem on perceived discrimination and depressive symptoms: a minority stress model for Asian international students. J Couns Psychol. 2008;55(4):451–62. 29. Iwata N, Buka S. Race/ethnicity and depressive symptoms: a crosscultural/ethnic comparison among university students in East Asia, North and South America. Soc Sci Med. 2002;55(12):2243–52. 30. Wong YJ, Koo K, Tran KK, Chiu Y-C, Mok Y. Asian American college students’ suicide ideation: a mixed-methods study. J Couns Psychol. 2011;58(2):197–209. 31. Wang KT, Wong YJ, Fu C-C. Moderation effects of perfectionism and discrimination on interpersonal factors and suicide ideation. J Couns Psychol. 2013;60(3):367–78.

X. Zhang et al. 32. Leong FTL, Leach MM, Yeh C, Chou E.  Suicide among Asian Americans: what do we know? What do we need to know? Death Stud. 2007;31(5):417–34. 33. Kramer EJ, Kwong K, Lee E, Chung H.  Cultural factors influencing the mental health of Asian Americans. West J Med. 2002;176(4):227–31. 34. Liu LL, Wang S, Fung J, Gudiño OG, Tao A, Lau AS. Psychology of Asian American children: contributions of cultural heritage and the minority experience. In: Chang EC, Downey CA, editors. Handbook of race and development in mental health. New  York: Springer; 2012. p. 147–67. 35. Ahn AJ, Kim BSK, Park YS.  Asian cultural values gap, cog nitive flexibility, coping strategies, and parent-child conflicts among Korean Americans. Cult Divers Ethn Minor Psychol. 2008;14(4):353–63. 36. Park YS, Kim BSK. Asian and European American cultural values and communication styles among Asian American and European American college students. Cult Divers Ethn Minor Psychol. 2008;14(1):47–56. 37. Xu Y, Farver JA, Zhang Z, Zeng Q, Yu L, Cai B. Mainland Chinese parenting styles and parent-child interaction. Int J Behav Dev. 2005;29(6):524–31. 38. Baumrind D.  Effects of authoritative parental control on child behavior. Child Dev. 1966;37(4):887–907. 39. Chao RK.  The parenting of immigrant Chinese and European American mothers. J Appl Dev Psychol. 2000;21(2):233–48. 40. Chen A, Casabianca J, Beam J. “We’re not even allowed to ask for help” debunking the myth of the model minority. New  York: Coalition for Asian American Children and Families & Pumphouse Projects; 2011. 41. Erikson EH. Identity and the life cycle: selected papers. New York: International University Press; 1959. 42. Smart JF, Smart DW.  Acculturative stress: the experience of the Hispanic immigrant. Couns Psychol. 1995;23(1):25–42. 43. Park S-Y. Depression and suicide ideation among Asian American youth: a twelve year longitudinal analysis. New  York: New  York University; 2015. 44. Berry JW.  Immigration, acculturation, and adaptation. Appl Psychol. 1997;46(1):5–34. 45. Berry JW, Kim U, Power S, Young M, Bujaki M. Acculturation attitudes in plural societies. Appl Psychol. 1989;38(2):185–206. 46. Iwamoto DK, Liu WM. The impact of racial identity, ethnic identity, Asian values, and race-related stress on Asian Americans and Asian international college students’ psychological well-being. J Couns Psychol. 2010;57(1):79–91. 47. Rhee S, Chang J, Rhee J. Acculturation, communication patterns, and self-esteem among Asian and Caucasian American adolescents. Adolescence. 2003;38(152):749–68. 48. Brand D. Education: the new Whiz kids. Time. 1987;130(9):42–51. 49. Breaking the Model Minority Myth [Internet]. Congressional Asian Pacific American Caucus. 2012. Available from: https://capac-chu. house.gov/media-center/model-minority-myth. 50. Mori SC.  Addressing the mental health concerns of international students. J Couns Dev. 2000;78(2):137–44. 51. Kempa ML, Thomas AJ. Culturally sensitive assessment and treatment of eating disorders. Eat Disord. 2000;8(1):17–30. 52. Chen JA, Courtwright A, Wu KC-C. The role of stigma and denormalization in suicide-prevention laws in East Asia: a sociocultural, historical, and ethical perspective. Harv Rev Psychiatry. 2017;25(5):229–40. 53. Yang LH, Kleinman A.  Face’ and the embodiment of stigma in China: the cases of schizophrenia and AIDS.  Soc Sci Med. 2008;67(3):398–408. 54. Hofstede G.  Cultural dimensions in management and planning. Asia Pac J Manag. 1984;1(2):81–99. 55. Joiner TE. Why people die by suicide. Cambridge, MA: Harvard University Press; 2005. p. 276.

6  Cross-Cultural Approaches to Mental Health Challenges Among Students 56. Van Orden KA, Witte TK, Cukrowicz KC, Braithwaite SR, Selby EA, Joiner TE. The interpersonal theory of suicide. Psychol Rev. 2010;117(2):575–600. 57. Chu J, Chi K, Chen K, Leino A. Ethnic variations in suicidal ideation and behaviors: a prominent subtype marked by nonpsychiatric factors among Asian Americans: ethnic variations in suicide construct subtypes. J Clin Psychol. 2014;70(12):1211–26. 58. Byrne P. Stigma of mental illness and ways of diminishing it. Adv Psychiatr Treat. 2000;6(1):65–72. 59. Lam CS, Tsang HWH, Corrigan PW, Lee Y-T, Angell B, Shi K, et al. Chinese lay theory and mental illness stigma: implications for research and practices. J Rehabil. 2010;76(1):35–40. 60. Nilsson JE, Berkel LA, Flores LY, Lucas MS. Utilization rate and presenting concerns of international students at a university counseling center: implications for outreach programming. J Coll Stud Psychother. 2004;19(2):49–59. 61. Corrigan P.  How stigma interferes with mental health care. Am Psychol. 2004;59(7):614–25. 62. Hinshaw SP. The stigmatization of mental illness in children and parents: developmental issues, family concerns, and research needs. J Child Psychol Psychiatry. 2005;46(7):714–34. 63. Kirmayer LJ.  Cultural variations in the clinical presentation of depression and anxiety: implications for diagnosis and treatment. J Clin Psychiatry. 2001;62(Suppl 13):22–8. discussion 29–30. 64. Cheng JKY, Fancher TL, Ratanasen M, Conner KR, Duberstein PR, Sue S, et al. Lifetime suicidal ideation and suicide attempts in Asian Americans. Asian Am J Psychol. 2010;1(1):18–30. 65. Isaac D. Culture-bound syndromes in mental health: a discussion paper. J Psychiatr Ment Health Nurs. 2013;20(4):355–61. 66. Cauce AM, Domenech-Rodríguez M, Paradise M, Cochran BN, Shea JM, Srebnik D, et  al. Cultural and contextual influences in mental health help seeking: a focus on ethnic minority youth. J Consult Clin Psychol. 2002;70(1):44–55. 67. Cheng D, Leong FTL, Geist R. Cultural differences in psychological distress between Asian and Caucasian American college students. J Multicult Couns Dev. 1993;21(3):182–90. 68. Leong FTL, Lau ASL.  Barriers to providing effective men tal health services to Asian Americans. Ment Health Serv Res. 2001;3(4):201–14. 69. Lee S, Juon H-S, Martinez G, Hsu CE, Robinson ES, Bawa J, et al. Model minority at risk: expressed needs of mental health by Asian American young adults. J Community Health. 2009;34(2): 144–52. 70. Chen CP.  Professional issues: common stressors among international college students: research and counseling implications. J Coll Couns. 1999;2(1):49–65. 71. Smith RA, Khawaja NG. A review of the acculturation experiences of international students. Int J Intercult Relat. 2011;35(6):699–713. 72. Dao TK, Donghyuck L, Chang HL.  Acculturation level, per ceived English fluency, perceived social support level, and depression among Taiwanese international students. Coll Stud J. 2007;41(2):287–96.

85

73. Trice AG.  Faculty perceptions of graduate international students: the benefits and challenges. J Stud Int Educ. 2003;7(4):379–403. 74. Andrade MS. International students in English-speaking universities: adjustment factors. J Res Int Educ. 2006;5(2):131–54. 75. Gebhard JG.  International students’ adjustment problems and behaviors. J Intercult Stud. 2012;2(2):158–64. 76. Hamamura T, Laird PG.  The effect of perfectionism and acculturative stress on levels of depression experienced by East Asian International students. J Multicult Couns Dev. 2014;42(4):205–17. 77. Kuo Y-H. Language challenges faced by international graduate students in the United States. J Intercult Stud. 2011;1(2):38–42. 78. Sümer S, Poyrazli S, Grahame K. Predictors of depression and anxiety among international students. J Couns Dev. 2008;86(4):429–37. 79. Leong F, Lee S-H, Chang D.  Counseling Asian Americans: client and therapist variables. In: Counseling across cultures [Internet]. Thousand Oaks: Sage; 2008 [cited 2017 Dec 13]. p.  113–28. Available from: http://sk.sagepub.com/books/ counseling-across-cultures/n7.xml. 80. Pedersen PB.  Counseling international students. Couns Psychol. 1991;19(1):10–58. 81. Geva E, Wiener J. Psychological assessment of culturally and linguistically diverse children and adolescents: a practitioner’s guide. New York: Springer Publishing Company; 2015. p. 336. 82. Yan K, Berliner DC.  Chinese international students’ academic stressors in the United States. Coll Stud J. 2009;43(4):939–60. 83. McNeal JU, Yeh C.  Development of consumer behavior patterns among Chinese children. J Consum Mark. 1997;14(1):45–59. 84. Rabia HA, Karkouti IM. A qualitative investigation of the factors affecting Arab international students’ persistence in the United States. Coll Stud J. 2017;51(3):347–54. 85. Wu H, Garza E, Guzman N. International student’s challenge and adjustment to college. Educ Res Int. 2015;2015:1–9. 86. Mallinckrodt B, Leong FTL. International graduate students, stress, and social support. J Coll Stud Dev. 1992;33(1):71–8. 87. Heggins WJ III, Jackson JFL. Understanding the collegiate experience for Asian international students at a Midwestern research university. Coll Stud J. 2003;37(3):379–91. 88. Yang B, Clum GA. Life stress, social support, and problem-solving skills predictive of depressive symptoms, hopelessness, and suicide ideation in an Asian student population: a test of a model. Suicide Life Threat Behav. 1994;24(2):127–39. 89. D’Zurilla TJ, Chang EC, Nottingham EJ, Faccini L. Social problem-solving deficits and hopelessness, depression, and suicidal risk in college students and psychiatric inpatients. J Clin Psychol. 1998;54(8):1091–107. 90. Chen JA, Yeung A, Liu L. The SWEET life: improving treatment engagement and emotional wellness in diverse clinical populations. Psychiatr Serv. 2017;68(12):1326. 91. Yeung A, Trinh N-HT, Chang TE, Fava M. The engagement interview protocol (EIP): improving the acceptance of mental health treatment among Chinese immigrants. Int J Cult Ment Health. 2011;4(2):91–105.

7

Depression After Traumatic Brain Injury Lauren B. Fisher, Garrett Thomas, Ryan A. Mace, and Ross Zafonte

Case Vignette

Jon sat down in my office, arriving to our session 10 min late, looking down and defeated. “There’s just so much confusion. I wasn’t able to get anything done. I don’t even remember what I was supposed to do. And I’m just so bad at time management. It’s useless.” Jon sustained a traumatic brain injury (TBI) on the job. Prior to his injury, he was climbing the ladder in his construction company, constantly putting in overtime, and always going the extra mile. He was vigilant about safety and was considered the most dependable worker on site. Nevertheless, Jon was in the wrong place at the wrong time when an on-site explosion threw him 50  ft off the ground. He briefly lost consciousness and was then rushed to the hospital. One and a half years later, he continues to struggle with

L. B. Fisher (*) • G. Thomas Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]; [email protected] R. A. Mace Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Department of Psychology, Suffolk University, Boston, MA, USA e-mail: [email protected] R. Zafonte Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Red Sox Foundation and Massachusetts General Hospital Home Base Program, Boston, MA, USA e-mail: [email protected]

light sensitivity, headaches, memory problems, disorganization, and difficulty managing time. In the last 3 months, Jon’s symptoms of depression have become increasingly apparent: low mood, lack of interest, reduced appetite, feelings of low self-worth, and fleeting thoughts about his own death. He continues to experience excessive fatigue, difficulty concentrating, and trouble with sleep which could be symptoms of the head injury, depression, or both. The impact of these symptoms on his daily functioning has been severely limiting, and he struggles to maintain independence. At the initial evaluation, Jon reported a family history of depression and a history of episodic dysthymia over the years. Yet, he had never experienced this severity of depression prior to the TBI or sought professional help. Physically, he had recovered substantially from the initial injury but was frustrated by the slow recovery of his knee and right arm. He was isolated and reported having few social supports. Attendance at rehabilitation appointments comprised the majority of his social interactions, and his only other social contacts were limited to his teenage children whom did not live with him. Cognitively, he frequently reported confusion, which was subsequently identified as delayed information processing, diminished executive function, and short-term memory problems. Prior to the injury, Jon had demonstrated excellent coping skills in managing his dysthymia (e.g., regular exercise, meditation, prayer), and the impact of low mood on his functioning had been minimal. Despite his efforts to maintain regular exercise, Jon had not achieved the same benefit from his previously effective coping skills since the injury. Furthermore, his perception of his abilities post-injury was negative; he was unable to return to work or manage the day-today tasks that he once took for granted. Jon struggled

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_7

87

88

L. B. Fisher et al.

with self-criticism, negative view of self, and a sense of hopelessness about his future. By completing a comprehensive assessment over several sessions, a biopsychosocial conceptualization of Jon’s difficulties and a mental health treatment plan were developed. His most impairing symptoms were prioritized in treatment: hopelessness, depressed mood, lack of interest, and social isolation. Likelihood of depression remission was optimized by engaging Jon in multiple treatment modalities to address his inherently interconnected psychiatric, cognitive, and physical symptoms. This included cognitive behavioral therapy (CBT), medication management, cognitive rehabilitation, physical therapy, and case management services. The involvement of mental health treaters who were sensitive to the specific challenges and needs of people with TBI was critical in engaging Jon in multidisciplinary treatment. Jon’s early experiences in CBT highlighted his cognitive difficulties, as he was unable to complete homework between appointments, struggled to remember what was discussed in prior sessions, and became easily overwhelmed. Before long, it became apparent that adapting CBT and tailoring to Jon’s specific needs could potentially improve his ability to learn coping skills and have a positive experience in psychotherapy.

Introduction: Traumatic Brain Injury (TBI) The Centers for Disease Control and Prevention [1] defines TBI as “a disruption in the normal function of the brain that can be caused by a bump, blow, or jolt to the head, or penetrating head injury.” TBI can also be caused by nonimpact forces such as blast waves or rapid acceleration and deceleration. Open head injuries involve penetration of the skull (e.g., gunshot wound) while closed injuries occur from non-penetrating blows to the head (e.g., falls, sports injury). TBI survivors may suffer from a range of physical, cognitive, emotional, and behavioral symptoms. Presentation widely varies based on individual factors (e.g., preexisting conditions, pre-injury functioning, age) and the nature of the head injury [2]. The health effects of TBI can persist and contribute to functional, interpersonal, and occupational impairment [3, 4]. Approximately 1.1% of the US population is living with long-term disability as a result of TBI [5]; one in five people hospitalized for a TBI does not return to work 1 year later [6]. In the United States, an estimated 1.7 million new TBI cases occur each year [7]. Falls (35%) and motor vehicle accidents (17%) are the leading causes of TBI in the

United States [7]. Rates of sport-related TBI are high among athletic youth, particularly in adolescents aged 12–18 [8]. In fact, the rate of emergency department visits for sports and recreation-related injuries with a diagnosis of concussion or TBI more than doubled among children (19 years or younger) from 2001 to 2012 [9]. Older adults are more likely to sustain TBI from falls [10], and blast injury is the most common cause of TBI among active military personnel [11]. The rate of TBI among active service members more than doubled from 2000 to 2011, which was likely due to an actual increase in TBI, greater awareness of the need to seek care, and enhanced screening [1]. An upward trend in TBI rates has also been observed in adults age 65 and older [12]. The yearly economic impact of TBI in the United States has been estimated to range from $60.4 to $221 billion [13, 14]. For patients with more severe TBI, average healthcare costs are 5.75 times greater than matched controls [13]. Official figures may underestimate the true burden of TBI because many cases often do not receive formal medical evaluation and treatment [1]. Brain damage resulting from head injury is caused by two processes. Primary injury refers to the damage that occurs at the moment of trauma when the brain is displaced within the skull. Primary injuries include cerebral contusion, blood vessel damage, and brain axonal shearing [15]. They are typically focal and produce symptoms associated with specific functions innervated by the damaged brain regions [16]. The initial impact also triggers “a cascade of secondary responses, leading to cell death, network dysfunction, and system-level changes” [16]. Secondary injury can exacerbate the structural brain damage caused by primary injury [17] and can result in diffuse cerebral dysfunction beyond the initial impact site [18]. Secondary injury can be acute (minutes to hours) or longer term, occurring over days or months postinjury. Neurochemical and metabolic changes that underlie TBI are complex (e.g., neurodegeneration and neuroinflammation) and have been summarized elsewhere [2, 16]. The Glasgow Coma Scale (GCS) is the most widely used measure of TBI severity [19]. This neurologic scale assesses eye opening, verbal response, and motor response to stage TBI as mild, moderate, or severe. However, consideration of other injury indicators is recommended to enhance TBI classification [1, 20]. TBI may be accompanied by altered or loss of consciousness (LOC) at the moment of impact; many quickly regain consciousness while some remain comatose for varying lengths of time. The extent of memory loss for events prior to the TBI (retrograde amnesia) or after the injury (post-traumatic amnesia) is sensitive to recovery of function [15]. Neuroimaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI), can identify structural brain damage indicative of TBI severity. Prolonged loss of con-

7  Depression After Traumatic Brain Injury

sciousness, post-traumatic amnesia, lower Glasgow Coma Scale scores, and neuroimaging abnormalities predict poorer outcomes [21, 22]. See [23] for a crosswalk of criteria for staging TBI severity. Mild TBI, commonly referred to as concussion, involves limited, if any, loss of consciousness and post-traumatic amnesia. Immediate symptoms may include nausea, vomiting, drowsiness, poor coordination, delayed responding, vacant stare, and sensory disturbances [24]. Other somatic symptoms (headache, dizziness, light sensitivity), cognitive complaints (forgetfulness, difficulty concentrating), mood disturbance (irritability, depression, anxiety), and sleep dysregulation may also emerge days to weeks post-TBI [15, 24]. Patients with moderate to severe TBI may exhibit more persistent or exacerbated symptoms: aphasia, dysarthria, loss of motor coordination, weakness or numbing of extremities, and psychomotor agitation [25]. Deficits in processing speed, attention, executive function, and memory are common in moderate TBI; global cognitive impairment can occur in very severe TBI [15, 26]. Additionally, patients with moderate to severe TBI often experience deficits in self-awareness and social cognition (e.g., emotion perception, cognitive empathy) [27], as well as behavioral changes, such as disinhibition, impulsivity, and emotional lability, which can interfere with interpersonal functioning and influence recovery. Increased severity of TBI significantly predicts poorer global outcomes and life satisfaction [28]. In mild TBI, patients are expected to achieve rapid and complete recovery [29, 30], though unfortunately this is often not the case [31, 32]. The sequelae of mild TBI are often grouped into a constellation of physical, cognitive, and emotional/behavioral symptoms known as “post-concussive syndrome” [33]. There is currently little agreement among researchers on the progression, duration, or resolution of post-concussive syndrome after mild TBI [32]. Results from a prospective study showed that 82% of patients with mild TBI reported at least one symptom of post-concussive syndrome at 6 and 12  months post-mild TBI [32]. Additionally, 33% of patients were functionally impaired 3  months post-mild TBI, and 22% were functionally impaired 1  year following mild TBI [32]. In a longitudinal study of patients with post-concussive syndrome, only 27% of participants eventually recovered (67% of which within the 1st year); no participants recovered from post-concussive syndrome lasting 3  years or longer [31]. Less is known about expectations for full recovery after moderate to severe TBI. Anecdotally, experienced clinicians often report that the fastest improvements tend to occur in the first 6 months after injury and may continue for several years after moderate to severe TBI depending on several injury-related and individual factors. Post-traumatic amnesia and time to follow commands (TFC)are generally considered the strongest predictors of long-term functioning in severe TBI [34,

89

35]. Further, moderate to severe TBI is associated with a greater likelihood of developing long-term disability [36, 37].

Depression After TBI TBI and mental illness commonly co-occur; an estimated 48% of people meet criteria for a psychiatric disorder at some point after TBI [38], and 40% of TBI patients suffer from two or more psychiatric disorders [39]. When TBI and MDD co-occur, it often impedes the recovery process and results in long-term impairment in functioning [40]. Consequently, comorbid TBI and psychiatric illness are associated with 3.4 times greater healthcare costs relative to TBI patients without psychiatric illness [27]. Major depressive disorder (MDD) is the most common psychiatric illness in patients with TBI [38] with prevalence ranging from 6% to 77% using clinical rating scales [41] and from 17% to 61% using diagnostic criteria [42]. The variation in reported prevalence rates is likely due to a lack of consistent and adequate methodology, including small sample sizes, selection bias, and retrospective reporting. Further, measures of depression vary to the extent that they assess somatic, cognitive, and psychological symptoms. This is particularly problematic given the symptom overlap between depression and TBI (i.e., fatigue, cognitive difficulties), which has notable implications for the assessment of MDD in this population (see discussion below). Risk for developing MDD is significant in the 1st year after TBI (53.1% of patients hospitalized for TBI) [43]; increased risk for depression persists many years after the injury [44]. Depressive symptoms are associated with worse overall functioning in the first 6  months [45] and up to 5–7 years after the injury [46]. Patients with comorbid MDD and TBI endorse greater severity and persistence of concussive symptoms (i.e., post-concussive syndrome) compared to TBI patients without MDD [47–49]. People with MDD following TBI experience greater problems with attention, memory, processing speed, and executive functioning than patients with TBI and no depression [50, 51]. MDD following TBI is associated with higher prevalence of aggression and suicidal behaviors in comparison to MDD without TBI [44, 52, 53]. Taken together, it is not surprising that patients with post-TBI MDD report lower quality of life at follow-up compared to TBI patients without MDD [43].

Assessment of Depression After TBI In discussing the literature on depression and TBI, it is critical to address key factors in the measurement of depression in this specific population. Key assessment challenges include the

90

L. B. Fisher et al.

Table 7.1  Select measures for assessing depression after traumatic brain injury (TBI) Measure Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) [55] Hamilton Rating Scale for Depression (HAM-D) [60]

Select research [39, 50, 56–59]

Type Clinicianrated

Length/format Semi-structured diagnostic interview

Clinicianrated

Semi-structured interview assessing symptom severity, 17 items

[61–63]

Patient Health Questionnaire (PHQ-9) [64]

Self-report

9 items, corresponds to DSM-IV criteria

[62, 65, 66]

Beck Depression Inventory-II (BDI-II) [67] Hospital Anxiety and Depression Scale (HADS) [70] Center for Epidemiologic Studies Depression Scale (CED-S) [73] Hopkins Symptom Checklist depression subscale (SCL-20) [78]

Self-report

21 items

[68, 69]

Self-report

14 items (7-item depression subscale, 7-item anxiety subscale) 20 items

[63, 71, 72] [74–77]

Self-report

20-item depression subscale (embedded in 90-item measure of general distress)

[62, 79, 80]

Self-report

42 items (3, 14-item scales: depression, anxiety, stress)

[72]

Depression Anxiety Stress Scales (DASS) [81]

Self-report

symptom overlap between depression and TBI (e.g., fatigue, difficulty with concentration) and impaired self-awareness commonly experienced post-TBI.  Some somatic complaints (e.g., headaches) may be symptoms of the injury itself, depression, or both. The presence of increased somatic symptoms in people with chronic medical illnesses is associated with greater likelihood of experiencing a depressive or anxiety disorder [54]. Given the challenges outlined above, assessment measures should be chosen carefully and interpreted with caution. Clinicians may benefit from examining responses to individual items rather than relying on total scores. Table  7.1 highlights several measures of depression that have been examined among people with TBI. This table is not a comprehensive summary of all depression measures utilized in depression and TBI research, rather a select number with some empirical support and theoretical promise. In this table, we highlighted some key considerations for each measure and possible advantages for use with people with TBI.

Mechanisms of Post-TBI Depression The mechanisms of post-TBI depression are not well understood. Some processes driving the onset and maintenance of post-TBI depression may be specific to TBI while others

Advantages for TBI population Considered “gold standard” for diagnosing depression Has been studied extensively in TBI One of the most widely used depression outcome measures in clinical trials Dropping items/modifying the scale can improve use in TBI sample [62] Developed for use among medical patients Widely used in health outcomes research Demonstrated strong psychometrics in TBI population May modify scoring algorithm slightly for excellent psychometrics [65] Efficient for depression screening Some support for use as depression screening tool Aims to exclude somatic items to minimize potential confound with TBI symptoms Valid, reliable, brief screening measure Used regularly in TBI research Sensitive to change in medical samples, though psychometrics improved with rating scale analysis Excludes somatic symptoms on depression scale (but includes somatic items on anxiety scale)

may be shared with people without TBI [40]. In contrast to global measures of functioning after TBI outcomes, injury severity does not significantly predict post-TBI depression [43, 82], which suggests the presence of indirect predictors. A complex interaction of genetic, developmental, and psychosocial factors likely contribute to the development of post-TBI depression [41, 77, 83–88] which will be explained in greater detail below. Figure  7.1 presents our proposed model for conceptualizing MDD in TBI. Consideration of post-TBI MDD characteristics is important because they may differentially influence treatment efficacy [41] and can be modifiable through intervention [43].

Biological Mechanisms TBI may cause changes in brain function that initiate the development of symptoms that mimic the clinical presentation of MDD in the acute or subacute post-TBI phase [89]. Increased extracellular glutamate following TBI results in abnormally high levels of intracellular calcium. Previous research has shown that an upregulation of glutamate and calcium is associated with hippocampal cell death in patients with MDD [90] and increased intracellular calcium has been associated with apoptosis, or programmed cell death [4, 91].

7  Depression After Traumatic Brain Injury

Post-injury factors

Pre-injury MDD

Coping skills

Other psychiatric illness

Social environment

Biological vulnerabilities

Cognition

Substance abuse

Physical functioning

Demographic factors

Emotional distress

This molecular path may provide valuable insight for the development of MDD following TBI because both populations are associated with reductions in brain volume, specifically in the hippocampus [90, 92–94]. Though not directly impacted, previous research suggests that the hippocampus is especially vulnerable to atrophy following TBI [95–98]. These findings have been modeled both in preclinical samples and in people with TBI [95]. Some evidence also suggests that neuronal and glial loss in the prefrontal and hippocampal regions of the brain may contribute to post-TBI depression [58, 99]. Moreover, reduction in hippocampal volume is also consistent with memory and executive ­functioning deficits seen in TBI.  Overlap in white matter abnormalities in both TBI and MDD using diffusion tensor imaging (DTI) may help understand common mechanisms implicated in post-TBI MDD [100]. One possible explanation for the reductions in brain volume associated with MDD and TBI is the reduced involvement of brain-derived neurotrophic factor (BDNF). Given that BDNF is primarily responsible for neurogenesis, subthreshold production of this protein may offer insight to a mechanism that underlies decreased regional brain volume found in both populations. Evidence for reduced BDNF has been observed in both MDD and TBI [101, 102]. Interestingly, many antidepressants are associated with an increase in BDNF and improved neuronal plasticity and neurogenesis [102]. BDNF may explain some individual differences in the development of MDD following TBI. BDNF is responsible for regulating energy responses throughout the body [103, 104]; therefore, people with decreased baseline level of BDNF may have fewer resources for recovery post-TBI. This could lead to a longer duration and possibly more severe symptoms associated with the TBI. Further, BDNF increases in response to physical activity, cognitive demands, and other energetic challenges [104]. Exercise may enhance neu-

Self-appraisal

Post–TBI MDD

Pre-injury factors

TBI

Fig. 7.1 Biopsychosocial model of post-traumatic brain injury (TBI) major depressive disorder (MDD)

91

rocognitive function via neurogenesis, neurotrophin upregulation, or a positive impact on overall health [105]. Research on the development of chronic traumatic encephalopathy (CTE) may also provide valuable insight for linking the development of MDD following TBI and the injury itself. There is growing public concern about CTE, a neurodegenerative disease caused by repeated head trauma and TBIs, particularly for athletes who play contact sports (e.g., American football) and military personnel. People with suspected CTE are poised to present with mood disturbances such as depression, emotional instability, suicidal ideation, and disinhibition [106, 107]. Yet, the link between these clinical findings and biology mechanisms that produce a distinct phenotype is unclear. These emotional and behavioral symptoms in CTE may stem from molecular abnormalities, including tau pathology, neurofibrillary tangles, microvascular disturbance, and neuroinflammation due to repeated head traumas [106, 108, 109]. Preclinical research suggests that a buildup of tau protein may be associated with the development of MDD [110], though available data on tau in MDD is limited [111]. Other biological markers of MDD in TBI, such as genetic risk factors, neurotransmitters, and neuroendocrine changes, warrant further research [41].

 sychosocial, Cognitive, and Environmental P Mechanisms Some studies have suggested that psychological and environmental factors may be more critical in the development of post-TBI depression than TBI-related changes in brain function [19, 82]. Pre-injury mental illness and demographic factors, such as lower education and ages 40–60 (relative to younger and older adults) at the time of head injury, predict poorer recovery (measured by Glasgow Coma Scale scores)

92

6 months post-TBI [112]. In particular, preexisting history of MDD and depression at the time of injury have been identified as the strongest predictors of post-TBI MDD [43]. Preinjury depression also predicts post-concussive syndrome after mild TBI [113]. Risk for MDD increases with number of prior concussions and may also exacerbate post-TBI symptoms and inhibit recovery rates [114–116]. Pre-injury substance abuse is common among TBI patients [117]; previous alcohol abuse is associated with increased risk for development of post-TBI MDD and alcohol abuse relapse [118]. Socioeconomic factors, such as unemployment and lower income, are also associated with increased vulnerability to post-TBI MDD [119]. Post-injury psychosocial and environmental factors can also maintain MDD in TBI and slow recovery rates [99]. Post-TBI MDD is associated with increased rates of suicide attempts [120] and use of maladaptive coping strategies including worry, wishful thinking, and self-blame [88]. A recent finding suggested that higher levels of catastrophizing thoughts (i.e., fearing the worst possible outcome) and fear-avoidance behavior are significantly associated with depression after TBI [121]. Perhaps consequently, patients with post-TBI MDD are more likely to endorse emotional distress and comorbid anxiety than TBI patients without MDD [43, 88]. MDD following TBI is associated with poorer global cognitive outcomes [122] and slower pace of cognitive recovery [123–125]. Patients with post-TBI MDD have been found to perform significantly worse on measures of memory, attention, and executive functioning [51]. The co-occurrence of depression and TBI-related cognitive impairment can manifest as forgetfulness, disorganization, mental inflexibility, disinhibition, inattention, and poor problem-solving. Patients with MDD and TBI may have greater difficulty with selfregulation, prioritization, motivating behavior, and selecting and employing effective coping strategies. In addition to performance-based indices, co-occurring MDD and TBI are also associated with greater subjective cognitive complaints [51]. A compelling model of post-TBI depression suggests that perceptions of impaired daily functioning and other psychosocial changes are strongly correlated with MDD [82]. Negative self-appraisals of disability, which may be distorted or overestimated, that reinforce depression can be targeted in CBT for post-TBI MDD. The confluence of cognitive, emotional, and physical difficulties in TBI can exacerbate disability and contribute to chronic stress. Physical injury, pain, and fatigue can individually contribute to post-TBI MDD and result in deterioration of functional ability, mobility, and productivity [69, 126, 127]. Post-TBI MDD can make it more difficult to return to work and reduce occupational performance, resulting in frustration and lower self-esteem. There is some evidence that depression may be more prevalent among those who are unemployed compared to those who are employed 5  years

L. B. Fisher et al.

after mild TBI [128]. Greater reliance on others post-TBI, such as requiring assistance completing activities of daily living, can burden the patient’s support system and negatively impact relationships. Negative post-injury changes in the ability for a patient’s family to provide social support can serve as a risk factor for MDD [127]. Apathy, anhedonia, hyperactivity, irritability, aggression, and disinhibited behavior can uniquely contribute to impaired social functioning in post-TBI MDD. Greater isolation may reinforce MDD and further delay reintegration into the patient’s community.

New Advances and Research Support Similar to our understanding of the mechanisms of post-TBI depression, there is limited understanding about the efficacy of treatment for depression after TBI. Unfortunately, there are no evidence-based guidelines for the treatment of MDD in patients with TBI. Preliminary studies of standard treatments for depression in patients with TBI and mood symptoms, such as pharmacological treatments [42, 61, 129–133], physical exercise [134–138], cognitive and/or behavioral-based therapies (e.g., CBT, mindfulness-based cognitive therapy) [139– 142], and multidisciplinary psychosocial interventions [143, 144], have been examined and present mixed findings [145] – all of which are discussed below in greater detail. Some case reports of transcranial magnetic stimulation (TMS) have also shown promise in reducing depressive symptoms [146, 147]. Although blue light therapy has been shown to alleviate fatigue and daytime sleepiness following TBI, there is no evidence of a treatment effect on depression [148].

Pharmacological Treatments Several studies have evaluated various pharmacological treatments for patients with TBI and comorbid psychiatric disorders. These previous studies have assessed the efficacy of well-known drugs including methylphenidate, sertraline, and citalopram. One experimental study found that methylphenidate was efficacious in improving cognitive function and alertness [130] while a separate literature review found that these effects were not supported by strong evidence [129]. One study found sertraline to be more effective than placebo in preventing the development of depressive symptoms in non-depressed patients in the first 3  months after TBI, though the effects are not likely to persist once the drug is discontinued [132]. Two additional studies failed to show significant differences in depressive symptoms following treatment with sertraline or placebo in people with MDD after TBI [61, 132]. The efficacy of citalopram did not differ from placebo in reducing relapse rates following remission of MDD after TBI [131]. Although some studies have found positive results, many pharmacotherapy studies failed to

7  Depression After Traumatic Brain Injury

demonstrate superiority of depression treatments over placebo. This may be due to a number of psychological and neurobiological factors associated with TBI that contributed to an enhanced placebo response [149].

Physical Exercise Evidence for the potential benefits of physical exercise following TBI is also mixed. Several studies assessing the efficacy of physical exercise found that aerobic exercise significantly improved mood and cognitive functioning compared to a control condition [134, 135]. Conversely, other similar studies found that aerobic exercise did not have a significant effect on psychological states in TBI [136, 137]. It is interesting to note that, even in studies with negative findings, groups that exercised longer (i.e., greater duration) reported greater improvement in mood scores than low-exercise groups [137]. This suggests the possibility of a duration threshold that must be attained to elicit the benefits of exercise following TBI.  Further research is needed to examine these trends.

 ognitive Behavior Therapy (CBT)-Based C Interventions Research on CBT for depressive symptoms in TBI have been encouraging. Studies evaluating the effects of CBT on patients with mood disorders following TBI found that the intervention was sufficient for improving emotional wellbeing [139, 141]. A separate study demonstrated that an adjunctive treatment of CBT and cognitive remediation also resulted in significantly improved emotional functioning compared to a wait-list control [140]. Finally, research assessing the efficacy of mindfulness-based cognitive therapy found significant reductions in depression on one, but not all, measures of depression [150]. Reported treatment effects have been modest, often indicating only partial symptom reduction and large variability. Moreover, none of the four studies mentioned above were designed specifically to treat people with MDD. This is problematic because the efficacy of CBT for MDD following TBI cannot be concluded from studies of people with subthreshold symptoms, given that comparatively greater illness severity, impairment in functioning, number of recurrent episodes, and risk of relapse are often evident in people with MDD [151].

Multidisciplinary Psychosocial Interventions Multidisciplinary psychosocial interventions have been shown to benefit patients with TBI and comorbid mood disorders. One study found that patients who engaged in a cog-

93

nitive rehabilitation program with close relatives had significantly reduced symptoms (cognitive, social, and emotional difficulties) from pre- to post-intervention based on self-report [144]. In a different randomized controlled trial, participants assigned to a community-based outreach rehabilitation program demonstrated significantly greater improvement in daily functioning, self-organization, and psychological well-being compared to the control group [143]. Of note, changes in depression, anxiety, socializing, and productive employment did not differ between the two groups [143]. The findings also suggested that community outreach interventions may yield long-term benefits several years following TBI [143].

 eed for Adapting Standard Depression N Treatment Although some studies of treatments for depression in TBI have been promising (particularly psychosocial interventions, including CBT), evidence is limited by the lack of adequately powered randomized controlled trials that are designed to prospectively evaluate and treat MDD. Without compelling research and evidence-based guidelines for the treatment of MDD in people with TBI, clinicians are forced to defer to recommendations used for the general psychiatric population. However, reliance on recommendations for general psychiatric populations is problematic when treating patients with TBI and depression because such patients may not respond as well to traditional psychotherapeutic interventions due to TBI-specific impairments (e.g., impaired cognition) [152]. A meta-analysis of 13 studies using prepost designs and controlled trials to examine pharmacological and non-pharmacological treatments for depression following mild TBI concluded that there is insufficient evidence to recommend a particular treatment for depression after mild TBI.  The overall effect size from the controlled trials suggested that active treatment is no more beneficial than placebo [153], which could imply that standard depression treatment should be adapted to adequately address other issues following TBI. Additionally, less than half of patients with MDD received antidepressant medication or psychosocial treatment in a large sample of patients with mild to severe TBI [43]. Modifying treatment for post-TBI MDD may help address current underutilization of mental health services by these patients. The unique treatment needs of people with TBI argue for the modification of psychological interventions for depression [154]. As described above, people with TBI typically experience significant neuropsychiatric sequelae, which can impede the rehabilitation process [155, 156]. Impaired attention in TBI can include lowered vigilance, increased distractibility, slowed processing speed, and impaired selective attention [157]. Many people also experience deficits in

94

executive functioning, such as problem-solving, working memory, abstract reasoning, planning, conceptual flexibility, and organization [158]. Given these cognitive difficulties, people with TBI are likely to struggle with the assumptions inherent in standard psychosocial treatments, particularly CBT. This includes the ability to learn and apply new coping skills in the context of sessions and complete self-monitoring, skills practice, and follow-through on experiments and activities between sessions. In fact, low adherence rates and feedback from an online CBT study highlighted patients’ limitations with reading, comprehension, and memory [139]. However, efforts at adapting CBT to compensate for the neuropsychiatric sequelae of TBI have been beneficial in improving emotional well-being [142] and reducing anxiety [159]. Two recent studies of TBI-tailored CBT were prospectively designed to target depression [56, 158] and are worth highlighting in detail. In the first, Ashman et al. [56] compared the efficacy of TBI-tailored CBT and supportive psychotherapy in a sample of 77 people with depression and mild to severe TBI and found no difference in outcomes between interventions. Of note, the study was comprised of a heterogeneous sample in terms of TBI severity and duration of time since injury (average >10 years), and it demonstrated overall low response to either treatment. The long duration of time since injury may indicate a sample of participants who have struggled with chronic depression for several years or been resistant to treatment and are likely to be qualitatively different from people who have experienced less time since TBI. The study utilized a very active comparison group, which may have limited the possibility of finding a significant difference between treatment groups. Further, the authors found no significant moderating variables, which precluded the opportunity to account for the nonsignificant findings. In the second study, Fann et  al. [158] examined 12 weeks of telephone and in-person CBT for MDD [160] tailored for people with TBI. Although preliminary findings suggest that minimally adapted CBT may be more helpful in treating MDD than usual care after the first 8 weeks of CBT, the study had (1) inconsistent findings across primary and secondary measures of depression, (2) a study design that included choice stratification and a biased “treatment as usual” group, and (3) a small, heterogeneous sample (18 subjects completed in-person CBT). At followup (16 weeks), the outcomes for the CBT group were similar to the treatment as usual group. Although these studies were prospectively designed to treat depression, further research is needed to address methodological limitations and remaining questions about the efficacy of an adapted CBT for MDD in people with TBI [157, 161]. Research in our group is currently testing the feasibility and acceptability of an adapted CBT for depression for people with moderate to severe TBI.

L. B. Fisher et al.

 reatment, Clinical Application, T and Recommendations for Practitioners Despite the lack of clear guidelines for treating MDD after TBI, patients with co-occurring MDD and TBI are prevalent in our clinics. Like our patient, Jon, discussed in the introduction to the chapter, these patients often present with a complex set of symptoms, with etiology that is often difficult to ascertain. How do we assess and treat these patients? Below are several suggestions that have been informed by research studies but do require future investigation before evidence-based recommendations can be made. The following recommendations are primarily geared toward psychosocial interventions, as pharmacological recommendations are beyond the scope of this chapter and are reviewed elsewhere [162].

Obtain Collateral Impaired self-awareness is a common symptom of TBI. Therefore, when beginning treatment with a new patient who has sustained a TBI, it is often useful to gather collateral information from a close friend, family member, significant other, or someone who knew the patient before the injury. Inclusion of a support person may be informal, through unstructured clinical interview, with the patient in the room, separate from the patient, or both. Clinicians may also opt to use semi-structured interviews (e.g., Glasgow Outcome Scale-Extended to assess current functioning) [163], behavior rating instruments (e.g., Frontal Systems Behavioral Scale to assess executive dysfunction) [164], and/or selfreport questionnaires (e.g., Awareness Questionnaire to assess impaired self-awareness) [165] with the support person in order to guide the clinician’s diagnostic assessment and formulation of a treatment plan. For people with severe cognitive impairment, it may be necessary to include a legally authorized representative in their care in order to obtain their consent to psychiatric treatment. A number of studies have addressed issues including cognitively impaired people in research and highlight the diminished capacity to consent and potential exploitation of this vulnerable population [166, 167]. However, there are no established guidelines that can be used to assess capacity to consent [168]. Some researchers have developed scales for evaluating decisional capacity to consent in research, including the MacArthur Competence Assessment Tool for Clinical Research [169] and the briefer, ten-item, UCSD Brief Assessment of Capacity to Consent [170], though these measures have not yet been validated in people with TBI.  Alternatively, the Capacity to Consent to Treatment Instrument [171] has been validated for evaluating medical decision-making in people with TBI and could be an option

7  Depression After Traumatic Brain Injury

95

for clinical practice, though administration time (approximately 20 min) may limit feasibility. Further consideration of a close friend or family member’s involvement in psychological treatment may help patients follow through with homework assignments, improve self-awareness, and generalize skills learned in treatment. A support person may also help with transportation, organization, coordination of care, and timeliness. Some patients will not have a support person to involve in treatment or may opt not to include a friend or family member in their treatment. In these instances, it is important to be mindful of constraints on understanding the patient’s current symptoms and level of functioning. Greater communication with multidisciplinary treaters may be warranted in order to gather multiple perspectives on current struggles and functioning.

whereby different disciplines offer various perspectives on multidisciplinary treatment. For example, our patient, Jon, often experiences low frustration tolerance and negative shifts in mood when presented with challenging cognitive tasks in cognitive rehabilitation training. Through communication with his CBT therapist, his speech-language pathologist has learned to ask Jon to identify his thoughts when she observes a change in affect. She encourages him to record the unhelpful thinking in his notebook so that he can later challenge it using skills learned in CBT. By addressing a psychological barrier to cognitive rehabilitation training, Jon’s speech-language pathologist improves the likelihood that he will progress in both treatments.

Family and Community Support

People with ongoing TBI sequelae often struggle with chronic headaches, light sensitivity, and cognitive and physical fatigue, which can impede engagement in treatment. When possible, the recommendation is for the clinician to be flexible. Many mental health treaters are often not able to fill their time slots when patients cancel last minute or may need to bill for missed appointments; however, there are several steps providers can take to help minimize frequent last-minute cancelations. Although the following considerations may be most relevant for people engaged in psychotherapy, some may also apply to medication management.

It may be beneficial for family members to also be involved in various types of peer and community support groups because the well-being of caregivers influences overall rehabilitation outcomes for survivors [172]. Notably, about one third of caregivers experience clinically significant psychological distress, which includes depression [173]. Peer support programs can be helpful in increasing knowledge of TBI, enhancing overall quality of life, improving general outlook, and enhancing ability to cope with depression postTBI in both patients and their families or caregivers [174]. Support groups may facilitate perceived self-efficacy and promote emotional stability.

Multidisciplinary Treatment Mental health providers work in a variety of care settings, and access to collaboration may be limited by their institution or site. However, people who have received treatment for TBIs often undergo extensive outpatient rehabilitation with a large multidisciplinary team. When possible, collaboration with physicians, speech-language pathologists, occupational therapists, and physical therapists can be a rich source of information and may also enhance psychiatric treatment. Mental health providers can contribute to multidisciplinary teams by providing information on the patient’s post-injury cognitive (e.g., self-appraisal of ability), psychological (e.g., coping skills), and social functioning (e.g., interpersonal support). Collaboration with physicians or therapists can alert mental health providers to physical limitations (e.g., light sensitivity, pain level) to facilitate personcentered modification of therapy. Enhanced communication could occur in clinical case conferences or group supervision

Treatment Structure

1. Length of treatment sessions: The traditional 50-min psychotherapy session may contribute to cognitive fatigue, or it may be difficult for patients to sustain attention and concentration. Consider the possibility of 30-min sessions, if needed. 2. Telehealth: Insurance companies are increasingly providing coverage for mental health treatment delivered via teleconferencing tools, and many hospitals are providing clinicians access to HIPAA-compliant, secure telehealth programs. In addition to the sequelae mentioned above, many people with TBI face barriers with driving and have physical injuries, which make them well-suited for virtual visits. Although nothing should replace an in-person initial assessment, follow-up virtual visits can increase treatment adherence for some patients. No guidelines currently exist to determine appropriateness for virtual visits. Therefore, you must use your own clinical judgment and consider factors such as level of risk and ability to sustain attention/limit distraction from the home environment. 3. Physical environment: Due to frequent and often distressing light sensitivity, fluorescent lighting should be avoided whenever possible. When fluorescent lighting is unavoidable, it may be helpful to offer the patient the possibility

96

of leaving the lights off during your session. Patients are often quite appreciative of this consideration, which can help build initial rapport, but sometimes have difficulty initiating the request. Sessions with Jon are routinely conducted in the “dark.” On more than one occasion, he has expressed his gratitude for leaving the lights off during session and his interpretation that his therapist just “gets it.” 4. Consistent scheduling: When possible, schedule patient appointments on the same day, at the same time each week. Given the frequent impairment in executive function following TBI, establishing structure and consistency can be beneficial for treatment adherence. It can also help limit the extent to which a patient needs to rely on memory to keep appointment times. 5 . Time of day: Ask patients about the time of day that is optimal for treatment sessions. Some patients notice that they have less cognitive fatigue in the morning, for example. Others have familial obligations, such as picking up children from school, and need to allow sufficient time so as not to be running late or fight traffic. Whenever possible with your own schedule, attempt to schedule appointments at a time of day that is “best” for accommodating patient limitations. 6 . Reminders: Due to frequent memory impairments, patients often benefit from appointment reminders. If you have the resources to provide appointment reminders (i.e., access to support staff), talk to patients about their preferred form of communication. Many hospitals and larger practices have adopted text messaging services, which, anecdotally, many patients who have TBIs find preferable. This allows patients to open the messages several times and reread details if they forget. Alternatively, some clinics provide automated phone calls or email reminders. (Note: If choosing a text messaging service to implement in your clinic, investigate the logistics. Some HIPAA-compliant programs have the benefit of enhanced security but may be complicated to access. Compared to traditional text messages, the additional steps required by such programs may be more difficult for patients with TBIs to follow.) It can also be beneficial to send patients between-session reminders to reinforce homework adherence and progress on goals. This may be particularly feasible in clinics that employ behavioral health coaches or care managers. Prior research has incorporated mid-week prompting to encourage homework compliance [140].

Provide Session Summaries Regardless of the form of treatment delivered, it may be helpful to provide patients with a written summary of the treatment session. Ideally, the summary sheet is completed

L. B. Fisher et al.

collaboratively throughout the session. In a CBT session, the summary may include an agenda, overview of the main concepts covered, compensatory strategies to be used throughout the week, a description of homework, and details about the next appointment. In our ongoing research study, we use a standard template to summarize every session, which promotes consistency and structure. Several other studies have also emphasized the importance of using memory aids in the form of writing down important points from sessions and homework tasks. In treatment with Jon, it quickly became apparent that it was essential to record homework/outside of session goals in order to optimize the chances of follow through.

Adapting Content Given the frequent cognitive limitations common in TBI, it is helpful to limit psychotherapy session content to one to two key concepts per session [159]. Subsequent sessions should be repetitive, which is important for retention of information and may result in longer courses of treatment than are typical in non-TBI settings. Similarly, it is important for psychiatrists and other mental health providers to be mindful of focusing each visit on a limited set of symptoms, as appropriate, and not attempting to make multiple changes at once. People with TBI can become easily overwhelmed and frustrated. It is important for the clinician to move at a slower pace, cover less material in one appointment, have more frequent checks for understanding, and acknowledge the patient’s ambivalence and uncertainty when present. A discussion of diagnostic impressions and acknowledgement of concerns may be reasonable goals for a first visit, especially for patients without a prior history of psychiatric treatment.

Provide a Patient Workbook Therapists delivering structured psychotherapy such as CBT may want to consider compiling handouts in a workbook or binder for patients. In some cases, this may be standard practice; however, we suggest modifying the handouts as appropriate. For example, handouts could include options for selection rather than blank spaces in order to minimize the need for memory strategies or generating new ideas [159]. In our ongoing research study, we have created our own version of the traditional CBT thought record. It includes enhanced directions, several prompts (rather than blank spaces), options for selection, and consolidated information (e.g., brief descriptions of cognitive distortions, questions to challenge unhelpful thoughts) that is traditionally covered in several handouts. Our version of a “thought worksheet” is meant to be comprehensive so that patients do not have to search for

7  Depression After Traumatic Brain Injury

supplemental handouts. Consolidating the information in one worksheet facilitates the steps of cognitive restructuring – identifying unhelpful thoughts, challenging unhelpful thoughts, and generating more adaptive thinking. Some patients may also benefit from additional handouts to practice and rehearse strategies above and beyond what is expected in standard treatments.

 eveloping Individualized Compensatory D Strategies As emphasized throughout the chapter, people with TBI experience a number of sequelae that have the potential to impede learning and acquisition of coping skills. Therefore, clinicians are encouraged to utilize a range of compensatory strategies, frequently utilized by other disciplines (e.g., speech-language pathologists, occupational therapists) to enhance treatment. Some research has shown promise in adapting CBT for people with anxiety [159] and depression [158]. Gallagher et  al. [175] outlined the most common adaptations to CBT for people with cognitive impairments after brain injury, many of which are described in this chapter. Our current research is further testing the hypothesis that adapted treatment will lead to significant reductions in depression among people with moderate to severe TBI. When neuropsychological testing results are available  – or if the possibility exists to refer for testing – clinicians can use this valuable data to directly inform strategies for maximal benefit [159]. A full neurocognitive testing battery is not necessary in order to be informative. Many rehabilitation providers administer a variety of subtests of specific cognitive domains as a part of their assessment and treatment (i.e., cognitive rehabilitation, occupational therapy). It is important not to overlook these recommendations for compensatory strategies that are often included in testing reports. For example, when testing results demonstrate a significant deficit in aspects of memory, recommendations for compensatory strategies could include: • When beginning something new and/or unfamiliar, or learning new information, it is important that the patient leaves himself enough time in the beginning to learn and understand what he is doing. • Complete one activity at a time. • Scheduled 10-min breaks every 30  min should be built into the patient’s work schedule. At that time, the patient can review completed and unfinished tasks. The patient can also use that time to move around or get a snack. Below, we list several additional strategies to consider utilizing with your patients. There is not a one size fits all approach, and we encourage you to individually tailor the

97

strategies and treatment to each of your patients, depending on their needs. • Audiotape sessions (i.e., with cellphones) and encourage patients to listen to the recording several times between sessions. • Establish a reminder/prompting system, such as setting alarms and calendar reminders on cell phones. • Utilize post-it notes or signs (which can be made in session) to display in strategic positions in the patient’s home/office/car. • Carry index card with important reminders in one’s wallet. • Utilize a calendar/appointment book/to-do list (this strategy is often emphasized for patients who have received cognitive rehabilitation, so it may not be novel to them.). • Identify distractors in their environment, and increase awareness of when the patient tends to get off task. This can be accomplished by completing homework assignments in a quiet setting and removing all unnecessary distractors (e.g., phone, TV). • Place therapy materials (e.g., session summaries, homework) in one visible area of the home (e.g., in a folder on the counter) and encourage patients to store them there.

Conclusion As illustrated throughout this chapter, people with depression after a TBI experience a number of symptoms that can impede their recovery from both conditions and impact overall quality of life. Our understanding of post-TBI depression is best understood from a biopsychosocial model (see Fig. 7.1 for our model), though more research is needed to understand why, how, and in which people MDD develops. Likewise, a dearth of research on treatments for post-TBI depression leaves clinicians without evidence-based recommendations to guide their treatment choices. We present several clinical considerations and recommendations that emphasize the importance of tailoring evidence-based treatments for depression for people with the most common cognitive and physiological sequelae of TBI (e.g., impaired cognition, ongoing headaches, light sensitivity). Future research should address content-based adaptations, which emphasize the importance of comorbid difficulties to maximize response to depression treatment. Treating depression after TBI can be both a challenging and rewarding endeavor that is likely to confront complex psychological issues, such as acceptance of changes in identity. Recovery in the context of ongoing cognitive and physical challenges is often daunting, even for those people who do not experience MDD. Evidence-based treatment, in combination with peer support groups, social support, and access

98

to resources, is greatly needed to optimize recovery from depression and other psychiatric illness following TBI.

FAQs: Common Questions and Answers Q1. Does the severity of TBI predict greater severity of depression? A1. There is no clear evidence to suggest that severity of TBI is a significant predictor of depression after injury, though this has been controversial in the literature. Although some researchers have found that people with more severe TBIs have a greater likelihood of experiencing worse depressive symptoms than people with less severe injuries [176], many researchers have found no correlation between injury severity and depressive severity post-TBI [43, 69, 177]. Some researchers have even argued the opposite: people with milder injuries may be more likely to experience greater depressive severity due to heightened awareness of their injury and limitations [47, 176]. In contrast to the controversial relationship between injury severity as a predictor of depressive severity post-TBI, research has consistently shown that history of depression prior to the injury and at the time of the injury is the strongest predictor of depression post-TBI [43]. Q2. How soon after TBI can you diagnose MDD? A2. The answer to this question may depend on several factors, including, but not limited to, severity of injury, loss of consciousness, and period of post-traumatic amnesia. Natural recovery after TBI is typically classified first by a period of impaired consciousness, followed by a posttraumatic confusional state with amnesia, and then a period of post-confusional improvement of cognitive abilities [178]. Although there are no established guidelines on the timeline for diagnosing MDD after TBI, depression should not be diagnosed during an amnesic state; patients should be fully oriented at the time of assessment. Further, given the cognitive improvement that occurs during the first several months following the termination of an amnesic state, it may be prudent to allow some time for natural recovery to occur before delivering a psychiatric diagnosis. Without evidencebased guidelines, clinical judgment is needed. Factors worthy of consideration may include past history of MDD and/or other psychiatric disorders, as well as depression at the time of injury. One study showed that about half of patients who became depressed at some point over the course of 1 year after TBI were identified by 3  months post-injury, suggesting that early assessment and treatment may be warranted [43]. A second study also supported the feasibility of identifying depres-

L. B. Fisher et al.

sion in patients with mild TBI by 3 months post-injury [76]. These findings contrast with earlier research that argued that depression typically develops after significant time has elapsed since the injury and the patient has greater awareness of the chronicity and long-term implications of the injury [85]. Other researchers highlight the importance of careful selection of assessment tools within the first 3  months post-TBI, given the role that highly transient somatic and cognitive complaints may play in early assessment [77]. For this reason, structured clinical interview using DSM-IV criteria may be preferred over other symptom severity measures. Some people will experience depression immediately following an injury, though the question remains whether or not the symptoms are best conceptualized as a depressive disorder, adjustment disorder, and/or sequelae of the brain injury itself (i.e., increased emotionality, irritability, fatigue, cognitive disturbance), to which the clinician must evaluate on a case-by-case basis. Q3.  Which depression measures are recommended for patients with TBI? A3. Earlier in the chapter, we reviewed select measures for assessing depression after TBI and highlighted the specific advantages for use in the TBI population. The Patient Health Questionnaire-9 (PHQ-9) is one appealing option because it is brief, consistent with the DSM-IV criteria for major depressive disorder, and widely used by providers. Research supports using a modified scoring algorithm for patients with TBI [65]. Alternatively, the Hospital Anxiety and Depression Scale (HADS) may be preferred as a brief self-report measure because it excludes somatic items that overlap with common TBI symptoms. As with the selection of all diagnostic and symptom severity measures, one must weigh the pros and cons in light of the intended purpose. See Table  7.1 for more information about eight select measures of depression. Q4.  When should interventions for depression post-TBI begin? A4. The answer is closely tied to the above discussion about the timing of assessment and diagnosis. It is also likely to depend on the clinician’s conceptualization of depressive symptoms and the patient’s level of awareness and motivation. Some have argued that people in the acute stage of injury (less than 6 months post-injury) have limited insight into their conditions and would be unlikely to benefit from insight-oriented treatments (e.g., cognitive therapy) [85]. However, there is a strong counterargument for considering CBT, as one of the purposes of CBT is to increase awareness of maladaptive thoughts, feelings, and behaviors [179]. Patients in the early acute phase may also be well-suited for psychopharmacological interventions to address the pathophysiological and

7  Depression After Traumatic Brain Injury

neurocognitive underpinnings of the illness [85]. When determining the appropriate timing for an intervention, it is also important to consider the individual’s risk factors (e.g., psychiatric history) and current psychosocial factors (e.g., social support, financial status, and ability to return to work), which will vary greatly among people depending upon the duration of time since injury. For people with TBI who do not have significant psychiatric symptoms, a CBT-informed group intervention aimed at building perceived self-efficacy may help prevent the future development of emotional distress [180, 181]. It is unknown whether or not a cognitive behavioral intervention could help prevent the development of MDD after TBI. Nevertheless, these findings highlight a potential role for early preventative intervention after TBI, especially for people with several risk factors for depression (e.g., pre-injury psychiatric history, limited social support). Q5. Given the range of cognitive and emotional symptoms associated with TBI, what should be addressed first in psychotherapy? A5. In the absence of strong evidence to guide recommendations for post-TBI depression, this is a challenging yet important question. Each patient should be considered individually, and assessment is a key component in determining a treatment plan. As with any new patient, it is important to conduct a thorough evaluation at the start of treatment, which includes assessment of psychiatric and cognitive symptoms. In addition to measures of depression discussed earlier in the chapter, mental health providers may want to incorporate brief, self-report measures as a feasible way to assess common sequelae after TBI.  Some possible measures may include Rivermead Post-Concussion Symptom Questionnaire (RPQ), a self-report questionnaire that assesses the presence and severity of somatic, cognitive, and emotional symptoms after TBI [182]; Neuro-QoL Item Bank v2.0 Cognition Function Short Form, an 8-item, self-rated questionnaire assessing cognitive function [183]; and Neuro-QoL Item Bank v1.0 Emotional and Behavioral Dyscontrol, an 8-item self-rated questionnaire assessing the patients’ experience of emotional and behavioral dyscontrol. The National Institutes of Health (NIH) provided funding for the development of Neuro-QoL, a measurement system that evaluates and monitors the physical, mental, and social consequences experienced by people with neurological conditions. Given the strong psychometric properties, growing research support, feasibility, and free and easy access, this set of measures is one of several options for gathering important data from new patients in order to help generate a treatment plan. It is important to collect information on TBI sequelae to assist clinicians in developing adaptations that are

99

likely to enhance depression treatment. Treatment planning has been conceptualized as a complex process involving sequential decisions, with consideration of information related to patient characteristics (e.g., psychiatric diagnoses, symptoms, problem areas), treatment context, presence or absence of social support, and treatment strategies [184]. Many clinicians address a patient’s central problem first and then move on to treat other problems in a sequential manner [185], while others attempt to treat comorbid conditions simultaneously. Other research has supported integrated treatment approaches, as with people suffering from severe mental illness and substance use disorders [186]. It is important to prioritize treating behaviors that may hinder a patient’s ability to fully engage with treatment, conceptualized in dialectal behavior therapy (DBT) as therapyinterfering behaviors [187]. More recent research has taken a transdiagnostic approach to treating psychopathology, meaning that it can be applied to a range of different disorders and problems [188], though this approach has not been studied in people with TBI. Thus far, there is no clear evidence to suggest that an individual’s degree of cognitive impairment will hinder their ability to benefit from psychological treatment. However, it is important to review neuropsychological test results when available and determine what treatment adaptations may be needed to compensate for cognitive impairment. In some cases, it can be helpful to initiate cognitive rehabilitation prior to beginning a psychosocial intervention, such as CBT. Many of the skills taught in cognitive rehabilitation, including strategies for improving executive function, organization, and memory, can enhance one’s ability to complete homework and achieve maximal benefit from CBT.  Tiersky et al. [140] demonstrated that CBT and cognitive remediation are effective in reducing psychological distress and improving cognitive functioning among people with mild to moderate TBI. Prospective research is needed to confirm the efficacy of sequential delivery of CBT and cognitive rehabilitation training for people with TBI and depression. Q6. Are suicidal thoughts and behaviors common after TBI? A6. People with TBI demonstrate elevated risk for suicidal ideation, suicidal behavior, and completed suicide compared to the general population [189]. People who have sustained a TBI are 1.55–3 times more likely to commit suicide than people without TBI in the general population [145, 190, 191]. After TBI, rates of suicidal ideation and suicide attempts have been estimated to range from 7% to 28% [44, 52] and 0.8% to 1.7% [44], respectively. Few studies have utilized longitudinal designs and examined predictors of suicidal behavior following TBI. Preliminary findings have demonstrated that sever-

100

L. B. Fisher et al.

ity of depression after injury, history of prior suicide attempt, history of bipolar disorder, and having less than a high school education predict suicidal ideation 1 year post-TBI [192]. Similar to the general population, postinjury psychiatric disturbance is strongly associated with post-injury suicidality [193]. Lastly, premorbid history of aggression/hostility in psychiatric patients may be a risk factor for post-injury suicidality in people with mild TBI [194]. Overall, severity of injury is not significantly related to the presence of suicidal behavior (i.e., suicidal thoughts, suicide attempts) post-injury [52, 192, 193]. In people with TBI, suicidal behavior may be a symptom of depression, or it may be related to pre-injury factors (i.e., psychiatric history, demographics) or other mood states (i.e., anxiety, impulsivity). In one study, about half of patients with TBI who reported suicidal ideation during the 1st year after injury also reported probable depression at the time of their first assessment [192]. In another study, 26.1% of participants who endorsed current suicidal ideation also met criteria for MDD [52]. In our research, we found that the majority of people with suicidal ideation 1  year post-TBI were likely to meet criteria for MDD; almost a tenth of them reported a suicide attempt in the 1st year after TBI [44]. Our findings suggested that suicidal ideation in this population may be largely conceptualized as a symptom of depression rather than a distinct neuropsychiatric sequela of TBI. In addition, nearly two thirds of people who reported at least one suicide attempt in the 1st year after TBI were likely to meet criteria for MDD at year one. Although the prevalence of depression among suicide attempters 1 year after TBI was high, it was comparatively lower than the rate of depression among suicide ideators 1  year after TBI.  This suggests that other factors (i.e., impulsivity, low frustration tolerance) may play an important role in predicting who attempts suicide in the 1st year after TBI [44]. Further research is needed to understand factors related to suicidality among people with TBI [91].

References 1. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Report to congress on traumatic brain injury in the United States: epidemiology and rehabilitation. Arch Phys Med Rehabil. 2015;96:1753–5. 2. Prins M, Greco T, Alexander D, Giza CC. The pathophysiology of traumatic brain injury at a glance. Model Mech. 2013;6:1307–15. 3. Riggio S, Wong M. Neurobehavioral sequelae of traumatic brain injury. Mt Sinai J Med. 2009;76:163–72. 4. Walker WC, Pickett TC. Motor impairment after severe traumatic brain injury: a longitudinal multicenter study. J Rehabil Res Dev. 2007;44:975–82.

5. Zaloshnja E, Miller T, Langlois JA, Selassie AW.  Prevalence of long-term disability from traumatic brain injury in the civilian population of the United States, 2005. J Head Trauma Rehabil. 2008;23(6):394–400. 6. Corso P, Finkelstein E, Miller T, Fiebelkorn I, Zaloshnja E. Incidence and lifetime costs of injuries in the United States. Inj Prev. 2006;12(4):212–8. 7. Faul M, Xu L, Wald MM, Coronado VG. Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002–2006. Atlanta: Centers for Disease Control and Prevention; 2010. 8. Selassie AW, Wilson DA, Pickelsimer EE, Voronca DC, Williams NR, Edwards JC. Incidence of sport-related traumatic brain injury and risk factors of severity: a population-based epidemiologic study. Ann Epidemiol. 2013;23:750–6. 9. Coronado VG, Haileyesus T, Cheng TA, Bell JM, HaarbauerKrupa J, Lionbarger MR, et  al. Trends in sports- and recreation-related traumatic brain injuries treated in US emergency departments: the National Electronic Injury Surveillance SystemAll Injury Program (NEISS-AIP) 2001–2012. J Head Trauma Rehabil. 2015;30(3):185. 10. Thompson HJ, McCormick WC, Kagan SH.  Traumatic brain injury in older adults: epidemiology, outcomes, and future implications. J Am Geriatr Soc. 2006;54:1590–5. 11. Elder GA, Cristian A.  Blast-related mild traumatic brain injury: mechanisms of injury and impact on clinical care. Mt Sinai J Med. 2009;76:111–8. 12. Albrecht JS, Hirshon JM, McCunn M, Bechtold KT, Rao V, Simoni-Wastila L, et  al. Increased rates of mild traumatic brain injury among older adults in US emergency departments, 2009– 2010. J Head Trauma Rehabil. 2016;31(5):E1–7. 13. Rockhill CM, Jaffe K, Zhou C, Fan M-Y, Katon W, Fann JR. Health care costs associated with traumatic brain injury and psychiatric illness in adults. J Neurotrauma. 2012;29(6):1038–46. 14. Orman JAL, Kraus JF, Zaloshnja E. Epidemiology. In: Silver JM, McAllister TW, Yudofsky SC, editors. Textbook of traumatic brain injury. 2nd ed. Washington, DC: American Psychiatric Publishing; 2011. p. 3–22. 15. Lucas JA, Addeo R. Traumatic brain injury and postconcussion syndrome. In: Snyder PJ, Nussbaum PD, Robins DL, editors. Clinical neuropsychology: a pocket handbook for assessment. Washington, DC: American Psychological Association. 2006; p. 351–80. 16. LaPlaca MC, Simon CM, Prado GR, Cullen DK.  CNS injury biomechanics and experimental models. Prog Brain Res. 2007;161:13–26. 17. Park E, Bell JD, Baker AJ. Traumatic brain injury: can the consequences be stopped? CMAJ. 2008;178:1163–70. 18. Smith DH, Meaney DF, Shull WH. Diffuse axonal injury in head trauma. J Head Trauma Rehabil. 2003;18:307–16. 19. Malec JF, Brown AW, Leibson CL, Flaada JT, Mandrekar JN, Diehl NN, et  al. The mayo classification system for traumatic brain injury severity. J Neurotrauma. 2007;24:1417–24. 20. Stein SC. Minor head injury: 13 is an unlucky number. J Trauma. 2001;50:759–60. 21. Baum J, Entezami P, Shah K, Medhkour A. Predictors of outcomes in traumatic brain injury. World Neurosurg. 2016;90:525–9. 22. Zador Z, Sperrin M, King AT. Predictors of outcome in traumatic brain injury: new insight using receiver operating curve indices and Bayesian network analysis. PLoS One. 2016;11:e0158762. 23. Brasure M, Lamberty GJ, Sayer NA, Nelson NW, MacDonald R, Ouellette J, et  al. AHRQ comparative effectiveness reviews. In: Multidisciplinary postacute rehabilitation for moderate to severe traumatic brain injury in adults. Rockville: Agency for Healthcare Research and Quality (US); 2012. 24. Kushner D. Mild traumatic brain injury: toward understanding manifestations and treatment. Arch Intern Med. 1998;158:1617–24.

7  Depression After Traumatic Brain Injury 25. Mayo Clinic. Traumatic brain injury. Diseases & conditions. Washington, DC: American Psychological Association. 2018. 26. Consensus conference. Rehabilitation of persons with traumatic brain injury. NIH consensus development panel on rehabilitation of persons with traumatic brain injury. JAMA. 1999;282(10):974–83. 27. McDonald S.  Impairments in social cognition following severe traumatic brain injury. J Int Neuropsychol Soc. 2013;19(3):231–46. 28. Cappa KA, Conger JC, Conger AJ. Injury severity and outcome: a meta-analysis of prospective studies on TBI outcome. Health Psychol. 2011;30:542–60. 29. Iverson GI, Zasler ND, Lange RT.  Post-concussion disorder. New York: Demos; 2007. 30. Carroll LJ, Cassidy JD, Peloso PM, Borg J, von Holst H, Holm L, et al. Prognosis for mild traumatic brain injury: results of the WHO collaborating centre task force on mild traumatic brain injury. J Rehabil Med. 2004;36:84–105. 31. Hiploylee C, Dufort PA, Davis HS, Wennberg RA, Tartaglia MC, Mikulis D, et al. Longitudinal study of Postconcussion syndrome: not everyone recovers. J Neurotrauma. 2017;34(8):1511–23. 32. McMahon P, Hricik A, Yue JK, Puccio AM, Inoue T, Lingsma HF, et al. Symptomatology and functional outcome in mild traumatic brain injury: results from the prospective TRACK-TBI study. J Neurotrauma. 2014;31(1):26–33. 33. Ryan LM, Warden DL.  Post concussion syndrome. Int Rev Psychiatry Abingdon Engl. 2003;15(4):310–6. 34. Tate RL, Broe GA, Cameron ID, Hodgkinson AE, Soo CA. PreInjury, Injury and early post-injury predictors of long-term functional and psychosocial recovery after severe traumatic brain injury. Brain Impair. 2005;6(2):75–89. 35. Davis KC, Slomine BS, Salorio CF, Suskauer SJ. Time to follow commands and duration of posttraumatic amnesia predict GOS-E Peds scores 1 to 2 years after TBI in children requiring inpatient rehabilitation. J Head Trauma Rehabil. 2016;31(2):E39–47. 36. Jallo JI, Narayan RK.  Craniocerebral trauma. In: Bradley WG, Daroff RB, Fenichel GM, editors. Neurology in clinical practice. Boston: Butterworth-Heinemann; 2000. p. 1055–87. 37. Selassie AW, Zaloshnja E, Langlois JA, Miller T, Jones P, Steiner C.  Incidence of long-term disability following traumatic brain injury hospitalization, United States, 2003. J Head Trauma Rehabil. 2008;23:123–31. 38. Koponen S, Taiminen T, Portin R, Himanen L, Isoniemi H, Heinonen H, et al. Axis I and II psychiatric disorders after traumatic brain injury: a 30-year follow-up study. Am J Psychiatry. 2002;159(8):1315–21. 39. Hibbard MR, Uysal S, Kepler K, Bogdany J, Silver J. Axis I psychopathology in individuals with traumatic brain injury. J Head Trauma Rehabil. 1998;13(4):24–39. 40. Rogers JM, Read CA.  Psychiatric comorbidity following traumatic brain injury. Brain Inj. 2007;21(13–14):1321–33. 41. Jorge RE, Starkstein SE.  Pathophysiologic aspects of major depression following traumatic brain injury. J Head Trauma Rehabil. 2005;20(6):475–87. 42. Rapoport MJ.  Depression following traumatic brain injury: epidemiology, risk factors and management. CNS Drugs. 2012;26(2):111–21. 43. Bombardier CH, Fann JR, Temkin NR, Esselman PC, Barber J, Dikmen SS. Rates of major depressive disorder and clinical outcomes following traumatic brain injury. JAMA. 2010;303:1938–45. 44. Fisher LB, Pedrelli P, Iverson GL, Bergquist TF, Bombardier CH, Hammond FM, et  al. Prevalence of suicidal behaviour following traumatic brain injury: longitudinal follow-up data from the NIDRR traumatic brain injury model systems. Brain Inj. 2016;30(11):1311–8. 45. Wilson J, Pettigrew L, Teasdale G. Emotional and cognitive consequences of head injury in relation to the Glasgow outcome scale. J Neurol Neurosurg Psychiatry. 2000;69(2):204–9.

101 46. Whitnall L, McMillan TM, Murray GD, Teasdale GM. Disability in young people and adults after head injury: 5–7 year follow up of a prospective cohort study. J Neurol Neurosurg Psychiatry. 2006;77(5):640–5. 47. Fann JR, Katon WJ, Uomoto JM, Esselman PC. Psychiatric disorders and functional disability in outpatients with traumatic brain injuries. Am J Psychiatry. 1995;152:1493–9. 48. Rutherford WH.  Sequelae of concussion caused by minor head injuries. Lancet. 1977;1:1–4. 49. Garden N, Sullivan KA. An examination of the base rates of postconcussion symptoms: the influence of demographics and depression. Appl Neuropsychol. 2010;17:1–7. 50. Rapoport MJ, McCullagh S, Shammi P, Feinstein A.  Cognitive impairment associated with major depression following mild and moderate traumatic brain injury. J Neuropsychiatry Clin Neurosci. 2005;17:61–5. 51. Chamelian L, Feinstein A.  The effect of major depression on subjective and objective cognitive deficits in mild to moderate traumatic brain injury. J Neuropsychiatry Clin Neurosci. 2006;18:33–8. 52. Tsaousides T, Cantor JB, Gordon WA.  Suicidal ideation following traumatic brain injury: prevalence rates and correlates in adults living in the community. J Head Trauma Rehabil. 2011;26(4):265–75. 53. Rao V, Rosenberg P, Bertrand M, Salehinia S, Spiro J, Vaishnavi S, et al. Aggression after traumatic brain injury: prevalence & correlates. J Neuropsychiatry Clin Neurosci. 2009;21(4):420–9. 54. Katon JW.  Epidemiology and treatment of depression in patients with chronic medical illness. Dialogues Clin Neurosci. 2011;13:7–23. 55. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured clinical interview for DSM-IV axis I disorders SCID-I: clinician version, administration booklet. Arlington: American Psychiatric Publishing; 1997. 56. Ashman T, Cantor JB, Tsaousides T, Spielman L, Gordon W.  Comparison of cognitive behavioral therapy and supportive psychotherapy for the treatment of depression following traumatic brain injury: a randomized controlled trial. J Head Trauma Rehabil. 2014;29(6):467–78. 57. Gould KR, Ponsford JL, Johnston L, Schonberger M. The nature, frequency and course of psychiatric disorders in the first year after traumatic brain injury: a prospective study. Psychol Med. 2011;41:2099–109. 58. Jorge RE, Robinson RG, Moser D, Tateno A, Crespo-Facorro B, Arndt S. Major depression following traumatic brain injury. Arch Gen Psychiatry. 2004;61:42–50. 59. Sliwinski M, Gordon WA, Bogdany J.  The Beck Depression Inventory: is it a suitable measure of depression for individuals with traumatic brain injury? J Head Trauma Rehabil. 1998;13(4):40–6. 60. Hamilton M.  A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. 61. Ashman TA, Cantor JB, Gordon WA, Spielman L, Flanagan S, Ginsberg A, et al. A randomized controlled trial of sertraline for the treatment of depression in persons with traumatic brain injury. Arch Phys Med Rehabil. 2009;90:733–40. 62. Dyer JR, Williams R, Bombardier CH, Vannoy S, Fann JR. Evaluating the psychometric properties of 3 depression measures in a sample of persons with traumatic brain injury and major depressive disorder. J Head Trauma Rehabil. 2016;31(3):225–32. 63. Schwarzbold ML, Diaz AP, Nunes JC, Sousa DS, Hohl A, Guarnieri R, et al. Validity and screening properties of three depression rating scales in a prospective sample of patients with severe traumatic brain injury. Rev Bras Psiquiatr. 2014;36(3):206–12. 64. Kroenke K, Spitzer RL, Williams JB.  The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

102 65. Fann JR, Bombardier CH, Dikmen S, Esselman P, Warms CA, Pelzer E, et  al. Validity of the patient health questionnaire-9  in assessing depression following traumatic brain injury. J Head Trauma Rehabil. 2005;20:501–11. 66. Cook KF, Bombardier CH, Bamer AM, Choi SW, Kroenke K, Fann JR. Do somatic and cognitive symptoms of traumatic brain injury confound depression screening? Arch Phys Med Rehabil. 2011;92(5):818–23. 67. Beck AT, Steer RA, Brown GK. Manual for the Beck Depression Inventory-II. San Antonio: Psychological Corporation; 1996. 68. Homaifar BY, Brenner LA, Gutierrez PM, Harwood JF, Thompson C, Filley CM, et  al. Sensitivity and specificity of the Beck Depression Inventory-II in persons with traumatic brain injury. Arch Phys Med Rehabil. 2009;90:652–6. 69. Rowland SM, Lam CS, Leahy B.  Use of the Beck Depression Inventory-II (BDI-II) with persons with traumatic brain injury: analysis of factorial structure. Brain Inj. 2005;19:77–83. 70. Zigmond AS, Snaith RP.  The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67(6):361–70. 71. Whelan-Goodinson R, Ponsford J, Schonberger M.  Validity of the hospital anxiety and depression scale to assess depression and anxiety following traumatic brain injury as compared with the structured clinical interview for DSM-IV.  J Affect Disord. 2009;114:94–102. 72. Dahm J, Wong D, Ponsford J. Validity of the depression anxiety stress scales in assessing depression and anxiety following traumatic brain injury. J Affect Disord. 2013;151:392–6. 73. Radloff LS.  The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401. 74. Bush BA, Novack TA, Schneider JJ, Madan A.  Depression following traumatic brain injury: the validity of the CES-D as a brief screening device. J Clin Psychol Med Settings. 2004;11(3):195–201. 75. McCauley SR, Pedroza C, Brown SA, Boake C, Levin HS, Goodman HS, et al. Confirmatory factor structure of the Center for Epidemiologic Studies-Depression scale (CES-D) in mild-tomoderate traumatic brain injury. Brain Inj. 2006;20(5):519–27. 76. Levin HS, McCauley SR, Josic CP, Boake C, Brown SA, Goodman HS, et  al. Predicting depression following mild traumatic brain injury. Arch Gen Psychiatry. 2005;62:523–8. 77. Dikmen SS, Bombardier CH, Machamer JE, Fann JR, Temkin NR. Natural history of depression in traumatic brain injury. Arch Phys Med Rehabil. 2004;85:1457–64. 78. Derogatis LR, Rickels K, Uhlenhuth EH, Covi L.  The Hopkins symptom checklist: a measure of primary symptom dimensions. In: Pinchot P, editor. Psychological measurements in psychopharmacology: problems in Pharmacopsychiatry. Basel: S Karger AG; 1974. p. 79–112. 79. Arundine A, Bradbury CL, Dupuis K, Dawson DR, Ruttan LA, Green REA.  Cognitive behavior therapy after acquired brain injury: maintenance of therapeutic benefits at 6 months posttreatment. J Head Trauma Rehabil. 2012;27(2):104–12. 80. Ramanathan DM, Wardecker BM, Slocomb JE, Hillary FG.  Dispositional optimism and outcome following traumatic brain injury. Brain Inj. 2011;25(4):328–37. 81. Lovibond SH, Lovibond PF.  Manual for the depression anxiety stress scales. Sidney: Psychology Foundation; 1995. 82. Malec JF, Brown AW, Moessner AM, Stump TE, Monahan P. A preliminary model for posttraumatic brain injury depression. Arch Phys Med Rehabil. 2010;91(7):1087–97. 83. Jorge RE, Robinson RG, Arndt SV, Starkstein SE, Forrester AW, Geisler F. Depression following traumatic brain injury: a 1 year longitudinal study. J Affect Disord. 1993;27:233–43. 84. Babin PR. Diagnosing depression in persons with brain injuries: a look at theories, the DSM-IV and depression measures. Brain Inj. 2003;17:889–900.

L. B. Fisher et al. 85. Moldover JE, Goldberg KB, Prout MF.  Depression after traumatic brain injury: a review of evidence for clinical heterogeneity. Neuropsychol Rev. 2004;14:143–54. 86. Rosenthal M, Christensen BK, Ross TP.  Depression following traumatic brain injury. Arch Phys Med Rehabil. 1998;79:90–103. 87. Moore AD, Stambrook M. Cognitive moderators of outcome following traumatic brain injury: a conceptual model and implications for rehabilitation. Brain Inj. 1995;9:109–30. 88. Curran CA, Ponsford JL, Crowe S.  Coping strategies and emotional outcome following traumatic brain injury: a comparison with orthopedic patients. J Head Trauma Rehabil. 2000;15:1256–74. 89. Fann JR, Burington B, Leonetti A, Jaffe K, Katon WJ, Thompson RS. Psychiatric illness following traumatic brain injury in an adult health maintenance organization population. Arch Gen Psychiatry. 2004;61(1):53–61. 90. Lee AL, Ogle WO, Sapolsky RM.  Stress and depression: possible links to neuron death in the hippocampus. Bipolar Disord. 2002;4(2):117–28. 91. Elmore S. Apoptosis: a review of programmed cell death. Toxicol Pathol. 2007;35:495–516. 92. Lucassen PJ, Müller MB, Holsboer F, Bauer J, Holtrop A, Wouda J, et  al. Hippocampal apoptosis in major depression is a minor event and absent from subareas at risk for glucocorticoid overexposure. Am J Pathol. 2001;158(2):453–68. 93. Girgis F, Pace J, Sweet J, Miller JP.  Hippocampal neurophysiologic changes after mild traumatic brain injury and potential neuromodulation treatment approaches. Front Syst Neurosci [Internet]. 2016. [cited 2018 Jan 25];10. Available from: https:// www.ncbi.nlm.nih.gov/pmc/articles/PMC4746250/. 94. Czéh B, Lucassen PJ.  What causes the hippocampal volume decrease in depression? Eur Arch Psychiatry Clin Neurosci. 2007;257(5):250–60. 95. Atkins CM. Decoding hippocampal signaling deficits after traumatic brain injury. Transl Stroke Res. 2011;2(4):546–55. 96. Tate DF, Bigler ED. Fornix and hippocampal atrophy in traumatic brain injury. Learn Mem Cold Spring Harb N. 2000;7(6):442–6. 97. Royo NC, Conte V, Saatman KE, Shimizu S, Belfield CM, Soltesz KM, et  al. Hippocampal vulnerability following traumatic brain injury: a potential role for neurotrophin-4/5 in pyramidal cell neuroprotection. Eur J Neurosci. 2006;23(5):1089–102. 98. Monti JM, Voss MW, Pence A, McAuley E, Kramer AF, Cohen NJ. History of mild traumatic brain injury is associated with deficits in relational memory, reduced hippocampal volume, and less neural activity later in life. Front Aging Neurosci. 2013;5:41. 99. Jorge RE, Acion L, Starkstein SE, Magnotta V.  Hippocampal volume and mood disorders after traumatic brain injury. Biol Psychiatry. 2007;62(4):332–8. 100. Maller JJ, Thomson RHS, Lewis PM, Rose SE, Pannek K, Fitzgerald PB.  Traumatic brain injury, major depression, and diffusion tensor imaging: making connections. Brain Res Rev. 2010;64(1):213–40. 101. Korley FK, Diaz-Arrastia R, Wu AH, Yue JK, Manley GT, Sair HI, et  al. Circulating brain-derived neurotrophic factor has diagnostic and prognostic value in traumatic brain injury. J Neurotrauma. 2016;33:215–25. 102. Lee B-H, Kim Y-K. The roles of BDNF in the pathophysiology of major depression and in antidepressant treatment. Psychiatry Investig. 2010. 23 07/09/received 09/07/accepted;7:231–5. 103. Rothman SM, Mattson MP.  Activity-dependent, stress-responsive BDNF signaling and the quest for optimal brain health and resilience throughout the lifespan. Neuroscience. 2013;239: 228–40. 104. Marosi K, Mattson MP. BDNF mediates adaptive brain and body responses to energetic challenges. Trends Endocrinol Metab TEM. 2014;25(2):89–98. 105. Fogelman D, Zafonte R. Exercise to enhance neurocognitive function after traumatic brain injury. PM R. 2012;4(11):908–13.

7  Depression After Traumatic Brain Injury 106. McKee AC, Cantu RC, Nowinski CJ, Hedley-Whyte ET, Gavett BE, Budson AE, et  al. Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. J Neuropathol Exp Neurol. 2009;68:709–35. 107. Stern RA, Riley DO, Daneshvar DH, Nowinski CJ, Cantu RC, McKee AC. Long-term consequences of repetitive brain trauma: chronic traumatic encephalopathy. PM R. 2011;3(10 Suppl 2):S460–7. 108. Lakhan SE, Kirchgessner A. Chronic traumatic encephalopathy: the dangers of getting “dinged”. Springerplus. 2012;1:2. 109. Tagge CA, Fisher AM, Minaeva OV, Gaudreau-Balderrama A, Moncaster JA, Zhang X-L, et  al. Concussion, microvascular injury, and early tauopathy in young athletes after impact head injury and an impact concussion mouse model. Brain [Internet]. [cited 2018 Jan 25]. Available from: https://doi.org/10.1093/brain/ awx350/4815697. 110. Yang C, Guo X, Wang GH, Wang HL, Liu ZC, Liu H, et  al. Changes in tau phosphorylation levels in the hippocampus and frontal cortex following chronic stress. Braz J Med Biol Res. 2014;47(3):237–44. 111. Brown EE, Iwata Y, Chung JK, Gerretsen P, Graff-Guerrero A. Tau in late-life depression: a systematic review and meta-analysis. J Alzheimers Dis JAD. 2016;54(2):615–33. 112. van der Naalt J, Timmerman ME, de Koning ME, van der Horn HJ, Scheenen ME, Jacobs B, et  al. Early predictors of outcome after mild traumatic brain injury (UPFRONT): an observational cohort study. Lancet Neurol. 2017;16:532–40. 113. Meares S, Shores EA, Taylor AJ, Batchelor J, Bryant RA, Baguley IJ, et al. The prospective course of postconcussion syndrome: the role of mild traumatic brain injury. Neuropsychology. 2011;25(4):454–65. 114. Chen J-K, Johnston KM, Petrides M, Ptito A.  Neural substrates of symptoms of depression following concussion in male athletes with persisting Postconcussion symptoms. Arch Gen Psychiatry. 2008;65(1):81. 115. Guskiewicz KM, Marshall SW, Bailes J, McCrea M, Harding HP, Matthews A, et  al. Recurrent concussion and risk of depression in retired professional football players. Med Sci Sports Exerc. 2007;39(6):903–9. 116. Chrisman SPD, Richardson LP. Prevalence of diagnosed depression in adolescents with history of concussion. J Adolesc Health. 2014;54(5):582–6. 117. Corrigan JD.  Substance abuse as a mediating factor in outcome from traumatic brain injury. Arch Phys Med Rehabil. 1995;76:302–9. 118. Jorge RE, Starkstein SE, Arndt S, Moser D, Crespo-Facorro B, Robinson RG. Alcohol misuse and mood disorders following traumatic brain injury. Arch Gen Psychiatry. 2005;62:742–9. 119. Seel RT, Kreutzer JS, Rosenthal M, Hammond FM, Corrigan JD, Black K. Depression after traumatic brain injury: a National Institute on Disability and Rehabilitation Research Model Systems multicenter investigation. Arch Phys Med Rehabil. 2003;84:177–84. 120. Silver JM, Kramer R, Greenwald S, Weissman M. The association between head injuries and psychiatric disorders: findings from the New Haven NIMH Epidemiologic Catchment Area Study. Brain Inj. 2001;15:935–45. 121. Wijenberg MLM, Stapert SZ, Verbunt JA, Ponsford JL, Van Heugten CM.  Does the fear avoidance model explain persistent symptoms after traumatic brain injury? Brain Inj. 2017;31:1597–604. 122. Fedoroff JP, Starkstein SE, Forrester AW, Geisler FH, Jorge RE, Arndt SV, et al. Depression in patients with acute traumatic brain injury. Am J Psychiatry. 1992;149:918–23. 123. Jorge RE, Robinson RG, Arndt S.  Are there symptoms that are specific for depressed mood in patients with traumatic brain injury? J Nerv Ment Dis. 1993;181:91–9.

103 124. Levin H, Kraus MF. The frontal lobes and traumatic brain injury. J Neuropsychiatry Clin Neurosci. 1994;6(4):443–54. 125. Mayberg H. Frontal lobe dysfunction in secondary depression. J Neuropsychiatry Clin Neurosci. 1994;6(4):428–42. 126. Wallace CA, Bogner J.  Awareness of deficits: emotional implications for persons with brain injury and their significant others. Brain Inj. 2000;14:549–62. 127. Leach LR, Frank RG, Bouman DE, Farmer J. Family functioning, social support and depression after traumatic brain injury. Brain Inj. 1994;8:599–606. 128. Franulic A, Carbonell CG, Pinto P, Sepulveda I.  Psychosocial adjustment and employment outcome 2, 5 and 10 years after TBI. Brain Inj. 2004;18(2):119–29. 129. Neurobehavioral Guidelines Working Group, Warden DL, Gordon B, McAllister TW, Silver JM, Barth JT, et al. Guidelines for the pharmacologic treatment of neurobehavioral sequelae of traumatic brain injury. J Neurotrauma. 2006;23(10):1468–501. 130. Lee H, Kim S-W, Kim J-M, Shin I-S, Yang S-J, Yoon J-S.  Comparing effects of methylphenidate, sertraline and placebo on neuropsychiatric sequelae in patients with traumatic brain injury. Hum Psychopharmacol. 2005;20(2):97–104. 131. Rapoport MJ, Mitchell RA, McCullagh S, Herrmann N, Chan F, Kiss A, et al. A randomized controlled trial of antidepressant continuation for major depression following traumatic brain injury. J Clin Psychiatry. 2010;71(9):1125–30. 132. Novack TA, Baños JH, Brunner R, Renfroe S, Meythaler JM.  Impact of early administration of sertraline on depressive symptoms in the first year after traumatic brain injury. J Neurotrauma. 2009;26(11):1921–8. 133. Fann JR, Bombardier CH, Temkin N, Esselman P, Warms C, Barber J, et  al. Sertraline for major depression during the year following traumatic brain injury: a randomized controlled trial. J Head Trauma Rehabil. 2017;32(5):332–42. 134. Driver S, Ede A.  Impact of physical activity on mood after TBI. Brain Inj. 2009;23(3):203–12. 135. Schwandt M, Harris JE, Thomas S, Keightley M, Snaiderman A, Colantonio A. Feasibility and effect of aerobic exercise for lowering depressive symptoms among individuals with traumatic brain injury: a pilot study. J Head Trauma Rehabil. 2012;27(2): 99–103. 136. Bateman A, Culpan FJ, Pickering AD, Powell JH, Scott OM, Greenwood RJ.  The effect of aerobic training on rehabilitation outcomes after recent severe brain injury: a randomized controlled evaluation. Arch Phys Med Rehabil. 2001;82(2):174–82. 137. Hoffman JM, Bell KR, Powell JM, Behr J, Dunn EC, Dikmen S, et al. A randomized controlled trial of exercise to improve mood after traumatic brain injury. PM R. 2010;2(10):911–9. 138. Wise EK, Hoffman JM, Powell JM, Bombardier CH, Bell KR. Benefits of exercise maintenance after traumatic brain injury. Arch Phys Med Rehabil. 2012;93(8):1319–23. 139. Topolovec-Vranic J, Cullen N, Michalak A, Ouchterlony D, Bhalerao S, Masanic C, et  al. Evaluation of an online cognitive behavioural therapy program by patients with traumatic brain injury and depression. Brain Inj. 2010;24:762–72. 140. Tiersky LA, Anselmi V, Johnston MV, Kurtyka J, Roosen E, Schwartz T, et  al. A trial of neuropsychologic rehabilitation in mild-spectrum traumatic brain injury. Arch Phys Med Rehabil. 2005;86:1565–74. 141. Bradbury CL, Christensen BK, Lau MA, Ruttan LA, Arundine AL, Green RE. The efficacy of cognitive behavior therapy in the treatment of emotional distress after acquired brain injury. Arch Phys Med Rehabil. 2008;89:S61–8. 142. Bédard M, Felteau M, Mazmanian D, Fedyk K, Klein R, Richardson J, et  al. Pilot evaluation of a mindfulness-based intervention to improve quality of life among individuals who sustained traumatic brain injuries. Disabil Rehabil. 2003;25: 722–31.

104 143. Powell J, Heslin J, Greenwood R. Community based rehabilitation after severe traumatic brain injury: a randomised controlled trial. J Neurol Neurosurg Psychiatry. 2002;72(2):193–202. 144. Svendsen H, Teasdale T, Pinner M.  Subjective experience in patients with brain injury and their close relatives before and after a rehabilitation programme. Neuropsychol Rehabil. 2004;14(5):495–515. 145. Brenner LA, Hoffberg AS, Shura RD, Bahraini N, Wortzel HS.  Interventions for mood-related issues post traumatic brain injury: novel treatments and ongoing limitations of current research. Curr Phys Med Rehabil Rep. 2013;1(3):143–50. 146. Nielson DM, McKnight CA, Patel RN, Kalnin AJ, Mysiw WJ. Preliminary guidelines for safe and effective use of repetitive transcranial magnetic stimulation in moderate to severe traumatic brain injury. Arch Phys Med Rehabil. 2015;96(4):S138–44. 147. Fitzgerald PB, Hoy KE, Maller JJ, Herring S, Segrave R, McQueen S, et al. Transcranial magnetic stimulation for depression after a traumatic brain injury: a case study. J ECT. 2011;27(1):38. 148. Sinclair KL, Ponsford JL, Taffe J, Lockley SW, Rajaratnam SMW.  Randomized controlled trial of light therapy for fatigue following traumatic brain injury. Neurorehabil Neural Repair. 2014;28(4):303–13. 149. Polich G, Iaccarino MA, Kaptchuk TJ, Morales-Quezada L, Zafonte R.  Placebo effects in traumatic brain injury. J Neurotrauma. 2018;35(11):1205–12. 150. Bédard M, Felteau M, Marshall S, Cullen N, Gibbons C, Dubois S, et al. Mindfulness-based cognitive therapy reduces symptoms of depression in people with a traumatic brain injury: results from a randomized controlled trial. J Head Trauma Rehabil. 2014;29(4):E13–22. 151. Judd LL, Akiskal HS, Maser JD, Zeller PJ, Endicott J, Coryell W, et al. A prospective 12-year study of subsyndromal and syndromal depressive symptoms in unipolar major depressive disorders. Arch Gen Psychiatry. 1998;55(8):694–700. 152. Burg JS, Williams R, Burright RG, Donovick PJ.  Psychiatric treatment outcome following traumatic brain injury. Brain Inj. 2000;14(6):513–33. 153. Barker-Collo S, Starkey N, Theadom A. Treatment for depression following mild traumatic brain injury in adults: a meta-analysis. Brain Inj. 2013;27(10):1124–33. 154. Mateer CA, Sira CS.  Cognitive and emotional consequences of TBI: intervention strategies for vocational rehabilitation. NeuroRehabilitation. 2006;21(4):315–26. 155. Nicholl J, LaFrance WC. Neuropsychiatric sequelae of traumatic brain injury. Semin Neurol. 2009;29(3):247–55. 156. Vaishnavi S, Rao V, Fann JR.  Neuropsychiatric problems after traumatic brain injury: unraveling the silent epidemic. Psychosomatics. 2009;50(3):198–205. 157. Zomeren AH, van Brouwer WH.  Clinical neuropsychology of attention. New York: Oxford University Press; 1994. 250 p. 158. Fann JR, Bombardier CH, Vannoy S, Dyer J, Ludman E, Dikmen S, et al. Telephone and in-person cognitive behavioral therapy for major depression after traumatic brain injury: a randomized controlled trial. J Neurotrauma. 2015;32:45–57. 159. Hsieh MY, Ponsford J, Wong D, Schonberger M, McKay A, Haines K. A cognitive behaviour therapy (CBT) programme for anxiety following moderate-severe traumatic brain injury (TBI): two case studies. Brain Inj. 2012;26:126–38. 160. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-IV-TR. 4th ed, text revision. Washington, DC: American Psychiatric Association; 2000. 943 p. 161. DeRubeis RJ, Gelfand LA, Tang TZ, Simons AD.  Medications versus cognitive behavior therapy for severely depressed outpatients: mega-analysis of four randomized comparisons. Am J Psychiatry. 1999;156(7):1007–13.

L. B. Fisher et al. 162. Silver JM, McAllister TW, Arciniegas DB. Depression and cognitive complaints following mild traumatic brain injury. Am J Psychiatry. 2009;166(6):653–61. 163. Wilson JT, Pettigrew LE, Teasdale GM. Structured interviews for the Glasgow outcome scale and the extended Glasgow outcome scale: guidelines for their use. J Neurotrauma. 1998;15(8):573–85. 164. Grace J, Malloy PF. Frontal systems behavior scale: professional manual. Lutz: Psychological Assessment Resources, Inc; 2001. 165. Sherer M. The awareness questionnaire. The Center for Outcome Measurement in Brain Injury [Internet]. 2004. Available from: http://www.tbims.org/combi/aq. 166. Johnson-Greene D.  Informed consent issues in traumatic brain injury research: current status of capacity assessment and recommendations for safeguards. J Head Trauma Rehabil. 2010;25:145–50. 167. Triebel KL, Novack TA, Kennedy R, Martin RC, Dreer LE, Raman R, et  al. Neurocognitive models of medical decisionmaking capacity in traumatic brain injury across injury severity. J Head Trauma Rehabil. 2016;31:E49–59. 168. Winslade WJ, Tovino SA. Research with brain-injured subjects. J Head Trauma Rehabil. 2004;19:513–5. 169. Appelbaum PS, Grisso T. MacCAT-CR: MacArthur competence assessment tool for clinical research. Sarasota: Professional Resource Press; 2001. 170. Jeste DV, Palmer BW, Appelbaum PS, Golshan S, Glorioso D, Dunn LB, et  al. A new brief instrument for assessing decisional capacity for clinical research. Arch Gen Psychiatry. 2007;64(8):966–74. 171. Marson DC, Ingram KK, Cody HA, Harrell LE.  Assessing the competency of patients with Alzheimer’s disease under different legal standards. A prototype instrument. Arch Neurol. 1995;52:949–54. 172. Anderson MI, Parmenter TR, Mok M. The relationship between neurobehavioural problems of severe traumatic brain injury (TBI), family functioning and the psychological well-being of the spouse/ caregiver: path model analysis. Brain Inj. 2002;16(9):743–57. 173. Kreutzer JS, Rapport LJ, Marwitz JH, Harrison-Felix C, Hart T, Glenn M, et  al. Caregivers’ well-being after traumatic brain injury: a multicenter prospective investigation. Arch Phys Med Rehabil. 2009;90(6):939–46. 174. Hibbard MR, Cantor J, Charatz H, Rosenthal R, Ashman T, Gundersen N, et al. Peer support in the community: initial findings of a mentoring program for individuals with traumatic brain injury and their families. J Head Trauma Rehabil. 2002;17(2):112–31. 175. Gallagher M, McLeod HJ, McMillan TM.  A systematic review of recommended modifications of CBT for people with cognitive impairments following brain injury. Neuropsychol Rehabil. 2016;0(0):1–21. 176. van Reekum R, Cohen T, Wong J.  Can traumatic brain injury cause psychiatric disorders? J Neuropsychiatry Clin Neurosci. 2000;12:316–27. 177. Peleg G, Barak O, Harel Y, Rochberg J, Hoofien D. Hope, dispositional optimism and severity of depression following traumatic brain injury. Brain Inj. 2009;23:800–8. 178. Povlishock JT, Katz DI. Update of neuropathology and neurological recovery after traumatic brain injury. J Head Trauma Rehabil. 2005;20:76–94. 179. Beck J. Cognitive behavior therapy: basics and beyond. 2nd ed. New York: Guilford Press; 2011. 180. Backhaus S, Neumann D, Parrot D, Hammond FM, Brownson C, Malec J. Examination of an intervention to enhance relationship satisfaction after brain injury: a feasibility study. Brain Inj. 2016;30:975–85. 181. Backhaus SL, Ibarra SL, Klyce D, Trexler LE, Malec JF.  Brain injury coping skills group: a preventative intervention for patients

7  Depression After Traumatic Brain Injury with brain injury and their caregivers. Arch Phys Med Rehabil. 2010;91:840–8. 182. King NS, Crawford S, Wenden FJ, Moss NE, Wade DT.  The Rivermead Post Concussion Symptoms Questionnaire: a measure of symptoms commonly experienced after head injury and its reliability. J Neurol. 1995;242(9):587–92. 183. Cella D, Lai J-S, Nowinski CJ, Victorson D, Peterman A, Miller D, et al. Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology. Neurology. 2012;78(23):1860–7. 184. Beutler L, Clarkin JF.  Systematic treatment selection: toward targeted therapeutic interventions. 1st ed. New York: Routledge; 1990. (Bruner/Mazel Integrative Psychotherapy, Book 3). 185. Clarkin JF, Kendall PC.  Comorbidity and treatment planning: summary and future directions. J Consult Clin Psychol. 1992;60:904–8. 186. Bellack AS, Bennett ME, Gearon JS, Brown CH, Yang Y. A randomized clinical trial of a new behavioral treatment for drug abuse in people with severe and persistent mental illness. Arch Gen Psychiatry. 2006;63:426–32. 187. Linehan MM.  Cognitive-behavioral treatment of borderline personality disorder. New York: Guilford Press; 1993. 188. Barlow DH, Farchione TJ, Bullis JR, Gallagher MW, MurrayLatin H, Sauer-Zavala S, et  al. The unified protocol for transdiagnostic treatment of emotional disorders compared with diagnosis-specific protocols for anxiety disorders: a randomized clinical trial. JAMA Psychiat. 2017;74:875–84.

105 189. Dreer LE, Tang X, Nakase-Richardson R, Pugh MJ, Cox MK, Bailey EK, et  al. Suicide and traumatic brain injury: a review by clinical researchers from the National Institute for Disability and Independent Living Rehabilitation Research (NIDILRR) and Veterans Health Administration Traumatic Brain Injury Model Systems. Curr Opin Psychol. 2017;22:73–8. 190. Harrison-Felix CL, Whiteneck GG, Jha A, DeVivo MJ, Hammond FM, Hart DM. Mortality over four decades after traumatic brain injury rehabilitation: a retrospective cohort study. Arch Phys Med Rehabil. 2009;90(9):1506–13. 191. Ventura T, Harrison-Felix C, Carlson N, Diguiseppi C, Gabella B, Brown A, et al. Mortality after discharge from acute care hospitalization with traumatic brain injury: a population-based study. Arch Phys Med Rehabil. 2010;91(1):20–9. 192. Mackelprang JL, Bombardier CH, Fann JR, Temkin NR, Barber JK, Dikmen SS. Rates and predictors of suicidal ideation during the first year after traumatic brain injury. Am J Public Health. 2014;104(7):e100–7. 193. Simpson G, Tate R.  Suicidality after traumatic brain injury: demographic, injury and clinical correlates. Psychol Med. 2002;32(4):687–97. 194. Oquendo MA, Friedman JH, Grunebaum MF, Burke A, Silver JM, Mann JJ. Suicidal behavior and mild traumatic brain injury in major depression. J Nerv Ment Dis. 2004;192(6):430–4.

Part III Medication Approaches

8

Personalized Medicine Simmie L. Foster, Samuel R. Petrie, David Mischoulon, and Maurizio Fava

Case Vignette

Susan is a 65-year-old married Caucasian woman with a long history of depression, anxiety, type 2 diabetes, and chronic pain due to diabetic neuropathy. She is postmenopausal. She presents for treatment of her depression, which started about 10 years ago after she retired from a busy position as CEO of a small local company. Her depression worsened over the past 2 years once she began to develop complications from her diabetes, in particular a severe neuropathy that limited her ability to be physically active. One of her major complaints is trouble falling asleep and staying asleep, which she attributes to the burning pain in her legs. She also endorses hopelessness that she will ever get better, wishing she were dead, decreased attention, and decreased motivation. Her husband, Bill, notes that Susan is “sensitive to medications” often needing to stop antidepressants because of side effects before reaching a therapeutic dose. Her past medication trials have included multiple selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs). She has never been on a tricyclic antidepressant and currently takes only lorazepam

0.5 mg at night for sleep. After a discussion of pharmacological and nonpharmacological treatments for comorbid depression, sleep disorder, and chronic pain, Susan is wary of starting a new medication since she has had such difficulty tolerating antidepressant medications in the past. She is interested in alternative therapies. The provider remembers reading about a study that recommended genotyping for patients over 65 years old starting nortriptyline and recommends a commercial kit offering genotyping of patients starting an antidepressant after failing multiple trials in the past. After discussing the pros and cons of genotyping with Susan, she decides to obtain the kit, which is partially covered by her insurance. While waiting for the results, Susan is referred to a holistic pain clinic where she can begin physical rehabilitation and cognitive behavioral therapy (CBT) for pain and sleep, in the hopes of becoming more active. Her genotype and recommended dosing for nortriptyline come back after a month. She is listed as a “poor metabolizer,” which could explain why she has had so many side effects from antidepressants. She is started at a very low dose of nortriptyline. Two months later, Susan returns, with substantial improvement in her depression, better sleep, and less pain. She is also more active after rehabilitation. She is tolerating the nortriptyline well, with only some dry mouth as a side effect.

Introduction S. L. Foster (*) · S. R. Petrie · D. Mischoulon · M. Fava Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]; [email protected]; [email protected]; [email protected]

Personalized medicine can be defined as the use of patient characteristics to guide treatment decisions. This is not at all a new concept; physicians have been personalizing medicine for thousands of years. For example, Hippocrates asserted,

© Springer Nature Switzerland AG 2019 B. G. Shapero et al. (eds.), The Massachusetts General Hospital Guide to Depression, Current Clinical Psychiatry, https://doi.org/10.1007/978-3-319-97241-1_8

109

110

“it’s far better to know what person has the disease than what disease the person has” [1]. Over the years, our use of personalization has become increasingly fine-grained and specific, moving from Hippocrates’s use of external observation of “complexions” and “humors” to classify patient subtypes [1] to present-day imaging of internal structures and even higher resolution cellular and molecular genotyping and phenotyping. The immediate future represents integration of the macro- and microscales: combining our ever-growing understanding of disease pathogenesis and molecular and imaging markers, with systems biology, database mining, and machine learning to individualize treatment. The concept of personalization has received considerable interest in the past several years in many fields of medicine. Although psychiatry is poised to be at the forefront of developing patient-specific diagnostics and therapeutics, there are as of yet few concrete guidelines for doing so; many of the potential methods are still in the research stage. In this chapter, we will discuss the historical background of personalized medicine, specifically how it relates to psychiatry and medication treatment of patients with MDD. Recent research advances in personalizing treatment for MDD will be emphasized, with a focus on predicting response to pharmacological treatment for patients with different characteristics. We will then give some recommendations for personalizing treatment of depressed patients in practice, and finally a brief overview of how current research may eventually improve our practice of precision psychiatry.

History Personalization has been a tenet of both Eastern and Western medicine for thousands of years. Traditional Chinese and Ayurvedic medicines have always emphasized that treatment must be tailored to the individual patient. In Western medicine, we have had a progression of personalization based on increasingly high-resolution observations of the biology (molecular and cellular function) underlying disease processes. Hippocrates, as mentioned above, classified patients with different combinations of four “humors,” and indicated that these combinations should determine treatment [1]. Paracelsus put forth the idea that we must take into account environment-patient interactions, introduced the concept of hormesis (the benefit of a treatment depends on the dose), and taught that medicine is based in science [1]. Motulsky, in the 1950s, proposed that medicine needed to be tailored to an individual’s makeup—a precursor to the present-day field of pharmacogenomics [1]. As scientists started to understand that individual responses to one’s environment and medications are influenced by biology, there was a movement toward making the practice of medicine more scientific that had the unintended consequence of depersonalizing medicine. In the 1960s,

S. L. Foster et al.

Alvan Feinstein pioneered the field of clinical epidemiology and set the stage for the teaching and practice of evidence-­ based medicine [2]. The gold standard to provide evidence that a medication is safe and effective is the double-blind, placebo-controlled, randomized clinical trial (RCT). The benefits of this scientific approach to medicine are clear (development of better quality and safer medications, and emphasis on mechanistic understanding)—but there are limitations. The first limitation is a problem of scale. A perfectly average individual does not exist, so treatments shown to be on average effective for a group of patients will by necessity be effective for some individuals and not others. If researchers start with a large enough group, characteristics of patients can be found that predict treatment response, but most studies are done with small samples and use heterogenous methods, making it difficult to interpret the combined data. The second limitation is an overreliance of “best available evidence” for taking care of individual patients [3]. The data obtained from an RCT are by necessity limited and usually do not include the full spectrum of data including subjective symptoms, progression of illness, and psychosocial factors, among others, that clinicians use in practice to make individual treatment decisions. The third limitation is bias. There are biases in how science is interpreted by society [4, 5] that can distort the findings and applicability of study results. The goal of the RCT to find an “effect” leads to substantial bias against considering individual variability in physiology and pathophysiology, since this variability masks the desired average efficacy. A striking example is the male bias of pharmacological studies that have led to many drugs being pulled from the market due to side effects in women [6, 7]. For example, women on average require half the dose of the hypnotic zolpidem; however, this was not discovered until after the drug was marketed [8]. Personalized approaches would avoid some of these biases by taking into account the variation (e.g., in hormonal levels or pharmacokinetics) of the individual rather than seeing the variation as a liability preventing the acquisition of standardized data (women were excluded from clinical trials for many years because of the idea that hormonal fluctuations associated with menstruation would introduce unacceptable variability in trial results). To give another example highlighting the advantages of personalized medicine, suppose we ask how a patient with a cough should be treated. If we were to average together the response to antibiotics of all patients with a cough, there would be a benefit of treatment only for patients with an infection and more specifically the patients with an infection caused by microbes susceptible to the chosen antibiotic—if we used the averaged result to make a treatment recommendation for the average cough patient, it would clearly benefit some patients and be potentially lethal for others. Before the acceptance of germ theory and the subsequent discovery of antibiotics, the options for personalization of cough treatment were limited. Now we know that cough can have infectious or noninfectious

8  Personalized Medicine

etiologies. The precision or personalized approach is to obtain data, for example, of a history suggesting infectious exposure, malignancy, COPD, autoimmunity, or allergy, physical findings, a sputum sample and culture, a chest X-Ray, and most importantly antibiotic susceptibility profile, and then prescribe treatment based on the results. In psychiatry, we are still gathering together the tools—the “germ theory” —that would let us be precise in our diagnosis and treatment. In the absence of these tools, psychiatrists have long been making assumptions about biology without knowing the underlying mechanisms. The practitioners with the best clinical judgment, based on history, presentation, and data, are often considered the best at predicting how the patient will respond to treatment. The patient’s symptoms and presentation may reflect the underlying biology of the disease process, and the clinician uses pattern recognition gained through clinical experience to guide treatment. We are now starting to understand more about the biology underlying psychiatric diseases, which eventually will allow us to develop the theory and procedures to personalize psychiatric therapy. The goal of this personalized approach is to advance our practice of medicine as a science while integrating our new appreciation of the biological variation of individuals to develop patient-specific pharmacological treatments. In the case of patients with MDD, the burgeoning toolkit for personalization contains two types of information: phenomenological and physiological. Phenomenological features are observed by the clinician or obtained through interactive diagnostic interviewing (semi-structured or not). These may be demographic (age, sex, ethnicity), historical (medical comorbidities, past history of depression, psychiatric comorbidities), syndromic clusters of symptoms (atypical vs melancholic vs anxious depression), or environmental factors (social stressors, allostatic load). These data tend to be relatively independent of mechanisms and can inform the clinician’s “best guess.” Physiological features or biomarkers are typically measures collected through procedures. For example, the collection of peripheral blood yielding circulating biomarkers (inflammatory moderators and mediators), genomic DNA (analyzed for pharmacogenetics), brain images obtained through various techniques such as functional magnetic resonance imaging (fMRI), and psychophysiological/neuropsychological data that are state-related (at a specific point in time), or fixed (exist independently from the state of illness or remission). The presumed goal of such measurements is to identify, understand, and therapeutically target a mechanism of disease. To personalize treatment, we integrate these two types of information, combining clinical phenomenology with objective measures, to ask two related questions: 1. Which patient characteristics will predict benefit from a treatment? 2. Which treatment will benefit the patient?

111

Traditionally, psychiatry has suffered from a dearth of objective measures of illness and evidence-based, molecularly targeted, treatments. However, the focus of psychiatry is understanding the patient as an individual, which may facilitate integration of objective data into a personalized model. Next we describe various types of phenomenological and physiological data and our burgeoning findings from research using these data to predict treatment response. We particularly focus on treatment response to pharmacological agents; however, personalization of psychotherapy will also play an important role in our ability to create effective individualized treatment. We note that our ability to personalize psychiatry is currently constrained by the limitations of our research methodology and will be enhanced as we find new and more effective ways to ask “how can I best treat this individual patient?”

Phenomenological Variables  emographic Variables Related to Treatment D Response  ex S The incidence of MDD is about twice as high in women as it is in men [9]. Men and women also demonstrate different response rates to certain groups of antidepressant medications. For example, numerous studies have shown that women have a greater response than men to selective serotonin reuptake inhibitors (SSRIs) [10–14]. On the other hand, men appear to respond better to imipramine, a tricyclic antidepressant (TCA) [10, 14]. Although the data supporting these findings are largely consistent, there have been a few conflicting studies indicating that further validation of these associations is still needed [15]. The observed sex differences in depression prevalence and treatment response may be due in part to hormonal factors. For instance, premenopausal women have higher rates of depression than men, whereas postmenopausal women have comparable rates [16]. With respect to treatment response, one study found that premenopausal women respond better than postmenopausal women to citalopram [17]. The specific effect of hormonal differences has been further suggested by trials showing that estrogen replacement therapy in postmenopausal women improves depressed mood [18], increases response to fluoxetine [19], and may reduce likelihood of developing depression [20, 21]. This finding indicates that the effect of hormones is complicated and may both predispose to and ameliorate depression. There has also been interest in neurosteroid treatment for depression, with studies performed in women with postpartum depression [22]. In addition to hormonal effects, there are also likely multiple other mechanisms that could contribute to differential treatment response

112

between men and women. For example, there are profound sex differences in neuroimmune interactions and inflammation (partly due to immune-­related genes on the X chromosome) that possibly impact treatment response [23–25]. Social factors also play a role, for example, gender differences in rates of having experienced sexual abuse and domestic violence, responsibility for childcare, and less opportunity for economic advancement [26]. In summary, there is preliminary evidence that women and men respond differently to various classes of monoamine-­ based antidepressant medications and that these differences may be related in part to hormonal and other mechanisms. Future research should further explore the biological underpinnings of sex differences in patients with depression as well as sex differences in pharmacokinetics and optimal dosing.

 ace and Ethnicity and Social and Environmental R Determinants There are many important differences in how individuals from differing racial and ethnic backgrounds experience depression. For example, African Americans, Asian Americans, Pacific Islanders, and Hispanics are all less likely than non-Hispanic Caucasians to believe that depression is biologically based and are also less likely to believe that antidepressant medications are effective [27]. Minority populations are also less likely to view medication treatment for depression as acceptable [28]. In addition to beliefs about depression, there are significant health disparities in the treatment of minority populations with major depressive disorder. Alegrίa and colleagues highlighted these disparities in a large survey [29] of 8762 depressed patients, which showed that minority groups are more likely to be misdiagnosed and underdiagnosed, less likely to access care, and subject to lower quality care. Please also see Chap. 4 on Race and Culture and Depression. One reason for the disparities in care experienced by minority populations is that providers may not always understand cultural differences in the conceptualization of depression. For example, Latin American individuals with depression and/or anxiety may describe their symptoms using cultural idioms such as susto or ataque de nervios [30, 31]. Similarly, a study by Yeung and Kam [32] showed that Chinese-American patients were more likely to explain their symptoms using physical words rather than mood-oriented words, perhaps because of a cultural difference in how Chinese-Americans view and experience depression [33]. This phenomenon sheds light on the risk for depression underdiagnosis in minority patients and underscores the need for practitioners to receive regular training in cultural competency. This is important for personalization since how individuals view the potential efficacy of treatment can impact their response to treatment [34]; treatment should be

S. L. Foster et al.

tailored to a patient’s worldview. The efficacy of culturally informed treatment vs. standard is discussed elsewhere (please see Chapters 4 and 6). With respect to our standard means of implementing personalized medicine, to date there is a paucity of studies examining differences in how racial and ethnic groups respond to antidepressant medications. The STAR*D study is one of the few large-scale trials to investigate this topic. The results showed that Caucasians have higher remission rates with citalopram treatment compared to minority populations [35]. Further analysis of this cohort by Murphy and colleagues [36] found that both socioeconomic factors and genetic factors contributed to the differences in citalopram remission in Caucasians and African-Americans. While socioeconomic factors had the most impact on treatment response, there was a small but significant effect of race and genetic ancestry (β = 0.050; p