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The nih almanac, national institute of mental health (nimh).
- Important Events
- Legislative Chronology
The mission of the National Institute of Mental Health (NIMH) is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery, and cure.
To continue fulfilling this vital public health mission, the Institute fosters innovative thinking and supports a full array of novel scientific perspectives to further discovery in the evolving science of the brain, behavior, and experience. In this way, breakthroughs in science can become breakthroughs for all people with mental illnesses.
To deliver high-quality, impactful research and promote translation of such research into clinical practice, services delivery, and policy, the Institute developed the NIMH Strategic Plan for Research to advance our mission and guide research. The most recent NIMH Strategic Plan for Research, published in 2020, builds on the successes of previous NIMH Strategic Plans, provides a framework for research to leverage new opportunities for scientific exploration, and addresses new challenges in mental health.
In this Strategic Plan for Research, NIMH outlines four high-level Goals as follows:
- Goal 1: Define the Brain Mechanisms Underlying Complex Behaviors
- Goal 2: Examine Mental Illness Trajectories Across the Life Span
- Goal 3: Strive for Prevention and Cures
- Goal 4: Strengthen the Public Health Impact of NIMH-Supported Research
These four Goals form a broad roadmap for the Institute’s research priorities over the next 5 years, beginning with fundamental science of the brain and behavior, and extending through evidence-based services that improve public health outcomes.
Important Events in NIMH History
1946 —On July 3, President Harry S. Truman signed the National Mental Health Act, which called for establishing a National Institute of Mental Health.
1946 —On August 15, U.S. Surgeon General Thomas Parran, Jr., M.D., and several nationally known psychiatrists attended the first meeting of the National Advisory Mental Health Council. The group was tasked with advising NIMH on its policies and activities.
1947 —On July 1, the U.S. Public Health Service Division of Mental Hygiene awarded the first mental health research grant, "Basic Nature of the Learning Process," to Winthrop N. Kellogg, Ph.D., of Indiana University.
1949 —On April 1, NIMH was formally established under the direction of psychiatrist and public health advocate Robert H. Felix, M.D., as one of the first four institutes of the National Institutes of Health (NIH).
1951 —In June, the first scientific director of NIMH, Seymour Kety, M.D., Ph.D., began building a joint basic intramural research program for NIMH and the newly created National Institute of Neurological Diseases and Blindness (NINDB). This research program evolved into the NIMH Intramural Research Program, the internal research division of NIMH. As of 2023, the NIMH Intramural Research Program comprised more than 650 staff and more than 40 research groups conducting basic, clinical, and translational research to advance understanding of the diagnosis, causes, treatment, and prevention of mental disorders.
1952 —On December 20, psychiatrist Robert A. Cohen, M.D., created a joint NIMH-NINDB clinical research program in time for the opening of the NIH Clinical Center in July 1953.
1955 —On July 28, The Mental Health Study Act of 1955 (Public Law [P.L.] 84-182) called for "an objective, thorough, and nationwide analysis and reevaluation of the human and economic problems of mental illness."
1956 —On August 2, Congress passed the Health Amendments Act of 1956 (P.L. 84-911). Title V of the legislation allowed NIMH to award “special project grants” about mental health. In expanding NIMH’s existing mandate, the act enabled NIMH to be more involved in community-based mental health efforts and programs.
1956 —On October 16, NIMH created the Psychopharmacology Service Center to coordinate the large-scale testing of new compounds. The congressionally funded effort, led by Jonathan O. Cole, M.D., spurred the discovery of blockbuster drugs chlorpromazine and meprobamate, to treat mental disorders. The center later evolved into the Early Clinical Drug Evaluation Unit, a collaborative program capable of conducting large nationwide clinical trials. This program, later renamed the New Clinical Drug Evaluation Unit, held an annual meeting that became a critical meeting in this domain, bringing together NIH researchers, academic investigators, industry scientists, U.S. and international regulators, and other professionals working in various aspects of drug development and clinical trials. Output from the program influenced the evolution of treatment research and the development of new treatments and treatment strategies.
1961 —The report, Action for Mental Health , assessed mental health conditions and resources throughout the United States "to arrive at a national program that would approach adequacy in meeting the individual needs of the mentally ill people of America." The report commanded the attention of President John F. Kennedy, who established a cabinet-level interagency committee to examine the recommendations and determine an appropriate federal response.
1963 —On February 5, 1963, President Kennedy submitted a special message to Congress—the first presidential message to the legislature on mental health issues. Energized by the president's focus, Congress passed the Mental Retardation Facilities and Community Mental Health Centers (CMHC) Construction Act (P.L. 88-164) on October 31, beginning a new era in federal support for mental health services. NIMH assumed responsibility for monitoring the nation's community mental health centers programs.
1965 —A provision in the Social Security Amendments of 1965 (P.L. 89-97) provided funds and a framework for a new Joint Commission on the Mental Health of Children to recommend national action for child mental health. The Community Mental Health Centers Act Amendments of 1965 (P.L. 89-105) also passed this year, which authorized grants to help pay the salaries of professional and technical personnel in federally funded community mental health centers.
1966 —In response to President Lyndon B. Johnson's pledge to apply scientific research to social challenges, NIMH refocused its efforts on fighting specific mental health problems. The institute established centers for research, training, and services covering topics such as schizophrenia, substance use, suicide prevention, crime, and child and family mental health. The National Center for Prevention and Control of Alcoholism was also established due to emerging public recognition of alcoholism as a disease.
1967 —On January 1, NIMH was separated from NIH by executive order and made an independent division within the U.S. Public Health Service. However, the NIMH Intramural Research Program, which conducted studies in the NIH Clinical Center and other NIH facilities, remained at NIH under an agreement for joint administration between NIH and NIMH.
1967 —On August 13, U.S. Department of Health, Education, and Welfare Secretary John W. Gardner, Ph.D., transferred administrative control of St. Elizabeths Hospital—the federal government's only civilian psychiatric hospital—to NIMH. Research was an important part of the work of St. Elizabeths through its Clinical Pharmacology Research Center, which made significant contributions to neurological and clinical research.
1968 —On April 1, NIMH became a component of the Health Services and Mental Health Administration within the U.S. Public Health Service.
1970 —On April 6, the U.S. Food and Drug Administration (FDA) approved lithium as a treatment for mania, a feature of bipolar disorder. This treatment, informed by NIMH-supported research, led to sharp drops in inpatient days and suicide rates among people with bipolar disorder and reduced economic costs associated with the illness. The FDA later approved lithium for the maintenance treatment of bipolar disorder.
1970 —On October 15, NIMH researcher Julius Axelrod, Ph.D., and two other researchers received the Nobel Prize in Physiology or Medicine for research on the chemistry of nerve transmission. Dr. Axelrod’s work established that norepinephrine was inactivated through "reuptake" by the same cells that secreted it. The discovery led to the development of selective serotonin reuptake inhibitors (SSRIs), the first blockbuster neuropharmacological medicine since the 1950s. SSRIs are a class of medications commonly used as antidepressants. They work by increasing brain levels of serotonin, a neurotransmitter involved in regulating mood, appetite, and sleep.
1970 —On December 31, the Comprehensive Alcohol Abuse and Alcoholism Prevention, Treatment, and Rehabilitation Act (P.L. 91-616) established the National Institute on Alcohol Abuse and Alcoholism within NIMH.
1970 —NIMH established the Center for Minority Group Mental Health Programs in response to concerns voiced by the Black Psychiatrists of America and other parties. The center marked NIMH's first official effort to increase the representation of people from minority groups among extramural awardees and intramural positions.
1972 —On March 21, Congress passed the Drug Abuse Office and Treatment Act (P.L. 92-255), which called for the future establishment of a National Institute on Drug Abuse (NIDA) within NIMH.
1973 —NIMH went through a series of organizational moves. The Institute temporarily rejoined NIH on July 2 with the abolishment of the Health Services and Mental Health Administration. The Alcohol, Drug Abuse, and Mental Health Administration (ADAMHA)—composed of NIAAA, NIDA, and NIMH—was established as the successor organization.
1974 —On May 14, Alcohol, Drug Abuse, and Mental Health Administration was officially established when President Richard Nixon signed the Comprehensive Alcohol Abuse and Alcoholism Prevention, Treatment, and Rehabilitation Act Amendments of 1974 (P.L. 93-282).
1975 —The Community Mental Health Centers Amendments of 1975 outlined requirements for national standards, quality assurance programs, and data collection, which set the stage for performance criterion in community mental health centers.
1977 —On February 17, President Jimmy Carter established the President's Commission on Mental Health by executive order. President Carter charged the commission with reviewing the nation’s mental health needs and making recommendations to the president on how best to meet these needs. First Lady Rosalyn Carter served as the honorary chair of the commission.
1978 —On April 27, The President's Commission on Mental Health submitted its final report to President Carter. The report contained more than 100 recommendations for expanding existing programs and creating new ones to make the community mental health center program more flexible and to extend mental health services. The report's appendices included three additional volumes with recommendations from more than 20 task panels focused on a wide range of mental health and substance use issues.
1980 —In October, NIMH released preliminary results from its Epidemiological Catchment Area Survey. Most notably, the survey found that nearly one in five Americans experienced a diagnosable psychiatric disorder within any six-month period. The effort, underway since 1977, was conducted by research teams at five universities and looked at rates of mental disorders in five cities. The extensive survey allowed for the accurate categorization of specific disorders in a general population for the first time.
1980 —On October 7, President Carter signed the Mental Health Systems Act (P.L. 96-398). The act created a complex federal, state, and local partnership focused on preventing mental illnesses. It expanded the Community Mental Health Center program and extended help to "chronically mentally ill individuals, children and youth, elderly individuals, racial and ethnic minorities, women, poor persons, and persons in rural areas."
1980 —NIMH participated in developing the U.S. Surgeon General’s Report, Toward a National Plan for the Chronically Mentally Ill , a sweeping effort to improve services and fine-tune various federal entitlement programs for those with severe, persistent mental disorders.
1981 —On August 13, President Ronald Reagan signed the Omnibus Budget Reconciliation Act of 1981 (P.L. 97-35). This act repealed the Mental Health Systems Act and consolidated the treatment and rehabilitation service programs of the Alcohol, Drug Abuse, and Mental Health Administration into a single block grant that enabled each state to administer its allocated funds. With the repeal of most of the community mental health legislation and the establishment of block grants, the federal role shifted to providing technical assistance to increase the capacity of state and local mental health service professionals.
1981 —On November 18, intramural NIMH researcher Louis Sokoloff, M.D., Ph.D., received the Lasker Basic Medical Research Award, considered the "American Nobel Prize" of clinical medical research. Previous researchers had found evidence for changing glucose levels in the brain but could not link those changes to specific brain regions. Dr. Sokoloff developed a noninvasive technique to track the movement of a radioactive analog of glucose in the brain, which allowed researchers to measure glucose metabolism and map brain function. This technique paved the way for the development of positron emission tomography (PET) imaging of the living brain.
1983 —In research supported by NIMH, zoologist Fernando Nottebohm, Ph.D., discovered the formation of new neurons in the brains of adult songbirds. This evidence of neurogenesis (the process by which new neurons are formed in the brain) opened an exciting and clinically promising new line of research in brain science. It was 15 years, however, before investigators found evidence for continued neurogenesis in the brains of adult humans.
1984 —Researchers in the NIMH Intramural Research Program published one of the earliest studies of seasonal affective disorder. The seminal study, which described patients who experienced depressive symptoms that emerged during the fall and winter and went away during the spring and summer, provided the first working definition of the disorder. Norman Rosenthal, M.D., and Thomas Wehr, M.D., both in the NIMH Clinical Psychobiology Branch, led the research. Frederick Goodwin, M.D., NIMH scientific director and chief of intramural research, also contributed. Study results also showed that light therapy had a robust antidepressant effect, effectively reducing depressive symptoms in people with seasonal affective disorder.
1987 —On October 1, the U.S. Department of Health and Human Services transferred administrative control of St. Elizabeths Hospital from NIMH to the city of Washington, D.C. NIMH retained research facilities on the hospital grounds.
1987 —The FDA approved SSRIs for treating depression. Building on the pioneering work of Dr. Axelrod and others at NIMH, researchers demonstrated the effectiveness of SSRIs as antidepressant medications. In the decades following FDA approval, SSRIs became one of the most widely prescribed antidepressants in the world due in part to their relatively mild side effects compared to other medications.
1989 —On July 25, in response to reports by the advisory councils of NIMH and the National Institute of Neurological Disorders and Stroke, President George H.W. Bush signed a declaration proclaiming the 1990s the "Decade of the Brain."
1989 —On September 25, NIMH staff, members of Congress, and mental health advocates attended a ceremony for the dedication of the NIMH Neuroscience Center and the NIMH Neuropsychiatric Research Hospital, located on the St. Elizabeths Hospital grounds.
1991 —Psychologist Marsha M. Linehan, Ph.D., and colleagues published findings from their NIMH-supported research on dialectical behavior therapy (DBT), a new treatment approach for people with borderline personality disorder. DBT enhanced standard change-oriented techniques from cognitive behavioral therapy with concepts of acceptance and validation of one’s present situation and emotional state. DBT also focused on helping people build skills to manage intense emotions, reduce self-destructive behaviors, and improve relationships. This study and later research showed that adults who received DBT engaged in fewer and less severe suicidal behaviors, had fewer inpatient days in the hospital, and were more likely to stay in therapy than those who received standard care. Later studies showed DBT to reduce suicidal behavior in adolescents. Later in her career, Dr. Linehan discussed her lived experience and how it helped inform novel strategies to treat mental illness and reduce suicide risk.
1992 —On July 10, President Bush signed the ADAMHA Reorganization Act (P.L. 102-321), abolishing the Alcohol, Drug Abuse, and Mental Health Administration. The research components of NIMH, NIAAA, and NIDA rejoined NIH, thereby reuniting NIMH with the leading medical research agency in the United States and ensuring the future of neuroscience and mental health research. The service components of each institute became part of a new U.S. Public Health Service agency—the Substance Abuse and Mental Health Services Administration. Within NIMH, new offices were created to support research on prevention, special populations, rural mental health, and HIV/AIDS.
1993 —NIMH coordinated a multi-institute effort to launch the Human Brain Project, a comprehensive neuroscience database accessible via an international computer network through cutting-edge imaging, computer, and network technologies.
1996 —On October 3, President Bill Clinton established the National Bioethics Advisory Commission. The commission issued the resulting report, Research Involving Persons With Mental Disorders that may affect Decisionmaking Capacity , in 1999. This report informed NIMH policies to safeguard and improve protections for human participants in clinical mental health research.
1996 —On July 18, NIMH initiated planning to integrate the Institute's peer review system for neuroscience, behavioral and social science, and AIDS research applications into the overall NIH peer review system.
1997 —At the request of Congress, NIH created the NIH Autism Coordinating Committee to increase the quality of research on autism spectrum disorder. The director of NIMH was made co-chair of the committee along with the director of the National Institute of Child Health and Human Development.
1998 —In September, NIMH launched several long-term, large-scale, multisite, community-based clinical studies to determine the effectiveness of certain treatments. Specifically, the studies focused on treatments for depression, treatments for bipolar disorder, and antipsychotic medications as part of treatment for schizophrenia and the management of psychosis and behavioral symptoms associated with Alzheimer's disease.
1999 —The NIMH Neuroscience Center and NIMH Neuropsychiatric Research Hospital were relocated from St. Elizabeths Hospital grounds in Washington, D.C., to the NIH Campus in Bethesda, Maryland, in response to the recommendations of the 1996 review of the NIMH Intramural Research Program by the Intramural Planning Committee.
1999 —In June, NIMH developed materials and helped organize the first White House Conference on Mental Health in Washington, D.C. The conference brought together national leaders, mental health scientific and clinical personnel, patients, and consumers to discuss needs and opportunities to understand and treat mental disorders.
1999 —In July, U.S. Surgeon General David Satcher, M.D., Ph.D., released The Surgeon General's Call to Action to Prevent Suicide . Another report, Mental Health: A Report of the Surgeon General , followed in December. NIMH and other federal agencies collaborated to prepare both landmark reports.
1999 —In December, the main findings from the Multimodal Treatment of ADHD study were published. NIMH sponsored the multisite study to compare the leading treatment approaches for attention-deficit/hyperactivity disorder (ADHD), one of the most common developmental disorders in childhood. Study participants included nearly 600 children, ages 7-9 years, seen at six study sites. In contrast to previous short-term studies, this study examined treatment effects for up to 14 months. Results showed that a combination treatment approach that included both medication and behavior therapy and a medication-only approach were both generally more effective in reducing ADHD symptoms compared to behavioral treatment alone or routine community care. The study also showed that these benefits lasted for as long as 14 months. Subsequent analyses and publications examined the impact of the interventions on various areas of functioning and the long-term course of youth in the study.
2000 —On October 9, Eric Kandel, M.D., Ph.D., Paul Greengard, Ph.D., and Arvid Carlsson, M.D., Ph.D., the 2000 Nobel Prize in Physiology or Medicine for their respective research on the functioning of signal transduction proteins in learning, memory, and movement. Dr. Kandel and Dr. Greengard conducted NIMH-supported research for more than 30 years. Dr. Kandel, who worked in the NIMH Intramural Research Program in the 1950s, received the prize for his research on the functional modification of synapses, which allow neurons to communicate in the brain. His work established that the formation of memories is a consequence of short- and long-term changes in the biochemistry of neurons and showed that these changes occur at the level of synapses.
2000 —On October 17, President Clinton signed the Children’s Health Act of 2000 (P.L. 106-310), which created the Interagency Autism Coordinating Committee to coordinate all autism-related efforts within the U.S. Department of Health and Human Services. By 2001, NIMH had been designated to lead implementation of the Interagency Autism Coordinating Committee, with the NIMH director as the committee chair.
2000 —On November 3, Nancy Andreasen, M.D., Ph.D., a psychiatrist whose research was supported by NIMH for many years, received the National Medal of Science for her groundbreaking work in schizophrenia that joined behavioral science with neuroscience and neuroimaging.
2000 —NIMH and other federal agencies collaborated to prepare a Report on the Surgeon General's Conference, Children's Mental Health: A National Action Agenda . Released by Surgeon General Dr. Satcher, this report indicated that the poor mental health of many children and adolescents in the United States represented a national public health crisis. The National Action Agenda outlined goals and strategies to improve services for children and adolescents with mental and emotional disorders.
2002 —In September, NIMH published a national conference report, Mental Health and Mass Violence: Evidence-Based Early Psychological Intervention for Victims/Survivors of Mass Violence: A Workshop to Reach Consensus on Best Practices . Although most people recover from a traumatic event over time, the report indicated that early psychological intervention guided by qualified mental health professionals could reduce the harmful psychological and emotional effects of exposure to mass violence. NIMH collaborated with the U.S. Department of Defense, other federal agencies, and the American Red Cross to prepare this report.
2003 —NIMH established the Limited Access Data Repository, the institute's first effort to provide an infrastructure that could support data sharing among extensive NIMH-funded clinical studies. The repository served as a platform for researchers to access datasets to conduct secondary analyses until 2017, when data from those clinical trials were moved to the NIMH Data Archive .
2004 —NIMH’s large-scale practical clinical trial—The Treatment of Adolescent Depression Study—published significant first-phase results on the most effective treatment for adolescents with depression. The study showed that a combination of cognitive behavioral therapy and the medication fluoxetine (the only FDA-approved antidepressant for children and adolescents at the time) was most effective at treating depression over 12 weeks.
The study's principal investigator, John March, M.D., M.P.H., presented additional results to NIMH’s National Advisory Mental Health Council in September 2006. These results, which extended the study’s time frame to 18 weeks, once again showed that the combination of cognitive behavioral therapy and fluoxetine provided the fastest, most effective treatment for adolescent depression. Although psychotherapy alone was a viable option for adolescents who could not take medication, it took an additional six months to achieve the improvement seen with treatment that included medication.
2005 —The Clinical Antipsychotic Trials of Intervention Effectiveness research program—another NIMH large-scale practical clinical trial— provided the first real-world test of antipsychotic medications for people with schizophrenia. Its first phase compared the effectiveness and side effects of four newer medications and one older medication for treating schizophrenia. All the medications—even the older, less expensive medication, perphenazine—showed comparable effectiveness. However, many people in the study stopped taking the medications due to intolerable side effects or a failure to control symptoms adequately.
Results from the first phase of the study were released in 2006 and showed that antipsychotic medications commonly prescribed to treat delusions, aggression, hallucinations, and similar symptoms of Alzheimer’s disease could also benefit some people with schizophrenia. Still, the medications were no more effective than a placebo when considering adverse side effects. The study advanced the field by directly comparing multiple antipsychotic drugs within a single trial. The extensive information provided by this direct comparison helped clinicians determine the best medication for individual patients.
2006 —The NIMH-funded Sequenced Treatment Alternatives to Relieve Depression (STAR*D) research program reported a series of results over the course of the year. A large-scale practical clinical trial led by NIMH, the study was the nation’s largest clinical trial of treatments for depression at the time. The systematic study showed that about 70% of participants achieved symptom-free status after 12 months, and many participants needed to try two or three medications before finding one that worked for them. The results provided real-world insight into depression treatment, highlighting the limitations of existing treatments and alternate options for people who do not respond to SSRIs. The study helped move the field toward personalized, measurement-based approaches to depression care.
2006 —On September 29, Aaron T. Beck, M.D., Professor Emeritus of Psychiatry at the University of Pennsylvania and a longtime NIMH-supported researcher, received the prestigious Albert Lasker Basic Medical Research Award for the development of cognitive behavioral therapy.
2006 —On December 19, President George W. Bush signed the Combating Autism Act of 2006 (P.L. 109-416). The measure called for increased research on autism spectrum disorder. The Interagency Autism Coordinating Committee, co-chaired by the NIMH director, was also reauthorized and chartered as a federal advisory committee.
2007 —Findings from the Systematic Treatment Enhancement Program for Bipolar Disorder research project, an NIMH practical clinical trial, revealed that people with bipolar disorder were more likely to recover from a depressive episode and stay well over the longer term if their treatment included both intensive psychotherapy and medication.
2008 —In August, NIMH published its Strategic Plan for Research (link is external) with four primary objectives:
- Promote discovery in the brain and behavioral sciences to fuel research on the causes of mental disorders
- Chart mental illness trajectories to determine when, where, and how to intervene
- Develop new and better interventions that incorporate the diverse needs and circumstances of people with mental illnesses
- Strengthen the public health impact of NIMH-supported research
2008 —The Child/Adolescent Anxiety Multimodal Study examined strategies for treating clinically significant anxiety among children ages 7-17 years. Results of the six-site clinical trial revealed that, although the combination of cognitive behavioral therapy and antidepressant medication was most effective at treating anxiety, each treatment alone was also effective. The findings indicated that clinicians and families have several viable treatment options for young people with anxiety disorders, depending on treatment availability and preferences.
2008 —The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) launched as a partnership between NIMH and the U.S. Army to conduct research to help reduce suicide rates among members of the military. The multisite, multiyear study, which included soldiers in all phases of service, was the largest study of mental health risk and resilience ever conducted among military personnel.
The study showed a rise in suicide deaths from 2004 to 2009, not only among currently and previously deployed soldiers but also among soldiers who were never deployed. Nearly half of soldiers who reported a previous suicide attempt indicated that their first attempt was prior to enlistment. Soldiers also reported higher rates of certain mental disorders than civilians. Results from Army STARRS have informed actionable strategies to enhance mental health and reduce suicide risk among members of the military and civilians.
2008 —Twelve NIMH staff members received the 2008 Hubert H. Humphrey Award for Service to America for their work addressing veterans' mental health needs. These staff members developed a new research initiative to support research that would describe and evaluate national, state, and local programs addressing the mental health needs of returning service members and their families.
2009 —The NIH Blueprint for Neuroscience Research launched the Human Connectome Project as a Blueprint Grand Challenge. Supported by NIMH and other Blueprint partners, the Human Connectome Project aimed to map the neural pathways that underlie human brain function. The effort expanded to measuring macroscale brain connections across the lifespan. The project led to new data models, informatics, and analytic tools that advanced researchers' ability to image and analyze brain connections. These advances played a major role in accelerating progress in the emerging human connectomics field and contributed to the formation of the Brain Research Through Advancing Innovative Neurotechnologies® Initiative, or The BRAIN Initiative®.
2009 —The Treatment of SSRI-Resistant Depression in Adolescents research project was an NIMH-funded practical clinical trial that examined difficult-to-treat depression among adolescents across multiple sites. The study showed that teens who did not respond to a first antidepressant medication were more likely to see symptom improvement if they switched to another antidepressant and added psychotherapy instead of only switching medications.
2010 —In July, NIMH launched the Research Domain Criteria (RDoC) initiative, providing a research framework for developing new ways of classifying mental disorders based on behavioral dimensions and neurobiological measures. The intent was to apply modern research approaches in genetics, neuroscience, and behavioral science to studying mental illnesses independently from the classification systems by which patients were typically grouped.
2010 —On November 10, NIMH intramural researcher Mortimer Mishkin, Ph.D., was awarded the National Medal of Science at a White House ceremony. In studies spanning more than five decades, Dr. Mishkin and colleagues examined the neural mechanisms underlying perception and memory. Dr. Mishkin's work explored how the brain processes input from vision, hearing, and touch to encode memory and shed light on the organization of memory and memory disorders in humans.
2011 —On July 6, the Grand Challenges in Global Mental Health initiative began. Co-led and funded by NIMH, the Grand Challenges brought together the largest-ever international Delphi panel—more than 400 participants representing work conducted in 60 countries—to determine priorities for research relevant to mental, neurological, and substance use disorders.
2011 —On August 25, NIMH was named by the White House as a "Champion of Change" for its efforts supporting research on suicide prevention. The initiative celebrated diverse individuals and organizations making an impact in communities and helping the country rise to the challenges of the 21st century.
2012 —On August 31, President Barack Obama signed an Executive Order (link is external) directing key federal departments, including NIH, to expand suicide prevention strategies and improve access to mental health and substance abuse treatment services for veterans, service members, and their families. The order also called for developing a National Research Action Plan with strategies to improve the diagnosis and treatment of post-traumatic stress disorder (PTSD) and other mental health conditions. NIMH led NIH’s participation in the action plan, which made significant progress in establishing common data elements to guide research on traumatic stress and suicide risk prevention and developing scalable interventions for PTSD and suicide prevention.
2012 —Researchers in the NIMH Intramural Research Program published the Ask Suicide-Screening Questions (ASQ) measure—a brief screening instrument clinicians can administer in 20 seconds to identify a patient's risk for suicide. An NIMH-led multisite study showed that a "yes" to any of the measure’s four questions identified 97% of young people at risk for suicide among those screened in pediatric emergency departments. Additional NIMH research subsequently validated the ASQ in pediatric inpatient care and integrated it into an evidence-based pathway for youth suicide prevention. This pathway served as a scientific basis for the Blueprint for Youth Suicide Prevention developed by the American Academy of Pediatrics and the American Foundation for Suicide Prevention.
In 2014, intramural researchers led a multisite study that confirmed the ASQ as a valid screening tool for suicide risk in adults. The researchers then expanded these studies into an ASQ Toolkit that clinicians can use to identify and manage suicide risk in both children and adults in a variety of medical settings. Intramural researchers have also worked with NIMH experts in global mental health and international collaborators to translate the ASQ into more than 20 languages and validate the ASQ through research in other countries. By enabling culturally responsive early identification and assessment of people at high risk for suicide, the ASQ Toolkit has enhanced suicide prevention for youth and adults in medical settings worldwide.
2013 —On April 2, President Obama announced the launch of the Brain Research Through Advancing Innovative Neurotechnologies Initiative or The BRAIN Initiative®——a major initiative focused on revolutionizing our understanding of the human brain. The president proposed $100 million for the first year of what he called “the next great American project.” NIH, the Defense Advanced Research Projects Agency, the National Science Foundation, and several private laboratories and foundations began working to develop the next generation of tools for decoding the language of the brain. Building on recent discoveries, the BRAIN Initiative aimed to accelerate the development and application of innovative technologies to produce a revolutionary, dynamic picture of the human brain. This dynamic picture would show how individual cells and complex neural circuits interact in both time and space, providing unprecedented opportunities to understand brain function and dysfunction.
2013 —On September 20, Thomas C. Südhof, M.D., and Richard H. Scheller, Ph.D., received the Lasker Basic Medical Research Award. The researchers, whose work had been supported by NIMH, were recognized for their work mapping the molecular mechanisms involved in neurotransmitter release. Dr. Südhof later received the 2013 Nobel Prize in Physiology or Medicine for his NIMH-supported research on how the brain sends and receives chemical messages.
2014 —NIMH launched the Emergency Department Screening for Teens at Risk for Suicide study in a network of hospital emergency departments across the United States as a part of the institute’s research agenda for suicide prevention. The study aimed to develop and test a personalized, computer-based suicide risk screening tool for teenagers that could improve screening and enable earlier intervention. In 2021, researchers involved in the study developed a computerized adaptive screening tool, which correctly identified more than 80% of youth who went on to attempt suicide in the three months following screening. The screener has offered a valuable tool for rapidly identifying youth at risk for suicide in emergency departments.
2014 —On April 2, the NIMH-funded BrainSpan Atlas of the Developing Human Brain consortium project reported its first major findings. The effort was intended to provide a comprehensive three-dimensional atlas of the brain and profile gene activity across the brain, beginning prenatally.
2014 —NIMH adopted a new policy for clinical trials that required future trials to follow an experimental therapeutics approach. Under this mechanism-based approach to intervention development and testing, trials are designed not only to test whether an intervention works but also to advance understanding of how the intervention works. The policy stipulated that clinical trials must also meet new recruitment, data sharing, and reporting standards.
2015 —NIMH issued a new Strategic Plan for Research . Informed by the successes and challenges of recent years, the plan updated the strategic objectives outlined in 2008 to balance the need for long-term investments in basic research with urgent mental health needs. The four strategic objectives in the 2015 plan were:
- Define the mechanisms of complex behaviors.
- Chart mental illness trajectories to determine when, where, and how to intervene.
- Strive for prevention and cures.
- Strengthen the public health impact of NIMH-supported research.
2015 —The Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) was launched as an extension of Army STARRS, representing a partnership between NIMH and the U.S. Army, U.S. Department of Defense, and U.S. Department of Veterans Affairs. STARRS-LS researchers continued to analyze Army STARRS data while also collecting new data to learn about suicide risk and mental health among military personnel throughout their Army careers and during the transition to civilian life.
2015 —Researchers in NIMH’s Recovery After an Initial Schizophrenia Episode (RAISE) initiative reported that treating people with first-episode psychosis using a team-based coordinated specialty care (CSC) approach produced better clinical and functional outcomes than typical community care. Findings from RAISE also showed that treatment was most effective for people who received care soon after psychosis symptoms began. Based on RAISE results, the Centers for Medicare & Medicaid Services (link is external) (CMS) posted an informational bulletin (link is external) for state Medicaid directors about covering CSC as an evidence-based treatment for first episode psychosis. The Veterans Health Administration and the U.S. Department of Labor also endorsed CSC. RAISE contributed to a new way to organize and deliver treatment and produced findings that changed the standard of practice for early schizophrenia treatment in the United States.
2015 —On February 6, NIMH announced the creation of the Early Psychosis Intervention Network (EPINET) , designed to link treatment centers for early psychosis in a network of evidence-based coordinated specialty care programs. The initiative was built on the insights developed during the NIMH RAISE initiative. By 2020, EPINET included a data coordinating center, eight scientific hubs, and more than 100 community clinics in a national learning health system aimed at improving services and outcomes for thousands of people experiencing an initial episode of psychosis.
2017— NIMH supported the launch of a major BRAIN Initiative effort to discover and catalog the brain's "parts list." This effort, known as the Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative Cell Census Network , was established as a cooperative network of comprehensive centers, specialized laboratories, and an integrated data center.
2017 —On April 29, results published from the Emergency Department Safety Assessment and Follow-up Evaluation study showed that hospital emergency departments can play a vital role in reducing suicide attempts among adults. The study was the largest emergency department-based suicide intervention trial ever conducted in the United States, taking place over five years in eight hospitals across seven states. The results showed that screening, followed by safety planning guidance and periodic phone check-ins after discharge, led to a 30% decrease in suicide attempts compared to standard emergency department care. The study was another example of NIMH’s prioritized suicide prevention research agenda.
2017 —On July 11, NIMH proposed the creation of the NIMH Data Archive to serve as an online resource for investigators seeking to share data, tools, methods, and analyses from research with human participants. The NIMH Data Archive, which was built upon the preexisting National Database for Autism Research, brought together other digital repositories, including the Research Domain Criteria Database, the National Database for Clinical Trials Related to Mental Illness, and the NIH Pediatric MRI Repository.
2018— NIMH released the first data set from the Adolescent Brain Cognitive Development ℠ study , the largest long-term study of brain development and child health ever conducted in the United States, to the scientific community through the NIMH Data Archive. The comprehensive dataset—including measures of brain development; social, emotional, and cognitive development; mental health; substance use and attitudes; gender identity and sexual health; and various physical health and environmental factors—allowed researchers to address numerous questions related to adolescent brain development to help inform future prevention and treatment efforts, public health strategies, and policy decisions.
2018— NIH launched the Helping to End Addiction Long-term® Initiative as an ambitious, high-priority effort across the institutes to speed scientific solutions to stem the opioid public health crisis. Launched in April 2018, the initiative focused on improving prevention and treatment strategies for opioid misuse and addiction and enhancing pain management. As a major partner in the initiative, NIMH led a research program focused on optimizing the delivery of services for people with opioid use disorder, mental disorders, and suicide risk. NIMH-supported efforts have included research to adapt the collaborative care model to treat co-occurring mental and substance use disorders and a program to reduce suicide deaths by identifying people at risk when seen in primary care settings.
2018— Researchers in the NIMH Intramural Research Program collaborated with the Indian Health Service (IHS) to pilot suicide risk screening in IHS emergency departments serving American Indian/Alaska Native communities. A follow-up quality improvement project further demonstrated the feasibility of suicide risk screening in IHS emergency departments. Intramural researchers subsequently used these findings to guide the implementation of suicide risk screening in more than 100 IHS medical settings (including 22 emergency departments) around the United States.
2019— On March 5, the FDA approved esketamine as a fast-acting and noninvasive treatment for depression that works via a different neurochemical pathway from other antidepressants. In 2006, NIMH intramural researchers Husseini Manji, M.D., and Carlos Zarate, M.D., along with Dennis Charney, M.D., reported findings from the first study investigating intravenous ketamine for people with treatment-resistant depression. They found that ketamine worked by blocking the NMDA receptor in brain cells, producing rapid, robust, and relatively sustained antidepressant effects in people who had already tried several antidepressant medications without seeing improvement. Their research also showed that ketamine stimulated the activity of the AMPA receptor. Dr. Manji built on this finding to produce an alternative way of delivering ketamine in the form of esketamine, which is delivered via nasal spray.
2019 —On March 19, the FDA approved the medication brexanolone as the first successful treatment for severe postpartum depression. In the 1980s and 1990s, NIMH intramural researcher Steven Paul, M.D., showed that the neurosteroid allopregnanolone promoted anesthesia during pregnancy by stimulating the inhibitory neurotransmitter GABA. Subsequent research demonstrated that brexanolone, an intravenous form of allopregnanolone, treated postpartum depression by continuing the stimulation of GABA into the postpartum period.
2019 —NIMH established the Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness research center program to support the advancement of clinical research and practice. The program was designed to leverage practice-based infrastructure, stakeholder engagement, and transdisciplinary research teams capable of incorporating insights from new fields and emerging technologies. These innovative components were expected to speed the translation of research into clinical practice. At launch, the program comprised research teams at eight centers. By 2023, the program had expanded to 14 centers focused on a range of populations and spanning a variety of real-world settings where services are delivered.
2020 —In response to the COVID-19 pandemic, NIMH funded research to understand the long-term mental health impacts of the pandemic and evaluate scalable interventions that could meet the increased mental health needs of diverse populations. NIMH committed to prioritizing research on COVID-19 and funding studies examining the pandemic’s ongoing impacts and effective ways to support mental health during public health emergencies.
2020 —NIMH published a new NIMH Strategic Plan for Research , which provided a framework for research to leverage new opportunities for scientific exploration and addressed new challenges in mental health. The four goals outlined in the 2020 plan formed a broad roadmap for the institute's research priorities, ranging from fundamental science to public health impact:
- Define the Brain Mechanisms Underlying Complex Behaviors
- Examine Mental Illness Trajectories Across the Life Span
- Strive for Prevention and Cures
- Strengthen the Public Health Impact of NIMH-Supported Research
2020 —NIH launched a public-private partnership to meet the urgent need for early therapeutic interventions for people at risk of developing schizophrenia. Part of the Accelerating Medicines Partnership® (AMP®) brought together NIH, the FDA, and multiple nonprofit and private organizations in a united effort to better understand underlying biological pathways and identify new treatment targets.
2021 —The BRAIN Initiative Cell Census Network unveiled an atlas of cell types and an anatomical neuronal wiring diagram for the mammalian primary motor cortex, derived from detailed studies of mice, monkeys, and humans. This publicly available resource represented the culmination of an international collaboration by more than 250 scientists at more than 45 institutions across three continents. The findings appeared in 17 associated papers published in a dedicated issue of the journal Nature.
2021 —In December, U.S. Surgeon General Vivek H. Murthy, M.D., issued The U.S. Surgeon General’s Advisory on Protecting Youth Mental Health. The advisory, developed with input from NIMH and other federal agencies, recognized mental health as an essential part of overall health and acknowledged the effects of the COVID-19 pandemic on youth mental health. The advisory included recommendations to increase timely data collection and research to identify and respond to youth mental health needs.
2022 —NIMH supported the launch of two transformative projects through the BRAIN Initiative: the BRAIN Initiative Cell Atlas Network and the Armamentarium for Precision Brain Cell Access. The BRAIN Initiative Cell Atlas Network represented the next step in NIH’s efforts to generate a complete reference atlas of cell types and circuits in the human brain across the lifespan. The Armamentarium for Precision Brain Cell Access aimed to generate tools that would allow researchers to target specific brain cells and neural circuits. Together, these BRAIN 2.0 projects aimed to transform our understanding of brain cell types and provide the precise tools needed to access them, helping unravel the complex workings of the human brain and inform treatments of brain disorders.
2023 —The White House Report on Mental Health Research Priorities, published in February, outlined administration-wide needs and opportunities to advance mental health research. Areas of emphasis included addressing mental health inequities, understanding and leveraging digital mental health interventions, and supporting and expanding the mental health workforce. NIMH substantively contributed to the development of the report, which highlighted several NIMH-supported research initiatives.
2023 —In May, U.S. Surgeon General Murthy issued The U.S. Surgeon General's Advisory on Social Media and Youth Mental Health. The advisory called for urgent action to clarify the mental health impacts of social media use, maximize the benefits and minimize the harms of social media platforms, and create safer and healthier online environments. NIMH and other federal agencies advised on the preparation of the report.
2023 —NIMH celebrated its 75th anniversary. A yearlong program of events launched with the inaugural scientific symposium, "The Evolution of Mental Health Research." Over the anniversary year, NIMH sponsored numerous activities, including symposiums, lectures, and sessions at scientific meetings. NIMH also shared stories of discovery and inspiration from its past, present, and future.
NIMH Legislative Chronology
1929 —P.L. 70-672 established two federal "narcotics farms" and authorized a Narcotics Division within PHS.
1930 —P.L. 71-357 redesignated the PHS Narcotics Division as the Division of Mental Hygiene.
1946 —P.L. 79-487, the National Mental Health Act, authorized the Surgeon General to improve the mental health of U.S. citizens through research into the causes, diagnosis, and treatment of psychiatric disorders.
1949 —NIMH was established April 1.
1953 —Reorganization Plan No. 1 assigned PHS to the newly created U.S. Department of Health, Education, and Welfare.
1955 —P.L. 84-182, the Mental Health Study Act, authorized NIMH to study and make recommendations on mental health and mental illness in the United States. The act also authorized the creation of the Joint Commission on Mental Illness and Health.
1956 —P.L. 84-830, the Alaska Mental Health Enabling Act, provided for territorial treatment facilities for mentally ill individuals in Alaska.
1963 —P.L. 88-164, the Mental Retardation Facilities and Community Mental Health Centers Construction Act provided for grants to assist in the construction of community mental health centers nationwide.
1965 —P.L. 89-105, amendments to P.L. 88-164, provided for grants to staff the new community mental health centers.
1966 —P.L. 89-793, the Narcotic Addict Rehabilitation Act of 1966, launched a national program for long-term treatment and rehabilitation of narcotic addicts.
1968 —January 1, NIMH became a component of the newly created Health Services and Mental Health Administration.
P.L. 90-574, Alcoholic and Narcotic Addict Rehabilitation Amendments of 1968, authorized funds for the construction and staffing of new facilities for the prevention of alcoholism and the treatment and rehabilitation of alcoholics.
1970 —P.L. 91-211, Community Mental Health Centers Amendments of 1970, authorized construction and staffing of centers for three additional years, prioritizing areas with a high number of people experiencing poverty.
P.L. 91-513, Comprehensive Drug Abuse Prevention and Control Act of 1970, expanded the national drug abuse program by extending the services of federally funded community treatment centers to non-narcotic drug abusers as well as addicts.
P.L. 91-6162-255, Comprehensive Alcohol Abuse and Alcoholism Prevention, Treatment, and Rehabilitation Act, established the National Institute on Alcohol Abuse and Alcoholism within NIMH.
1972 —P.L. 92-255, Drug Abuse Office and Treatment Act of 1972, provided that a National Institute on Drug Abuse be established within NIMH.
1973 —NIMH rejoined NIH. NIMH later became a component of the Alcohol, Drug Abuse, and Mental Health Administration (ADAMHA).
1974 —P.L. 93-282, the Comprehensive Alcohol Abuse and Alcoholism Prevention, Treatment, and Rehabilitation Act Amendments of 1974, authorized the establishment of ADAMHA.
1978 —P.L. 95-622, the Community Mental Health Centers (CMHC) Extension Act of 1978, revised and extended programs under the CMHC Act and established the President's Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research.
1979 —P.L. 96-88, the Department of Education Organization Act, created the Department of Education and renamed HEW the Department of Health and Human Services (HHS).
1980 —P.L. 96-398, the Mental Health Systems Act expanded the community mental health centers program and created a federal-state-local partnership to help the chronically ill, children, the elderly, minorities, women, the poor, and rural populations.
1981 —P.L. 97-35, the Omnibus Reconciliation Act, repealed many of the provisions of P.L. 96-398 and consolidated ADAMHA's treatment and rehabilitation programs into a single block grant that enabled each state to administer allocated funds.
1983 —P.L. 98-24, Alcohol Abuse Amendments of 1983, consolidated the existing authorization for ADAMHA and the institutes within it into a new title V of the PHS act.
1984 —P.L. 98-509, Alcohol Abuse, Drug Abuse, and Mental Health Amendments, authorized funding for block grants for fiscal years 1985 through 1987 and extended authorizations for federal activities in alcohol and drug abuse research, information dissemination, and development of new treatment methods.
1986 —P.L. 99-660, The State Comprehensive Mental Health Services Plan Act required states to submit to HHS community-based mental health services plans for the chronically mentally ill.
1992 —P.L. 102-321, the ADAMHA Reorganization Act, abolished ADAMHA, created the Substance Abuse and Mental Health Services Administration, and transferred NIMH research activities to NIH.
2000 —P.L. 106-310, The Children's Health Act of 2000, Title I Autism expanded and coordinated activities of the NIH with respect to research on autism, including the establishment of not less than five centers of excellence to conduct basic and clinical research into autism. The act also mandated that the HHS Secretary establish an Interagency Autism Coordinating Committee (IACC) to coordinate autism research. NIMH was designated the NIH lead for this activity.
2006 —P.L. 109-416, the Combating Autism Act of 2006, provided for expanded activities related to autism spectrum disorder (ASD)-related research, surveillance, prevention, treatment, and education. The act called for NIH-funded research to address the entire scope of ASD; provided for a review of regional centers of excellence for autism research and epidemiology; authorized activities to increase public awareness, improve the use of evidence-based interventions, and increase early screening for autism; and called on the Interagency Autism Coordinating Committee to enhance information sharing.
2010 —P.L. 111-148, the Patient Protection and Affordable Care Act, contained a section encouraging NIMH to continue relevant research on the mental health effects of pregnancy outcomes, including carrying to term and parenting, placing for adoption, miscarriage, and abortion., The Act also contained a “Sense of the Congress” authorizing the NIMH Director to conduct a longitudinal study of the “relative mental health consequences for women of resolving a pregnancy.”
2014 —P.L. 113-157, the Autism Collaboration, Accountability, Research, Education, and Support (CARES) Act of 2014 reauthorized federal efforts related to autism spectrum disorder within HHS. The legislation directed the HHS Secretary to designate an existing HHS official to implement ASD activities, taking into account the Interagency Autism Coordinating Committee (IACC) Strategic Plan, and to ensure that federal ASD activities were not unnecessarily duplicative; expanded the IACC membership; requested a report on young adults and transitioning youth; and reauthorized the IACC and authorized appropriations for fiscal years 2015 through 2019.
2016 — P.L. 114-255, the 21st Century Cures Act of 2016, provided critical tools and resources to advance biomedical research. The legislation authorized multiyear funding to four new innovative scientific initiatives at NIH—the All of Us Research Program; the Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative; the Cancer Moonshot; and the Regenerative Medicine Innovation Project . The law also appointed the directors of NIMH, NIDA, and NIAAA as ex-officio members of various SAMHSA advisory councils.
2019 – P.L. 116-60, the Autism Collaboration, Accountability, Research, Education, and Support (CARES) Act of 2019 reauthorized federal efforts related to ASD within HHS as noted in P.L. 113-157 and authorized appropriations through fiscal year 2024. The legislation enhanced NIH ASD activities, reauthorized the IACC and expanded the committee membership, and requested a report on the health and well-being of individuals with ASD across the lifespan. NIMH continued to be the designated NIH lead for this activity.
Biographical Sketch of NIMH Director, Joshua A. Gordon, M.D., Ph.D.
Joshua A. Gordon, M.D., Ph.D., is the director of the National Institute of Mental Health, the lead federal agency for research on mental disorders.
Dr. Gordon pursued a combined M.D.-Ph.D. degree at the University of California, San Francisco (UCSF). Medical school coursework in psychiatry and neuroscience convinced him that the greatest need, and greatest promise, for biomedical science was in these areas. During his Ph.D. thesis with Dr. Michael Stryker, Dr. Gordon pioneered the methods necessary to study brain plasticity in the mouse visual system.
Upon completion of the dual degree program at UCSF, Dr. Gordon went to Columbia University for his psychiatry residency and research fellowship because of the breadth and depth of the research opportunities there. Working with Dr. Rene Hen, Dr. Gordon and colleagues studied the role of the hippocampus, a brain structure known to be important for memory and emotional processes associated with anxiety and depression. He joined the Columbia faculty in 2004 as an assistant professor in the Department of Psychiatry.
Dr. Gordon’s research focuses on the analysis of neural activity in mice carrying mutations of relevance to psychiatric disease. His lab studied genetic models of these diseases from an integrative neuroscience perspective, focused on understanding how a given disease mutation leads to a behavioral phenotype across multiple levels of analysis. To this end, he employs a range of systems neuroscience techniques, including in vivo imaging, anesthetized and awake behavioral recordings, and optogenetics, which is the use of light to control neural activity. His research has direct relevance to schizophrenia, anxiety disorders, and depression.
In addition to his research, Dr. Gordon was an associate director of the Columbia University/New York State Psychiatric Institute Adult Psychiatry Residency Program, where he directed the neuroscience curriculum and administered research training programs for residents. He also maintained a general psychiatric practice, caring for patients who suffer from the illnesses he studied in his lab at Columbia.
Dr. Gordon’s work has been recognized by several prestigious awards, including the Brain and Behavior Research Foundation’s NARSAD Young Investigator Award, the Rising Star Award from the International Mental Health Research Organization, the A.E. Bennett Research Award from the Society of Biological Psychiatry, and the Daniel H. Efron Research Award from the American College of Neuropsychopharmacology.
Nimh programs, offices and divisions, office of the director.
Office on AIDS Office of Autism Research Coordination Office of Clinical Research Office of Genomics Research Coordination Office for Disparities Research and Workforce Diversity Office of Management Office of Rural Mental Health Research Office of Science Policy, Planning, and Communications Office of Technology Development and Coordination
Division of Neuroscience and Basic Behavioral Science
The Division of Neuroscience and Basic Behavioral Science (DNBBS) provides support for research programs in the areas of basic neuroscience, genetics, basic behavioral science, research training, resource development, technology development, drug discovery, and research dissemination. The Division has the responsibility, in cooperation with other components of the Institute and the research community, for ensuring that relevant basic science knowledge is generated and then harvested to create improved diagnosis, treatment, and prevention of mental and behavioral disorders.
Areas of High Priority:
- Develop new and use existing physiological and computational models to understand the biological functions of genes, gene products, cells, and brain circuits in normal and abnormal mental function.
- Elucidate how cognitive, affect, stress, and motivational processes interact and their role(s) in mental disorders through functional studies spanning levels of analysis (genomic, molecular, cellular, circuits, behavior) during development and throughout the life span.
- Elucidate fundamental mechanisms (e.g., genetic, biological, behavioral, environmental) of complex social behavior.
- Identify in diverse populations from the United States and around the world genetic variants, epigenetic mechanisms, and gene-environment interactions that influence vulnerability to mental disorders, endophenotypes, and pharmacologic response profiles.
- Identify biological markers (e.g., genetic, proteomic, imaging) in model systems and humans that could be further validated as methods for diagnosing and/or detecting risk/vulnerability, onset, progress, and/or severity of mental disorders.
- Identify and validate new molecular targets and tools for drug discovery relevant to the treatment of mental disorders.
Branches within the Division of Neuroscience and Basic Behavioral Science
Behavioral Science and Integrative Neuroscience Research Branch Genomics Research Branch Molecular, Cellular, and Genomic Neuroscience Research Branch Office of Research Training and Career Development Small Business Innovation Research and Small Business Technology Transfer Programs
Division of Translational Research
The Division of Translational Research (DTR) directs, plans, and supports programs of research and research training that translate knowledge from basic science to discover the etiology, pathophysiology, and trajectory of mental disorders, and develops effective interventions for children and adults. DTR supports integrative, multidisciplinary research on the following areas: the phenotypic characterization and risk factors for psychiatric disorders; neurobehavioral mechanisms of psychopathology; trajectories of risk and resilience based on the interactive influences of genetics, brain development, environment, and experience; and design and testing of innovative psychosocial, psychopharmacologic, and somatic treatment interventions.
- Delineate specific neural circuits contributing to one or more major mental disorders or subtypes of mental disorders.
- Develop, test, and validate biological markers (e.g., genetic, proteomic, imaging) for diagnosing or detecting risk/vulnerability, onset, progression, and/or severity of mental disorders to prevent disorders, serve as criteria to personalize treatment, and evaluate treatment response.
- Develop models to predict treatment response and vulnerability to side effects of psychotropic medications and approaches to prevent or ameliorate treatment-emergent side effects (e.g., delineate the mechanisms through which specific psychotropic medications produce adverse metabolic and cardiovascular events, and begin to develop models to predict which patients are at high risk for developing these complications.
- Identify mechanisms (e.g., genetic, biological, behavioral, environmental) that confer vulnerability to psychiatric illnesses, and develop early interventions (pharmacological and/or psychosocial) for reducing the severity and incidence of psychopathology.
- Evaluate the safety and efficacy of novel mechanism pharmacological agents and/or behavioral interventions that target domains of psychopathology inadequately addressed by current therapies or prevention strategies.
- Develop, test, and validate methods to assess domains of psychopathology for use in clinical trials in order to increase the efficiency of the mental illness treatment development critical path, emphasizing approaches based on partnerships with FDA and industry.
- Delineate neurobehavioral mechanisms responsible for the development of psychopathology, including critical and sensitive periods in brain development and the effects of sex, behavior, and experience on the brain.
- Utilize behavioral phenotypes reflecting dimensional processes (e.g., attention, mood regulation) to maximize discovery of underlying neural systems and genes, and refine behavioral assessment tools so that they are comparable across age, species, and social experience (e.g., socioeconomic status, culture).
- Test integrative models incorporating biological, behavioral, and experiential factors in the development of psychopathology, and utilize longitudinal research to track trajectories of risk and protection based on the combined and interactive influences among these factors.
- Based on expanded knowledge of neurobehavioral trajectories, identify early signs of risk and develop novel and targeted preventive and treatment interventions.
- Assess the mechanisms of action of efficacious interventions in the brain.
Branches within the Division of Translational Research
Adult Psychopathology and Psychosocial Intervention Research Branch Adult Pathophysiology and Biological Interventions Development Branch Developmental Mechanisms and Trajectories of Psychopathology Branch Geriatrics and Aging Processes Research Branch Developmental Mechanisms and Trajectories of Psychopathology Branch Biomarker and Intervention Development for Childhood-Onset Mental Disorders Branch Traumatic Stress Research Program Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Program (Adult Psychopathology) Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Program (Child Psychopathology) Research Training and Career Development Program
Division of AIDS Research
The Division of AIDS Research (DAR) supports research to reduce the incidence of HIV/AIDS worldwide and to decrease the burden of living with HIV/AIDS. DAR-supported research encompasses a broad range of studies that includes basic and clinical neuroscience of HIV infection to understand and alleviate the consequences of HIV infection of the central nervous system (CNS), and basic and applied behavioral science to prevent new HIV infections and limit morbidity and mortality among those infected. DAR places a high priority on interdisciplinary research across multiple populations, including racial and ethnic minorities, over the life span.
The portfolio on the basic neuroscience of HIV infection includes research to elucidate the mechanisms underlying HIV-induced neuropathogenesis; to understand the motor and cognitive impairments that result from HIV infection of the CNS; to develop novel treatments to prevent or mitigate the neurobehavioral complications of HIV infection; and to minimize the neurotoxicities induced by long-term use of antiretroviral therapy. Critical approaches to this effort require molecular, cellular, and genetic studies to delineate the pathophysiologic mechanisms that lead to disrupted neuronal function and to identify potential targets for therapeutic intervention. In addition, eradication of the virus from HIV-infected individuals to achieve a cure or a functional cure is a high priority.
The behavioral science research agenda emphasizes developing and testing behavioral interventions that can be effectively integrated with biomedical approaches to significantly impact the epidemic. The behavioral science agenda targets prevention of both transmission and acquisition of HIV, adherence to intervention components to reduce the burden of disease, and studies that address the behavioral consequences of HIV/AIDS. A strong component of integrating behavioral and biomedical approaches is expanding collaboration with other NIH institutes and federal agencies to leverage resources and broaden the impact of this research.
- Expand approaches to integrate behavioral science with effective biomedical strategies for HIV prevention.
- Advance the development and testing of interventions delivered beyond the individual level, by incorporating appropriate context into intervention development and testing.
- Increase intervention potency and long-term maintenance of effects, with an emphasis on targeting high-risk vulnerable populations.
- Develop strategies to increase HIV-testing and improve linkage to care and timely treatment initiation.
- Develop and test interventions to improve HIV treatment outcomes through optimal treatment adherence and sustained engagement in care.
- Support implementation science and operations research to enhance dissemination strategies and public health impact of effective interventions.
- Examine evolving pathophysiologic mechanisms of HIV-associated neurocognitive disorders (HAND) in the setting of long-term antiretroviral therapy and the development of novel therapeutic approaches to mitigate CNS complications of HIV infection.
- Support the use of state-of-the-art genetic approaches to identify and validate viral and host genetic factors that influence the pathophysiology of HAND.
- Define and characterize HIV persistence in the CNS in the context of suppressive highly active antiretroviral therapy, and foster translational research to enable therapeutic eradication of HIV-1 from the brain.
Branches within the Division of AIDS Research
Developmental and Clinical Neuroscience of HIV Prevention and Treatment Branch HIV Prevention and Care Continuum, Co-Morbidities, and Translational Research Branch HIV Neuropathogenesis, Genetics, and Therapeutics Branch AIDS Research Centers Program Training, Fellowship, and Health Disparities Programs Small Business Innovation Research (SBIR) Program and Small Business Technology Transfer (STTR) Program
Division of Services and Intervention Research
The Division of Services and Intervention Research supports two critical areas of research:
- Intervention research to evaluate the effectiveness of pharmacologic, psychosocial (psychotherapeutic and behavioral), somatic, rehabilitative, and combination interventions on mental and behavior disorders, including acute and longer-term therapeutic effects on functioning across domains (such as school, family, and peer functioning) for children, adolescents, and adults.
- Mental health services research.
The interventions focus is broad and inclusive with respect to the heterogeneity of patients, the severity and chronicity of disorders, and the variety of community and institutional settings in which treatment is provided. It includes clinical trials evaluating the effectiveness of known efficacious interventions as well as studies evaluating modified or adapted forms of interventions for use with additional populations (such as women and ethnic and racial groups), new settings (public sector, pediatric primary care, schools, other non-academic settings, communities at large), and people with co-occurring disorders. Other foci include: identifying subgroups who may be more likely to benefit from treatment, evaluating the combined or sequential use of interventions (such as to extend effect among refractory subgroups), determining the optimal length of intervention, establishing the utility of continuation or maintenance treatment (that is, for prevention of relapse or recurrence), and evaluating the long-term impact of efficacious interventions on symptoms and functioning.
Services research covers all mental health services research issues across the life span and disorders, including but not limited to:
- Services organization, delivery (process and receipt of care), and related health economics at the individual, clinical, program, community, and systems levels in specialty mental health, general health, and other delivery settings (such as the workplace).
- Interventions to improve the quality and outcomes of care (including diagnostic, treatment, preventive, and rehabilitation services).
- Enhanced capacity for conducting services research.
- The clinical epidemiology of mental disorders across all clinical and service settings.
- The dissemination and implementation of evidence-based interventions into service settings.
The Division also provides biostatistical analysis and clinical trials operations expertise for research studies; analyzes and evaluates national mental health needs and community research partnership opportunities; and supports research on health disparities.
- Develop innovative interventions, including treatment regimens, prevention strategies, and innovative service delivery approaches; and personalize them for optimal use in diverse populations (e.g., across geographic locations, underserved groups, those with comorbid conditions, and all age groups).
- Test interventions through effectiveness research and practical clinical trials, to ensure that they are safe, maximize recovery and functioning, are cost-effective, and are personalized (e.g., by determining optimal lengths, combinations, and sequences of interventions as well as subgroups in which they work best).
- Reduce the significant burden and mortality associated with suicidality through research on early detection, assessment, interventions, and services for individuals at risk in populations of all ages.
- Identify effective dissemination and implementation processes and mechanisms to increase the uptake of scientifically informed treatments and services.
- Employ strategic partnerships and community engagement/participation to enhance research capacity and infrastructure to conduct research in underserved and diverse populations as well as in traditional and nontraditional service settings.
- Identify new targets for innovative intervention (development/refinement) and service delivery models through research that examines the burdens from mental illness as well as the current use, benefits, safety, costs, and unmet needs for mental health care.
Branches within the Division of Services and Intervention Research
Treatment and Preventive Intervention Research Branch Services Research and Clinical Epidemiology Branch Office of Research Training and Career Development Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs
Division of Extramural Activities (DEA)
The Division of Extramural Activities: (1) provides leadership and advice in developing, implementing, and coordinating extramural programs and policies; (2) represents the Institute on extramural program and policy issues within the Department and with outside organizations; (3) provides scientific and technical peer and objective review of applications for grants, cooperative agreements, and contracts; (4) provides information and guidelines for grant applications; (5) oversees National Advisory Mental Health Council activities; (6) provides committee management services for peer review, council, and any other Federal Advisory Committee Act--related committee meetings that are required at NIMH; and (7) awards grants, ensuring that applications chosen for funding comply with federal laws, regulations, and policies prior to award, which involves critical communication with the grantee throughout the pre-award, award, and post-award processes.
Branches within the Division of Extramural Activities (DEA)
Extramural Policy Branch Grants Management Branch Extramural Review Branch
Division of Intramural Research Programs
The Division of Intramural Research Programs (DIRP) at the National Institute of Mental Health is the internal research division of NIMH. The Division plans and conducts basic, clinical, and translational research to advance understanding of the diagnosis, causes, treatment, and prevention of psychiatric disorders. DIRP conducts state-of-the-art research that utilizes the unique resources of the National Institutes of Health (NIH), provides an environment conducive to the training and development of clinical and basic scientists, and, in part, complements extramural research activities.
Labs, Clinics, and Branches
Behavioral Endocrinology Branch Clinical and Translational Neuroscience Branch Emotion and Development Branch Experimental Therapeutics & Pathophysiology Branch Functional Neural Circuits Unit Genetic Epidemiology Research Branch Human Genetics Branch Laboratory of Brain and Cognition Laboratory of Cellular and Molecular Regulation Laboratory of Molecular Biology Laboratory of Molecular and Cellular Neurobiology Laboratory of Neuropsychology Molecular Imaging Branch Section on Behavioral Neuroscience Section on Critical Brain Dynamics Section on Light and Circadian Rhythms (SLCR) Section on Neuroadaptation and Protein Metabolism Section on Neurobiology of Fear and Anxiety Section on Neuroplasticity Section on Synapse Development Plasticity Unit on Neural Computation and Behavior Unit on Neurobiology of Affective Memory Unit on Neuromodulation and Synaptic Integration
Sections & Units Attached to the Scientific Director’s Office
Section on Affective Cognitive Neuroscience Unit on Statistical Genomics Unit on Neuroplasticity
Unit on Neural Computation and Behavior Unit on Genetics of Cognition & Behavior Section on Fundamental Neuroscience Section on Neuroadaptation and Protein Metabolism Section on Neurobiology of Fear and Anxiety Section on Neuroendocrine Immunology Section on Pharmacology
This page last reviewed on November 8, 2023
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- 1 Department of Psychiatry.
- 2 Universitair Psychiatrisch Centrum - Katholieke Universiteit Leuven (UPC-KUL), Campus Gasthuisberg.
- 3 Health Services Research Unit, IMIM (Hospital del Mar Medical Research Institute).
- 4 Department of Epidemiologic and Psychosocial Research, National Institute of Psychiatry Ramón de la Fuente Muñiz.
- 5 Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam.
- 6 Department for Psychology, Clinical Psychology and Psychotherapy, Friedrich-Alexander University Erlangen Nuremberg.
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- 10 Department of Psychology, Harvard University.
- 11 Department of Psychiatry, Harvard Medical School.
- 12 Department of Health Care Policy, Harvard Medical School.
- 13 Department of Psychiatry, University of Cape Town.
- 14 Harvard Medical School.
- PMID: 30211576
- PMCID: PMC6193834
- DOI: 10.1037/abn0000362
Increasingly, colleges across the world are contending with rising rates of mental disorders, and in many cases, the demand for services on campus far exceeds the available resources. The present study reports initial results from the first stage of the WHO World Mental Health International College Student project, in which a series of surveys in 19 colleges across 8 countries (Australia, Belgium, Germany, Mexico, Northern Ireland, South Africa, Spain, United States) were carried out with the aim of estimating prevalence and basic sociodemographic correlates of common mental disorders among first-year college students. Web-based self-report questionnaires administered to incoming first-year students (45.5% pooled response rate) screened for six common lifetime and 12-month DSM-IV mental disorders: major depression, mania/hypomania, generalized anxiety disorder, panic disorder, alcohol use disorder, and substance use disorder. We focus on the 13,984 respondents who were full-time students: 35% of whom screened positive for at least one of the common lifetime disorders assessed and 31% screened positive for at least one 12-month disorder. Syndromes typically had onsets in early to middle adolescence and persisted into the year of the survey. Although relatively modest, the strongest correlates of screening positive were older age, female sex, unmarried-deceased parents, no religious affiliation, nonheterosexual identification and behavior, low secondary school ranking, and extrinsic motivation for college enrollment. The weakness of these associations means that the syndromes considered are widely distributed with respect to these variables in the student population. Although the extent to which cost-effective treatment would reduce these risks is unclear, the high level of need for mental health services implied by these results represents a major challenge to institutions of higher education and governments. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
- Diagnostic and Statistical Manual of Mental Disorders
- Health Surveys
- Mental Disorders / diagnosis
- Mental Disorders / epidemiology*
- Mental Health
- Mental Health Services
- Students / statistics & numerical data
- World Health Organization
- Young Adult
Grants and funding
- R01 DA016558/DA/NIDA NIH HHS/United States
- R56 MH109566/MH/NIMH NIH HHS/United States
- R01 MH070884/MH/NIMH NIH HHS/United States
- R03 TW006481/TW/FIC NIH HHS/United States
- R01 MH069864/MH/NIMH NIH HHS/United States
- R13 MH066849/MH/NIMH NIH HHS/United States
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Assessing and Improving the Quality in Mental Health Services
1 Faculty of Economics and Management, Open University of Cyprus, Latsia, Nicosia, Cyprus
2 Department of Psychiatry, Medical School, University of Cyprus, Nicosia, Cyprus
3 Mental Health Services, Athalassa Psychiatric Hospital, Nicosia, Cyprus
Michael A. Talias
Background: The mental health of the population consists of the three essential pillars of quality of life, economy, and society. Mental health services take care of the prevention and treatment of mental disorders and through them maintain, improve, and restore the mental health of the population. The purpose of this study is to describe the methodology for qualitative and quantitative evaluation and improvement of the mental health service system. Methods: This is a narrative review study that searches the literature to provide criteria, indicators, and methodology for evaluating and improving the quality of mental health services and the related qualitative and quantitative indicators. The bibliography was searched in popular databases PubMed, Google Scholar, CINAHL, using the keywords “mental”, “health”, “quality”, “indicators”, alone or in combinations thereof. Results: Important quality indicators of mental health services have been collected and presented, and modified where appropriate. The definition of each indicator is presented here, alongside its method of calculation and importance. Each indicator belongs to one of the eight dimensions of quality assessment: (1) Suitability of services, (2) Accessibility of patients to services, (3) Acceptance of services by patients, (4) Ability of healthcare professionals to provide services, (5) Efficiency of health professionals and providers, (6) Continuity of service over time (ensuring therapeutic continuity), (7) Efficiency of health professionals and services, (8) Safety (for patients and for health professionals). Discussion/Conclusions: Accessibility and acceptability of service indicators are important for the attractiveness of services related to their use by the population. Profitability indicators are important economic indicators that affect the viability and sustainability of services, factors that are now taken into account in any health policy. All of the indicators mentioned are related to public health, affecting the quality of life, morbidity, mortality, and life expectancy, directly or indirectly. The systematic measurement and monitoring of indicators and the measurement and quantification of quality through them, are the basis for evidence-based health policy for improvement of the quality of mental health services.
The burden of illnesses from mental health disorders is by far the highest of all health problems worldwide, accounting for 13% of the total burden of illness from all diseases. More specifically, mental illness accounts for 32.4% of years lost due to mental illness or disability (YLDs) and 13% of disability-adjusted life years (DALYs), which is the exact measure of disease burden [ 1 ]. DALYs corresponding to the burden of mental illness is the sum of YLDs along with years lost due to premature death from mental illness (YLL). At the EU Member State level, the cost of mental disorders is estimated at 3–4% of GDP, mainly due to the loss of productivity [ 2 ]. Providing high-quality psychiatric care to mental patients, especially in the context of the EU, is an obligation of the welfare state, a duty of the mental health professionals, and the patients’ right.
The unmet need for quality in mental health services that responds to patients’ needs and respect citizens’ right to mental health can be achieved over time through strategic planning, annual evaluation and targeted improvement of quality in the provided mental health services. In addition, a qualitative and quantitative evaluation with indicators of quality in mental health services is needed, in order to identify and reveal the needs of disadvantaged social groups, to facilitate the state intervening politically to ensure universal and equal access to services for the population [ 3 ].
Evaluation/measurement of existing quality is a prerequisite for its improvement: What cannot be evaluated and measured cannot be improved. The evaluation of the quality of mental health services at the national and international levels is qualitative and quantitative. However, this is a complicated process as there are no common scientific language, objectives, and priorities and there are no common examples in this area, and for most countries—even for those that are developed—there are clearly unmet needs in this area [ 4 ].
There are significant differences in the quality of mental health services both between and within countries, which clinically take the form of different distances, within each health system, between the clinical guidelines of international scientific societies and daily clinical practice. Clinical decisions about the same symptom and illness differ from country to country because the systems in place for psychiatry are different, and therefore the resources and opportunities for quality mental health care are different.
Studies evaluating the quality of interventions show that, as a rule, daily clinical practice is always below the level set by national and international guidelines [ 5 ]. The differences in adherence to the guidelines are therefore explained as well as the quality indicators. Focusing on evaluation and quality improvement can reduce the heterogeneity of clinical decisions and optimize the outcome of cases treated, to some extent, without overlooking the fact that that clinical medicine is not only a science but also an art, as described in the Hippocratic Oath.
For quality assessment in health systems, there are some useful weighted generic tools that may initially help [ 6 ], such as the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS) [ 7 ] and WHO-Quality Rights [ 8 ], although these are not available either in Greek or in many other languages. Their contribution consists of a general descriptive assessment of the quality of the mental health sector of health systems.
In this study, we propose and describe the methodology and procedure for a thorough evaluation of the quality of mental health services at the health system level. Evaluation is based on evaluation criteria, and each criterion has its own specific evaluation indicators. Different criteria mean different problems. The indicators do not suggest a solution to the problem under consideration. Indicators collectively can help to clarify and quantify policy objectives and strategies for an optimized Mental Health System.
The purpose of the project is twofold: first, to propose an integrated model of mental health services evaluation, and second to target specific interventions to improve the quality of mental health services at the level of the health system.
2. Materials and Methods
In order to search for criteria, indicators and methodology for evaluating and improving the quality of mental health services and related qualitative and quantitative indicators, a bibliography was searched in large databases PubMed, Google Scholar, CINAHL, using the keywords “mental”, “health”, “quality”, “indicators”, alone or in combinations thereof. Peer review journals were searched with English keywords for articles published in peer-reviewed journals possessing an English abstract without setting restrictions for the languages of the main text. Mental health databases maintained by Eurostat, WHO, and OECD were also searched.
To evaluate and measure the quality of mental health services, we propose and describe an eight-dimensional model in this study. Each dimension is an independent, quality assessment criterion. The model comes from the Canadian Health System General Rating System but is modified and enriched by the author specifically for mental health services and includes only mental health-related indicators [ 9 ]. Other authors have used simplified three-dimensional models [ 10 ] where all dimensions are concentrated in three categories: (1) clinical improvement, (2) patient safety, (3) overall patient experience of treatment/care, which qualifies for evaluation in structural/clinical level, but is not sufficiently detailed to assess at a macroscopic level the mental health system. We consider that the quality evaluation should be multidimensional and interdisciplinary. Therefore, it was considered necessary to include financial dimensions as well, since any development of a new service is also assessed at a cost, sustainability, and sustainable development level.
The eight dimensions (criteria) in which mental health services are evaluated are as follows: (1) appropriateness of services provided, (2) accessibility of patients to services provided, (3) acceptability of the services from the patients, (4) competence of mental health care providers, (5) effectiveness of mental health professionals (6) therapeutic continuity in the mental health system, (7) the efficiency of health professionals, (8) the safety of patients and health care providers. Indicators of each dimension can be characterized as indicators of structure, indicators of process, and indicators of the outcome, as suggested by other researchers in the evaluation of the quality of mental health services [ 6 ].
Each of the dimensions has aspects called indicators, which are described in detail in the text. Specifically, for each indicator is described (a) the background and the general meaning of the index, (b) the definition that includes how the index is calculated, and (c) the performance target for mental health services, quantitatively or qualitatively: The performance target is the benchmark.
3.1. Appropriateness Criterion, or Appropriateness Dimension. Indicators of Appropriateness of Mental Health Services
3.1.1. the number of chronic patients hospitalized in psychiatric hospitals rather than rehabilitation in an outpatient setting.
This is a structural indicator of the mental health system, which indicates the adequacy or inadequacy of outpatient psychiatric structures created in parallel with deinstitutionalization. During the long period of deinstitutionalization, most patients with chronic mental disorders were discharged from psychiatric hospitals who had been staying for months or years and referred for psychiatric care to community mental health units as outpatients. The definition of the chronic disease, as well as the relevant references, has now been added in the manuscript. Chronic disease is one lasting three months or more, by the definition of the U.S. National Center for Health Statistics [ 11 , 12 ]. A patient has to be hospitalized for at least three months in order to be considered for chronic hospitalization.
These patients at best are now living in independent or sheltered apartments, alone, with friends or relatives, and in many cases they reside in approved hostels and are monitored by a social worker, community nurse, and community nursing psychiatrist. Psychiatric hospitals shrank in the number of beds and have changed shape and use: They have maintained short-stay clinics, day-care units, emergency clinics, and rehabilitation clinics, while closing long-term clinics that were replaced by less isolated community services, not only for patients with mental disorders but also for patients with developmental disabilities.
This indicator is calculated as a fraction where the numerator is the number of patients with chronic mental disorders and those with developmental disabilities living in the psychiatric hospital, and the denominator is the total number of patients in the hospital.
The performance target is to reduce gradually, year by year, patients who are permanently hospitalized while being transferred to community services. Therefore, this indicator can be used to evaluate the progress of a hospital year on year, as long as the indicator is calculated on an annual basis. The indicator can also be used for comparison between countries with similar psychiatric therapeutic cultures and deinstitutionalization approach, as is the case in the EU Member States. Note that the purpose of this indicator is to evaluate mental health services with the aim of improving them and thereby improving the quality of life of patients. For this to happen, it is not enough to dismiss patients from the psychiatric clinic, but also to provide adequate community mental health structures. The indicator should, therefore, be assessed against the adequacy of community structures, as otherwise, the indicator may improve (with the closure of psychiatric clinics and the dismissal of patients), but the quality of life, morbidity, and mortality of some patients may deteriorate if there is not sufficient development of community structures.
3.1.2. Number of Cases That Could Avoid Admission to Hospital with Appropriate External Intervention
This is a structural indicator, focusing on assessing the adequacy of appropriate outpatient mental health structures. In the mental health system, there are a number of patients admitted to the hospital (one or more times) per year due to the lack of availability of corresponding outpatient services. Unnecessary hospital admission and hospitalization are costly to the system and not necessarily beneficial to the patient as the patient does not receive proper care.
This indicator is a fraction where the numerator is the number of inpatients admitted due to inadequate outpatient structure and intervention, while the denominator is the number of inpatients patients treated in the same clinic during the same time period.
Obviously, the performance target is that the fraction becomes smaller and smaller with each passing year. The larger the fraction, the greater the inadequacy of outpatient structures (e.g., community psychiatry, community nursing). At the system level, the indicator can be used to compare the adequacy of outpatient mental health care between the EU Member States.
3.1.3. Number of Cases Treated without Indication
This is an indicator that assesses the structure of Mental Health Services and is indicative of the availability of specific structures. In particular, it assesses whether the patient is treated in the appropriate setting (hospitalized or outpatient), that is, if he or she receives appropriate personalized treatment. In psychiatry, it is very important that the treatment provided is tailored to the individual bio-psychosocial needs of each patient. Therefore, the psychosocial context is always taken into account. Inappropriate treatment may be due to the lack of this structure (e.g., lack of a section on eating disorders) or the completeness of this structure (e.g., patient being admitted to a psychiatric hospital due to the fullness of the psychiatric ward of the general hospital) or due to lack of specialization and unclear roles between departments (e.g., a patient with depression and alcohol dependence may be hospitalized (1) in a special alcohol dependence and rehabilitation clinic, or (2) a general hospital’s psychiatric clinic, or (3) in the psychiatric hospital, or (4) in internal medicine ward of the general hospital).
This indicator is calculated as a fraction where the number is the number of patients who received appropriate care in the clinic who were hospitalized over a period of one year. The denominator is the total number of patients treated in the same clinic during the same period.
The performance objective is that all patients receive appropriate treatment, that is, the fraction value as close to the unit as possible. The indicator should be calculated on a yearly basis to show the course of improvement of the health system over time, for example, by comparing this year’s score with the last year’s score. It can also be used at the level of an individual clinic, which shows the extent to which the clinic is treating cases that are not of its specialty (e.g., non-psychotic patients that are voluntarily hospitalized in the psychiatric hospital due to lack of beds in the psychiatric clinic of the general hospital or psychiatric clinic). The indicator can also be used to compare health systems between them, e.g., between the EU Member States.
3.2. Accessibility Criterion, or Otherwise Accessibility Dimensions. Accessibility and Accessibility Indicators
3.2.1. waiting time at the accident and emergency department.
It is important for prognostic reasons that the mentally ill patient who comes to the Accident and Emergency Department (A&E) does not have to wait long until he or she is examined by the A&E physician and later by the psychologist. Long waiting may be associated with a worsening of the prognosis in some cases. Some patients who come to A&E may be at risk due to acute medical disease or worsening chronic disease. Especially for those suffering from an acute psychiatric disorder, long waiting times can lead to a particularly adverse development, especially for patients with aggression, self-harm, or suicidality.
This is a process indicator. The indicator is a fraction where the numerator is the number of patients who came to the A&E and served in less than 4 h, and the denominator is the total number of patients admitted to the A&E over the same period of one year.
The performance target published by the National Health System can be used as a reference. Specifically, the UK’s National Health System (NHS) has set a target of 95% of patients admitted in A&E to be served there in less than 4 h [ 13 ]. This does not just mean starting the examination, but that within four hours the patient has to be fully managed by the A&E, that is, the patient has been admitted, or referred to another unit or discharged by the Accident and Emergency Department to go home. However, even the NHS itself cannot fully meet this self-imposed target, as only 75% of cases are fully managed within 4 h [ 14 ].
3.2.2. Waiting Days for Evaluation in the Outpatient Clinic
This is a process indicator. Citizens have equal rights to health, and this means that the health system must provide citizens equal access and coverage. There are many factors that may reduce citizens’ access to the health system, but the most common and most important are the waiting lists. Another equally important factor that may reduce accessibility is the uneven geographical distribution of services. An important factor that can reduce accessibility is the health system itself since only modern health services in developed countries are characterized by universal coverage and equal access by citizens.
This indicator is a number that shows the average length of waiting lists (in days) for both general and specialized mental health services. This indicator may be referred to as the waiting list for mental health professionals, that is the psychiatrist, psychologist, social worker, mental health nurse, psychotherapist, counselor, or occupational therapist.
The goal of the performance is to eliminate waiting lists from the health system. The existence of a waiting list leads to a loss of public confidence in the health system. It leads beneficiaries not to use the services to which they are entitled, and to purchase services from private providers outside the health system, by making direct out-of-pocket payments or by buying private insurance contracts.
3.2.3. Waiting Days for Admission to the Clinic (Waiting Lists)
Psychiatric clinics can be roughly divided into two categories, depending on the patient admission procedure. One category is emergency psychiatry clinics that treat a patient with a temporary safe, compulsory care order. Usually, these clinics are in psychiatric hospitals and rarely in general hospitals. The other category is the psychiatric clinics that treat patients only on a voluntary basis, at the direct request of the patient and after the patient is first informed and has signed the consent form. Second-category psychiatric clinics are usually located in general hospitals and rarely in psychiatric hospitals. There are also mixed clinics where cases are treated with compulsory or voluntary hospitalization. There are no waiting lists for mandatory hospitalizations. For voluntary care, there may be long waiting lists sometimes. Long-term waiting lists usually refer to cases of long-term voluntary care, such as treatment for personality disorders or even more often to drug and alcohol detoxification and rehabilitation centers.
This is a structure indicator. The indicator is the average number of days a patient has to wait before being admitted to the clinic. Waiting lists are due to under-staffing and result in reduced accessibility for beneficiaries. The lack of transparency in waiting lists can conceal corruption, i.e., the use of public power for personal gain.
The goal is to eliminate waiting lists to ensure equal access for beneficiaries to treatment.
3.2.4. Percentage of the Population Having Access to the Health System
This is a structural indicator of the system that concerns citizens’ accessibility to the system. Ideally, health systems should ensure universal coverage and equal access to the citizens of the country, but also to all EU citizens, on the basis of the European acquis on cross-border health between the Member States. Indicatively, it is reported that in Cyprus [ 11 , 15 ] and Greece [ 16 , 17 ] the entire population now has access to the country’s health services. However, there are residents (legal or illegal) who do not have access to the services because they are not citizens (they do not have citizenship/nationality). Special arrangements should be made for these categories to have access to mental health services for those suffering from acute or chronic mental disorders and, in particular, for those suffering from chronic substance dependence. Denial of access of this population to mental health services results in exacerbation of social problems and bio-psychosocial dysfunction for the patient.
The indicator is a fraction where the nominator is the number of residents of the country having access to the country’s mental health services, and the denominator is the total number of residents of the country. The health system access must include legal residents and non-legal residents as well as temporary residents, as all of these people are affected and affect public health. Certainly, there are statistical difficulties in measuring the population who are not citizens of the country as they are a hidden population that is not recorded in the population censuses.
The benchmark and performance target is full (100%) population access to mental health services. This seems to have been achieved in some countries for their citizens (e.g., in Cyprus and Greece). However, the objective of achieving this must be the accessibility to services for both EU citizens as well as for illegal immigrants/refugees. Whether or not some of them may be deported in the future, the country of residence must provide them with health services for the time they are on their territory, in the context of patients’ rights to access health services regardless of whether legal cases are pending against them. This is due to the fact that the right of patients to health refers to patients in the territory of the country, and not only to the subpopulation of patients who are citizens of the country.
3.3. Acceptability Criterion or Acceptability Dimension. Indicators of Acceptability
3.3.1. average patient satisfaction rating.
Patients themselves, as service users and customers, evaluate the quality of service provided by the provider. Mentally ill patients may not have the full critical ability to evaluate the service provided, especially if they suffer from acute psychosis, without or with reduced insight, and are treated with a temporary compulsory safe care order. For this reason, mental patients respond to patient satisfaction questionnaires at the end of their hospitalization, and in particular, when their critical capacity has been restored, and questions can be sufficiently understood.
This is an outcome indicator. The indicator is the average of the ratings of patient responses to patient satisfaction questionnaires/scales.
The average value of responses can be assessed in two ways. Firstly, by comparing directly with the responses given by patients treated in other relevant centers in the country or abroad. Second, by comparing the responses of patients in the same center over time. The analysis of the answers reveals and identifies the weaknesses of the center/service being evaluated, and improvements are planned while comparing this year’s answers with those of previous years. The index evaluates whether this service improves over time. Obviously, patients’ evaluation questionnaires should be systematically answered over time by all patients who are discharged and not confined to a specific study of a specific time period. Such questionnaires are integrated into modern comprehensive assessment programs (360 degrees), where everyone involved in health services is evaluated by everyone.
3.3.2. Recording Patient Experiences
Recording patients’ experiences in free-text formats is important for quality assessment and for designing improvement interventions. The recording should be done on a systematic basis over time. Registration should be anonymous and should be done by all patients upon discharge from the clinic so that there is no conflict of interest. The appropriate time to respond in writing to the patient is to ask about their experience on the day of removal, immediately after being discharged, but before the patient left the hospital.
It is an indicator of the qualitative evaluation of the processes and the outcome. It is a qualitative record of the experience the patient has experienced while in the hospital or in general in the treatment of the patient.
Reduction in the number of negative patient experiences compared to the previous year in the same clinic can be used as a performance target.
3.3.3. Patients’ Rights to Information and Protection of Their Personal Data
This is a process indicator that refers to the mental patient’s access to their medical information. The patient alone has the right to know all the information that is related to the patient’s health. The medical record belongs to the patient. The above are also regulated by the General Data Protection Regulation (General Data Protection Rules, GDPR) [ 18 , 19 ]. Member States have incorporated these aspects of the General Data Protection Regulation into separate legislation, such as the Electronic Health Act of the Republic of Cyprus [ 20 ].
The index is scored on a questionnaire that includes questions about the patient’s satisfaction from the mental health service, regarding the personal data management, and the extent to which the patient is aware of their rights with regard to their personal sensitive data.
The performance objective is to improve the results compared to the previous year’s results of the same service. This indicates an improved, over time, communication, and information of the patients regarding their rights, but also an adaptation of the mental health service to the General Data Protection Regulation.
3.3.4. Patient Rights to Health Services
Beneficiaries of health systems often do not fully know their rights and obligations within the health system. Accessibility often differs between different services in the same health system, but the obligations of the beneficiaries, as well as any financial obligations (co-payments), also vary. Accessibility is also affected by the beneficiary’s educational level, financial status, accessibility to the Internet, and distance to the nearest medical service.
This is a process indicator. The indicator is a mean score of patients’ responses to a questionnaire with questions about the basic rights of patients, as provided to beneficiaries by the health system, but also as provided for by the legislation of the Member State and the European acquis.
The performance target is to improve the index compared to the previous year’s average patient rating in the same questionnaire.
3.3.5. Number of Patients Leaving the Clinic with a Discharge Note
Patients hospitalized in psychiatric hospitals or in general hospital’s psychiatric clinics are admitted, on a voluntary or on a compulsory basis, for hospitalization. They may be hospitalized on a voluntary basis have become aware of the need for treatment (insight) or maybe hospitalized on a mandatory basis without being aware of the need for treatment (no insight). In the latter case, the patient is treated under a court order for a temporary mandatory safe hospitalization and dismissed when symptoms have subsided, and the patient’s critical mental capacity has returned. Consequently, both of the above categories of patients (with some exceptions) have recovered their mental capacity as a result of the treatment and are entitled to be informed in writing of their health status, receiving a notice upon completion of their treatment and discharge from the clinic.
This is a process indicator, which relates to patients’ access to information. The index is a fraction, where the nominator is the number of patients discharged from the clinic with a discharge note, and the denominator is the total number of patients discharged from the psychiatric clinic.
The performance target is all of the patients to receive an information note, the day of their discharge from the clinic.
3.3.6. Number of Patients Using the Suggestion/Complaint Box
In order to assess and improve the quality of care provided, mental health services can easily install a box of suggestions and/or complaints in any inpatient and outpatient setting, e.g., outpatient clinic. Subsequently, psychiatrists encourage patients (inpatient and outpatient) to write a letter or fill out a form with their suggestions for improving the service (or their complaints) and adding it to the box. Patients’ notes can be a valuable tool for qualitative-rather than quantitative-assessment of the quality of each mental health facility because they contain descriptions of the experiences patients have had in their treatment in the particular clinical setting.
This is a process indicator. This indicator is a fraction: The nominator is the number of patients who used the suggestion/complaint box during a year, and the denominator is the total number of patients discharged from the clinic during the same time period.
There is no published benchmark for this indicator because it is a quality evaluation indicator. Letters/notes describing patients’ experiences are useful for evaluating the quality of care. An increase in the number of complaints per year may indicate a decline in quality due to under-staffing or under-financing.
Alternatively, all clinics (inpatient and outpatient) could suggest to all patients on the day of discharge from the clinic to write a summary or fill in a form/questionnaire (in print or electronic form), revealing their treatment experiences. In some hospitals/clinics, this form/questionnaire is considered a patient’s obligation. Other hospitals could choose to motivate the patient to complete the form/questionnaire. The incentive could be economical, in the form of a reduction in co-payments, for example, to medications that patients may receive from the hospital pharmacy prior to discharge.
3.4. Competence Criterion or Competence Dimension. Indicators of Competence
3.4.1. lifelong learning program for mental health professionals.
In modern mental health systems, lifelong learning programs for mental health professionals are essential for maintaining the quality of services, productivity, and satisfaction of patients and employees.
This is a structure indicator. The indicator is a fraction where the numerator is the number of mental health professionals in a lifelong training program, and the denominator is the total of mental health professionals working in the same institution.
The goal is to increase the index year after year, with the ultimate goal of ensuring that all employees in the organization enjoy lifelong learning.
3.4.2. Lifelong Learning Program with Quantification of Training for Mental Health Professionals
Scientific developments in the field of mental health are running at a rapid pace, while the environment is changing. Therefore, there is a need for continuous modification of the bio-psychosocial diagnostic and therapeutic interventions that a well-trained health professional should provide. To this end, health professionals must have lifelong training. It is good practice for the training to be quantified in units to ensure the required training on an annual basis.
This is a process indicator. The index is a fraction where the numerator is the number of specific specialty mental health practitioners who follow a Lifelong Curriculum that is quantifying in units, and the denominator is the total number of health professionals of the same specialty.
The aim is to increase the index score, year by year, with the ultimate goal of ensuring that all mental health professionals follow a qualitative and quantifiable educational system with training units.
3.5. Effectiveness Criterion or Effectiveness Dimension. Indicators of Effectiveness
3.5.1. percentage of patients who have improved as a result of treatment.
Patients are usually admitted to mental health clinics during an acute mental disorder or during exacerbation of a chronic mental disorder. Patients are discharged from the clinic after receiving appropriate treatment and improvement. Appropriate therapeutic interventions are usually a combination of biological interventions (psychotropic drugs), non-medication interventions (psychotherapy, counseling), and social interventions. The “percentage of patients who have improved” is a useful and, therefore, common and popular indicator of the quality of mental health service [ 10 ].
This is an outcome indicator. The index is defined as the fraction the nominator of which is the number of patients identified in the discharge note as “improved”, and the denominator is the total number of patients discharged from the same mental health clinic over the same time period, usually over a period of one year. Patient improvement may refer to the initial symptoms present at the day of admission, but may also refer to the patient’s psychosocial functioning as assessed by a clinical examination or to a scale such as the Global Assessment of Functioning (GAF) [ 21 , 22 ].
The proportion of patients who have improved may vary from clinic to clinic, depending on the type of diagnosis and the severity of the cases. However, most patients are expected to show improvement as a result of the treatment provided in the clinic. It is recommended that all clinics publish the “percentage of patients showing improvement” index so that quality can be monitored and compared with other clinics over time. A common reason for the patient not improving may be the patient’s early voluntary discharge from the clinic despite the psychiatrist’s dissenting opinion that the patient has to remain in the clinic until completion of treatment. The performance target varies depending on the severity of the cases being treated by each clinic. However, the indicator is particularly useful for improving the clinic through comparisons over time.
3.5.2. Extra Mortality in Patients with Schizophrenia Overall and by Sex
Patients with schizophrenia have a lower life expectancy compared to the general population. This decline is a result of many factors, such as cognitive decline, poverty, lack of social support, reduced ability to co-operate in treatments for physical ailments (e.g., diabetes, hypertension), and an increased likelihood of suicide.
This is an outcome indicator. Extra mortality in patients diagnosed with schizophrenia is defined as a fraction where the total mortality of schizophrenia patients is the numerator, and the overall mortality of the general population is the denominator. Both the numerator and the denominator include only patients aged 15 to 74 years, according to OECD recommendations [ 23 ].
This indicator should be presented for men, women, and the general population. A number of benchmarks have been published from time to time [ 23 ]. According to OECD published data, Latvia has the lowest rate, 1.7 for men and 2.4 for women. The highest index is reported by Finland and is 6.6 for men and 6.1 for women. Only Finland and Denmark report higher rates for men, while other OECD members report higher rates for women than men.
3.5.3. Extra Mortality in Patients with Bipolar Disorder Overall and by Sex
Patients with bipolar disorder have increased mortality and reduced life expectancy. This is the result of many factors, including an increased risk of suicide compared to the general population, increased correlation with substance use disorders, reduced income due to loss of work, obesity, and cognitive impairment and brain atrophy (mainly of the white matter bundles) as a consequence of the natural course of the disease.
This is an outcome indicator. The index “increased mortality in patients with bipolar disorder” is a fraction, with numerator the total mortality in patients with bipolar disorder (type-1 or type-2), and denominator the number of patients with general mortality in the general population. The numerator, as well as the denominator, includes only ages 15 to 74, according to OECD guidelines, as mortality in the rest of the age range is not affected by bipolar disorder [ 23 ].
This indicator should be presented for men, women, and the general population. Israel has published the lowest values, which are 2.8 for men and 2.6 for women. The OECD has published benchmarks according to which: Finland has the highest values for women, i.e., 5.2. Korea has the highest values for men, i.e., 3.7. All countries, with the exception of Israel, have a higher mortality rate for bipolar disorder in women than in men [ 23 ].
3.5.4. Number of Psychiatric Beds per 100,000 Inhabitants
This is an indication of the state of mental health in a country. A very small number of psychiatric beds in a country can mean that the country does not give enough money for mental health; that is, mental health is not in the country’s priorities. On the other hand, too many beds may mean that the country has not undergone sufficient psychiatric reform/deinstitutionalization, and/or that there is not a sufficient number of community psychiatric services.
This is a structure indicator. The index is a fraction where the number is the number of psychiatric beds in the country, and the number is 100,000. The index is published on Eurostat on an annual basis by all Member States.
The benchmark is the average number of psychiatric beds in the Member States. The performance target is to bring health systems closer to the European average in terms of the number of psychiatric beds in each country. The average number of psychiatric beds in Europe of 28 Member States is 69 beds per 100,000 inhabitants [ 24 ]. Indicatively, psychiatric beds in Cyprus are only 21/100,000 inhabitants, in Greece 74/100,000, in Belgium 136/100,000, in Germany 128/100,000, and in France 84/100,000, according to published data in the European Union Statistical Office (Eurostat) [ 24 ]. Monitoring the course of the number of psychiatric beds in each country can yield useful conclusions about the mental health of each member country. An extremely low number of psychiatric beds indicates underemployment and lack of funding in mental health, and that mental health is not a priority for that country. On the other hand, an extremely high number of beds may indicate that psychiatric reform in some countries has not progressed sufficiently, although mental health policy in countries with high scientific tradition and mental health research (number of publications in peer review psychiatry journals of high impact factor) is very important to be taken into account.
3.5.5. Inpatient Suicides
The suicide of a patient while hospitalized in a psychiatric hospital is a very rare event. However, it is the worst that can happen.
This is an outcome indicator. The Inpatient Suicide Index is a fraction, in which the numerator is the number of inpatient patients who committed suicide during their stay in the facility (general hospital, psychiatric hospital, prison hospital, prison setting) and denominator is the number of patients in the respective unit structure that have a primary or secondary diagnosis of a mental disorder.
Inpatient suicide is a very rare event, so the performance target should be zero. The benchmark is close to zero in most countries. Indicatively, the Czech Republic reported the lowest rates with 0.0% and Spain with 0.02% of inpatient suicides in a 2013 study. Estonia gave the highest rate of 0.025% for the same year, which is, nevertheless, very low [ 23 ].
3.5.6. Suicides of Patients Hospitalized in a Psychiatric Facility in the Last Month and Previous Year
This is an indicator that is particularly characteristic of the quality of mental health services provided in each country. It is also characteristic of the continuity of care provided to potentially suicidal patients who have been discharged from mental health clinics of any type.
This is an outcome indicator. This indicator is a fraction where the numerator is the number of suicides recorded in the last month and the last 12 months, and the denominator is the number of patients discharged with primary or secondary psychiatric diagnoses during the past month and the last 12 months respectively. For each case, the follow-up period begins the day of the discharge from the inpatient clinic.
For both of these indicators, the UK is considered the benchmark country, with less than 0.2 suicides per 100 discharged patients each year, or less than 0.1% suicide rate for the first month after being discharged from a psychiatric hospital. Slovenia has the highest value in the suicide rate for the first year after the discharge, with more than 0.8% for the first year or more than 0.3 for the first month after the discharge [ 23 ].
3.5.7. Suicides in the Country Last Year
This is an outcome measure that is a powerful predictor of the level of mental health of each country’s population. The indicator is not stable but is influenced by environmental factors. The index is heavily influenced by the country’s economic level, with prices rising during the economic crisis [ 25 ]. In Greece, suicide attempts and suicides increased significantly during the financial crisis [ 26 ].
The index is a fraction where the numerator is the number of suicides in the country in one year per 100,000 population, and the denominator is 100,000. In addition to the overall index, it is informative to present the index for men and women separately.
Greece and Cyprus are benchmarks for this indicator, with the lowest value of five suicides per 100,000 population per country. The indicator is considered typical for the level of Psychiatry and Mental Health in each country. Lithuania has the highest rate in the index, with 30 suicides per 100,000 population per year. Liechtenstein, Cyprus, Greece, and Italy have the lowest rates with 2,4,5,6 suicides per 100,000 population per year respectively. For the EU Member States, the average is 11 suicides per 100,000 population per year [ 27 ]. However, overall for the WHO European Region, which includes the poorest European states outside the EU (a total of 53 countries including the EU-28 Member States), suicides are 21.2 per 100,000 population per year [ 28 ].
3.5.8. Percentage of Patients Who Completed the Addiction Treatment Program
The indicator relates to voluntary inpatient treatments in psychiatric clinics or detox/rehab clinics. Any patient who enters such a treatment setting can either complete the treatment and be discharged, or interrupt and leave the clinic before completing despite the physician’s contrary opinion. When many patients discontinue voluntary treatment, this may be indicative of (1) lack of education by health professionals, (2) excessive rigor of the program, (3) excessive severity of the disease of patients admitted to the clinic (severe drug/alcohol use disorder), (4) lack of human resources, staff are not numerous enough for quality hospitalization/monitoring/intervention.
This is a process indicator. The index is a fraction where the number of patients who completed the treatment program is the numerator, and the denominator is the total number of admissions to the same clinic over the same period of one year.
The index can be used for comparison between similar clinics of the same or other hospitals. However, it is more useful for monitoring the course of the same clinic over time, comparing this year with the previous year’s score. In international literature, some authors prefer to calculate the dropout rate. The performance target is to improve the index against last year’s values in the same clinic.
3.5.9. Percentage of Substance Abusers Who Have Reduced Consumption as a Consequence of Treatment
The modern approach to the treatment of substance use disorders (detoxification, rehabilitation, harm reduction) does not only include treatments that are oriented towards complete abstinence. Previously, treatment programs were always abstinence oriented, and these approaches were commonly known as “abstinence-oriented therapeutic communities”. In the modern era, the “Harm Reduction” approach is popular, where complete abstinence is desirable but not necessary for the achievement of treatment. Treatment is defined as an increase in bio-psychosocial functionality, even if the patient remains a sporadic user of the legal or illegal substance. Increased bio-psychosocial functionality means rehabilitation of physical illnesses related to use (e.g., infections, liver disease, etc.), rehabilitation of psychological stability (treatment of withdrawal syndrome, depression, etc.), social rehabilitation (back to home, family, work, interpersonal relationships, no more unlawful actions and issues with law, police, courts, prisons, etc.). In order to evaluate the effectiveness of therapeutic interventions, conservative endpoints such as days until relapse, which refer to days of abstinence after leaving the program, were previously selected. Additionally, “the percentage of patients who completed the treatment program” is a criterion referring to the percentage of patients who do not discontinue treatment before it is completed. More modern and less conservative evaluation indicators of therapeutic interventions used in the harm reduction approach are “average daily consumption”, as well as the number of “days where daily consumption exceeded” two international alcohol units or another threshold set by the psychiatrist/researcher.
This is an outcome indicator. The index is a fraction where the number of patients who have restricted consumption after the treatment program is the numerator, and the denominator is the number of patients who participated in that treatment program.
The performance target is the index value getting closer to the unit through continuous program improvements. The index can be used as a program self-improvement tool but can also be used to compare different programs when these programs have a therapeutic goal of reducing consumption.
3.5.10. Percentage of Patients Readmitted in One Month
The indicator refers to the phenomenon of “revolving door.” Patients with coexisting severe psychosocial problems and a lack of supportive environment return to the clinic soon after their discharge. Some of these patients suffer from severe chronic recurrent diseases and frequent readmission may indicate inadequate structure for their medical care, for example, a patient with a severe personality disorder hospitalized in a general hospital’s mental health clinic.
This is an outcome indicator. The index is a fraction where the numerator is the number of patients readmitted within the first month after their release, and the denominator denotes the total number of patients dismissed from the psychiatric clinic within the same time period.
The goal is the avoidance (or decrease the number) of readmissions to a psychiatric clinic within the first-month post-discharge, but instead to continue patient’s treatment on an outpatient basis. This presupposes the existence or establishment of appropriate outpatient structures for community psychiatry and day hospital for mental patients.
3.5.11. Percentage of Patients Who Continued Relapse Prevention Therapy after Detoxification
Treatments for drug dependencies, whether they are drug dependencies or behavioral dependencies, consist of two major distinct phases. The first phase is detoxification; that is, the treatment of withdrawal syndrome, achieved in combination with pharmacotherapy and psychotherapeutic interventions. The second phase is relapse prevention, which lasts much longer and is also done with a combination of pharmacotherapy and psychotherapeutic interventions. Often the first phase (detoxification) takes place in the inpatient clinic, followed by the second phase (relapse prevention), which takes place in the outpatient clinic.
This is an outcome indicator. The indicator is the percentage of patients who continue into the second phase after the first phase is completed.
The goal of the performance is for all patients to continue and complete the second phase. The reason many patients do not go on the relapse prevention phase is that by the end of the first phase, the patient is already abstaining from alcohol while the symptomology of withdrawal syndrome has completely subsided. This gives the patient the illusion that they have completely regained control and do not need further treatment. The patients who do not enter the relapse prevention program may have increased risk for relapse.
3.5.12. Number of Abstinence Days after Discharge from the Clinic (Days until Relapse)
Substance use disorders, such as alcohol and illicit drug dependence, are chronic relapsing mental disorders and as such, are classified in the Psychiatric Disorders Classification Systems, such as DSM-5 of the American Psychiatric Association [ 29 ] and ICD-11 [ 30 ] of the World Health Organization. Since these are chronic recurrent disorders, this means they have relapses and remissions. Consequently, relapses should not be interpreted as a failure of treatment, but there are embedded in the natural course of the disease. The purpose of treatment is to reduce the number, frequency, duration, and intensity of relapses.
This is an outcome indicator. The indicator is the average number of days that the patient remains in abstinence from alcohol or the illicit recreational substance of dependence after the completion of a specific treatment plan. The program may be for example, a short-term detoxification program or a long-term inpatient therapy program, e.g. in a therapeutic community setting.
There is no published benchmark. The index can be used to compare similar programs with one another, as well as to document treatment program improvement over time. The index can also be used as a clinical evaluation endpoint in research protocols for the evaluation of therapeutic interventions over time (randomized clinical trials, randomized control trials, RCTs). The indicator is about abstinence-oriented therapeutic interventions or treatment programs, and not so much the modern harm reduction interventions.
3.5.13. Number of Days with Consumption More Than Two Units of Alcohol (Iu, International Units)
In the treatment of addictions, complete abstinence has been, in the past, the only treatment option. In recent years, the therapeutic option of controlled consumption has also been added. This option is mainly recommended for patients that are suffering from addiction on legal substances such as alcohol, and not for patients addicted to illegal substances. However, in legal and illegal substances of abuse, the harm is dose dependent, which is high, and uncontrolled consumption is certainly more harmful than low controlled consumption. This also applies to behavioral dependencies such as the gambling use disorder, where mildly controlled betting may be less harmful than uncontrollable. Consequently, the reduction in consumption is accompanied by a reduction of harm, to both substance dependencies and behavioral dependencies.
This is an outcome indicator. The indicator refers to alcohol, where there is a quantified dose. In particular, an international unit (IU) is equivalent to a drink containing 12 grams of ethanol, that is, about a small beer, a glass of wine, or a glass of whiskey. It is an outcome measure where it evaluates patients completing a treatment plan for addiction, with a therapeutic goal of controlled use. The indicator is a fraction, where the numerator is the average number of days that patients consume more than two units of alcohol, and the denominator is the sum of the days of patients completing the treatment program under study.
There is no published benchmark. The indicator is useful for evaluating treatment programs aimed at reducing consumption, i.e., controlled use. These are harm reduction programs and not complete abstinence programs. The index is also useful as an endpoint in research programs to reduce the harm from alcohol use.
3.5.14. The Average Consumption of Alcohol or of Other Recreational Substance of Abuse after Discharge from the Clinic
Addiction is a chronic relapsing mental disorder, which means that relapses are included in the natural course of the disease. Many patients that are suffering from drug use disorder are starting their treatment under genuine internal motivation. Others, however, have only external motivation. Therefore, efforts are made by the therapists during the treatment to transform this external motivation into internal motivation. Some patients do not want, are not ready, or cannot discontinue the drug and/or alcohol use, so reducing consumption is a temporary harm reduction intervention, as the harm is dose dependent.
This is an outcome indicator. The indicator is the average alcohol consumption per day for the following year after completion of a treatment program. Accordingly, the indicator can also be used for illegal euphoric substances.
There is no published benchmark. The indicator is important for evaluating treatment plans and/or individual therapeutic interventions. It can also be used in harm reduction research or drug evaluation protocols aimed at reduced alcohol or drug consumption.
3.6. The Criterion of Therapeutic Continuity or Therapeutic Continuity Dimension. Therapeutic Continuity Indicators
3.6.1. percentage of patients continuing an outpatient follow-up program after completion of the inpatient treatment program.
Detoxification and rehabilitation therapies from legal and illegal additives often contain two distinct phases of treatment. The first stage—usually within the clinic—is detoxification, that is to say, the discontinuation of the addictive substance and the prevention/treatment of impending withdrawal syndrome. In the second phase—usually on an external basis—it is prevented from being a relapse, by managing/treating the strong/irrepressible desire (craving) for drug/alcohol use. Many patients abandoned the program after completing the first phase of treatment, mistakenly assuming that they have gained control of consumption, and therefore, no further treatment would be needed to prevent a recurrence.
This is an outcome indicator. The indicator is a fraction, where the numerator is the number of patients continuing on an outpatient program after completion of detoxification, and the denominator is the number of patients who have started detoxification therapy.
The goal of performance for all of the patients is, after completing the internal phase, to continue to their outpatient relapse prevention program.
3.6.2. Percentage of Patients Attending the First Follow-Up Appointment as an Outpatient after Being Discharged from the Psychiatric Clinic
In psychiatry, as a general rule, when a patient completes a period of hospitalization and is discharged from the clinic, the patient continues to be an outpatient in the outpatient clinic. The occurrence of the first appointment is indicative of the patient’s compliance/co-operation or otherwise adherence with the treatment, which is related to the patient’s mental state at the time of discharge, as well as to the therapeutic relationship established between the patient and the therapist during the internal phase of treatment.
This is an outcome indicator. The index is a fraction where the numerator is the number of dismissed patients who appeared at the first scheduled appointment after discharge, and the denominator is the number of patients who appeared together with those who did not appear at the first scheduled appointment.
Generally, for patients with mental health problems who were given their first appointment one week after completion of internal therapy and discharged from the clinic, a satisfactory performance target is the appearance of 80% of patients, according to published data [ 31 ].
3.6.3. Percentage of Patients Attending the First Follow-Up Appointment after Discharge from an Inpatient Clinic for Treatment of Substance Use Disorder
Addiction/dependence or, more precisely, Substance Use Disorder is a chronic, relapsing and remitting mental disorder. Usually, this patient population suffers from co-morbidities, mainly with personality disorders, and this is what diminishes their commitment and co-operation in treatment.
This is an outcome indicator. The index is a fraction where the numerator is the number of patients who completed the detoxification/rehabilitation program and then discharged and received their first appointment as outpatients. The denominator is the number of discharged patients who attended together with those who did not attend the first scheduled appointment as outpatients.
For the general population of patients with mental health disorders who have their first re-evaluation appointment one week after discharge, the performance target is 59% according to published data [ 31 ]. This compliance rate for patients with substance use disorder is significantly lower than the general population of patients with mental disorders who come to the first appointment after dismissal, which is 82.4%, according to the findings of the same study. The large difference may be due to the reduced compliance/co-operation of the subgroup of mentally ill patients with substance use disorder, due to co-morbidities with personality disorders.
3.6.4. Percentage of Patients Who Attended the Next Two Appointments after Being Discharged from Hospital
The phenomenon of patients losing their appointments at an outpatient clinic is occasionally observed. However, it is not observed with the same frequency in all areas of the General Health System. Lost appointments show difficulty in this structure for maintaining the therapeutic continuity, which is necessary to improve prognosis.
This is an outcome indicator. The index is a fraction where the numerator is the number of cases that came to the next two appointments after discharged from the hospital. The denominator is the total number of discharged patients from the clinic at the same time period.
There is no published benchmark. The purpose of the indicator is to improve the clinic itself. A low score means that the clinic has difficulties maintaining patients for long-term follow-up, that is, maintaining therapeutic continuity. The causes need to be sought and corrected. In searching for the causes of missed appointments, it is important to ask the patients’ opinions by using questionnaires, oral interviews, or written free text. Patients should always be taken into account.
3.6.5. Number of Patients Attending Two Consecutive Appointments
The continuity of therapeutic care is an important indicator of the quality of the system. It helps to complete treatment, reduce complications, prevent relapse, and improve the prognosis of patients with mental disorders.
This is an outcome indicator. The index is a fraction where the numerator is the number of patients with a chronic mental disorder who came to two consecutive appointments, and the denominator is the total number of patients.
The goal is to increase the number of patients who do not miss their scheduled appointments, year after year, that is, they have years of constant monitoring in the health system.
3.6.6. Number of Referrals
The number of referrals indicates continuity of care (therapeutic continuity) but also indicates the extent to which a mental health service operates interdisciplinary.
This is a process indicator. The index is a fraction where the numerator is the number of patients who received a referral from a physician on the day of discharge from the clinic. The denominator is the total number of patients dismissed from the clinic during the same period.
There is no published performance target for the referral rate, but the index can be used by clinical managers as a tool to improve communication between medical specialties and between different disciplines, as well as an indicator of ongoing clinical evaluation.
3.7. Efficiency Criterion or Efficiency Dimension. Efficiency Indicators
3.7.1. number of mental health professionals per 100,000 population.
The number of healthcare professionals per 100,000 population, mainly psychiatrists per 100,000 population and mental health nurses per 100,000 population, varies between countries, and in many cases, this is indicative of the population’s access to mental health services, which may also affect the quality of mental health services.
It is an indicator of the structure of the country’s mental health services. The index is a fraction of the number of mental health professionals (e.g., psychiatrists) per 100,000 inhabitants.
Specifically for psychiatrists, according to the WHO regarding the WHO European region, there are 13 psychiatrists per 100,000 inhabitants, while in the European Union of 28 Member States, the number is much higher [ 32 ]. The performance target for each Member State is the convergence index to the European average. The European average is 43.5 mental health professionals per 100,000 inhabitants. Indicatively low in the index is Africa and South Asia, where the WHO lists only 1.4 and 4.8 mental health professionals per 100,000 population, respectively [ 33 ]. Specifically, in the WHO European Region, in every 100 mental health professionals are included ten psychiatrists, one Child and Adolescent psychiatrist, three physicians of other specialties, 23 mental health nurses, five psychologists, one social worker, half of an occupational therapist and half of a speech therapist (that is, one speech therapist per 200,000 population) [ 28 ].
3.7.2. Percentage of the Workforce in the Country That Is Working in the Health System
In the EU Member States, the health system is a major employer: Much of the country’s workforce is employed by public or private health services. Comparing the staffing of different health systems can lead to different conclusions, especially when comparing public health indicators. The low score on this indicator may explain the low accessibility of beneficiaries in certain areas/services of the health system.
This is a structure indicator. The indicator is a fraction where the numerator is the number of mental health professionals working in the health system, and the denominator is the total workforce in the country.
The performance target is intended to be close to the European average in each Member State. The deviation from the European average may indicate the need for adjustments, either by funding, an increase of human resources or productivity. The WHO has published data that in Europe, 50 health professionals per 100,000 population [ 28 ] work in the field of mental health. According to the same report, specifically for the list of rich countries as defined by the World Bank-including Greece and Cyprus-there are currently employed 71.7 mental health professionals per 100,000 inhabitants [ 28 ].
3.7.3. Number of Patients per Physician per Day, in the Outpatient Clinic
Psychiatric evaluation, counseling, and intervention require the formulation of a therapeutic relationship and quality communication between the psychiatrist and the patient at each session, and this is a time-consuming task that cannot be done in a hurry. Time is a limiting factor in the number of patients that each psychiatrist can see per day of an outpatient clinic. When there are many patients per psychiatrist per day of outpatient care, the time available per patient is less, resulting in a decline in the quality of service provided.
This is a structure indicator. The index is a fraction where the numerator is the number of outpatients that the psychiatrist has served in clinic during the year, and the denominator denotes the number of full working days in an outpatient clinic.
The performance target is that the number of patients is about 10 per day in the outpatient clinic and certainly not more than 20, meaning each session lasts about 40 minutes, with short breaks between sessions. For Cyprus, the Union of Medical Doctors of the Public Sector (PASYKI) has set the maximum number of patients that a specialist should not exceed in one full clinical working day, in the secondary specialist care outpatient clinic, in 20 patients per day. However, difficult cases within specialties such as Psychiatry contribute to a rate greater than one patient per hour.
3.7.4. The Average Length of Hospitalization
Patients with the same diagnosis in different clinics need a different (greater or lesser) number of days of hospitalization until their hospitalization is complete. This is related to staffing, experience, clinical workload, availability of labs and imaging laboratories, and many other parameters related to the availability of logistical and human resources.
This is an outcome indicator. The numerator is the total number of days of hospitalization per clinic per year, and the denominator is the total number of patients hospitalized in the clinic over the same time period.
The purpose is quick, but not hasty, case management. For this reason, the index should be used to compare similar clinics and similar cases, for example, by using of Diagnosis Related Groups (DRGs).
3.7.5. The Average Cost of Hospitalization per Patient per Doctor, Clinic, Field, Hospital
The average cost of hospitalization is an important parameter for financial planning, financial management of the hospital, and its sustainability and sustainable development. The average cost of hospitalization can be calculated per patient, per entry diagnosis, per physician, per clinic, per department and per hospital.
This is an outcome indicator, which is influenced by the days of hospitalization of each case. The total operating cost of the hospital, ward, clinic, is divided by the total days of hospitalization, and the cost per day of hospitalization is calculated in each case. Then the amount is multiplied by the average number of days of hospitalization of patients and thus, the average cost per patient is calculated. The average cost per patient per doctor is also calculated correspondingly since, for patients with the same diagnosis, the days of hospitalization differ between different physicians but also between different hospitals and between different clinics of the same hospital. The average cost per hospitalization is also calculated per diagnosis/treatment using the Disease-Related Groups (DRGs) method, based on which a specific hospital fee is set for each DRG. The DRGs method is not currently available in all hospitals.
It is not easy to define the benchmark that is a performance target, as costs should be correlated with the outcome of the incidents using an outcome indicator. The index is very useful for comparing hospital costs between hospitals in the same country. However, it can also be used for between-countries comparisons if the cost of hospitalization is corrected for the difference in Gross Domestic Product (GDP) between the two Member States. However, in any health system, the average cost indicator must be calculated, and the clinics, hospitals, and physicians that deviate from this should be mobilized towards convergence.
3.7.6. The Average Cost per Day of Hospitalization per Clinic, Sector, and Hospital
The average cost per patient varies between clinics, departments, and hospitals for a variety of reasons. For the management of patients with the same diagnosis, there can be a great difference in the days of hospitalization but also in the type and cost of examinations performed between different clinics, departments, hospitals. System sustainability and sustainable development are important parameters in all modern health systems, at least as far as the EU Member States are concerned. Therefore, the cost per day of hospitalization is an important parameter.
This is an outcome measure that is the cost of each day of hospitalization. The total cost of the hospital, ward, and clinic is divided by the total days of hospitalization, and the cost per day of hospitalization is calculated in each case. The index is a fraction, where the numerator is the total cost of operating the clinic for one year (salaries, building, medicines, consumables, etc.), and the denominator is the total patient-days of a year of the clinic.
This is a very useful index on the basis of which managers can compare the cost between clinics and between physicians of the same specialty. However, evaluation cannot be done only by cost indicators, but clinical outcomes must be taken into account, together with other epidemiological health indicators. It is not possible to make direct cost comparisons between different clinics and different countries because there are many different factors, payrolls, procedures, diagnoses of cases admitted, therapeutic protocols that determine the days of hospitalization in each case. The indicator is very useful for comparing costs between the same clinics located in different hospitals in the same Member State. For example, a comparison of the cost per day of hospitalization could be performed between the two General Hospitals Psychiatric Clinic in two different cities.
3.7.7. The Average Cost of Medication Consumption per Patient and per Day of Hospitalization, per Physician, Clinic, Field, Hospital
With up-to-date medical folder software included in the Integrated Hospital Information System, the total cost of a drug for the entire hospital per year and then per patient, clinic and/or physician can be calculated. The cost per patient depends largely on the diagnosis, but it also depends on other parameters, such as over-prescribing and choosing expensive prototypes in place of cheap generics. Therefore, the pharmaceutical cost of treating the same cases may vary between different hospitals and/or different doctors.
This is an outcome indicator. The indicator is the average cost of medication consumption per patient, doctor, clinic, hospital sector. To calculate it, we first calculate the total pharmaceutical cost of each hospital, clinic, department, physician, using a suitable filter from the software. We then divide by the total number of patients or days of hospitalization of patients treated by the respective hospital, clinic, field, physician, and thus the average cost of medication consumption is calculated.
As with any cost index, one must take into account the prognosis and outcome of patients. It is expected that when a clinic treats many severely ill patients, it will have high pharmaceutical costs per patient and many days of hospitalization per patient. However, the expense indicators are useful for comparing physicians and clinicians of the same specialty, as well as for approximating prescription patterns. As a benchmark it may additionally be used the theoretically expected drug consumption in DDDs, according to the definition of Worldwide Health Organization (WHO): “The Defined Daily Dose (DDD) is the assumed average maintenance dose per day for a drug used for its main indication in adults” [ 34 ]. The index is suitable for setting a performance target for the financial planning of each structure, especially for structures limited by global/closed budgets. It can also help clinicians and physicians who are far from average to reduce their pharmaceutical spending.
3.7.8. The Average Cost of Diagnostic Examinations per Physician, Clinic, Field, Hospital
Each hospital has a different ability to perform clinical, laboratory, and biochemical tests, due to differences in equipment and training of human resources. Some doctors write more or fewer tests than others, especially in health systems where there are no protocols to determine which specialties can order each laboratory diagnostic test. On the other hand, many times, whether or not a test is performed is limited by the patient’s private security plan, especially in countries with a multi-insurance health system.
This is a process indicator. The indicator is a fraction where the numerator is the total number of diagnostic tests, and the denominator is the number of patients that can refer to the whole clinic, outpatient clinic, field, physician, hospital. The results of the indicator can be presented in all ways to allow comparisons between clinics, doctors, and hospitals, but also to monitor the course of increases or decreases in costs each year.
There is no internationally accepted benchmark due to the large differences in the cost of these services between the Member States. A comparison could be made, between the Member States, after weighting of the data, for example, on the basis of GDP per capita of each country. The index can be more easily used for within-hospital and between-hospitals comparisons.
3.7.9. Cost per Diagnosis Related Group (DRG)
Diagnosis Related Groups (DRGs), have been developed at Yale University and were primarily implemented in the USA and in particular in the Medicare health subsystem, but were quickly transmitted and implemented (same or modified) in many other countries including Greece. The rationale is that each diagnosis based on the ICD-10 classification system corresponds to a specific DRG, which in turn corresponds to standardized specific costs for the hospital that has been calculated and determined in advance. The hospital for each hospitalization of the patient will be compensated on the basis of the patient’s DRGs, as shown by the diagnosis referred to the patient’s discharge note. The doctor sets the diagnosis by using ICD-10 or ICD-11 codes, and the accounting software corresponds the ICD code to the appropriate DRG code, on the basis of correspondence tables. For each DRG, the hospital accounting office will receive the corresponding compensation from the relevant funding source (e.g., Ministry of Health, an insurance company, insurance agency, etc.).
This is a process indicator that is indicative of the economic organization of the health system. The indicator receives YES/NO values indicating whether the DRGs system is applied to the particular health system or hospital or clinic. If the indicator receives a YES value, further quantification of the estimate is considered useful, i.e., the percentage of the hospital funding that is made through DRGs payments.
The most important DRGs for psychiatry-which are a benchmark-are those of Psychosis, that is, the funding of the hospital that uses DRGs for the care and treatment of patients with a chronic psychotic disorder (e.g., schizophrenia).
3.7.10. Cost per Current Procedural Terminology
The existence and employment of the Current Procedural Terminology (CPT) in the health system, for specific and coded terminology, is very useful for storing and communicating information about medical services and procedures. In particular, this process is useful for administrative, financial, and analytical reasons. The CPT is, therefore, a common language that contributes to mutual understanding between physicians, patients, insurance companies (both in a mono-insurance and multi-insurance environment) and the payers of the services provided [ 35 ].
For example, in Cyprus, where the Health System (General Health System, GHS) is mono-insurance (single-insurance), all costs go through the Health Insurance Organization (HIO), which is a single-payer mono-insurance organization. HIO recently published the proposed compensation for the medical practice of any specialty under the CPT system [ 36 ]. Based on the above model which includes typical examples of medical transactions and fees for each medical specialty, each physician can make assumptions and predictions about their own median income, and the system can calculate median and average income and thus protect and ensure the viability of the health system.
Indicatively, a typical psychiatric visit (with CPT code CY001 and weight of 2 units) together with psychiatric assessment and/or psychiatric counseling (with CPT code 99242 and weight of 2.7 points) of half an hour duration, has a total CPT weight of 3.8 points. At a unit price of 15 euros, this psychiatric session lasting half an hour and weighing 3.8 units is compensated at 3.8 × 15 = 57 euros [ 36 ].
This is a process indicator. The Index receives the values YES/NO depending on whether the hospital system in question operates with a CPT algorithm for compensations/payments, and then lists the cost per CPT. This way, cost comparisons can be made between hospitals and health systems.
There is no published performance target because the cost of each medical practice in each country is multifactorial, and it is difficult to make valid cross-country comparisons. However, the indicator is useful for tracking costs over time as well as for planning health system sustainability.
3.7.11. Doctors to Nurses Ratio
The ratio of the number of doctors to the number of nurses is an important indicator of the quality of the clinic. Poor quality may be due to a lack of skilled nurses and, as a result, poor quality of hospitalization or a lack of doctors’ and nurses’ competence. Therefore, poor quality is due to the effort to perform medicine without enough doctors.
This is a structure indicator. The numerator is the number of doctors in the clinic, and the denominator is the number of nurses in the same clinic, hospital, or system.
The published WHO score for this indicator can be set as a performance target, as described [ 37 ]. Indicatively, the National Health System (NHS) of the United Kingdom has one psychiatrist for every five mental health nurses. The National Health System of Greece (ESY) has one psychiatrist for every four mental health nurses. In the Mental Health Services of the Public Sector of the General Health System of Cyprus (GHS) there is only one psychiatrist per 14 mental health nurses, while in the Department of Addiction Psychiatry of the Mental Health Services one psychiatrist corresponds to 28 mental health nurses [ 38 ].
3.7.12. Doctors per Nurse per Bed in the Hospital
Following the recent financial crisis that has shaken Europe’s health systems, it has become clear that resources are scarce and need to be utilized to the best of their ability to ensure the viability and sustainability of the system.
This is a structure indicator. The index is a fraction where the numerator is the number of doctors, and the denominator is the number of beds in the clinic. The index is multiplied by a coefficient showing the average bed coverage of the clinic (mean completeness of the clinic). That is, for an average annual coverage of 80%, the ratio should be multiplied by 0.8 to increase the accuracy of the rating. The indicator may also be used to evaluate the staffing of the clinic, namely paramedical staff, and other professionals.
The index is used to evaluate staffing and find areas of under-staffing and possible over-staffing in the health system. It can be used for comparison between clinics of the same or different hospitals but also between hospitals in the same country or between different countries, especially between the EU Member States, where a target of performance is the convergence in policies for health systems and services.
3.7.13. Economic Sustainability and Sustainable Development
Economic viability or sustainability refers to the strategic planning of the mental health system in such a way as to ensure endless financing and production of services, with the least possible risk of collapsing for the mental health services. Sustainable development additionally provides planning for the development and evolution of the system over time.
The Index is the answer YES/NO to the question of whether the mental health system has a viability study and whether the strategic planning of the system includes a separate chapter on strategic planning and sustainable development.
The performance target is the writing of a viability/sustainability study as well as a separate chapter on the sustainable development of the mental health system, in the strategic planning manual, which is also the benchmark. In addition to the health system, the indicator could be assessed at the level of the individual organizations included and constituting the system, but also at the level of hospitals and/or clinics.
3.7.14. Average Beds Occupancy
The fullness of the beds in a clinic or hospital indicates that the available resources are being used to a sufficient extent but also indicate the need to expand the number of beds and, therefore, to increase staffing. This is a useful indicator that helps answer the question of how many doctors and nurses are needed for that particular clinic. The index is also particularly useful for comparisons between clinics of the same hospital and for comparisons between different hospitals.
This is a process indicator. The numerator is the total number of days of hospitalization per year of the clinic. This equals the number of beds, multiplied with patients’ average number of days in the hospital. The denominator is the theoretically maximum number of patient-days that this clinic could serve; that is, the number of beds in the clinic multiplies by 365 days of the year. A similar indicator is the average rotation interval, i.e., how many days a bed is left empty, which can be indirectly calculated from the average occupancy and number of beds.
The average occupancy rate of hospitals in European countries, which is 87%, can be used as a reference [ 39 , 40 ]. Occupancy below 80% can be a waste of resources, while occupancy 90% or above can lead the staff to occupational burnout syndrome as well as to increased hospital costs due to many on-call times. The purpose is to avoid having empty beds. Of course, this does not mean 100% occupancy, but certainly close to 90%, as there are several reasons a bed may be vacant (e.g., death, transfer to another clinic, cancellation of scheduled admission, or voluntary early departure from the clinic without the completion of the treatment plan).
3.7.15. Average Inflow Rate (Average Number of Patients per Bed per Year)
The volume of work in a clinic is not only reflected by the average occupancy but also by the rate of rotation of patients in the beds. For example, two clinics with the same number of beds, where one hospital has twice the number of patients per year than the other.
This is an outcome indicator. The index is a fraction where the total number of patients treated in the clinic per year is the numerator, and the denominator is the number of beds in that clinic.
Based on published WHO data as a benchmark, bed needs can be calculated for an area depending on its population. In particular, in the WHO European Area, 60 beds and 453 patient admissions per year correspond for every 100,000 population. This corresponds to 7.6 patients per bed per year, i.e., 1.6 months or 48 days average hospital stay [ 28 ]. The index varies depending on the diagnosis and the severity of the cases being treated by a clinic. There is, of course, an indicative number of days of hospitalization for patients with a specific diagnosis, and this is described in the KEN/DRGs lists. By comparing clinical doctors with this indicator, each physician can try to converge to the average score of other clinicians of the same specialty. Specifically, for psychiatric clinics of the general hospitals, for Europe, the benchmark is 34.3 beds and 89.6 patients’ admissions per 100,000 population per year. Whereas, for the psychiatric hospitals, the corresponding benchmank is 12.3 beds and 160.5 patients’ admissions per 100,000 population per year [ 28 ].
3.7.16. Average Bed Rotation Interval
Making the most of the resources of a clinic and especially beds is not an easy task and requires a high degree of organization. Often some beds remain vacant and this reduces the utilization of the resources of the clinic. For example, when a clinic discharges patients on a daily basis, but admits patients only on specific days of the week, when the outpatient clinic and/or the Emergency Medicine Department of the hospital is working.
This is a process indicator. The indicator represents the days that the bed remains empty until the next patient arrives. The numerator is the number of empty-bed days (obviously may be greater than 365), and the denominator is the number of the beds of the clinic, multiplied by the 365 days of the year.
The goal of each clinic’s performance is to limit as much as possible the empty-bed days in the clinic, thereby reducing costs.
3.7.17. Cost of Mental Health Services Compared to Health System Costs
The cost of health systems in the EU Member States is about 10% of each country’s GDP. A small percentage of this is attributed to mental health services.
The indicator shows the cost of mental health as a percentage of the country’s total health costs.
The indicator identifies unmet needs in mental health. It also points to areas where mental health is not yet a priority. Therefore, funding for the development of mental health services needs to be increased. There are no published benchmarks for this indicator. However, the indicator can be calculated indirectly, as there are published WHO data on governments’ per capita spending on mental health in each country. For the WHO European Region (54 countries) the per capita expenditure is US $21.7 per year, while the corresponding per capita expenditure globally is only US $2.5 [ 28 ]. According to the WHO report, in the European Union, the entire cost is covered by the state, with little or no cost to the patient for mental services [ 28 ].
3.7.18. Existence of Strategic Plan and Action Plan
Strategic Plans and Action Plans are essential elements of health policy as they largely determine the quality of services in the present and the future. This indicator is also recommended by the WHO [ 7 ].
This is a process indicator. The indicator is qualitative and answers YES/NO to two key questions: (1) Is a written strategic plan available, and (2) is a written action plan available. These should be answered for the service as a whole and for each structure separately: (1) If there are timetables, (2) if there is written accountability at the end of each academic/clinical year, (3) if there is a fixed/global budget.
The performance goal is the answer YES to all queries. The budget should be separate for each structure, and it should be described as a percentage of the overall state budget for health. In addition, the expenditure on mental health should be specified as a percentage of the total expenditure of the health system (which includes both public and private mental health). It is not enough just to define budgets, but each individual budget must be accompanied by the sources of funding.
3.7.19. Mental Health Research Index
The quality of mental health services can also be assessed by the number of ongoing research prjects, research protocols, doctoral theses and master theses, as a percentage of the total number of studies related to the health system. Published articles in scientific journals that use the peer review system should also be taken into account.
This is an outcome indicator. The indicator is the number of mental health studies, in relation to all health studies carried out in the country or health system or in the general hospital. The most important criterion is the overall impact of research on mental health, as shown by the overall impact factor of published mental health studies over a five-year period.
No benchmark has been published, but the aim is that mental health research should be at least quantitatively proportional to the percentage of funding for mental health services in relation to overall funding.
3.8. Safety Criterion or Safety Dimension. Indicators of Safety
3.8.1. number of incidents of verbal violence.
Verbal violence and verbal abuse of patients or staff are not permitted in mental health clinics. Patients who are admitted on a voluntary basis are usually informed in writing, and sign a written informed consent form, which commits them not to exhibit verbal or other violence against staff and patients, during their treatment in the inpatient ward and/or the outpatient clinic.
This is an outcome indicator. The indicator is a fraction where the numerator is the number of cases of verbal violence/abuse from patients to other patients or to clinical staff over the period of a year. The denominator is the total number of admissions during the same year.
There are no published performance targets for acceptable rates of verbal violence. The lower, the better.
3.8.2. Number of Incidents of Physical Violence
Physical violence and/or verbal abuse to patients or staff is not permitted in mental health clinics, as in other clinics. Patients admitted to the clinic on a voluntary basis usually sign an informed consent document (informed consent) stating that they will avoid any form of violence during their hospitalization or treatment in the outpatient clinic.
This is an outcome indicator. The index is a fraction where the numerator is the number of cases of physical or verbal violence from patients to other patients or to staff during the year. The denominator is the number of patient admissions during the same time period.
There is no published acceptable rate of domestic violence cases per year. The objective is to continuously reduce the incidence of violence in terms of frequency, intensity, and duration.
3.8.3. Number of Cases of Illegal Drug Use in the Clinic
Substance use disorder is a chronic, recurrent mental disorder. Co-morbidities are common, so patients may be referred—depending on the primary problem—to a general psychiatric clinic, or to more specialized clinics called detoxification and rehabilitation clinics, or other clinics for the treatment of dependence. It is not uncommon for a patient addicted to illicit euphoric substances to try to bring and use within the clinic the substance to which the patient is addicted (e.g., heroin). The frequency of these undesirable behaviors is an indicator of safety and quality of health care as they relate to the number, ability, training level, and experience of staff.
This is an outcome indicator. The indicator is a fraction where the numerator is the number of patients that showed positive urine tests for illicit substance use within the clinic during their hospitalization, and the denominator is the total number of patients admitted to the clinic during the same period.
No commonly accepted published data were found regarding an acceptable frequency of these adverse events. The less the better. The index can be used for the improvement of the clinic, year by year.
3.8.4. Number of Cases of Alcohol Use in the Clinic
Alcohol use disorder is a chronic recurrent mental disorder. Patients often present with co-morbidities, so the clinic in which they will be hospitalized is selected on the basis of the pre-existing health problem, and may be the general hospital psychiatric clinic, the internal medicine clinic, or the alcohol detoxification and rehabilitation center. It is not uncommon for an alcohol-dependent patient to try and bring alcohol into the clinic. The frequency of such undesirable behaviors is an indicator of safety and quality of care and is related to the number of staff competence, suitability, training, and experience.
This is an outcome indicator. The index is a fraction where the numerator is the number of cases of alcohol use in the clinic, and the denominator is the total number of admissions in the clinic during the same period.
There is no published benchmark, but the index can be used with the aim of improving the quality of the clinic year after year.
3.8.5. Personal Data Security
The General Data Protection Rule, as accepted and adopted by the European Parliament in 2016 [ 18 ], has brought about many changes in the data of patients with mental disorders, in the practice of psychiatry and other mental health professions, but also in the field of mental health in general. The patient’s personal data includes information such as name, age, place of residence, marital status, educational level, racial origin, political beliefs, religion, ideologies (political, philosophical), union, status health (diagnoses, treatments), erotic life and erotic preferences, criminal history (register of prosecutions and convictions). All of the above may be included in a mental health record, as they are often released as the patient answers open-ended and closed-ended questions during psychiatric interviewing and evaluation. Many of the above data are particularly important in forming the image of one’s personality. Therefore, these are regarded as sensitive personal data. Sensitive personal data are the health status (diagnoses, medications, hospitalizations), social welfare data (benefits, financial status), criminal history (prosecutions, convictions), ethnicity, nationality, citizenship, race, political choices, religious/philosophical beliefs, union, union, union (syndicate), erotic life (marital status, sexual preferences).
The General Data Protection Regulation regulates how health is collected, registered, organized, maintained/stored for a certain period, modified, exported, used, transmitted, disseminated, disposed of, linked to other databases, the combination of databases, databases connection, blocking, deletion, and destruction of personal data. It specifies in detail the conditions for the collection and processing of personal data, and in addition, for the sensitive personal data, it specifies that it may only be collected and processed after permission from the authorized Authority for the Protection of Personal Data. It also requires the consent of the subject whose data will be kept in the file.
This is a process indicator. The indicator determines whether the General Mental Health Regulation is followed in this mental health service and responds with YES/NO. Then, in free text format, detailed explanations can be given about areas of GDPR that do not apply to this structure/service and why.
The target of performance is the full implementation of GDPR in all Mental Health areas. In addition, another target is a yearly report that describes GDPR areas that are not being implemented, so that progress can be made until it is fully implemented.
3.8.6. Existence of Institutionalized Internal and External Evaluation of Patients’ Human Rights
Patients’ rights are defined by international conventions, the United Nations (UN) and the European acquis. The assessment of the human rights situation of the mentally ill must be assessed in writing through statutory procedures at (1) at the local hospital/prison level, (2) at the national level, (3) at the international level. An important body of the Council of Europe, which assesses patients’ rights in compulsory treatment and detention structures, is the European Commission for the Prevention of Torture and Inhuman or Degrading Treatment or Punishment (CPT). The CPT pursues a policy of transparency by publishing on its website reports for each EU Member State it visits and evaluates [ 41 ].
This is a process indicator. The indicator is qualitative and receives the values YES/NO to questions whether there is an established written procedure for evaluating patients’ rights on a yearly basis (a) at a local intra-service level, (b) at national level (e.g., independent patient rights evaluation committee), (c) internationally (by an independent international organization). It should also be noted if these reports are published online so that they are transparent and accessible to patients and the general public.
In a WHO survey, only 28% of countries have regular evaluations on a yearly basis by a special commission or an independent body, which assesses whether legislation is in line with international human rights conventions (26). The performance target is to establish such a process, with timetables and transparency in the publication of evaluation findings and reports.
Quality assessment in the provision of mental health services is a multidimensional process and difficult to implement. The difficulty is compounded by the lack of reliable biological markers in psychiatric evaluation, which makes it difficult to quantify any improvement after hospitalization, or in general after hospital or out-of-hospital pharmacotherapy and psychotherapy interventions [ 42 ]. The availability of biomarkers is going to increase in the near future as a great amount of research is already oriented in this direction [ 43 ].
The appropriateness of clinical structure and treatment is an important dimension of quality. Despite the progress in de-institutionalization in the mental health systems of most EU Member States, however, avoiding unnecessary admissions in the psychiatric clinic sometimes still remains a challenge. Additionally, a challenge is the continuity of rehabilitation in the community immediately after discharge from the hospital. For this to happen, appropriate outpatient structures are needed, which is not always possible, as many countries have been in a hurry to over-de-institutionalize, which has led to a deterioration in the prognosis and quality of life for a subgroup of chronic patients whose quality of life have decreased whilst morbidity has increased due to lack of appropriate community mental health services [ 44 ]. In this case, there is an unmet need for coordination and close co-operation between the general practitioners and community mental health services in order to improve appropriateness of referals [ 45 ]. The three indicators for qualitative assessment of appropriateness included in the review adequately quantify this dimension of services.
Accessibility to services is crucial for early treatment, and therefore for prognosis. Accessibility in mental health services is a core issue that affects all underprivileged people with mental health issues and is more evident in vulnerable subpopulations as the immigrants and refugees [ 46 , 47 ]. Accessibility also relates to cultural indicators such as stigma levels. Stigma has three dimensions: it is the set of negative mentalities, prejudice, and discrimination related to mental illness. The stigma comes from and concerns citizens in general and mental health professionals in particular and in particular the mentally ill and their relatives. The stigma discourages the patient from going to the doctor for early treatment, while later undermining the patient’s psychosocial recovery and social reintegration. High accessibility is related to less stigma and vice versa in the context of equity in healthcare, human rights, community involvement, continuity of care until patients’ recovery [ 48 ]. However, beyond stigma, accessibility also depends on the adequacy and availability of appropriate hospital and community mental health structures [ 44 , 49 , 50 ], as well as the absence of waiting lists for evaluations, hospitalizations, and outpatient therapies, as can be described.
In assessing the quality of mental health services, the degree of acceptability and overall opinion of service users is valuable and should be taken into account [ 51 , 52 , 53 , 54 ]. User evaluation is qualitative through interviews and complaint/suggestion boxes but also quantitative, through weighted questionnaires to which users can systematically respond, for example on the day of their dismissal from the clinic [ 55 ]. Factors that are playing an important role in forming patients’ opinion is respect and trust, feeling safety, information and explanation about clinical decisions, and family involvement in the treatment and rehabilitation process [ 56 , 57 ]. Particular attention should be paid to the evaluation by patients of their experience of hospitalization with regard to the degree of application of the General Data Protection Rule (GDPR).
The competence of mental health professionals is included in the evaluation and evaluated through the development of lifelong learning programs. There is increasing evidence and published guidance from the World Psychiatric Association (WPA) and European Psychiatric Association (EPA) regarding the importance of cultural competence for mental health professionals, in the current context of increased migration in the European Union as well as many other countries [ 58 , 59 , 60 , 61 ]. Additionally, of great importance is the development of competence for the treatment of sensitive populations, such as the sexual and gender minority populations [ 62 ]. In the field of Child and Adolescent Psychiatry, the availability of competent clinicians in the field of child sexual abuse is also considered to be of great importance [ 63 ].
The efficacy of therapeutic interventions is an important element of the quality of mental health services and is evaluated through indicators assessing the outcome of therapies and, more generally, the prognosis of patients with specific diagnoses and the structural characteristics of services.
Ensuring therapeutic continuity is an important element of quality and is evaluated both by the seamless referral, mobility, and accessibility of patients to the most appropriate health care services as well as by the smooth transition from inpatient care to outpatient services. Despite the debate between continuity of care versus specialization of teams, continuity of care has been related to better social functioning, especially when primary care and mental health care, addiction and social welfare services are oriented towards recovery [ 64 , 65 , 66 ].
Evaluating the effectiveness of mental health services is a multifaceted quality control process. It includes indicators of mainly economic interests that are important for the sustainability and sustainable development of structures, services, and the mental health system. Recruitment, funding, and resource utilization are important aspects that should be clearly included in the strategic planning and action plan of the organization.
Safety is an important element of quality and includes the safety of patients as well as health professionals. Violence in mainly verbal and rarely physical, and in danger are mainly male nurses who are working in the emergency psychiatry ward. Training in communication skills and de-escalating skills are important pillars of violence prevention in mental health services settings [ 56 , 67 , 68 ]. In addition to the security of individuals, the security of their personal data is of particular importance. Personal data relating to mental health services are by definition sensitive personal data. Therefore, their protection is strictly regulated by the EU General Data Protection Regulation.
The review includes the author’s most important quality indicators that cover all aspects of mental health service quality assessment. The current trend in measuring the quality of mental health has been taken into account, where research interest has now shifted from measuring structures and services to prognosis, outcome, and outcome measurement [ 69 ]. Nor has it escaped our attention that the over-focus on outcomes often overlooks populations with low access to mental health services, mainly due to their disadvantaged socio-economic status [ 70 ]. Efforts have been made so that the findings can be generalized in order to provide a tool for quality assessment and improvement in any health system.
However, it is a limitation of the study that that we did not search and, therefore, we did not include in this study, the body of published literature that did not have an English abstract. We considered only studies with an English abstract and we excluded those not referring to the developed countries, to the extent possible. The indicators presented in this study focus mainly on issues that are important in developed countries such as Cyprus, Greece and the rest of the EU Member States, rather than to poor underdeveloped or developing countries. Poor and underdeveloped countries may have other priorities, such as simply providing food and shelter/asylum for patients with reduced functionality due to mental disorders. Although even providing housing for the homeless with severe mental disorders is not sometimes a simple process, it is still a challenge even for the modern welfare state and the developed mental health services of wealthy European countries [ 71 ]. However, investing in mental health, regardless of the country’s GDP, has been described as having an excellent value-for-money relationship [ 72 ] and as a ‘best buy’ investment.
Another limitation of the study is the fact that it is not a systematic review but rather a narrative review, with a focus on clinically relevant indicators, that is, those that will ensure clinical quality. In particular, of the many indicators proposed in the literature, those with the greatest clinical relevance have been selected, which have as direct an impact as possible on improving the quality of mental health services enjoyed by the patient, and not on indicators that may have specialized economic, political or academic/research interest. Likewise, due to size limitations, no emphasis has been placed on preventive psychiatric and quality of life in healthy or non-clinical populations, despite the fact that such indicators are available in the literature since the importance of prevention is high.
In order to be used in practice, the above quality assessment indicators, for improvement of the quality of mental health services, they must be converted into electronic form, so that they can be automatically and systematically measured by appropriate e-health software. This will enable the indicators to be calculated automatically and their scores made available to the management team at any time. The existence of an integrated IT system for the collection of mental health data and indicators has been considered essential, and the establishment of a specialized independent European Mental Health Observatory within the EU has been proposed as an urgent need [ 73 ]. At Member State local level, in the context of administrative transparency in the use of resources now demanded by the public administration rules, it would be beneficial for index scores to be published each year on the Internet with open access to the public. This will not only ensure political transparency in the use of state resources, but it will also provide research data from authors working in the fields of health policy and planning. Future research directions in the may include the use of data envelopment analysis (DEA) methodology for the estimation of production frontiers and benchmarking of the mental health services [ 74 ] as well as the Grossman model of health demand of medical care [ 75 ], as already has been done in other fields of health studies.
The long-term goal of this study is to provide an opportunity for Mental Health professionals to use research evidence, as should be expected, to underpin the public and mental health policy. A limited number of studies have so far partially tackled the problem of connecting the quality assessment issues with the performance of mental health systems [ 76 , 77 , 78 ]. This study provides a holistic approach to connect the quality aspects of a Mental Health System with its efficiency. Furthermore, the increasing availability of healthcare data will enhance the possibility of driving an important decision on how to build up a complex mental health system efficiently using Big data [ 79 , 80 ]. It is important to note that a set of indicators can be used simultaneously, and these can be used to frame the issues and to define the problems under consideration. Different criteria mean different problems. The indicators do not suggest a solution to the problem under consideration. Indicators collectively can help to clarify and quantify policy objectives and strategies for an optimized Mental Healthcare system.
Mental health is an important social parameter related to well-being, quality of life, human rights, but also to economics, creativity, productivity, sustainability, and sustainable development. Mental health is, therefore, socially good and the welfare state has a responsibility to maintain and improve it, through a system of mental health services of high quality, but also through interprofessional health education and preventive psychiatry programs. Improving the quality of mental health services requires assessing the existing quality and measuring and quantifying it so that comparisons can be made feasible over time at local, state, and transnational levels. Through the evaluation and measurement of quality with the proposed indicators, the areas of the mental health system that need to be emphasized and improved are revealed. Additionally, the optimization of available human resources and funding is optimized, and the mental health policies and planning of mental health services, based on scientific evidence, are encouraged.
L.S. and M.A.T. conceptualization, L.S. wrote the paper, M.A.T. reviewed and edited the paper, M.A.T. supervision. All authors have read and agreed to the published version of the manuscript.
This research received no external funding.
Conflicts of Interest
The authors declare no conflicts of interest.
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- Review Article
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- Published: 22 April 2020
Deep learning in mental health outcome research: a scoping review
- Chang Su 1 ,
- Zhenxing Xu 1 ,
- Jyotishman Pathak 1 &
- Fei Wang 1
Translational Psychiatry volume 10 , Article number: 116 ( 2020 ) Cite this article
- Psychiatric disorders
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients’ historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
Mental illness is a type of health condition that changes a person’s mind, emotions, or behavior (or all three), and has been shown to impact an individual’s physical health 1 , 2 . Mental health issues including depression, schizophrenia, attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), etc., are highly prevalent today and it is estimated that around 450 million people worldwide suffer from such problems 1 . In addition to adults, children and adolescents under the age of 18 years also face the risk of mental health disorders. Moreover, mental health illnesses have also been one of the most serious and prevalent public health problems. For example, depression is a leading cause of disability and can lead to an increased risk for suicidal ideation and suicide attempts 2 .
To better understand the mental health conditions and provide better patient care, early detection of mental health problems is an essential step. Different from the diagnosis of other chronic conditions that rely on laboratory tests and measurements, mental illnesses are typically diagnosed based on an individual’s self-report to specific questionnaires designed for the detection of specific patterns of feelings or social interactions 3 . Due to the increasing availability of data pertaining to an individual’s mental health status, artificial intelligence (AI) and machine learning (ML) technologies are being applied to improve our understanding of mental health conditions and have been engaged to assist mental health providers for improved clinical decision-making 4 , 5 , 6 . As one of the latest advances in AI and ML, deep learning (DL), which transforms the data through layers of nonlinear computational processing units, provides a new paradigm to effectively gain knowledge from complex data 7 . In recent years, DL algorithms have demonstrated superior performance in many data-rich application scenarios, including healthcare 8 , 9 , 10 .
In a previous study, Shatte et al. 11 explored the application of ML techniques in mental health. They reviewed literature by grouping them into four main application domains: diagnosis, prognosis, and treatment, public health, as well as research and clinical administration. In another study, Durstewitz et al. 9 explored the emerging area of application of DL techniques in psychiatry. They focused on DL in the studies of brain dynamics and subjects’ behaviors, and presented the insights of embedding the interpretable computational models into statistical context. In contrast, this study aims to provide a scoping review of the existing research applying DL methodologies on the analysis of different types of data related to mental health conditions. The reviewed articles are organized into four main groups according to the type of the data analyzed, including the following: (1) clinical data, (2) genetic and genomics data, (3) vocal and visual expression data, and (4) social media data. Finally, the challenges the current studies faced with, as well as future research directions towards bridging the gap between the application of DL algorithms and patient care, are discussed.
Deep learning overview
ML aims at developing computational algorithms or statistical models that can automatically infer hidden patterns from data 12 , 13 . Recent years have witnessed an increasing number of ML models being developed to analyze healthcare data 4 . However, conventional ML approaches require a significant amount of feature engineering for optimal performance—a step that is necessary for most application scenarios to obtain good performance, which is usually resource- and time-consuming.
As the newest wave of ML and AI technologies, DL approaches aim at the development of an end-to-end mechanism that maps the input raw features directly into the outputs through a multi-layer network structure that is able to capture the hidden patterns within the data. In this section, we will review several popular DL model architectures, including deep feedforward neural network (DFNN), recurrent neural network (RNN) 14 , convolutional neural network (CNN) 15 , and autoencoder 16 . Figure 1 provides an overview of these architectures.
a Deep feedforward neural network (DFNN). It is the basic design of DL models. Commonly, a DFNN contains multiple hidden layers. b A recurrent neural network (RNN) is presented to process sequence data. To encode history information, each recurrent neuron receives the input element and the state vector of the predecessor neuron, and yields a hidden state fed to the successor neuron. For example, not only the individual information but also the dependence of the elements of the sequence x 1 → x 2 → x 3 → x 4 → x 5 is encoded by the RNN architecture. c Convolutional neural network (CNN). Between input layer (e.g., input neuroimage) and output layer, a CNN commonly contains three types of layers: the convolutional layer that is to generate feature maps by sliding convolutional kernels in the previous layer; the pooling layer is used to reduce dimensionality of previous convolutional layer; and the fully connected layer is to make prediction. For the illustrative purpose, this example only has one layer of each type; yet, a real-world CNN would have multiple convolutional and pooling layers (usually in an interpolated manner) and one fully connected layer. d Autoencoder consists of two components: the encoder, which learns to compress the input data into a latent representation layer by layer, whereas the decoder, inverse to the encoder, learns to reconstruct the data at the output layer. The learned compressed representations can be fed to the downstream predictive model.
Deep feedforward neural network
Artificial neural network (ANN) is proposed with the intention of mimicking how human brain works, where the basic element is an artificial neuron depicted in Fig. 2a . Mathematically, an artificial neuron is a nonlinear transformation unit, which takes the weighted summation of all inputs and feeds the result to an activation function, such as sigmoid, rectifier (i.e., rectified linear unit [ReLU]), or hyperbolic tangent (Fig. 2b ). An ANN is composed of multiple artificial neurons with different connection architectures. The simplest ANN architecture is the feedforward neural network (FNN), which stacks the neurons layer by layer in a feedforward manner (Fig. 1a ), where the neurons across adjacent layers are fully connected to each other. The first layer of the FNN is the input layer that each unit receives one dimension of the data vector. The last layer is the output layer that outputs the probabilities that a subject belonging to different classes (in classification). The layers between the input and output layers are the hidden layers. A DFNN usually contains multiple hidden layers. As shown in Fig. 2a , there is a weight parameter associated with each edge in the DFNN, which needs to be optimized by minimizing some training loss measured on a specific training dataset (usually through backpropagation 17 ). After the optimal set of parameters are learned, the DFNN can be used to predict the target value (e.g., class) of any testing data vectors. Therefore, a DFNN can be viewed as an end-to-end process that transforms a specific raw data vector to its target layer by layer. Compared with the traditional ML models, DFNN has shown superior performance in many data mining tasks and have been introduced to the analysis of clinical data and genetic data to predict mental health conditions. We will discuss the applications of these methods further in the Results section.
a An illustration of basic unit of neural networks, i.e., artificial neuron. Each input x i is associated with a weight w i . The weighted sum of all inputs Σ w i x i is fed to a nonlinear activation function f to generate the output y j of the j -th neuron, i.e., y j = f (Σ w i x i ). b Illustrations of the widely used nonlinear activation function.
Recurrent neural network
RNNs were designed to analyze sequential data such as natural language, speech, and video. Given an input sequence, the RNN processes one element of the sequence at a time by feeding to a recurrent neuron. To encode the historical information along the sequence, each recurrent neuron receives the input element at the corresponding time point and the output of the neuron at previous time stamp, and the output will also be provided to the neuron at next time stamp (this is also where the term “recurrent” comes from). An example RNN architecture is shown in Fig. 1b where the input is a sequence of words (a sentence). The recurrence link (i.e., the edge linking different neurons) enables RNN to capture the latent semantic dependencies among words and the syntax of the sentence. In recent years, different variants of RNN, such as long short-term memory (LSTM) 18 and gated recurrent unit 19 have been proposed, and the main difference among these models is how the input is mapped to the output for the recurrent neuron. RNN models have demonstrated state-of-the-art performance in various applications, especially natural language processing (NLP; e.g., machine translation and text-based classification); hence, they hold great premise in processing clinical notes and social media posts to detect mental health conditions as discussed below.
Convolutional neural network
CNN is a specific type of deep neural network originally designed for image analysis 15 , where each pixel corresponds to a specific input dimension describing the image. Similar to a DFNN, CNN also maps these input image pixels to the corresponding target (e.g., image class) through layers of nonlinear transformations. Different from DFNN, where only fully connected layers are considered, there are typically three types of layers in a CNN: a convolution–activation layer, a pooling layer, and a fully connected layer (Fig. 1c ). The convolution–activation layer first convolves the entire feature map obtained from previous layer with small two-dimensional convolution filters. The results from each convolution filter are activated through a nonlinear activation function in the same way as a DFNN. A pooling layer reduces the size of the feature map through sub-sampling. The fully connected layer is analogous to the hidden layer in a DFNN, where each neuron is connected to all neurons of the previous layer. The convolution–activation layer extracts locally invariant patterns from the feature maps. The pooling layer effectively reduces the feature dimensionality to avoid model overfitting. The fully connected layer explores the global feature interactions as in DFNNs. Different combinations of these three types of layers constitute different CNN architectures. Because of the various characteristics of images such as local self-similarity, compositionality, and translational and deformation invariance, CNN has demonstrated state-of-the-art performance in many computer vision tasks 7 . Hence, the CNN models are promising in processing clinical images and expression data (e.g., facial expression images) to detect mental health conditions. We will discuss the application of these methods in the Results section.
Autoencoder is a special variant of the DFNN aimed at learning new (usually more compact) data representations that can optimally reconstruct the original data vectors 16 , 20 . An autoencoder typically consists of two components (Fig. 1d ) as follows: (1) the encoder, which learns new representations (usually with reduced dimensionality) from the input data through a multi-layer FNN; and (2) the decoder, which is exactly the reverse of the encoder, reconstructs the data in their original space from the representations derived from the encoder. The parameters in the autoencoder are learned through minimizing the reconstruction loss. Autoencoder has demonstrated the capacity of extracting meaningful features from raw data without any supervision information. In the studies of mental health outcomes, the use of autoencoder has resulted in desirable improvement in analyzing clinical and expression image data, which will be detailed in the Results section.
The processing and reporting of the results of this review were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 21 . To thoroughly review the literature, a two-step method was used to retrieve all the studies on relevant topics. First, we conducted a search of the computerized bibliographic databases including PubMed and Web of Science. The search strategy is detailed in Supplementary Appendix 1 . The literature search comprised articles published until April 2019. Next, a snowball technique was applied to identify additional studies. Furthermore, we manually searched other resources, including Google Scholar, and Institute of Electrical and Electronics Engineers (IEEE Xplore), to find additional relevant articles.
Figure 3 presents the study selection process. All articles were evaluated carefully and studies were excluded if: (1) the main outcome is not a mental health condition; (2) the model involved is not a DL algorithm; (3) full-text of the article is not accessible; and (4) the article is written not in English.
In total, 57 studies, in terms of clinical data analysis, genetic data analysis, vocal and visual expression data analysis, and social media data analysis, which met our eligibility criteria, were included in this review.
A total of 57 articles met our eligibility criteria. Most of the reviewed articles were published between 2014 and 2019. To clearly summarize these articles, we grouped them into four categories according to the types of data analyzed, including (1) clinical data, (2) genetic and genomics data, (3) vocal and visual expression data, and (4) social media data. Table 1 summarizes the characteristics of these selected studies.
Previous studies have shown that neuroimages can record evidence of neuropsychiatric disorders 22 , 23 . Two common types of neuroimage data analyzed in mental health studies are functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) data. In fMRI data, the brain activity is measured by identification of the changes associated with blood flow, based on the fact that cerebral blood flow and neuronal activation are coupled 24 . In sMRI data, the neurological aspect of a brain is described based on the structural textures, which show some information in terms of the spatial arrangements of voxel intensities in 3D. Recently, DL technologies have been demonstrated in analyzing both fMRI and sMRI data.
One application of DL in fMRI and sMRI data is the identification of ADHD 25 , 26 , 27 , 28 , 29 , 30 , 31 . To learn meaningful information from the neuroimages, CNN and deep belief network (DBN) models were used. In particular, the CNN models were mainly used to identify local spatial patterns and DBN models were to obtain a deep hierarchical representation of the neuroimages. Different patterns were discovered between ADHDs and controls in the prefrontal cortex and cingulated cortex. Also, several studies analyzed sMRIs to investigate schizophrenia 32 , 33 , 34 , 35 , 36 , where DFNN, DBN, and autoencoder were utilized. These studies reported abnormal patterns of cortical regions and cortical–striatal–cerebellar circuit in the brain of schizophrenia patients, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. Moreover, the use of DL in neuroimages also targeted at addressing other mental health disorders. Geng et al. 37 proposed to use CNN and autoencoder to acquire meaningful features from the original time series of fMRI data for predicting depression. Two studies 31 , 38 integrated the fMRI and sMRI data modalities to develop predictive models for ASDs. Significant relationships between fMRI and sMRI data were observed with regard to ASD prediction.
Challenges and opportunities
The aforementioned studies have demonstrated that the use of DL techniques in analyzing neuroimages can provide evidence in terms of mental health problems, which can be translated into clinical practice and facilitate the diagnosis of mental health illness. However, multiple challenges need to be addressed to achieve this objective. First, DL architectures generally require large data samples to train the models, which may pose a difficulty in neuroimaging analysis because of the lack of such data 39 . Second, typically the imaging data lie in a high-dimensional space, e.g., even a 64 × 64 2D neuroimage can result in 4096 features. This leads to the risk of overfitting by the DL models. To address this, most existing studies reported to utilize MRI data preprocessing tools such as Statistical Parametric Mapping ( https://www.fil.ion.ucl.ac.uk/spm/ ), Data Processing Assistant for Resting-State fMRI 40 , and fMRI Preprocessing Pipeline 41 to extract useful features before feeding to the DL models. Even though an intuitive attribute of DL is the capacity to learn meaningful features from raw data, feature engineering tools are needed especially in the case of small sample size and high-dimensionality, e.g., the neuroimage analysis. The use of such tools mitigates the overfitting risk of DL models. As reported in some selected studies 28 , 31 , 35 , 37 , the DL models can benefit from feature engineering techniques and have been shown to outperform the traditional ML models in the prediction of multiple conditions such as depression, schizophrenia, and ADHD. However, such tools extract features relying on prior knowledge; hence may omit some information that is meaningful for mental outcome research but unknown yet. An alternative way is to use CNN to automatically extract information from the raw data. As reported in the previous study 10 , CNNs perform well in processing raw neuroimage data. Among the studies reviewed in this study, three 29 , 30 , 37 reported to involve CNN layers and achieved desirable performances.
As a low-cost, small-size, and high temporal resolution signal containing up to several hundred channels, analysis of electroencephalogram (EEG) data has gained significant attention to study brain disorders 42 . As the EEG signal is one kind of streaming data that presents a high density and continuous characteristics, it challenges traditional feature engineering-based methods to obtain sufficient information from the raw EEG data to make accurate predictions. To address this, recently the DL models have been employed to analyze raw EEG signal data.
Four articles reviewed proposed to use DL in understanding mental health conditions based on the analysis of EEG signals. Acharya et al. 43 used CNN to extract features from the input EEG signals. They found that the EEG signals from the right hemisphere of the human brain are more distinctive in terms of the detection of depression than those from the left hemisphere. The findings provided shreds of evidence that depression is associated with a hyperactive right hemisphere. Mohan et al. 44 modeled the raw EEG signals by DFNN to obtain information about the human brain waves. They found that the signals collected from the central (C3 and C4) regions are marginally higher compared with other brain regions, which can be used to distinguish the depressed and normal subjects from the brain wave signals. Zhang et al. 45 proposed a concatenated structure of deep recurrent and 3D CNN to obtain EEG features across different tasks. They reported that the DL model can capture the spectral changes of EEG hemispheric asymmetry to distinguish different mental workload effectively. Li et al. 46 presented a computer-aided detection system by extracting multiple types of information (e.g., spectral, spatial, and temporal information) to recognize mild depression based on CNN architecture. The authors found that both spectral and temporal information of EEG are crucial for prediction of depression.
EEG data are usually classified as streaming data that are continuous and are of high density. Despite the initial success in applying DL algorithms to analyze EEG data for studying multiple mental health conditions, there exist several challenges. One major challenge is that raw EEG data gathered from sensors have a certain degree of erroneous, noisy, and redundant information caused by discharged batteries, failures in sensor readings, and intermittent communication loss in wireless sensor networks 47 . This may challenge the model in extracting meaningful information from noise. Multiple preprocessing steps (e.g., data denoising, data interpolation, data transformation, and data segmentation) are necessary for dealing with the raw EEG signal before feeding to the DL models. Besides, due to the dense characteristics in the raw EEG data, analysis of the streaming data is computationally more expensive, which poses a challenge for the model architecture selection. A proper model should be designed relatively with less training parameters. This is one reason why the reviewed studies are mainly based on the CNN architecture.
Electronic health records
Electronic health records (EHRs) are systematic collections of longitudinal, patient-centered records. Patients’ EHRs consist of both structured and unstructured data: the structured data include information about a patient’s diagnosis, medications, and laboratory test results, and the unstructured data include information in clinical notes. Recently, DL models have been applied to analyze EHR data to study mental health disorders 48 .
The first and foremost issue for analyzing the structured EHR data is how to appropriately handle the longitudinal records. Traditional ML models address this by collapsing patients’ records within a certain time window into vectors, which comprised the summary of statistics of the features in different dimensions 49 . For instance, to estimate the probability of suicide deaths, Choi et al. 50 leveraged a DFNN to model the baseline characteristics. One major limitation of these studies is the omittance of temporality among the clinical events within EHRs. To overcome this issue, RNNs are more commonly used for EHR data analysis as an RNN intuitively handles time-series data. DeepCare 51 , a long short-term memory network (LSTM)-based DL model, encodes patient’s long-term health state trajectories to predict the future outcomes of depressive episodes. As the LSTM architecture appropriately captures disease progression by modeling the illness history and the medical interventions, DeepCare achieved over 15% improvement in prediction, compared with the conventional ML methods. In addition, Lin et al. 52 designed two DFNN models for the prediction of antidepressant treatment response and remission. The authors reported that the proposed DFNN can achieve an area under the receiver operating characteristic curve (AUC) of 0.823 in predicting antidepressant response.
Analyzing the unstructured clinical notes in EHRs refers to the long-standing topic of NLP. To extract meaningful knowledge from the text, conventional NLP approaches mostly define rules or regular expressions before the analysis. However, it is challenging to enumerate all possible rules or regular expressions. Due to the recent advance of DL in NLP tasks, DL models have been developed to mine clinical text data from EHRs to study mental health conditions. Geraci et al. 53 utilized term frequency-inverse document frequency to represent the clinical documents by words and developed a DFNN model to identify individuals with depression. One major limitation of such an approach is that the semantics and syntax of sentences are lost. In this context, CNN 54 and RNN 55 have shown superiority in modeling syntax for text-based prediction. In particular, CNN has been used to mine the neuropsychiatric notes for predicting psychiatric symptom severity 56 , 57 . Tran and Kavuluru 58 used an RNN to analyze the history of present illness in neuropsychiatric notes for predicting mental health conditions. The model engaged an attention mechanism 55 , which can specify the importance of the words in prediction, making the model more interpretable than their previous CNN model 56 .
Although DL has achieved promising results in EHR analysis, several challenges remain unsolved. On one hand, different from diagnosing physical health condition such as diabetes, the diagnosis of mental health conditions lacks direct quantitative tests, such as a blood chemistry test, a buccal swab, or urinalysis. Instead, the clinicians evaluate signs and symptoms through patient interviews and questionnaires during which they gather information based on patient’s self-report. Collection and deriving inferences from such data deeply relies on the experience and subjectivity of the clinician. This may account for signals buried in noise and affect the robustness of the DL model. To address this challenge, a potential way is to comprehensively integrate multimodal clinical information, including structured and unstructured EHR information, as well as neuroimaging and EEG data. Another way is to incorporate existing medical knowledge, which can guide model being trained in the right direction. For instance, the biomedical knowledge bases contain massive verified interactions between biomedical entities, e.g., diseases, genes, and drugs 59 . Incorporating such information brings in meaningful medical constraints and may help to reduce the effects of noise on model training process. On the other hand, implementing a DL model trained from one EHR system into another system is challenging, because EHR data collection and representation is rarely standardized across hospitals and clinics. To address this issue, national/international collaborative efforts such as Observational Health Data Sciences and Informatics ( https://ohdsi.org ) have developed common data models, such as OMOP, to standardize EHR data representation for conducting observational data analysis 60 .
Multiple studies have found that mental disorders, e.g., depression, can be associated with genetic factors 61 , 62 . Conventional statistical studies in genetics and genomics, such as genome-wide association studies, have identified many common and rare genetic variants, such as single-nucleotide polymorphisms (SNPs), associated with mental health disorders 63 , 64 . Yet, the effect of the genetic factors is small and many more have not been discovered. With the recent developments in next-generation sequencing techniques, a massive volume of high-throughput genome or exome sequencing data are being generated, enabling researchers to study patients with mental health disorders by examining all types of genetic variations across an individual’s genome. In recent years, DL 65 , 66 has been applied to identify genetic risk factors associated with mental illness, by borrowing the capacity of DL in identifying highly complex patterns in large datasets. Khan and Wang 67 integrated genetic annotations, known brain expression quantitative trait locus, and enhancer/promoter peaks to generate feature vectors of variants, and developed a DFNN, named ncDeepBrain, to prioritized non-coding variants associated with mental disorders. To further prioritize susceptibility genes, they designed another deep model, iMEGES 68 , which integrates the ncDeepBrain score, general gene scores, and disease-specific scores for estimating gene risk. Wang et al. 69 developed a novel deep architecture that combines deep Boltzmann machine architecture 70 with conditional and lateral connections derived from the gene regulatory network. The model provided insights about intermediate phenotypes and their connections to high-level phenotypes (disease traits). Laksshman et al. 71 used exome sequencing data to predict bipolar disorder outcomes of patients. They developed a CNN and used the convolution mechanism to capture correlations of the neighboring loci within the chromosome.
Although the use of genetic data in DL in studying mental health conditions shows promise, multiple challenges need to be addressed. For DL-based risk c/gene prioritization efforts, one major challenge is the limitation of labeled data. On one hand, the positive samples are limited, as known risk SNPs or genes associated with mental health conditions are limited. For example, there are about 108 risk loci that were genome-wide significant in ASD. On the other hand, the negative samples (i.e., SNPs, variants, or genes) may not be the “true” negative, as it is unclear whether they are associated with the mental illness yet. Moreover, it is also challenging to develop DL models for analyzing patient’s sequencing data for mental illness prediction, as the sequencing data are extremely high-dimensional (over five million SNPs in the human genome). More prior domain knowledge is needed to guide the DL model extracting patterns from the high-dimensional genomic space.
Vocal and visual expression data
The use of vocal (voice or speech) and visual (video or image of facial or body behaviors) expression data has gained the attention of many studies in mental health disorders. Modeling the evolution of people’s emotional states from these modalities has been used to identify mental health status. In essence, the voice data are continuous and dense signals, whereas the video data are sequences of frames, i.e., images. Conventional ML models for analyzing such types of data suffer from the sophisticated feature extraction process. Due to the recent success of applying DL in computer vision and sequence data modeling, such models have been introduced to analyze the vocal and/or visual expression data. In this work, most articles reviewed are to predict mental health disorders based on two public datasets: (i) the Chi-Mei corpus, collected by using six emotional videos to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 , 74 , 75 , collected within human–computer interaction scenario. The proposed models include CNNs, RNNs, autoencoders, as well as hybrid models based on the above ones. In particular, CNNs were leveraged to encode the temporal and spectral features from the voice signals 76 , 77 , 78 , 79 , 80 and static facial or physical expression features from the video frames 79 , 81 , 82 , 83 , 84 . Autoencoders were used to learn low-dimensional representations for people’s vocal 85 , 86 and visual expression 87 , 88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76 , 81 , 84 , 85 , 86 , 87 , 88 , 89 , 90 . Few studies focused on analyzing static images using a CNN architecture to predict mental health status. Prasetio et al. 91 identified the stress types (e.g., neutral, low stress, and high stress) from facial frontal images. Their proposed CNN model outperforms the conventional ML models by 7% in terms of prediction accuracy. Jaiswal et al. 92 investigated the relationship between facial expression/gestures and neurodevelopmental conditions. They reported accuracy over 0.93 in the diagnostic prediction of ADHD and ASD by using the CNN architecture. In addition, thermal images that track persons’ breathing patterns were also fed to a deep model to estimate psychological stress level (mental overload) 93 .
From the above summary, we can observe that analyzing vocal and visual expression data can capture the pattern of subjects’ emotion evolution to predict mental health conditions. Despite the promising initial results, there remain challenges for developing DL models in this field. One major challenge is to link vocal and visual expression data with the clinical data of patients, given the difficulties involved in collecting such expression data during clinical practice. Current studies analyzed vocal and visual expression over individual datasets. Without clinical guidance, the developed prediction models have limited clinical meanings. Linking patients’ expression information with clinical variables may help to improve both the interpretability and robustness of the model. For example, Gupta et al. 94 designed a DFNN for affective prediction from audio and video modalities. The model incorporated depression severity as the parameter, linking the effects of depression on subjects’ affective expressions. Another challenge is the limitation of the samples. For example, the Chi-Mei dataset contains vocal–visual data from only 45 individuals (15 with bipolar disorder, 15 with unipolar disorder, and 15 healthy controls). Also, there is a lack of “emotion labels” for people’s vocal and visual expression. Apart from improving the datasets, an alternative way to solve this challenge is to use transfer learning, which transfers knowledge gained with one dataset (usually more general) to the target dataset. For example, some studies trained autoencoder in public emotion database such as eNTERFACE 95 to generate emotion profiles (EPs). Other studies 83 , 84 pre-trained CNN over general facial expression datasets 96 , 97 for extracting face appearance features.
Social media data
With the widespread proliferation of social media platforms, such as Twitter and Reddit, individuals are increasingly and publicly sharing information about their mood, behavior, and any ailments one might be suffering. Such social media data have been used to identify users’ mental health state (e.g., psychological stress and suicidal ideation) 6 .
In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 , 99 , 100 , 101 , depression identification 102 , 103 , 104 , 105 , 106 , and estimation of suicide risk 103 , 105 , 107 , 108 , 109 . In general, the core concept across these work is to mine the textual, and where applicable graphical, content of users’ social media posts to discover cues for mental health disorders. In this context, the RNN and CNN were largely used by the researchers. Especially, RNN usually introduces an attention mechanism to specify the importance of the input elements in the classification process 55 . This provides some interpretability for the predictive results. For example, Ive et al. 103 proposed a hierarchical RNN architecture with an attention mechanism to predict the classes of the posts (including depression, autism, suicidewatch, anxiety, etc.). The authors observed that, benefitting from the attention mechanism, the model can predict risk text efficiently and extract text elements crucial for making decisions. Coppersmith et al. 107 used LSTM to discover quantifiable signals about suicide attempts based on social media posts. The proposed model can capture contextual information between words and obtain nuances of language related to suicide.
Apart from text, users also post images on social media. The properties of the images (e.g., color theme, saturation, and brightness) provide some cues reflecting users’ mental health status. In addition, millions of interactions and relationships among users can reflect the social environment of individuals that is also a kind of risk factors for mental illness. An increasing number of studies attempted to combine these two types of information with text content for predictive modeling. For example, Lin et al. 99 leveraged the autoencoder to extract low-level and middle-level representations from texts, images, and comments based on psychological and art theories. They further extended their work with a hybrid model based on CNN by integrating post content and social interactions 101 . The results provided an implication that the social structure of the stressed users’ friends tended to be less connected than that of the users without stress.
The aforementioned studies have demonstrated that using social media data has the potential to detect users with mental health problems. However, there are multiple challenges towards the analysis of social media data. First, given that social media data are typically de-identified, there is no straightforward way to confirm the “true positives” and “true negatives” for a given mental health condition. Enabling the linkage of user’s social media data with their EHR data—with appropriate consent and privacy protection—is challenging to scale, but has been done in a few settings 110 . In addition, most of the previous studies mainly analyzed textual and image data from social media platforms, and did not consider analyzing the social network of users. In one study, Rosenquist et al. 111 reported that the symptoms of depression are highly correlated inside the circle of friends, indicating that social network analysis is likely to be a potential way to study the prevalence of mental health problems. However, comprehensively modeling text information and network structure remains challenging. In this context, graph convolutional networks 112 have been developed to address networked data mining. Moreover, although it is possible to discover online users with mental illness by social media analysis, translation of this innovation into practical applications and offer aid to users, such as providing real-time interventions, are largely needed 113 .
Discussion: findings, open issues, and future directions
The purpose of this study is to investigate the current state of applications of DL techniques in studying mental health outcomes. Out of 2261 articles identified based on our search terms, 57 studies met our inclusion criteria and were reviewed. Some studies that involved DL models but did not highlight the DL algorithms’ features on analysis were excluded. From the above results, we observed that there are a growing number of studies using DL models for studying mental health outcomes. Particularly, multiple studies have developed disease risk prediction models using both clinical and non-clinical data, and have achieved promising initial results.
DL models “think to learn” like a human brain relying on their multiple layers of interconnected computing neurons. Therefore, to train a deep neural network, there are multiple parameters (i.e., weights associated links between neurons within the network) being required to learn. This is one reason why DL has achieved great success in the fields where a massive volume of data can be easily collected, such as computer vision and text mining. Yet, in the health domain, the availability of large-scale data is very limited. For most selected studies in this review, the sample sizes are under a scale of 10 4 . Data availability is even more scarce in the fields of neuroimaging, EEG, and gene expression data, as such data reside in a very high-dimensional space. This then leads to the problem of “curse of dimensionality” 114 , which challenges the optimization of the model parameters.
One potential way to address this challenge is to reduce the dimensionality of the data by feature engineering before feeding information to the DL models. On one hand, feature extraction approaches can be used to obtain different types of features from the raw data. For example, several studies reported in this review have attempted to use preprocessing tools to extract features from neuroimaging data. On the other hand, feature selection that is commonly used in conventional ML models is also an option to reduce data dimensionality. However, the feature selection approaches are not often used in the DL application scenario, as one of the intuitive attributes of DL is the capacity to learn meaningful features from “all” available data. The alternative way to address the issue of data bias is to use transfer learning where the objective is to improve learning a new task through the transfer of knowledge from a related task that has already been learned 115 . The basic idea is that data representations learned in the earlier layers are more general, whereas those learned in the latter layers are more specific to the prediction task 116 . In particular, one can first pre-train a deep neural network in a large-scale “source” dataset, then stack fully connected layers on the top of the network and fine-tune it in the small “target” dataset in a standard backpropagation manner. Usually, samples in the “source” dataset are more general (e.g., general image data), whereas those in the “target” dataset are specific to the task (e.g., medical image data). A popular example of the success of transfer learning in the health domain is the dermatologist-level classification of skin cancer 117 . The authors introduced Google’s Inception v3 CNN architecture pre-trained over 1.28 million general images and fine-tuned in the clinical image dataset. The model achieved very high-performance results of skin cancer classification in epidermal (AUC = 0.96), melanocytic (AUC = 0.96), and melanocytic–dermoscopic images (AUC = 0.94). In facial expression-based depression prediction, Zhu et al. 83 pre-trained CNN on the public face recognition dataset to model the static facial appearance, which overcomes the issue that there is no facial expression label information. Chao et al. 84 also pre-trained CNN to encode facial expression information. The transfer scheme of both of the two studies has been demonstrated to be able to improve the prediction performance.
Diagnosis and prediction issues
Unlike the diagnosis of physical conditions that can be based on lab tests, diagnoses of the mental illness typically rely on mental health professionals’ judgment and patient self-report data. As a result, such a diagnostic system may not accurately capture the psychological deficits and symptom progression to provide appropriate therapeutic interventions 118 , 119 . This issue accordingly accounts for the limitation of the prediction models to assist clinicians to make decisions. Except for several studies using the unsupervised autoencoder for learning low-dimensional representations, most studies reviewed in this study reported using supervised DL models, which need the training set containing “true” (i.e., expert provided) labels to optimize the model parameters before the model being used to predict labels of new subjects. Inevitably, the quality of the expert-provided diagnostic labels used for training sets the upper-bound for the prediction performance of the model.
One intuitive route to address this issue is to use an unsupervised learning scheme that, instead of learning to predict clinical outcomes, aims at learning compacted yet informative representations of the raw data. A typical example is the autoencoder (as shown in Fig. 1d ), which encodes the raw data into a low-dimensional space, from which the raw data can be reconstructed. Some studies reviewed have proposed to leverage autoencoder to improve our understanding of mental health outcomes. A constraint of the autoencoder is that the input data should be preprocessed to vectors, which may lead to information loss for image and sequence data. To address this, recently convolutional-autoencoder 120 and LSTM-autoencoder 121 have been developed, which integrate the convolution layers and recurrent layers with the autoencoder architecture and enable us to learn informative low-dimensional representations from the raw image data and sequence data, respectively. For instance, Baytas et al. 122 developed a variation of LSTM-autoencoder on patient EHRs and grouped Parkinson’s disease patients into meaningful subtypes. Another potential way is to predict other clinical outcomes instead of the diagnostic labels. For example, several selected studies proposed to predict symptom severity scores 56 , 57 , 77 , 82 , 84 , 87 , 89 . In addition, Du et al. 108 attempted to identify suicide-related psychiatric stressors from users’ posts on Twitter, which plays an important role in the early prevention of suicidal behaviors. Furthermore, training model to predict future outcomes such as treatment response, emotion assessments, and relapse time is also a promising future direction.
The field of mental health is heterogeneous. On one hand, mental illness refers to a variety of disorders that affect people’s emotions and behaviors. On the other hand, though the exact causes of most mental illnesses are unknown to date, it is becoming increasingly clear that the risk factors for these diseases are multifactorial as multiple genetic, environmental, and social factors interact to influence an individual’s mental health 123 , 124 . As a result of domain heterogeneity, researchers have the chance to study the mental health problems from different perspectives, from molecular, genomic, clinical, medical imaging, physiological signal to facial, and body expressive and online behavioral. Integrative modeling of such multimodal data means comprehensively considering different aspects of the disease, thus likely obtaining deep insight into mental health. In this context, DL models have been developed for multimodal modeling. As shown in Fig. 4 , the hierarchical structure of DL makes it easily compatible with multimodal integration. In particular, one can model each modality with a specific network and combine them by the final fully connected layers, such that parameters can be jointly learned by a typical backpropagation manner. In this review, we found an increasing number of studies have attempted to use multimodal modeling. For example, Zou et al. 28 developed a multimodal model composed of two CNNs for modeling fMRI and sMRI modalities, respectively. The model achieved 69.15% accuracy in predicting ADHD, which outperformed the unimodal models (66.04% for fMRI modal-based and 65.86% for sMRI modal-based). Yang et al. 79 proposed a multimodal model to combine vocal and visual expression for depression cognition. The model results in 39% lower prediction error than the unimodal models.
One can model each modality with a specific network and combine them using the final fully-connected layers. In this way, parameters of the entire neural network can be jointly learned in a typical backpropagation manner.
Due to the end-to-end design, the DL models usually appear to be “black boxes”: they take raw data (e.g., MRI images, free-text of clinical notes, and EEG signals) as input, and yield output to reach a conclusion (e.g., the risk of a mental health disorder) without clear explanations of their inner working. Although this might not be an issue in other application domains such as identifying animals from images, in health not only the model’s prediction performance but also the clues for making the decision are important. For example in the neuroimage-based depression identification, despite estimation of the probability that a patient suffers from mental health deficits, the clinicians would focus more on recognizing abnormal regions or patterns of the brain associated with the disease. This is really important for convincing the clinical experts about the actions recommended from the predictive model, as well as for guiding appropriate interventions. In addition, as discussed above, the introduction of multimodal modeling leads to an increased challenge in making the models more interpretable. Attempts have been made to open the “black box” of DL 59 , 125 , 126 , 127 . Currently, there are two general directions for interpretable modeling: one is to involve the systematic modification of the input and the measure of any resulting changes in the output, as well as in the activation of the artificial neurons in the hidden layers. Such a strategy is usually used in CNN in identifying specific regions of an image being captured by a convolutional layer 128 . Another way is to derive tools to determine the contribution of one or more features of the input data to the output. In this case, the widely used tools include Shapley Additive Explanation 129 , LIME 127 , DeepLIFT 130 , etc., which are able to assign each feature an importance score for the specific prediction task.
Connection to therapeutic interventions
According to the studies reviewed, it is now possible to detect patients with mental illness based on different types of data. Compared with the traditional ML techniques, most of the reviewed DL models reported higher prediction accuracy. The findings suggested that the DL models are likely to assist clinicians in improved diagnosis of mental health conditions. However, to associate diagnosis of a condition with evidence-based interventions and treatment, including identification of appropriate medication 131 , prediction of treatment response 52 , and estimation of relapse risk 132 still remains a challenge. Among the reviewed studies, only one 52 proposed to target at addressing these issues. Thus, further efforts are needed to link the DL techniques with the therapeutic intervention of mental illness.
Another important direction is to incorporate domain knowledge. The existing biomedical knowledge bases are invaluable sources for solving healthcare problems 133 , 134 . Incorporating domain knowledge could address the limitation of data volume, problems of data quality, as well as model generalizability. For example, the unified medical language system 135 can help to identify medical entities from the text and gene–gene interaction databases 136 could help to identify meaningful patterns from genomic profiles.
Recent years have witnessed the increasing use of DL algorithms in healthcare and medicine. In this study, we reviewed existing studies on DL applications to study mental health outcomes. All the results available in the literature reviewed in this work illustrate the applicability and promise of DL in improving the diagnosis and treatment of patients with mental health conditions. Also, this review highlights multiple existing challenges in making DL algorithms clinically actionable for routine care, as well as promising future directions in this field.
World Health Organization. The World Health Report 2001: Mental Health: New Understanding, New Hope (World Health Organization, Switzerland, 2001).
Marcus, M., Yasamy, M. T., van Ommeren, M., Chisholm, D. & Saxena, S. Depression: A Global Public Health Concern (World Federation of Mental Health, World Health Organisation, Perth, 2012).
Hamilton, M. Development of a rating scale for primary depressive illness. Br. J. Soc. Clin. Psychol. 6 , 278–296 (1967).
CAS PubMed Google Scholar
Dwyer, D. B., Falkai, P. & Koutsouleris, N. Machine learning approaches for clinical psychology and psychiatry. Annu. Rev. Clin. Psychol. 14 , 91–118 (2018).
PubMed Google Scholar
Lovejoy, C. A., Buch, V. & Maruthappu, M. Technology and mental health: the role of artificial intelligence. Eur. Psychiatry 55 , 1–3 (2019).
Wongkoblap, A., Vadillo, M. A. & Curcin, V. Researching mental health disorders in the era of social media: systematic review. J. Med. Internet Res. 19 , e228 (2017).
PubMed PubMed Central Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436 (2015).
Miotto, R., Wang, F., Wang, S., Jiang, X. & Dudley, J. T. Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinformatics 19 , 1236–1246 (2017).
Durstewitz, D., Koppe, G. & Meyer-Lindenberg, A. Deep neural networks in psychiatry. Mol. Psychiatry 24 , 1583–1598 (2019).
Vieira, S., Pinaya, W. H. & Mechelli, A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74 , 58–75 (2017).
Shatte, A. B., Hutchinson, D. M. & Teague, S. J. Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 49 , 1426–1448 (2019).
Murphy, K. P. Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012).
Biship, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer-Verlag, Berlin, 2007).
Bengio, Y., Simard, P. & Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. Learn. Syst. 5 , 157–166 (1994).
CAS Google Scholar
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86 , 2278–2324 (1998).
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. & Manzagol, P. A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 , 3371–3408 (2010).
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Cogn. modeling. 5 , 1 (1988).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9 , 1735–1780 (1997).
Cho, K., Van Merriënboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: encoder-decoder approaches. In Proc . SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation 103–111 (Doha, Qatar, 2014).
Liou, C., Cheng, W., Liou, J. & Liou, D. Autoencoder for words. Neurocomputing 139 , 84–96 (2014).
Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151 , 264–269 (2009).
Schnack, H. G. et al. Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage 84 , 299–306 (2014).
O’Toole, A. J. et al. Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J. Cogn. Neurosci. 19 , 1735–1752 (2007).
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412 , 150 (2001).
Kuang, D. & He, L. Classification on ADHD with deep learning. In Proc . Int. Conference on Cloud Computing and Big Data 27–32 (Wuhan, China, 2014).
Kuang, D., Guo, X., An, X., Zhao, Y. & He, L. Discrimination of ADHD based on fMRI data with deep belief network. In Proc . Int. Conference on Intelligent Computing 225–232 (Taiyuan, China, 2014).
Farzi, S., Kianian, S. & Rastkhadive, I. Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach. In Proc . 5th Int. Symposium on Computational and Business Intelligence 96–99 (Dubai, United Arab Emirates, 2017).
Zou, L., Zheng, J. & McKeown, M. J. Deep learning based automatic diagnoses of attention deficit hyperactive disorder. In Proc . 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) 962–966 (Montreal, Canada, 2017).
Riaz A. et al. Deep fMRI: an end-to-end deep network for classification of fMRI data. In Proc . 2018 IEEE 15th Int. Symposium on Biomedical Imaging . 1419–1422 (Washington, DC, USA, 2018).
Zou, L., Zheng, J., Miao, C., Mckeown, M. J. & Wang, Z. J. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access. 5 , 23626–23636 (2017).
Sen, B., Borle, N. C., Greiner, R. & Brown, M. R. A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PLoS ONE 13 , e0194856 (2018).
Zeng, L. et al. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30 , 74–85 (2018).
Pinaya, W. H. et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci. Rep. 6 , 38897 (2016).
CAS PubMed PubMed Central Google Scholar
Pinaya, W. H., Mechelli, A. & Sato, J. R. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: a large-scale multi-sample study. Hum. Brain Mapp. 40 , 944–954 (2019).
Ulloa, A., Plis, S., Erhardt, E. & Calhoun, V. Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia. In Proc . 25th IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP) 1–6 (Boston, MA, USA, 2015).
Matsubara, T., Tashiro, T. & Uehara, K. Deep neural generative model of functional MRI images for psychiatric disorder diagnosis. IEEE Trans. Biomed. Eng . 99 (2019).
Geng, X. & Xu, J. Application of autoencoder in depression diagnosis. In 2017 3rd Int. Conference on Computer Science and Mechanical Automation (Wuhan, China, 2017).
Aghdam, M. A., Sharifi, A. & Pedram, M. M. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J. Digit. Imaging 31 , 895–903 (2018).
Shen, D., Wu, G. & Suk, H. -I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19 , 221–248 (2017).
Yan, C. & Zang, Y. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci. 4 , 13 (2010).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16 , 111–116 (2019).
Herrmann, C. & Demiralp, T. Human EEG gamma oscillations in neuropsychiatric disorders. Clin. Neurophysiol. 116 , 2719–2733 (2005).
Acharya, U. R. et al. Automated EEG-based screening of depression using deep convolutional neural network. Comput. Meth. Prog. Biol. 161 , 103–113 (2018).
Mohan, Y., Chee, S. S., Xin, D. K. P. & Foong, L. P. Artificial neural network for classification of depressive and normal. In EEG Proc . 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences 286–290 (Kuala Lumpur, Malaysia, 2016).
Zhang, P., Wang, X., Zhang, W. & Chen, J. Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27 , 31–42 (2018).
Li, X. et al. EEG-based mild depression recognition using convolutional neural network. Med. Biol. Eng. Comput . 47 , 1341–1352 (2019).
Patel, S., Park, H., Bonato, P., Chan, L. & Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9 , 21 (2012).
Smoller, J. W. The use of electronic health records for psychiatric phenotyping and genomics. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177 , 601–612 (2018).
Wu, J., Roy, J. & Stewart, W. F. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med. Care. 48 , S106–S113 (2010).
Choi, S. B., Lee, W., Yoon, J. H., Won, J. U. & Kim, D. W. Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea. J. Affect. Disord. 231 , 8–14 (2018).
Pham, T., Tran, T., Phung, D. & Venkatesh, S. Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform. 69 , 218–229 (2017).
Lin, E. et al. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front. Psychiatry 9 , 290 (2018).
Geraci, J. et al. Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression. Evid. Based Ment. Health 20 , 83–87 (2017).
Kim, Y. Convolutional neural networks for sentence classification. arXiv Prepr. arXiv 1408 , 5882 (2014).
Yang, Z. et al. Hierarchical attention networks for document classification. In Proc . 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1480–1489 (San Diego, California, USA, 2016).
Rios, A. & Kavuluru, R. Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores. J. Biomed. Inform. 75 , S85–S93 (2017).
Dai, H. & Jonnagaddala, J. Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks? PLoS ONE 13 , e0204493 (2018).
Tran, T. & Kavuluru, R. Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks. J. Biomed. Inform. 75 , S138–S148 (2017).
Samek, W., Binder, A., Montavon, G., Lapuschkin, S. & Müller, K.-R. Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28 , 2660–2673 (2016).
Hripcsak, G. et al. Characterizing treatment pathways at scale using the OHDSI network. Proc. Natl. Acad. Sci . USA 113 , 7329–7336 (2016).
McGuffin, P., Owen, M. J. & Gottesman, I. I. Psychiatric Genetics and Genomics (Oxford Univ. Press, New York, 2004).
Levinson, D. F. The genetics of depression: a review. Biol. Psychiatry 60 , 84–92 (2006).
Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50 , 668 (2018).
Mullins, N. & Lewis, C. M. Genetics of depression: progress at last. Curr. Psychiatry Rep. 19 , 43 (2017).
Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51 , 12–18 (2019).
Yue, T. & Wang, H. Deep learning for genomics: a concise overview. Preprint at arXiv:1802.00810 (2018).
Khan, A. & Wang, K. A deep learning based scoring system for prioritizing susceptibility variants for mental disorders. In Proc . 2017 IEEE Int. Conference on Bioinformatics and Biomedicine (BIBM) 1698–1705 (Kansas City, USA, 2017).
Khan, A., Liu, Q. & Wang, K. iMEGES: integrated mental-disorder genome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes. BMC Bioinformatics 19 , 501 (2018).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362 , eaat8464 (2018).
Salakhutdinov, R. & Hinton, G. Deep Boltzmann machines. In Proc . 12th Int. Conference on Artificial Intelligence and Statistics 448–455 (Clearwater, Florida, USA, 2009).
Laksshman, S., Bhat, R. R., Viswanath, V. & Li, X. DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning. Hum. Mutat. 38 , 1217–1224 (2017).
CAS PubMed Central Google Scholar
Huang, K.-Y. et al. Data collection of elicited facial expressions and speech responses for mood disorder detection. In Proc . 2015 Int. Conference on Orange Technologies (ICOT) 42–45 (Hong Kong, China, 2015).
Valstar, M. et al. AVEC 2013: the continuous audio/visual emotion and depression recognition challenge. In Proc . 3rd ACM Int. Workshop on Audio/Visual Emotion Challenge 3–10 (Barcelona, Spain, 2013).
Valstar, M. et al. Avec 2014: 3d dimensional affect and depression recognition challenge. In Proc . 4th Int. Workshop on Audio/Visual Emotion Challenge 3–10 (Orlando, Florida, USA, 2014).
Valstar, M. et al. Avec 2016: depression, mood, and emotion recognition workshop and challenge. In Proc . 6th Int. Workshop on Audio/Visual Emotion Challenge 3–10 (Amsterdam, The Netherlands, 2016).
Ma, X., Yang, H., Chen, Q., Huang, D. & Wang, Y. Depaudionet: an efficient deep model for audio based depression classification. In Proc . 6th Int. Workshop on Audio/Visual Emotion Challenge 35–42 (Amsterdam, The Netherlands, 2016).
He, L. & Cao, C. Automated depression analysis using convolutional neural networks from speech. J. Biomed. Inform. 83 , 103–111 (2018).
Li, J., Fu, X., Shao, Z. & Shang, Y. Improvement on speech depression recognition based on deep networks. In Proc . 2018 Chinese Automation Congress (CAC) 2705–2709 (Xi’an, China, 2018).
Yang, L., Jiang, D., Han, W. & Sahli, H. DCNN and DNN based multi-modal depression recognition. In Proc . 2017 7th Int. Conference on Affective Computing and Intelligent Interaction 484–489 (San Antonio, Texas, USA, 2017).
Huang, K. Y., Wu, C. H. & Su, M. H. Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses. Pattern Recogn. 88 , 668–678 (2019).
Dawood, A., Turner, S. & Perepa, P. Affective computational model to extract natural affective states of students with Asperger syndrome (AS) in computer-based learning environment. IEEE Access. 6 , 67026–67034 (2018).
Song, S., Shen, L. & Valstar, M. Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features. In Proc . 13th IEEE Int. Conference on Automatic Face & Gesture Recognition 158–165 (Xi’an, China, 2018).
Zhu, Y., Shang, Y., Shao, Z. & Guo, G. Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans. Affect. Comput. 9 , 578–584 (2018).
Chao, L., Tao, J., Yang, M. & Li, Y. Multi task sequence learning for depression scale prediction from video. In Proc . 2015 Int. Conference on Affective Computing and Intelligent Interaction (ACII) 526–531 (Xi’an, China, 2015).
Yang, T. H., Wu, C. H., Huang, K. Y. & Su, M. H. Detection of mood disorder using speech emotion profiles and LSTM. In Proc . 10th Int. Symposium on Chinese Spoken Language Processing (ISCSLP) 1–5 (Tianjin, China, 2016).
Huang, K. Y., Wu, C. H., Su, M. H. & Chou, C. H. Mood disorder identification using deep bottleneck features of elicited speech. In Proc . 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 1648–1652 (Kuala Lumpur, Malaysia, 2017).
Jan, A., Meng, H., Gaus, Y. F. B. A. & Zhang, F. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Trans. Cogn. Dev. Syst. 10 , 668–680 (2017).
Su, M. H., Wu, C. H., Huang, K. Y. & Yang, T. H. Cell-coupled long short-term memory with l-skip fusion mechanism for mood disorder detection through elicited audiovisual features. IEEE Trans. Neural Netw. Learn. Syst . 31 (2019).
Harati, S., Crowell, A., Mayberg, H. & Nemati, S. Depression severity classification from speech emotion. In Proc . 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 5763–5766 (Honolulu, HI, USA, 2018).
Su, M. H., Wu, C. H., Huang, K. Y., Hong, Q. B. & Wang, H. M. Exploring microscopic fluctuation of facial expression for mood disorder classification. In Proc . 2017 Int. Conference on Orange Technologies (ICOT) 65–69 (Singapore, 2017).
Prasetio, B. H., Tamura, H. & Tanno, K. The facial stress recognition based on multi-histogram features and convolutional neural network. In Proc . 2018 IEEE Int. Conference on Systems, Man, and Cybernetics (SMC) 881–887 (Miyazaki, Japan, 2018).
Jaiswal, S., Valstar, M. F., Gillott, A. & Daley, D. Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. In Proc . 12th IEEE Int. Conference on Automatic Face & Gesture Recognition 762–769 (Washington, DC, USA, 2017).
Cho, Y., Bianchi-Berthouze, N. & Julier, S. J. DeepBreath: deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In Proc . 2017 7th Int. Conference on Affective Computing and Intelligent Interaction (ACII) 456–463 (San Antonio, Texas, USA, 2017).
Gupta, R., Sahu, S., Espy-Wilson, C. Y. & Narayanan, S. S. An affect prediction approach through depression severity parameter incorporation in neural networks. In Proc . 2017 Int. Conference on INTERSPEECH 3122–3126 (Stockholm, Sweden, 2017).
Martin, O., Kotsia, I., Macq, B. & Pitas, I. The eNTERFACE'05 audio-visual emotion database. In Proc . 22nd Int. Conference on Data Engineering Workshops 8–8 (Atlanta, GA, USA, 2006).
Goodfellow, I. J. et al. Challenges in representation learning: A report on three machine learning contests. In Proc . Int. Conference on Neural Information Processing 117–124 (Daegu, Korea, 2013).
Yi, D., Lei, Z., Liao, S. & Li, S. Z.. Learning face representation from scratch. Preprint at arXiv 1411.7923 (2014).
Lin, H. et al. User-level psychological stress detection from social media using deep neural network. In Proc . 22nd ACM Int. Conference on Multimedia 507–516 (Orlando, Florida, USA, 2014).
Lin, H. et al. Psychological stress detection from cross-media microblog data using deep sparse neural network. In Proc . 2014 IEEE Int. Conference on Multimedia and Expo 1–6 (Chengdu, China, 2014).
Li, Q. et al. Correlating stressor events for social network based adolescent stress prediction. In Proc . Int. Conference on Database Systems for Advanced Applications 642–658 (Suzhou, China, 2017).
Lin, H. et al. Detecting stress based on social interactions in social networks. IEEE Trans. Knowl. Data En. 29 , 1820–1833 (2017).
Cong, Q. et al. X-A-BiLSTM: a deep learning approach for depression detection in imbalanced data. In Proc . 2018 IEEE Int. Conference on Bioinformatics and Biomedicine (BIBM) 1624–1627 (Madrid, Spain, 2018).
Ive, J., Gkotsis, G., Dutta, R., Stewart, R. & Velupillai, S. Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health. In Proc . Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic 69–77 (New Orleans, Los Angeles, USA, 2018).
Sadeque, F., Xu, D. & Bethard, S. UArizona at the CLEF eRisk 2017 pilot task: linear and recurrent models for early depression detection. CEUR Workshop Proc . 1866 (2017).
Fraga, B. S., da Silva, A. P. C. & Murai, F. Online social networks in health care: a study of mental disorders on Reddit. In Proc . 2018 IEEE/WIC/ACM Int. Conference on Web Intelligence (WI) 568–573 (Santiago, Chile, 2018).
Gkotsis, G. et al. Characterisation of mental health conditions in social media using Informed Deep Learning. Sci. Rep. 7 , 45141 (2017).
Coppersmith, G., Leary, R., Crutchley, P. & Fine, A. Natural language processing of social media as screening for suicide risk. Biomed. Inform. Insights 10 , 1178222618792860 (2018).
Du, J. et al. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Med. Inform. Decis. Mak. 18 , 43 (2018).
Alambo, A. et al. Question answering for suicide risk assessment using Reddit. In Proc . IEEE 13th Int. Conference on Semantic Computing 468–473 (Newport Beach, California, USA, 2019).
Eichstaedt, J. C. et al. Facebook language predicts depression in medical records. Proc. Natl Acad. Sci. USA 115 , 11203–11208 (2018).
Rosenquist, J. N., Fowler, J. H. & Christakis, N. A. Social network determinants of depression. Mol. Psychiatry 16 , 273 (2011).
Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In Proc. 2017 Int. Conference on Learning Representations (Toulon, France, 2017).
Rice, S. M. et al. Online and social networking interventions for the treatment of depression in young people: a systematic review. J. Med. Internet Res. 16 , e206 (2014).
Hastie, T., Tibshirani, R. & Friedman, J. The elements of statistical learning: data mining, inference, and prediction. Springer Series in Statistics. Math. Intell. 27 , 83–85 (2009).
Torrey, L. & Shavlik, J. in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques 242–264 (IGI Global, 2010).
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Proc . Advances in Neural Information Processing Systems 3320–3328 (Montreal, Canada, 2014).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 , 115 (2017).
Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. Psychiatr. Assoc. 167 , 748–751 (2010).
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. & Hartmann, J. A. Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiatry 74 , 528–534 (2017).
Guo, X., Liu, X., Zhu, E. & Yin, J. Deep clustering with convolutional autoencoders. In Proc . Int. Conference on Neural Information Processing 373–382 (Guangzhou, China, 2017).
Srivastava, N., Mansimov, E. & Salakhudinov, R. Unsupervised learning of video representations using LSTMs. In Proc . Int. Conference on Machine Learning 843–852 (Lille, France, 2015).
Baytas, I. M. et al. Patient subtyping via time-aware LSTM networks. In Proc . 23rd ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining 65–74 (Halifax, Canada, 2017).
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®) (American Psychiatric Pub, Washington, DC, 2013).
Biological Sciences Curriculum Study. In: NIH Curriculum Supplement Series (Internet) (National Institutes of Health, USA, 2007).
Noh, H., Hong, S. & Han, B. Learning deconvolution network for semantic segmentation. In Proc . IEEE Int. Conference on Computer Vision 1520–1528 (Santiago, Chile, 2015).
Grün, F., Rupprecht, C., Navab, N. & Tombari, F. A taxonomy and library for visualizing learned features in convolutional neural networks. In Proc. 33rd Int. Conference on Machine Learning (ICML) Workshop on Visualization for Deep Learning (New York, USA, 2016).
Ribeiro, M. T., Singh, S. & Guestrin, C. Why should I trust you?: Explaining the predictions of any classifier. In Proc . 22nd ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining 1135–1144 (San Francisco, CA, 2016).
Zhang, Q. S. & Zhu, S. C. Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19 , 27–39 (2018).
Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. In Proc . 31st Conference on Neural Information Processing Systems 4765–4774 (Long Beach, CA, 2017).
Shrikumar, A., Greenside, P., Shcherbina, A. & Kundaje, A. Not just a black box: learning important features through propagating activation differences. In Proc . 33rd Int. Conference on Machine Learning (New York, NY, 2016).
Gawehn, E., Hiss, J. A. & Schneider, G. Deep learning in drug discovery. Mol. Inform. 35 , 3–14 (2016).
Jerez-Aragonés, J. M., Gómez-Ruiz, J. A., Ramos-Jiménez, G., Muñoz-Pérez, J. & Alba-Conejo, E. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif. Intell. Med. 27 , 45–63 (2003).
Zhu, Y., Elemento, O., Pathak, J. & Wang, F. Drug knowledge bases and their applications in biomedical informatics research. Brief. Bioinformatics 20 , 1308–1321 (2018).
Su, C., Tong, J., Zhu, Y., Cui, P. & Wang, F. Network embedding in biomedical data science. Brief. Bioinform . https://doi.org/10.1093/bib/bby117 (2018).
Bodenreider, O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32 (suppl_1), D267–D270 (2004).
Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43 , D447–D452 (2014).
The work is supported by NSF 1750326, R01 MH112148, R01 MH105384, R01 MH119177, R01 MH121922, and P50 MH113838.
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Chang Su, Zhenxing Xu, Jyotishman Pathak & Fei Wang
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C.S., Z.X. and F.W. planned and structured the whole paper. C.S. and Z.X. conducted the literature review and drafted the manuscript. J.P. and F.W. reviewed and edited the manuscript.
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Su, C., Xu, Z., Pathak, J. et al. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 10 , 116 (2020). https://doi.org/10.1038/s41398-020-0780-3
Received : 31 August 2019
Revised : 17 February 2020
Accepted : 26 February 2020
Published : 22 April 2020
DOI : https://doi.org/10.1038/s41398-020-0780-3
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Mental health and the pandemic: what u.s. surveys have found.
The coronavirus pandemic has been associated with worsening mental health among people in the United States and around the world . In the U.S, the COVID-19 outbreak in early 2020 caused widespread lockdowns and disruptions in daily life while triggering a short but severe economic recession that resulted in widespread unemployment. Three years later, Americans have largely returned to normal activities, but challenges with mental health remain.
Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic. These findings reflect a snapshot in time, and it’s possible that attitudes and experiences may have changed since these surveys were fielded. It’s also important to note that concerns about mental health were common in the U.S. long before the arrival of COVID-19 .
Three years into the COVID-19 outbreak in the United States , Pew Research Center published this collection of survey findings about Americans’ challenges with mental health during the pandemic. All findings are previously published. Methodological information about each survey cited here, including the sample sizes and field dates, can be found by following the links in the text.
The research behind the first item in this analysis, examining Americans’ experiences with psychological distress, benefited from the advice and counsel of the COVID-19 and mental health measurement group at Johns Hopkins Bloomberg School of Public Health.
At least four-in-ten U.S. adults (41%) have experienced high levels of psychological distress at some point during the pandemic, according to four Pew Research Center surveys conducted between March 2020 and September 2022.
Young adults are especially likely to have faced high levels of psychological distress since the COVID-19 outbreak began: 58% of Americans ages 18 to 29 fall into this category, based on their answers in at least one of these four surveys.
Women are much more likely than men to have experienced high psychological distress (48% vs. 32%), as are people in lower-income households (53%) when compared with those in middle-income (38%) or upper-income (30%) households.
In addition, roughly two-thirds (66%) of adults who have a disability or health condition that prevents them from participating fully in work, school, housework or other activities have experienced a high level of distress during the pandemic.
The Center measured Americans’ psychological distress by asking them a series of five questions on subjects including loneliness, anxiety and trouble sleeping in the past week. The questions are not a clinical measure, nor a diagnostic tool. Instead, they describe people’s emotional experiences during the week before being surveyed.
While these questions did not ask specifically about the pandemic, a sixth question did, inquiring whether respondents had “had physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart” when thinking about their experience with the coronavirus outbreak. In September 2022, the most recent time this question was asked, 14% of Americans said they’d experienced this at least some or a little of the time in the past seven days.
More than a third of high school students have reported mental health challenges during the pandemic. In a survey conducted by the Centers for Disease Control and Prevention from January to June 2021, 37% of students at public and private high schools said their mental health was not good most or all of the time during the pandemic. That included roughly half of girls (49%) and about a quarter of boys (24%).
In the same survey, an even larger share of high school students (44%) said that at some point during the previous 12 months, they had felt sad or hopeless almost every day for two or more weeks in a row – to the point where they had stopped doing some usual activities. Roughly six-in-ten high school girls (57%) said this, as did 31% of boys.
On both questions, high school students who identify as lesbian, gay, bisexual, other or questioning were far more likely than heterosexual students to report negative experiences related to their mental health.
Mental health tops the list of worries that U.S. parents express about their kids’ well-being, according to a fall 2022 Pew Research Center survey of parents with children younger than 18. In that survey, four-in-ten U.S. parents said they’re extremely or very worried about their children struggling with anxiety or depression. That was greater than the share of parents who expressed high levels of concern over seven other dangers asked about.
While the fall 2022 survey was fielded amid the coronavirus outbreak, it did not ask about parental worries in the specific context of the pandemic. It’s also important to note that parental concerns about their kids struggling with anxiety and depression were common long before the pandemic, too . (Due to changes in question wording, the results from the fall 2022 survey of parents are not directly comparable with those from an earlier Center survey of parents, conducted in 2015.)
Among parents of teenagers, roughly three-in-ten (28%) are extremely or very worried that their teen’s use of social media could lead to problems with anxiety or depression, according to a spring 2022 survey of parents with children ages 13 to 17 . Parents of teen girls were more likely than parents of teen boys to be extremely or very worried on this front (32% vs. 24%). And Hispanic parents (37%) were more likely than those who are Black or White (26% each) to express a great deal of concern about this. (There were not enough Asian American parents in the sample to analyze separately. This survey also did not ask about parental concerns specifically in the context of the pandemic.)
Looking back, many K-12 parents say the first year of the coronavirus pandemic had a negative effect on their children’s emotional health. In a fall 2022 survey of parents with K-12 children , 48% said the first year of the pandemic had a very or somewhat negative impact on their children’s emotional well-being, while 39% said it had neither a positive nor negative effect. A small share of parents (7%) said the first year of the pandemic had a very or somewhat positive effect in this regard.
White parents and those from upper-income households were especially likely to say the first year of the pandemic had a negative emotional impact on their K-12 children.
While around half of K-12 parents said the first year of the pandemic had a negative emotional impact on their kids, a larger share (61%) said it had a negative effect on their children’s education.
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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
- Open access
- Published: 29 April 2022
Positive mental health in psychotherapy: a qualitative study from psychotherapists’ perspectives
- Sherilyn Chang 1 ,
- Rajeswari Sambasivam 1 ,
- Esmond Seow 1 ,
- Mythily Subramaniam 1 ,
- Hanita Ashok Assudani 2 ,
- Geoffrey Chern-Yee Tan 3 , 4 ,
- Sharon Huixian Lu 2 &
- Janhavi Ajit Vaingankar 1
BMC Psychology volume 10 , Article number: 111 ( 2022 ) Cite this article
There is growing evidence in the literature on the use of positive mental health (PMH) interventions among clinical samples. This qualitative study aims to explore the definitions of PMH from psychotherapists’ perspectives, and to examine views and attitudes related to the construct.
Focus group discussions were conducted with psychotherapists at a tertiary psychiatric institute. Focus group sessions were transcribed verbatim and transcripts were analyzed using an inductive thematic approach.
Five themes related to psychotherapists’ definition of PMH were identified: (1) acceptance; (2) normal functioning and thriving in life; (3) resilience; (4) positive overall evaluation of life; (5) absence of negative emotions and presence of positive emotion states. Themes related to views and attitudes towards PMH were: (1) novel and valuable for psychotherapy; (2) reservations with terminology; (3) factors influencing PMH.
PMH in psychotherapy is a multidimensional concept that means more than symptom management and distress reduction in clients. There is potential value for its application in psychotherapy practice, though some concerns need to be addressed before it can be well integrated.
Peer Review reports
Positive mental health (PMH) reflects a state of mental wellbeing that goes beyond the mere absence of psychopathology. It encompasses emotional and psychological wellbeing, and functioning in psychological, social and societal domains [ 1 ]. In relation to emotional wellbeing, Diener et al.’s definition on subjective wellbeing is often drawn upon as it looks at an individual’s overall evaluation of their life and emotional experiences such as life satisfaction, positive affect and negative affect [ 2 ]. This is also seen as the hedonic approach to wellbeing that focuses on pleasure attainment and pain avoidance [ 3 ]. In contrast, the eudemonic approach ‘focuses on meaning and self-realization and defines wellbeing in terms of the degree to which a person is fully functioning’ [ 4 ]. A closely related concept of psychological wellbeing has been conceptualized as consisting of six dimensions: autonomy, environmental mastery, personal growth, purpose in life, positive relations with others, and self-acceptance [ 5 ]. In addition to emotional and psychological wellbeing, Keyes also considers social wellbeing as essential in identifying a thriving individual as flourishing [ 6 ]. Some studies have also identified spirituality (related to religious beliefs and practices) as an important domain of PMH [ 7 , 8 ].
Traditionally psychotherapy, and also clinical care in general, focus on alleviating symptoms and are largely aimed at correcting deficits resulting from disruption of normal functioning [ 9 , 10 ]. There have been calls to shift away from this deficit-based view of mental health to promoting wellbeing, and to evaluate both PMH dimensions and psychopathology when providing psychotherapy and in conducting research [ 1 , 11 , 12 ]. Distinct psychotherapeutic interventions that are theoretically grounded in positive psychology, a scientific field that studies contributing factors of human flourishing and optimal function [ 13 ], explicitly targets wellbeing outcomes. Some examples include therapies such as the wellbeing therapy [ 14 ] and positive psychotherapy [ 15 ]. Other non-positive psychology approaches such as mindfulness-based interventions and gratitude-promoting exercises have also been incorporated into traditional psychotherapies to enhance wellbeing.
Several studies have examined the effectiveness of PMH interventions in improving outcomes among clinical samples. A meta-analysis by Goldberg et al. reported equivalent efficacy of mindfulness-based interventions to first-line, evidence-based psychological and psychiatric treatments in symptoms reduction, with effects most consistent for depression, pain, smoking and addictions [ 16 ]. In another meta-analysis examining the effects of positive psychology interventions on wellbeing and distress, the authors found small effect sizes for such interventions on improving wellbeing and depression, and moderate improvements for anxiety among clinical samples [ 17 ]. A recently published article by Jankowski et al. provided a comprehensive review of the various types of interventions in psychotherapy to promote wellbeing and the efficacy of these treatments [ 11 ]. The authors found support for these approaches in enhancing wellbeing and urged ‘researchers and psychotherapists to continue to integrate symptom reduction and wellbeing promotion into psychotherapy approaches aimed at fostering client flourishing’. Given that ‘good outcomes’ of psychotherapy constituted more than symptom alleviation and included outcomes such as with gaining acceptance and self-understanding, alongside developing a sense of mastery and self-compassion [ 18 ], there is value in exploring the application of PMH interventions in psychotherapy.
To date there has been no study that has examined the concept of PMH among psychotherapists and to understand their attitudes towards a PMH based approach in psychotherapy. As a step towards exploring ways in which PMH interventions can be incorporated into psychotherapy practice, it is imperative to first understand the concept of PMH from the point of view of psychotherapists in clinical settings. This could provide insights into the attitudes of psychotherapists towards PMH and identify potential challenges and difficulties in integrating PMH interventions into psychotherapy in a clinical setting. The present qualitative study thus aims to explore the definitions of PMH from psychotherapists’ perspectives and its application in their practice, and to examine their views related to the concept of PMH.
Study design and setting
A qualitative study was conducted at a tertiary psychiatric hospital in Singapore and data collection took place between April and November 2019. This study used an interpretivist approach to gain an in-depth understanding of psychotherapists’ definitions and views of PMH, and this enabled an understanding of psychotherapy practices from the practitioners’ perspectives to yield clinical applications and inform future research. The Consolidated Criteria for Reporting Qualitative Research (COREQ; Additional file 1 : Appendix A) was used to guide the reporting of this study [ 19 ].
Participants for this study were professionals who provided psychotherapy to individuals with mental health issues at private or public institutions in Singapore. Purposive sampling was adopted to ensure appropriate representation of psychotherapists by work experience. Psychotherapists were invited to participate in the research study through connections from personal network and also via word of mouth (none of the recruited participants were personally acquainted with study team members who were present during the interview), and were contacted through phone calls and emails to provide them with further details of the study. Inclusion criteria for the study were individuals aged 21 years and above, experienced in providing psychotherapy to people with mental health problems at public or private institutes, and able to provide consent. The study was approved by the institutional ethics committee and all participants had provided written informed consent prior to their participation. This study was conducted in accordance with the Declaration of Helsinki.
Qualitative data was collected during focus group discussions (FGDs) conducted with psychotherapists. Each FGD session lasted between 1.5–2 h, had 4–6 participants, and was facilitated by a female senior researcher (JV), who has a background in epidemiology (MSc) and is trained in qualitative research methodologies and has domain expertise in the area of mental wellbeing. Study team members (RS, ES or SC), who were researchers with bachelor degrees in psychology and had prior experience in conducting qualitative research, were present during the session as a note taker. Participants completed a short questionnaire that collected information pertaining to their sociodemographic background and clinical experience. As part of icebreaking activity before the FGD began, all participants and study members who were present briefly introduced themselves regarding their work and personal interests. An interview guide was used during the FGDs to facilitate discussion (see Table 1 for brief guide). This interview guide was developed with inputs from clinicians and psychologists from the study team to set the questions in the context of psychotherapy. Participants were first presented with an overarching question on what PMH means to them in their practice, and were then given time to pen down their thoughts on cue cards. These cards served as aids to facilitate subsequent discussion. As far as possible, the discussions followed the experiences of the participants and clarifications were sought when needed. Participants were also encouraged to share their opinions on the viewpoints raised by other participants during the discussions. Recruitment of participants and FGDs continued until repetition of themes occurred and no new information was evident (i.e. data saturation achieved). All the FGDs were audiotaped and transcribed verbatim for analysis. Quality checks on the transcripts were performed; after which the transcripts were anonymized to safeguard the participants’ identity.
Thematic data analysis was conducted to analyze the data where common underlying themes were identified inductively from the data [ 20 ]. NVivo software (Version 11) was used to code and organize the data. One transcript each was assigned to three study team members (JV, SC, ES) who read through the respective transcript repeatedly and thoroughly to familiarize themselves with the content. Each team member noted meaningful content in the transcript to generate codes inductively which were later combined to form emergent themes. Study team members then gathered to discuss the codes and themes obtained, and a list of preliminary themes was identified. This was used to code the remaining transcripts, and new codes and themes were created to capture any new content that emerged. After all transcripts were reviewed, various themes were combined to produce higher-order themes. Any disagreements between team members were resolved through discussions to reach consensus.
Lincoln and Guba’s criteria to assess the trustworthiness of a study looks at credibility, transferability, dependability and confirmability [ 21 ], and these criteria can be applied in conducting thematic analysis [ 22 ]. In terms of data accuracy, all FGD sessions were audio-recorded and transcribed verbatim by a team member; study team members (other than the person who transcribed the interview) performed checks on the transcripts to ensure its quality and accuracy. Raw audio recordings and verbatim transcripts were stored in well-organized archives until verification was completed, and records of observation notes, coded transcripts and discussion notes were kept to provide an audit trail of the code generation process and serves to provide dependability and confirmability. Findings were reviewed by members in the study team which included researchers and also psychotherapists and this addresses credibility of the study. Detailed descriptions of the research process and in reporting of results can provide information to other researchers on the transferability of findings in another study population.
A total of 7 FGDs were conducted with 38 participants for the study. The participants’ age ranged between 27 and 63 years, were mostly females (84.2%), of Chinese ethnicity (81.6%), and the majority had obtained a post-graduate degree (94.7%; Table 2 ). All participants had received formal training in varied psychotherapy modalities including cognitive behavioral therapy, positive behavioral management, exposure and response prevention, eye movement desensitization and reprocessing, acceptance and commitment therapy, schema-focused therapy, emotion focused therapy, solution focused brief therapy, psychodynamic therapy, dialectical behavioral therapy, mindfulness-based therapy etc. For the majority of participants, their clientele comprised adults presenting with mental disorders including mood disorders and anxiety disorders. Others worked with children and adolescents with childhood disorders, elderly population with dementia, or individuals who needed life coaching.
Thematic analysis of the qualitative data identified five themes pertaining to psychotherapists’ definition of PMH: (1) acceptance; (2) normal functioning and thriving in life; (3) resilience; (4) positive overall evaluation of life; (5) absence of negative emotions and presence of positive emotion states. Their views on the concept of PMH could be examined from the following three themes: (1) novel and valuable for psychotherapy; (2) reservations with terminology; (3) factors influencing PMH. Figure 1 a, b present the coding trees derived from the coding process with the subthemes and themes shown. The following section describes the themes in further details and salient quotes that underscore the essence of the theme are presented.
a Coding tree of themes identified in the coding process pertaining to psychotherapists’ definition of PMH. b Coding tree of themes identified in the coding process pertaining to psychotherapists’ views on the concept of PMH
Definitions of positive mental health
This was a common theme discussed by participants from various FGDs. PMH was defined as having the ability to accept things that happened in life and acknowledging the outcomes that resulted. Acceptance was in relation to not just negative events, but also acceptance of ‘difficult emotions’ and where one is in life.
It’s about accepting where you are in life and… as well as… growing in that journey to acceptance and being at peace with that. – FGD 3 Same way like you were talking about ACT (Acceptance and Commitment Therapy) just now, it’s accepting it, even if they just accept what has happened to them, I think it’s already positive mental health. – FGD 7
In a related note, a participant described PMH as having contentment in life and described how acceptance of situation contributed to contentment.
Positive mental health to me is finding content, which is a bit like peace, whatever the circumstance… a lot of it is perception, how you see certain things, like certain circumstances that you might not be able to control. So I mean modifying or coming to terms with what I can accept and what I can change. I think that helps; gives me contentment and peace. – FGD 6
(2) Normal functioning and thriving in life
For the participants, having PMH was defined as being able to function normally. At the individual level, a functioning person was described as someone leading a balanced and healthy lifestyle, and able to manage stress and not be overwhelmed by it. The idea of optimal functioning pertained to various aspects in life including occupation, relationship with others, and being an active and contributing member of the society.
PMH is not about like the mental condition. It is about, you know, how we make these conditions and maybe other life issues not to interfere with our life. So it’s about living that life, you know, despite all the obstacles and difficulties. – FGD 3
Some participants moved beyond the notion of basic psychosocial functioning to describe PMH in terms of thriving which encompassed the idea of growth.
I wrote it (PMH) as the ability to thrive in very stressful environment [be]cause I think the way I see PMH is not just the absence of mental health issues but [it] is also the ability to kind of progress and really to be able to kind of expand on your own potential. – FGD 4 … they (clients) are kind of bootstrapped. They are self-corrective. They may come to you with a presenting problem, but if you just drop a few hints along the way, a bit of psycho-edu[cation] here, a bit of coaching there, they are able to extrapolate that to other problems independently on their own. So I think that’s also important. It’s not just where you are now, it’s whether you have the capacity to adapt and grow. – FGD 6
In defining PMH, the concept of resilience was frequently brought up by participants and it at times co-occurred alongside the theme on functioning. Yet this is a distinct theme from functioning in that rather than focusing on outcomes, it describes a trait or skillset that promotes wellbeing.
I would see it (PMH) as resilience, the ability to deal with challenges and the ability to function. – FGD 1 Okay for me positive mental health is being able to cope with the demands and challenges of life. So it’s a bit like mental resilience… sometimes you have negative emotions and being able to cope with that or cope with the demands. – FGD 2
Resilience was often described by participants as a trait that would help their clients to ‘bounce back’ from adversities, and also as a coping resource to support normal functioning in spite of challenges. One participant discussed how having emotional resilience can aid distressed clients to self-regulate by learning to not internalize events that occurred around them.
(4) Positive overall evaluation of life
The keywords in this theme were ‘quality of life’, ‘good life’, ‘fulfilled life’ and ‘life satisfaction’. Definitions captured in this theme described the concept of PMH as an all-encompassing, overall evaluation of one’s life that generated a broad sense of wellness or a feeling of ‘good living’.
Good living, like you’re not just alive; but you are living well, so living well… I think it’s defined differently by different people. So to person A living well might be ‘I’m a able to look after my grandkids’, that’s living well… to summarize it’s the person’s own idea of a good life, a good quality of life. – FGD 1
While elaborating the concepts of life fulfillment and the ideal life in the context of PMH, keywords such as ‘goals’, ‘values’, ‘purpose’, ‘meaningful’ and ‘aspirations’ were often mentioned and participants described these as constituents of a ‘good life’.
… a feeling of living a life that is consistent with one’s values… If someone values career, then he is living a life that is working towards that. If my value is family, I’m living a life that allows me to spend time with my family in a way that I consider meaningful. – FGD 5 Positive mental health is leveraging on people’s needs and values to bring them closer to their fulfilment… To me, fulfilment is living their own values, living their lives according to their own values. And being able to meet their needs. – FGD 6
(5) Absence of negative emotions and presence of positive emotion states
This theme relates to the emotional state of an individual and the definitions of PMH encompassed the absence of distress and the presence of positive emotions. PMH was defined as the removal of mental illness symptoms or distress, and also it meant experiencing positive emotions and state such as ‘happiness’, ‘hope’ and ‘joy’.
Freedom in mind, having peace, having calm. And there is no mental illness or distress and managing with difficulties. – FGD 4 I’ve written that firstly, positive mental health is being hopeful and laughing often. – FGD 5
Views on the concept of PMH
(1) Novel and valuable for psychotherapy
For some participants, PMH was a novel concept which could be defined in various ways by different individuals. For one participant, it gave the ‘impression of mindfulness’ which is the ‘third wave of therapy at the moment’, and some participants compared it to positive psychology.
So I think positive mental health is a new change, so it’s like a new science where you hear a lot of people saying that oh it’s important, it’s crucial but the research out there is very limited to back up all this evidence, but we do see the trends of positive mental health is emerging too. – FGD 2
Participants generally agreed on the importance of individuals to have PMH, with one participant stating it as ‘our birth right’, and another participant citing it to be ‘imperative for a healthy society’. A number of participants acknowledged the roles that they could potentially play as psychotherapists in introducing PMH concepts to their clients, as evident from the following quotes:
Like traditionally the way therapy was created was for like to remove disorder. That’s why I think the newer age therapists are saying that we really need to go further where there’s this idea of growth. I think that’s where the newer age therapists try to incorporate it as part of therapy. – FGD 4 I think for me they (PMH-based interventions) definitely have a space in psychotherapy and they help to balance out between always talking about problems as compared to, well, talking about what were you like before all the problems and what would it be like without the problems. So it balances out the conversation a little bit as compared to every time you come in we talk about your difficulties. – FGD 5
Not all participants, however, concurred with the relevance and significance of PMH, particularly in the context of clinical setting and the profile of clientele that they saw.
I think positive psychology is not that much used in our setting maybe because we have quite a lot of patients in quite severe conditions and talking about positive psychology is like… we are at this level and then you are talking about positive psychology. So maybe in our setting, clinical setting, we don’t really talk about positive psychology and I find that it’s more of a marketing thing… like it’s great and we are doing these classes in school and all that but I think there are other things that are more important to be done. – FGD 2
(2) Reservations with the terminology
A number of participants expressed reservations with the term ‘positive’ that was being used, either with respect to ‘positive mental health’ or ‘positive psychology’. To some participants, such usage implied that clients have to strive towards a positive state all the time, which is ‘not natural’ and ‘an impossible setup’ for them, when instead a simple improvement or progression could in fact be thought of as ‘positive’.
Because from clinical psychology background, it’s about treating mental illness. So it’s like if they (clients) can reach a neutral level or it may be back to baseline, then it’s something the patient may know to achieve, so positive means it sounds to me like up there (pointing to higher level). That you know even myself cannot be completely happy all the time. – FGD 3 The word ‘positive’ here is very misleading. And it’s exaggerating people’s expectations… it’s like wherever you are, if things get in anyway slightly better… that’s already positive. It need not necessary be like you have to have ten steps of growth, not really. – FGD 7
Some participants felt that this terminology carries a connotation and dichotomizes mental health either into the positive or negative realm, and that did not accurately reflect the entirety of what mental health should be in their psychotherapy practice.
I guess one of the main core tenets of psychotherapy is to bring flexibility and balance in the ideas or the perspective that we share about ourselves and other people. So I guess with a connotation, where you kind of put ‘positive’ in front of a word, it doesn’t sit really well in a lot of practices that we do encourage in psychotherapy. – FGD 1 When you term it as positive it becomes very dichotomous, very off-putting… when [what] we want to talk is more about adaptability, workability, more neutral rather than there’s a negative or positive connotation. – FGD 1
They suggested alternative terms such as ‘mental wellness’, ‘positive living’, or sticking to words that were used by their clients, for instance ‘better life’ if that was what the client explicitly stated.
(3) Factors influencing PMH
Participants described several factors that could influence PMH and these were broadly classified into three categories: individual level, interpersonal level, and community and social cultural level. At the individual level, it was about clients’ personality and them having basic self-care which included things like exercise, proper sleep hygiene and healthy coping mechanism. For some participants, it was also about the clients having goals and purpose in life that could motivate them and which contribute to better wellbeing.
I think the other is having that sense of meaning and purpose, so feeling that I have meaningful visions, pursuits or meaningful job that I can contribute meaningfully to my system and the society at large. – FGD 3
At the interpersonal level, participants discussed interpersonal relationship with others that could influence PMH. This included support received from family, friends, or a significant other who provided the feeling of being ‘connected’ with others. A couple of participants noted the impact of mismatched values or misaligned expectations in relationships with others could have on the individuals.
But I think the other part is in the relationship with their significant other, the manner of how these values are transmitted or being talked about. Sometimes it can cause a lot of distress when they have different values. That’s where they have a lot of conflicts, especially when mental illness comes into the system which is a new thing, it can actually distraught the whole thing. – FGD 3
In terms of factors at the community level and social cultural level, a number of participants described how addressing stigma could be a step forward in improving PMH. One way to do so could be to reframe the idea of PMH:
But I was just wondering like why can’t PMH be same as growth and development so not assuming that you have a problem, but you just want to be resilient or be with some more resources. – FGD 4
Participants also suggested creating awareness and improving mental health literacy, particularly amongst the youth and within the school setting.
We are so driven by academic literacy that that’s pretty much all we know right, to achieve and strive, achieve and strive. And if we don’t get it then we fail. But there’s no emotional literacy and acceptance in that that is being taught in schools. – FGD 1
This was an exploratory study conducted to understand psychotherapists’ definitions of PMH and their views of this construct and its application in their clinical practice. From the findings reported in this study, it was observed that PMH was a multidimensional concept and while defined in varied manners, four main themes emerged from this qualitative inquiry. These themes identified are in many ways reflective of the conceptualizations of PMH and wellbeing in the current literature.
PMH in psychotherapy for the participants meant clients are able to alleviate distress and experience positive emotions. Considering that many of the study participants worked in clinical setting with clients who sought treatment for mood and anxiety disorders, it is expected that reducing distress would be a component described. This theme is in line with the hedonic traditions of mental health where the focus is on feeling well [ 23 ]. The hedonic approach also looks at life satisfaction which concurred with the theme on positive overall evaluation of life that was identified in this study. The theme on normal functioning and thriving in life identified in this study is reflective of the eudemonic viewpoint in which the focus is on functioning well psychologically and socially [ 24 ], and parallels could also be drawn with Ryff’s and Keyes’s concept of personal growth [ 5 ].
It was unclear at first glance if the theme on resilience accorded well with the hedonic and eudemonic traditions of conceptualizing PMH. A recent systematic review identified ‘growth’, ‘personal resources’ and ‘social resources’ as conceptualizations of resilience within adult mental health research [ 25 ]. In this sense it is comparable with Ryff’s and Keyes’s dimensions of personal growth and environmental mastery [ 5 ] where in the former individuals seeks development as a person, and in the latter being able to tap into individual and surrounding resources. Nevertheless, several authors have also suggested to include definitions of PMH that encompassed skills and coping strategies to achieve wellbeing [ 8 , 26 , 27 ]. Vaillant also proposed a cross-cultural definition of PMH that included viewing mental health as resilience [ 28 ]. Furthermore, this might be a pertinent concept for our study participants in the context of psychotherapy as clients are usually distressed and are seeking help to resolve their issues and return to normality, or to ‘bounce back’.
Results from this study showed that psychotherapists in our study, whose self-reported primary psychotherapeutic orientation was not amongst those in the fourth wave of psychotherapies (value- and virtue-oriented approaches such as positive psychology interventions, loving-kindness and compassion meditation and spiritually informed therapies; see [ 9 ]), generally see the value and potential in introducing PMH to their clients. However, PMH being novel and a ‘new science’ for some participants, unfamiliarity with it might act as a barrier for application in clinical settings. For one, some participants raised a point on the limitation of its use among clients presenting with more severe conditions. At this point, it might be worth highlighting that studies have been conducted among clinical samples which included patients with major depressive disorder and schizophrenia, and they provided preliminary evidence on the effectiveness of wellbeing interventions in improving wellbeing and reducing distress [ 11 , 17 , 29 ], with effects comparable with those of conventional cognitive behavioral therapy [ 30 ]. A qualitative study conducted among service users with psychosis to investigate their experience of positive psychotherapy also reported promising results. Feedback given was generally positive and participants provided instances of how the intervention supported them in making significant changes to their work and life domains [ 31 ]. In all, these studies lend support for the application of PMH interventions and incorporating them into psychotherapy practice.
Perhaps then the question to contemplate on is when or at which stage of therapy should interventions with elements of PMH be introduced to clients. Some therapists believed that while meaning in life is an underlying issue for all problems, it is not appropriate to address this with all clients in therapy. Client’s readiness and also presence of other pressing issues are factors to be considered [ 32 ]. In a similar vein, McNulty and Fincham noted that the effects of wellbeing traits and processes (e.g. optimism, positivity) are contextual based [ 33 ]; the interaction between a person’s characteristics and the social environment influences how these play out in either promoting or compromising wellbeing. Thus, psychotherapists would need to consider the circumstances in which to initiate PMH interventions, and future studies can seek to examine such factors that could potentially influence the effectiveness of these interventions.
Another finding worth discussing is that a number of participants were skeptical towards the use of the word ‘positive’. It is hard to discern if this reservation among our study participants is attributable to their background in clinical psychology and hence, the focus on deficits, or the unfamiliarity with PMH construct. The contention being that this terminology creates a dichotomy which is not an accurate nor ideal portrayal of mental health, and working towards a positive state all the time is not inherently desirable nor achievable. This echoes the argument by McNulty and Fincham that because psychological traits and processes have to be best understood in context [ 33 ], it would be prudent to avoid labeling them as positive or negative. However, as some authors have noted, these could be some common misconceptions surrounding positive psychological interventions [ 34 , 35 ]. Rather, practitioners and researchers of PMH are advocating for a more balanced focus between illness and wellness. What this suggests is not replacing conventional psychotherapy modalities with PMH interventions, but instead complementing or supplementing the existing treatment options with them.
There are some limitations of this study to be noted. Firstly, some of the participants were acquainted with each other in the FGD session and that could potentially introduce participant bias in a way that their responses reflected the group’s sentiment rather than their own personal opinions. This was minimized by setting the scene from the beginning of the session where participants were explicitly informed that this was an exploratory study and there were no right or wrong answers to begin with. Participants were consistently asked if they agreed or disagreed with what was mentioned, and were encouraged to express their personal opinion in relation to the point raised. Secondly, the large majority of the participants were from public health institutions; only three participants were employed in private practice and one had experience in both. It is possible that differences in work practices and views exists between psychotherapists in public versus private setting, and this could limit the generalizability of the study findings.
With the growing evidence and support for PMH and wellbeing interventions in the literature, it is an opportune time to explore service providers’ perspectives and views towards the use of these interventions in psychotherapy. This study found that the concept of PMH carried multiple meanings for psychotherapists in their practice that meant more than reduction of distress and alleviation of symptoms. It was generally agreed that PMH is an important concept and has a place in psychotherapy for clients, though some concerns may need to be addressed before it is introduced to them. Findings generated from this study provided valuable insights to understanding potential facilitators and barriers in integrating PMH interventions into psychotherapy.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available due to requirements mandated by the institutional review board (IRB) and funders, but may be available from the corresponding author on reasonable request. Access may be granted subject to the IRB and the research collaborative agreement guidelines.
Trompetter HR, Lamers SMA, Westerhof GJ, et al. Both positive mental health and psychopathology should be monitored in psychotherapy: confirmation for the dual-factor model in acceptance and commitment therapy. Behav Res Ther. 2017;91:58–63. https://doi.org/10.1016/j.brat.2017.01.008 .
Article PubMed Google Scholar
Diener E, Heintzelman SJ, Kushlev K, et al. Findings all psychologists should know from the new science on subjective well-being. Can Psychol Can. 2017;58:87–104.
Article Google Scholar
Kahneman D, Diener E, Schwarz N (1999) Well-being: the foundations of hedonic psychology. xii, 593–xii, 593
Ryan RM, Deci EL. On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Annu Rev Psychol. 2001;52:141–66. https://doi.org/10.1146/annurev.psych.52.1.141 .
Ryff CD, Keyes CLM. The structure of psychological well-being revisited. J Pers Soc Psychol. 1995;69:719–27.
Keyes CLM. The mental health continuum: from languishing to flourishing in life. J Health Soc Behav. 2002;43:207–22.
Mirabzadeh A, Baradaran Eftekhari M, Falahat K, et al. Positive mental health from the perspective of Iranian society: A qualitative study [version 2; peer review: 2 approved]. F1000Research. 2018. https://doi.org/10.12688/f1000research.13394.2 .
Article PubMed PubMed Central Google Scholar
Vaingankar JA, Subramaniam M, Lim YW, et al. From well-being to positive mental health: conceptualization and qualitative development of an instrument in Singapore. Qual life Res Int J Qual life Asp Treat Care Rehabil. 2012;21:1785–94. https://doi.org/10.1007/s11136-011-0105-3 .
Peteet JR. A fourth wave of psychotherapies: moving beyond recovery toward well-being. Harv Rev Psychiatry. 2018;26:90–5. https://doi.org/10.1097/HRP.0000000000000155 .
VanderWeele TJ, McNeely E, Koh HK. Reimagining health-flourishing. JAMA. 2019;321:1667–8. https://doi.org/10.1001/jama.2019.3035 .
Jankowski PJ, Sandage SJ, Bell CA, et al. Virtue, flourishing, and positive psychology in psychotherapy: an overview and research prospectus. Psychotherapy. 2020;57:291–309.
Wood AM, Tarrier N. Positive clinical psychology: a new vision and strategy for integrated research and practice. Clin Psychol Rev. 2010;30:819–29. https://doi.org/10.1016/j.cpr.2010.06.003 .
Gable SL, Haidt J. What (and why) is positive psychology? Rev Gen Psychol. 2005;9:103–10. https://doi.org/10.1037/1089-26126.96.36.199 .
Fava GA, Ruini C, Rafanelli C, et al. Well-being therapy of generalized anxiety disorder. Psychother Psychosom. 2005;74:26–30. https://doi.org/10.1159/000082023 .
Seligman MEP, Rashid T, Parks AC. Positive psychotherapy. Am Psychol. 2006;61:774–88.
Goldberg SB, Tucker RP, Greene PA, et al. Mindfulness-based interventions for psychiatric disorders: a systematic review and meta-analysis. Clin Psychol Rev. 2018;59:52–60. https://doi.org/10.1016/j.cpr.2017.10.011 .
Chakhssi F, Kraiss JT, Sommers-Spijkerman M, Bohlmeijer ET. The effect of positive psychology interventions on well-being and distress in clinical samples with psychiatric or somatic disorders: a systematic review and meta-analysis. BMC Psychiatry. 2018;18:211. https://doi.org/10.1186/s12888-018-1739-2 .
Binder P-E, Holgersen H, Nielsen GH. What is a “good outcome” in psychotherapy? A qualitative exploration of former patients’ point of view. Psychother Res. 2010;20:285–94. https://doi.org/10.1080/10503300903376338 .
Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care J Int Soc Qual Heal Care. 2007;19:349–57. https://doi.org/10.1093/intqhc/mzm042 .
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101. https://doi.org/10.1191/1478088706qp063oa .
Lincoln Y, Guba EG. Naturalistic inquiry. Newbury Park: Sage; 1985.
Book Google Scholar
Nowell LS, Norris JM, White DE, Moules NJ. Thematic analysis: striving to meet the trustworthiness criteria. Int J Qual Methods. 2017;16:1609406917733847. https://doi.org/10.1177/1609406917733847 .
Deci EL, Ryan RM. Hedonia, eudaimonia, and well-being: an introduction. J Happiness Stud. 2008;9:1–11. https://doi.org/10.1007/s10902-006-9018-1 .
Keyes CLM. Mental illness and/or mental health? Investigating axioms of the complete state model of health. J Consult Clin Psychol. 2005;73:539–48. https://doi.org/10.1037/0022-006X.73.3.539 .
Ayed N, Toner S, Priebe S. Conceptualizing resilience in adult mental health literature: a systematic review and narrative synthesis. Psychol Psychother Theory, Res Pract. 2019;92:299–341. https://doi.org/10.1111/papt.12185 .
Fusar-Poli P, Salazar de Pablo G, De Micheli A, et al. What is good mental health? A scoping review. Eur Neuropsychopharmacol. 2020;31:33–46. https://doi.org/10.1016/j.euroneuro.2019.12.105 .
Galderisi S, Heinz A, Kastrup M, et al. Toward a new definition of mental health. World Psychiatry. 2015;14:231–3. https://doi.org/10.1002/wps.20231 .
Vaillant GE. Positive mental health: is there a cross-cultural definition? World Psychiatry. 2012;11:93–9. https://doi.org/10.1016/j.wpsyc.2012.05.006 .
Geerling B, Kraiss JT, Kelders SM, et al. The effect of positive psychology interventions on well-being and psychopathology in patients with severe mental illness: A systematic review and meta-analysis. J Posit Psychol. 2020;15:572–87. https://doi.org/10.1080/17439760.2020.1789695 .
Chaves C, Lopez-Gomez I, Hervas G, Vazquez C. A comparative study on the efficacy of a positive psychology intervention and a cognitive behavioral therapy for clinical depression. Cognit Ther Res. 2017;41:417–33. https://doi.org/10.1007/s10608-016-9778-9 .
Brownell T, Schrank B, Jakaite Z, et al. Mental health service user experience of positive psychotherapy. J Clin Psychol. 2015;71:85–92. https://doi.org/10.1002/jclp.22118 .
Hill CE, Kanazawa Y, Knox S, et al. Meaning in life in psychotherapy: the perspective of experienced psychotherapists. Psychother Res. 2017;27:381–96. https://doi.org/10.1080/10503307.2015.1110636 .
McNulty JK, Fincham FD. Beyond positive psychology? Toward a contextual view of psychological processes and well-being. Am Psychol. 2012;67:101–10. https://doi.org/10.1037/a0024572 .
Magyar-Moe JL, Owens RL, Conoley CW. Positive psychological interventions in counseling: what every counseling psychologist should know Ψ. Couns Psychol. 2015;43:508–57. https://doi.org/10.1177/0011000015573776 .
Hart KE, Sasso T. Mapping the contours of contemporary positive psychology. Can Psychol Can. 2011;52:82–92.
This study was supported by the Singapore Ministry of Health’s National Medical Research Council under the Centre Grant Programme (NMRC/CG/M002/2017).
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Sherilyn Chang, Rajeswari Sambasivam, Esmond Seow, Mythily Subramaniam & Janhavi Ajit Vaingankar
Department of Psychology, Institute of Mental Health, Singapore, Singapore
Hanita Ashok Assudani & Sharon Huixian Lu
Department of Mood and Anxiety, Institute of Mental Health, Singapore, Singapore
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Singapore Institute of Clinical Sciences, A*STAR, Singapore, Singapore
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JAV, RS, ES, HAA, GCYT and SHXL designed the interview guide, approached and consented research participants. The project was supervised by MS. JAV and SC conducted interviews with the participants. Analysis of data was performed by SC, JAV and ES, and the first draft of the manuscript was written by SC. All authors critically reviewed the manuscript. All authors read and approved the final manuscript.
Correspondence to Janhavi Ajit Vaingankar .
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The study was approved by the institutional ethics committee (National Healthcare Group Domain Specific Review Board; DSRB Ref No.: 2018/00870). All participants had provided written informed consent prior to their participation. This study was conducted in accordance with the Declaration of Helsinki.
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Chang, S., Sambasivam, R., Seow, E. et al. Positive mental health in psychotherapy: a qualitative study from psychotherapists’ perspectives. BMC Psychol 10 , 111 (2022). https://doi.org/10.1186/s40359-022-00816-6
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DOI : https://doi.org/10.1186/s40359-022-00816-6
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Why the reliance on data? Findings and statistics from research studies can impact us emotionally, add credibility to an article, and ground us in the real world. However, the importance of research findings reaches far beyond providing knowledge to the general population. Research and evaluation studies — those studies that assess a program’s impact — are integral to promoting mental health and reducing the burden of mental illness in different populations.
Mental health research identifies biopsychosocial factors — how biological, psychological and social functioning are interacting — detecting trends and social determinants in population health. That data greatly informs the current state of mental health in the U.S. and around the world. Findings from such studies also influence fields such as public health, health care and education. For example, mental health research and evaluation can impact public health policies by assisting public health professionals in strategizing policies to improve population mental health.
Research helps us understand how to best promote mental health in different populations. From its definition to how it discussed, mental health is seen differently in every community. Thus, mental health research and evaluation not only reveals mental health trends but also informs us about how to best promote mental health in different racial and ethnic populations. What does mental health look like in this community? Is there stigma associated with mental health challenges? How do individuals in the community view those with mental illness? These are the types of questions mental health research can answer.
Data aids us in understanding whether the mental health services and resources that are available meet mental health needs. Many times the communities where needs are the greatest are the ones where there are limited services and resources available. Mental health research and evaluation informs public health professionals and other relevant stakeholders of the gaps that currently exist so they can prioritize policies and strategies for communities where gaps are the greatest.
Research establishes evidence for the effectiveness of public health policies and programs. Mental health research and evaluation help develop evidence for the effectiveness of healthcare policies and strategies as well as mental health promotion programs. This evidence is crucial for showcasing the value and return on investment for programs and policies, which can justify local, state and federal expenditures. For example, mental health research studies evaluating the impact of Mental Health First Aid (MHFA) have revealed that individuals taking the course show increases in knowledge about mental health, greater confidence to assist others in distress, and improvements in their own mental wellbeing. They have been fundamental in assisting organizations and instructors in securing grant funding to bring MHFA to their communities.
The findings from mental health research and evaluation studies provide crucial information about the specific needs within communities and the impacts of public education programs like MHFA. These studies provide guidance on how best to improve mental health in different contexts and ensure financial investments go towards programs proven to improve population mental health and reduce the burden of mental illness in the U.S.
In 2021, in a reaffirmation of its dedication and commitment to mental health and substance use research and community impact, Mental Health First Aid USA introduced MHFA Research Advisors. The group advises and assists Mental Health First Aid USA on ongoing research and future opportunities related to individual MHFA programs, including Youth MHFA, teen MHFA and MHFA at Work.
Through this advisory group and evaluation efforts at large, Mental Health First Aid USA will #BeTheDifference for mental health research and evaluation across communities in the US.
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Factors that influence mental health of university and college students in the UK: a systematic review
- Fiona Campbell 1 ,
- Lindsay Blank 1 ,
- Anna Cantrell 1 ,
- Susan Baxter 1 ,
- Christopher Blackmore 1 ,
- Jan Dixon 1 &
- Elizabeth Goyder 1
BMC Public Health volume 22 , Article number: 1778 ( 2022 ) Cite this article
Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students.
To identify factors associated with mental health of students in higher education.
We undertook a systematic review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010–2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.
We included 31 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy.
Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.
Peer Review reports
Poor mental health of students in further and higher education is an increasing concern for public health and policy [ 1 , 2 , 3 , 4 ]. A 2020 Insight Network survey of students from 10 universities suggests that “1 in 5 students has a current mental health diagnosis” and that “almost half have experienced a serious psychological issue for which they felt they needed professional help”—an increase from 1 in 3 in the same survey conducted in 2018 [ 5 ]. A review of 105 Further Education (FE) colleges in England found that over a three-year period, 85% of colleges reported an increase in mental health difficulties [ 1 ]. Depression and anxiety were both prevalent and widespread in students; all colleges reported students experiencing depression and 99% reported students experiencing severe anxiety [ 5 , 6 ]. A UK cohort study found that levels of psychological distress increase on entering university [ 7 ], and recent evidence suggests that the prevalence of mental health problems among university students, including self-harm and suicide, is rising, [ 3 , 4 ] with increases in demand for services to support student mental health and reports of some universities finding a doubling of the number of students accessing support [ 8 ]. These common mental health difficulties clearly present considerable threat to the mental health and wellbeing of students but their impact also has educational, social and economic consequences such as academic underperformance and increased risk of dropping out of university [ 9 , 10 ].
Policy changes may have had an influence on the student experience, and on the levels of mental health problems seen in the student population; the biggest change has arguably been the move to widen higher education participation and to enable a more diverse demographic to access University education. The trend for widening participation has been continually rising since the late 1960s [ 11 ] but gained impetus in the 2000s through the work of the Higher Education Funding Council for England (HEFCE). Macaskill (2013) [ 12 ] suggests that the increased access to higher education will have resulted in more students attending university from minority groups and less affluent backgrounds, meaning that more students may be vulnerable to mental health problems, and these students may also experience greater challenges in making the transition to higher education.
Another significant change has been the introduction of tuition fees in 1998, which required students to self fund up to £1,000 per academic year. Since then, tuition fees have increased significantly for many students. With the abolition of maintenance grants, around 96% of government support for students now comes in the form of student loans [ 13 ]. It is estimated that in 2017, UK students were graduating with average debts of £50,000, and this figure was even higher for the poorest students [ 13 ]. There is a clear association between a student’s mental health and financial well-being [ 14 ], with “increased financial concern being consistently associated with worse health” [ 15 ].
The extent to which the increase in poor mental health is also being seen amongst non-students of a similar age is not well understood and warrants further study. However, the increase in poor mental health specifically within students in higher education highlights a need to understand what the risk factors are and what might be done within these settings to ensure young people are learning and developing and transitioning into adulthood in environments that promote mental wellbeing.
Commencing higher education represents a key transition point in a young person’s life. It is a stage often accompanied by significant change combined with high expectations of high expectations from students of what university life will be like, and also high expectations from themselves and others around their own academic performance. Relevant factors include moving away from home, learning to live independently, developing new social networks, adjusting to new ways of learning, and now also dealing with the additional greater financial burdens that students now face.
The recent global COVID-19 pandemic has had considerable impact on mental health across society, and there is concern that younger people (ages 18–25) have been particularly affected. Data from Canada [ 16 ] indicate that among survey respondents, “almost two-thirds (64%) of those aged 15 to 24 reported a negative impact on their mental health, while just over one-third (35%) of those aged 65 and older reported a negative impact on their mental health since physical distancing began” (ibid, p.4). This suggests that older adults are more prepared for the kind of social isolation which has been brought about through the response to COVID-19, whereas young adults have found this more difficult to cope with. UK data from the National Union of Students reports that for over half of UK students, their mental health is worse than before the pandemic [ 17 ]. Before COVID-19, students were already reporting increasing levels of mental health problems [ 2 ], but the COVID-19 pandemic has added a layer of “chronic and unpredictable” stress, creating the perfect conditions for a mental health crisis [ 18 ]. An example of this is the referrals (both urgent and routine) of young people with eating disorders for treatment in the NHS which almost doubled in number from 2019 to 2020 [ 19 ]. The travel restrictions enforced during the pandemic have also impacted on student mental health, particularly for international students who may have been unable to commence studies or go home to see friends and family during holidays [ 20 ].
With the increasing awareness and concern in the higher education sector and national bodies regarding student mental health has come increasing focus on how to respond. Various guidelines and best practice have been developed, e.g. ‘Degrees of Disturbance’ [ 21 ], ‘Good Practice Guide on Responding to Student Mental Health Issues: Duty of Care Responsibilities for Student Services in Higher Education’ [ 22 ] and the recent ‘The University Mental Health Charter’ [ 2 ]. Universities UK produced a Good Practice Guide in 2015 called “Student mental wellbeing in higher education” [ 23 ]. An increasing number of initiatives have emerged that are either student-led or jointly developed with students, and which reflect the increasing emphasis students and student bodies place on mental health and well-being and the increased demand for mental health support: Examples include: Nightline— www.nightline.ac.uk , Students Against Depression— www.studentsagainstdepression.org , Student Minds— www.studentminds.org.uk/student-minds-and-mental-wealth.html and The Alliance for Student-Led Wellbeing— www.alliancestudentwellbeing.weebly.com/ .
Although requests for professional support have increased substantially [ 24 ] only a third of students with mental health problems seek support from counselling services in the UK [ 12 ]. Many students encounter barriers to seeking help such as stigma or lack of awareness of services [ 25 ], and without formal support or intervention, there is a risk of deterioration. FE colleges and universities have identified the need to move beyond traditional forms of support and provide alternative, more accessible interventions aimed at improving mental health and well-being. Higher education institutions have a unique opportunity to identify, prevent, and treat mental health problems because they provide support in multiple aspects of students’ lives including academic studies, recreational activities, pastoral and counselling services, and residential accommodation.
In order to develop services that better meet the needs of students and design environments that are supportive of developing mental wellbeing it is necessary to explore and better understand the factors that lead to poor mental health in students.
The overall aim of this review was to identify, appraise and synthesise existing research evidence that explores the aetiology of poor mental health and mental wellbeing amongst students in tertiary level education. We aimed to gain a better understanding of the mechanisms that lead to poor mental health amongst tertiary level students and, in so doing, make evidence-based recommendations for policy, practice and future research priorities. Specific objectives in line with the project brief were to:
To co-produce with stakeholders a conceptual framework for exploring the factors associated with poorer mental health in students in tertiary settings. The factors may be both predictive, identifying students at risk, or causal, explaining why they are at risk. They may also be protective, promoting mental wellbeing.
To conduct a review drawing on qualitative studies, observational studies and surveys to explore the aetiology of poor mental health in students in university and college settings and identify factors which promote mental wellbeing amongst students.
To identify evidence-based recommendations for policy, service provision and future research that focus on prevention and early identification of poor mental health
Identification of relevant evidence.
The following inclusion criteria were used to guide the development of the search strategy and the selection of studies.
We included students from a variety of further education settings (16 yrs + or 18 yrs + , including mature students, international students, distance learning students, students at specific transition points).
Universities and colleges in the UK. We were also interested in the context prior to the beginning of tertiary education, including factors during transition from home and secondary education or existing employment to tertiary education.
Any factor shown to be associated with mental health of students in tertiary level education. This included clinical indicators such as diagnosis and treatment and/or referral for depression and anxiety. Self-reported measures of wellbeing, happiness, stress, anxiety and depression were included. We did not include measures of academic achievement or engagement with learning as indicators of mental wellbeing.
We included cross-sectional and longitudinal studies that looked at factors associated with mental health outcomes in Table 5 .
Data extraction and quality appraisal
We extracted and tabulated key data from the included papers. Data extraction was undertaken by one reviewer, with a 10% sample checked for accuracy and consistency The quality of the included studies were evaluated using the Newcastle-Ottawa Scale [ 26 ] and the findings of the quality appraisal used in weighting the strength of associations and also identifying gaps for future high quality research.
Involvement of stakeholders
We recruited students, ex-students and parents of students to a public involvement group which met on-line three times during the process of the review and following the completion of the review. During a workshop meeting we asked for members of the group to draw on their personal experiences to suggest factors which were not mentioned in the literature.
Methods of synthesis
We undertook a narrative synthesis [ 27 ] due to the heterogeneity in the exposures and outcomes that were measured across the studies. Data showing the direction of effects and the strength of the association (correlation coefficients) were recorded and tabulated to aid comparison between studies.
Searches were conducted in the following electronic databases: Medline, Applied Social Sciences Index and Abstracts (ASSIA), International Bibliography of Social Sciences (IBSS), Science,PsycINFO and Science and Social Sciences Ciatation Indexes. Additional searches of grey literature, and reference lists of included studies were also undertaken.
The search strategy combined a number of terms relating to students and mental health and risk factors. The search terms included both subject (MeSH) and free-text searches. The searches were limited to papers about humans in English, published from 2010 to June 2020. The flow of studies through the review process is summarised in Fig. 1 .
The full search strategy for Medline is provided in Appendix 1 .
Thirty-one quantitative, observational studies (39 papers) met the inclusion criteria. The total number of students that participated in the quantitative studies was 17,476, with studies ranging in size from 57 to 3706. Eighteen studies recruited student participants from only one university; five studies (10 publications) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] included seven or more universities. Six studies (7 publications) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] only recruited first year students, while the majority of studies recruited students from a range of year groups. Five studies [ 39 , 42 , 43 , 44 , 45 ] recruited only, or mainly, psychology students which may impact on the generalisability of findings. A number of studies focused on students studying particular subjects including: nursing [ 46 ] medicine [ 47 ], business [ 48 ], sports science [ 49 ]. One study [ 50 ] recruited LGBTQ (lesbian, gay, bisexual, transgender, intersex, queer/questioning) students, and one [ 51 ] recruited students who had attended hospital having self-harmed. In 27 of the studies, there were more female than male participants. The mean age of the participants ranged from 19 to 28 years. Ethnicity was not reported in 19 of the studies. Where ethnicity was reported, the proportion that were ‘white British’ ranged from 71 – 90%. See Table 1 for a summary of the characteristics of the included studies and the participants.
Design and quality appraisal of the included studies
The majority of included studies ( n = 22) were cross-sectional surveys. Nine studies (10 publications) [ 35 , 36 , 39 , 41 , 43 , 50 , 51 , 52 , 53 , 62 ] were longitudinal in design, recording survey data at different time points to explore changes in the variables being measured. The duration of time that these studies covered ranged from 19 weeks to 12 years. Most of the studies ( n = 22) only recruited participants from a single university. The use of one university setting and the large number of studies that recruited only psychology students weakens the wider applicability of the included studies.
Included studies ( n = 31) measured a wide range of variables and explored their association with poor mental health and wellbeing. These included individual level factors: age, gender, sexual orientation, ethnicity and a range of psychological variables. They also included factors that related to mental health variables (family history, personal history and mental health literacy), pre-university factors (childhood trauma and parenting behaviour. University level factors including social isolation, adjustment and engagement with learning. Their association was measured against different measures of positive mental health and poor mental health.
Measurement of association and the strength of that association has some limitations in addressing our research question. It cannot prove causality, and nor can it capture fully the complexity of the inter-relationship and compounding aspect of the variables. For example, the stress of adjustment may be manageable, until it is combined with feeling isolated and out of place. Measurement itself may also be misleading, only capturing what is measureable, and may miss variables that are important but not known. We included both qualitative and PPI input to identify missed but important variables.
The wide range of variables and different outcomes, with few studies measuring the same variable and outcomes, prevented meta-analyses of findings which are therefore described narratively.
The variables described were categorised during the analyses into the following categories:
Vulnerabilities – factors that are associated with poor mental health
Individual level factors including; age, ethnicity, gender and a range of psychological variables were all measured against different mental health outcomes including depression, anxiety, paranoia, and suicidal behaviour, self-harm, coping and emotional intelligence.
Six studies [ 40 , 42 , 47 , 50 , 60 , 63 ] examined a student’s ages and association with mental health. There was inconsistency in the study findings, with studies finding that age (21 or older) was associated with fewer depressive symptoms, lower likelihood of suicide ideation and attempt, self-harm, and positively associated with better coping skills and mental wellbeing. This finding was not however consistent across studies and the association was weak. Theoretical models that seek to explain this mechanism have suggested that older age groups may cope better due to emotion-regulation strategies improving with age [ 67 ]. However, those over 30 experienced greater financial stress than those aged 17-19 in another study [ 63 ].
Four studies [ 33 , 40 , 64 , 68 ] examined the association between poor mental health and sexual orientation status. In all of the studies LGBTQ students were at significantly greater risk of mental health problems including depression [ 40 ], anxiety [ 40 ], suicidal behaviour [ 33 , 40 , 64 ], self harm [ 33 , 40 , 64 ], use of mental health services [ 33 ] and low levels of wellbeing [ 68 ]. The risk of mental health problems in these students compared with heterosexual students, ranged from OR 1.4 to 4.5. This elevated risk may reflect the greater levels of isolation and discrimination commonly experienced by minority groups.
Nine studies [ 33 , 38 , 39 , 40 , 42 , 47 , 50 , 60 , 63 ] examined whether gender was associated mental health variables. Two studies [ 33 , 47 ] found that being female was statistically significantly associated with use of mental health services, having a current mental health problem, suicide risk, self harm [ 33 ] and depression [ 47 ]. The results were not consistent, with another study [ 60 ] finding the association was not significant. Three studies [ 39 , 40 , 42 ] that considered mediating variables such as adaptability and coping found no difference or very weak associations.
Two studies [ 47 , 60 ] examined the extent to which ethnicity was associated with mental health One study [ 47 ] reported that the risks of depression were significantly greater for those who categorised themselves as non-white (OR 8.36 p = 0.004). Non-white ethnicity was also associated with poorer mental health in another cross-sectional study [ 63 ]. There was no significant difference in the McIntyre et al. (2018) study [ 60 ]. The small number of participants from ethnic minority groups represented across the studies means that this data is very limited.
Six studies [ 33 , 40 , 42 , 50 , 60 ] explored the association of a concept that related to a student’s experiences in childhood and before going to university. Three studies [ 40 , 50 , 60 ] explored the impact of ACEs (Adverse Childhood Experiences) assessed using the same scale by Feletti (2009) [ 69 ] and another explored the impact of abuse in childhood [ 46 ]. Two studies examined the impact of attachment anxiety and avoidance [ 42 ], and parental acceptance [ 46 , 59 ]. The studies measured different mental health outcomes including; positive and negative affect, coping, suicide risk, suicide attempt, current mental health problem, use of mental health services, psychological adjustment, depression and anxiety.
The three studies that explored the impact of ACE’s all found a significant and positive relationship with poor mental health amongst university students. O’Neill et al. (2018) [ 50 ] in a longitudinal study ( n = 739) showed that there was in increased likelihood in self-harm and suicidal behaviours in those with either moderate or high levels of childhood adversities (OR:5.5 to 8.6) [ 50 ]. McIntyre et al. (2018) [ 60 ] ( n = 1135) also explored other dimensions of adversity including childhood trauma through multiple regression analysis with other predictive variables. They found that childhood trauma was significantly positively correlated with anxiety, depression and paranoia (ß = 0.18, 0.09, 0.18) though the association was not as strong as the correlation seen for loneliness (ß = 0.40) [ 60 ]. McLafferty et al. (2019) [ 40 ] explored the compounding impact of childhood adversity and negative parenting practices (over-control, overprotection and overindulgence) on poor mental health (depression OR 1.8, anxiety OR 2.1 suicidal behaviour OR 2.3, self-harm OR 2.0).
Gaan et al.’s (2019) survey of LGBTQ students ( n = 1567) found in a multivariate analyses that sexual abuse, other abuse from violence from someone close, and being female had the highest odds ratios for poor mental health and were significantly associated with all poor mental health outcomes [ 33 ].
While childhood trauma and past abuse poses a risk to mental health for all young people it may place additional stresses for students at university. Entry to university represents life stage where there is potential exposure to new and additional stressors, and the possibility that these students may become more isolated and find it more difficult to develop a sense of belonging. Students may be separated for the first time from protective friendships. However, the mechanisms that link childhood adversities and negative psychopathology, self-harm and suicidal behaviour are not clear [ 40 ]. McLafferty et al. (2019) also measured the ability to cope and these are not always impacted by childhood adversities [ 40 ]. They suggest that some children learn to cope and build resilience that may be beneficial.
McLafferty et al. (2019) [ 40 ] also studied parenting practices. Parental over-control and over-indulgence was also related to significantly poorer coping (OR -0.075 p < 0.05) and this was related to developing poorer coping scores (OR -0.21 p < 0.001) [ 40 ]. These parenting factors only became risk factors when stress levels were high for students at university. It should be noted that these studies used self-report, and responses regarding views of parenting may be subjective and open to interpretation. Lloyd et al.’s (2014) survey found significant positive correlations between perceived parental acceptance and students’ psychological adjustment, with paternal acceptance being the stronger predictor of adjustment.
Autistic students may display social communication and interaction deficits that can have negative emotional impacts. This may be particularly true during young adulthood, a period of increased social demands and expectations. Two studies [ 56 ] found that those with autism had a low but statistically significant association with poor social problem-solving skills and depression.
Mental health history
Three studies [ 47 , 51 , 68 ] investigated mental health variables and their impact on mental health of students in higher education. These included; a family history of mental illness and a personal history of mental illness.
Students with a family history or a personal history of mental illness appear to have a significantly greater risk of developing problems with mental health at university [ 47 ]. Mahadevan et al. (2010) [ 51 ] found that university students who self-harm have a significantly greater risk (OR 5.33) of having an eating disorder than a comparison group of young adults who self-harm but are not students.
Buffers – factors that are protective of mental wellbeing
Twelve studies [ 29 , 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 , 64 ] assessed the association of a range of psychological variables and different aspects of mental wellbeing and poor mental health. We categorised these into the following two categories: firstly, psychological variables measuring an individual’s response to change and stressors including adaptability, resilience, grit and emotional regulation [ 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 ] and secondly, those that measure self-esteem and body image [ 29 , 64 ].
The evidence from the eight included quantitative studies suggests that students with psychological strengths including; optimism, self-efficacy [ 70 ], resilience, grit [ 58 ], use of positive reappraisal [ 49 ], helpful coping strategies [ 42 ] and emotional intelligence [ 41 , 46 ] are more likely to experience greater mental wellbeing (see Table 2 for a description of the psychological variables measured). The positive association between these psychological strengths and mental well-being had a positive affect with associations ranging from r = 0.2–0.5 and OR1.27 [ 41 , 43 , 46 , 49 , 54 ] (low to moderate strength of association). The negative associations with depressive symptoms are also statistically significant but with a weaker association ( r = -0.2—0.3) [ 43 , 49 , 54 ].
Denovan (2017a) [ 43 ] in a longitudinal study found that the association between psychological strengths and positive mental wellbeing was not static and that not all the strengths remained statistically significant over time. The only factors that remained significant during the transition period were self-efficacy and optimism, remaining statistically significant as they started university and 6 months later.
Only one study [ 59 ] explored family factors associated with the development of psychological strengths that would equip young people as they managed the challenges and stressors encountered during the transition to higher education. Lloyd et al. (2014) [ 59 ] found that perceived maternal and paternal acceptance made significant and unique contributions to students’ psychological adjustment. Their research methods are limited by their reliance on retrospective measures and self-report measures of variables, and these results could be influenced by recall bias.
Two studies [ 29 , 64 ] considered the impact of how individuals view themselves on poor mental health. One study considered the impact of self-esteem and the association with non-accidental self-injury (NSSI) and suicide attempt amongst 734 university students. As rates of suicide and NSSI are higher amongst LGBT (lesbian, gay, bisexual, transgender) students, the prevalence of low self-esteem was compared. There was a low but statistically significant association between low self-esteem and NSSI, though not for suicide attempt. A large survey, including participants from seven universities [ 42 ] compared depressive symptoms in students with marked body image concerns, reporting that the risk of depressive symptoms was greater (OR 2.93) than for those with lower levels of body image concerns.
Mental health literacy and help seeking behaviour
Two studies [ 48 , 68 ] investigated attitudes to mental illness, mental health literacy and help seeking for mental health problems.
University students who lack sufficient mental health literacy skills to be able to recognise problems or where there are attitudes that foster shame at admitting to having mental health problems can result in students not recognising problems and/or failing to seek professional help [ 48 , 68 ]. Gorcyznski et al. (2017) [ 68 ] found that women and those who had a history of previous mental health problems exhibited significantly higher levels of mental health literacy. Greater mental health literacy was associated with an increased likelihood that individuals would seek help for mental health problems. They found that many students find it hard to identify symptoms of mental health problems and that 42% of students are unaware of where to access available resources. Of those who expressed an intention to seek help for mental health problems, most expressed a preference for online resources, and seeking help from family and friends, rather than medical professionals such as GPs.
Kotera et al. (2019) [ 48 ] identified self-compassion as an explanatory variable, reducing social comparison, promoting self-acceptance and recognition that discomfort is an inevitable human experience. The study found a strong, significant correlation between self-compassion and mental health symptoms ( r = -0.6. p < 0.01).
There again appears to be a cycle of reinforcement, where poor mental health symptoms are felt to be a source of shame and become hidden, help is not sought, and further isolation ensues, leading to further deterioration in mental health. Factors that can interrupt the cycle are self-compassion, leading to more readiness to seek help (see Fig. 2 ).
Poor mental health – cycles of reinforcement
Nine studies [ 33 , 38 , 41 , 46 , 51 , 54 , 60 , 64 , 65 ] examined the concepts of loneliness and social support and its association with mental health in university students. One study also included students at other Higher Education Institutions [ 46 ]. Eight of the studies were surveys, and one was a retrospective case control study to examine the differences between university students and age-matched young people (non-university students) who attended hospital following deliberate self-harm [ 51 ].
Included studies demonstrated considerable variation in how they measured the concepts of social isolation, loneliness, social support and a sense of belonging. There were also differences in the types of outcomes measured to assess mental wellbeing and poor mental health. Grouping the studies within a broad category of ‘social factors’ therefore represents a limitation of this review given that different aspects of the phenomena may have been being measured. The tools used to measure these variables also differed. Only one scale (The UCLA loneliness scale) was used across multiple studies [ 41 , 60 , 65 ]. Diverse mental health outcomes were measured across the studies including positive affect, flourishing, self-harm, suicide risk, depression, anxiety and paranoia.
Three studies [ 41 , 60 , 62 ] measuring loneliness, two longitudinally [ 41 , 62 ], found a consistently positive association between loneliness and poor mental health in university students. Greater loneliness was linked to greater anxiety, stress, depression, poor general mental health, paranoia, alcohol abuse and eating disorder problems. The strength of the correlations ranged from 0–3-0.4 and were all statistically significant (see Tables 3 and 4 ). Loneliness was the strongest overall predictor of mental distress, of those measured. A strong identification with university friendship groups was most protective against distress relative to other social identities [ 60 ]. Whether poor mental health is the cause, or the result of loneliness was explored further in the studies. The results suggest that for general mental health, stress, depression and anxiety, loneliness induces or exacerbates symptoms of poor mental health over time [ 60 , 62 ]. The feedback cycle is evident, with loneliness leading to poor mental health which leads to withdrawal from social contacts and further exacerbation of loneliness.
Factors associated with protecting against loneliness by fostering supportive friendships and promoting mental wellbeing were also identified. Beliefs about the value of ‘leisure coping’, and attributes of resilience and emotional intelligence had a moderate, positive and significant association with developing mental wellbeing and were explored in three studies [ 46 , 54 , 66 ].
The transition to and first year at university represent critical times when friendships are developed. Thomas et al. (2020) [ 65 ] explored the factors that predict loneliness in the first year of university. A sense of community and higher levels of ‘social capital’ were significantly associated with lower levels of loneliness. ‘Social capital’ scales measure the development of emotionally supportive friendships and the ability to adjust to the disruption of old friendships as students transition to university. Students able to form close relationships within their first year at university are less likely to experience loneliness (r-0.09, r- 0.36, r- 0.34). One study [ 38 ] investigating the relationship between student experience and being the first in the family to attend university found that these students had lower ratings for peer group interactions.
Young adults at university and in higher education are facing multiple adjustments. Their ability to cope with these is influenced by many factors. Supportive friendships and a sense of belonging are factors that strengthen coping. Nightingale et al. (2012) undertook a longitudinal study to explore what factors were associated with university adjustment in a sample of first year students ( n = 331) [ 41 ]. They found that higher skills of emotion management and emotional self-efficacy were predictive of stable adjustment. These students also reported the lowest levels of loneliness and depression. This group had the skills to recognise their emotions and cope with stressors and were confident to access support. Students with poor emotion management and low levels of emotional self-efficacy may benefit from intervention to support the development of adaptive coping strategies and seeking support.
The positive and negative feedback loops
The relationship between the variables described appeared to work in positive and negative feedback loops with high levels of social capital easing the formation of a social network which acts as a critical buffer to stressors (see Fig. 3 ). Social networks and support give further strengthening and reinforcement, stimulating positive affect, engagement and flourishing. These, in turn, widen and deepen social networks for support and enhance a sense of wellbeing. Conversely young people who enter the transition to university/higher education with less social capital are less likely to identify with and locate a social network; isolation may follow, along with loneliness, anxiety, further withdrawal from contact with social networks and learning, and depression.
Triggers – factors that may act in combination with other factors to lead to poor mental health
Stress is seen as playing a key role in the development of poor mental health for students in higher education. Theoretical models and empirical studies have suggested that increases in stress are associated with decreases in student mental health [ 12 , 43 ]. Students at university experience the well-recognised stressors associated with academic study such as exams and course work. However, perhaps less well recognised are the processes of transition, requiring adapting to a new social and academic environment (Fisher 1994 cited by Denovan 2017a) [ 43 ]. Por et al. (2011) [ 46 ] in a small ( n = 130 prospective survey found a statistically significant correlation between higher levels of emotional intelligence and lower levels of perceived stress ( r = 0.40). Higher perceived stress was also associated with negative affect in two studies [ 43 , 46 ], and strongly negatively associated with positive affect (correlation -0.62) [ 54 ].
Eleven studies [ 35 , 39 , 47 , 51 , 52 , 54 , 60 , 63 , 65 , 83 , 84 ] explored university variables, and their association with mental health outcomes. The range of factors and their impact on mental health variables is limited, and there is little overlap. Knowledge gaps are shown by factors highlighted by our PPI group as potentially important but not identified in the literature (see Table 5 ). It should be noted that these may reflect the focus of our review, and our exclusion of intervention studies which may evaluate university factors.
High levels of perceived stress caused by exam and course work pressure was positively associated with poor mental health and lack of wellbeing [ 51 , 52 , 54 ]. Other potential stressors including financial anxieties and accommodation factors appeared to be less consistently associated with mental health outcomes [ 35 , 38 , 47 , 51 , 60 , 62 ]. Important mediators and buffers to these stressors are coping strategies and supportive networks (see conceptual model Appendix 2 ). One impact of financial pressures was that students who worked longer hours had less interaction with their peers, limiting the opportunities for these students to benefit from the protective effects of social support.
Red flags – behaviours associated with poor mental health and/or wellbeing
Engagement with learning and leisure activities.
Engagement with learning activities was strongly and positively associated with characteristics of adaptability [ 39 ] and also happiness and wellbeing [ 52 ] (see Fig. 4 ). Boulton et al. (2019) [ 52 ] undertook a longitudinal survey of undergraduate students at a campus-based university. They found that engagement and wellbeing varied during the term but were strongly correlated.
Engagement and wellbeing
Engagement occurred in a wide range of activities and behaviours. The authors suggest that the strong correlation between all forms of engagement with learning has possible instrumental value for the design of systems to monitor student engagement. Monitoring engagement might be used to identify changes in the behaviour of individuals to assist tutors in providing support and pastoral care. Students also were found to benefit from good induction activities provided by the university. Greater induction satisfaction was positively and strongly associated with a sense of community at university and with lower levels of loneliness [ 65 ].
The inte r- related nature of these variables is depicted in Fig. 4 . Greater adaptability is strongly associated with more positive engagement in learning and university life. More engagement is associated with higher mental wellbeing.
Denovan et al. (2017b) [ 54 ] explored leisure coping, its psychosocial functions and its relationship with mental wellbeing. An individual’s beliefs about the benefits of leisure activities to manage stress, facilitate the development of companionship and enhance mood were positively associated with flourishing and were negatively associated with perceived stress. Resilience was also measured. Resilience was strongly and positively associated with leisure coping beliefs and with indicators of mental wellbeing. The authors conclude that resilient individuals are more likely to use constructive means of coping (such as leisure coping) to proactively cultivate positive emotions which counteract the experience of stress and promote wellbeing. Leisure coping is predictive of positive affect which provides a strategy to reduce stress and sustain coping. The belief that friendships acquired through leisure provide social support is an example of leisure coping belief. Strong emotionally attached friendships that develop through participation in shared leisure pursuits are predictive of higher levels of well-being. Friendship bonds formed with fellow students at university are particularly important for maintaining mental health, and opportunities need to be developed and supported to ensure that meaningful social connections are made.
The ‘broaden-and-build theory’ (Fredickson 2004 [ 85 ] cited by [ 54 ]) may offer an explanation for the association seen between resilience, leisure coping and psychological wellbeing. The theory is based upon the role that positive and negative emotions have in shaping human adaptation. Positive emotions broaden thinking, enabling the individual to consider a range of ways of dealing with and adapting to their environment. Conversely, negative emotions narrow thinking and limit options for adapting. The former facilitates flourishing, facilitating future wellbeing. Resilient individuals are more likely to use constructive means of coping which generate positive emotion (Tugade & Fredrickson 2004 [ 86 ], cited by [ 54 ]). Positive emotions therefore lead to growth in coping resources, leading to greater well-being.
Health behaviours at university
Seven studies [ 29 , 31 , 38 , 45 , 51 , 54 , 66 ] examined how lifestyle behaviours might be linked with mental health outcomes. The studies looked at leisure activities [ 63 , 80 ], diet [ 29 ], alcohol use [ 29 , 31 , 38 , 51 ] and sleep [ 45 ].
Depressive symptoms were independently associated with problem drinking and possible alcohol dependence for both genders but were not associated with frequency of drinking and heavy episodic drinking. Students with higher levels of depressive symptoms reported significantly more problem drinking and possible alcohol dependence [ 31 ]. Mahadevan et al. (2010) [ 51 ] compared students and non-students seen in hospital for self-harm and found no difference in harmful use of alcohol and illicit drugs.
Poor sleep quality and increased consumption of unhealthy foods were also positively associated with depressive symptoms and perceived stress [ 29 ]. The correlation with dietary behaviours and poor mental health outcomes was low, but also confirmed by the negative correlation between less perceived stress and depressive symptoms and consumption of a healthier diet.
Physical activity and participation in leisure pursuits were both strongly correlated with mental wellbeing ( r = 0.4) [ 54 ], and negatively correlated with depressive symptoms and anxiety ( r = -0.6, -0.7) [ 66 ].
Thirty studies measuring the association between a wide range of factors and poor mental health and mental wellbeing in university and college students were identified and included in this review. Our purpose was to identify the factors that contribute to the growing prevalence of poor mental health amongst students in tertiary level education within the UK. We also aimed to identify factors that promote mental wellbeing and protect against deteriorating poor mental health.
Loneliness and social isolation were strongly associated with poor mental health and a sense of belonging and a strong support network were strongly associated with mental wellbeing and happiness. These associations were strongly positive in the eight studies that explored them and are consistent with other meta-analyses exploring the link between social support and mental health [ 87 ].
Another factor that appeared to be protective was older age when starting university. A wide range of personal traits and characteristics were also explored. Those associated with resilience, ability to adjust and better coping led to improved mental wellbeing. Better engagement appeared as an important mediator to potentially explain the relationship between these two variables. Engagement led to students being able to then tap into those features that are protective and promoting of mental wellbeing.
Other important risk factors for poor mental wellbeing that emerged were those students with existing or previous mental illness. Students on the autism spectrum and those with poor social problem-solving also were more likely to suffer from poor mental health. Negative self-image was also associated with poor mental health at university. Eating disorders were strongly associated with poor mental wellbeing and were found to be far more of a risk in students at university than in a comparative group of young people not in higher education. Other studies of university students also found that pre-existing poor mental health was a strong predictor of poor mental health in university students [ 88 ].
At a family level, the experience of childhood trauma and adverse experiences including, for example, neglect, household dysfunction or abuse, were strongly associated with poor mental health in young people at university. Students with a greater number of ‘adverse childhood experiences’ were at significantly greater risk of poor mental health than those students without experience of childhood trauma. This was also identified in a review of factors associated with depression and suicide related outcomes amongst university undergraduate students [ 88 ].
Our findings, in contrast to findings from other studies of university students, did not find that female gender associated with poor mental health and wellbeing, and it also found that being a mature student was protective of mental wellbeing.
Exam and course work pressure was associated with perceived stress and poor mental health. A lack of engagement with learning activities was also associated with poor mental health. A number of variables were not consistently shown to be associated with poor mental health including financial concerns and accommodation factors. Very little evidence related to university organisation or support structures was assessed in the evidence. One study found that a good induction programme had benefits for student mental wellbeing and may be a factor that enables students to become a part of a social network positive reinforcement cycle. Involvement in leisure activities was also found to be associated with improved coping strategies and better mental wellbeing. Students with poorer mental health tended to also eat in a less healthy manner, consume more harmful levels of alcohol, and experience poorer sleep.
This evidence review of the factors that influence mental health and wellbeing indicate areas where universities and higher education settings could develop and evaluate innovations in practice. These include:
Interventions before university to improve preparation of young people and their families for the transition to university.
Exploratory work to identify the acceptability and feasibility of identifying students at risk or who many be exhibiting indications of deteriorating mental health
Interventions that set out to foster a sense of belonging and identify
Creating environments that are helpful for building social networks
Improving mental health literacy and access to high quality support services
This review has a number of limitations. Most of the included studies were cross-sectional in design, with a small number being longitudinal ( n = 7), following students over a period of time to observe changes in the outcomes being measured. Two limitations of these sources of data is that they help to understand associations but do not reveal causality; secondly, we can only report the findings for those variables that were measured, and we therefore have to support causation in assuming these are the only factors that are related to mental health.
Furthermore, our approach has segregated and categorised variables in order to better understand the extent to which they impact mental health. This approach does not sufficiently explore or reveal the extent to which variables may compound one another, for example, feeling the stress of new ways of learning may not be a factor that influences mental health until it is combined with a sense of loneliness, anxiety about financial debt and a lack of parental support. We have used our PPI group and the development of vignettes of their experiences to seek to illustrate the compounding nature of the variables identified.
We limited our inclusion criteria to studies undertaken in the UK and published within the last decade (2009–2020), again meaning we may have limited our inclusion of relevant data. We also undertook single data extraction of data which may increase the risk of error in our data.
Understanding factors that influence students’ mental health and wellbeing offers the potential to find ways to identify strategies that enhance the students’ abilities to cope with the challenges of higher education. This review revealed a wide range of variables and the mechanisms that may explain how they impact upon mental wellbeing and increase the risk of poor mental health amongst students. It also identified a need for interventions that are implemented before young people make the transition to higher education. We both identified young people who are particularly vulnerable and the factors that arise that exacerbate poor mental health. We highlight that a sense of belonging and supportive networks are important buffers and that there are indicators including lack of engagement that may enable early intervention to provide targeted and appropriate support.
Availability of data and materials
Further details of the study and the findings can be provided on request to the lead author ([email protected]).
Association of Colleges. Association of Colleges’ survey on students with mental health conditions in further education. London: 2017.
Hughes G, Spanner L. The University Mental Health Charter. Leeds: Student Minds; 2019.
Sivertsen B, Hysing M, Knapstad M, Harvey AG, Reneflot A, Lønning KJ, et al. Suicide attempts and non-suicidal self-harm among university students: prevalence study. BJPsych Open. 2019;5(2):e26.
Article PubMed PubMed Central Google Scholar
Storrie K, Ahern K, Tuckett A. A systematic review: students with mental health problems—a growing problem. Int J Nurs Pract. 2010;16(1):1–6.
Article PubMed Google Scholar
Pereira S, Reay K, Bottell J, Walker L, Dzikiti C, Platt C, Goodrham C. Student Mental Health Survey 2018: A large scale study into the prevalence of student mental illness within UK universities. 2019.
Bayram N, Bilgel N. The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students. Soc Psychiatry Psychiatr Epidemiol. 2008;43(8):667–72.
Bewick B, Koutsopoulou G, Miles J, Slaa E, Barkham M. Changes in undergraduate students’ psychological well-being as they progress through university. Stud High Educ. 2010;35(6):633–45.
Article Google Scholar
Thorley C. Not By Degrees: Not by degrees: Improving student mental health in the UK’s universities. London: IPPR; 2017.
Eisenberg D, Golberstein E, Hunt JB. Mental health and academic success in college. BE J Econ Anal Pol. 2009;9(1):1–37.
Hysenbegasi A, Hass SL, Rowland CR. The impact of depression on the academic productivity of university students. J Ment Health Policy Econ. 2005;8(3):145.
PubMed Google Scholar
Chowdry H, Crawford C, Dearden L, Goodman A, Vignoles A. Widening participation in higher education: analysis using linked administrative data. J R Stat Soc A Stat Soc. 2013;176(2):431–57.
Macaskill A. The mental health of university students in the United Kingdom. Br J Guid Couns. 2013;41(4):426–41.
Belfield C, Britton J, van der Erve L. Higher Education finance reform: Raising the repayment threshold to£ 25,000 and freezing the fee cap at £ 9,250: Institute for Fiscal Studies Briefing note. London: Institute for Fiscal Studies; 2017. Available from https://ifs.org.uk/publications/9964 .
Benson-Egglenton J. The financial circumstances associated with high and low wellbeing in undergraduate students: a case study of an English Russell Group institution. J Furth High Educ. 2019;43(7):901–13.
Jessop DC, Herberts C, Solomon L. The impact of financial circumstances on student health. Br J Health Psychol. 2005;10(3):421–39.
(2020) SCSC. Canadians’ mental health during the COVID-19 pandemic. 2020.
(NUS) NUoS. Coronavirus Student Survey phase III November 2020. 2020.
Hellemans K, Abizaid A, Gabrys R, McQuaid R, Patterson Z. For university students, COVID-19 stress creates perfect conditions for mental health crises. The Conversation. 2020. Available from: https://theconversation.com/for-university-students-covid-19-stress-creates-perfect-conditions-for-mental-health-crises-149127 .
England N. Children and Young People with an Eating Disorder Waiting Times: NHS England; 2021 [Available from: https://www.england.nhs.uk/statistics/statistical-work-areas/cyped-waiting-times/
King JA, Cabarkapa S, Leow FH, Ng CH. Addressing international student mental health during COVID-19: an imperative overdue. Australas Psychiatry. 2020;28(4):469.
Rana R, Smith E, Walking J. Degrees of disturbance: the new agenda; the Impact of Increasing Levels of Psychological Disturbance Amongst Students in Higher Education. England: Association for University and College Counselling Rugby; 1999.
AMOSSHE. Responding to student mental health issues: 'Duty of Care' responsibilities for student services in higher education. https://www.amosshe.org.uk/resources/Documents/AMOSSHE_Duty_of_Care_2001.pdf [accessed 24.12.2020]. (2001).
Universities UK. Student mental wellbeing in higher education. Good practice guide. London: Universities UK; 2015.
Williams M, Coare P, Marvell R, Pollard E, Houghton A-M, Anderson J. 2015. Understanding provision for students with mental health problems and intensive support needs: Report to HEFCE by the Institute for Employment Studies (IES) and Researching Equity, Access and Partnership (REAP). Institute for Employment Studies.
Hunt J, Eisenberg D. Mental health problems and help-seeking behavior among college students. J Adolesc Health. 2010;46(1):3–10.
Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. In.: Oxford; 2000.
Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. 2020;368:l6890.
El Ansari W, Adetunji H, Oskrochi R. Food and mental health: relationship between food and perceived stress and depressive symptoms among university students in the United Kingdom. Cent Eur J Public Health. 2014a;22(2):90–7.
El Ansari W, Dibba E, Stock C. Body image concerns: levels, correlates and gender differences among students in the United Kingdom. Cent Eur J Public Health. 2014b;22(2):106–17.
Ansari EL, W, Oskrochi R, Stock C. Symptoms and health complaints and their association with perceived stress: Students from seven universities in England, Wales and Northern Ireland. J Public Health. 2013;21(5):413–25.
El Ansari W, Sebena R, Stock C. Do importance of religious faith and healthy lifestyle modify the relationships between depressive symptoms and four indicators of alcohol consumption? A survey of students across seven universities in England, Wales, and Northern Ireland. Subst Use Misuse. 2014c;49(3):211–20.
El Ansari W, Stock C. Is the health and wellbeing of university students associated with their academic performance? Cross sectional findings from the United Kingdom. International Journal of Environmental Research & Public Health [Electronic Resource]. 2010;7(2):509–27.
Gnan GH, Rahman Q, Ussher G, Baker D, West E, Rimes KA. General and LGBTQ-specific factors associated with mental health and suicide risk among LGBTQ students. J Youth Stud. 2019;22(10):1393–408.
Jackson SL, Dritschel B. Modeling the impact of social problem-solving deficits on depressive vulnerability in the broader autism phenotype. Res Aut Spectr Disord. 2016;21:128–38.
Richardson T, Elliott P, Roberts R. The impact of tuition fees amount on mental health over time in British students. J Public Health. 2015;37(3):412–8.
Article CAS Google Scholar
Richardson T, Mma Y, Jansen M, Elliott P, Roberts R. Financial difficulties and psychosis risk in British undergraduate students: a longitudinal analysis. J Public Ment Health. 2018;17(2):61–8.
Thomas L, Briggs P, Hart A, Kerrigan F. Understanding social media and identity work in young people transitioning to university. Comput Hum Behav. 2017;76:541–53.
Hixenbaugh P, Dewart H, Towell T. What enables students to succeed? An investigation of socio-demographic, health and student experience variables. Psychodyn Pract. 2012;18(3):285–301.
Holliman A, Martin A, Collie R. Adaptability, engagement, and degree completion: a longitudinal investigation of university students. Educ Psychol. 2018;38(6):785–99.
McLafferty M, Armour C, Bunting B, Ennis E, Lapsley C, Murray E, et al. Coping, stress, and negative childhood experiences: the link to psychopathology, self-harm, and suicidal behavior. Psychic J. 2019;8(3):293–306.
Nightingale S, Roberts S, Tariq V, Appleby Y, Barnes L, Harris R, et al. Trajectories of university adjustment in the United Kingdom: EMOTION management and emotional self-efficacy protect against initial poor adjustment. Learn Individ Differ. 2013;27:174–81.
Berry K, Kingswell S. An investigation of adult attachment and coping with exam-related stress. Br J Guid Couns. 2012;40(4):315.
Denovan A, Macaskill A. Stress and subjective well-being among first year UK undergraduate students. J Happiness Stud. 2017a;18(2):505–25.
Hassel S, Ridout N. An investigation of first-year students’ and lecturers’ expectations of university education. Front Psychol. 2018;8:2218.
Norbury R, Evans S. Time to think: subjective sleep quality, trait anxiety and university start time. Psychiatry Res. 2019;271:214–9.
Por J, Barriball L, Fitzpatrick J, Roberts J. Emotional intelligence: its relationship to stress, coping, well-being and professional performance in nursing students. Nurse Educ Today. 2011;31(8):855.
Honney K, Buszewicz M, Coppola W, Griffin M. Comparison of levels of depression in medical and non-medical students. Clin Teach. 2010;7(3):180–4.
Kotera Y, Conway E, Van Gordon W. Mental health of UK university business students: Relationship with shame, motivation and self-compassion. Journal of Education for Business. 2019;94(1):11–20.
Oliver EJ, Markland D, Hardy J. Interpretation of self-talk and post-lecture affective states of higher education students: a self-determination theory perspective. Br J Educ Psychol. 2010;80(Pt 2):307–23.
O’Neill S, McLafferty M, Ennis E, Lapsley C, Bjourson T, Armour C, et al. Socio-demographic, mental health and childhood adversity risk factors for self-harm and suicidal behaviour in College students in Northern Ireland. J Affect Disord. 2018;239:58–65.
Mahadevan S, Hawton K, Casey D. Deliberate self-harm in Oxford University students, 1993–2005: a descriptive and case-control study. Soc Psychiatry Psychiatr Epidemiol. 2010;45(2):211–9.
Boulton CA, Hughes E, Kent C, Smith JR, Williams HTP. Student engagement and wellbeing over time at a higher education institution. PLoS One [Electronic Resource]. 2019;14(11): e0225770.
Davies EL, Paltoglou AE. Public self-consciousness, pre-loading and drinking harms among university students. Subst Use Misuse. 2019;54(5):747–57.
Denovan A, Macaskill A. Stress, resilience and leisure coping among university students: Applying the broaden-and-build theory. Leisure Studies. 2017b;36(6):852–65.
El Ansari W, Vallentin-Holbech L, Stock C. Predictors of illicit drug/s use among university students in Northern Ireland, Wales and England. Glob J Health Sci. 2015;7(4):18–29.
Freeth M, Bullock T, Milne E. The distribution of and relationship between autistic traits and social anxiety in a UK student population. Autism. 2013;17(5):571–81.
Jessop DC, Reid M, Solomon L. Financial concern predicts deteriorations in mental and physical health among university students. Psychology Health. 2020;35(2):196–209.
Kannangara CS, Allen RE, Waugh G, Nahar N, Khan SZN, Rogerson S, Carson J. All that glitters is not grit: Three studies of grit in university students. Front Psychol. 2018;9:1539.
Lloyd J, Ward T, Young J. Do parental interpersonal power and prestige moderate the relationship between parental acceptance and psychological adjustment in U.K. Students? Cross-Cultural Research. The Journal of Comparative Social Science. 2014;48(3):326–35.
McIntyre JC, Worsley J, Corcoran R, Harrison Woods P, Bentall RP. Academic and non-academic predictors of student psychological distress: the role of social identity and loneliness. J Ment Health. 2018;27(3):230–9.
Ribchester C, Ross K, Rees EL. Examining the impact of pre-induction social networking on the student transition into higher education. Innov Educ Teach Int. 2014;51(4):355–65.
Richardson T, Elliott P, Roberts R. Relationship between loneliness and mental health in students. J Public Ment Health. 2017a;16(2):48–54.
Richardson T, Elliott P, Roberts R, Jansen M. A Longitudinal Study of Financial Difficulties and Mental Health in a National Sample of British Undergraduate Students. Community Ment Health J. 2017;53(3):344–52.
Taylor PJ, Dhingra K, Dickson JM, McDermott E. Psychological Correlates of Self-Harm within Gay, Lesbian and Bisexual UK University Students. Arch Suicide Res. 2020;24(sup1):41–56.
Thomas L, Orme E, Kerrigan F. Student loneliness: The role of social media through life transitions. Comput Educ. 2020;146:103754.
Tyson P, Wilson K, Crone D, Brailsford R, Laws K. Physical activity and mental health in a student population. J Ment Health. 2010;19(6):492–9.
Folkman S. The Oxford handbook of stress, health, and coping. Oxford: Oxford University Press; 2011.
Gorczynski P, Sims-schouten W, Hill D, Wilson JC. Examining mental health literacy, help seeking behaviours, and mental health outcomes in UK university students. J Ment Health Train Educ Pract. 2017;12(2):111–20.
Felitti VJ. Adverse childhood experiences and adult health. Acad Pediatr. 2009;9(3):131–2.
Denovan A, Macaskill A. An interpretative phenomenological analysis of stress and coping in first year undergraduates. Br Educ Res J. 2013;39(6):1002–24.
Bandura A. Self-efficacy: The foundation of agency. Control of human behavior, mental processes, and consciousness: Essays in honor of the 60th birthday of August Flammer. 2000;16.
Martin AJ, Nejad H, Colmar S, Liem GAD. Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. J Psychol Couns Sch. 2012;22(1):58–81.
Lazarus RS, Folkman S. Stress, appraisal, and coping: Springer publishing company; 1984.
Gross JJ. Emotion regulation: Past, present, future. Cogn Emot. 1999;13(5):551–73.
Mayer JD, Salovey P, Caruso DR. TARGET ARTICLES:" Emotional Intelligence: Theory, Findings, and Implications". Psychol Inq. 2004;15(3):197–215.
Duckworth AL, Peterson C, Matthews MD, Kelly DR. Grit: perseverance and passion for long-term goals. J Pers Soc Psychol. 2007;92(6):1087.
Snyder CR, Ilardi SS, Cheavens J, Michael ST, Yamhure L, Sympson S. The role of hope in cognitive-behavior therapies. Cognit Ther Res. 2000;24(6):747–62.
Scheier MF, Carver CS, Bridges MW. Optimism, pessimism, and psychological well-being. 2001.
Seligman ME. Positive psychology in practice: Wiley; 2012.
Masten AS. Ordinary magic: Lessons from research on resilience in human development. Education Canada. 2009;49(3):28–32.
Rosenberg M, Schooler C, Schoenbach C, Rosenberg F. Global self-esteem and specific self-esteem: Different concepts, different outcomes. Am Sociol Rev. 1995:141–56.
Oliver EJ, Markland D, Hardy J. Interpretation of self-talk and post-lecture affective states of higher education students: A self-determination theory perspective. Br J Educ Psychol. 2010;80(2):307–23.
Hofmann W, Friese M, Strack F. Impulse and self-control from a dual-systems perspective. Perspect Psychol Sci. 2009;4(2):162–76.
Aceijas C, Waldhausl S, Lambert N, Cassar S, Bello-Corassa R. Determinants of health-related lifestyles among university students. Perspect Public Health. 2017;137(4):227–36.
Fredrickson BL. The broaden–and–build theory of positive emotions. Philos Trans R Soc Lond B Biol Sci. 2004;359(1449):1367–77.
Tugade MM, Fredrickson BL, Feldman Barrett L. Psychological resilience and positive emotional granularity: Examining the benefits of positive emotions on coping and health. J Pers. 2004;72(6):1161–90.
Harandi TF, Taghinasab MM, Nayeri TD. The correlation of social support with mental health: A meta-analysis. Electron physician. 2017;9(9):5212.
Sheldon E, Simmonds-Buckley M, Bone C, Mascarenhas T, Chan N, Wincott M, Gleeson H, Sow K, Hind D, Barkham M. Prevalence and risk factors for mental health problems in university undergraduate students: A systematic review with meta-analysis. J Affect Disord. 2021;287:282–92.
We acknowledge the input from our public advisory group which included current and former students, and family members of students who have struggled with their mental health. The group gave us their extremely valuable insights to assist our understanding of the evidence.
This project was supported by funding from the National Institute for Health Research as part of the NIHR Public Health Research Programme (fuding reference 127659 Public Health Review Team). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
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Campbell, F., Blank, L., Cantrell, A. et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health 22 , 1778 (2022). https://doi.org/10.1186/s12889-022-13943-x
Received : 03 February 2022
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Published : 20 September 2022
DOI : https://doi.org/10.1186/s12889-022-13943-x
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Mental Illness, Especially Later in Life, Can Increase the Risk of Dementia
Research is revealing an inextricable link between mental disorders and neurological disorders.
By Dana G. Smith
In 2021, Sharon Niederhaus, then 77, lost her husband of more than 50 years. Her daughter, Kristin Henry, said her mother was never the same afterward. “We feel like we lost both our parents at the same time,” Ms. Henry said, referring to her and her brother.
Ms. Niederhaus drank heavily in the wake of her husband’s death. Her alcohol use had initially increased eight years earlier, when she became his caregiver after he had a stroke; after he died, it got even worse. In another alarming development, she started messaging with a con man who claimed to be her former boss. Over the course of two years, he swindled her out of hundreds of thousands of dollars.
Ms. Henry and her brother tried everything to help their mother: They staged an intervention, confiscated her phone, took on power of attorney and sent her to rehab. The entire time, they thought her problems were related to the alcohol; her doctors said she was also likely experiencing brain fog because of her grief.
It wasn’t until she was referred to a neuropsychologist in early 2023 that things became clear: In addition to having alcohol addiction, Ms. Niederhaus also has dementia.
Research is mounting that psychiatric conditions, like depression, addiction and schizophrenia, are inextricably linked to neurological disorders, most notably different forms of dementia.
“This arbitrary distinction between a psychiatric condition and a neurological condition feels a little bit outdated,” said Ruth Morin, a clinical neuropsychologist at Hoag Hospital in Orange County, Calif., who treats Ms. Niederhaus. “It’s all happening in the brain.”
Scientists are working to understand exactly how the two types of conditions are connected, but evidence suggests that having a mental illness, especially in old age, is associated with a greater risk of developing dementia.
How are late-life mental disorders and dementia related?
The association is best established between depression and dementia: A 2020 commission named late-life depression (after age 65) as one of 12 major risk factors for dementia. Building on this, new research suggests that the connection exists for virtually any mental health disorder that is diagnosed later in life. The study examined the medical records of nearly 800,000 people in Sweden and found that, compared to people without dementia, people with the condition were about 70 percent more likely to develop a new mental illness three years before the onset of their dementia. In the year before the dementia diagnosis, that risk rose to roughly 300 percent.
Experts have two main theories about what’s behind the connection. One is that the onset of a new psychiatric condition, like substance abuse or anxiety, in a person’s 70s or 80s could actually be an early sign of dementia. In these cases, “the neurodegeneration in the brain is also causing psychiatric symptoms,” said Dr. Sara Garcia-Ptacek, a neurologist and assistant professor at the Karolinska Institutet in Sweden, who led the study.
The other theory is that the two diagnoses are independent — a diagnosis of depression later in life, say, may be just that — but the toll the mental illness takes ends up triggering or accelerating a person’s dementia. “Depression is going to reduce our cognitive capacity, and that means that maybe a biological process that has been going on for a number of years suddenly comes to light,” Dr. Garcia-Ptacek said.
Not everyone who develops a psychiatric disorder later in life will also develop dementia, but given how often the two types of conditions occur together, Dr. Garcia-Ptacek advised that doctors order cognitive testing for older adults with new psychiatric symptoms, as a precaution.
Is mental illness earlier in life also associated with dementia?
The connection between psychiatric health and neurological health is less clear when a person is diagnosed with mental illness earlier on. There is some evidence that a mental health episode, particularly depression, in a person’s 30s, 40s or 50s is linked to an increased risk of developing dementia down the road, but not every study shows that.
For example, one study published in 2017 tracked more than 10,000 adults between the ages of 35 and 55 for 30 years and found that high scores on a test assessing symptoms of depression — not a formal diagnosis — were not associated with an increased risk for dementia if they occurred earlier in life. However, there was an increased risk if the depression symptoms occurred in late adulthood, roughly a decade before a dementia diagnosis.
Backing this up, a 2022 meta-analysis found that depressive episodes after age 60 nearly doubled a person’s risk for dementia, but if they occurred before then, the risk increased by 17 percent.
One major caveat is that severe mental illness (for example, the kind that requires hospitalization) in early or mid adulthood does seem to be associated with a substantially greater risk of dementia. A study published last year that looked at 30 years of health records for 1.7 million people in New Zealand found that those who had been hospitalized for a mental health disorder at any point during adulthood were three to four times as likely to be diagnosed with dementia later.
One way to explain the possible association between mental disorders earlier in life and neurological disorders later is that there could be a direct link in the brain between the two types of conditions. For example, both Alzheimer’s disease and major depressive disorder are associated with decreased volume in an area of the brain called the hippocampus, which is involved in both memory and mood.
The connection could also be attributed to behaviors associated with mental illness that increase the risk for a neurodegenerative disease, said Terrie Moffitt, a psychology professor at Duke University who led the New Zealand study. For example, people with psychiatric disorders tend to be more isolated , not sleep as well , be less physically healthy and have higher rates of chronic conditions like heart disease and diabetes — all things that increase the risk for dementia .
“It’s really tough to say what is driving the association,” Dr. Morin said. “But I think the takeaway is you should take this seriously, because we do know that there is an association.”
However, the experts also stressed that having a psychiatric condition is by no means a guarantee that you’re going to develop dementia, and there are things you can do to reduce your risk.
The first priority should be to get treatment for any psychiatric disorder, Dr. Morin said. That will help limit the severity of the condition, which can mitigate the risk of developing dementia.
Taking care of your physical health can lower your risk of dementia as well. “Physical activity, eating a healthy diet, staying socially connected, having mental stimulation — all of these are great,” Dr. Garcia-Ptacek said.
These are “factors that are within people’s control,” Dr. Morin said, and they “can have really a drastic effect” on a person’s future.
Dana G. Smith is a reporter for the Well section, where she has written about everything from psychedelic therapy to exercise trends to Covid-19. More about Dana G. Smith
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Study shows link between mental and physical health
Psychiatric patients almost twice as likely to have multiple physical ailments.
A new study published in BMJ Mental Health has found that individuals with severe mental illness are almost twice as likely to report physical multimorbidity, emphasising the critical importance of addressing the intersection between mental and physical health.
The research, led by Anglia Ruskin University (ARU) in collaboration with the University of Cambridge's Biomedical Research Centre, involved an extensive analysis of 19 different studies, encompassing data from 194,123 psychiatric patients across the world, with a comparison to 7,660,590 individuals in control groups.
Multimorbidity is when a person is affected by any combination of chronic disease with at least one other physical health condition, and the researchers found the psychiatric patients were 1.84 times more likely to report multimorbidity than the control group.
The study found that people with severe mental health issues also report physical conditions including metabolic diseases, hypertension, epilepsy, respiratory, vascular, kidney, and gastrointestinal diseases, as well as cancer.
As of 2019, nearly one billion people were living with a mental disorder, making it a leading cause of disability worldwide. According to Mind, one in four people will experience a mental health problem of some kind each year in England.
Previous research has found that a large percentage of individuals in need of mental health services lack access to effective, affordable, and quality mental healthcare, especially in low-income countries. For instance, 71% of individuals with psychosis worldwide do not receive necessary mental health services, with a vast disparity between high-income and low-income countries.
Lead author Lee Smith, Professor of Public Health at Anglia Ruskin University (ARU), said: "Mental health underpins our individual and collective abilities to make decisions, build relationships, and shape the world we live in. It is evident from our research that individuals with severe mental illness are at a significantly higher risk of experiencing physical multimorbidity.
"This complex relationship between severe mental illness and physical multimorbidity has far-reaching implications, including decreased treatment compliance, increased risk of treatment failure, increased treatment costs, relapsing disease, worsening prognosis, and reduced life expectancy.
"Poor clinical management of physical comorbidities in people with mental disorders exacerbates the issue, leading to an increased burden on individuals, their communities, and healthcare systems. A holistic approach is urgently needed to improve the physical, mental, and social outcomes of individuals dealing with severe mental illness and physical multimorbidity."
- Mental Health Research
- Chronic Illness
- Diseases and Conditions
- Down Syndrome
- Mental Health
- Disorders and Syndromes
- Philosophy of mind
- Developmental disability
- Substance abuse
- Public health
Materials provided by Anglia Ruskin University . Note: Content may be edited for style and length.
Journal Reference :
- Damiano Pizzol, Mike Trott, Laurie Butler, Yvonne Barnett, Tamsin Ford, Sharon AS Neufeld, Anya Ragnhildstveit, Christopher N Parris, Benjamin R Underwood, Guillermo Felipe López Sánchez, Matt Fossey, Carol Brayne, Emilio Fernandez-Egea, Guillaume Fond, Laurent Boyer, Jae Il Shin, Shahina Pardhan, Lee Smith. Relationship between severe mental illness and physical multimorbidity: a meta-analysis and call for action . BMJ Mental Health , 2023; 26 (1): e300870 DOI: 10.1136/bmjment-2023-300870
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What Do We Know About Healthy Aging?
Many factors influence healthy aging. Some of these, such as genetics, are not in our control. Others — like exercise, a healthy diet, going to the doctor regularly, and taking care of our mental health — are within our reach. Research supported by NIA and others has identified actions you can take to help manage your health, live as independently as possible, and maintain your quality of life as you age. Read on to learn more about the research and the steps you can take to promote healthy aging.
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Taking care of your physical health
Taking care of your mental health, taking care of your cognitive health.
While scientists continue to actively research how to slow or prevent age-related declines in physical health, they’ve already discovered multiple ways to improve the chances of maintaining optimal health later in life. Taking care of your physical health involves staying active, making healthy food choices, getting enough sleep, limiting your alcohol intake, and proactively managing your health care. Small changes in each of these areas can go a long way to support healthy aging.
Get moving: Exercise and physical activity
A study of adults 40 and older found that taking 8,000 steps or more per day, compared to only taking 4,000 steps, was associated with a 51% lower risk of death from all causes. You can increase the number of steps you get each day by doing activities that keep your body moving, such as gardening, walking the dog, and taking the stairs instead of the elevator.
Although it has many other benefits, exercise is an essential tool for maintaining a healthy weight. Adults with obesity have an increased risk of death, disability, and many diseases such as type 2 diabetes and high blood pressure. However, thinner is not always healthier either. Being or becoming too thin as an older adult can weaken your immune system, increase the risk of bone fracture, and in some cases may be a symptom of disease. Both obesity and underweight conditions can lead to loss of muscle mass, which may cause a person to feel weak and easily worn out.
As people age, muscle function often declines. Older adults may not have the energy to do everyday activities and can lose their independence. However, exercise can help older adults maintain muscle mass as they age. In a 2019 investigation of data from NIA’s Baltimore Longitudinal Study of Aging , researchers found that moderate to vigorous physical activity is strongly associated with muscle function, regardless of age. This suggests that exercise may be able to prevent age-related decline in muscle function.
In addition to helping older adults live better, maintaining muscle mass can help them live longer. In another study , researchers found that in adults older than 55, muscle mass was a better predictor of longevity than was weight or body mass index (BMI).
What can you do?
Although many studies focus on the effects of physical activity on weight and BMI, research has found that even if you’re not losing weight, exercise can still help you live longer and better. There are many ways to get started . Try being physically active in short spurts throughout the day or setting aside specific times each week to exercise. Many activities, such as brisk walking or yoga, are free or low cost and do not require special equipment. As you become more active, you will start feeling energized and refreshed after exercising instead of exhausted. The key is to find ways to get motivated and get moving.
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Healthy eating: Make smart food choices
Much of the research shows that the Mediterranean-style eating pattern, which includes fresh produce, whole grains, and healthy fats, but less dairy and more fish than a traditional American diet, may have a positive impact on health. A 2021 study analyzing the eating patterns of more than 21,000 participants found that people closely following the Mediterranean-style pattern had a significantly lower risk of sudden cardiac death .
A low-salt diet called Dietary Approaches to Stop Hypertension (DASH) has also been shown to deliver significant health benefits. Studies testing the DASH diet found that it lowers blood pressure, helps people lose weight, and reduces the risk of type 2 diabetes and heart disease.
Yet another eating pattern that may support healthy aging is the MIND diet, which combines a Mediterranean-style eating pattern with DASH. Researchers have found that people who closely follow the MIND diet have better overall cognition — the ability to clearly think, learn, and remember — compared to those with other eating styles.
Try starting with small changes by adopting one or two aspects of the Mediterranean-style eating pattern or MIND diet. Several studies have shown that incorporating even a part of these eating patterns, such as more fish or more leafy greens, into your daily eating habits can improve health outcomes. One study of 182 older adults with frequent migraines found that a diet lower in vegetable oil and higher in fatty fish could reduce migraine headaches . Another study that followed almost 1,000 older adults over five years found that consumption of green leafy vegetables was significantly associated with slower cognitive decline .
Even if you haven’t thought much about healthy eating until recently, changing your diet now can still improve your well-being as an older adult. If you are concerned about what you eat, talk with your doctor about ways you can make better food choices.
Learn more about healthy eating and smart food choices for healthy aging .
Getting a good night’s sleep
Sleep quality matters for memory and mood. In one study of adults older than 65, researchers found that those who had poor sleep quality had a harder time problem-solving and concentrating than those who got good quality sleep. Another study , which looked at data from nearly 8,000 people, showed that those in their 50s and 60s who got six hours of sleep or less a night were at a higher risk of developing dementia later in life. This may be because inadequate sleep is associated with the buildup of beta-amyloid , a protein involved in Alzheimer’s disease. Poor sleep may also worsen depression symptoms in older adults. Emerging evidence suggests that older adults who were diagnosed with depression in the past, and do not get quality sleep, may be more likely to experience their depression symptoms again.
More generally, a 2021 study found that older adults who did not sleep well and napped often were at greater risk of dying within the next five years. Conversely, getting good sleep is associated with lower rates of insulin resistance, heart disease, and obesity. Sleep can also improve your creativity and decision-making skills, and even your blood sugar levels .
There are many things you can do to help you sleep better, such as following a regular sleep schedule. Try to fall asleep and get up at the same time each day. Avoid napping late in the day, as this may keep you awake at night. Exercise can help you sleep better, too, if it isn’t too close to bedtime. Research suggests that behavioral interventions, such as mindfulness meditation , can also improve sleep quality.
Learn more about how to get a good night’s sleep and check out this infographic .
It doesn’t matter how old you are or how long you’ve been smoking, research confirms that even if you’re 60 or older and have been smoking for decades, quitting will improve your health. Quitting smoking at any age will:
- Lower your risk of cancer, heart attack, stroke, and lung disease
- Improve your blood circulation
- Improve your sense of taste and smell
- Increase your ability to exercise
- Set a healthy example for others
One study found that among men 55 to 74 years old and women 60 to 74 years old, current smokers were three times more likely to die within the six-year follow-up period than those who had never smoked.
If you smoke, quit. Quitting smoking is good for your health and may add years to your life. One study of nearly 200,000 people demonstrated that older adults who quit smoking between the ages of 45 and 54 lived about six years longer compared to those who continued to smoke. Adults who quit between the ages of 55 to 64 lived about four years longer. It is never too late to stop smoking and reap the benefits of breathing easier, having more energy, saving money, and improving your health.
Read more about how to quit smoking as an older adult.
Alcohol and other substances
Like all adults, older adults should avoid or limit alcohol consumption. In fact, aging can lead to social and physical changes that make older adults more susceptible to alcohol misuse and abuse and more vulnerable to the consequences of alcohol. Alcohol dependence or heavy drinking affects every organ in the body, including the brain.
A comprehensive study from the National Institute on Alcohol Abuse and Alcoholism shows that alcohol consumption among older adults, especially women, is on the rise. The researchers also found evidence that certain brain regions show signs of premature aging in alcohol-dependent men and women. In addition, heavy drinking for extended periods of time in older adults may contribute to poor heart health, as shown in this 2016 study . These studies suggest that stopping or limiting the use of alcohol could improve heart health and prevent the accelerated aging seen with heavy alcohol use.
In addition to being cautious with alcohol, older adults and their caregivers should be aware of other substances that can be misused or abused. Because older adults are commonly prescribed opioids for pain and benzodiazepines for anxiety or trouble sleeping, they may be at risk for misuse and dependence on these substances. One study of adults age 50 and older showed that misuse of prescription opioids or benzodiazepines is associated with thoughts of suicide.
Learn about the current U.S. guidelines for drinking and when to avoid alcohol altogether. It’s important to be aware of how much you are drinking and the harm that drinking can cause. If you or a loved one needs help with substance abuse or alcohol use, talk with your doctor or a mental health professional. You can also try finding a support group for older adults with substance or alcohol abuse issues.
Learn about substance use in older adults and get tips on how to stop drinking alcohol or drink less alcohol .
Go to the doctor regularly
In recent years, scientists have developed and improved upon laboratory, imaging, and similar biological tests that help uncover and monitor signs of age-related disease. Harmful changes in the cells and molecules of your body may occur years before you start to experience any symptoms of disease. Tests that detect these changes can help medical professionals diagnose and treat disease early, improving health outcomes.
Visit the doctor at least yearly and possibly more depending on your health. You cannot reap the benefits of medical advancements without regular trips to the doctor for physical exams and other tests. Regular screenings can uncover diseases and conditions you may not yet be aware of, such as diabetes, cancer, and cardiovascular disease. If you only seek medical attention when you’re experiencing symptoms, you may lose the chance of having your doctor catch a disease in its earliest stages, when it would be most treatable. Regular check-ups can help ensure you could start treatment months or years earlier than would have been possible otherwise.
Read about how you can make the most of your appointment with your doctor .
Mental health, or mental wellness, is essential to your overall health and quality of life. It affects how we think, feel, act, make choices, and relate to others. Managing social isolation, loneliness, stress, depression, and mood through medical and self-care is key to healthy aging.
Social isolation and loneliness
Several recent studies show that older adults who are socially isolated or feel lonely are at higher risk for heart disease, depression, and cognitive decline. A 2021 study of more than 11,000 adults older than age 70 found that loneliness was associated with a greater risk of heart disease. Another recent study found that socially isolated older adults experienced more chronic lung conditions and depressive symptoms compared to older adults with social support.
Feeling lonely can also impact memory. A study of more than 8,000 adults older than 65 found that loneliness was linked to faster cognitive decline.
Research also shows that being socially active can benefit older adults. A study of more than 3,000 older adults found that making new social contacts was associated with improved self-reported physical and psychological well-being. Being social may also help you reach your exercise goals. A 2019 study found that older adults who had regular contact with friends and family were more physically active than those who did not.
Staying connected with others may help boost your mood and improve your overall well-being. Stay in touch with family and friends in person or over the phone. Scheduling time each day to connect with others can help you maintain connections. Meet new people by taking a class to learn something new or hone a skill you already have.
Learn about loneliness and social isolation and get tips for how to stay connected.
Stress is a natural part of life and comes in many forms. Sometimes stress arises from difficult events or circumstances. Positive changes, like the birth of a grandchild or a promotion, can cause stress too. Research shows that constant stress can change the brain , affect memory, and increase the risk of developing Alzheimer’s or related dementias.
Older adults are at particular risk for stress and stress-related problems. A recent study examined how levels of the stress hormone cortisol change over time. Researchers have found that cortisol levels in a person’s body increase steadily after middle-age, and that this age-related increase in stress may drive changes in the brain. A meta-analysis funded by the National Institute of Mental Health supports the notion that stress and anxiety rewire the brain in ways that can impact memory, decision-making, and mood.
Finding ways to lower stress and increase emotional stability may support healthy aging. In an analysis of data from the Baltimore Longitudinal Study of Aging, scientists followed 2,000 participants for more than five decades, monitoring their mood and health. The data reveal that individuals who were emotionally stable lived on average three years longer than those who had a tendency toward being in a negative or anxious emotional state. Long-term stress also may contribute to or worsen a range of health problems, including digestive disorders, headaches, and sleep disorders.
You can help manage stress with meditation techniques, physical activity, and by participating in activities you enjoy . Keeping a journal may also help you identify and challenge negative and unhelpful thoughts. Reach out to friends and family who can help you cope in a positive way.
Read about more ways to manage stress .
Depression and overall mood
Although different than depression, which is a serious medical disorder, mood changes can also influence aging. A 2020 longitudinal study demonstrated a link between positive mood and better cognitive control. Further studies are necessary to determine whether changes that improve mood could improve cognition. The way you think about aging can also make a difference. Research shows that whether you hold negative or positive views about aging may impact health as you age. Negative beliefs about aging may increase undesirable health outcomes , Alzheimer’s disease biomarkers , and cellular aging . Meanwhile, positive beliefs about aging may decrease the risk of developing dementia and obesity .
Depression , even when severe, can be treated. As soon as you begin noticing signs, it’s important to get evaluated by a health care professional. In addition to deep sadness or numbness, lack of sleep and loss of appetite are also common symptoms of depression in older adults. If you think you or a loved one may have depression, start by making an appointment to see your doctor or health care provider. If you are thinking of harming yourself, get help immediately — call the 24-hour 988 Suicide & Crisis Lifeline at 988 or 800-273-TALK (800-273-8255).
Leisure activities and hobbies
Research on music, theater, dance, creative writing, and other participatory arts shows promise for improving older adults’ quality of life and well-being, from better cognitive function, memory, and self-esteem to reduced stress and increased social interaction. Even hobbies as simple as taking care of a pet can improve your health. According to a 2020 study , pet ownership (or regular contact with pets) was associated with better cognitive function, and in some cases, better physical function.
Look for opportunities to participate in activities. Get out and about by going to a sporting event, trying a new restaurant, or visiting a museum. Learn how to cook or play a musical instrument. Consider volunteering at a school, library, or hospital to become more active in your community.
Learn more about participating in activities you enjoy .
Cognition — the ability to clearly think, learn, and remember — often changes as we age. Although some people develop Alzheimer’s or other types of dementia, many older adults experience more modest changes in memory and thinking. Research shows that healthy eating, staying active, and learning new skills may help keep older adults cognitively healthy.
How different factors affect cognitive health
If you think your daily choices don’t make a difference, data from an NIH study with 3,000 participants show otherwise. Researchers scored participants on five healthy lifestyle factors, all of which have important health benefits:
- At least 150 minutes per week of moderate- to vigorous-intensity physical activity
- Not smoking
- Not drinking heavily
- A high-quality, Mediterranean-style diet
- Engagement in mentally stimulating activities, such as reading, writing letters, and playing games
The findings show that making these small, daily changes can add up to significant health benefits. Those who followed at least four of these healthy lifestyle behaviors had a 60% lower risk of developing Alzheimer’s. Even practicing just two or three activities lowered the risk by 37%. While results from observational studies such as this one cannot prove cause and effect, they point to how a combination of modifiable behaviors may mitigate Alzheimer's risk and identify promising avenues to be tested in clinical trials.
New clinical trials are also testing the benefits of tightly controlling blood pressure on healthy aging. These trials are based on a 2019 study , with data supporting the idea that intensive blood pressure control may slow age-related brain damage and even mild cognitive impairment, which can increase the risk for Alzheimer’s or a related dementia.
Researchers continue work to understand how we might prevent Alzheimer’s and other forms of age-related cognitive decline. NIA is currently funding more than 350 active clinical trials on Alzheimer’s and related dementias, 100 of which use nondrug interventions , such as exercise, diet, cognitive training, sleep, or combination therapies.
Read about what we know about preventing Alzheimer’s disease .
How cognitive training affects health outcomes
But there is some evidence that exercising your brain by learning a new skill can improve memory function. A study of adults 60 and older showed that sustained engagement in cognitively demanding, novel activity enhanced memory function. In particular, the new skills learned in this study were 1) learning how to use computer software to edit photos and 2) learning how to quilt. Learning a new game, instrument, craft, or other skill can be fun and may have the added benefit of staving off memory loss as you age.
Learn more about cognitive health .
Taking care of your physical, mental, and cognitive health is important for healthy aging. Even making small changes in your daily life can help you live longer and better. In general, you can support your physical health by staying active, eating and sleeping well, and going to the doctor regularly. Take care of your mental health by interacting with family and friends, trying to stay positive, and participating in activities you enjoy. Taking steps to achieve better physical and mental health may reduce your risk for Alzheimer’s and related dementias as you age.
There is still a lot to learn, though, about how people age and what habits support healthy aging. Scientists are exploring these questions with studies that look at physical, mental, and cognitive health. You can be a part of scientific progress by joining a clinical trial or research study in person or online. All types of volunteers are needed, including caregivers, older adults with medical conditions, and those who are healthy.
To explore all trials funded by NIH, visit ClinicalTrials.gov . To find Alzheimer’s and related dementias research studies, visit the Clinical Trials Finder at Alzheimers.gov . Every treatment available today is due to people like you who choose to participate in clinical research.
Learn more about clinical trials .
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For more information.
NIA Information Resource Center 800-222-2225 [email protected] www.nia.nih.gov
NIA Alzheimer’s and related Dementias Education and Referral (ADEAR) Center 800-438-4380 [email protected] www.nia.nih.gov/alzheimers The NIA ADEAR Center offers information and free print publications about Alzheimer’s and related dementias for families, caregivers, and health professionals. ADEAR Center staff answer telephone, email, and written requests and make referrals to local and national resources.
This content is provided by the NIH National Institute on Aging (NIA). NIA scientists and other experts review this content to ensure it is accurate and up to date.
Content reviewed: February 23, 2022
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