Eric J. Lenze, MD

“Brain health conditions are a key contributor to the global burden of disease and disability. The devastating impact of these conditions is even more apparent as the world faces global challenges such as the COVID-19 pandemic, climate change, societal inequity, and more. To make progress, we need a fundamental shift in our approach. Convergence Brain Health deftly makes the case that a convergence approach is urgently needed to tackle the global brain health crisis and provides a roadmap for change.”

— Victor Dzau MD, President, National Academy of Medicine

Even before the COVID-19 pandemic, the world was in the midst of an escalating global brain health crisis with severe physical, social, and economic ramifications. According to a recent World Health Organization report, around 450 million individuals were suffering from mental health conditions, marking mental health disorders among the leading causes of ill-health and disability worldwide.

 The Organisation for Economic Co-operation and Development (OECD) estimates that the consequences of mental ill health cost up to 

Due to social and physical distancing, unemployment and underemployment, stress and other factors, there have been significant increases in issues such as depression, anxiety, social isolation, substance abuse, loneliness, and cognitive decline in older adults.

 In addition, as observed after the SARS pandemic, it is expected that COVID-19 survivors may endure long term cognitive and psychiatric consequences, including suicide, posttraumatic stress disorders, and depression.

 Furthermore, brain health is a prototypical 

 in that it is extremely complex and textured by innumerable variables from biology, sociology, genetics, psychology, and the natural environment to economics and beyond.

 Compounding these issues are challenges at all levels of innovation around brain health, listed in 

Table 1. Challenges and Opportunities for the Brain Health Field

Progress in the 21st century will be molded by our collective response to the global brain health crisis.

 Fortunately, beacons of hope are beginning to shine. Stigma has been reduced in high-income country settings, allowing individuals to overcome the barriers to seeking care.

 The COVID-19 pandemic has—in a positive sense—lowered the perceived and actual barriers to technology adoption, ushering in a new era of technology-driven brain health care. Some governments are beginning to increase spending on brain health, and venture capital funding for brain health start-ups has never been more in vogue. PitchBook data shows that 146 deals generated nearly $1.6 billion in venture capital investments as of December 10th, 2020. A decade ago, there were only 3 deals, worth $6.6 million, whereas last year saw a total of 

However, these advances on their own cannot solve the myriad global challenges posed by the brain health emergency. A paradigm shift is needed to address the unprecedented complexities associated with the current global brain health crisis—a crisis now inextricably linked to concurrent interacting crises surrounding discrimination and inequality, mass misinformation, environmental catastrophes, accelerated climate change, entrenched economic models, population displacements and immigrant status, and more.

 Governments, employers, and healthcare systems around the world need to acknowledge the scope of the problem and embrace solutions to empower consumers to take charge of their own brain health and to protect existing levels of health, well-being, and social inclusion.

In response to the evolving issues, convergence science integrates knowledge, tools, and thought strategies from diverse fields to address challenges that exist at the interface of 

 Convergence science not only recognizes the need to respond to apparent societal problems, but also emphasizes approaches which allow us to consider a myriad of the known unknowns and improves the odds of us quickly adapting to newly identified gaps.

 Acknowledging the serendipity of accidentally obtained knowledge permits 

 Taking these approaches when investigating solutions to brain health challenges permits greater flexibility and utility when unforeseen challenges are inevitably met.

Brains are the indispensable drivers of human progress. Why not invest more heavily in them? There are currently 

 allocated to Environment, Sustainability, and Governance investing around the world.

 If only a portion of this amount were invested in brain health, we could produce major improvements for our society.

 note that this present moment is a clarion call for the philanthropic community, which is uniquely positioned to lead an approach in 2 distinct ways. In the short term, the immediate mental health crisis can be mitigated by supporting interventions for the most vulnerable, the development and deployment of rapidly available mental health tools and technologies, and research to drive evidence-based responses to COVID-19-related mental health challenges. Much work is needed to address the long-term mental health impacts caused by the pandemic. There is an ongoing and urgent need for targeted investment in research to better understand and treat mental health conditions, build capacity and diversity within the mental health system, and advocate for social and policy solutions to improve the ecosystem of mental health care.

Given the critical need to nurture healthier, more resilient, and increasingly flexible brains worldwide, we have introduced the concept of Brain Capital—broadly defined as a form of capital which prioritizes brain skills and brain health.

The OECD Neuroscience-inspired Policy Initiative seeks to place Brain Capital at the center of a new narrative to fuel societal resilience in mitigating economic shocks, and to create preparedness should future societies fundamentally transform, for instance through irreversible damage to the environment. The OECD’s New Approaches to Economic Challenges Unit has created the Neuroscience-inspired Policy Initiative. This initiative seeks to reconceptualize and revitalize the economy, laying the groundwork to identify relevant metrics while building a transdisciplinary network of stakeholders. The initiative will rapidly refine and advance the concept of Brain Capital via a series of research projects, economic modelling, seminars, and clear policy analyses and recommendations.

In this context, our Brain Capital Grand Strategy supports the focus of the OECD initiative. The Grand Strategy has 3 main components: considering Brain Capital in all policies, developing a comprehensive investment plan to support Brain Capital, and generating a Brain Capital Index.

Brain health issues should be addressed in the context of improving performance within the fields of innovation and entrepreneurship. Because most 

 are created by firms less than 5 years old,

 creative and innovative entrepreneurs are needed to drive social and economic growth at the regional and national levels. In many ways, together with unprecedented government investment in many countries, entrepreneurs stand as a critical firewall against recession and deprivation. They serve as economic first responders when (exogenous) catastrophic events produce national and global economic shocks. 

In the United States, deaths due to suicide, opioid overdoses, and other addictions and poisonings (deaths of despair) are a barrier to complete economic recovery from COVID-19.

 Approximately 20% of prime-aged males who have dropped out of the labor force are 

 in these deaths, and many of them will not take up jobs even when available due to the severity of their addiction.

A senior economist at The Brookings Institution is leading an initiative to coordinate the many efforts to deal with this crisis. An important role for the task force would be to coordinate with the many burgeoning local level efforts to address addiction and despair, and these local organizations could greatly benefit from ties to a federal-level coordinating agency. Among other things, this agency could serve as an 

 Without addressing the linked issues of mental health and labor market drop-out simultaneously, it will be difficult to solve the tragic mental health crisis or achieve the goal of widely shared economic prosperity.

It is also essential that, given the complexity of the challenges being addressed, freedom to fail is offered liberally. Flat organizational structures, which are agile and empower individuals to make decisions in their specific areas and roles without need to constantly consult with managers at higher levels, are important. However, these decisions should be aligned with the mission and not deviate from the already traced goals and strategy. In 

, we provide examples of organizations and buildings which foster Convergence Brain Health activities.

Table 2. Infusing Convergence Brain Health Practices Into Various Levels of Innovation

Existing models of innovation are not adequate—suffering from siloing and a lack of speed. There are now a number of more contemporary models which may propel the brain health field forward more quickly and effectively. 

For example, The Davos Alzheimer Collaborative (DAC) is a public-private partnership committed to a collective global response against the challenges Alzheimer disease presents to millions of families around the world.

 The DAC aims to raise around USD $700 million for a 6-year plan to accelerate and diversify innovation in Alzheimer disease research. The 3 main components of DAC include a global cohort developed to identify new targets for potential treatments, a global clinical trial support platform to reduce the cost and time to bring new treatments to market, and a healthcare system prepared to get new treatments to patients. The DAC project will enable novel biomarker development, connect global researchers using the data platform provided by the Alzheimer Disease Data Initiative, and keep individuals with the lived experience of Alzheimer disease at the center of its efforts.

The first big data wave occurred over a decade ago, fueled by the reduced cost of -omics techniques (eg, genomics, transcriptomics), increased access to neuroimaging equipment, and the transition to electronic medical records. Insights from this data, however, have not translated into dramatically improved outcomes for patients. Diagnostic clarity is often elusive and treatment selection remains primarily trial-and-error. A second wave of big data proliferation is already underway with the increased implementation of mobile applications and wearable devices.

 Solutions are desperately needed to resolve current bottlenecks in transforming data into insights and action. 

Furthermore, according to economist Klaus Schwab, PhD, we are on the verge of the Fourth Industrial Revolution, which will see a rise in cyber-physical systems that blur “the lines between the physical, digital, and biological spheres.”

 Virtual reality environments, brain-computer interfaces, wearable or implantable devices, and mobile applications will continue to contribute to the current data explosion. Artificial intelligence integrates knowledge from a myriad of disciplines, including computer science, data science, mathematics, engineering, physics, statistics, linguistics, signal processing, ethics, and philosophy. Artificial intelligence is no longer a novelty, but a requisite set of convergence tools for navigating the future.

As psychological disorders are mediated by complex interactions between genetic and environmental factors it will be imperative to dedicate significant effort to understanding the modifiable risk factors emerging from the environment.

 Since the environment includes biological, psychosocial, and ecological components, it is likely to be more complex than the genes, proteins, and metabolites that are currently analyzed by a variety of tools. Therefore, within the framework of convergence brain health it will be necessary to develop highly powerful and complex tools that record, measure, and analyze the myriad environmental factors. This effort will be pursued as it has been with the omics technologies but with a much larger scope, ranging from psychophysiological data collected from millions of individual smartphones to satellite tracking of air pollution in big cities. Important ethical issues, however, will have to be addressed with some urgency.

Operationalizing a convergent clinical framework will require effective direction and utilization of the continuing data explosion.

 The role of digital clinical operating systems is therefore critical for allowing effective patient care, as well as ongoing research. These clinical operating systems house data from clinical encounters, imaging, pathology, personal medical devices, wearables, and finance. Analytics methods include the use of mathematical and algorithmic-based processing of data resources, including subfields of artificial intelligence, such as machine learning, natural language processing, and visual analytics to generate descriptive, predictive, and prescriptive models to analyze and derive insight from data. Machine learning has been utilized in predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation.

Convergent approaches often require consideration of many different data types. Data harmonization involves creating a single consistent macro-data source from multiple, potentially disparate micro-datasets. Contributing datasets are structured to remove inconsistencies, creating harmony, such that the final dataset provides a cohesive view of a subject from many different perspectives. Machine learning enables automatization of this process, permitting integration of clinical, behavioral, -omic, imaging, neurosignals, and other datatypes for data mining and predictive modelling. Deep learning in combination with multidimensional biological data (eg, genomics, proteomics) enables examination of the impact of genetic variation or other system disturbances on transcription, translation, signaling, and resultant phenotypic variation. 

Perioperative brain health is an emerging example of the need for a convergence approach in brain health science. The average individual undergoes 9 surgical procedures in his or her lifetime, typically at an older age when the brain is more vulnerable to perioperative insults such as stress, illness, and immobility.

 As such, the perioperative period is a very high-risk period for older adults, and brain health impairments are abundant with considerable negative impacts. For example, the US National Health and Aging Trends Study found clinically significant depressive and anxiety symptoms in 39% and 24% older adults in rehabilitation facilities, respectively, in older adults recently in rehabilitation facilities (vs. 10% and 9% for older adults in the community).

Similarly, a study in the UK of postoperative Intensive Care Unit patients found rates of 40% and 46%, respectively.

 Depression and anxiety are connected to poor postoperative outcomes, including persistent pain, delirium, longer hospital stays, poor functional recovery, lower quality of life, and increased chance of both falls and rehospitalization.

A new field of perioperative mental health is responding to the need to integrate mental and cognitive healthcare with surgical and postsurgical care. The increasing number of older adults undergoing surgery creates high demand for this innovation, so that care can move beyond an emphasis only on surgical success and anesthetic safety, to a convergent brain health view of outcomes, such as improved functional recovery, reduced rehospitalization, and decreased long-term morbidity. These innovations are further driven by bundled care models, which include preoperative optimization, in-hospital care, and postoperative rehabilitation. With the recent genesis of the Center for Perioperative Mental Health, funded by the National Institute of Mental Health (P50MH122351), surgeons, anesthesiologists, nurses, physical and occupational therapists, and surgical patients will work together with psychiatrists in an accelerated laboratory for translating scientific advances to practical clinical care that improves the lives of older adults.

With the emergence of so many digital innovations, care providers are presented with more tools than ever. However, such a crowd of novel options adds complexity and uncertainty. To be clinically useful, such point solutions must first be vetted and then clustered into symphonies shown to play well together to optimize collective benefit for specific conditions. In the dementia field, for example, it is encouraging to see multidomain interventions being deployed.39 In the future, a therapeutic guidance system may make sense, similar to those used to advance precision medicine, such as oncology and neurosurgery.

To develop a workforce that can harness and deploy a convergence brain health framework, increased inclusion and empowerment of women and other traditionally under-represented populations is required. Building institutions that are both inclusive and meritocratic is health promoting in its own right. But more importantly, it is a powerful mechanism for producing the additional talent, fresh perspectives, and new ideas that are needed.

Meanwhile, certain behavioral characteristics among individual innovators should be cultivated. MacRae and Furnham

 articulated 3 dimensions of a high-potential individual suited to convergent teamwork building on Silzer and Church’s model of potential.

 There are 3 core dimensions: foundational, growth, and career dimensions. These dimensions explain how personality traits are foundational to potential because they represent fundamental and stable individual differences. Personality traits, especially conscientiousness and adjustment, contribute to success in nearly any type of work and have, “higher predictive ability of potential in roles that are more complex, challenging, and demanding such as that of the convergence practitioner.”

Career dimensions are the unique and specific skills, expertise, and occupational knowledge required for success in a specific career or role: “Convergence practice requires sublimating one’s own objectives into the vision and the objectives of the team.”

 MacRae and Furnham explain in detail how personality traits fit within a model of convergence practitioner potential and expand on 10 behaviors that are related to career dimensions of potential.

 These behavioral and workplace cultural characteristics allow us to formulate screening criteria for potential new entrants into convergent brain health work, identify those who are high potential convergence practitioners, as well as consider training approaches for those within our existing brain health structures.

Leaders must assume a critical role in optimizing transdisciplinary collaboration and facilitating the emergence of discoveries from these endeavors.

 Gray outlined 3 responsibilities of transdisciplinary leaders: cognitive leadership, structural leadership, and processual leadership:

. Effective cognitive leadership provides a vision that links and motivates transdisciplinary researchers to step beyond their disciplinary lens, relax old assumptions, and search for creative frame-breaking solutions.

. Effective structural leadership adds value by creating bridges and links among unconnected individuals, labs, groups, or departments.

- Processual Leadership: Effective processual leadership encourages trust and turns conflict into constructive interactions.

These leadership approaches should be kept in mind when fostering Convergence Brain Health within an organization. Other 

 of a convergence workplace include a flat organizational structure with minimal discipline silos, a culture of trust and mutual respect, and an open acceptance of the need to respectfully challenge entrenched ways of thinking.

 Teams should be humble in acknowledging that nobody has cracked the many complex problems to date, and likely never will due to unsolvable uncertainty. However, being open to novel and seemingly eccentric ideas may help navigate such problems and identify ways for their best possible management.

Teams in a convergence workplace must embrace the three somehow radical tenets outlined in Jim Dator, PhD’s 

: 1) The future cannot be predicted because the future does not exist; 2) Any useful idea about the future should appear to be ridiculous; and 3) We shape our tools and thereafter our tools shape us.

 An optimal convergence science environment should have a strong guiding vision, will be capable of identifying myriad possible alternative futures, and better guide the organization toward the desired future.

Convergence Brain Health provides direction for transformational change in how teams could work and how organizations need to be built and operated. As we embark on building the new normal of our post-COVID-19 world, Convergence Brain Health will play a key role in this rapidly unfolding change. Indeed, Convergence Brain Health provides an opportunity to grow global Brain Capital and enter a new era of innovation and progress, leading to greater health justice for all people and preparing for the unprecedented brain health challenges of the 21st century.

is cofounder of the PRODEO Institute, adjunct associate professor with IMPACT at Deakin University, instructor in brain health diplomacy at the Global Brain Health Institute, and co-lead of the OECD Neuroscience-inspired Policy Initiative.

is Director of Innovation at a major mental health association and expert in residence at Georgetown University, and the author of Wired This Way.

is an associate with the PRODEO Institute and Thiel Fellow at Stanford University.

is a professor of psychiatry at University of California in Los Angeles (UCLA).

is Distinguished and Emeritus Professor of Psychiatry, University of Pittsburgh, Pittsburgh, PA.

is the global head of Science for Minds at Johnson & Johnson.

is a health policy researcher at the Global Brain Health Institute.

is a social gerontologist at the Global Brain Health Institute.

is a research professor and Director of Chambers-Grundy Center for Transformative Neuroscience at the University of Nevada, Las Vegas.

is a venture partner at Alsop Louie Partners.

is professor and vice chair of Psychiatry & Behavioral Services at Baylor College of Medicine.

is special advisor to the OECD secretary general and head of the OECD New Approaches to Economic Challenges Unit.

is the Wallace & Lucille Renard Professor of Psychiatry and the Director of the Healthy Mind Lab at Washington University School of Medicine in St. Louis, Missouri.

is head of strategic projects and alliances at Delix Therapeutics.

is a professor in the department of Integrative Structural and Computational Biology at Scripps Research Institute.

is Head Stakeholder Liaison Alzheimer disease at Biogen.

is a professor of Law and Health Sciences at the University of Surrey School of Law and adjunct assistant professor of Medicine at the David Geffen School of Medicine at UCLA.

is the founding director of the Center for Brain Health at UT Dallas.

is a T. Boone Pickens Distinguished Scientist at the Center for BrainHealth, a co- director at Global Brain Health Institute, professor emeritus at Trinity College Dublin, and founding director of its Institute of Neuroscience.

is the Leo Pasvolsky Senior Fellow at the Brookings Institution, a College Park Professor at the University of Maryland, and a Senior Scientist at Gallup.

is a postdoctoral researcher, Center for Precision Health School of Biomedical Informatics at the University of Texas Health Science Center at Houston.

is Researcher at the Department of Aquatic Sciences and Assessment; Section for Ecology and Biodiversity at the Sveriges lantbruksuniversitet.

is Assistant Clinical Professor of Psychiatry and Biobehavioral Sciences at David Geffen School of Medicine at UCLA and UCLA Olive View Medical Center. 

is a neuroscientist at the Global Brain Health Institute.

MB is supported by a NHMRC Senior Principal Research Fellowship (1156072). MB has received Grant/Research Support from the NIH, Cooperative Research Centre, Simons Autism Foundation, Cancer Council of Victoria, Stanley Medical Research Foundation, Medical Benefits Fund, National Health and Medical Research Council, Medical Research Futures Fund, Beyond Blue, Rotary Health, A2 milk company, Meat and Livestock Board, Woolworths, Avant and the Harry Windsor Foundation, has been a speaker for Abbot, Astra Zeneca, Janssen and Janssen, Lundbeck and Merck and served as a consultant to Allergan, Astra Zeneca, Bioadvantex, Bionomics, Collaborative Medicinal Development, Janssen and Janssen, Lundbeck Merck, Pfizer and Servier – all unrelated to this work. HAE is an employee of PRODEO LLC. ES is an employee of PRODEO LLC. ASC is an official employee of Biogen Int and the VP of Euresearch. Her contribution to this work is based on her personal opinion and might not reflect the one of the organizations with whom she is affiliated. AI has received Grant/Research Support from ANID/FONDECYT Regular (1210195); FONCYT-PICT 2017-1820; ANID/FONDAP/15150012; Alzheimer’s Association GBHI ALZ UK-20-639295; Sistema General de Regalías [BPIN2018000100059] Universidad del Valle [CI 5316], and the Multi-Partner Consortium to Expand Dementia Research in Latin America [ReDLat, supported by National Institutes of Health, National Institutes of Aging (R01 AG057234), Alzheimer’s Association (SG-20-725707), Rainwater Charitable Foundation (Tau Consortium), and Global Brain Health Institute)]. EJL has received support from the NIH, Patient-Centered Outcomes Research Institute, Taylor Family Institute for Innovative Psychiatric Disorders, and Center for Brain Research in Mood Disorders at Washington University.

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Convergence Mental Health: A Transdisciplinary Approach to Innovation.

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https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-europe-2020_82129230-en

4. Holmes EA, O’Connor RC, Perry VH, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. 

5. Blazer D. Social isolation and loneliness in older adults-A mental health/public health challenge. 

6. Vahia IV. COVID-19, aging, and mental health: Lessons from the first six months. 

7. Orben A, Tomova L, Blakemore SJ. The effects of social deprivation on adolescent development and mental health. 

8. Steenblock C, Todorov V, Kanczkowski W, et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the neuroendocrine stress axis. 

9. Milner A, King TL, LaMontagne AD, et al. Underemployment and its impacts on mental health among those with disabilities: evidence from the HILDA cohort. 

10. Koralnik IJ, Tyler KL. COVID-19: a global threat to the nervous system. 

11. Varatharaj A, Thomas N, Ellul MA, et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study

12. Mak IW, Chu CM, Pan PC, et al. Long-term psychiatric morbidities among SARS survivors. 

13. Taquet M, Geddes JR, Husain M, et al. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records

14. Stony Brook University. What’s a wicked problem

https://www.stonybrook.edu/commcms/wicked-problem/about/What-is-a-wicked-problem

Articles

How can we face the current pandemic while addressing the brain health crisis?  

Boosting Brain Health After COVID-19: A Convergence Solution 

Slideshows

Although not a DSM diagnosis, fear of falling is recognized as a common source of functional impairment and distress. The author presents 7 commonly prescribed medication categories that may increase fall risk in older adults.

Caution: 7 Medications That May Increase Falls in Your Older Patient

is Professor of Psychiatry, Washington University, St Louis, MO.

In the elderly, falls are a common and important problem. In the US, fall-related injuries cost approximately $30 billion per year, and about 30% of older adults fall at least once in a given year.

 One of the most feared consequences of falling is a hip fracture, which occurs in almost 400,000 older adults each year. Other consequences include other fractures, head injuries, and less serious injuries that can nonetheless impair function and quality of life.

Moreover, after a fall-related injury, many older adults can have a fear of falling, a common geriatric condition marked by anxiety and low self-confidence about walking safely. Although not a DSM diagnosis, fear of falling is recognized as a common source of functional impairment and distress (and increased risk of falls) in older adults.

Falls are highly relevant to psychiatry because they are often preventable, and one of the most highly feasible prevention strategies concerns psychotropic drugs. Simply stated: if we reduce the use of psychotropic drugs that are known to cause falls (or better yet, avoid such drugs in the first place), we will greatly improve our older patients’ quality of life by preventing falls and fall-related injuries.

Case 1: Your patient is a 74-year-old woman who was referred to you by her primary care physician for management of a complicated psychotropic medication regimen. Her family is very concerned about her gait instability. Upon meeting her, you see obvious ataxia, and at times she has to hold on to the wall for support when she walks. She has had 3 falls in the past 6 months, one of which was serious enough to require a visit to the emergency department. Her family wants to know whether her medications increase her risk of falls-and what should be done about them.

How to reduce falls in older psychiatric patients

First, review your patient’s entire list of medications (not just the “psych” drugs) to look for any drugs that are known to cause falls, in order to stop them (or better yet, not to start them in the first place). There are 2 kinds of medication-related problems: the first are individual drugs that increase fall risk; the second are drug interactions that increase fall risk. For both of these, the most common drugs are sedatives, and in particular benzodiazepines. The Table lists commonly used medications in older adults that increase fall risk.

Be aware of the following clinical points about benzodiazepines and falls in older adults:

There are no “safe benzos”: all benzodiazepines have fall risk, including short-acting medications such as alprazolam.

 There’s no therapeutic index for older adults: there is no dose at which benzodiazepines are effective for anxiety disorders yet safe from fall risk.

 Sedation equals fall risk: a patient who complains of daytime sedation or shows signs of sedation on examination is a patient whose reaction time and postural control are reduced.

 Risk is dose-related: the higher the dose, the greater the risk of falls. This can be an issue with the highly potent benzodiazepines such as clonazepam. A dosage of clonazepam 1 mg twice daily might not sound like a high dosage to many practitioners, but 1 mg of clonazepam is equal in potency to 2 mg of alprazolam or 4 mg of lorazepam.

Avoid the use of benzodiazepines, especially long-term use. Benzodiazepines initiated in a hospital or consultative setting (eg, the patient is anxious) should have an automatic stop in the order (eg, “stop prescription upon discharge”).

Do not prescribe benzodiazepines in the first place: many safer, effective treatments exist. Since the 2 main reasons for a benzodiazepine prescription are insomnia and anxiety, use behavioral methods as a first-line intervention. Cognitive behavioral therapy, brief behavioral therapy, and sleep hygiene are all good first-line choices for insomnia; cognitive behavioral therapy, relaxation training, and mindfulness training are all effective for chronic anxiety. In addition, both insomnia and anxiety disorders have effective medication regimens that are safer than benzodiazepines: trazodone, ramelteon, and melatonin for insomnia; and SSRIs, SNRIs, and mirtazapine for anxiety.

Taper and try to stop benzodiazepines in older adults, especially in those with obvious fall risk (such as recent falls or ataxia). A good rule of thumb is to go slow: a small dose reduction every 2 weeks should avoid overt withdrawal and more insidious rebound effects.

A final point about reducing and stopping benzodiazepines is to remember that these medications are habit-forming. Clearly describe their risks in ways that patients understand and remember (eg, “this medication causes hip fractures”; “it is bad for your brain, like having Alzheimer’s”). Reiterate these risks, using the same memorable language, at follow-up appointments.

Any sedative can cause falls. Aside from benzodiazepines, sleeping medications (particularly the “z-drugs”-zolpidem, zaleplon, and eszopiclone), centrally acting anticholinergics and antihistamines, and some other psychotropic drugs (eg, gabapentin; some antipsychotics) can increase the risk of falls. Many of these drugs are “non-psychiatric” drugs, such as anticholinergics for the bowel or bladder, antihistamines for allergies, and over-the-counter sleep medications such as diphenhydramine. Because these drugs adversely affect the brain, identifying and stopping them is the psychiatrist’s job, as doing so reduces patients’ fall risk, helps their cognitive function, and reduces their risk of delirium and cognitive decline. The centrally acting anticholinergics can often be stopped immediately, or if they are needed, they can be switched to a less toxic alternative. Thus, many of these sedating drugs are easy to fix with a “stop or switch” approach.

This article should not be taken as a recommendation to stop or avoid all fall-risk medications. Indeed, many may be necessary for stabilizing a patient psychiatrically (eg, antipsychotics or mood stabilizers for severe mental illness) or medically (eg, antiseizure medications). This article cannot provide a one-size-fits-all recommendation in such cases; they will be classic “risk versus risk” decisions that consider the risk of a patient falling (or suffering cognitive impairment) against the risk of clinically worsening. In fact, there are scenarios in which patients must receive medications that are hazardous to them; for example, antipsychotics in dementia with behavior disturbance. In such cases, communicate with patients and their families this balance of risks and benefits, and aim for a shared decision-making process.

Two types of drug combinations are known to cause falls. The first is “cumulative” (or pharmacodynamic) interactions. For example, an older adult who takes 2 sedative drugs is at greater risk for falling than an older adult who takes one (assuming same dose). Remember that some of these hazardous combinations may include something “over the counter” (eg, alcohol plus a sedative medication).

The second drug interaction is pharmacokinetic: one drug interferes with the metabolism of another, raising its concentration and making it toxic. Some antidepressants are notorious for these interactions-namely, paroxetine (strong CYP 2D6 and 2B6 inhibitor), fluoxetine and duloxetine (moderate CYP 2D6 and 2B6 inhibitors), and bupropion (strong CYP 2D6 inhibitor). For example, a common antidepressant combination strategy is paroxetine plus bupropion. This makes sense from a pharmacodynamic perspective, as paroxetine is an SSRI while bupropion is a norepinephrine and a dopamine reuptake inhibitor. But it is a nightmare from a pharmacokinetic perspective: paroxetine inhibits 2D6 (thus, it inhibits its own metabolism, producing non-linear pharmacokinetics) and 2B6, which metabolizes bupropion. Bupropion in turn also inhibits 2D6 (further inhibiting paroxetine’s metabolism). The result can be a positive feedback loop in which the 2 drugs increase each other’s concentration and cause adverse effects, including falls.

 For this reason, it is important to be aware of the pharmacokinetic effects of common psychotropics.

Do commonly prescribed antidepressants cause falls in older adults?

Case 2: Your patient is a 60-year-old woman whom you are treating for recurrent major depression with an SSRI. She has responded well and comes for routine follow-up. However, at this visit, she wants to stop the antidepressant. She says she recently read that antidepressants cause falls in older adults, and she is afraid of falling because she has osteopenia and her mother died of complications of a hip fracture. What should you do?

Several observational studies have reported that antidepressants are associated with falls in older adults. This includes SSRIs and SNRIs. It should also be noted that a host of additional “risks” of these antidepressants have been reported in observational studies, including all-cause mortality, suicide, and bone loss. However, there is no evidence that antidepressants cause falls (or any of these other risks) in older adults, as no randomized controlled trial has demonstrated this association. In spite of the absence of conclusive evidence, some may argue observational studies are sufficient to assert that these antidepressants do cause falls and therefore that clinical practice and policy should reflect this risk. Such a viewpoint might recommend that antidepressants be less commonly used in older adults, perhaps in favor of psychotherapy, and that they be reserved for the most severe acute cases and then be tapered off.

Yet, in medicine, observational studies are typically insufficient to argue causality (with some notable exceptions, such as smoking and lung cancer). Observational studies are insufficient because they cannot rule out alternative explanations for the association. Other explanations for the antidepressant-falls association are that falls cause people to be placed on antidepressants, and that older adults who are prescribed antidepressants in the real world tend to be people who are more fall-prone in the first place-because they are medically ill and functionally impaired (and depressed!). No observational study can completely control for confounding variables.

Given the unresolved controversy, psychotropics in the elderly are an example of the typical “risk versus risk” consideration that we commonly grapple with in psychiatry; namely, does the risk of treatment outweigh the risk of non-treatment? In older adults, the known risks of untreated major depression and anxiety disorders usually outweigh the unknown risk of falls with antidepressants. This does not mean that antidepressants are completely benign in older adults. Evidence is needed-in the form of large randomized controlled trials-to determine whether antidepressants do or do not cause falls.

One study, Optimizing Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM), may provide some answers. OPTIMUM is a large randomized controlled trial of commonly used augmentation and switch options (such as aripiprazole augmentation, or switch to bupropion) for older adults with treatment-resistant depression. OPTIMUM is ongoing, and results will not be available for several years.

What should clinicians do now? As psychiatrists, it is our job to inform patients of known risks of medications (such as tardive dyskinesia with long-term antipsychotic use), not unknown and unproven ones such as falls with antidepressants. At the same time, we can take the following measures to reduce falls in our patients:

• Ask about recent falls: “Have you fallen in the past 6 months?”

• Assess gait stability at in-person visits

• For those with unsteady gait or multiple recent falls, assess for possible causes, particularly those amenable to psychiatric management

As the 2 cases depict, concern about falls is common in geriatric psychiatry, and understanding and mitigating this risk is a core competency for any psychiatrist who sees older adults. We can do much to prevent falls by reducing risky medications, but there is not sufficient evidence to attribute falls to antidepressants as a class.

Dr. Lenze has received research support from National Institutes of Health, FDA, McKnight Brain Research Foundation, Taylor Family Institute for Innovative Psychiatric Research, Center for Brain Research in Mood Disorders, Barnes Jewish Foundation, Takeda, Lundbeck, Janssen, and Alkermes.

-This article was supported by funding from the National Institutes of Health and the Taylor Family Institute for Innovative Psychiatric Research and Center for Brain Research in Mood Disorders (at Washington University). The funding source(s) had no role in the preparation, review, or approval of the manuscript.

 Centers for Disease Control and Prevention. Costs of Falls Among Older Adults. August 2016. 

https://www.cdc.gov/homeandrecreationalsafety/falls/fallcost.html

 Iaboni A, Flint AJ. The complex interplay of depression and falls in older adults: a clinical review. 

 Joo JH, Lenze EJ, Mulsant BH, et al. Risk factors for falls during treatment of late-life depression. 

 Gebara MA, Lipsey KL, Karp JF, et al. Cause or effect? Selective serotonin reuptake inhibitors and falls in older adults: a systematic review. 

Psychotropic Drugs and Falls in Older Adults

Who should care about aging? This question is particularly germane in mental health. The articles in this Special Report remind us of the importance of understanding and focusing on the stresses, problems, and treatment-related issues in this population.

I’ll try to answer the “who should care?” question in 2 ways. First, there is a national and global imperative to ensure the optimal mental health of older adults. This is because of an unprecedented demographic shift: most of us are living longer, and we are having fewer children. The result is that people 60 years and older will become an enormous segment of our global society. By 2050, they will be one-third of the population of developed nations and one-fifth of developing nations. This adds up to 2 billion seniors worldwide by 2050.

The implications of this demographic change for the importance of mental health care are staggering. On the one hand, if older adults are disabled, burdensome to caregivers, and frequent users of health care (which occurs when they are depressed, anxious, psychotic, or demented), the net burden on society will be tremendous. On the other hand, this demographic shift need not be a painful one. If effective mental health treatments help prevent or provide remission of mental health symptoms associated with aging, we can look forward to an era of productive old age.

For the second answer to “who should care?” my answer is “look in the mirror.” You’ll spot someone who (hopefully) will someday join the ranks of older adults, and (sadly) face illness, disability, and grief. Therefore, you may have to fend off or get treated for anxiety disorders, depression, dementia, and the like. It is thus a personal imperative to see that good geriatric mental health care is available.

Unfortunately, as the articles in this 2-part Special Report highlight, we are a long way from ideal care. 

, problems that exemplify the clini-cian’s concern “am I doing more harm than good with these treatments?” These concerns in older adults have recently extended to SSRIs and similar antidepressants, in which elevated bone loss and fall risks seen in observational studies remind us that we cannot simply rely on data from industry studies done with young, healthy, and entirely unrepresentative samples.

We need to remember that depression, anxiety disorders, and psychosis, while different in many ways in older adults, are just as treatable as their corresponding disorders in young adults. As 

, not a curative, profession: we cannot cure, but we can and should bring our patients to remission!

For those still looking in the mirror with dread, we should remember that there are many positive mental aspects of aging. Older adults are the demographic with the highest levels of happiness. In Part 2 of this Special Report, to be published in the September issue, 

 in the face of major life stressors, and we need to reassure our older patients that they have significant strengths (such as wisdom, a lifetime of experience, beliefs, and social support) that can be used when they need mental health care. As 

 point out, problem-solving therapy and other therapies adapted for older adults can often help 

Finally, cognitive dysfunction is considered to be a late-life consequence of type 2 diabetes. 

-obesity, insulin resistance, and the metabolic syndrome-that may be responsible for cognition difficulties in the elderly.

This Special Report gathers articles from talented clinicians/scientists who make up the present and the future of geriatric mental health research. Although the aging trend is described as “graying,” we should feel that the future in aging is bright.

Who Should Care About Geriatric Mental Health? Check the Mirror . . .

Despite high prevalence and negative consequences of anxiety disorders in later life, this area has received little research attention. A relatively small number of outcome investigations on late-life anxiety have focused on the impact of pharmacological and psychotherapeutic treatments.

Community prevalence rates for anxiety disorders in older adults range from 3.5% to 10.2%, suggesting a higher prevalence than for late-life depression (Beekman et al., 1998; Bland et al., 1988). Anxiety disorders are even more common among older adults with medical illness and in senior housing facilities (Junginger et al., 1993; Tolin et al., 2005). Generalized anxiety disorder (GAD) is one of the most common anxiety disorders, with community prevalence rates ranging from 1.9% to 7.3% (Beekman et al., 1998; Blazer et al., 1991). Anxiety symptoms that do not meet DSM criteria are even more prevalent, with rates from 15% to 20% in community samples and even higher in medically ill populations (Himmelfarb and Murrell, 1984; Mehta et al., 2003; Wittchen et al., 2002). For example, panic attacks, but not necessarily DSM-diagnosable panic disorder, are common in chronic obstructive pulmonary disease (COPD). Anxiety symptoms and disorders are associated with many negative consequences such as decreased physical activity and functional status, poorer self-perceptions of health, decreased life satisfaction, increased loneliness, increased physical disability, decreased quality of life, and increased service utilization (Brenes et al., 2005a, 2005b; Lenze et al., 2001; Stanley et al., 2001; van Balkom et al., 2000).

The first line of treatment for anxiety in later life is typically pharmacological. Benzodiazepines are the most commonly used anxiety management medication, followed by serotonergic antidepressants, buspirone (BuSpar), and venlafaxine (Effexor) (Mamdani et al., 2005). Recent research has also examined the impact of psychotherapy, typically cognitive-behavioral therapy (CBT), for late-life anxiety.

Approximately half of older patients diagnosed with an anxiety disorder in primary care are prescribed an anxiolytic or antidepressant medication (Stanley et al., 2001). All pharmacological interventions reviewed are approved treatment options for anxiety disorders. Benzodiazepines are the most frequently prescribed medications for anxiety in later life, with usage rates of 10% to as high as 43% for individuals with persistent anxiety (Gleason et al., 1998; Gray et al., 2003; Mamdani et al., 2005; Schuurmans et al., 2005). However, benzodiazepines can lead to an increased risk of falls, and possibly increased cognitive decline, in older people (Paterniti et al., 2002).

 Only three randomized controlled trials have investigated the efficacy of benzodiazepines for anxiety disorders in later life. Two of these focused on GAD (Bresolin et al., 1988; Frattola et al., 1992) and one on an earlier, comparable category of anxiety neurosis (Koepke et al., 1982). The data from these trials suggest the utility of benzodiazepines for anxiety disorders in older adults. Despite the results demonstrating efficacy, limited and short-term use of benzodiazepines is recommended given the possible negative consequences (Sheikh and Cassidy, 2000).

. Five pharmacotherapy studies of late-life anxiety disorders have tested antidepressant medications. Two of these studies included patients with panic disorder (PD), one included a mixed group of patients with GAD, PD or obsessive-compulsive disorder (OCD), and two included patients with GAD. Sheikh and Swales (1999) found promising results when comparing the efficacy of imipramine (Janimine, Tofranil), alprazolam (Xanax) and placebo in patients with PD. A follow-up investigation demonstrated improvement in anxiety symptoms in patients with PD using sertraline (Zoloft) (Sheikh et al., 2004). A sample with mixed anxiety disorders showed high response rates to fluvoxamine (Luvox) (Wylie et al., 2000). Interestingly, none of the three patients with PD responded to treatment. Katz and colleagues (2002) conducted a secondary analysis of data from patients age 60 years and older who participated in five randomized clinical trials of venlafaxine for GAD. Venlafaxine demonstrated superiority to placebo in this investigation. In the only prospective, randomized trial to date of a serotonergic antidepressant, Lenze and colleagues (2005), found citalopram (Celexa) superior to placebo, and these gains were maintained at 32-week follow-up for patients who continued taking the medication (Blank et al., in press).

Most psychotherapy studies of late-life anxiety have used CBT with patients with GAD. We found evidence for the efficacy of CBT for GAD in seven studies. The CBT protocols in these investigations typically consisted of education about anxiety, relaxation training, cognitive therapy and systematic desensitization to worry-provoking thoughts and situations, most commonly administered in a group format. Control conditions included waiting list, minimal contact control, usual care, supportive therapy and a discussion group attention placebo. Data suggest that at follow-up, gains are maintained, particularly when booster sessions are given.

One study found that supportive therapy (which emphasized empathic listening) and CBT were equally effective in reducing anxiety and depressive symptoms (Stanley et al., 1996). Another investigation compared CBT to a minimal contact control condition and demonstrated superiority of CBT, even at 12-month follow-up (Stanley et al., 2003a). Wetherell and colleagues (2003), compared active (CBT and discussion group attention placebo ) and inactive (waiting list) conditions and demonstrated roughly equal efficacy in active conditions compared to waiting list. Interestingly, CBT was only superior to discussion group placebo on one measure of anxiety, and this finding was no longer significant at six-month follow-up. A small study of primary care patients demonstrated the effectiveness of individually administered CBT compared to usual care (Stanley et al., 2003b). This investigation was an important initial effort to disseminate anxiety treatment options into a primary care setting, where the majority of older adults with GAD present for treatment.

Other approaches to the treatment of late-life anxiety have added techniques to the standard CBT protocol. One approach used an enhanced form of individually administered CBT (Mohlman et al., 2003). Enhanced CBT included the standard CBT elements as well as additional memory aids (e.g., reminder phone calls, weekly review of concepts and techniques). Overall, results suggest that CBT enhancements improved treatment outcome compared to waiting list. A follow-up investigation compared enhanced CBT to a waiting list among patients with varying levels of executive functioning (Mohlman and Gorman, 2005). Results suggested that individuals with impaired executive functioning did not respond to CBT whereas those with intact and improved executive functioning were more likely to respond. Another approach used a CBT protocol based on the concept of intolerance of uncertainty as the key feature of GAD (Ladouceur et al., 2004). The majority of patients in this study no longer met criteria for GAD upon completion of treatment.

Much less research has been conducted in older adult populations with anxiety disorders other than GAD. Some research suggests the effectiveness of CBT for PD, agoraphobia, OCD, specific phobia and social phobia. There have been no randomized, controlled psychosocial interventions conducted exclusively with older adults with PD. However, a non-controlled study produced promising results demonstrating the effectiveness of CBT in older PD patients (Swales et al., 1996). A retrospective chart review of older inpatients treated for OCD using exposure and response prevention, along with other elements (e.g., pharmacotherapy, milieu therapy) showed that staff and self-report improvement ratings were high (Carmin et al., 1998).

Several studies have demonstrated the effectiveness of CBT in samples with mixed anxiety disorder diagnoses. Radley and colleagues (1997), found efficacy for CBT in a multiple baseline design in a small sample of patients with a variety of anxiety disorders.

A small case series of patients with various anxiety disorder diagnoses also found improvement in anxiety symptoms following CBT (King and Barrowclough, 1991). Another study used a home-based individual format, which demonstrated significant effects for CBT relative to supportive therapy (Barrowclough et al., 2001). Gains were maintained at 12-month follow-up. Gorenstein and colleagues (2005) found equivalent effects for CBT plus medication management relative to medication management alone.

Overall, available data suggest the potential utility of both pharmacological and psychotherapeutic treatments for late-life anxiety disorders, especially GAD. The CBT literature has demonstrated the effectiveness of CBT compared to no treatment, but evidence does not consistently support the benefits of CBT relative to other psychosocial treatment options (e.g., supportive therapy). Pharmacological treatment is more effective than placebo in the treatment of late-life anxiety, and some evidence suggests that it is more effective than CBT (Wetherell et al., 2005).

Limitations of this literature include homogeneous and unrepresentative samples in terms of age, functional status, ethnicity, education, or medical health; neglect of anxiety disorders other than GAD; and a relative dearth of research on treatment in settings more typically used by anxious older adults (e.g., primary care). Another concern is the failure to investigate long-term maintenance. In most CBT studies, follow-up is limited to 12 months or less. Pharmacological trials have typically not examined the nature and efficacy of various maintenance options.

Psychotherapy attrition rates are higher among older adults (21% to 39%) than among younger adults (approximately 10%) (Gould et al., 2004). Attrition from pharmacotherapy trials is somewhat lower (15% to 23%) and is consistent with attrition rates in younger adults with anxiety disorders (Katz et al., 2002; Lenze et al., 2005). Psychotherapy trials typically analyze data only from completers, whereas pharmacological trials more often analyze on an intent-to-treat basis. Furthermore, psychotherapy studies typically use multiple outcome measures, which complicate the interpretation of inconsistent results across measures. Finally, there is no psychotherapy comparison condition equivalent to a pill placebo.

Although initial results point to the efficacy of pharmacological and psychotherapeutic treatment for geriatric anxiety disorders, more research is needed on other intervention techniques, patient samples, and anxiety disorder diagnoses. More research is also needed to determine whether medication is an acceptable and effective long-term management tool for these chronic disorders. Long-term follow-up of patients and creative methods for the dissemination of these treatments remain important goals for future research, as do combinations of pharmacological and psychotherapeutic treatment and stepped care approaches.

Barrowclough C, King P, Colville J et al. (2001), A randomized trial of the effectiveness of cognitive-behavioral therapy and supportive counseling for anxiety symptoms in older adults. J Consult Clin Psychol 69(5):756-762.


Beekman AT, Bremmer MA, Deeg DJ et al. (1998), Anxiety disorders in later life: a report from the Longitudinal Aging Study Amsterdam. Int J Geriatr Psychiatry 13(10):717-726.


Bland RC, Newman SC, Orn H (1988), Prevalence of psychiatric disorders in the elderly in Edmonton. Acta Psychiatr Scand Suppl 338:57-63.


Blank S, Lenze EJ, Mulsant BH et al. (in press), Outcomes of late-life anxiety disorders during 32 weeks of citalopram treatment. J Clin Psychiatry.


Blazer D, George LK, Hughes D (1991), The epidemiology of anxiety disorders: an age comparison. In: Anxiety in the Elderly: Treatment and Research, Salzman C, Lebowitz BD, eds. New York: Springer Pub. Co., pp17-30.
