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• June 01, 2024 | VOL. 181, NO. 6 CURRENT ISSUE pp.461-564
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The Critical Relationship Between Anxiety and Depression

• Ned H. Kalin , M.D.

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Anxiety and depressive disorders are among the most common psychiatric illnesses; they are highly comorbid with each other, and together they are considered to belong to the broader category of internalizing disorders. Based on statistics from the Substance Abuse and Mental Health Services Administration, the 12-month prevalence of major depressive disorder in 2017 was estimated to be 7.1% for adults and 13.3% for adolescents ( 1 ). Data for anxiety disorders are less current, but in 2001–2003, their 12-month prevalence was estimated to be 19.1% in adults, and 2001–2004 data estimated that the lifetime prevalence in adolescents was 31.9% ( 2 , 3 ). Both anxiety and depressive disorders are more prevalent in women, with an approximate 2:1 ratio in women compared with men during women’s reproductive years ( 1 , 2 ).

Across all psychiatric disorders, comorbidity is the rule ( 4 ), which is definitely the case for anxiety and depressive disorders, as well as their symptoms. With respect to major depression, a worldwide survey reported that 45.7% of individuals with lifetime major depressive disorder had a lifetime history of one or more anxiety disorder ( 5 ). These disorders also commonly coexist during the same time frame, as 41.6% of individuals with 12-month major depression also had one or more anxiety disorder over the same 12-month period. From the perspective of anxiety disorders, the lifetime comorbidity with depression is estimated to range from 20% to 70% for patients with social anxiety disorder ( 6 ), 50% for patients with panic disorder ( 6 ), 48% for patients with posttraumatic stress disorder (PTSD) ( 7 ), and 43% for patients with generalized anxiety disorder ( 8 ). Data from the well-known Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study demonstrate comorbidity at the symptom level, as 53% of the patients with major depression had significant anxiety and were considered to have an anxious depression ( 9 ).

Anxiety and depressive disorders are moderately heritable (approximately 40%), and evidence suggests shared genetic risk across the internalizing disorders ( 10 ). Among internalizing disorders, the highest level of shared genetic risk appears to be between major depressive disorder and generalized anxiety disorder. Neuroticism is a personality trait or temperamental characteristic that is associated with the development of both anxiety and depression, and the genetic risk for developing neuroticism also appears to be shared with that of the internalizing disorders ( 11 ). Common nongenetic risk factors associated with the development of anxiety and depression include earlier life adversity, such as trauma or neglect, as well as parenting style and current stress exposure. At the level of neural circuits, alterations in prefrontal-limbic pathways that mediate emotion regulatory processes are common to anxiety and depressive disorders ( 12 , 13 ). These findings are consistent with meta-analyses that reveal shared structural and functional brain alterations across various psychiatric illnesses, including anxiety and major depression, in circuits involving emotion regulation ( 13 ), executive function ( 14 ), and cognitive control ( 15 ).

Anxiety disorders and major depression occur during development, with anxiety disorders commonly beginning during preadolescence and early adolescence and major depression tending to emerge during adolescence and early to mid-adulthood ( 16 – 18 ). In relation to the evolution of their comorbidity, studies demonstrate that anxiety disorders generally precede the presentation of major depressive disorder ( 17 ). A European community-based study revealed, beginning at age 15, the developmental relation between comorbid anxiety and major depression by specifically focusing on social phobia (based on DSM-IV criteria) and then asking the question regarding concurrent major depressive disorder ( 18 ). The findings revealed a 19% concurrent comorbidity between these disorders, and in 65% of the cases, social phobia preceded major depressive disorder by at least 2 years. In addition, initial presentation with social phobia was associated with a 5.7-fold increased risk of developing major depressive disorder. These associations between anxiety and depression can be traced back even earlier in life. For example, childhood behavioral inhibition in response to novelty or strangers, or an extreme anxious temperament, is associated with a three- to fourfold increase in the likelihood of developing social anxiety disorder, which in turn is associated with an increased risk to develop major depressive disorder and substance abuse ( 19 ).

It is important to emphasize that the presence of comor‐bid anxiety symptoms and disorders matters in relation to treatment. Across psychiatric disorders, the presence of significant anxiety symptoms generally predicts worse outcomes, and this has been well demonstrated for depression. In the STAR*D study, patients with anxious major depressive disorder were more likely to be severely depressed and to have more suicidal ideation ( 9 ). This is consistent with the study by Kessler and colleagues ( 5 ), in which patients with anxious major depressive disorder, compared with patients with nonanxious major depressive disorder, were found to have more severe role impairment and more suicidal ideation. Data from level 1 of the STAR*D study (citalopram treatment) nicely illustrate the impact of comorbid anxiety symptoms on treatment. Compared with patients with nonanxious major depressive disorder, those 53% of patients with an anxious depression were less likely to remit and also had a greater side effect burden ( 20 ). Other data examining patients with major depressive disorder and comorbid anxiety disorders support the greater difficulty and challenge in treating patients with these comorbidities ( 21 ).

This issue of the Journal presents new findings relevant to the issues discussed above in relation to understanding and treating anxiety and depressive disorders. Drs. Conor Liston and Timothy Spellman, from Weill Cornell Medicine, provide an overview for this issue ( 22 ) that is focused on understanding mechanisms at the neural circuit level that underlie the pathophysiology of depression. Their piece nicely integrates human neuroimaging studies with complementary data from animal models that allow for the manipulation of selective circuits to test hypotheses generated from the human data. Also included in this issue is a review of the data addressing the reemergence of the use of psychedelic drugs in psychiatry, particularly for the treatment of depression, anxiety, and PTSD ( 23 ). This timely piece, authored by Dr. Collin Reiff along with a subgroup from the APA Council of Research, provides the current state of evidence supporting the further exploration of these interventions. Dr. Alan Schatzberg, from Stanford University, contributes an editorial in which he comments on where the field is in relation to clinical trials with psychedelics and to some of the difficulties, such as adequate blinding, in reliably studying the efficacy of these drugs ( 24 ).

In an article by McTeague et al. ( 25 ), the authors use meta-analytic strategies to understand the neural alterations that are related to aberrant emotion processing that are shared across psychiatric disorders. Findings support alterations in the salience, reward, and lateral orbital nonreward networks as common across disorders, including anxiety and depressive disorders. These findings add to the growing body of work that supports the concept that there are common underlying factors across all types of psychopathology that include internalizing, externalizing, and thought disorder dimensions ( 26 ). Dr. Deanna Barch, from Washington University in St. Louis, writes an editorial commenting on these findings and, importantly, discusses criteria that should be met when we consider whether the findings are actually transdiagnostic ( 27 ).

Another article, from Gray and colleagues ( 28 ), addresses whether there is a convergence of findings, specifically in major depression, when examining data from different structural and functional neuroimaging modalities. The authors report that, consistent with what we know about regions involved in emotion processing, the subgenual anterior cingulate cortex, hippocampus, and amygdala were among the regions that showed convergence across multimodal imaging modalities.

In relation to treatment and building on our understanding of neural circuit alterations, Siddiqi et al. ( 29 ) present data suggesting that transcranial magnetic stimulation (TMS) targeting can be linked to symptom-specific treatments. Their findings identify different TMS targets in the left dorsolateral prefrontal cortex that modulate different downstream networks. The modulation of these different networks appears to be associated with a reduction in different types of symptoms. In an editorial, Drs. Sean Nestor and Daniel Blumberger, from the University of Toronto ( 30 ), comment on the novel approach used in this study to link the TMS-related engagement of circuits with symptom improvement. They also provide a perspective on how we can view these and other circuit-based findings in relation to conceptualizing personalized treatment approaches.

Kendler et al. ( 31 ), in this issue, contribute an article that demonstrates the important role of the rearing environment in the risk to develop major depression. Using a unique design from a Swedish sample, the analytic strategy involves comparing outcomes from high-risk full sibships and high-risk half sibships where at least one of the siblings was home reared and one was adopted out of the home. The findings support the importance of the quality of the rearing environment as well as the presence of parental depression in mitigating or enhancing the likelihood of developing major depression. In an accompanying editorial ( 32 ), Dr. Myrna Weissman, from Columbia University, reviews the methods and findings of the Kendler et al. article and also emphasizes the critical significance of the early nurturing environment in relation to general health.

This issue concludes with an intriguing article on anxiety disorders, by Gold and colleagues ( 33 ), that demonstrates neural alterations during extinction recall that differ in children relative to adults. With increasing age, and in relation to fear and safety cues, nonanxious adults demonstrated greater connectivity between the amygdala and the ventromedial prefrontal cortex compared with anxious adults, as the cues were being perceived as safer. In contrast, neural differences between anxious and nonanxious youths were more robust when rating the memory of faces that were associated with threat. Specifically, these differences were observed in the activation of the inferior temporal cortex. In their editorial ( 34 ), Dr. Dylan Gee and Sahana Kribakaran, from Yale University, emphasize the importance of developmental work in relation to understanding anxiety disorders, place these findings into the context of other work, and suggest the possibility that these and other data point to neuroscientifically informed age-specific interventions.

Taken together, the papers in this issue of the Journal present new findings that shed light onto alterations in neural function that underlie major depressive disorder and anxiety disorders. It is important to remember that these disorders are highly comorbid and that their symptoms are frequently not separable. The papers in this issue also provide a developmental perspective emphasizing the importance of early rearing in the risk to develop depression and age-related findings important for understanding threat processing in patients with anxiety disorders. From a treatment perspective, the papers introduce data supporting more selective prefrontal cortical TMS targeting in relation to different symptoms, address the potential and drawbacks for considering the future use of psychedelics in our treatments, and present new ideas supporting age-specific interventions for youths and adults with anxiety disorders.

Disclosures of Editors’ financial relationships appear in the April 2020 issue of the Journal .

1 Substance Abuse and Mental Health Services Administration (SAMHSA): Key substance use and mental health indicators in the United States: results from the 2017 National Survey on Drug Use and Health (HHS Publication No. SMA 18-5068, NSDUH Series H-53). Rockville, Md, Center for Behavioral Health Statistics and Quality, SAMHSA, 2018. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.htm Google Scholar

2 Kessler RC, Chiu WT, Demler O, et al. : Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication . Arch Gen Psychiatry 2005 ; 62:617–627, correction, 62:709 Crossref , Medline ,  Google Scholar

3 Merikangas KR, He JP, Burstein M, et al. : Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A) . J Am Acad Child Adolesc Psychiatry 2010 ; 49:980–989 Crossref , Medline ,  Google Scholar

4 Kessler RC, McGonagle KA, Zhao S, et al. : Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey . Arch Gen Psychiatry 1994 ; 51:8–19 Crossref , Medline ,  Google Scholar

5 Kessler RC, Sampson NA, Berglund P, et al. : Anxious and non-anxious major depressive disorder in the World Health Organization World Mental Health Surveys . Epidemiol Psychiatr Sci 2015 ; 24:210–226 Crossref , Medline ,  Google Scholar

6 Dunner DL : Management of anxiety disorders: the added challenge of comorbidity . Depress Anxiety 2001 ; 13:57–71 Crossref , Medline ,  Google Scholar

7 Kessler RC, Sonnega A, Bromet E, et al. : Posttraumatic stress disorder in the National Comorbidity Survey . Arch Gen Psychiatry 1995 ; 52:1048–1060 Crossref , Medline ,  Google Scholar

8 Brawman-Mintzer O, Lydiard RB, Emmanuel N, et al. : Psychiatric comorbidity in patients with generalized anxiety disorder . Am J Psychiatry 1993 ; 150:1216–1218 Link ,  Google Scholar

9 Fava M, Alpert JE, Carmin CN, et al. : Clinical correlates and symptom patterns of anxious depression among patients with major depressive disorder in STAR*D . Psychol Med 2004 ; 34:1299–1308 Crossref , Medline ,  Google Scholar

10 Hettema JM : What is the genetic relationship between anxiety and depression? Am J Med Genet C Semin Med Genet 2008 ; 148C:140–146 Crossref , Medline ,  Google Scholar

11 Hettema JM, Neale MC, Myers JM, et al. : A population-based twin study of the relationship between neuroticism and internalizing disorders . Am J Psychiatry 2006 ; 163:857–864 Link ,  Google Scholar

12 Kovner R, Oler JA, Kalin NH : Cortico-limbic interactions mediate adaptive and maladaptive responses relevant to psychopathology . Am J Psychiatry 2019 ; 176:987–999 Link ,  Google Scholar

13 Etkin A, Schatzberg AF : Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders . Am J Psychiatry 2011 ; 168:968–978 Link ,  Google Scholar

14 Goodkind M, Eickhoff SB, Oathes DJ, et al. : Identification of a common neurobiological substrate for mental illness . JAMA Psychiatry 2015 ; 72:305–315 Crossref , Medline ,  Google Scholar

15 McTeague LM, Huemer J, Carreon DM, et al. : Identification of common neural circuit disruptions in cognitive control across psychiatric disorders . Am J Psychiatry 2017 ; 174:676–685 Link ,  Google Scholar

16 Beesdo K, Knappe S, Pine DS : Anxiety and anxiety disorders in children and adolescents: developmental issues and implications for DSM-V . Psychiatr Clin North Am 2009 ; 32:483–524 Crossref , Medline ,  Google Scholar

17 Kessler RC, Wang PS : The descriptive epidemiology of commonly occurring mental disorders in the United States . Annu Rev Public Health 2008 ; 29:115–129 Crossref , Medline ,  Google Scholar

18 Ohayon MM, Schatzberg AF : Social phobia and depression: prevalence and comorbidity . J Psychosom Res 2010 ; 68:235–243 Crossref , Medline ,  Google Scholar

19 Clauss JA, Blackford JU : Behavioral inhibition and risk for developing social anxiety disorder: a meta-analytic study . J Am Acad Child Adolesc Psychiatry 2012 ; 51:1066–1075 Crossref , Medline ,  Google Scholar

20 Fava M, Rush AJ, Alpert JE, et al. : Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report . Am J Psychiatry 2008 ; 165:342–351 Link ,  Google Scholar

21 Dold M, Bartova L, Souery D, et al. : Clinical characteristics and treatment outcomes of patients with major depressive disorder and comorbid anxiety disorders: results from a European multicenter study . J Psychiatr Res 2017 ; 91:1–13 Crossref , Medline ,  Google Scholar

22 Spellman T, Liston C : Toward circuit mechanisms of pathophysiology in depression . Am J Psychiatry 2020 ; 177:381–390 Link ,  Google Scholar

23 Reiff CM, Richman EE, Nemeroff CB, et al. : Psychedelics and psychedelic-assisted psychotherapy . Am J Psychiatry 2020 ; 177:391–410 Link ,  Google Scholar

24 Schatzberg AF : Some comments on psychedelic research (editorial). Am J Psychiatry 2020 ; 177:368–369 Link ,  Google Scholar

25 McTeague LM, Rosenberg BM, Lopez JW, et al. : Identification of common neural circuit disruptions in emotional processing across psychiatric disorders . Am J Psychiatry 2020 ; 177:411–421 Link ,  Google Scholar

26 Caspi A, Moffitt TE : All for one and one for all: mental disorders in one dimension . Am J Psychiatry 2018 ; 175:831–844 Link ,  Google Scholar

27 Barch DM : What does it mean to be transdiagnostic and how would we know? (editorial). Am J Psychiatry 2020 ; 177:370–372 Abstract ,  Google Scholar

28 Gray JP, Müller VI, Eickhoff SB, et al. : Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies . Am J Psychiatry 2020 ; 177:422–434 Link ,  Google Scholar

29 Siddiqi SH, Taylor SF, Cooke D, et al. : Distinct symptom-specific treatment targets for circuit-based neuromodulation . Am J Psychiatry 2020 ; 177:435–446 Link ,  Google Scholar

30 Nestor SM, Blumberger DM : Mapping symptom clusters to circuits: toward personalizing TMS targets to improve treatment outcomes in depression (editorial). Am J Psychiatry 2020 ; 177:373–375 Abstract ,  Google Scholar

31 Kendler KS, Ohlsson H, Sundquist J, et al. : The rearing environment and risk for major depression: a Swedish national high-risk home-reared and adopted-away co-sibling control study . Am J Psychiatry 2020 ; 177:447–453 Abstract ,  Google Scholar

32 Weissman MM : Is depression nature or nurture? Yes (editorial). Am J Psychiatry 2020 ; 177:376–377 Abstract ,  Google Scholar

33 Gold AL, Abend R, Britton JC, et al. : Age differences in the neural correlates of anxiety disorders: an fMRI study of response to learned threat . Am J Psychiatry 2020 ; 177:454–463 Link ,  Google Scholar

34 Gee DG, Kribakaran S : Developmental differences in neural responding to threat and safety: implications for treating youths with anxiety (editorial). Am J Psychiatry 2020 ; 177:378–380 Abstract ,  Google Scholar

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• Review Article
• Published: 03 October 2022

How COVID-19 shaped mental health: from infection to pandemic effects

• Brenda W. J. H. Penninx   ORCID: orcid.org/0000-0001-7779-9672 1 , 2 ,
• Michael E. Benros   ORCID: orcid.org/0000-0003-4939-9465 3 , 4 ,
• Robyn S. Klein 5 &
• Christiaan H. Vinkers   ORCID: orcid.org/0000-0003-3698-0744 1 , 2  

Nature Medicine volume  28 ,  pages 2027–2037 ( 2022 ) Cite this article

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• Epidemiology
• Infectious diseases
• Neurological manifestations
• Psychiatric disorders

The Coronavirus Disease 2019 (COVID-19) pandemic has threatened global mental health, both indirectly via disruptive societal changes and directly via neuropsychiatric sequelae after SARS-CoV-2 infection. Despite a small increase in self-reported mental health problems, this has (so far) not translated into objectively measurable increased rates of mental disorders, self-harm or suicide rates at the population level. This could suggest effective resilience and adaptation, but there is substantial heterogeneity among subgroups, and time-lag effects may also exist. With regard to COVID-19 itself, both acute and post-acute neuropsychiatric sequelae have become apparent, with high prevalence of fatigue, cognitive impairments and anxiety and depressive symptoms, even months after infection. To understand how COVID-19 continues to shape mental health in the longer term, fine-grained, well-controlled longitudinal data at the (neuro)biological, individual and societal levels remain essential. For future pandemics, policymakers and clinicians should prioritize mental health from the outset to identify and protect those at risk and promote long-term resilience.

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In 2019, the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO), with 590 million confirmed cases and 6.4 million deaths worldwide as of August 2022 (ref. 1 ). To contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, many national and local governments implemented often drastic restrictions as preventive health measures. Consequently, the pandemic has not only led to potential SARS-CoV-2 exposure, infection and disease but also to a wide range of policies consisting of mask requirements, quarantines, lockdowns, physical distancing and closure of non-essential services, with unprecedented societal and economic consequences.

As the world is slowly gaining control over COVID-19, it is timely and essential to ask how the pandemic has affected global mental health. Indirect effects include stress-evoking and disruptive societal changes, which may detrimentally affect mental health in the general population. Direct effects include SARS-CoV-2-mediated acute and long-lasting neuropsychiatric sequelae in affected individuals that occur during primary infection or as part of post-acute COVID syndrome (PACS) 2 —defined as symptoms lasting beyond 3–4 weeks that can involve multiple organs, including the brain. Several terminologies exist for characterizing the effects of COVID-19. PACS also includes late sequalae that constitute a clinical diagnosis of ‘long COVID’ where persistent symptoms are still present 12 weeks after initial infection and cannot be attributed to other conditions 3 .

Here we review both the direct and indirect effects of COVID-19 on mental health. First, we summarize empirical findings on how the COVID-19 pandemic has impacted population mental health, through mental health symptom reports, mental disorder prevalence and suicide rates. Second, we describe mental health sequalae of SARS-CoV-2 virus infection and COVID-19 disease (for example, cognitive impairment, fatigue and affective symptoms). For this, we use the term PACS for neuropsychiatric consequences beyond the acute period, and will also describe the underlying neurobiological impact on brain structure and function. We conclude with a discussion of the lessons learned and knowledge gaps that need to be further addressed.

Impact of the COVID-19 pandemic on population mental health

Independent of the pandemic, mental disorders are known to be prevalent globally and cause a very high disease burden 4 , 5 , 6 . For most common mental disorders (including major depressive disorder, anxiety disorders and alcohol use disorder), environmental stressors play a major etiological role. Disruptive and unpredictable pandemic circumstances may increase distress levels in many individuals, at least temporarily. However, it should be noted that the pandemic not only resulted in negative stressors but also in positive and potentially buffering changes for some, including a better work–life balance, improved family dynamics and enhanced feelings of closeness 7 .

Awareness of the potential mental health impact of the COVID-19 pandemic is reflected in the more than 35,000 papers published on this topic. However, this rapid research output comes with a cost: conclusions from many papers are limited due to small sample sizes, convenience sampling with unclear generalizability implications and lack of a pre-COVID-19 comparison. More reliable estimates of the pandemic mental health impact come from studies with longitudinal or time-series designs that include a pre-pandemic comparison. In our description of the evidence, we, therefore, explicitly focused on findings from meta-analyses that include longitudinal studies with data before the pandemic, as recently identified through a systematic literature search by the WHO 8 .

Self-reported mental health problems

Most studies examining the pandemic impact on mental health used online data collection methods to measure self-reported common indicators, such as mood, anxiety or general psychological distress. Pooled prevalence estimates of clinically relevant high levels of depression and anxiety symptoms during the COVID-19 pandemic range widely—between 20% and 35% 9 , 10 , 11 , 12 —but are difficult to interpret due to large methodological and sample heterogeneity. It also is important to note that high levels of self-reported mental health problems identify increased vulnerability and signal an increased risk for mental disorders, but they do not equal clinical caseness levels, which are generally much lower.

Three meta-analyses, pooling data from between 11 and 61 studies and involving ~50,000 individuals or more 13 , 14 , 15 , compared levels of self-reported mental health problems during the COVID-19 pandemic with those before the pandemic. Meta-analyses report on pooled effect sizes—that is, weighted averages of study-level effect sizes; these are generally considered small when they are ~0.2, moderate when ~0.5 and large when ~0.8. As shown in Table 1 , meta-analyses on mental health impact of the COVID-19 pandemic reach consistent conclusions and indicate that there has been a heterogeneous, statistically significant but small increase in self-reported mental health problems, with pooled effect sizes ranging from 0.07 to 0.27. The largest symptom increase was found when using specific mental health outcome measures assessing depression or anxiety symptoms. In addition, loneliness—a strong correlate of depression and anxiety—showed a small but significant increase during the pandemic (Table 1 ; effect size = 0.27) 16 . In contrast, self-reported general mental health and well-being indicators did not show significant change, and psychotic symptoms seemed to have decreased slightly 13 . In Europe, alcohol purchase decreased, but high-level drinking patterns solidified among those with pre-pandemic high drinking levels 17 . When compared to pre-COVID levels, no change in self-reported alcohol use (effect size = −0.01) was observed in a recent meta-analysis summarizing 128 studies from 58 (predominantly European and North American) countries 18 .

What is the time trajectory of self-reported mental health problems during the pandemic? Although findings are not uniform, various large-scale studies confirmed that the increase in mental health problems was highest during the first peak months of the pandemic and smaller—but not fully gone—in subsequent months when infection rates declined and social restrictions eased 13 , 19 , 20 . Psychological distress reports in the United Kingdom increased again during the second lockdown period 15 . Direct associations between anxiety and depression symptom levels and the average number of daily COVID-19 cases were confirmed in the US Centers for Disease Control and Prevention (CDC) data 21 . Studies that examined longer-term trajectories of symptoms during the first or even second year of the COVID-19 pandemic are more sparse but revealed stability of symptoms without clear evidence of recovery 15 , 22 . The exception appears to be for loneliness, as some studies confirmed further increasing trends throughout the first COVID-19 pandemic year 22 , 23 . As most published population-based studies were conducted in the early time period in which absolute numbers of SARS-CoV2-infected individuals were still low, the mental health impacts described in such studies are most likely due to indirect rather than direct effects of SARS-CoV-2 infection. However, it is possible that, in longer-term or later studies, these direct and indirect effects may be more intertwined.

The extent to which governmental policies and communication have impacted on population mental health is a relevant question. In cross-country comparisons, the extent of social restrictions showed a dose–response relationship with mental health problems 24 , 25 . In a review of 33 studies worldwide, it was concluded that governments that enacted stringent measures to contain the spread of COVID-19 benefitted not only the physical but also the mental health of their population during the pandemic 26 , even though more stringent policies may lead to more short-term mental distress 25 . It has been suggested that effective communication of risks, choices and policy measures may reduce polarization and conspiracy theories and mitigate the mental health impact of such measures 25 , 27 , 28 .

In sum, the general pattern of results is that of an increase in mental health symptoms in the population, especially during the first pandemic months, that remained elevated throughout 2020 and early 2021. It should be emphasized that this increase has a small effect size. However, even a small upward shift in mental health problems warrants attention as it has not yet shown to be returned to pre-pandemic levels, and it may have meaningful cumulative consequences at the population level. In addition, even a small effect size may mask a substantial heterogeneity in mental health impact, which may have affected vulnerable groups disproportionally (see below).

Mental disorders, self-harm and suicide

Whether the observed increase in mental health problems during the COVID-19 pandemic has translated into more mental disorders or even suicide mortality is not easy to answer. Mental disorders, characterized by more severe, disabling and persistent symptoms than self-reported mental health problems, are usually diagnosed by a clinician based on the International Classification of Diseases, 10th Revision (ICD-10) or the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) criteria or with validated semi-structured clinical interviews. However, during the COVID-19 pandemic, research systematically examining the population prevalence of mental disorders has been sparse. Unfortunately, we can also not strongly rely on healthcare use studies as the pandemic impacted on healthcare provision more broadly, thereby making figures of patient admissions difficult to interpret.

On a global scale and based on imputations and modeling from survey data of self-reported mental health problems, the Global Burden of Disease (GBD) study 29 estimated that the COVID-19 pandemic has led to a 28% (95% uncertainty interval (UI): 25–30) increase in major depressive disorders and a 26% (95% UI: 23–28) increase in anxiety disorders. It should be noted that these estimations come with high uncertainty as the assumption that transient pandemic-related increases in mental symptoms extrapolate into incident mental disorders remains disputable. So far, only four longitudinal population-based studies have measured and compared current mental (that is, depressive and anxiety) disorder prevalence—defined using psychiatric diagnostic criteria—before and during the pandemic. Of these, two found no change 30 , 31 , one found a decrease 32 and one found an increase in prevalence of these disorders 33 . These studies were local, limited to high-income countries, often small-scale and used different modes of assessment (for example, online versus in-person) before and during the pandemic. This renders these observational results uncertain as well, but their contrast to the GBD calculations 29 is striking.

Time-series analysis of monthly suicide trends in 21 middle-income to high-income countries across the globe yielded no evidence for an increase in suicide rates in the first 4 months of the pandemic, and there was evidence of a fall in rates in 12 countries 34 . Also in the United States, there was a significant decrease in suicide mortality in the first pandemic months but a slight increase in mortality due to drug overdose and homicide 35 . A living systematic review 36 also concluded that, throughout 2020, there was no observed increase in suicide rates in 20 studies conducted in North America, Europe and Asia. Analyses of electronic health record data in the primary care setting showed reduced rates of self-harm during the first COVID-19 pandemic year 37 . In contrast, emergency department visits for self-harm behavior were unchanged 38 or increased 39 . Such inconsistent findings across healthcare settings may reflect a reluctance in healthcare-seeking behavior for mental healthcare issues. In the living systematic review, eight of 11 studies that examined service use data found a significant decrease in reported self-harm/suicide attempts after COVID lockdown, which returned to pre-lockdown levels in some studies with longer follow-up (5 months) 36 .

In sum, although calculations based on survey data predict a global increase of mental disorder prevalence, objective and consistent evidence for an increased mental disorder, self-harm or suicide prevalence or incidence during the first pandemic year remains absent. This observation, coupled with the only small increase in mental health symptom levels in the overall population, may suggest that most of the general population has demonstrated remarkable resilience and adaptation. However, alternative interpretations are possible. First, there is a large degree of heterogeneity in the mental health impact of COVID-19, and increased mental health in one group (for example, due to better work–family balance and work flexibility) may have masked mental health problems in others. Various societal responses seen in many countries, such as community support activities and bolstering mental health and crisis services, may have had mitigating effects on the mental health burden. Also, the relationship between mental health symptom increases during stressful periods and its subsequent effects on the incidence of mental disorders may be non-linear or could be less visible due to resulting alternative outcomes, such as drug overdose or homicide. Finally, we cannot rule out a lag-time effect, where disorders may take more time to develop or be picked up, especially because some of the personal financial or social consequences of the COVID pandemic may only become apparent later. It should be noted that data from low-income countries and longer-term studies beyond the first pandemic year are largely absent.

Which individuals are most affected by the COVID-19 pandemic?

There is substantial heterogeneity across studies that evaluated how the COVID pandemic impacted on mental health 13 , 14 , 15 . Although our society as a whole may have the ability to adequately bounce back from pandemic effects, there are vulnerable people who have been affected more than others.

First, women have consistently reported larger increases in mental health problems in response to the COVID-19 pandemic than men 13 , 15 , 29 , 40 , with meta-analytic effect sizes being 44% 15 to 75% 13 higher. This could reflect both higher stress vulnerability or larger daily life disruptions due to, for example, increased childcare responsibilities, exposure to home violence or greater economic impact due to employment disruptions that all disproportionately fell to women 41 , thereby exacerbating the already existing pre-pandemic gender inequalities in depression and anxiety levels. In addition, adolescents and young adults have been disproportionately affected compared to younger children and older adults 12 , 15 , 29 , 40 . This may be the result of unfavorable behavioral and social changes (for example, school closure periods 42 ) during a crucial development phase where social interactions outside the family context are pivotal. Alarmingly, even though suicide rates did not seem to increase at the population level, studies in China 43 and Japan 44 indicated significant increases in suicide rates in children and adolescents.

Existing socio-cultural disparities in mental health may have further widened during the COVID pandemic. Whether the impact is larger for individuals with low socio-economic status remains unclear, with contrasting meta-analyses pointing toward this group being protected 15 or at increased risk 40 . Earlier meta-analyses did not find that the mental health impact of COVID-19 differed across Europe, North America, Asia and Oceania 13 , 14 , but data are lacking from Africa and South America. Nevertheless, a large-scale within-country comparison in the United States found that the mental health of Black, Hispanic and Asian respondents worsened relatively more during the pandemic compared to White respondents. Moreover, White respondents were more likely to receive professional mental healthcare during the pandemic, and, conversely, Black, Hispanic, and Asian respondents demonstrated higher levels of unmet mental healthcare needs during this time 45 .

People with pre-existing somatic conditions represent another vulnerable group in which the pandemic had a greater impact (pooled effect size of 0.25) 13 . This includes people with conditions such as epilepsy, multiple sclerosis or cardiometabolic disease as well as those with multiple comorbidities. The disproportionate impact may reflect this groupʼs elevated COVID-19 risk and, consequently, more perceived stress and fear of infection, but it could also reflect disruptions of regular healthcare services.

Healthcare workers faced increased workload, rapidly changing and challenging work environments and exposure to infections and death, accompanied by fear of infecting themselves and their families. High prevalences of (subthreshold) depression (13% 46 ), depressive symptoms (31% 47 ), (subthreshold) anxiety (16% 46 ), anxiety symptoms (23% 47 ) and post-traumatic stress disorder (~22% 46 , 47 ) have been reported in healthcare workers. However, a meta-analysis did not find a larger mental health impact of the pandemic as compared to the general population 40 , and another meta-analysis (of 206 studies) found that the mental health status of healthcare workers was similar to or even better than that of the general population during the first COVID year 48 . However, it is important to note that these meta-analyses could not differentiate between frontline and non-frontline healthcare workers.

Finally, individuals with pre-existing mental disorders may be at increased risk for exacerbation of mental ill-health during the pandemic, possibly due to disease history—illustrating a higher genetic and/or environmental vulnerability—but also due to discontinuity of mental healthcare. Already before the pandemic, mental health systems were under-resourced and disorganized in most countries 6 , 49 , but a third of all WHO member states reported disruptions to mental and substance use services during the first 18 months of the pandemic 50 , with reduced, shortened or postponed appointments and limited capacity for acute inpatient admissions 51 , 52 . Despite this, there is no clear evidence that individuals with pre-existing mental disorders are disproportionately affected by pandemic-related societal disruptions; the effect size for pandemic impact on self-reported mental health problems was similar in psychiatric patients and the general population 13 . In the United States, emergency visits for ten different mental disorders were generally stable during the pandemic compared to earlier periods 53 . In a large Dutch study 22 , 54 with multiple pre-pandemic and during-pandemic assessments, there was no difference in symptom increase among patients relative to controls (see Fig. 1 for illustration). In absolute terms, however, it is important to note that psychiatric patients show much higher symptom levels of depression, anxiety, loneliness and COVID-fear than healthy controls. Again, variation in mental health changes during the pandemic is large: next to psychiatric patients who showed symptom decrease due to, for example, experiencing relief from social pressures, there certainly have been many patients with symptom increases and relapses during the pandemic.

Trajectories of mean depressive symptoms (QIDS score), anxiety symptoms (BAI score), loneliness (De Jong questionnaire score) and Fear of COVID-19 score before and during the first year of the COVID-19 pandemic in healthy controls (blue line, n  = 378) and in patients with depressive and/or anxiety disorders (red line, n  = 908). The x -axis indicates time with one pre-COVID assessment (averaged over up to five earlier assessments conducted between 2006 and 2019) and 11 online assessments during April 2020 through February 2021. Symbols indicate the mean score during the assessment with 95% CIs. As compared to pre-COVID assessment scores, the figure shows a statistically significant increase of depression and loneliness symptoms during the first pandemic peak (April 2020) in healthy controls but not in patients (for more details, see refs. 22 , 54 ). Asterisks indicate where subsequent wave scores differ from the prior wave scores ( P  < 0.05). The figure also illustrates the stability of depressive and anxiety symptoms during the first COVID year, a significant increase in loneliness during this period and fluctuations of Fear of COVID-19 score that positively correlate with infection rates in the Netherlands. Raw data are from the Netherlands Study of Depression and Anxiety (NESDA), which were re-analyzed for the current plots to illustrate differences between two groups (healthy controls versus patients). BAI, Beck Anxiety Inventory; QIDS, Quick Inventory of Depressive Symptoms.

Impact of COVID-19 infection and disease on mental health and the brain

Not only the pandemic but also COVID-19 itself can have severe impact on the mental health of affected individuals and, thus, of the population at large. Below we describe acute and post-acute neuropsychiatric sequelae seen in patients with COVID-19 and link these to neurobiological mechanisms.

Neuropsychiatric sequelae in individuals with COVID-19

Common symptoms associated with acute SARS-CoV-2 infection include headache, anosmia (loss of sense of smell) and dysgeusia (loss of sense of taste). The broader neuropsychiatric impact is dependent on infection severity and is very heterogeneous (Table 2 ). It ranges from no neuropsychiatric symptoms among the large group of asymptomatic COVID-19 cases to milder transient neuropsychiatric symptoms, such as fatigue, sleep disturbance and cognitive impairment, predominantly occurring among symptomatic patients with COVID-19 (ref. 55 ). Cognitive impairment consists of sustained memory impairments and executive dysfunction, including short-term memory loss, concentration problems, word-finding problems and impaired daily problem-solving, colloquially termed ‘brain fog’ by patients and clinicians. A small number of infected individuals become severely ill and require hospitalization. During hospital admission, the predominant neuropsychiatric outcome is delirium 56 . Delirium occurs among one-third of hospitalized patients with COVID-19 and among over half of patients with COVID-19 who require intensive care unit (ICU) treatment. These delirium rates seem similar to those observed among individuals with severe illness hospitalized for other general medical conditions 57 . Delirium is associated with neuropsychiatric sequalae after hospitalization, as part of post-intensive care syndrome 58 , in which sepsis and inflammation are associated with cognitive dysfunction and an increased risk of a broad range of psychiatric symptoms, from anxiety to depression and psychotic symptoms with hallucinations 59 , 60 .

A subset of patients with COVID-19 develop PACS 61 , which can include neuropsychiatric symptoms. A large meta-analysis summarizes 51 studies involving 18,917 patients with a mean follow-up of 77 days (range, 14–182 days) 62 . The most prevalent neuropsychiatric symptom associated with COVID-19 was sleep disturbance, with a pooled prevalence of 27.4%, followed by fatigue (24.4%), cognitive impairment (20.2%), anxiety symptoms (19.1%), post-traumatic stress symptoms (15.7%) and depression symptoms (12.9%) (Table 2 ). Another meta-analysis that assessed patients 12 weeks or more after confirmed COVID-19 diagnosis found that 32% experienced fatigue, and 22% experienced cognitive impairment 63 . To what extent neuropsychiatric symptoms are truly unique for patients with COVID remains unclear from these meta-analyses, as hardly any study included well-matched controls with other types of respiratory infections or inflammatory conditions.

Studies based on electronic health records have examined whether higher levels of neuropsychiatric symptoms truly translate into a higher incidence of clinically overt mental disorders 64 , 65 . In a 1-year follow-up using the US Veterans Affairs database, 153,848 survivors of SARS-CoV-2 infection exhibited an increased incidence of any mental disorder with a relative risk of 1.46 and, specifically, 1.35 for anxiety disorders, 1.39 for depressive disorders and 1.38 for stress and adjustment disorders, compared to a contemporary group and a historical control group ( n  = 5,859,251) 65 . In absolute numbers, the incident risk difference attributable to SARS-CoV-2 for mental disorders was 64 per 1,000 individuals. Taquet et al. 64 analyzed electronic health records from the US-based TriNetX network with over 81 million patients and 236,379 COVID-19 survivors followed for 6 months. In absolute numbers, 6-month incidence of hospital contacts related to diagnoses of anxiety, affective disorder or psychotic disorder was 7.0%, 4.5% and 0.4%, respectively. Risks of incident neurological or psychiatric diagnoses were directly correlated with COVID-19 severity and increased by 78% when compared to influenza and by 32% when compared to other respiratory tract infections. In contrast, a medical record study involving 8.3 million adults confirmed that neuropsychiatric disorders were significantly elevated among COVID-19 hospitalized individuals but to a similar extent as in hospitalized patients with other severe respiratory disease 66 . In line with this, a study using language processing of clinical notes in electronic health records did not find an increase in fatigue, mood and anxiety symptoms among COVID-19 hospitalized individuals when compared to hospitalized patients for other indications and adjusted for sociodemographic features and hospital course 67 . It is important to note that research based only on hospital records might be influenced by increased health-seeking behavior that could be differential across care settings or by increased follow-up by hospitals of patients with COVID-19 (compared to patients with other conditions).

Consequently, whether PACS symptoms form a unique pattern due to specific infection with SARS-CoV-2 remains debatable. Prospective case–control studies that do not rely on hospital records but measure the incidence of neuropsychiatric symptoms and diagnoses after COVID-19 are still scarce, but they are critical for distinguishing causation and confounding when characterizing PACS and the uniqueness of neuropsychiatric sequalae after COVID-19 (ref. 68 ). Recent studies with well-matched control groups illustrate that long-term consequences may not be so unique, as they were similar to those observed in patients with other diseases of similar severity, such as after acute myocardial infarction or in ICU patients 56 , 66 . A first prospective follow-up study of COVID-19 survivors and control patients matched on disease severity, age, sex and ICU admission found similar neuropsychiatric outcomes, regarding both new-onset psychiatric diagnosis (19% versus 20%) and neuropsychiatric symptoms (81% versus 93%). However, moderate but significantly worse cognitive outcomes 6 months after symptom onset were found among survivors of COVID-19 (ref. 69 ). In line with this, a longitudinal study of 785 participants from the UK Biobank showed small but significant cognitive impairment among individuals infected with SARS-CoV-2 compared to matched controls 70 .

Numerous psychosocial mechanisms can lead to neuropsychiatric sequalae of COVID-19, including functional impairment; psychological impact due to, for example, fear of dying; stress of being infected with a novel pandemic disease; isolation as part of quarantine and lack of social support; fear/guilt of spreading COVID-19 to family or community; and socioeconomic distress by lost wages 71 . However, there is also ample evidence that neurobiological mechanisms play an important role, which is discussed below.

Neurobiological mechanisms underlying neuropsychiatric sequelae of COVID-19

Acute neuropsychiatric symptoms among patients with severe COVID-19 have been found to correlate with the level of serum inflammatory markers 72 and coincide with neuroimaging findings of immune activation, including leukoencephalopathy, acute disseminated encephalomyelitis, cytotoxic lesions of the corpus callosum or cranial nerve enhancement 73 . Rare presentations, including meningitis, encephalitis, inflammatory demyelination, cerebral infarction and acute hemorrhagic necrotizing encephalopathy, have also been reported 74 . Hospitalized patients with frank encephalopathies display impaired blood-brain barrier (BBB) integrity with leptomeningeal enhancement on brain magnetic resonance images 75 . Studies of postmortem specimens from patients who succumbed to acute COVID-19 reveal significant neuropathology with signs of hypoxic damage and neuroinflammation. These include evidence of BBB permeability with extravasation of fibrinogen, microglial activation, astrogliosis, leukocyte infiltration and microhemorrhages 76 , 77 . However, it is still unclear to what extent these findings differ from patients with similar illness severity due to acute non-COVID illness, as these brain effects might not be virus-specific effects but rather due to cytokine-mediated neuroinflammation and critical illness.

Post-acute neuroimaging studies in SARS-CoV-2-recovered patients, as compared to control patients without COVID-19, reveal numerous alterations in brain structure on a group level, although effect sizes are generally small. These include minor reduction in gray matter thickness in the various regions of the cortex and within the corpus collosum, diffuse edema, increases in markers of tissue damage in regions functionally connected to the olfactory cortex and reductions in overall brain size 70 , 78 . Neuroimaging studies of post-acute COVID-19 patients also report abnormalities consistent with micro-structural and functional alterations, specifically within the hippocampus 79 , 80 , a brain region critical for memory formation and regulating anxiety, mood and stress responses, but also within gray matter areas involving the olfactory system and cingulate cortex 80 . Overall, these findings are in line with ongoing anosmia, tremors, affect problems and cognitive impairment.

Interestingly, despite findings mentioned above, there is little evidence of SARS-CoV-2 neuroinvasion with productive replication, and viral material is rarely found in the central nervous system (CNS) of patients with COVID-19 (refs. 76 , 77 , 81 ). Thus, neurobiological mechanisms of SARS-CoV-2-mediated neuropsychiatric sequelae remain unclear, especially in patients who initially present with milder forms of COVID-19. Symptomatic SARS-CoV-2 infection is associated with hypoxia, cytokine release syndrome (CRS) and dysregulated innate and adaptive immune responses (reviewed in ref. 82 ). All these effects could contribute to neuroinflammation and endothelial cell activation (Fig. 2 ). Examination of cerebrospinal fluid in patients with neuroimaging findings revealed elevated levels of pro-inflammatory, BBB-destabilizing cytokines, including interleukin-6 (IL-6), IL-1, IL-8 and mononuclear cell chemoattractants 83 , 84 . Whether these cytokines arise from the periphery, due to COVID-19-mediated CRS, or from within the CNS, is unclear. As studies generally lack control patients with other severe illnesses, the specificity of such findings to SARS-CoV-2 also remains unclear. Systemic inflammatory processes, including cytokine release, have been linked to glial activation with expression of chemoattractants that recruit immune cells, leading to neuroinflammation and injury 85 . Cerebrospinal fluid concentrations of neurofilament light, a biomarker of neuronal damage, were reportedly elevated in patients hospitalized with COVID-19 regardless of whether they exhibited neurologic diseases 86 . Acute thromboembolic events leading to ischemic infarcts are also common in patients with COVID-19 due to a potentially increased pro-coagulant process secondary to CRS 87 .

(1) Elevation of BBB-destabilizing cytokines (IL-1β and TNF) within the serum due to CRS or local interactions of mononuclear and endothelial cells. (2) Virus-induced endotheliitis increases susceptibility to microthrombus formation due to platelet activation, elevation of vWF and fibrin deposition. (3) Cytokine, mononuclear and endothelial cell interactions promote disruption of the BBB, which may allow entry of leukocytes expressing IFNg into the CNS (4), leading to microglial activation (5). (6) Activated microglia may eliminate synapses and/or express cytokines that promote neuronal injury. (7) Injured neurons express IL-6 which, together with IL-1β, promote a ‘gliogenic switch’ in NSCs (8), decreasing adult neurogenesis. (9) The combination of microglial (and possibly astrocyte) activation, neuronal injury and synapse loss may lead to dysregulation of NTs and neuronal circuitry. IFNg, interferon-g; NSC, neural stem cell; NT, neurotransmitter; TJ, tight junction; TNF, tumor necrosis factor; vWF, von Willebrand factor.

It is also unclear whether hospitalized patients with COVID-19 may develop brain abnormalities due to hypoxia or CRS rather than as a direct effect of SARS-CoV-2 infection. Hypoxia may cause neuronal dysfunction, cerebral edema, increased BBB permeability, cytokine expression and onset of neurodegenerative diseases 88 , 89 . CRS, with life-threatening levels of serum TNF-α and IL-1 (ref. 90 ) could also impact BBB function, as these cytokines destabilize microvasculature endothelial cell junctional proteins critical for BBB integrity 91 . In mild SARS-CoV-2 infection, circulating immune factors combined with mild hypoxia might impact BBB function and lead to neuroinflammation 92 , as observed during infection with other non-neuroinvasive respiratory pathogens 93 . However, multiple studies suggest that the SARS-CoV-2 spike protein itself may also induce venous and arterial endothelial cell activation and endotheliitis, disrupt BBB integrity or cross the BBB via adoptive transcytosis 94 , 95 , 96 .

Reducing neuropsychiatric sequelae of COVID-19

The increased risk of COVID-19-related neuropsychiatric sequalae was most pronounced during the first pandemic peak but reduced over the subsequent 2 years 64 , 97 . This may be due to reduced impact of newer SARS-CoV-2 strains (that is, Omicron) but also protective effects of vaccination, which limit SARS-CoV-2 spread and may, thus, prevent neuropsychiatric sequalae. Fully vaccinated individuals with breakthrough infections exhibit a 50% reduction in PACS 98 , even though vaccination does not improve PACS-related neuropsychiatric symptoms in patients with a prior history of COVID-19 (ref. 99 ). As patients with pre-existing mental disorders are at increased risk of SARS-CoV-2 infection, they deserve to be among the prioritization groups for vaccination efforts 100 .

Adequate treatment strategies for neuropsychiatric sequelae of COVID-19 are needed. As no specific evidence-based intervention yet exists, the best current treatment approach is that for neuropsychiatric sequelae arising after other severe medical conditions 101 . Stepped care—a staged approach of mental health services comprising a hierarchy of interventions, from least to most intensive, matched to the individual’s need—is efficacious with monitoring of mental health and cognitive problems. Milder symptoms likely benefit from counseling and holistic care, including physiotherapy, psychotherapy and rehabilitation. Individuals with moderate to severe symptoms fulfilling psychiatric diagnoses should receive guideline-concordant care for these disorders 61 . Patients with pre-existing mental disorders also deserve special attention when affected by COVID-19, as they have shown to have an increased risk of COVID-19-related hospitalization, complications and death 102 . This may involve interventions to address their general health, any unfavorable socioenvironmental factors, substance abuse or treatment adherence issues.

Lessons learned, knowledge gaps and future challenges

Ultimately, it is not only the millions of people who have died from COVID-19 worldwide that we remember but also the distress experienced during an unpredictable period with overstretched healthcare systems, lockdowns, school closures and changing work environments. In a world that is more and more globalized, connectivity puts us at risk for future pandemics. What can be learned from the last 2 years of the COVID-19 pandemic about how to handle future and longstanding challenges related to mental health?

Give mental health equal priority to physical health

The COVID-19 pandemic has demonstrated that our population seems quite resilient and adaptive. Nevertheless, even if society as a whole may bounce back, there is a large group of people whose mental health has been and will be disproportionately affected by this and future crises. Although various groups, such as the WHO 8 , the National Health Commission of China 103 , the Asia Pacific Disaster Mental Health Network 104 and a National Taskforce in India 105 , developed mental health policies early on, many countries were late in realizing that a mental health agenda deserves immediate attention in a rapidly evolving pandemic. Implementation of comprehensive and integrated mental health policies was generally inconsistent and suboptimal 106 and often in the shadow of policies directed at containing and reducing the spread of SARS-CoV-2. Leadership is needed to convey the message that mental health is as important as physical health and that we should focus specific attention and early interventions on those at the highest risk. This includes those vulnerable due to factors such as low socioeconomic status, specific developmental life phase (adolescents and young adults), pre-existing risk (poor physical or somatic health and early life trauma) or high exposure to pandemic-related (work) changes—for example, women and healthcare personnel. This means that not only should investment in youth and reducing health inequalities remain at the top of any policy agenda but also that mental health should be explicitly addressed from the start in any future global health crisis situation.

Communication and trust is crucial for mental health

Uncertainty and uncontrollability during the pandemic have challenged rational thinking. Negative news travels fast. Communication that is vague, one-sided and dishonest can negatively impact on mental health and amplify existing distress and anxiety 107 . Media reporting should not overemphasize negative mental health impact—for example, putative suicide rate increases or individual negative experiences—which could make situations worse than they actually are. Instead, communication during crises requires concrete and actionable advice that avoids polarization and strengthens vigilance, to foster resilience and help prevent escalation to severe mental health problems 108 , 109 .

Rapid research should be collaborative and high-quality

Within the scientific community, the topic of mental health during the pandemic led to a multitude of rapid studies that generally had limited methodological quality—for example, cross-sectional designs, small or selective sampling or study designs lacking valid comparison groups. These contributed rather little to our understanding of the mental health impact of the emerging crisis. In future events that have global mental health impact, where possible, collaborative and interdisciplinary efforts with well-powered and well-controlled prospective studies using standardized instruments will be crucial. Only with fine-grained determinants and outcomes can data reliably inform mental health policies and identify who is most at risk.

Do not neglect long-term mental health effects

So far, research has mainly focused on the acute and short-term effects of the pandemic on mental health, usually spanning pandemic effects over several months to 1 year. However, longer follow-up of how a pandemic impacts population mental health is essential. Can societal and economic disruptions after the pandemic increase risk of mental disorders at a later stage when the acute pandemic effects have subsided? Do increased self-reported mental health problems return to pre-pandemic levels, and which groups of individuals remain most affected in the long-term? We need to realize that certain pandemic consequences, particularly those affecting income and school/work careers, may become visible only over the course of several years. Consequently, we should maintain focus and continue to monitor and quantify the effects of the pandemic in the years to come—for example, by monitoring mental healthcare use and suicide. This should include specific at-risk populations (for example, adolescents) and understudied populations in low-income and middle-income countries.

Pay attention to mental health consequences of infectious diseases

Even though our knowledge on PACS is rapidly expanding, there are still many unanswered questions related to who is at risk, the long-term course trajectories and the best ways to intervene early. Consequently, we need to be aware of the neuropsychiatric sequelae of COVID-19 and, for that matter, of any infectious disease. Clinical attention and research should be directed toward alleviating potential neuropsychiatric ramifications of COVID-19. Next to clinical studies, studies using human tissues and appropriate animal models are pivotal to determine the CNS region-specific and neural-cell-specific effects of SARS-CoV-2 infection and the induced immune activation. Indeed, absence of SARS-CoV-2 neuroinvasion is an opportunity to learn and discover how peripheral neuroimmune mechanisms can contribute to neuropsychiatric sequelae in susceptible individuals. This emphasizes the importance of an interdisciplinary approach where somatic and mental health efforts are combined but also the need to integrate clinical parameters after infection with biological parameters (for example, serum, cerebrospinal fluid and/or neuroimaging) to predict who is at risk for PACS and deliver more targeted treatments.

Prepare mental healthcare infrastructure for pandemic times

If we take mental health seriously, we should not only monitor it but also develop the resources and infrastructure necessary for rapid early intervention, particularly for specific vulnerable groups. For adequate mental healthcare to be ready for pandemic times, primary care, community mental health and public mental health should be prepared. In many countries, health services were not able to meet the population’s mental health needs before the pandemic, which substantially worsened during the pandemic. We should ensure rapid access to mental health services but also address the underlying drivers of poor mental health, such as mitigating risks of unemployment, sexual violence and poverty. Collaboration in early stages across disciplines and expertise is essential. Anticipating disruption to face-to-face services, mental healthcare providers should be more prepared for consultations, therapy and follow-up by telephone, video-conferencing platforms and web applications 51 , 52 . The pandemic has shown that an inadequate infrastructure, pre-existing inequalities and low levels of technological literacy hindered the use and uptake of e-health, both in healthcare providers and in patients across different care settings. The necessary investments can ensure rapid upscaling of mental health services during future pandemics for those individuals with a high mental health need due to societal changes, government measures, fear of infection or infection itself.

Even though much attention has been paid to the physical health consequences of COVID-19, mental health has unjustly received less attention. There is an urgent need to prepare our research and healthcare infrastructures not only for adequate monitoring of the long-term mental health effects of the COVID-19 pandemic but also for future crises that will shape mental health. This will require collaboration to ensure interdisciplinary and sound research and to provide attention and care at an early stage for those individuals who are most vulnerable—giving mental health equal priority to physical health from the very start.

WHO Coronavirus (COVID-19) Dashboard (WHO, 2022; https://covid19.who.int/

Rando, H. M. et al. Challenges in defining long COVID: striking differences across literature, electronic health records, and patient-reported information. Preprint at https://www.medrxiv.org/content/10.1101/2021.03.20.21253896v1 (2021).

Nalbandian, A. et al. Post-acute COVID-19 syndrome. Nat. Med. 27 , 601–615 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Abbafati, C. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204–1222 (2020).

Article   Google Scholar  

Penninx, B. W., Pine, D. S., Holmes, E. A. & Reif, A. Anxiety disorders. Lancet 397 , 914–927 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Herrman, H. et al. Time for united action on depression: a Lancet –World Psychiatric Association Commission. Lancet 399 , 957–1022 (2022).

Article   PubMed   Google Scholar  

Radka, K., Wyeth, E. H. & Derrett, S. A qualitative study of living through the first New Zealand COVID-19 lockdown: affordances, positive outcomes, and reflections. Prev. Med. Rep. 26 , 101725 (2022).

Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact (WHO, 2022).

Dragioti, E. et al. A large-scale meta-analytic atlas of mental health problems prevalence during the COVID-19 early pandemic. J. Med. Virol. 94 , 1935–1949 (2022).

Zhang, S. X. et al. Mental disorder symptoms during the COVID-19 pandemic in Latin America—a systematic review and meta-analysis. Epidemiol. Psychiatr. Sci. 31 , e23 (2022).

Zhang, S. X. et al. Meta-analytic evidence of depression and anxiety in Eastern Europe during the COVID-19 pandemic. Eur. J. Psychotraumatol . 13 , 2000132 (2022).

Racine, N. et al. Global prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: a meta-analysis. JAMA Pediatr. 175 , 1142–1150 (2021).

Robinson, E., Sutin, A. R., Daly, M. & Jones, A. A systematic review and meta-analysis of longitudinal cohort studies comparing mental health before versus during the COVID-19 pandemic in 2020. J. Affect. Disord. 296 , 567–576 (2022).

Article   CAS   PubMed   Google Scholar  

Prati, G. & Mancini, A. D. The psychological impact of COVID-19 pandemic lockdowns: a review and meta-analysis of longitudinal studies and natural experiments. Psychol. Med. 51 , 201–211 (2021).

Patel, K. et al. Psychological distress before and during the COVID-19 pandemic among adults in the United Kingdom based on coordinated analyses of 11 longitudinal studies. JAMA Netw. Open 5 , e227629 (2022).

Ernst, M. et al. Loneliness before and during the COVID-19 pandemic: a systematic review with meta-analysis. Am. Psychol . 77 , 660–677 (2022).

Kilian, C. et al. Changes in alcohol use during the COVID-19 pandemic in Europe: a meta-analysis of observational studies. Drug Alcohol Rev . 41 , 918–931 (2022).

Acuff, S. F., Strickland, J. C., Tucker, J. A. & Murphy, J. G. Changes in alcohol use during COVID-19 and associations with contextual and individual difference variables: a systematic review and meta-analysis. Psychol. Addict. Behav. 36 , 1–19 (2022).

Varga, T. V. et al. Loneliness, worries, anxiety, and precautionary behaviours in response to the COVID-19 pandemic: a longitudinal analysis of 200,000 Western and Northern Europeans. Lancet Reg. Health Eur . 2 , 100020 (2021).

Fancourt, D., Steptoe, A. & Bu, F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: a longitudinal observational study. Lancet Psychiatry 8 , 141–149 (2021).

Jia, H. et al. National and state trends in anxiety and depression severity scores among adults during the COVID-19 pandemic—United States, 2020–2021. MMWR Morb. Mortal. Wkly. Rep. 70 , 1427–1432 (2021).

Kok, A. A. L. et al. Mental health and perceived impact during the first Covid-19 pandemic year: a longitudinal study in Dutch case–control cohorts of persons with and without depressive, anxiety, and obsessive-compulsive disorders. J. Affect. Disord. 305 , 85–93 (2022).

Su, Y. et al. Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: a systematic review and meta-analysis. Int. Psychogeriatr. https://doi.org/10.1017/S1041610222000199 (2022).

Knox, L., Karantzas, G. C., Romano, D., Feeney, J. A. & Simpson, J. A. One year on: what we have learned about the psychological effects of COVID-19 social restrictions: a meta-analysis. Curr. Opin. Psychol. 46 , 101315 (2022).

Aknin, L. B. et al. Policy stringency and mental health during the COVID-19 pandemic: a longitudinal analysis of data from 15 countries. Lancet Public Health 7 , e417–e426 (2022).

Lee, Y. et al. Government response moderates the mental health impact of COVID-19: a systematic review and meta-analysis of depression outcomes across countries. J. Affect. Disord. 290 , 364–377 (2021).

Wu, J. T. et al. Nowcasting epidemics of novel pathogens: lessons from COVID-19. Nat. Med. 27 , 388–395 (2021).

Brooks, S. K. et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 395 , 912–920 (2020).

Santomauro, D. F. et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 398 , 1700–1712 (2021).

Knudsen, A. K. S. et al. Prevalence of mental disorders, suicidal ideation and suicides in the general population before and during the COVID-19 pandemic in Norway: a population-based repeated cross-sectional analysis. Lancet Reg. Health Eur . 4 , 100071 (2021).

Ayuso-Mateos, J. L. et al. Changes in depression and suicidal ideation under severe lockdown restrictions during the first wave of the COVID-19 pandemic in Spain: a longitudinal study in the general population. Epidemiol. Psychiatr. Sci . 30 , e49 (2021).

Vloo, A. et al. Gender differences in the mental health impact of the COVID-19 lockdown: longitudinal evidence from the Netherlands. SSM Popul. Health 15 , 100878 (2021).

Winkler, P. et al. Prevalence of current mental disorders before and during the second wave of COVID-19 pandemic: an analysis of repeated nationwide cross-sectional surveys. J. Psychiatr. Res. 139 , 167–171 (2021).

Pirkis, J. et al. Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries. Lancet Psychiatry 8 , 579–588 (2021).

Faust, J. S. et al. Mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicides during the pandemic, March–August 2020. JAMA 326 , 84–86 (2021).

John, A. et al. The impact of the COVID-19 pandemic on self-harm and suicidal behaviour: update of living systematic review. F1000Res. 9 , 1097 (2020).

Steeg, S. et al. Temporal trends in primary care-recorded self-harm during and beyond the first year of the COVID-19 pandemic: time series analysis of electronic healthcare records for 2.8 million patients in the Greater Manchester Care Record. EClinicalMedicine 41 , 101175 (2021).

Rømer, T. B. et al. Psychiatric admissions, referrals, and suicidal behavior before and during the COVID-19 pandemic in Denmark: a time-trend study. Acta Psychiatr. Scand. 144 , 553–562 (2021).

Holland, K. M. et al. Trends in US emergency department visits for mental health, overdose, and violence outcomes before and during the COVID-19 pandemic. JAMA Psychiatry 78 , 372–379 (2021).

Kunzler, A. M. et al. Mental burden and its risk and protective factors during the early phase of the SARS-CoV-2 pandemic: systematic review and meta-analyses. Global Health 17 , 34 (2021).

Flor, L. S. et al. Quantifying the effects of the COVID-19 pandemic on gender equality on health, social, and economic indicators: a comprehensive review of data from March, 2020, to September, 2021. Lancet 399 , 2381–2397 (2022).

Viner, R. et al. School closures during social lockdown and mental health, health behaviors, and well-being among children and adolescents during the first COVID-19 wave: a systematic review. JAMA Pediatr. 176 , 400–409 (2022).

Zheng, X. Y. et al. Trends of injury mortality during the COVID-19 period in Guangdong, China: a population-based retrospective analysis. BMJ Open 11 , e045317 (2021).

Tanaka, T. & Okamoto, S. Increase in suicide following an initial decline during the COVID-19 pandemic in Japan. Nat. Hum. Behav. 5 , 229–238 (2021).

Thomeer, M. B., Moody, M. D. & Yahirun, J. Racial and ethnic disparities in mental health and mental health care during the COVID-19 pandemic. J. Racial Ethn. Health Disparities https://doi.org/10.1007/s40615-021-01006-7 (2022).

Hill, J. E. et al. The prevalence of mental health conditions in healthcare workers during and after a pandemic: systematic review and meta-analysis. J. Adv. Nurs. 78 , 1551–1573 (2022).

Marvaldi, M., Mallet, J., Dubertret, C., Moro, M. R. & Guessoum, S. B. Anxiety, depression, trauma-related, and sleep disorders among healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 126 , 252–264 (2021).

Phiri, P. et al. An evaluation of the mental health impact of SARS-CoV-2 on patients, general public and healthcare professionals: a systematic review and meta-analysis. EClinicalMedicine 34 , 100806 (2021).

Jorm, A. F., Patten, S. B., Brugha, T. S. & Mojtabai, R. Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry 16 , 90–99 (2017).

Third Round of the Global Pulse Survey on Continuity of Essential Health Services during the COVID-19 Pandemic (WHO, 2021).

Baumgart, J. G. et al. The early impacts of the COVID-19 pandemic on mental health facilities and psychiatric professionals. Int. J. Environ. Res. Public Health 18 , 8034 (2021).

Raphael, J., Winter, R. & Berry, K. Adapting practice in mental healthcare settings during the COVID-19 pandemic and other contagions: systematic review. BJPsych Open 7 , e62 (2021).

Anderson, K. N. et al. Changes and inequities in adult mental health-related emergency department visits during the COVID-19 pandemic in the US. JAMA Psychiatry 79 , 475–485 (2022).

Pan, K. Y. et al. The mental health impact of the COVID-19 pandemic on people with and without depressive, anxiety, or obsessive-compulsive disorders: a longitudinal study of three Dutch case–control cohorts. Lancet Psychiatry 8 , 121–129 (2021).

Dantzer, R., O’Connor, J. C., Freund, G. G., Johnson, R. W. & Kelley, K. W. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat. Rev. Neurosci. 9 , 46–56 (2008).

Nersesjan, V. et al. Central and peripheral nervous system complications of COVID-19: a prospective tertiary center cohort with 3-month follow-up. J. Neurol. 268 , 3086–3104 (2021).

Wilson, J. E. et al. Delirium. Nat. Rev. Dis. Prim . 6 , 90 (2020).

Rawal, G., Yadav, S. & Kumar, R. Post-intensive care syndrome: an overview. J. Transl. Intern. Med. 5 , 90–92 (2017).

Pandharipande, P. P. et al. Long-term cognitive impairment after critical illness. N. Engl. J. Med. 369 , 1306–1316 (2013).

Girard, T. D. et al. Long-term cognitive impairment after hospitalization for community-acquired pneumonia: a prospective cohort study. J. Gen. Intern. Med. 33 , 929–935 (2018).

Crook, H., Raza, S., Nowell, J., Young, M. & Edison, P. Long covid—mechanisms, risk factors, and management. BMJ 374 , n1648 (2021).

Badenoch, J. B. et al. Persistent neuropsychiatric symptoms after COVID-19: a systematic review and meta-analysis. Brain Commun . 4 , fcab297 (2021).

Ceban, F. et al. Fatigue and cognitive impairment in post-COVID-19 syndrome: a systematic review and meta-analysis. Brain Behav. Immun. 101 , 93–135 (2022).

Taquet, M., Geddes, J. R., Husain, M., Luciano, S. & Harrison, P. J. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. Lancet Psychiatry 8 , 416–427 (2021).

Xie, Y., Xu, E. & Al-Aly, Z. Risks of mental health outcomes in people with covid-19: cohort study. BMJ 376 , e068993 (2022).

Kieran Clift, A. et al. Neuropsychiatric ramifications of severe COVID-19 and other severe acute respiratory infections. JAMA Psychiatry 79 , 690–698 (2022).

Castro, V. M., Rosand, J., Giacino, J. T., McCoy, T. H. & Perlis, R. H. Case–control study of neuropsychiatric symptoms following COVID-19 hospitalization in 2 academic health systems. Mol. Psych. (in the press).

Amin-Chowdhury, Z. & Ladhani, S. N. Causation or confounding: why controls are critical for characterizing long COVID. Nat. Med. 27 , 1129–1130 (2021).

Nersesjan, V. et al. Neuropsychiatric and cognitive outcomes in patients 6 months after COVID-19 requiring hospitalization compared with matched control patients hospitalized for non-COVID-19 illness. JAMA Psychiatry 79 , 486–497 (2022).

Douaud, G. et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 604 , 697–707 (2022).

Zhang, H. et al. Psychological experience of COVID-19 patients: a systematic review and qualitative meta-synthesis. Am. J. Infect. Control 50 , 809–819 (2022).

Mazza, M. G. et al. Anxiety and depression in COVID-19 survivors: role of inflammatory and clinical predictors. Brain Behav. Immun. 89 , 594–600 (2020).

Moonis, G. et al. The spectrum of neuroimaging findings on CT and MRI in adults With COVID-19. AJR Am. J. Roentgenol. 217 , 959–974 (2021).

Asadi-Pooya, A. A. & Simani, L. Central nervous system manifestations of COVID-19: a systematic review. J. Neurol. Sci . 413 , 116832 (2020).

Lersy, F. et al. Cerebrospinal fluid features in patients with Coronavirus Disease 2019 and neurological manifestations: correlation with brain magnetic resonance imaging findings in 58 patients. J. Infect. Dis. 223 , 600–609 (2021).

Thakur, K. T. et al. COVID-19 neuropathology at Columbia University Irving Medical Center/New York Presbyterian Hospital. Brain 144 , 2696–2708 (2021).

Cosentino, G. et al. Neuropathological findings from COVID-19 patients with neurological symptoms argue against a direct brain invasion of SARS-CoV-2: a critical systematic review. Eur. J. Neurol. 28 , 3856–3865 (2021).

Tian, T. et al. Long-term follow-up of dynamic brain changes in patients recovered from COVID-19 without neurological manifestations. JCI Insight 7 , e155827 (2022).

Lu, Y. et al. Cerebral micro-structural changes in COVID-19 patients—an MRI-based 3-month follow-up study. EClinicalMedicine 25 , 100484 (2020).

Qin, Y. et al . Long-term microstructure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations. J. Clin. Invest . 131 , e147329 (2021).

Matschke, J. et al. Neuropathology of patients with COVID-19 in Germany: a post-mortem case series. Lancet Neurol. 19 , 919–929 (2020).

Shivshankar, P. et al. SARS-CoV-2 infection: host response, immunity, and therapeutic targets. Inflammation 45 , 1430–1449 (2022).

Manganotti, P. et al. Cerebrospinal fluid and serum interleukins 6 and 8 during the acute and recovery phase in COVID-19 neuropathy patients. J. Med. Virol. 93 , 5432–5437 (2021).

Farhadian, S. et al. Acute encephalopathy with elevated CSF inflammatory markers as the initial presentation of COVID-19. BMC Neurol . 20 , 248 (2020).

Francistiová, L. et al. Cellular and molecular effects of SARS-CoV-2 linking lung infection to the brain. Front. Immunol . 12 , 730088 (2021).

Paterson, R. W. et al. Serum and cerebrospinal fluid biomarker profiles in acute SARS-CoV-2-associated neurological syndromes. Brain Commun . 3 , fcab099 (2021).

Cryer, M. J. et al. Prothrombotic milieu, thrombotic events and prophylactic anticoagulation in hospitalized COVID-19 positive patients: a review. Clin. Appl. Thromb. Hemost . 28 , 10760296221074353 (2022).

Nalivaeva, N. N. & Rybnikova, E. A. Editorial: Brain hypoxia and ischemia: new insights into neurodegeneration and neuroprotection. Front. Neurosci . 13 , 770 (2019).

Brownlee, N. N. M., Wilson, F. C., Curran, D. B., Lyttle, N. & McCann, J. P. Neurocognitive outcomes in adults following cerebral hypoxia: a systematic literature review. NeuroRehabilitation 47 , 83–97 (2020).

Del Valle, D. M. et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat. Med. 26 , 1636–1643 (2020).

Daniels, B. P. et al. Viral pathogen-associated molecular patterns regulate blood–brain barrier integrity via competing innate cytokine signals. mBio 5 , e01476-14 (2014).

Reynolds, J. L. & Mahajan, S. D. SARS-COV2 alters blood brain barrier integrity contributing to neuro-inflammation. J. Neuroimmune Pharmacol. 16 , 4–6 (2021).

Bohmwald, K., Gálvez, N. M. S., Ríos, M. & Kalergis, A. M. Neurologic alterations due to respiratory virus infections. Front. Cell. Neurosci . 12 , 386 (2018).

Khaddaj-Mallat, R. et al. SARS-CoV-2 deregulates the vascular and immune functions of brain pericytes via spike protein. Neurobiol. Dis . 161 , 105561 (2021).

Qian, Y. et al. Direct activation of endothelial cells by SARS-CoV-2 nucleocapsid protein is blocked by simvastatin. J Virol. 95 , e0139621 (2021).

Rhea, E. M. et al. The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice. Nat. Neurosci. 24 , 368–378 (2021).

Magnúsdóttir, I. et al. Acute COVID-19 severity and mental health morbidity trajectories in patient populations of six nations: an observational study. Lancet Public Health 7 , e406–e416 (2022).

Antonelli, M. et al. Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case–control study. Lancet Infect. Dis. 22 , 43–55 (2022).

Wisnivesky, J. P. et al. Association of vaccination with the persistence of post-COVID symptoms. J. Gen. Intern. Med . 37 , 1748–1753 (2022).

De Picker, L. J. et al. Severe mental illness and European COVID-19 vaccination strategies. Lancet Psychiatry 8 , 356–359 (2021).

Cohen, G. H. et al. Comparison of simulated treatment and cost-effectiveness of a stepped care case-finding intervention vs usual care for posttraumatic stress disorder after a natural disaster. JAMA Psychiatry 74 , 1251–1258 (2017).

Vai, B. et al. Mental disorders and risk of COVID-19-related mortality, hospitalisation, and intensive care unit admission: a systematic review and meta-analysis. Lancet Psychiatry 8 , 797–812 (2021).

Xiang, Y. T. et al. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry 7 , 228 (2020).

Newnham, E. A. et al. The Asia Pacific Disaster Mental Health Network: setting a mental health agenda for the region. Int. J. Environ. Res. Public Health 17 , 6144 (2020).

Article   CAS   PubMed Central   Google Scholar  

Dandona, R. & Sagar, R. COVID-19 offers an opportunity to reform mental health in India. Lancet Psychiatry 8 , 9–11 (2021).

Qiu, D. et al. Policies to improve the mental health of people influenced by COVID-19 in China: a scoping review. Front. Psychiatry 11 , 588137 (2020).

Su, Z. et al. Mental health consequences of COVID-19 media coverage: the need for effective crisis communication practices. Global Health 17 , 4 (2021).

Petersen, M. B. COVID lesson: trust the public with hard truths. Nature 598 , 237 (2021).

van der Bles, A. M., van der Linden, S., Freeman, A. L. J. & Spiegelhalter, D. J. The effects of communicating uncertainty on public trust in facts and numbers. Proc. Natl Acad. Sci. USA 117 , 7672–7683 (2020).

Titze-de-Almeida, R. et al. Persistent, new-onset symptoms and mental health complaints in Long COVID in a Brazilian cohort of non-hospitalized patients. BMC Infect. Dis. 22 , 133 (2022).

Carfì, A., Bernabei, R. & Landi, F. Persistent symptoms in patients after acute COVID-19. JAMA 324 , 603–605 (2020).

Bliddal, S. et al. Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients. Sci. Rep. 11 , 13153 (2021).

Kim, Y. et al. Post-acute COVID-19 syndrome in patients after 12 months from COVID-19 infection in Korea. BMC Infect. Dis . 22 , 93 (2022).

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Acknowledgements

The authors thank E. Giltay for assistance on data analyses and production of Fig. 1 . B.W.J.H.P. discloses support for research and publication of this work from the European Union’s Horizon 2020 research and innovation programme-funded RESPOND project (grant no. 101016127).

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• Open access
• Published: 22 November 2012

Quality of life of people with mental health problems: a synthesis of qualitative research

• Janice Connell 1 ,
• John Brazier 2 ,
• Alicia O’Cathain 1 ,
• Myfanwy Lloyd-Jones 2 &
• Suzy Paisley 3  

Health and Quality of Life Outcomes volume  10 , Article number:  138 ( 2012 ) Cite this article

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To identify the domains of quality of life important to people with mental health problems.

A systematic review of qualitative research undertaken with people with mental health problems using a framework synthesis.

We identified six domains: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; and hope and hopelessness. Firstly, symptoms or ‘ill-being’ were an intrinsic aspect of quality of life for people with severe mental health problems. Additionally, a good quality of life was characterised by the feeling of being in control (particularly of distressing symptoms), autonomy and choice; a positive self-image; a sense of belonging; engagement in meaningful and enjoyable activities; and feelings of hope and optimism. Conversely, a poor quality life, often experienced by those with severe mental health difficulties, was characterized by feelings of distress; lack of control, choice and autonomy; low self-esteem and confidence; a sense of not being part of society; diminished activity; and a sense of hopelessness and demoralization.

Conclusions

Generic measures fail to address the complexity of quality of life measurement and the broad range of domains important to people with mental health problems.

Introduction

There has been a shift in mental health services from an emphasis on treatment focused on reducing symptoms, based on a narrow notion of health and disease, to a more holistic approach which takes into consideration both well-being and functioning [ 1 ]. Mental health services in the United Kingdom, for example, are now being planned and commissioned based on psychological formulations addressing a person’s wider well-being, need, and functional outcome alongside, or sometimes in place of, diagnostic categories and clinical ideas of cure and outcome [ 2 ]. At the same time, there has been an increasing use of generic measures of health related quality of life like EQ-5D and SF-36 in assessing the benefits of health care interventions in order to inform decisions about provision and reimbursement (eg National Institute for Health and Clinical Excellence) [ 3 ] and for assessing patient reported outcomes [ 4 ]. It is claimed these generic measures are appropriate for both physical and mental health conditions; however some argue they are not suitable for people with severe mental health problems, particularly psychosis [ 5 , 6 ].

One of the challenges of using the concept ‘quality of life’ as a basis for outcome measurement is that it can be defined, and therefore measured, in innumerable ways. The assumptions underlying such measurement can be influenced by both academic discipline and ideological perspective [ 7 ]. As a result there are many different overlapping models of quality of life including objective and subjective indicators, needs satisfaction, psychological and subjective well-being models, health, functioning and social models [ 8 ]. One on-going tension is whether a measure should have a subjective or objective orientation. A subjective orientation may emphasise the importance of ‘being’, which in turn can be viewed either in hedonistic terms as the experience of current happiness or pleasure, or as a more eudemonic approach which considers the more pervading attributes of self-fulfilment, realisation or actualization [ 9 , 10 ]. A subjective evaluative approach may also be taken which asks people to rate how satisfied they are with their lives and aspects of it [ 11 ]. On the other hand, a more objective approach used in social policy places its emphasis on meeting needs, whether they are healthy, have sufficient income for food and satisfactory living conditions, are well educated and have access to resources [ 9 , 12 ]. A review of eleven instruments for measuring quality of life for people with severe mental illness identified that the most commonly assessed domains are employment or work, health, leisure, living situation, and relationships [ 13 ]. These measures combine an objective with a subjective approach that establishes levels of satisfaction with these different objective life domains. However, concerns have been raised regarding the limited coverage of domains assessed in such instruments [ 14 , 15 ]. Furthermore, it is criticised that measures have primarily been generated from the perspective of mental health professionals or other experts using a top-down approach rather than by an assessment of what individuals with mental health problems perceive to be important to their quality of life [ 15 ]. These are also important potential criticisms of the generic measures of health related quality of life like the EQ-5D and SF-36 [ 5 ].

The aim of this literature review was to examine the quality of life domains that are important from the perspective of an individual with mental health problems. This research was part of a larger project considering the applicability and suitability of generic health related quality of life measures for people with mental health problems (MRC project number G0801394).

We sought to identify all primary qualitative research studies (involving methods such as interviews and focus groups) which explicitly asked adults with mental health problems what they considered to be important to their quality of life or how their quality of life had been affected by their mental health problems.

A range of approaches is available for synthesizing qualitative research [ 16 ]. Paterson et al. [ 17 ] recommend that the choice is made on the basis of the nature of the research question and design, the prevailing paradigm, and the researcher’s personal preference. In this review, framework synthesis was used. This is based on the ‘framework’ approach for the analysis of primary data [ 18 ] and is a highly structured approach to organizing and analyzing data which permits the expansion and refinement of an a priori framework to incorporate new themes emerging from the data [ 16 ]. It is appropriate here because the aim of our wider study was to identify whether existing outcomes measures are useful for measuring quality of life for people with mental health problems.

Search methods

Systematic reviews of clinical effectiveness evidence require extensive searching based on a clearly focussed search question. Defining a focussed question was neither possible nor appropriate here because a pre-specified search question would have imposed on the search process an a priori conceptual understanding of the topic under review. Given the abstract nature of the relevant concepts and associated search vocabulary, and given the exploratory and inductive nature of the review process, we needed to use an iterative approach to searching. This incorporated a number of different search techniques including keyword searching, taking advice from experts, hand searching and citation searching of relevant references and world-wide-web searching. The iterative approach provided a means of accommodating within the search process new themes emerging from the review as the scope of our conceptual understanding developed. The identification of relevant search terms was an evolving process. Four search iterations were undertaken. The choice of search terms used in earlier iterations was based on our initial understanding of the review topic and on papers identified by experts at the outset of the review. The choice of search terms used in later iterations was informed by the review of evidence identified by earlier search iterations. Key terms included mental health; mental illness; mental disorder; quality of life; well-being; well being; life satisfaction; life functioning; life change; recovery; subjective experience; lived experience; lifestyle; coping; adaptation; qualitative; qualitative research. For a full list of search terms and details of the evolving search iterations see Additional file 1 : Appendix 1 and Additional file 2: Appendix 2. Database searches were undertaken between October 2009 and April 2010 and included Medline, ASSIA, CINAHL, PsycINFO, and Web of Science. The searches were not restricted by date, language or country.

Inclusion and exclusion criteria

• Quality of life

The search started from a premise of not imposing a pre-conceived definition or model of ‘quality of life’. Whilst some studies retrieved had an explicit aim to explore quality of life we found other studies with very similar findings to those which explicitly examined the concept of quality of life even though quality of life was not the subject of investigation. These studies examined the concepts of: recovery, lived experience, subjective experience, psychosocial issues, health needs, and strategies for living. Complexities thus arose in deciding whether the studies were about the same substantive concept of quality of life or were tapping into a separate but overlapping concept. As Sandelowski [ 19 ] states ‘often research purposes and questions are so broadly stated it is only by looking at the kinds of findings produced that topical similarity can be determined’. We were aware of the danger that the inclusion of these studies could introduce themes that were not central to the concept of quality of life but were rather allied to a separate but related concept. A pragmatic decision was made to examine the research aims and interview questions of those studies which did not directly investigate the concept of quality of life and only include those which asked broad open-ended questions about how participants’ mental health affected their lives, what was important to or would improve their lives, or equated their findings with quality of life in some way. We excluded studies that deliberately started with a premise of the importance of any particular domain of quality of life or were structured solely around a pre-conceived list of domains.

Qualitative research

We included primary qualitative research studies that used qualitative interviews or focus groups data to identify the views of individuals with mental health problems. We excluded studies that used content analysis which presented results as a frequency list with no supporting participant quotes. Some studies sought the views of people with mental health problems and of carers or professionals; in such cases, we only included those studies in which the views of people with mental health problems could be separately identified.

• Mental health

We included research on all mood disorders (eg depression, bi-polar, mania), neurosis and stress related disorders (eg anxiety, phobias, post traumatic stress disorder) personality disorders and schizophrenia, schizotypal and delusional disorders. Included studies had to state that participants had mental health problems as identified either through diagnosis, or through attendance at an establishment for people with mental health problems. Studies where mental health problems were secondary to a physical health problem were excluded.

The use of quality assessment in reviews of qualitative research is contested. Quality assessment is usually used in framework synthesis but this may be associated with its use alongside systematic reviews of effectiveness [ 16 ]. In this review, articles were not quality assessed and systematically excluded on the basis of quality. However, it was of paramount importance that any included study elicited the perspective of individuals with mental health problems and where this appeared not to be the case they were excluded. Consequently, studies were excluded when it was strongly suspected that the views of the researcher, or the method of analysis, had overly influenced the findings. These articles were examined and discussed at length by the research team before being excluded.

Although the searches were not restricted to English language articles, non-English language articles were excluded because of the potential for mis-interpretation. Five potentially relevant articles were excluded on the grounds of language (Figure  1 ).

PRISMA flow diagram of searched articles.

Data extraction and analysis

The following details of the studies were extracted: mental health problem studied; author affiliation; time and location of study; number and demographic details of participants; research aims and questions; recruitment and sampling methods; and method of data collection and analysis. Themes within the findings and discussion sections were extracted for the thematic analysis.

Framework analysis [ 18 ] was used to allow the identification of common and variable patterns of themes within and across different studies. The first stage of framework analysis- familiarisation - was undertaken by reading all included papers. The second stage involved examining the findings from these papers to identify initial themes for a thematic framework. These ten initial descriptive themes were either identified as main themes from more than one study, or arose consistently across studies. These were: activity; relationships; the self; the future/aspirations; symptoms/well-being/emotions; spirituality; control/coping; insight/education; health care services/interventions; and resources/basic needs. The third stage, data organisation, involved charting data from the findings and discussion sections that corresponded to each theme. Text was transferred verbatim to ensure contextual accuracy. It was common for text to be identified as supporting more than one theme, for example a quote describing how work was good for their self-esteem would be placed in the thematic categories ‘activity’ and ‘self’. At the next stage each initial theme was examined and further sub-themes identified and documented within the framework chart. To assist with the final stage of framework - mapping - the sub-themes were listed and examined for their conceptual similarities and differences. To aid this process, we searched the wider literature to find papers which would help us to understand the data, to make connections between sub-themes, and to assist in the development of our final themes. For example, ‘belonging’ was an emerging theme, and we identified Hagerty et al’s [ 20 ] research which explored and defined this concept. We then returned to our framework chart to re-examine our data in light of the wider literature. Other influential literature was on the theory of ‘doing, being, becoming’ [ 21 ], ill-being vs well-being and intrinsic and extrinsic quality of life [ 22 , 23 ] and demoralization [ 24 ]. We have reported this literature when describing the theme in the findings because it was influential in shaping our understanding of the theme. The themes and domains from the included papers were presented and organised in contrasting styles by the authors of those papers. Depending upon the theoretical background of the researcher, and the method of analysis used, this resulted in themes which were either objective and descriptive (e.g. relationships, occupation) or abstract or metaphoric in their presentation (e.g. ‘Upset and calm changes patterns of being with and apart from others’). For the latter, whether a theme was major or minor was the subjective view of the authors. We have reported a theme as being a major theme within the studies if it was: a) a titled theme within the study findings b) was reported as being represented throughout the data or c) formed a substantive part of those studies that used abstract or metaphoric themes or of those that were not organised thematically. For transparency the original themes or section titles from the original papers have been presented after the quotes provided to illustrate our findings.

Validation and trustworthiness

Validation procedures were incorporated into the review at all stages. Two researchers (JC and MLJ) independently identified articles from the first search iteration, and compared results to clarify the inclusion and exclusion criteria. Potential full articles were identified from further searches by the primary researcher and independently checked by the second researcher. The included articles were examined independently by both researchers to identify the main themes for the initial framework. Disagreements at all stages were resolved by discussion. Additionally, a multidisciplinary team of researchers met regularly in addition to meetings with clinicians and a user representative to discuss and challenge the inclusion and exclusion criteria, thematic framework, and conceptual interpretations and conclusions.

Description of included studies

Thirteen studies were identified from 16 articles [ 25 – 40 ]; two had fuller reports available, one an internal report [ 25 , 26 ] and the other a dissertation [ 27 , 28 ], the fuller reports [ 26 , 28 ] have been referenced in the findings. Further, one study indicated that not all emerging themes were presented in the paper and had a supplementary paper dedicated to the impact of bi-polar disorder on work functioning, which was included in our analysis [ 37 , 38 ]. The studies were published between 1994 and 2010 in a number of countries: Canada (5), UK (3), Sweden (2), USA (1), Australia (1) and New Zealand (1). The professional affiliations of the first author were occupational therapy (5), nursing (4), psychology (2), psychiatry (1) and social work (1). The mental health disorder most frequently represented was schizophrenia (or other psychotic disorder): this was the only population researched in three studies and the majority population in a further two. Three studies included individuals with bi-polar disorder only and one panic disorder only. Other studies had a mixed population including the above disorders plus persons with personality disorder, severe depression, and anxiety disorders. Two studies did not specify the disorder; they included persons described as having ‘enduring mental health problems’ and ‘psychiatric disability’.

Two studies had a primarily positive orientation in that they asked ‘what is required for a good quality of life’, and four studies a negative orientation through asking ‘how has your mental health affected your quality of life’. The remainder considered both ‘what had helped and hindered quality of life’. Most studies presented their findings descriptively, and four had a conceptual/abstract orientation. Further details of the studies [ 25 – 40 ] can be found in Table  1 .

We identified six major themes: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; and hope and hopelessness. The themes identified within each of the studies can be found in Table  2 .

Well-being and Ill-being

Well-being has long been regarded as an important dimension of health related quality of life scales [ 14 ]. The emotional component of subjective well-being consists of high levels of positive affect (experiencing pleasant emotions and moods), and lack of low levels of negative affect (experiencing few unpleasant emotions and moods) [ 23 ]. Within our papers, symptoms of mental illness and aspects of emotional well-being were intertwined, with an emphasis on the negative rather than the positive. This suggested that ill-being, which is more akin to distress and the symptoms of mental illness, is an important aspect of quality of life for those with severe mental health problems.

The most evident ‘ill-being’ themes were general feelings of distress from symptoms; the experience of psychosis/mania; depressed mood; problems with energy and motivation and fear and anxiety.

Distress from symptoms

Distress, or the subjective experience of the symptoms of mental illness, was evident in the majority of studies [ 26 , 28 , 30 – 32 , 35 , 36 , 40 ] and a major theme in four [ 28 , 30 , 35 , 40 ]. The subjective experience of mental illness was described as wretched [ 36 ] a burden, debilitating, painful [ 40 ], tormenting [ 35 ], and as having a tyrannical power over life [ 28 ]. Pre-occupation with the symptoms of mental health problems interfered greatly with the most basic tasks of everyday living [ 26 , 28 , 31 , 40 ], making it difficult to deal with anything but the present moment [ 40 ]. Instead life was consumed with coping on a daily basis and living ‘one day at a time’ - sometimes on a moment to moment basis [ 28 , 31 , 34 ].

Symptoms of mental illness were described primarily in negative and restrictive ways . Subjects reported continually trying to deal with the symptoms , describing symptoms as “ a great burden .” The symptoms seemed to be so encompassing that these men had difficulty seeing beyond the pain of today . “ This illness is a great burden . Day - to - day survival is a big question , and I just feel in a turmoil a lot of the time ”; “ I ’ ve had terrible suffering for over 20 years.” [ 40 - A pervasive feeling of distress ]

Experience of psychosis/mania

Distressing symptoms reported included hallucinations and delusions (particularly hearing voices, thought disturbances and paranoia) [ 26 , 28 , 30 ], reality disorientation [ 28 ], mania, and hypomania [ 38 ], feelings of discomfort, weirdness or oddness [ 28 ], and irritability or agitation [ 30 ]. These symptoms could interfere directly with day to day living by having an effect on behaviour control [ 26 , 30 , 35 , 38 ], concentration, memory or decision making [ 26 , 30 , 31 ] and sense of self-identity [ 28 , 31 , 37 ].

“ When I hear voices erm , that stops me from doing a day to day existence , I ’ m preoccupied with the voices ”; “ … the voices , how they ’ ve affected my life , erm , er just day to day living basically … Erm just er , getting out , getting out and doing things er … go to the shops , erm , erm , cooking , anything , anything like that ”; “ I daren ’ t go out now , thoughts in my head , make me think bad things ; I get paranoid when there ’ s crowds of people. ” [ 26 - Fear of exacerbating mental health difficulties ].

Depressed mood

Depression was a diagnosis of a proportion of participants in two of the studies [ 28 , 29 ] and bi-polar disorder the primary diagnosis in three further studies [ 35 , 37 , 39 ]. Negative affect, in the more severe form of depression including feeling suicidal [ 26 ] (as opposed to simply being sad, unhappy), was also identified in studies where the primary diagnosis was psychosis related [ 26 , 30 , 34 , 36 , 40 ]. It was also the symptoms of depression in bi-polar patients that were reported as being particularly distressing [ 35 ], together with the unpredictability and instability of mood [ 35 , 38 ].

Energy and motivation

Depression was often expressed as associated with a lack of energy and/or motivation. Although energy and motivation might be regarded as two distinct concepts (physical and psychological), they were closely associated and for the most part reported together within the primary research. Energy, or lack of it, was a major theme in one study [ 39 ] and all but three of the primary research articles [ 32 , 33 , 35 ] described the debilitating effects of lack of energy. The three studies where energy and motivation were not evident focused on the nursing implications of panic disorder [ 32 ] the psycho-social issues related to bipolar disorder [ 35 ] and the positive determinants of health [ 33 ]. Participants reported feeling generally drained of energy [ 26 , 28 – 30 , 38 , 40 ] associated with a lack of motivation, enthusiasm, or interest in things [ 26 , 28 – 31 , 34 , 36 , 40 ]. The side effects of medication [ 26 ] or problems with sleep [ 26 , 30 ] were reported as having a causal effect.

“ The quality of my life in the last few years has been horrible , because it has taken so much energy and struggle to get through so many things …. I ’ ve got to get out , go out and do things or go to concerts or go to school things , or go to meetings or something , and doing some of those things is so tough , to make yourself , you know , get up and go . Just getting up to go out for a walk was really hard for me , whereas walking is one of my , you know , I love to go out and walk .” [ 29 - Distant hopes fuel the relentless struggle to carry on ]

Because lack of energy was a problem, conserving energy for those activities that brought pleasure and joy was important [ 39 ]. Whilst lack of energy was the dominant theme, hypomanic states in bi-polar disorder were associated with increased energy and enthusiasm but were often short-lived with a return to a usual depressed state [ 38 ].

Fear and anxiety

Two studies reported that ‘fear’ was a theme that was represented throughout their interview data [ 28 , 30 ]. Fear, anxiety, or worry was present in some form in all of the studies. The subjective experiences of the symptoms were reported as being very frightening [ 26 , 28 , 31 , 32 , 35 , 40 ]. This tended to be identified in the studies on schizophrenia, bi-polar disorder and panic disorder. As a consequence, individuals lived in fear of relapse or a return to hospital [ 26 , 28 , 30 ]. There were associated financial worries which had implications for planning for the future and making commitments [ 30 , 34 , 37 ].

Living day to day with a psychotic illness was described as a very frightening and isolating experience . The participants described their sense of fear while experiencing symptoms , watchfulness for reoccurrence of illness , concerns over safety , experiences of anxiety and rejection in interactions with others , avoidance of stressors , feelings that they were being treated as “ fragile ” by their families , and a sense of powerlessness in gaining control over symptoms [ 27 - The experience of illness ]

Anxiety in social situations was especially evident and took various forms including anxiety about leaving the house, crowds and public places [ 26 ], concerns for their own safety [ 26 , 28 , 36 ], and that of others [ 26 ], worrying about what others thought of them and how they appeared [ 26 , 28 , 30 ], worries and concerns within relationships [ 29 ] and fears of rejection [ 40 ]. Worries concerning relapse or aggravation of symptoms and social anxiety often resulted in the avoidance of any activity or situation which might be perceived as stressful [ 26 , 28 , 30 , 33 ] thus limiting the possibilities of improving other aspects of quality of life.

Avoiding situations they had previously enjoyed because of fear of how they would appear or that the stress associated with those situations would mean deterioration in mental health : “ I ’ ve cut down on the sort of positions I get myself in … because of bad experiences in the past …. you just try less things with the fear that you ’ re going to get very ill again and go to hospital ” [ 30 - reduced control of behaviour and actions ]

Within the studies reviewed there tended to be an emphasis on the absence of ill-being rather than the presence of well-being. However, the positive themes identified that were important to people included an overall sense of well-being [ 31 , 33 , 39 ], feeling healthy [ 31 ], peaceful, calm and relaxed [ 26 , 30 , 33 , 39 ], stable [ 30 , 35 ], safe [ 33 , 39 ] and free from worry and demands [ 33 , 39 ]. Enjoyment or happiness were not identifiable themes within the reviewed studies but were associated primarily with the need for activities to be enjoyable [ 31 , 36 , 39 ].

Physical well-being

Physical health was not a strong theme within the reviewed studies. The compounding effects of physical health problems were indicated in two studies [ 26 , 36 ] and physical health was listed as the second most important aspect of quality of life by participants in another [ 37 ]. A healthy lifestyle was considered beneficial which included exercise, avoiding drugs and generally taking care of oneself [ 26 , 28 , 33 ].

Control, autonomy and choice

The importance of aspects of choice and control to quality of life was identified in eight studies [ 26 , 28 , 30 , 31 , 34 , 35 , 37 , 39 ] and was a main theme in three of these [ 30 , 34 , 35 ]. It was often discussed in the context of the availability of external resources which enabled choice and control, including medication and treatment, support, information and finances.

Symptom control

One of the most evident aspects of control was the management of the most distressing or pervading aspects of mental illness, particularly for those with psychosis related disorders [ 26 , 28 , 31 – 35 , 38 , 39 ]. Control was usually described as being achieved through medication [ 26 , 28 , 33 – 35 , 39 ]. Having control meant that individuals could move beyond ‘the all encompassing world of their illness’ [ 28 ] and instead attend to other important areas of their lives [ 28 , 31 ]. However, medication could also have a detrimental effect on quality of life through side effects, [ 26 , 28 , 30 , 34 ] feelings of dependency, [ 34 , 35 ] and fear of the consequences of not taking it [ 30 ]. It was therefore necessary to find the right medication to balance symptom management and side effects [ 26 , 28 , 32 , 33 , 35 , 39 ] as a means to a sense of well-being [ 28 ].

“ I ’ m on good medication , no symptoms , no side effects . I used to go through all the side - effects and symptoms and I don ’ t have anything now . . before that , I never really felt human . . I ’ m human , I ’ m flesh , you know like that in my mind and , it ’ s just a good feeling . I can ’ t explain how I was , used to be but since I ’ ve been on this medication I feel like a human … I don ’ t have any side - effects or anything or any problems . . I just take my pills and go . Like I feel like a human being … it ’ s just great ”; “ I think for me , apparently the most important one is just managing the illness … different medications , side - effects , knowing what they are … for me there ' s been limited discomfort ” [ 28 - Experience of illness - gaining control ]

The concept of control was particularly important for those with bipolar disorder, and was related to an inability to control or pre-empt the onset of mood episodes or their behaviour [ 35 , 38 ] and to a need for stability [ 35 ].

Being informed and having an understanding and insight about the illness was considered to be important [ 26 , 28 , 32 , 34 , 39 ]. To achieve this it was important to have an accurate diagnosis [ 32 , 33 ]. This meant that people could receive effective medication [ 33 ], knew what to expect for the future [ 28 , 33 , 39 ] and could develop strategies to manage their illness and deal with it better [ 28 , 34 ]. This was regarded as a first step on the way to recovery [ 32 ] and improving quality of life [ 39 ].

Independence/dependence

There was a complex relationship between independence, dependence, and support. Both support [ 26 , 28 , 31 , 33 , 34 , 37 , 39 , 40 ] and independence [ 32 , 33 , 37 ], particularly financial independence [ 37 ] were regarded as being important for quality of life. Support helped people manage their illness, access resources, and increase their self-confidence [ 33 ]. However, it could also result in feelings of dependency [ 26 , 37 ] with a resulting loss of a sense of control and self-esteem [ 37 ]. Hence there could be a dilemma between wanting help and support and at the same time resenting it [ 29 ]. On the other hand, choosing to be dependent could enhance power and control [ 39 ]. Personal autonomy, finding the optimum balance between support and independence, was therefore important to quality of life [ 25 , 33 ].

“ I think that ’ s a big part of what I recognize now as quality of life is feeling I can take care of myself without being heavily dependent on a long - term basis on either the welfare system or my Dad , unless I ’ m choosing to do so for a specific reason ” [ 33 - Independence : ‘ Or rather , not being independent , but not being dependent ]

Personal strength, determination, and self-sufficiency were also regarded as important [ 26 , 32 , 33 , 38 ]. It meant people were able to make use of available resources and develop self-help and personal coping strategies [ 26 , 28 , 32 , 37 ] which in turn promoted independence and a sense of control [ 28 , 32 , 37 ].

The concept of choice was most associated with the availability of financial resources [ 26 , 28 , 30 , 33 , 34 , 37 ] and with limited employment opportunities [ 26 , 28 , 30 , 34 , 38 , 40 ]. Having sufficient financial resources meant people could more readily have a healthy lifestyle [ 33 ], engage in activities that promoted well-being [ 26 , 28 , 30 , 33 ], facilitate the attainment of an optimum balance between dependency and independence [ 26 , 37 ], have a choice in their surroundings [ 26 , 28 , 34 ] and be able to plan for the future [ 30 ].

“ I ’ d have had more money if I ’ d stayed in the [ job ] … I ’ d have been able to board the animals and go on holiday . I would have been able to afford a bigger house maybe even have some help with some of my domestic tasks . yes . it ’ s limited my choices ”…“ Lack of control of your finances because what you get in benefits goes immediately what with all the things you have to pay out for . So you have to be very careful … That ’ s another sort of loss of control of part of your life which doesn ’ t make you feel very good about yourself ” [ 30 - Financial constraints on activities and plans ]

Also of value was being able to choose whether or not to take part in things (particularly social activities), [ 28 , 34 ] flexible work conditions, [ 38 ] when and with whom to disclose mental illness, [ 34 ] and choices associated with mental health services, workers and interventions [ 26 ].

Self-perception

A number of aspects of self associated with quality of life were identified: self-efficacy - having a belief and confidence in your own abilities; self-identity - having a perception of self and knowing who you are; self-esteem - having a sense of self-worth and self-respect; and self-stigma - internalizing the negative views of others. These were linked to a further theme of self-acceptance. These self concepts were closely associated and used interchangeably within the studies reviewed making them difficult to differentiate. Aspects of the self and self-perception were a major theme in three studies [ 28 , 32 , 35 ] and were present in some form within all of the other studies except one [ 29 ] which had an abstract analytical style and only had undertones suggesting low self- esteem/image.

Self-identity

Problems related to self-identity, having a sense of self and ‘knowing who you are’ appeared particularly to be related to bi-polar disorder, schizophrenia, and panic disorder. The studies described difficulties with having a coherent sense of self, identity, and personality [ 31 , 32 , 35 , 37 , 39 ].

‘ when you end up in the hospital with a full - blown mania and you think that you ’ re a king and you ’ re screaming at the top of your lungs … trying to eat your hospital bed and , and … you don ’ t know how to deal with it or , or how to be . You don ’ t know how to become yourself again . You don ’ t know what happened to you . It ’ s like your identity has been changed . It ’ s like somebody hands you a different driver ’ s license and you ’ re like , ‘ Well who is this person ?’ [ 37 - Identity ]

This loss of a sense of self necessitated a re-negotiation [ 31 ] or reclaiming [ 32 ] of self, based on self-acceptance, self-knowledge and understanding, [ 31 , 32 , 37 , 39 ] and relationships with reliable others [ 39 ]. Spirituality also had a role in achieving a sense of self [ 28 ].

Self-efficacy

This concept was expressed in the reviewed studies primarily as a lack of self-confidence, but also as feelings of inadequacy, uselessness, failure, an inability to cope, and helplessness [ 26 , 28 , 30 – 33 , 35 , 37 ]. Mental health problems were associated with a lack of confidence [ 26 , 31 , 32 , 35 , 38 ]. This lack of confidence limited day to day functioning and activities [ 26 ], and access to helpful resources [ 26 ] and affected choice and opportunities in employment [ 26 , 28 , 38 ] and relationships [ 26 , 28 ]. Bipolar disorder could be associated with an increase in self-confidence during manic episodes [ 38 ].

Self-esteem and self-acceptance

The theme of self- esteem includes the concepts of self-image, worth, value, and shame, and a view of the self as ‘defective’ [ 26 , 28 , 30 , 35 – 37 , 40 ]. It was primarily reported as a negative concept closely associated with loss of self-identity [ 37 ] and confidence [ 35 ]. Occupational activity was considered particularly important for self-esteem and status [ 28 , 36 , 38 ], as was the satisfaction gained from helping others [ 28 ]. However, the difficulties encountered in obtaining employment often resulted in a lowering of self-esteem [ 30 , 40 ]. A closely related concept to self-esteem was the positive concept of self-acceptance, [ 28 , 32 , 37 , 40 ] acceptance of the self as a person with an illness [ 32 ], or the belief that the illness did not represent everything that they were [ 28 , 37 ].

Self-stigma

The theme of ‘the self’ was closely related to the next theme of ‘belonging’, particularly through the concepts of ‘stigma’ and ‘feeling normal’ (see below). This inter-relationship is most evident in the concept of self-stigmatization, an internalisation of the negative views of others [ 28 ].

Individuals living with severe and persistent mental illnesses suffer from a form of stigma - self - stigma - perhaps the most powerful of all stigmas as it affects the inner sense of self in very profound ways . ‘ I stigmatize myself . I just have a very low self - image . I ' m kind of hard on myself for not conducting myself the way I should be … not being as productive as I could be . It ' s a reflection from general community ' s perceptions of what this illness is all about . […] [ 27 - Sense of Self : Self doubt , criticism - a barrier ]

The concept of belonging has been defined as the experience of integration and personal involvement in a system or environment at differing interpersonal levels. It can have two dimensions: ‘valued involvement’ - the experience of feeling valued, needed, accepted; and ‘fit’ - the person’s perception that his or her characteristics articulate with, or complement, the system or environment [ 20 ].

Of the primary research studies included in the review, one identified ‘connecting and belonging’ as being important to quality of life [ 34 ]. Others identified closely related main themes: being part of a social context [ 33 ], rejection and isolation from the community [ 35 ], a need for acceptance by others [ 40 ], social support [ 37 ], relationships [ 28 ], barriers placed on relationships [ 30 ], labeling and attitudes from others [ 30 ], stigma [ 28 , 30 , 37 , 40 ], alienation [ 40 ], detachment and isolation [ 30 ].

Relationships

Relationships were clearly central to the concept of ‘belonging’. These relationships included close connections with family and friends and also more casual relations with the local community, in the workplace, with service providers or with society at large. The complex nature of relationships and the positive and/or negative effects on quality of life were evident in all the primary studies.

The provision of support was a particularly strong theme, being a major theme in three studies [ 33 , 37 , 39 ]. Both practical [ 26 , 28 , 32 , 33 , 37 , 39 ] and emotional [ 26 , 28 , 32 – 34 , 37 , 39 ] care and support was identified as important to quality of life. This could be from family and friends [ 26 , 28 , 31 , 33 , 34 , 37 , 39 , 40 ] or peers and work colleagues [ 28 , 38 , 39 ]. Also important was the support received from professionals [ 26 , 28 , 32 , 33 , 35 , 39 ]. When families and professionals were unsupportive, quality of life declined [ 26 , 28 , 39 ].

“[.] if you have schizophrenia or you have mental illnesses a lot of support helps , helps you get back on track ”; “ The support that they give me means a lot to me . I wouldn ' t be where I am today without my family and my friends . They ' ve supported me in every little way that they could … like my Mom will drive me to doctor ' s appointments … just having my family in [ name ] living around me … I know that if , if I can ' t get somewhere myself I can always rely on family members to take me ” [ 28 - Relationships with supportive family members ]

Within the reviewed studies the most predominant benefits of good and reliable relationships were to feel accepted and understood [ 26 , 28 , 33 – 35 , 37 , 40 ], and having company, camaraderie and shared interests [ 28 – 31 , 33 , 34 , 36 ]. Good relationships also satisfied the need for love, care, and affection [ 26 , 28 , 33 , 34 , 37 ], facilitated the experience of joy, fun, and happiness [ 29 , 33 ], someone to talk to/share problems with [ 26 , 28 , 29 , 33 , 39 ], to feel needed/helpful to others [ 28 , 30 , 33 , 39 ], to have people in whom one had trust and confidence [ 26 , 33 , 39 ] and who provided motivation and encouragement [ 33 ].

Connecting with others and achieving a sense of belonging emerged as key to quality of life : “ You need friends to be happy … you need affection , you need to be loved by people , or else you would never get ahead in life . You will always be miserable and unhappy ” [ 34 - Connecting and Belonging ]

Given the importance of others, their well-being was also important to the quality of life of the study participants [ 33 ].

Whilst relationships which satisfied the need to belong were important, difficulties forming and maintaining these relationships were evident [ 26 , 28 – 30 , 34 – 36 , 40 ]. These difficulties included problems and tensions within supportive long term relationships [ 26 , 28 , 29 , 35 , 37 ].

“ My Dad considers me a problem son . My mother thinking it ' s going to be a bit of a problem … you ' re not treated with the same kind of respect that you were before … you ' re not given the same kind of credibility … it ' s not , not quite the same . You don ' t feel a part anymore . You ' re separated … You ' re not even part of your family … you don ' t feel part of the community ; I don ' t feel part of anything .” [ 28 - Negative reactions from family members - a barrier ]

Problems with relationships represented a complex multidirectional interaction between the person and society at varying interpersonal levels. This interaction involved the effect of the person’s illness when relating to others, other people’s subsequent reactions and attitudes to them, and the effect of those reactions and attitudes in further exacerbating symptoms of anxiety and depression and affecting the person’s perception of themself. Examples of the barriers experienced in connecting and relating to people included cognitive and thought disorders resulting in problems with concentration and attention [ 28 , 30 , 40 ] problems controlling behaviour [ 30 , 35 , 37 ] including acting out [ 30 , 37 ], irritability, volatile or inappropriate behaviour [ 37 ], grandiosity or self-inflation [ 37 ], and feelings of anxiety when talking to or being around people, including problems with trust and paranoia [ 26 , 28 , 38 ].

Stigma can be defined as ‘any condition, attribute, trait or behaviour that symbolically identifies the bearer as culturally unacceptable or inferior’ [ 41 ]. Stigmatisation was a major theme in four of the studies [ 28 , 30 , 37 , 40 ] and evident in three others [ 26 , 34 , 35 ]. The experience and perception of negative reactions on the part of family, friends, service providers, employers, and society at large was shown to have a detrimental effect on quality of life. Individuals felt that they were perceived as lesser human beings who were discriminated against and treated accordingly [ 28 ] and that they were feared, avoided, or not accepted, which in turn led to feelings of rejection, marginalization, or being written off [ 28 , 30 , 35 , 37 , 40 ]. As a result, disclosure of mental illness was problematic and often avoided, and this had consequences for employment and close relationships [ 28 , 34 , 37 ]. Stigma had a detrimental effect on most aspects of life, including relationships [ 26 , 28 , 30 , 37 ], employment and career [ 26 , 28 , 30 , 37 ], going out and pursuing leisure activities [ 26 , 30 ], obtaining services [ 28 ], and planning for the future [ 28 ]. Stigma was considered to be more predominant in bipolar than unipolar depression [ 37 ].

Feeling normal

A major barrier to achieving a sense of belonging was that informants were not perceived by others – and often did not perceive themselves – as “normal” [ 34 ]. Whilst feeling normal was something they held in high regard, instead they were aware of being perceived differently and consequently treated differently [ 40 ]. Feelings that they were different, and attempts to appear normal, do normal things, or be accepted as normal, formed a theme that permeated many of the studies reviewed [ 28 , 30 , 31 , 34 , 35 , 40 ], being a major theme of three [ 28 , 31 , 34 ]. This is consistent with the dimension of ‘fit’ within the concept of ‘belonging’ - the person’s perception that his or her characteristics articulate with, or complement, the system or environment [ 20 ].

… most informants expressed a need to both feel and be perceived as normal . For example , Informant 2 remarked , “ The thing is that I want to be a normal person and achieve something in my life ,” and Informant 25 stated , “ I ’ d like to be treated as equal in society .” Informants spoke about not feeling like other persons and implied that this set them apart . As Informant 16 stated , I don ’ t want to be mentally ill , I wanna be normal so I can study normally , go to school normally , get married , this and that . […]” [ 34 - Connecting and belonging : being normal ]

Loneliness/isolation/alienation

Feelings of isolation, loneliness, and particularly the concept of alienation can be regarded as the antithesis of a sense of belonging. Whilst highlighted as a main theme in one study only [ 40 ], these feelings were evident within the themes of relationships and stigmatization in all studies except one [ 31 ]. The symptoms of mental illness, the barriers these caused in the formation of relationships, the stigma and consequential effects on the self, together with feelings of being different and not accepted, resulted in a pervasive sense of loneliness and isolation. People chose isolation, or avoided relationships, as a way of protecting themselves against rejection and dealing with the fears of how they appeared and what others thought of them [ 28 , 30 , 40 ]. The effects of being consistently treated as undesirable or different became internalised and further influenced their sense of self [ 28 ]. Isolation was further compounded by the feelings that they were the only person suffering in this way [ 28 ]. Hence, isolation was not just feeling as though they did not have any friends but became a painful feeling of despair that affected all aspects of life.

“ I think one of the things about schizophrenia , I don ' t know whether it ' s schizophrenia or whether it ' s , it happens in other mental illnesses too , is this terrible , terrible kind of inner isolation feeling , like you ' re the only person … who is going through what you are going through and you , and you ' re completely alone […] it ' s just a terrible , painful sense of utter loneliness and isolation .” [ 28 - The Tyranny of Psychosis - a barrier ]

For quality of life, people wanted a reciprocal relationship with others [ 33 , 37 ] which involved understanding and acceptance [ 26 , 28 , 33 – 35 , 37 , 40 ]. This could be achieved through ‘supportive own’, those who share their illness and experiences [ 28 , 33 , 34 , 37 , 39 ], or through belonging to a religious community [ 26 ]. However, it was also possible to have a sense of belonging to a social network that was ultimately not beneficial to quality of life [ 37 ], and difficulties disentangling ‘real’ spiritual experience from hyper-religiosity when hypo/manic could make belonging to a religious community problematic [ 37 ].

By ‘doing’, a person achieves a sense of self, mastery, and successfully participates in the external world [ 21 , 42 ]. The importance of activity in some form to quality of life was expressed in all of the studies except one (which examined panic disorder from a nursing perspective) [ 32 ]. There was a difference in emphasis between studies: some focused specifically on the benefit of employment [ 30 , 35 , 38 , 40 ] and others on activity or occupation in its broader sense, including both employment and leisure activity [ 26 , 28 , 31 , 33 , 34 , 36 , 39 ]. Whatever the type of activity, it was stressed that it should be meaningful or fulfilling [ 26 , 28 , 31 , 33 , 34 , 36 , 39 ], enjoyable, [ 31 , 36 , 39 ] and suited to need and capabilities [ 26 , 31 , 33 ].

The benefit of activity is that it can provide the means for many of the factors important to quality of life discussed above. It is through activity that the opportunity arises to interact with others and hence develop a sense of belonging [ 34 ]. Activity can also improve mood [ 26 , 28 , 31 , 33 , 34 ], increase energy and/or motivation [ 28 , 34 , 39 ], relieve stress [ 26 ] and boredom [ 34 , 36 ] and provide a distraction from problems [ 26 , 33 , 34 ]. It also helps self-esteem and self-confidence, engenders a positive self-identity, [ 26 , 28 , 30 , 34 , 36 , 38 , 40 ] and enables people to take control of their lives [ 34 ].

One further factor is how activity provides order, routine, and structure [ 30 , 33 , 34 , 37 , 39 ]. Routine and structure can be achieved through employment [ 30 , 37 ], childcare [ 30 , 37 ] or activity in general, be it work or leisure [ 33 , 34 , 37 , 39 ]. However, one study highlighted how too much structure could be problematic and that what was important was flexibility and choice [ 37 ]. Having a physiological routine - particularly regular sleep, meals, and exercise - was considered important for general well-being [ 37 , 39 ].

Positive outcomes that could be derived from the strategy of using activity to structure and fill time included increased motivation , diversion from present problems , and avoidance of negative moods : [.] “ The actual work , whatever it is , is good for the mind and soul … you forget yourself . You forget your own problems when you are working ” [.] “ In the morning I have to do something . Some job or something I should do . Otherwise , I become bored and then become depressed because I don ’ t have anything to do …. when I have nothing to do I become sad and unhappy and become very depressed , and I don ’ t know what to do . It is very difficult .” [ 34 - Managing time ].

Whilst activity was almost universally considered to be beneficial, taking part could be difficult if the activity was too demanding and not suited to needs [ 33 , 38 , 39 ]. The symptoms of mental illness could make difficult even the most rudimentary of activities, such as self-care, cooking and shopping, [ 26 , 36 ] and taking up employment was especially problematic [ 31 ]. Even potentially enjoyable leisure activities were avoided because of concern regarding other people’s reactions, [ 26 , 30 , 36 ] problems relating to people, [ 36 ] and the associated fear and stress resulting in a deterioration in health [ 26 , 30 ]. Lack of money also put a restriction on enjoyable pastimes [ 30 ].

For those who were employed, interpersonal relationships at work were particularly affected due to social withdrawal and irritability, or interfering, inappropriate, or volatile behaviour during hypomania, although work productivity could increase during hypomania [ 38 ].

Hope and hopelessness

Integral to the concept of hope is having dreams, goals and a positive view of the future. The importance to quality of life of having dreams and goals or personal achievement was evident in six of the studies [ 28 , 29 , 31 , 33 , 34 , 37 ], the importance of activity and/or life in general being fulfilling and having some meaning and purpose was also evident [ 28 , 33 , 34 , 37 , 40 ]. Both having dreams and goals and having meaning and purpose in life were necessary to instigate change, make plans, and to move forward. Again, the difficulty of achieving this was stressed [ 28 , 31 , 34 ]. Losses experienced in the past affected the view of the future with a perception of reduced opportunities and choices [ 35 ] and diminished hopes and dreams, [ 29 , 31 ] particularly in the fields of employment [ 30 , 38 , 40 ] and relationships [ 29 , 40 ]. Loss and the effect of past experiences was a theme in seven of the studies,[ 26 , 28 – 31 , 35 , 38 , 40 ] and a major theme in three of these [ 29 , 31 , 35 ]. These losses included the loss of life roles generally, and more specifically the loss of work and career opportunities, relationship and the parental role, skills and ability, time, financial losses, and, ultimately, the loss of a sense of self and identity. Losses which had occurred in the past were perceived as a burden [ 28 ] with a pervasive sense of ‘something missing’ [ 40 ] which had long-lasting effects and made life a constant struggle [ 29 , 35 , 40 ]. Participants compared their own lives negatively with those of others [ 29 , 35 , 40 ], or with their own lives before illness struck [ 29 , 31 ], and all this brought about feelings of failure, of being cheated, and a sense of unfairness [ 35 , 38 , 40 ].

Past losses, including the loss of meaning and purpose in life, a sense of helplessness and inability to cope, all brought about a sense of hopelessness, necessitating a renegotiation and a lowering of aspirations and priorities [ 28 , 29 , 31 ].

The concepts of ‘hope’ and ‘hopelessness’ permeated the review studies [ 29 , 30 , 35 , 39 , 40 ] and formed a major theme for two [ 29 , 35 ]. Hopelessness was an expression of the view that life would never change for the better, and brought about a pervasive feeling of distress [ 40 ]. Conversely, hope provided a catalyst for change and a better life [ 39 ].

" Well , my whole life feels problematic , I feel as if I ' m not going anywhere … I know it sounds negative and I ' m not really negative like this all the time , but you know , I find it hard , projecting myself into the future , and leading a happy life . I don ' t think my life is very happy at the moment , it ' s not very fulfilling . I haven ' t got any real struggles at the moment , but it could be better . I don ' t know if it ' s because of the illness or the sort of person I am …"; " I don ' t have hope that I ' ll ever have a nice boyfriend , I don ' t have any hope that I ' ll get married , I don ' t have any hope that I ' ll work a full week — week after week after week . I don ' t really have hope for stability …" [ 35 - Bipolar Patients ' View of Their Future ]

We identified six major themes associated with quality of life for those with mental health problems: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; and hope and hopelessness.

Measuring quality of life for people with mental health problems is of interest currently because of concerns about the emphasis of mental health services on reducing symptoms. Yet our review identified the importance of distress and symptom control from the perspective of people with mental health problems. Amongst academic circles quality of life has confusingly come to be known as anything which is not clinical [ 14 ]. However, this review of the qualitative literature indicates that, when those with severe mental health problems are interviewed, the distress related to symptoms is integral to their quality of life, and in some instances seeing beyond this distress is difficult.

One of the strongest themes revealed by the review was a sense of belonging achieved principally by good quality relationships and lack of stigma. It has been stated that people are fundamentally motivated by a need to belong [ 43 ], and that belonging is the missing conceptual link in understanding mental health and mental illness [ 44 ]. Our review also indicates that negative social relationships are detrimental to quality of life. This is supported by research that shows that, whilst a large social network and satisfaction with social relations are associated with a better quality of life [ 45 ], negative social interactions and stigma are related to a worse quality of life [ 46 ]. Social exchange theory emphasizes that social interaction entails both rewards and costs, and that negative social outcomes can have a greater impact on well-being than positive outcomes [ 47 ]. There is also evidence that loneliness is caused more by a lack of intimate connections than by a lack of social contact [ 48 ]. Hence, the important factor is the sense of belonging, rather than social contact. So, whilst there is a strong argument that those people who experience supportive, caring, loving relationships and have a sense of belonging have a better quality of life, it is less clear which is the more detrimental - to experience and risk the negative impact of uncaring and disrespectful relationships, rejection and stigma, or to protect oneself through self-isolation.

As good and poor relationships can have a positive or negative impact, so activity can both help and hinder quality of life. For some, the severity of symptoms can mean that basic self-care and day to day functioning are difficult. Activity beyond perceived capabilities can also result in feelings of anxiety, which in turn can lead to deterioration in other mental health symptoms such as hearing voices and paranoia. This results in avoidance of any potentially stressful situations. This finding is supported by the findings of research into the occupational activity of those with severe mental illness which indicated that, though employment was valued, people made choices constrained by fear of relapse, and entered, avoided, and shaped their social and occupational activity to remain well [ 49 ] It was found that doing too much could exacerbate symptoms, yet doing too little could also cause illness, and therefore people with severe mental illness sought out daily occupations with structure, flexibility, and easily met demands over which they had control [ 49 ]. Therefore, to achieve well-being and quality of life, people need to find a balance and be enabled towards what they are best fitted [ 50 ].

Although avoidance of social and occupational activity may reduce anxiety and the occurrence of other related symptoms, at the same time it can compromise other aspects of quality of life. The consequent reduction in choice and opportunity has a detrimental effect on self-esteem and confidence. However, self-worth is gained through positive social feedback and successfully engaging in activity. Lack of self-esteem has also been shown to increase the risk of psychiatric disorders, the development of delusions, and the maintenance of psychotic symptoms [ 51 ]. The perception of self is therefore both a cause and a consequence of mental health, and can therefore be regarded as being pivotal to quality of life.

In relation to the finding of the importance of hope and hopelessness to quality of life, parallels can be seen between the results of this review and the concept of demoralization [ 24 , 52 ] whereby a persistent inability to cope with internally or externally induced stresses result in feelings of helplessness, incompetence, and loss of mastery and control leading to diminished self-esteem, hopelessness and demoralization which in turn adds to the distress of symptoms and further reduces a person’s capacity to cope. The demoralized person clings to a small number of habitual activities, avoids novelty and challenge, and fears making long term plans [ 24 , 52 ]. This feeling of demoralization further impacts upon ill-being and, if untreated, leads to chronic distress and possible suicide [ 24 , 53 ].

Strengths and limitations of review

The primary studies included those with severe mental health problems only, with a majority having schizophrenia or psychotic disorders. Where there was a mixed population, studies rarely indicated any differences between people with different diagnoses. The findings may therefore have biases towards those with psychotic rather than affective disorders. The evidence base could therefore be improved by undertaking research with a wider range of mental health conditions.

Findings from the primary studies could be negatively or positively oriented depending upon the approach: research that asked how the illness had affected quality of life led to negative concepts (e.g. fear/stigma/isolation) whereas research that asked what would improve participants’ lives resulted in positive concepts (e.g. love, support, understanding). Some research papers addressed both, and identified factors that both helped and hindered quality of life. There was a greater emphasis on negative than positive concepts in the primary studies, and this has influenced the analysis and subsequent findings.

The range of themes included in the reviewed articles was extensive, in this review we have focused on those that are most closely associated with ‘health related’ quality of life.

Setting boundaries

There were difficulties setting boundaries around themes because of the strong inter-relationship of the different domains which make up quality of life. To avoid repetition, sub-themes have been placed in the main theme with which they were considered to be most strongly associated, but aspects of these themes could be placed in other themes. For example, ‘feeling normal’ has been included under the main theme of ‘belonging’ but could also be regarded as an element of ‘ill-being/ well-being’ and ‘the self’. Likewise, symptom management through medication is also an aspect of ‘well-being’ but probably due to the emphasis on psychosis related disorders in the reviewed studies it was the control aspect of medication use that predominated.

Complexities also arose when setting boundaries around the concept of quality of life. It was evident that there was a considerable overlap in findings with studies examining ‘recovery’, ‘lived/subjective experience’, ‘psychosocial issues’, ‘health needs’, and ‘strategies for living’. After much discussion and deliberation within the team, these studies were excluded from the review. Since completing our analysis a systematic review of the concept of ‘personal recovery’ has been undertaken [ 54 ] a concept previously defined as ‘a way of living a satisfying, hopeful, and contributing life even with limitations caused by illness’ [ 55 ]. Interestingly, they identified five recovery processes comprising ‘connectedness’, ‘hope and optimism about the future’, ‘identity’, ‘meaning in life’ and ‘empowerment’ which are very similar to our own final themes. They do not include ‘well-being’ and this may be due to the rejection of an emphasis on symptoms within the recovery movement. This suggests that the concepts of ‘recovery’ and ‘quality of life’ are very closely related. This is important to understand as the concept of ‘recovery’ is gaining prominence as a guiding principle for mental health services [ 56 ].

Implications for measuring quality of life

The findings of this review indicate six major themes associated with quality of life for those with mental health problems: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; and hope and hopelessness. This provides important evidence for critically examining the content of measures currently being used in mental health and particularly the generic measures of health related quality of life like EQ-5D that are being used to inform resource allocation decisions and the monitoring of outcomes. Concerns with the generic measures have been that they are designed by experts with little or no input from people with mental health problems and their coverage is too limited. The EQ-5D, for example, has the following five dimensions of health: mobility, self-care, usual activities, pain and discomfort, and depression and anxiety. Respondents are asked to report their level of problems (no problems, some/moderate problems or severe/extreme problem) on each dimension to provide a position on the EQ-5D health state classification. A key concern raised about this measure is the focus on physical health rather than mental health problems [ 5 , 6 ]. These can be seen as a combination of physical functioning (mobility, self-care), well-being (depression and anxiety), social functioning (that may be included in usual activities) and physical symptoms (pain and discomfort). There is only a modest degree of fit between these EQ-5D dimensions and the six themes within our review. Anxiety and depression may reflect, however crudely, ill-being (though not well-being). Usual activity is again rather crude, but arguably covers aspects of activity. However it makes no allowance for the finding that some activity can have a negative as well as a positive impact. This leaves the themes of control, autonomy and choice; self-perception; belonging; and hope/hopelessness which are not addressed within the EQ-5D.

The findings of this review can help to provide useful evidence for examining the content validity of different measures. This evidence can be used alongside quantitative psychometric evidence on the performance of measures in different groups. In the case of EQ-5D, for example, recent reviews have found supporting evidence for construct validity and responsiveness in people with depression and personality disorder, but reflected the concerns about their appropriateness for those with anxiety, bipolar disorder and schizophrenia [ 57 – 59 ].

A good quality of life is characterized by feelings of well-being, control and autonomy, a positive self-perception, a sense of belonging, participation in enjoyable and meaningful activity, and a positive view of the future. In contrast, a poor quality of life is associated with feelings of distress, lack of control over symptoms and life in general, a negative perception of self, stigmatization and rejection, diminished activity and difficulties with day to day functioning, and a negative outlook. These life domains interact in a complex and reciprocal way. Generic measures of quality of life may fail to address this complexity and the rich and broad range of domains important to people with mental health problems.

Gladis MM, Gosch EA, Dishuk NM, Crits-Christoph P: Quality of life: Expanding the scope of clinical significance. J Consult Clin Psychol 1999, 67: 320–331.

Article   CAS   PubMed   Google Scholar  

British Psychological Society: Psychological health and well-being . Leicester: A new ethos for mental health; 2009.

Google Scholar  

National Institute for Health and Clinical Excellence (NICE): Guide to the methods of technology appraisal . London: NICE; 2008.

Gilbody SM, House AO, Sheldon T: Routine administration of Health Related Quality of Life (HRQoL) and needs assessment instruments to improve psychological outcome – a systematic review. Psychol Med 2002, 32: 1345–1356.

Brazier J: Is the EQ-5D fit for purpose in mental health. Br J Psychiatry 2010, 197: 348–349.

Article   PubMed   Google Scholar  

Saarni SI, Viertiö D, Perälä J, Koskinen S, Lönnqvist J, Suvisaari J: Quality of life of people with schizophrenia, bipolar disorder and other psychotic disorders. Br J Psychiatry 2010, 197: 386–394.

Barry MM, Zissi A: Quality of life as an outcome measure in evaluating mental health services: a review of the empirical evidence. Soc Psychiatry Psychiatr Epidemiol 1997, 32: 38–47.

Brown J, Bowling A, Flynn T: Models of quality of life: a taxonomy and systematic review of the literature . FORUM Project: University of Sheffield; 2004.

Phillips D: Quality of Life: Concept, Policy and Practice . New York: Routledge; 2006.

Book   Google Scholar  

McMahan EA, Estes D: Measuring lay conceptions of well-being: the beliefs about well-being scale. J Happiness Stud 2011, 12: 267–287.

Article   Google Scholar  

Diener E, Emmons RA, Larsen RJ, Griffin S: The satisfaction with life scale. J Pers Assess 1985, 49: 71–75.

Johansson S: Conceptualizing and measuring quality of life for national policy. Soc Indic Res 2001, 58: 13–32.

Van Nieuwenhuizen C, Schene AH, Boevink WA, Wolf JRLM: Measuring the quality of life of clients with severe mental illness: a review of instruments. Psychiatr Rehab J 2011, 4: 33–42.

Bowling A: Measuring Health. A review of quality of life measurement scales . Buckingham: Open University Press; 1997.

Hunt SM: The problem of quality of life. Qual Life Res 1997, 6(3):205–212.

Barnett-Page E, Thomas J: Methods for the synthesis of qualitative research: a critical review. BMC Med Res Methodol 2009. [ http://www.biomedcentral.com/1471–2288/9/59 ]

Paterson BL, Thorne SE, Canam C, Jillings C: Meta-Study of Qualitative Health Research: A Practical Guide to Meta-Analysis and Meta-Synthesis . Thousand Oaks, CA: Sage; 2001.

Ritchie J, Spencer L: Qualitative data analysis for applied policy research. In Analysing Qualitative Data . Edited by: Bryman A, Burgess RG. London: Routledge; 1994:173–194.

Chapter   Google Scholar  

Sandelowski M, Docherty S, Emden C: Qualitative meta-synthesis: Issues and techniques. Res Nurs Health 1997, 20(4):365–371.

Hagerty BMK, Lynch-Sauer J, Patusky KL, Bouwsema M, Collier P: Sense of belonging: A vital mental health concept. Arc Psychiatr Nurs 1992, 6(3):172–17.

Article   CAS   Google Scholar  

Fidler GS, Fidler JW: Doing and becoming: purposeful action and self actualization. Am J Occup Ther 1978, 32: 5,305–310.

de Leval N: The three time dimensions synoptic scale (3TSS) for depressive population. Quality of Life News Letter 2001, 26: 15–16.

Diener E: Guidelines for national indicators of subjective well-being and Ill-being. Appl Res Qual Life 2006, 1: 151–157.

Clarke DM, Kissane DW: Demoralization: its phenomenology and importance. Aust N Zeal J Psychiatry 2002, 36: 733–742.

Cook S, Chambers E: What helps and hinders people with psychotic conditions doing what they want in their daily lives. Br J Occup Ther 2009, 72(6):238–248.

Chambers E, Cook S Internal report. University of Sheffield. What helps and hinders people with psychotic conditions doing what they want in their daily lives: The views of people with psychotic conditions 2006.

Corring DJ, Cook JV: Use of qualitative methods to explore the quality-of-life construct from a consumer perspective. Psychiatr Serv 2007, 58: 240–244.

Corring DJ PhD Dissertation. In “Being normal”: Quality of life domains for persons with a mental illness . University of Western Ontario: Department of Rehabilitation Sciences; 2005.

Fisher MA, Mitchell GJ: Patients’ views of quality of life: transforming the knowledge base of nursing. [see comment]. Clin Nurse Spec 1998, 12: 99–105.

Gee L, Pearce E, Jackson M: Quality of life in schizophrenia: a grounded theory approach. Health Qual Life Outcomes 2003, 1: 31.

Article   PubMed Central   PubMed   Google Scholar  

Gould A, DeSouza S, Rebeiro-Gruhl KL: And then I lost that life: a shared narrative of four young men with schizophrenia. Br J Occ Ther 2005, 68: 467–473.

Hamer HP, McCallin AM, Garrett N: Searching for self: the layers and labels of panic disorder: a New Zealand study. Nurs Health Sci 2009, 11: 51–57.

Hedberg L, Skärsäter I: The importance of health for persons with psychiatric disabilities. J Psychiatr Ment Health Nurs 2009, 16: 455–461.

Laliberte-Rudman D, Yu B, Scott E, Pajouhandeh P: Exploration of the perspectives of persons with schizophrenia regarding quality of life. Am J Occup Ther 2000, 54: 137–147.

Lim L, Nathan P, O'Brien-Malone A, Williams S, Lim L, Nathan P, et al .: A qualitative approach to identifying psychosocial issues faced by bipolar patients. J Nerv Ment Dis 2004, 192: 810–817.

Mayers CA: Quality of life: priorities for people with enduring mental health problems. Br J Occ Ther 2000, 63(12):591–6.

Michalak EE, Yatham LN, Kolesar S, Lam RW, Michalak EE, Yatham LN, et al .: Bipolar disorder and quality of life: a patient-centered perspective. Qual Life Res 2006, 15: 25–37.

Michalak EE, Yatham LN, Maxwell V, Hale S, Lam RW, Michalak EE, et al .: The impact of bipolar disorder upon work functioning: a qualitative analysis. Bipolar Disord 2007, 9: 126–143.

Rusner M, Carlsson G, Brunt D, Nyström M: A dependence that empowers - the meaning of the conditions that enable a good life with bipolar disorder. Int J Qual Stud Health Wellbeing 2010, 5(1):4653. 10.3402/qhw.v5i1.4653

Vallenga BA, Christenson J: Persistent and severely mentally ill clients' perceptions of their mental illness. Issues Ment Health Nurs 1994, 15: 359–371.

Goffman E: Stigma: Notes on the Management of Spoiled Identity . London: Penguin; 1963.

Erikson EH: Childhood and Society . New York: W W Norton; 1963.

Baumeister R, Leary M: The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychol Bull 1995, 117(3):497–529.

Anant SS: The need to belong. Can Ment Health 1966, 14: 21–21.

Hansson L: Determinants of quality of life in people with severe mental illness. Acta Psychiatr Scand Suppl 2006, 113(429):46–50.

El-Badri S, Mellsop G: Stigma and quality of life as experienced by people with mental illness. Australas Psychiatry 2007, 15(3):195–200.

Newsom JT, Rook KS, Nishishiba M, Sorkin DH, Mahan TL: Understanding the relative importance of positive and negative social exchanges: examining specific domains and appraisals. J Gerontol B Psychol Sci Soc Sci 2005, 60(6):304–312.

Reis HT: The role of intimacy in interpersonal relations. J Soc Clin Psychol 1990, 9: 15–30.

Nagle S, Cook J, Polatajko HJ: I’m doing as much as i can: occupational choices of persons with a severe and persistent mental illness. J Occup Sci 2002, 9(2):72–81.

Wilcock AA: Reflections on doing being and becoming. Can J Occup Ther 1998, 65(5):279–85.

Romm KL, Rossberg JI, Hansen CF, Haug E, Andreassen OA: Melle: Self-esteem is associated with premorbid adjustment and positive psychotic symptoms in early psychosis. BMC Psychiatry 2011, 11: 136.

Frank JD: Psychotherapy. the restoration of morale. Am J Psychiat 1974, 131: 271–274.

Strada AE: Grief, demoralization and depression: diagnostic challenges and treatment modalities. Prim Psychiatry 2009, 16(5):49–55.

Leamy M, Bird V, Le Boutillier C, Williams J, Slade M: A conceptual framework for personal recovery in mental health: systematic review and narrative synthesis. Brit J Psychiat 2011, 199: 445–452.

Anthony WA: Recovery from mental illness: the guiding vision of the mental health service system in the 1990s. Psychosoc Rehabil J 1993, 16(4):11–23.

Care Services Improvement Partnership; Royal College of Psychiatrists; Social Care Institute for Excellence: A common purpose: Recovery in future mental health services . Leeds: Care Services Improvement Partnership; 2007.

Brazier J, Connell J, Papaioannou D, Parry G, O'Cathain A, Mukuria C, Mulhern B, Parry G: Validating generic preference-based measures of health in mental health populations and estimating mapping functions for widely used measures. Health Technol Asses forthcoming

Papaioannou D, Brazier JE, Parry G: How to measure quality of life for cost effectiveness analyses in personality disorders? A systematic review. J Pers Disord in press

Papaioannou D, Brazier J, Parry G: How valid and responsive are generic health status measures, such as the EQ-5D and SF-36, in schizophrenia? A systematic review. Value Health 2011, 14(6):907–920.

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Acknowledgements

Funding sources for this research were the Medical Research Council Ref No. G0801394. We would also like to thank Prof. Michael Barkham, Prof. Glenys Parry and Eleni Chambers for their helpful and wise comments on draft versions of the paper.

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JC, primary researcher, screened titles and abstracts, reviewed included papers, conducted additional searches, developed the conceptual framework, analysed the data, and drafted the manuscript. JB, principal investigator, reviewed included papers, reviewed and advised on the conceptual framework. AOC, co-investigator, reviewed included papers, reviewed and advised on the conceptual framework. MLJ, co-investigator, screened titles and abstracts, reviewed included papers, and developed the conceptual framework. SP developed the search strategy, conducted the electronic database searches and drafted the related section of the manuscript. All authors reviewed and revised drafts and approved the final manuscript.

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Connell, J., Brazier, J., O’Cathain, A. et al. Quality of life of people with mental health problems: a synthesis of qualitative research. Health Qual Life Outcomes 10 , 138 (2012). https://doi.org/10.1186/1477-7525-10-138

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E-collection: Public Mental Health

Mental health and mental disorder in the european journal of public health.

Jutta Lindert President of the EUPHA Section on Public Mental Health 

Mental Health and mental disorder including suicide and suicidal behavior have been a neglected issue in Public Health for many years. Yet, mental disorders rank among the disorders which contribute enormous suffering for affected persons and their families, high burden of disability adjusted life years (DALYS), and high economic and societal direct and indirect costs. People with severe mental illness have increased risk for premature mortality and thus a shorter life expectancy ( Ösby U ).

The fact that mental disorders are the leading causes of the burden of disease make research in mental disorders and policies to promote mental health a Public Health priority, worldwide. More knowledge on the scope and extent of mental health and mental disorders, the relationship between mental health and mental disorder and the determinants of mental health and mental disorder is highly needed.  As examples of determinants a variety of determinants of mental disorders (e.g., economic, social factors, relationship factors, factors related to the physical environment) have been identified. Economic factors such as relative deprivation ( Gunnarsdóttir H ), social factors such as social adversities ( Rajaleid K ) and working related factors, relationship factors such as violence and victimization, and factors related to the physical environment have been identified.

Social adversities over the life course have not only short term but also long-term effects on mental health and social adversities in adolescence predict trajectories of internalized mental ill-health symptoms. Working related factors related to mental disorders are employment status ( Katikireddi S ), working conditions ( Kouvonen A ), and employment history ( von Bonsdorff MB ). In the study from the Netherlands by von Bonsdorf discontinuous employment during mid-career was associated with poorer self-reported physical and mental functioning around the age of retirement. Herewith the long term effects of exposures to social adversities such as financial stress and interrupted employment histories were highlighted. Many studies have investigated how unemployment history influences health, less attention has been paid to the reverse causal direction; how health may influence the risk of employment history and the risk of becoming unemployed. However, an interrupted employment history might be both an indicator for mental disorders and a determinant of mental disorders as people with poor mental and physical health are at increased risk of job loss. ( Kaspersen SL )

Additionally, and importantly, studies investigated relationship factors and mental disorders and mental health. Relationships might include relationships between individuals, groups and communities. A study by Palm et al. showed the significant impact violence and abuse has on women`s mental health. In this study young women visiting youth health centers in Sweden answered a questionnaire constructed from standardized instruments addressing violence victimization (emotional, physical, sexual and family violence), socio-demographics, substance use and physical and mental health ( Palm A ). Yet the relationships between violence and health need further investigation, might it be the impact of war on mental health ( Lindert J ) or the impact of family relationships, physical abuse and early adversities, gun violence, domestic violence, bullying and cyber-bullying? 

Besides economic, social or relationship factors environment related factors may have a significant contribution for mental health, such as exposure to asbestos. The results obtained in the Asbestos-Related Diseases Cohort (ARDCO) study confirm that environment related factors need to be investigated and linked to the field of Public Mental Health. ( Mounchetrou Njoya I ).

Yet the relationships of mental health and mental disorders need further investigation. However, we need more longitudinal population based studies on trajectories of mental disorders, determinants and mechanisms of mental health and mental disorders and how positive trajectories of mental health can be supported. If we want to promote mental health, reduce mental disorders and improve Public Mental Health, we need to produce studies with data from more countries ( Bøe T ). Studies published in the EJPH on mental disorder and mental health trajectories are good examples and will allow us not only to better understand variations between and within countries but mental disorders' trajectories and develop effective and cost-effective interventions.

Financial difficulties in childhood and adult depression in Europe Tormod Bøe, Mirza Balaj, Terje A. Eikemo, Courtney L. McNamara, Erling F. Solheim Eur J Public Health (2017) 27 (suppl_1): 96-101.

Suicide mortality in Belgium at the beginning of the 21st century: differences according to migrant background Mariska Bauwelinck, Patrick Deboosere, Didier Willaert, Hadewijch Vandenheede Eur J Public Health (2017) 27 (1): 111-111

Relative deprivation in the Nordic countries-child mental health problems in relation to parental financial stress  Hrafnhildur Gunnarsdóttir, Gunnel Hensing, Lene Povlsen, Max Petzold Eur J Public Health (2016) 26 (2): 277-282

Health and unemployment: 14 years of follow-up on job loss in the Norwegian HUNT Study Silje L Kaspersen, Kristine Pape, Gunnhild Å. Vie, Solveig O. Ose, Steinar Krokstad, David Gunnell, Johan H. Bjørngaard  Eur J Public Health (2016) 26 (2): 312-317. 

Employment status and income as potential mediators of educational inequalities in population mental health Srinivasa Vittal Katikireddi, Claire L. Niedzwiedz CL, Frank Popham Eur J Public Health (2016) 26 (5): 814-816

Changes in psychosocial and physical working conditions and common mental disorders  Anne Kouvonen, Minna Mänty, Tea Lallukka, Eero Lahelma, Ossi Rahkonen Eur J Public Health (2016). pii: ckw019. [Epub ahead of print])

Refugees mental health-A public mental health challenge Jutta Lindert, Mauro G. Carta, Ingo Schäfer, Richard F. Mollica Eur J Public Health (2016) 26 (3): 374-375

Anxious and depressive symptoms in the French Asbestos Related Diseases Cohort: risk factors and self-perception of risk Ibrahim Mounchetrou Njoya, Christophe Paris, Jerome Dinet, Amadine Luc, Joelle Lighezzolo-Alnot, Jean-Claude Pairon, Isabelle Thaon Eur J Public Health (2017) 27 (2): 359-366

Mortality trends in cardiovascular causes in schizophrenia, bipolar and unipolar mood disorder in Sweden 1987-2010 Urban Ösby, Jeanette Westman, Jonas Hällgren, Mika Gissler Eur J Public Health (2016) 26 (5): 867-871

Violence victimisation-a watershed for young women's mental and physical health Anna Palm, Ingela Danielsson, Alkistis Skalkidou, Niclas Olofsson, Ulf Högberg  Eur J Public Health (2016) 26 (5): 861-867

Social adversities in adolescence predict unfavourable trajectories of internalized mental health symptoms until middle age: results from the Northern Swedish Cohort  Kristiina Rajaleid, Tapio Nummi, Hugo Westerlund, Pekka Virtanen, Per E. Gustafsson, Anne Hammarström Eur J Public Health (2016) 26 (1): 23-29

Mid-career work patterns and physical and mental functioning at age 60-64: evidence from the 1946 British birth cohort Mikaela B. von Bonsdorff, Diana Kuh, Monika E. von Bonsdorff, Rachel Cooper Eur J Public Health (2016) 26 (3): 486-491

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• Research article
• Open access
• Published: 24 October 2019

A scoping review of the literature on the current mental health status of physicians and physicians-in-training in North America

• Mara Mihailescu   ORCID: orcid.org/0000-0001-6878-1024 1 &
• Elena Neiterman 2  

BMC Public Health volume  19 , Article number:  1363 ( 2019 ) Cite this article

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This scoping review summarizes the existing literature regarding the mental health of physicians and physicians-in-training and explores what types of mental health concerns are discussed in the literature, what is their prevalence among physicians, what are the causes of mental health concerns in physicians, what effects mental health concerns have on physicians and their patients, what interventions can be used to address them, and what are the barriers to seeking and providing care for physicians. This review aims to improve the understanding of physicians’ mental health, identify gaps in research, and propose evidence-based solutions.

A scoping review of the literature was conducted using Arksey and O’Malley’s framework, which examined peer-reviewed articles published in English during 2008–2018 with a focus on North America. Data were summarized quantitatively and thematically.

A total of 91 articles meeting eligibility criteria were reviewed. Most of the literature was specific to burnout ( n  = 69), followed by depression and suicidal ideation ( n  = 28), psychological harm and distress ( n  = 9), wellbeing and wellness ( n  = 8), and general mental health ( n  = 3). The literature had a strong focus on interventions, but had less to say about barriers for seeking help and the effects of mental health concerns among physicians on patient care.

Conclusions

More research is needed to examine a broader variety of mental health concerns in physicians and to explore barriers to seeking care. The implication of poor physician mental health on patients should also be examined more closely. Finally, the reviewed literature lacks intersectional and longitudinal studies, as well as evaluations of interventions offered to improve mental wellbeing of physicians.

Peer Review reports

The World Health Organization (WHO) defines mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community.” [ 41 ] One in four people worldwide are affected by mental health concerns [ 40 ]. Physicians are particularly vulnerable to experiencing mental illness due to the nature of their work, which is often stressful and characterized by shift work, irregular work hours, and a high pressure environment [ 1 , 21 , 31 ]. In North America, many physicians work in private practices with no access to formal institutional supports, which can result in higher instances of social isolation [ 13 , 27 ]. The literature on physicians’ mental health is growing, partly due to general concerns about mental wellbeing of health care workers and partly due to recognition that health care workers globally are dissatisfied with their work, which results in burnout and attrition from the workforce [ 31 , 34 ]. As a consequence, more efforts have been made globally to improve physicians’ mental health and wellness, which is known as “The Quadruple Aim.” [ 34 ] While the literature on mental health is flourishing, however, it has not been systematically summarized. This makes it challenging to identify what is being done to improve physicians’ wellbeing and which solutions are particularly promising [ 7 , 31 , 33 , 37 , 38 ]. The goal of our paper is to address this gap.

This paper explores what is known from the existing peer-reviewed literature about the mental health status of physicians and physicians-in-training in North America. Specifically, we examine (1) what types of mental health concerns among physicians are commonly discussed in the literature; (2) what are the reported causes of mental health concerns in physicians; (3) what are the effects that mental health concerns may have on physicians and their patients; (4) what solutions are proposed to improve mental health of physicians; and (5) what are the barriers to seeking and providing care to physicians with mental health concerns. Conducting this scoping review, our goal is to summarize the existing research, identifying the need for a subsequent systematic review of the literature in one or more areas under the study. We also hope to identify evidence-based interventions that can be utilized to improve physicians’ mental wellbeing and to suggest directions for future research [ 2 ]. Evidence-based interventions might have a positive impact on physicians and improve the quality of patient care they provide.

A scoping review of the academic literature on the mental health of physicians and physicians-in-training in North America was conducted using Arksey and O’Malley’s [ 2 ] methodological framework. Our review objectives and broad focus, including the general questions posed to conduct the review, lend themselves to a scoping review approach, which is suitable for the analysis of a broader range of study designs and methodologies [ 2 ]. Our goal was to map the existing research on this topic and identify knowledge gaps, without making any prior assumptions about the literature’s scope, range, and key findings [ 29 ].

Stage 1: identify the research question

Following the guidelines for scoping reviews [ 2 ], we developed a broad research question for our literature search, asking what does the academic literature tell about mental health issues among physicians, residents, and medical students in North America ? Burnout and other mental health concerns often begin in medical training and continue to worsen throughout the years of practice [ 31 ]. Recognizing that the study and practice of medicine plays a role in the emergence of mental health concerns, we focus on practicing physicians – general practitioners, specialists, and surgeons – and those who are still in training – residents and medical students. We narrowed down the focus of inquiry by asking the following sub-questions:

What types of mental health concerns among physicians are commonly discussed in the literature?

What are the reported causes of mental health problems in physicians and what solutions are available to improve the mental wellbeing of physicians?

What are the barriers to seeking and providing care to physicians suffering from mental health problems?

Stage 2: identify the relevant studies

We included in our review empirical papers published during January 2008–January 2018 in peer-reviewed journals. Our exclusive focus on peer-reviewed and empirical literature reflected our goal to develop an evidence-based platform for understanding mental health concerns in physicians. Since our focus was on prevalence of mental health concerns and promising practices available to physicians in North America, we excluded articles that were more than 10 years old, suspecting that they might be too outdated for our research interest. We also excluded papers that were not in English or outside the region of interest. Using combinations of keywords developed in consultation with a professional librarian (See Table  1 ), we searched databases PUBMed, SCOPUS, CINAHL, and PsychNET. We also screened reference lists of the papers that came up in our original search to ensure that we did not miss any relevant literature.

Stage 3: literature selection

Publications were imported into a reference manager and screened for eligibility. During initial abstract screening, 146 records were excluded for being out of scope, 75 records were excluded for being outside the region of interest, and 4 papers were excluded because they could not be retrieved. The remaining 91 papers were included into the review. Figure  1 summarizes the literature search and selection.

PRISMA Flow Diagram

Stage 4: charting the data

A literature extraction tool was created in Microsoft Excel to record the author, date of publication, location, level of training, type of article (empirical, report, commentary), and topic. Both authors coded the data inductively, first independently reading five articles and generating themes from the data, then discussing our coding and developing a coding scheme that was subsequently applied to ten more papers. We then refined and finalized the coding scheme and used it to code the rest of the data. When faced with disagreements on narrowing down the themes, we discussed our reasoning and reached consensus.

Stage 5: collating, summarizing, and reporting the results

The data was summarized by frequency and type of publication, mental health topics, and level of training. The themes inductively derived from the data included (1) description of mental health concerns affecting physicians and physicians-in-training; (2) prevalence of mental health concerns among this population; (3) possible causes that can explain the emergence of mental health concerns; (4) solutions or interventions proposed to address mental health concerns; (5) effects of mental health concerns on physicians and on patient outcomes; and (6) barriers for seeking and providing help to physicians afflicted with mental health concerns. Each paper was coded based on its relevance to major theme(s) and, if warranted, secondary focus. Therefore, one paper could have been coded in more than one category. Upon analysis, we identified the gaps in the literature.

Characteristics of included literature

The initial search yielded 316 records of which 91 publications underwent full-text review and were included in our scoping review. Our analysis revealed that the publications appear to follow a trend of increase over the course of the last decade reflecting the growing interest in physicians’ mental health. More than half of the literature was published in the last 4 years included in the review, from 2014 to 2018 ( n  = 55), with most publications in 2016 ( n  = 18) (Fig.  2 ). The majority of papers ( n  = 36) focused on practicing physicians, followed by papers on residents ( n  = 22), medical students ( n  = 21), and those discussing medical professionals with different level of training ( n  = 12). The types of publications were mostly empirical ( n  = 71), of which 46 papers were quantitative. Furthermore, the vast majority of papers focused on the United States of America (USA) ( n  = 83), with less than 9% focusing on Canada ( n  = 8). The frequency of identified themes in the literature is broken down into prevalence of mental health concerns ( n  = 15), causes of mental health concerns ( n  = 18), effects of mental health concerns on physicians and patients ( n  = 12), solutions and interventions for mental health concerns ( n  = 46), and barriers to seeking and providing care for mental health concerns ( n  = 4) (Fig.  3 ).

Number of sources by characteristics of included literature

Frequency of themes in literature ( n  = 91)

Mental health concerns and their prevalence in the literature

In this thematic category ( n  = 15), we coded the papers discussing the prevalence of specific mental health concerns among physicians and those comparing physicians’ mental health to that of the general population. Most papers focused on burnout and stress ( n  = 69), which was followed by depression and suicidal ideation ( n  = 28), psychological harm and distress ( n  = 9), wellbeing and wellness ( n  = 8), and general mental health ( n  = 3) (Fig.  4 ). The literature also identified that, on average, burnout and mental health concerns affect 30–60% of all physicians and residents [ 4 , 5 , 8 , 9 , 15 , 25 , 26 ].

Number of sources by mental health topic discussed ( n  = 91)

There was some overlap between the papers discussing burnout, depression, and suicidal ideation, suggesting that work-related stress may lead to the emergence of more serious mental health problems [ 3 , 12 , 21 ], as well as addiction and substance abuse [ 22 , 27 ]. Residency training was shown to produce the highest rates of burnout [ 4 , 8 , 19 ].

Causes of mental health concerns

Papers discussing the causes of mental health concerns in physicians formed the second largest thematic category ( n  = 18). Unbalanced schedules and increasing administrative work were defined as key factors in producing poor mental health among physicians [ 4 , 5 , 6 , 13 , 15 , 27 ]. Some papers also suggested that the nature of the medical profession itself – competitive culture and prioritizing others – can lead to the emergence of mental health concerns [ 23 , 27 ]. Indeed, focus on qualities such as rigidity, perfectionism, and excessive devotion to work during the admission into medical programs fosters the selection of students who may be particularly vulnerable to mental illness in the future [ 21 , 24 ]. The third cluster of factors affecting mental health stemmed from structural issues, such as pressure from the government and insurance, fragmentation of care, and budget cuts [ 13 , 15 , 18 ]. Work overload, lack of control over work environment, lack of balance between effort and reward, poor sense of community among staff, lack of fairness and transparency by decision makers, and dissonance between one’s personal values and work tasks are the key causes for mental health concerns among physicians [ 20 ]. Govardhan et al. conceptualized causes for mental illness as having a cyclical nature - depression leads to burnout and depersonalization, which leads to patient dissatisfaction, causing job dissatisfaction and more depression [ 19 ].

Effects of mental health concerns on physicians and patients

A relatively small proportion of papers (13%) discussed the effects of mental health concerns on physicians and patients. The literature prioritized the direct effect of mental health on physicians ( n  = 11) with only one paper focusing solely on the indirect effects physicians’ mental health may have on patients. Poor mental health in physicians was linked to decreased mental and physical health [ 3 , 14 , 15 ]. In addition, mental health concerns in physicians were associated with reduction in work hours and the number of patients seen, decrease in job satisfaction, early retirement, and problems in personal life [ 3 , 5 , 15 ]. Lu et al. found that poor mental health in physicians may result in increased medical errors and the provision of suboptimal care [ 25 ]. Thus physicians’ mental wellbeing is linked to the quality of care provided to patients [ 3 , 4 , 5 , 10 , 17 ].

Solutions and interventions

In this largest thematic category ( n  = 46) we coded the literature that offered solutions for improving mental health among physicians. We identified four major levels of interventions suggested in the literature. A sizeable proportion of literature discussed the interventions that can be broadly categorized as primary prevention of mental illness. These papers proposed to increase awareness of physicians’ mental health and to develop strategies that can help to prevent burnout from occurring in the first place [ 4 , 12 ]. Some literature also suggested programs that can help to increase resilience among physicians to withstand stress and burnout [ 9 , 20 , 27 ]. We considered the papers referring to the strategies targeting physicians currently suffering from poor mental health as tertiary prevention . This literature offered insights about mindfulness-based training and similar wellness programs that can increase self-awareness [ 16 , 18 , 27 ], as well as programs aiming to improve mental wellbeing by focusing on physical health [ 17 ].

While the aforementioned interventions target individual physicians, some literature proposed workplace/institutional interventions with primary focus on changing workplace policies and organizational culture [ 4 , 13 , 23 , 25 ]. Reducing hours spent at work and paperwork demands or developing guidelines for how long each patient is seen have been identified by some researchers as useful strategies for improving mental health [ 6 , 11 , 17 ]. Offering access to mental health services outside of one’s place of employment or training could reduce the fear of stigmatization at the workplace [ 5 , 12 ]. The proposals for cultural shift in medicine were mainly focused on promoting a less competitive culture, changing power dynamics between physicians and physicians-in-training, and improving wellbeing among medical students and residents. The literature also proposed that the medical profession needs to put more emphasis on supporting trainees, eliminating harassment, and building strong leadership [ 23 ]. Changing curriculum for medical students was considered a necessary step for the cultural shift [ 20 ]. Finally, while we only reviewed one paper that directly dealt with the governmental level of prevention, we felt that it necessitated its own sub-thematic category because it identified the link between government policy, such as health care reforms and budget cuts, and the services and care physicians can provide to their patients [ 13 ].

Barriers to seeking and providing care

Only four papers were summarized in this thematic category that explored what the literature says about barriers for seeking and providing care for physicians suffering from mental health concerns. Based on our analysis, we identified two levels of factors that can impact access to mental health care among physicians and physicians-in-training.

Individual level barriers stem from intrinsic barriers that individual physicians may experience, such as minimizing the illness [ 21 ], refusing to seek help or take part in wellness programs [ 14 ], and promoting the culture of stoicism [ 27 ] among physicians. Another barrier is stigma associated with having a mental illness. Although stigma might be experienced personally, literature suggests that acknowledging the existence of mental health concerns may have negative consequences for physicians, including loss of medical license, hospital privileges, or professional advancement [ 10 , 21 , 27 ].

Structural barriers refer to the lack of formal support for mental wellbeing [ 3 ], poor access to counselling [ 6 ], lack of promotion of available wellness programs [ 10 ], and cost of treatment. Lack of research that tests the efficacy of programs and interventions aiming to improve mental health of physicians makes it challenging to develop evidence-based programs that can be implemented at a wider scale [ 5 , 11 , 12 , 18 , 20 ].

Our analysis of the existing literature on mental health concerns in physicians and physicians-in-training in North America generated five thematic categories. Over half of the reviewed papers focused on proposing solutions, but only a few described programs that were empirically tested and proven to work. Less common were papers discussing causes for deterioration of mental health in physicians (20%) and prevalence of mental illness (16%). The literature on the effects of mental health concerns on physicians and patients (13%) focused predominantly on physicians with only a few linking physicians’ poor mental health to medical errors and decreased patient satisfaction [ 3 , 4 , 16 , 24 ]. We found that the focus on barriers for seeking and receiving help for mental health concerns (4%) was least prevalent. The topic of burnout dominated the literature (76%). It seems that the nature of physicians’ work fosters the environment that causes poor mental health [ 1 , 21 , 31 ].

While emphasis on burnout is certainly warranted, it might take away the attention paid to other mental health concerns that carry more stigma, such as depression or anxiety. Establishing a more explicit focus on other mental health concerns might promote awareness of these problems in physicians and reduce the fear such diagnosis may have for doctors’ job security [ 10 ]. On the other hand, utilizing the popularity and non-stigmatizing image of “burnout” might be instrumental in developing interventions promoting mental wellbeing among a broad range of physicians and physicians-in-training.

Table  2 summarizes the key findings from the reviewed literature that are important for our understanding of physician mental health. In order to explicitly summarize the gaps in the literature, we mapped them alongside the areas that have been relatively well studied. We found that although non-empirical papers discussed physicians’ mental wellbeing broadly, most empirical papers focused on medical specialty (e.g. neurosurgeons, family medicine, etc.) [ 4 , 8 , 15 , 19 , 25 , 28 , 35 , 36 ]. Exclusive focus on professional specialty is justified if it features a unique context for generation of mental health concerns, but it limits the ability to generalize the findings to a broader population of physicians. Also, while some papers examined the impact of gender on mental health [ 7 , 32 , 39 ], only one paper considered ethnicity as a potential factor for mental health concerns and found no association [ 4 ]. Given that mental health in the general population varies by gender, ethnicity, age, and sexual orientation, it would be prudent to examine mental health among physicians using an intersectional analysis [ 30 , 32 , 39 ]. Finally, of the empirical studies we reviewed, all but one had a cross-sectional design. Longitudinal design might offer a better understanding of the emergence and development of mental health concerns in physicians and tailor interventions to different stages of professional career. Additionally, it could provide an opportunity to evaluate programs’ and policies’ effectiveness in improving physicians’ mental health. This would also help to address the gap that we identified in the literature – an overarching focus on proposing solutions with little demonstrated evidence they actually work.

This review has several limitations. First, our focus on academic literature may have resulted in overlooking the papers that are not peer-reviewed but may provide interesting solutions to physician mental health concerns. It is possible that grey literature – reports and analyses published by government and professional organizations – offers possible solutions that we did not include in our analysis or offers a different view on physicians’ mental health. Additionally, older papers and papers not published in English may have information or interesting solutions that we did not include in our review. Second, although our findings suggest that the theme of burnout dominated the literature, this may be the result of the search criteria we employed. Third, following the scoping review methodology [ 2 ], we did not assess the quality of the papers, focusing instead on the overview of the literature. Finally, our research was restricted to North America, specifically Canada and the USA. We excluded Mexico because we believed that compared to the context of medical practice in Canada and the USA, which have some similarities, the work experiences of Mexican physicians might be different and the proposed solutions might not be readily applicable to the context of practice in Canada and the USA. However, it is important to note that differences in organization of medical practice in Canada and the USA do exist, as do differences across and within provinces in Canada and the USA. A comparative analysis can shed light on how the structure and organization of medical practice shapes the emergence of mental health concerns.

The scoping review we conducted contributes to the existing research on mental wellbeing of American and Canadian physicians by summarizing key knowledge areas and identifying key gaps and directions for future research. While the papers reviewed in our analysis focused on North America, we believe that they might be applicable to the global medical workforce. Identifying key gaps in our knowledge, we are calling for further research on these topics, including examination of medical training curricula and its impact on mental wellbeing of medical students and residents, research on common mental health concerns such as depression or anxiety, studies utilizing intersectional and longitudinal approaches, and program evaluations assessing the effectiveness of interventions aiming to improve mental wellbeing of physicians. Focus on the effect physicians’ mental health may have on the quality of care provided to patients might facilitate support from government and policy makers. We believe that large-scale interventions that are proven to work effectively can utilize an upstream approach for improving the mental health of physicians and physicians-in-training.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

United States of America

World Health Organization

Ahmed N, Devitt KS, Keshet I, Spicer J, Imrie K, Feldman L, et al. A systematic review of the effects of resident duty hour restrictions in surgery: impact on resident wellness, training, and patient outcomes. Ann Surg. 2014;259(6):1041–53.

Article   Google Scholar  

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

Atallah F, McCalla S, Karakash S, Minkoff H. Please put on your own oxygen mask before assisting others: a call to arms to battle burnout. Am J Obstet Gynecol. 2016;215(6):731.e1.

Baer TE, Feraco AM, Tuysuzoglu Sagalowsky S, Williams D, Litman HJ, Vinci RJ. Pediatric resident burnout and attitudes toward patients. Pediatrics. 2017;139(3):e20162163. https://doi.org/10.1542/peds.2016-2163 .

Article   PubMed   Google Scholar  

Blais R, Safianyk C, Magnan A, Lapierre A. Physician, heal thyself: survey of users of the Quebec physicians health program. Can Fam Physician. 2010;56(10):e383–9.

PubMed   PubMed Central   Google Scholar  

Brennan J, McGrady A. Designing and implementing a resiliency program for family medicine residents. Int J Psychiatry Med. 2015;50(1):104–14.

Cass I, Duska LR, Blank SV, Cheng G, NC dP, Frederick PJ, et al. Stress and burnout among gynecologic oncologists: a Society of Gynecologic Oncology Evidence-based Review and Recommendations. Gynecol Oncol. 2016;143(2):421–7.

Chan AM, Cuevas ST, Jenkins J 2nd. Burnout among osteopathic residents: a cross-sectional analysis. J Am Osteopath Assoc. 2016;116(2):100–5.

Chaukos D, Chad-Friedman E, Mehta DH, Byerly L, Celik A, McCoy TH Jr, et al. Risk and resilience factors associated with resident burnout. Acad Psychiatry. 2017;41(2):189–94.

Compton MT, Frank E. Mental health concerns among Canadian physicians: results from the 2007-2008 Canadian physician health study. Compr Psychiatry. 2011;52(5):542–7.

Cunningham C, Preventing MD. Burnout. Trustee. 2016;69(2):6–7 1.

PubMed   Google Scholar  

Daskivich TJ, Jardine DA, Tseng J, Correa R, Stagg BC, Jacob KM, et al. Promotion of wellness and mental health awareness among physicians in training: perspective of a national, multispecialty panel of residents and fellows. J Grad Med Educ. 2015;7(1):143–7.

Dyrbye LN, Shanafelt TD. Physician burnout: a potential threat to successful health care reform. JAMA. 2011;305(19):2009–10.

Article   CAS   Google Scholar  

Epstein RM, Krasner MS. Physician resilience: what it means, why it matters, and how to promote it. Acad Med. 2013;88(3):301–3.

Evans RW, Ghosh K. A survey of headache medicine specialists on career satisfaction and burnout. Headache. 2015;55(10):1448–57.

Fahrenkopf AM, Sectish TC, Barger LK, Sharek PJ, Lewin D, Chiang VW, et al. Rates of medication errors among depressed and burnt out residents: prospective cohort study. BMJ. 2008;336(7642):488–91.

Fargen KM, Spiotta AM, Turner RD, Patel S. The importance of exercise in the well-rounded physician: dialogue for the inclusion of a physical fitness program in neurosurgery resident training. World Neurosurg. 2016;90:380–4.

Gabel S. Demoralization in Health Professional Practice: Development, Amelioration, and Implications for Continuing Education. J Contin Educ Health Prof 2013 Spring. 2013;33(2):118–26.

Google Scholar  

Govardhan LM, Pinelli V, Schnatz PF. Burnout, depression and job satisfaction in obstetrics and gynecology residents. Conn Med. 2012;76(7):389–95.

Jennings ML, Slavin SJ. Resident wellness matters: optimizing resident education and wellness through the learning environment. Acad Med. 2015;90(9):1246–50.

Keller EJ. Philosophy in medical education: a means of protecting mental health. Acad Psychiatry. 2014;38(4):409–13.

Krall EJ, Niazi SK, Miller MM. The status of physician health programs in Wisconsin and north central states: a look at statewide and health systems programs. WMJ. 2012;111(5):220–7.

Lemaire JB, Wallace JE. Burnout among doctors. BMJ. 2017;358:j3360.

Linzer M, Bitton A, Tu SP, Plews-Ogan M, Horowitz KR, Schwartz MD, et al. The end of the 15-20 minute primary care visit. J Gen Intern Med. 2015;30(11):1584–6.

Lu DW, Dresden S, McCloskey C, Branzetti J, Gisondi MA. Impact of burnout on self-reported patient care among emergency physicians. West J Emerg Med. 2015;16(7):996–1001.

Maslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol. 2001;52:397–422.

McClafferty H, Brown OW. Section on integrative medicine, committee on practice and ambulatory medicine, section on integrative medicine. Physician health and wellness. Pediatrics. 2014;134(4):830–5.

Miyasaki JM, Rheaume C, Gulya L, Ellenstein A, Schwarz HB, Vidic TR, et al. Qualitative study of burnout, career satisfaction, and well-being among US neurologists in 2016. Neurology. 2017;89(16):1730–8.

Peterson J, Pearce P, Ferguson LA, Langford C. Understanding scoping reviews: definition, purpose, and process. JAANP. 2016;29:12–6.

Przedworski JM, Dovidio JF, Hardeman RR, Phelan SM, Burke SE, Ruben MA, et al. A comparison of the mental health and well-being of sexual minority and heterosexual first-year medical students: a report from the medical student CHANGE study. Acad Med. 2015;90(5):652–9.

Ripp JA, Privitera MR, West CP, Leiter R, Logio L, Shapiro J, et al. Well-being in graduate medical education: a call for action. Acad Med. 2017;92(7):914–7.

Salles A, Mueller CM, Cohen GL. Exploring the relationship between stereotype perception and Residents’ well-being. J Am Coll Surg. 2016;222(1):52–8.

Shiralkar MT, Harris TB, Eddins-Folensbee FF, Coverdale JH. A systematic review of stress-management programs for medical students. Acad Psychiatry. 2013;37(3):158–64.

Sikka R, Morath J, Leape L. The quadruple aim: care, health, cost and meaning in work. BMJ Qual Saf. 2015;24(10):608–10. https://doi.org/10.1136/bmjqs-2015-004160 .

Tawfik DS, Phibbs CS, Sexton JB, Kan P, Sharek PJ, Nisbet CC, et al. Factors Associated With Provider Burnout in the NICU. Pediatrics. 2017;139(5):608. https://doi.org/10.1542/peds.2016-4134 Epub 2017 Apr 18.

Turner TB, Dilley SE, Smith HJ, Huh WK, Modesitt SC, Rose SL, et al. The impact of physician burnout on clinical and academic productivity of gynecologic oncologists: a decision analysis. Gynecol Oncol. 2017;146(3):642–6.

West CP, Dyrbye LN, Erwin PJ, Shanafelt TD. Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet. 2016;388(10057):2272.

Williams D, Tricomi G, Gupta J, Janise A. Efficacy of burnout interventions in the medical education pipeline. Acad Psychiatry. 2015;39(1):47–54.

Woodside JR, Miller MN, Floyd MR, McGowen KR, Pfortmiller DT. Observations on burnout in family medicine and psychiatry residents. Acad Psychiatry. 2008;32(1):13–9.

World Health Organization. (2001). Mental disorders affect one in four people.

World Health Organization. Promoting mental health: concepts, emerging evidence, practice (Summary Report). Geneva: World Health Organization; 2004.

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M.M. and E.N. were involved in identifying the relevant research question and developing the combinations of keywords used in consultation with a professional librarian. M.M. performed the literature selection and screening of references for eligibility. Both authors were involved in the creation of the literature extraction tool in Excel. Both authors coded the data inductively, first independently reading five articles and generating themes from the data, then discussing their coding and developing a coding scheme that was subsequently applied to ten more papers. Both authors then refined and finalized the coding scheme and M.M. used it to code the rest of the data. M.M. conceptualized and wrote the first copy of the manuscript, followed by extensive drafting by both authors. E.N. was a contributor to writing the final manuscript. All authors read and approved the final manuscript.

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Mihailescu, M., Neiterman, E. A scoping review of the literature on the current mental health status of physicians and physicians-in-training in North America. BMC Public Health 19 , 1363 (2019). https://doi.org/10.1186/s12889-019-7661-9

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Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.

Author Summary

One of the major challenges of neuroscience research is to integrate the results of the large number of published research studies in order to better understand how psychological functions are mapped onto brain systems. In this research, we take advantage of a large database of neuroimaging studies, along with text mining methods, to extract information about the topics that are found in the brain imaging literature and their mapping onto reported brain activation data. We also show that this method can be used to identify new relations between psychological functions and mental disorders, through their shared brain activity patterns. This work provides a new way to discover the underlying structure that relates brain function and mental processes.

Citation: Poldrack RA, Mumford JA, Schonberg T, Kalar D, Barman B, Yarkoni T (2012) Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping. PLoS Comput Biol 8(10): e1002707. https://doi.org/10.1371/journal.pcbi.1002707

Editor: Olaf Sporns, Indiana University, United States of America

Received: May 14, 2012; Accepted: August 2, 2012; Published: October 11, 2012

Copyright: © Poldrack et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by NIH grant RO1MH082795 (to RAP) and F32NR012081 (to TY) and by the Texas Emerging Technology Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Competing interests: The authors have declared that no competing interests exist.

Introduction

The search for clues regarding the underlying causes of mental disorders has led to the notion that these disorders may be best understood in terms of a set of underlying psychological and/or neural mechanisms that stand between genes and environment on the one hand and psychiatric diagnoses on the other hand. Such intermediate phenotypes, or “endophenotypes”, may provide the traction that has eluded research using diagnostic categories as primary phenotypes [1] , [2] . They may also provide the means to better understand the structure the underlying psychological dimensions that appear to underlie overlapping categories of mental disorders [3] , [4] .

The identification of endophenotypes requires an understanding the basic structure of mental functions and their associated brain networks. For more than 30 years, cognitive neuroscientists have used neuroimaging methods (including EEG/MEG, PET, and fMRI) in an attempt to address this question. This work has led to a large body of knowledge about associations between specific psychological processes or tasks and activity in brain regions or networks. However, this knowledge has not led to a commensurate improvement in our understanding of the basic mental operations that may be subserved by particular brain systems. Instead, diverse literatures often assign widely varying functions to the same networks. A prime example is the anterior cingulate cortex, which has been associated with such widespread functions as conflict monitoring, error processing, pain, and interoceptive awareness. In order to understand the unique functions that are subserved by brain regions or networks, a different approach is necessary; namely, we need to analyze data obtained across a broad range of mental domains and understand how these domains are organized with regard to neural function and structure.

The identification of basic operations can be understood statistically as a problem of latent structure identification; that is, what are the latent underlying mental functions and brain networks that give rise to to the broad range of observed behaviors and patterns of brain activity and neuropsychiatric disorders? The focus within cognitive neuroscience on establishing associations between activation and specific hypothesized processes has hindered the ability to identify such latent structures. However, within the fields of machine learning and text mining, a number of powerful approaches have been developed to estimate the latent structure that generates observed data, assuming that large enough datasets are available. In the present work, we take advantage of one class of such generative models to develop a new approach to identifying the underlying latent structure of mental processing and the associated brain functions, which we refer to as “topic mapping”. We examine the latent conceptual structure of the fMRI literature by mining the full text from a large text corpus comprising more than 5,800 articles from the neuroimaging literature, and model the relation between these topics and associated brain activation using automated methods for extracting activation coordinates from published papers. This analysis uncovers conceptual structure and activation patterns consistent with those observed in previous neuroimaging meta-analyses, which provides confirmation of the approach, while also providing some novel suggestions regarding structure/function relationships. We then use this approach to identify the topical structure of terms related neuropsychiatric diseases, and use multivariate methods to identify relations between these the mental and disorder domains based on common brain activation patterns. This approach provides an empirical means of discovering novel endophenotypes that may underlie mental disorders, as well providing new insights into the relations between diagnostic categories.

Within the fields of information retrieval and computer science, research into document retrieval has led to the development of a set of techniques for estimating the latent structure underlying a set of documents. Early work in this area treated documents as vectors in a high-dimensional space, and used matrix decomposition techniques such as singular value decomposition to identify the latent semantic structure of the documents [5] . More recently, researchers in this domain have developed approaches that are based on generative models of documents. One popular approach, known generically as “topic models” [6] , treats each document as a mixture of a small number of underlying “topics”, each of which is associated with a distribution over words. Generating a document via this model involves sampling a topic and then sampling over words within the chosen topic; using Bayesian estimation techniques, it is possible to invert this model and estimate the topic and word distributions given a set of documents. The particular topic modeling technique that we employ here, known as latent Dirichlet allocation (LDA: [7] ), has been shown to be highly effective at extracting the structure of large text corpuses. For example [8] , used this approach to characterize the topical structure of science by analyzing 10 years of abstracts from PNAS , showing that it was able to accurately extract the conceptual structure of this domain.

We characterized the latent structure of the cognitive neuroscience literature by applying latent Dirichlet allocation to a corpus of 5,809 articles (using an expanded version of the corpus developed in [9] ), which were selected on the basis of reporting fMRI activation in a standardized coordinate format. An overview of the entire data processing workflow is presented in Figure 1 . This technique estimates a number of underlying latent “topics” that generate the observed text, where each topic is defined by a distribution over words. The dimensionality (i.e., number of topics) is estimated using a cross-validation approach; the documents are randomly split into 8 sets, and for each set a topic model is trained on the remaining data and then used to estimate the empirical likelihood of the held-out documents [10] . Plots of the empirical likelihood of left-out documents as a function of the number of topics are shown in Figure 2 , and histograms of the number of documents per topic and number of topics per document are shown in figure 3 .

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https://doi.org/10.1371/journal.pcbi.1002707.g002

https://doi.org/10.1371/journal.pcbi.1002707.g003

Initial application of LDA to the full-text corpus identified a number of topics that were related to mental function, but also many topics related to methodological or linguistic aspects of the documents. Because we were specifically interested in estimating the conceptual structure of mental processes, we examined each document in the corpus and identified each occurrence of any of the 605 terms (both single words and phrases) that are present as mental concepts in the Cognitive Atlas ( http://www.cognitiveatlas.org ); the topic model was then estimated using this limited word set (treating each word or phrase as a single-word token). The Cognitive Atlas is a curated collaborative ontology that aims to describe mental functions, and contains terms spanning across nearly all domains of psychological function [11] . The cross-validation analysis identified 130 as the optimal number of topics for this dataset. Examples of these topics are shown in Figure 4 , and the full list is presented in Table S1 . In large part these topics are consistent with the topics that are the focus of research in the cognitive neuroscience literature. The topics with the highest number of associated documents were those related to very common features of neuroimaging tasks such as movement (topic 20), emotion (topic 93), audition (topic 74), attention (topic 43), and working memory (topic 61). Each of these was associated with more than 400 documents in the corpus. At the other end of the spectrum were more focused topics that loaded on fewer than 200 documents, such as topic 121 (regret,surprise), topic 71 (narrative, discourse), and topic 108 (empathy, pain). The results of this analysis suggest that topic modeling applied to the limited term set of mental functions can successfully extract the conceptual structure of psychological processes at multiple levels within the current text corpus.

https://doi.org/10.1371/journal.pcbi.1002707.g004

In order to further examine the effects of topic dimensionality, we compared the results obtained across several values for the number of topics (10,50, 100, and 250). We chose the term “language” and identified all topics for each model in which that term occurred in the top five terms. We then examined the correlation in the loading vector across documents for each set of levels, in order to identify the hierarchical graph relating topics across levels (see Figure 5 ). This analysis showed that increasing the topic dimensionality resulted in finer-grained topics; for example, with 10 topics there was a single matching topic that included “meaning”, “reading”, and “comprehension”, whereas each of these was split into a separate set of topics in the 50-topic model, and further subdivided as the dimensionality increased. This suggests that although the cross validation resulted in a particular “best” dimensionality, in reality there is relevant information at many different levels which differs in grain size.

All topics with “language in their top 5 terms were first identified from the results for topic models fit to the data at 10, 50, 100, and 250 topics. At each level, each topic is linked to the topic at the previous level with which it had the highest correlation in its document loadings. The values on each edge reflect the correlation in the topic loading vector across documents between the two levels.

https://doi.org/10.1371/journal.pcbi.1002707.g005

Topic mapping

While concordance with the existing literature is reassuring, the true promise of this approach is in its ability to uncover novel associations between functions and activation, and the topic mapping analysis did in fact identify some unexpected associations, particularly when looking at negative associations. Two interesting examples are evident in Figure 4 . First, topic 61 was associated with the bilateral fronto-parietal network usually associated with working memory, but it also exhibited strong and focused negative association in the right amygdala; this means that the amygdala was significantly less likely to be activated in studies that loaded on this topic relative to those that did not. This is particularly interesting in light of further exploration of the literature using the PubBrain tool ( http://www.pubbrain.org ) which identified a number of studies that have noted amygdala activation in association with working memory tasks (cf. [13] ). Another example is topic 71 (associated with auditory processing) which was negatively associated with activation in a broad set of regions previously implicated in emotional function, such as orbitofrontal cortex, striatum, and amygdala. Whether such negative associations reflect truly negative relations in activation between these networks or reflect features of the tasks used in these domains remains to be determined, but such unexpected associations could suggest novel hypotheses about relations between specific brain networks. These are only two examples of potential novel discoveries using Topic Mapping.; future studies will be needed to systematically examine all possible new findings emerging from the usage of this tool.

Mapping the neural basis of neuropsychiatric disorders

Based on the results from the foregoing analyses, we then examined whether it was possible to obtain new insights about the organization of brain disorders using the topic mapping approach developed above. We estimated a set of topics using only terms related to brain disorders, based on a lexicon of mental disorders terms derived from the NIFSTD Dysfunction ontology [14] along with the DSM-IV. The optimal dimensionality of 60 based on cross-validation was found to produce multiple topics with exactly the same word distribution, so we used the largest number of topics yielding a unique set of word distributions across topics, which was 29 topics. Examples of these topics and the associated topic maps are presented in Figure 6 .

Topics are ordered in terms of the number of documents loading on the topic; color maps reflect the correlation coefficient between topic loading and activation across documents. The images are presented in radiological convention (i.e., left-right reversed).

https://doi.org/10.1371/journal.pcbi.1002707.g006

The results of this analysis are largely consistent with results from prior meta-analyses and known functional anatomy of the various disorders, but are novel in highlighting relations between some of the disorders. For example, Topic 7 demonstrates the relations between bipolar disorder, schizophrenia, and mood disorders, with activation centered on the medial prefrontal cortex, basal ganglia, and amygdala. Topic 8 highlights relations between obesity and eating disorders and drug abuse, with activation in the ventral striatum and ventromedial prefrontal cortex. Topic 14 demonstrates relations between a set of externalizing disorders (drug abuse, conduct disorder, alcoholism, antisocial personality disorder, and cannabis related disorder) with activation focused in the striatum, amygdala, orbitofrontal cortex, and dorsal prefrontal cortex. Conversely, Topic 25 demonstrates relations between a set of internalizing disorders (anxiety disorder, panic disorder, phobia, obsessive compulsive disorder, agoraphobia, and post traumatic stress disorder), with a very similar pattern of activation, though notably weaker in the striatum. One striking result of these analyses is the similarity of the patterns of brain activity associated with the mention of all of these different disorders. This could arise either from the fact that this particular set of limbic brain systems is the seat of all major psychiatric disorders, or the fact that these disorders are commonly mentioned in relation to tasks or cognitive domains that happen to preferentially engage these brain systems.

We further characterized the relations between different disorder concepts in their associated neural activations by clustering the disorder topics based on their associated brain activation patterns using hierarchical clustering. The results of this analysis are shown in Figure 7 . The results show the degree to which the neural patterns associated with the use of particular sets of mental disorder terms exhibit a consistent systematic structure. The clustering breaks into four large groups, comprising language disorders, mood/anxiety disorders and drug abuse, psychotic disorders, and autism and memory disorders. What is particularly interesting is that, although none of the topic maps associated with the term “schizophrenia” showed strong activation, the fact that they cluster together in this analysis suggests that they are nonetheless similar in the patterns of activation that are reported in the associated papers; however, this could also reflect the fact that a relatively small number of tasks is used in the literature, and thus any concordance could be driven by overlap of tasks that are commonly mentioned in the context of schizophrenia. Despite such limitations, these results provide further confirmation that the present analysis, while largely based on studies involving healthy adults, can nonetheless accurately characterize the neural basis of mental disorders as described in the literature.

Euclidean distance was used as the distance metric for clustering, and hierarchical clustering was performed using Ward's method. The colored blocks show the four major groupings obtained by cutting the tree at a height of 2.0. Abbreviations: APH: aphasia, DLX:dyslexia, SLI: specific language impairment, DA: drug abuse, AD:Alzheimer's disease, DEP:depressive disorder, MDD:major depressive disorder, ANX:anxiety disorder, PAN: panic disorder, BPD: bipolar disorder, CD: conduct disorder, GAM: gambling, MD: mood disorder, PD: Parkinson's disease, OCD: obsessive compulsive disorder, PHO: phobia, EAT: eating disorder, SZ: schizophrenia, OBE: obesity, COC: cocaine related disorder, PSY: psychotic disorder, PAR: paranoid disorder, SZTY: schizotypal personality disorder, TIC: tic disorder, ALC: alcoholism, ALX: alexia, ADD: attention deficit disorder, AMN: amnesia, AUT: autism, ASP: Asperger syndrome.

https://doi.org/10.1371/journal.pcbi.1002707.g007

Empirical discovery of endophenotypes

https://doi.org/10.1371/journal.pcbi.1002707.t001

The first canonical variate (#0) demonstrated associations between a number of both internalizing and externalizing disorders (anxiety, depression, obesity, gambling) which were centered around the involvement of emotional processes (such as mood and fear) and reward-related decision processes. Another canonical variate (#1) was focused on memory processes, and identified a cluster of disorders including classical memory disorders (amnesia and Alzheimer's disease) as well as schizophrenia. Another (#2) focused on language processes and was associated with activity in left prefrontal, temporal, and parietal regions.

The results of the CCA analysis provide a potential new window into the complex psychological and neural underpinnings of schizophrenia and its relation to other psychiatric disorders. Across different canonical variates, schizophrenia is related to mood and decision making processes (components 0 and 3), memory processes (component 5), and social perception (component 10). These could potentially relate to different aspects of schizophrenic symptomatology, such as the distinctions between positive versus negative symptoms or between cognitive versus affective impairments. Further, they provide novel potential targets for genetic association studies, which have struggled to identify meaningful and replicable associations between schizophrenic symptoms or endophenotypes and genetic polymorphisms (cf. [16] ).

We also performed CCA directly using topic-document loading vectors, in order to determine whether the results differed from CCA computed on neural loading vectors; the results are presented in Table 2 . The results of this analysis are quite concordant with the foregoing analyses based on activation patterns, but one noticeable difference between the two analyses is that the activation-based CCA analysis appeared to cluster disorders more broadly, whereas many of the components found in the text-based analysis had only a single disorder. This may reflect the fact that disorders are less neurally distinct than is suggested by what is written by authors, but could also reflect greater noise in the neural data; further work will be necessary to better understand the unique contributions of activation-based and text-based analyses.

https://doi.org/10.1371/journal.pcbi.1002707.t002

It is clear that neuroimaging can provide important evidence regarding the functional organization of the brain, but one of the most fundamental questions in cognitive neuroscience has been whether it can provide any new insights into psychological function [17] – [19] . The results presented here demonstrate how large databases of neuroimaging data can provide new insights into the structure of psychological processes, by laying bare their relations within a similarity space defined by neural function. The present results highlight the importance of “discovery science” approaches that take advantage of modern statistical techniques to characterize large, high-dimensional datasets (cf. [20] ). Just as the fields of molecular biology and genomics have been revolutionized by this approach [21] , we propose that the hypothesis-generating approach supported by data mining tools can serve as a powerful complement to more standard hypothesis-testing approaches [22] .

There is growing recognition that the diagnostic categories used in psychiatry are not reflective of sharp parallel biological distinctions; instead, a growing body of behavioral, genetic, and neuroimaging data suggest that these different disorders fall along a set of underlying continuous dimensions which likely relate to particular basic psychological processes [3] , [4] . The results presented here are consistent with that viewpoint, and further show how endophenotypes for groups of disorders can be empirically discovered via data mining, even if those disorders were not the primary aims of the studies being mined. This approach would likely be even more powerful using databases that were focused on imaging data from studies of patients. In addition, this approach has the potential to characterize the genetic architecture of these disorders through mining of genetic association data; unfortunately, genetic terms are not sufficiently frequent in the Neurosynth database to support robust mapping of relationships to genes, but future analyses using enhanced databases has the potential to discover additional relations between neurocognitive components and genetic contributions.

The present work is limited by several features of the data that were used in the analyses. The first limitation arises from the fact that we rely upon the presence of particular terms in the text, rather than on manual annotation of the relevance of those terms. Thus, obvious issues such as polysemy (e.g., the multiple senses of the term “working memory”) and negation can be problematic, though these issues could potentially be addressed using more powerful natural language processing. A second limitation arises from the meta-analytic nature of the activation data used in the analyses, which are reconstructed from a very sparse representation of the original data. A third limitation is that the activation maps are associated only with complete documents, not with specific terms within the document, and this coarseness undoubtedly adds a significant amount of noise to the modeling results. These limitations necessitate caution in drawing strong conclusions from the results reported here. At the same time, the concordance of many of the results with previous analyses using different datasets and analysis approaches suggests that these limitations have not greatly undermined the power of the technique. We propose that the approach outlined here is likely to be most useful for inspiring novel hypotheses rather than for confirming existing hypotheses, which means that any such results will be just the first step in a research program that must also include hypothesis-driven experimentation.

Another potential limitation of the present work is that the fact that a number of the parameters in the analyses were set arbitrarily. While the dimensionality of the topic models was determined using an automated method, there remain parameter settings (such as smoothness of the word and topic distributions) that must be chosen arbitrarily (in our case, we chose them based on previously published results). The results of the topic model are quite robust; for example, we saw very similar results when performing the topic models on the original set of 4,393 papers from the earlier paper by Yarkoni et al. compared to the results from the corpus of 5,809 papers. It is also evident from Figure 5 that there is strong continuity in topics across different dimensionalities, with single topics at lower dimensionalities splitting into multiple finer-grained topics at higher dimensionalities. We have chosen model parameters that appear to give sensible results relative to prior findings, but the possibility remains that different parameterizations or analysis approaches could lead to different outcomes; future research will need to explore this question in more detail. We would also note that some of these limitations may be offset by the fact that the analyses presented here are almost fully automated, which removes many possible opportunities for research bias to affect the results.

The present work follows and extends other recent work that has aimed to mine the relations between mental function and brain function using coordinate-based meta-analyses. Smith et al. [23] analyzed the BrainMap database (which is similar to the database used here, but is created via manual annotation and thus has lower coverage but greater specificity and accuracy than the Neurosynth database). This work showed that independent components analysis applied to the meta-analytic data was able to identify networks very similar to those observed in resting-state fMRI time series, and that these could be related to specific aspects of psychological function via the annotations in the BrainMap database. Laird et al [24] extended this by showing that behavioral functions could be clustered together based on these meta-analytic maps. The present work further extends those previous studies by showing that the structure of the psychological domain can be identified in an unsupervised manner using topic modeling across both cognitive function and mental disorder domains, and that these can further be used to identify potential endophenotypes that share common neural patterns across these two domains. Visual examination of the ICA components presented in the Smith and Laird papers shows substantial overlap with the topic maps identified in the present study. In future work, we hope to directly compare the topic mapping results with the maps identified in those papers, to further characterize the utility of each approach.

In summary, we have shown how large neuroimaging and text databases can be used to identify novel relations between brain, mind, and mental disorders. The approach developed here has the potential to enable new discoveries about the neural and cognitive bases of neuropsychiatric disorders, and to provide empirically-driven functional characterizations of patterns of brain activation. The results also highlight the importance of the availability of large open datasets in cognitive neuroscience to enable discovery-based science as a complement to hypothesis-driven research.

Materials and Methods

Code to implement all of the analyses reported here, along with all of the auxiliary files, are available at https://github.com/poldrack/LatentStructure .

Data extraction

The full text from the Neurosynth corpus was used for the text mining analyses. The sources of these data as well as the process for automated extraction of activation coordinates are described in detail in [9] .

Peak image creation

Synthetic activation peak images were created from the extracted activation coordinates by placing a sphere (10 mm radius) at each activation location, at 3 mm resolution using the MNI305 template. Activations detected to be in Talairach space were first converted to MNI305 coordinates using the Lancaster transform [25] .

Topic modeling

We ran two topic modeling analyses using limited sets of terms to obtain focused topics in specific domains. In the first, we used 605 mental concept terms from the Cognitive Atlas database mentioned previously. In the second, we used a set of 55 terms describing mental disorders; these were obtained by taking the NIFSTD Dysfunction ontology and removing all terms not relevant to psychiatric disorders, and then adding a set of missing terms that described additional disorders listed in the DSM-IV. In each case, we processed the full text corpus and created restricted documents containing only terms that were present in the respective term list (along with synonyms, which were mapped back to the base term), and then performed topic modeling on those restricted documents. The median number of terms per document after filtering was 127 for cognitive terms and 3 for disease terms.

For each dataset, the optimal number of topics was determined by performing a grid search across a range of dimensionality values (from 10 to 250 in steps of 10). Each document set was split into 8 random sets of documents, and 8 separate models were trained, in each case leaving out one subset of documents. The empirical likelihood of the left-out documents was then estimated using an importance sampling method as implemented in MALLET [10] .

In order to identify the hierarchical relations between topics across different dimensionalities (as shown in Figure 5 ), the topic models from the first crossvalidation fold for each level (10, 50, 100, and 250 topics) were used; because 1/8 of the data were excluded as test data, these models were thus trained on a total of 5082 documents (using the same documents across all different dimensionalities). Hierarchical relations between levels were identified by computing the correlation between the document loading vectors for each lower-level topic and all higher-level topics, and then assigning the link according to the maximum correlation.

Disorder clustering

Disorders were clustered using hierarchical clustering (Ward's method) applied to the Euclidean distance matrix computed across voxels for the disorder-based topic maps (Pearson r values).

Canonical correlation analysis

Supporting Information

Complete list of topics identified through application of latent Dirichlet allocation to the text corpus filtered for Cognitive Atlas terms. The top 5 words shown for each topic are those which had the highest loading for that topic across documents. The number of documents that loaded on each topic is also listed.

https://doi.org/10.1371/journal.pcbi.1002707.s001

Complete list of topics identified through application of latent Dirichlet allocation to the text corpus filtered for mental disorder terms. The top 5 words shown for each topic are those which had the highest loading for that topic across documents. The number of documents that loaded on each topic is also listed.

https://doi.org/10.1371/journal.pcbi.1002707.s002

Acknowledgments

Thanks to Robert Bilder, Eliza Congdon, Steve Hanson, Oluwasanmi Koyejo, Jonathan Pillow, and Fred Sabb for helpful comments on a draft of this paper and to Daniela Witten for assistance with the R PMA package.

Author Contributions

Conceived and designed the experiments: RAP TS DK BB TY. Performed the experiments: RAP TY. Analyzed the data: RAP JAM TY. Contributed reagents/materials/analysis tools: RAP JAM DK BB TY. Wrote the paper: RAP JAM TS TY.

• View Article
• Google Scholar
• 6. Steyvers M, Griffiths T (2007) Probabilistic topic models. In: Landauer T, McNamara D, Dennis S, Kintsch W, editors. Latent Semantic Analysis: A Road to Meaning.
• 10. Wallach HM, Murray I, Salakhutdinov R, Mimno D (2009) Evaluation methods for topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY, USA: ACM, ICML 2009. pp. 1105–1112. Available: http://doi.acm.org/10.1145/1553374.1553515 .
• 26. McCallum AK (2002) Mallet: A machine learning for language toolkit. Available: http://mallet.cs.umass.edu .

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Addressing the unprecedented behavioral-health challenges facing Generation Z

Nearly two years after the COVID-19 pandemic began in the United States, Gen Zers, ranging from middle school students to early professionals, are reporting higher rates of anxiety, depression, and distress than any other age group. 1 Ages for Generation Z can vary, with some analysis including ages as young as nine. In this article, we focus on those between the ages of 16 and 24, and define millennials as 25 to 40; Ramin Mojtabai and Mark Olfson, “National trends in mental health care for US adolescents,” JAMA Psychiatry , March 25, 2020, Volume 77, Number 7; Martin Seligman, The Optimistic Child: A Revolutionary Approach to Raising Resilient Children , Boston, MA: Mariner Books, 2007; Gen Z respondents are 1.5 times as likely to report having felt anxious or depressed, compared with the average respondent, according to the McKinsey Consumer Health Insights Survey, conducted in June 2021—a nationally representative survey of 2,906 responses, including 316 Gen Z responses. The mental-health challenges among this generation are so concerning that US surgeon general Vivek Murthy issued a public health advisory on December 7, 2021, to address the “youth mental health crisis” exacerbated by the COVID-19 pandemic. 2 Protecting youth mental health: US surgeon general’s advisory , Office of the Surgeon General, December 7, 2021.

About the authors

The article is a collaborative effort by Erica Coe , Jenny Cordina , Kana Enomoto , Raelyn Jacobson , Sharon Mei, and Nikhil Seshan, representing views of the McKinsey’s Healthcare Systems & Services and Public & Social Sector Practices.

A series of consumer surveys and interviews conducted by McKinsey indicate stark differences among generations, with Gen Z  reporting the least positive life outlook, including lower levels of emotional and social well-being than older generations. One in four Gen Z respondents reported feeling more emotionally distressed (25 percent), almost double the levels reported by millennial and Gen X respondents (13 percent each), and more than triple the levels reported by baby boomer respondents (8 percent). 3 These research efforts have been focused on Gen Zers between the ages of 16 and 24 when compared with samples of millennials (aged 25 to 40), Gen Xers (aged 41 to 56), and baby boomers (aged 57 to 76). And the COVID-19 pandemic has only amplified this challenge (see sidebar, “The disproportionate impact of the COVID-19 pandemic”). While consumer surveys are, of course, subjective and Gen Z is not the only generation to experience distress, employers, educators, and public health leaders may want to consider the sentiment of this emerging generation as they plan for the future.

The disproportionate impact of the COVID-19 pandemic

While Gen Z is less vulnerable to the physical impacts of the COVID-19 pandemic, they bear unique burdens due to their life stage, including emotional stress and grief from the pandemic, high rates of job loss and unemployment, and educational challenges from remote or interrupted learning. The effects of the pandemic may be especially felt by recent college graduates, many of whom have encountered difficulties finding jobs, had their previously secured job offers rescinded, or were unable to apply to graduate school due to the timing of the lockdowns in March 2020. In April 2020, workers aged 18 to 24 faced 27 percent unemployment, with 13 percent of this segment ceasing to look for work. While employment has largely recovered, this segment has exited the workforce at twice the rate of other age groups  since the start of the pandemic. The inequitable impact of the pandemic by race extends to Gen Z employment as well, where Black, Hispanic/Latino, and Asian American and Pacific Islander (AAPI) workers aged 18 to 24 faced up to 1.8 times the unemployment rates of their White counterparts. 1 McKinsey analysis of the US Census Bureau Current Population Survey as of November 2020.

In our sample, Gen Z respondents were more likely to report having been diagnosed with a behavioral-health condition (for example, mental or substance use disorder) than either Gen Xers or baby boomers. 4 Gen Z respondents were 1.4 to 2.3 times more likely to report that they had been diagnosed with a mental-health condition and 1.9 to 4.1 times more likely to be diagnosed with a substance-use disorder than both Gen Xers and baby boomers. Based on the McKinsey Consumer Behavioral Health Survey conducted in November–December 2020—a nationally representative survey of 1,523 responses, including an oversample of Gen Z respondents (aged 16 to 24, n = 874). Gen Z respondents were also two to three times more likely than other generations to report thinking about, planning, or attempting suicide in the 12-month period spanning late 2019 to late 2020.

Gen Z also reported more unmet social needs than any other generation. 5 Also referred to as social determinants of health or social needs, including income, employment, education, food, housing, transportation, social support, and safety. These basic needs, if unmet, can negatively affect health. In addition, factors such as race, ethnicity, gender and sexual orientation, disability, and age can influence health status. Fifty-eight percent of Gen Z reported two or more unmet social needs, compared with 16 percent of people from older generations. These perceived unmet social needs, including income, employment, education, food, housing, transportation, social support, and safety, are associated with higher self-reported rates of behavioral-health conditions. As indicated in a recent nationwide survey, people with poor mental health were two times as likely to report an unmet basic need as those with good mental health, and four times as likely to have three or more unmet basic needs. 6 2019 McKinsey Social Determinants of Health Survey, n = 2,010, where respondents included those with Medicare or Medicaid coverage, individuals with coverage through the individual market who had household incomes below 250 percent of the federal poverty level, and individuals who were uninsured and had household income below 250 percent of the federal poverty level.

As these young adults work to develop their resilience, Gen Zers may seek out the holistic approach to health they have come to expect, which includes physical health, behavioral health, and social needs, as future students, employees, and customers.

Characteristics of Gen Z consumers in the healthcare ecosystem

Gen Z’s specific needs suggest that improving their behavioral healthcare will require stakeholders to increase access and deliver appropriate, timely services.

Gen Z is less likely to seek help

Gen Z respondents were more likely to report having a behavioral-health diagnosis but less likely to report seeking treatment compared with other generations (Exhibit 1). For instance, Gen Z is 1.6 to 1.8 times more likely to report not seeking treatment for a behavioral-health condition than millennials. There are several factors that may account for Gen Z’s lack of seeking help: developmental stage, disengagement from their healthcare, perceived affordability, and stigma associated with mental or substance use disorders within their families and communities. 7 Before age 25, the human brain is not fully developed. Awareness of long-term consequences and the ability to curb impulsive behavior are some of the last functions to mature. Thus, adolescents and young adults, across generations and not just Gen Z, may be less likely to engage in activities such as routine or preventive healthcare. For more, see Investing in the health and well-being of young adults , Institute of Medicine and National Research Council, 2015.

Gen Z respondents identified as less engaged in their healthcare than other respondents (Exhibit 2). About two-thirds of Gen Z respondents fell into lower engagement segments of healthcare consumers, compared with one-half of respondents from other generations. Gen Z and other people in these less engaged segments reported that they feel less in control of their health and lifespan, are less health-conscious, and are less proactive about maintaining good health. One-third of Gen Z respondents fell into the least engaged segment, who reported the lowest motivation to improve their health and the least comfort talking about behavioral-health challenges with doctors. 8 Disadvantaged, disconnected users are more resigned to their health and less engaged and active in improving it. They value convenience but are often not engaged digitally.

Another driver for Gen Z’s reduced help-seeking may be the perceived affordability of mental-health services. One out of four Gen Z respondents said they could not afford mental-health services, which had the lowest perceived affordability of all services surveyed. 9 Services surveyed include healthcare, health insurance, internet services, necessary transportation, financial services, housing, and nutritious food. Across the board, Americans with mental and substance use disorders bear a disproportionate share of out-of-pocket healthcare costs for a range of reasons, including the fact that many behavioral-health providers do not accept insurance . “I found the perfect therapist for me but I couldn’t afford her, even with insurance,” said one Gen Z respondent. “The absolute biggest barrier to gaining mental-health treatment has been financial,” added another.

In addition, stigma associated with mental and substance use disorders and a lack of family support may be a substantial barrier in seeking mental healthcare. Many Gen Zers rely on parents for transportation or health insurance and may fear interacting with their parents about mental-health topics. This factor is particularly relevant for communities of color, who report perceiving a higher level of stigma associated with behavioral-health conditions. 10 Mental health: Culture, race, and ethnicity; A supplement to mental health; A report of the surgeon general , US Department of Health and Human Services, August 2001: A 1998 study cited in the supplement found that only 12 percent of Asians would mention their mental-health problems to a friend or relative (compared with 25 percent of Whites), only 4 percent of Asians would seek help from a psychiatrist or specialist (compared with 26 percent of Whites), and only 3 percent of Asians would seek help from a physician (compared with 13 percent of Whites). Children of immigrants also may internalize guilt because of their parents’ sacrifices or may have behavioral-health concerns minimized by their parents, who may state or think their children “have it much easier” than they did growing up. 11 Mental Health America , “To be the child of an immigrant,” blog entry by Kenna Chick, accessed December 1, 2021.

Gen Z relies on emergency care, social media, and digital tools when they do seek help

When they do seek support for behavioral-health issues, Gen Z may not be turning to regular outpatient mental-health services and instead may rely on emergency care, social media, and digital tools .

Gen Zers rely on acute sites of care more often than older generations, with Gen Z respondents one to four times more likely to report using the ER, and two to three times more likely to report using crisis services or behavioral-health urgent care in the past 12 months. Gen Z also makes up nearly three-quarters of Crisis Text Line’s users. 12 Everybody hurts 2020: What 48 million messages say about the state of mental health in America , Crisis Text Line, February 10, 2020. One Gen Z respondent expressed her frustration, saying, “Seems [like the] only option is an emergency room visit, otherwise I have to wait weeks to see a psychiatrist.”

Almost one in four Gen Zers also reported that it is “extremely” or “very” challenging to get help during a behavioral-health crisis. This lack of access is concerning for a generation two to three times more likely to report seeking treatment in the past 12 months for suicidal ideation or attempted suicide, than any other generation.

Many Gen Zers also indicated their first step in managing behavioral-health challenges was going to TikTok or Reddit for advice from other young people, following therapists on Instagram, or downloading relevant apps. This reliance on social media may be due, in part, to the provider shortages in many parts of the country: 64 percent of counties in the United States have a shortage of mental-health providers. Furthermore, 56 percent of counties in the United States are without a psychiatrist (corresponding to 9 percent of the total population), and 73 percent of counties are without a child and adolescent psychiatrist (corresponding to 19 percent of the total population). 13 Oleg Bestsennyy, Greg Gilbert, Alex Harris, and Jennifer Rost, “ Telehealth: A quarter-trillion-dollar post-COVID-19 reality ?,” McKinsey, July 9, 2021; Vulnerable Populations dashboard, McKinsey’s Center for Societal Benefit through Healthcare, accessed December 1, 2021.

Gen Z is less satisfied with the behavioral-health services they receive

Gen Zers say the behavioral healthcare system overall is not meeting their expectations—Gen Zers who received behavioral healthcare were less likely to report being satisfied with the services they received than other generations. For example, compared with older generations, Gen Z reports lower satisfaction with behavioral-health services received through outpatient counseling/therapy (3.7 out of 5.0 for Gen Z, compared with 4.1 for Gen X) or intensive outpatient (3.1 for Gen Z, compared with 3.8 for older generations). 14 Mean differences are significantly different, at a 90 percent confidence level. One Gen Z respondent said, “Struggling to find a psychologist whom I was comfortable with and cared enough to remember my name and what we did the week before” was the most significant barrier to care. Another said, “I have trust issues and find it difficult to talk with therapists about my problems. I also had a very bad experience with a therapist, which made this problem worse.”

Although we have seen high penetration of telehealth in psychiatry (share of telehealth outpatient and office visits claims were at 50 percent in February 2021), 15 Vulnerable Populations: Data Over Time Database, McKinsey Center for Societal Benefit through Healthcare, April 2021. Gen Z has the lowest satisfaction with tele-behavioral health (Gen Z rates their satisfaction with telehealth at a 3.8 out of 5.0, compared with older generations, who rate it 4.1) and digital app/tools (3.5 out of 5.0 for Gen Z, compared with 4.0 for older generations). 16 Mean differences are significantly different, at a 90 percent confidence level. Around telehealth, Gen Zers cited reasons for dissatisfaction such as telehealth therapy feeling “less official” or “less professional,” as well as more difficult to form a trusting connection with a therapist. For apps, Gen Z respondents noted a lack of personalization, as well as a lack of diversity—both in terms of the racial and ethnic diversity of the stories they presented, and in the problems that the apps offered tools to address. In creating and improving behavioral-health tools, it is crucial to employ a user-centered design approach to develop functionality and experiences that Gen Zers actually want.

In creating and improving behavioral-health tools, it is crucial to employ a user-centered design approach to develop functionality and experiences that Gen Zers actually want.

Gen Z cares about diversity when choosing a healthcare provider

Racial and ethnic diversity in the behavioral-health workforce is also important. According to McKinsey’s COVID-19 Consumer Survey, racial and ethnic minority respondents reported valuing racial and ethnic diversity when choosing a physician, citing their physician’s race more frequently than White respondents as a consideration. 17 Thirteen percent of Black respondents, 9 percent of Asian respondents, and 8 percent of Hispanic/Latino respondents cited their physician’s race when selecting the physicians that they see, compared with 4 percent of Whites. Because Gen Z cares deeply about diversity, there are opportunities to integrate care and early intervention by offering a more racially and ethnically diverse behavioral-health workforce and culturally relevant digital tools. 18 According to surveys conducted by the Pew Research Center, most Gen Zers see the country’s growing racial and ethnic diversity as a good thing: Ruth Igielnik and Kim Parker, “On the cusp of adulthood and facing an uncertain future: What we know about Gen Z so far,” Pew Research Center, May 14, 2020.

Potential stakeholder actions to address the needs of Generation Z

In our article “ Unlocking whole person care through behavioral health ,” we outline six potential actions integral to improving the quality of care and experience for millions with behavioral-health conditions. Many of those levers apply to Gen Z, but further tailoring is needed to best meet the needs of this emerging generation. Promising areas to explore could include the emerging role of digital and telehealth; the need for stronger community-based response to behavioral-health crises; better meeting the needs of Gen Z where they live, work, and go to school; promoting mental-health literacy; investing in behavioral health at parity with physical health; and supporting a holistic approach that embraces behavioral, physical, and social aspects of health.

Need for action now

Gen Z is our next generation of leaders, activists, and politicians; many of them have already taken on adult responsibilities as they start climate movements, lead social justice marches, and drive companies to align more closely with their values. Healthcare leaders, educators, and employers all have a role to play in supporting the behavioral health of Gen Z. By taking a tailored, generational approach to designing messages, products, and services, stakeholders can meaningfully improve the behavioral health of Gen Z and help them achieve their full potential. This investment could be viewed as a down payment on our future that will bear social and economic returns for years to come.

Erica Coe is a partner in McKinsey’s Atlanta office and coleads the Center for Societal Benefit through Healthcare, Jenny Cordina is a partner in the Detroit office and leads McKinsey’s Consumer Health Insights research, Kana Enomoto is a senior expert in the Washington, DC, office and coleads the Center for Societal Benefit through Healthcare, Raelyn Jacobson is an associate partner in the Seattle office, Sharon Mei is an expert in the New York office, and Nikhil Seshan is a consultant in the Philadelphia office.

The authors wish to thank Tamara Baer, Eric Bochtler, Emma Dorn, Erin Harding, Brad Herbig, Jimmy Sarakatsannis, and Boya Wang for their contributions to this paper.

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Key Data on Health and Health Care by Race and Ethnicity

Nambi Ndugga , Latoya Hill , and Samantha Artiga Published: June 11, 2024

Executive Summary

Introduction.

Racial and ethnic disparities in health and health care remain a persistent challenge in the United States. The COVID-19 pandemic’s uneven impact on people of color drew increased attention to inequities in health and health care, which have been documented for decades and reflect longstanding structural and systemic inequities rooted in historical and ongoing racism and discrimination. KFF’s 2023 Survey on Racism, Discrimination, and Health documents ongoing experiences with racism and discrimination, including in health care settings. While inequities in access to and use of health care contribute to disparities in health, inequities across broader social and economic factors that drive health, often referred to as social determinants of health , also play a major role. Using data to identify disparities and the factors that drive them is important for developing interventions and directing resources to address them, as well as for assessing progress toward achieving greater equity over time.

This analysis examines how people of color fare compared to White people across 64 measures of health, health care, and social determinants of health using the most recent data available from federal surveys and administrative sets as well as the 2023 KFF Survey on Racism, Discrimination, and Health , which provides unique nationally-representative measures of adults’ experiences with racism and discrimination, including in health care (see About the Data). Where possible, we present data for six groups: White, Asian, Hispanic, Black, American Indian or Alaska Native (AIAN), and Native Hawaiian or Pacific Islander (NHPI). People of Hispanic origin may be of any race, but we classify them as Hispanic for this analysis. We limit other groups to people who identify as non-Hispanic. When the same or similar measures are available in multiple datasets, we use the data that allow us to disaggregate for the largest number of racial and ethnic groups. Future analyses will reflect new federal standards that will utilize a combined race and ethnicity approach for collecting information and include a new category for people who identify as Middle Eastern or North African. Unless otherwise noted, differences described in the text are statistically significant at the p<0.05 level.

We include data for smaller population groups wherever available. Instances in which the unweighted sample size for a subgroup is less than 50 or the relative standard error is greater than 30% — which are outside of what we would typically include in analysis like this — are noted in the figures, and confidence intervals for those measures are included in the figure. Although these small sample sizes may impact the reliability, validity, and reproducibility of data, they are important to include because they point to potential underlying disparities that are hidden without disaggregated data. For some data measures throughout this brief we refer to “women” but recognize that other individuals also give birth, including some transgender men, nonbinary, and gender-nonconforming persons.

Key Takeaways

Black, Hispanic, and AIAN people fare worse than White people across the majority of examined measures of health and health care and social determinants of health (Figure 1). Black people fare better than White people for some cancer screening and incidence measures, although they have higher rates of cancer mortality. Despite worse measures of health coverage and access and social determinants of health, Hispanic people fare better than White people for some health measures, including life expectancy, some chronic diseases, and most measures of cancer incidence and mortality. These findings may, in part, reflect variation in outcomes among subgroups of Hispanic people , with better outcomes for some groups, particularly recent immigrants to the U.S. Examples of some key findings include:

• Nonelderly AIAN (19%) and Hispanic (18%) people were more than twice as likely as their White counterparts (7%) to be uninsured as of 2022.
• Among adults with any mental illness, Hispanic (40%), Black (38%), and Asian (36%) adults were less likely than White adults (56%) to receive mental health services as of 2022.
• Roughly, six in ten Hispanic (63%), AIAN (63%), and Black (58%) adults went without a flu vaccine in the 2022-2023 season, compared to less than half of White adults (49%).
• AIAN (67.9 years) and Black (72.8 years) people had a shorter life expectancy compared to White people (77.5 years) as of 2022, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2022; however, all racial and ethnic groups experienced a small increase in life expectancy between 2021 and 2022.
• Black (10.9 per 1,000) and AIAN (9.1 per 1,000) infants were at least two times as likely to die as White infants (4.5 per 1,000) as of 2022. Black and AIAN women also had the highest rates of pregnancy-related mortality.
• AIAN (24%) and Black (21%) children were more than three times as likely to have food insecurity as White children (6%), and Hispanic children (15%) were over twice as likely to have food insecurity than White children (6%) as of 2022.

Asian people in the aggregate fare the same or better compared to White people for most examined measures. However, they fare worse for some measures, including receipt of some routine care and screening services, and some social determinants of health, including home ownership, crowded housing, and experiences with racism. They also have higher shares of people who are noncitizens or who have limited English proficiency (LEP), which could contribute to barriers to accessing health coverage and care. Moreover, the aggregate data may mask underlying disparities among subgroups of the Asian population. Asian people also report experiences with discrimination in daily life, which is associated with adverse effects on mental health and well-being.

Data gaps largely prevent the ability to identify and understand health disparities for NHPI people. Data are insufficient or not disaggregated for NHPI people for a number of the examined measures. Among available data, NHPI people fare worse than White people for the majority of measures. There are no significant differences for some measures, but this largely reflects the smaller sample size for NHPI people in many datasets, which limits the power to detect statistically significant differences.

These data highlight the importance of continued efforts to address disparities in health and health care and show that it will be key for efforts to address factors both within and beyond the health care system. While these data provide insight into the status of disparities, ongoing data gaps and limitations hamper the ability to get a complete picture, particularly for smaller population groups and among subgroups of the broader racial and ethnic categories. As the share of people who identify as multiracial grows, it will be important to develop improved methods for understanding their experiences. How data are collected and reported by race and ethnicity is important for understanding disparities and efforts to address them. Recent changes to federal standards for collecting and reporting racial and ethnic data are intended to better represent the diversity of the population and will likely support greater disaggregation of data to identify and address disparities.

Racial Diversity Within the U.S. Today

Total population by race and ethnicity.

About four in ten people (42%) in the United States identify as people of color (Figure 2). This group includes 19% who are Hispanic, 12% who are Black, 6% who are Asian, 1% who are AIAN, less than 1% who are NHPI, and 5% who identify as another racial category, including individuals who identify as more than one race. The remaining 58% of the population are White. The share of the population who identify as people of color has been growing over time, with the largest growth occurring among those who identify as Hispanic or Asian. The racial diversity of the population is expected to continue to increase, with people of color projected to account for over half of the population by 2050. Recent changes to how data on race and ethnicity are collected and reported may also influence measures of the diversity of the population.

RACIAL DIVERSITY BY STATE

Certain areas of the country—particularly in the South, Southwest, and parts of the West—are more racially diverse than others (Figure 3). Overall, the share of the population who are people of color ranges from 10% or fewer in Maine, Vermont, and West Virginia to 50% or more of the population in California, District of Columbia, Georgia, Hawaii, Maryland, Nevada, New Mexico, and Texas. Most people of color live in the South and West. More than half (59%) of the Black population resides in the South, and nearly eight in ten Hispanic people live in the West (38%) or South (39%). About three quarters of the NHPI population (75%), almost half (49%) of the AIAN population, and 43% of the Asian population live in the Western region of the country.

TOTAL POPULATION BY AGE, RACE, AND ETHNICITY

People of color are younger compared to White people. Hispanic people are the youngest racial and ethnic group, with 31% ages 18 or younger and 56% below age 35 (Figure 4). Roughly half of Black (48%), AIAN (50%), and NHPI (51%) people are below age 35, compared to 42% of Asian people and 38% of White people.

Health Coverage, Access to and Use of Care

Racial disparities in health coverage, access, and use.

Overall, Hispanic and AIAN people fare worse compared to White people across most examined measures of health coverage, and access to and use of care (Figure 5). Black people fare worse than White people across half of these measures, and experiences for Asian people are mostly similar to or better than White people across these examined measures. NHPI people fare worse than White people across some measures, but several measures lacked sufficient data for a reliable estimate for NHPI people.

HEALTH COVERAGE

Despite gains in health coverage across racial and ethnic groups over time, nonelderly AIAN, Hispanic, NHPI, and Black people remain more likely to be uninsured compared to their White counterparts. After the Affordable Care Act (ACA), Medicaid, and Marketplace coverage expansions took effect in 2014, all racial and ethnic groups experienced large increases in coverage . Beginning in 2017, coverage gains began reversing and the number of uninsured people increased for three consecutive years. However, between 2019 and 2022, there were small gains in coverage across most racial and ethnic groups, with pandemic enrollment protections in Medicaid and enhanced ACA premium subsidies. Despite these gains over time, disparities in health coverage persist as of 2022. Nonelderly AIAN (19%) and Hispanic (18%) people have the highest uninsured rates (Figure 6). Uninsured rates for nonelderly NHPI (13%) and Black (10%) people are also higher than the rate for their White counterparts (7%). Nonelderly White (7%) and Asian (6%) people have the lowest uninsured rates.

ACCESS TO AND USE OF CARE

Most groups of nonelderly adults of color are more likely than nonelderly White adults to report not having a usual doctor or provider and going without care. Roughly one third (36%) of Hispanic adults, one quarter of AIAN (25%) and NHPI (24%) adults, and about one in five (21%) Asian adults report not having a personal health care provider compared to 17% of White adults (Figure 7). The share of Black adults who report not having a personal health care provider is the same as their White counterparts (17% for both). In addition, Hispanic (21%), NHPI (18%), AIAN (16%), and Black (14%) adults are more likely than White adults (11%) to report not seeing a doctor in the past 12 months because of cost, while Asian adults (8%) are less likely than White adults to say they went without a doctor visit due to cost. Hispanic (32%) and AIAN (31%) adults are more likely than White adults (28%) to say they went without a routine checkup in the past year, while Asian (26%), NHPI (24%), and Black (20%) adults are less likely to report going without a checkup. Hispanic and AIAN (both 45%) and Black (40%) adults are more likely than White adults (34%) to report going without a visit to a dentist or dental clinic in the past year.

In contrast to the patterns among adults, racial and ethnic differences in access to and use of care are more mixed for children. Nearly one in ten (9%) Hispanic children lack a usual source of care when sick compared to 5% of White children, but there are no significant differences for other groups for which data are available (Figure 8). Similar shares of Hispanic (7%), Asian (7%), and Black (4%) children went without a health care visit in the past year as White children (6%). However, higher shares of Asian (23%) and Black (21%) children went without a dental visit in the past year compared to White children (17%). Data are not available for NHPI children for these measures, and data for AIAN children should be interpreted with caution due to small sample sizes and large standard errors.

Among adults with any mental illness, Black, Hispanic, and Asian adults are less likely than White adults to report receiving mental health services. Roughly half (56%) of White adults with any mental illness report receiving mental health services in the past year. (Figure 9). In contrast, about four in ten (40%) Hispanic adults and just over a third of Black (38%) and Asian (36%) adults with any mental illness report receiving mental health care in the past year. Data are not available for AIAN and NHPI adults.

Experiences across racial and ethnic groups are mixed regarding receipt of recommended cancer screenings (Figure 10). Among women ages 50-74 (the age group recommended for screening prior to updates in 2024, which lowered the starting age to 40), Black people (24%) are less likely than White people (29%) to go without a recent mammogram. In contrast, AIAN (41%) and Hispanic (35%) people are more likely than White people (29%) to go without a mammogram. Among those recommended for colorectal cancer screening, Hispanic, Asian, AIAN, NHPI, and Black people are more likely than White people to not be up to date on their screening. Increases in cancer screenings, particularly for breast, colorectal, and prostate cancers, have been identified as one of the drivers of the decline in cancer mortality over the past few decades.

Racial and ethnic differences persist in flu and childhood vaccinations (Figure 11). Roughly six in ten Hispanic (63%), AIAN (63%), and Black (58%) adults went without a flu vaccine in the 2022-2023 season compared to about half (49%) of White adults. However, among children, White children (44%) are more likely than Asian (28%) and Hispanic (39%) children to go without the flu vaccine; data are not available to assess flu vaccinations among NHPI adults and children. In 2019-2020, AIAN (42%), Black (37%), and Hispanic (33%) children were more likely than White children (28%) to have not received all recommended childhood immunizations.

Health Status and Outcomes

Racial disparities in health status and outcomes.

Black and AIAN people fare worse than White people across the majority of examined measures of health status and outcomes (Figure 12). In contrast, Asian and Hispanic people fare better than White people for a majority of examined health measures. Nearly half of the examined measures did not have data available for NHPI people, limiting the ability to understand their experiences. Among available data, NHPI people fare worse than White people for more than half of the examined measures.   

LIFE EXPECTANCY

AIAN and Black people have a shorter life expectancy at birth compared to White people, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2021. Life expectancy at birth represents the average number of years a group of infants would live if they were to experience the age-specific death rates prevailing during a specified period. Life expectancy declined by 2.7 years between 2019 and 2021, largely reflecting an increase in excess deaths due to COVID-19, which disproportionately impacted Black, Hispanic, and AIAN people. AIAN people experienced the largest life expectancy decline of 6.6 years, followed by Hispanic (4.2 years) and Black people (4.0 years), and a smaller decline of 2.4 years for White people. Asian people had the smallest decline in life expectancy of 2.1 years between 2019 and 2021. Provisional data from 2022 show that overall life expectancy increased across all racial and ethnic groups between 2021 and 2022, but racial disparities persist (Figure 13). Life expectancy is lowest for AIAN people at 67.9 years, followed by Black people at 72.8 years, while White and Hispanic people have higher life expectancies of 77.5 and 80 years, respectively, and Asian people have the highest life expectancy at 84.5 years. Life expectancies are even lower for AIAN and Black males, at 64.6 and 69.1 years, respectively. Data are not available for NHPI people.

SELF-REPORTED HEALTH STATUS

Black, Hispanic, and AIAN adults are more likely to report fair or poor health status than their White counterparts, while Asian adults are less likely to indicate fair or poor health. Nearly three in ten (29%) AIAN adults and roughly two in ten Hispanic (23%) and Black (21%) adults report fair or poor health status compared to 16% of White adults (Figure 14). One in ten Asian adults report fair or poor health status.

BIRTH RISKS AND OUTCOMES

NHPI (62.8 per 100,000), Black (39.9 per 100,000), and AIAN (32 per 100,000) women have the highest rates of pregnancy-related mortality (deaths within one year of pregnancy) between 2017-2019, while Hispanic women (11.6 per 100,000) have the lowest rate (Figure 15). More recent data for maternal mortality, which measures deaths that occur during pregnancy or within 42 days of pregnancy, shows that Black women (49.5 per 100,000) have the highest maternal mortality rate across racial and ethnic groups in 2022 (Figure 16). However, maternal mortality rates decreased significantly across most racial and ethnic groups between 2021 and 2022. Experts suggest the decline may reflect a return to pre-pandemic levels following the large increase in maternal death rates due to COVID-19 related deaths. The Dobbs decision eliminating the constitutional right to abortion could widen the already large disparities in maternal health as people of color may face disproportionate challenges accessing abortions due to state restrictions.

Black, AIAN, and NHPI women have higher shares of preterm births, low birthweight births, or births for which they received late or no prenatal care compared to White women (Figure 17). Additionally, Asian women are more likely to have low birthweight births than White women. Notably, NHPI women (22%) are four times more likely than White women (5%) to begin receiving prenatal care in the third trimester or to receive no prenatal care at all.

Teen birth rates have declined over time, but the birth rates among Black, Hispanic, AIAN, and NHPI teens are over two times higher than the rate among White teens (Figure 18). In contrast, the birth rate for Asian teens is more than four times lower than the rate for White teens.

Infants born to women of color are at higher risk for mortality compared to those born to White women. Infant mortality rates have declined over time although provisional 2022 data suggest a slight increase relative to 2021. As of 2022, Black (10.9 per 1,000) and AIAN (9.1 per 1,000) infants are at least two times as likely to die as White infants (4.5 per 1,000) (Figure 19). NHPI infants (8.5 per 1,000) are nearly twice as likely to die as White infants (4.5 per 1,000). Asian infants have the lowest mortality rate at 3.5 per 1,000 live births.

HIV AND AIDS DIAGNOSIS INDICATORS

Black, Hispanic, NHPI, and AIAN people are more likely than White people to be diagnosed with HIV or AIDS, the most advanced stage of HIV infection. In 2021, the HIV diagnosis rate for Black people is roughly eight times higher than the rate for White people, and the rate for Hispanic people is about four times higher than the rate for White people (Figure 20). AIAN and NHPI people also have higher HIV diagnosis rates compared to White people. Similar patterns are present in AIDS diagnosis rates, the most advanced stage of HIV, reflecting barriers to treatment. Black people have a roughly nine times higher rate of AIDS diagnosis compared to White people, and Hispanic, AIAN, and NHPI people also have higher rates of AIDS diagnoses. Most groups have seen decreases in HIV and AIDS diagnosis rates since 2013, although the HIV diagnosis rate has remained stable for Hispanic people and increased for AIAN and NHPI people.

Among people ages 13 and older living with diagnosed HIV infection, viral suppression rates are lower among AIAN (64%), Hispanic (64%), NHPI (63%), and Black (62%) people compared with White (72%) and Asian (70%) people (Figure 21) . Viral suppression refers to having less than 200 copies of HIV per milliliter of blood. Increasing the viral suppression rate among people with HIV is one of the key strategies of the Ending the HIV Epidemic in the U.S. initiative. Viral suppression promotes optimal health outcomes for people with HIV and also offers a preventive benefit as when someone is virally suppressed, they cannot sexually transmit HIV.

CHRONIC DISEASE AND CANCER

The prevalence of chronic disease varies across racial and ethnic groups and by type of disease. Diabetes rates for AIAN (18%), Black (16%), and Hispanic (13%) adults are all higher than the rate for White adults (11%). AIAN people (11%) are more likely to have had a heart attack or heart disease than White people (8%), while rates for Black (6%), NHPI (6%), Hispanic (4%) and Asian (3%) people are lower than White people. Black (12%) and AIAN (13%) adults have higher rates of asthma compared to their White counterparts (10%), while rates for Hispanic (8%) and Asian (5%) adults are lower, and the rate for NHPI is the same (10%). Among children, Black children (16%) are nearly twice as likely to have asthma compared to White children (9%), while Asian children (6%) have a lower asthma rate (Figure 22). Differences are not significant for other racial and ethnic groups, and data are not available for NHPI children.

AIAN, NHPI, and Black people are roughly twice as likely as White people to die from diabetes, and Black people are more likely than White people to die from heart disease (Figure 23). Hispanic people (28.3 per 100,000) also have a higher diabetes death rate compared to White people (21.3 per 100,000). In contrast, Asian people (17.2 per 100,000) are less likely than White people (21.3 per 100,000) to die from diabetes, and AIAN, Hispanic, and Asian people have lower heart disease death rates than their White counterparts.

People of color generally have lower rates of new cancer cases compared to White people, but Black people have higher incidence rates for some cancer types (Figure 24). Black people have lower rates of cancer incidence compared to White people for cancer overall, and most of the leading types of cancer examined. However, they have higher rates of new colon, and rectum, and prostate cancer. AIAN people have a higher rate of colon and rectum cancer than White people. Other groups have lower cancer incidence rates than White people across all examined cancer types.

Although Black people do not have higher cancer incidence rates than White people overall and across most types of cancer, they are more likely to die from cancer. Black people have a higher cancer death rate than White people for cancer overall and for most of the leading cancer types (Figure 25). In contrast, Hispanic, Asian and Pacific Islander, and AIAN people have lower cancer mortality rates across most cancer types compared to White people. The higher mortality rate among Black people despite similar or lower rates of incidence compared to White people could reflect a combination of factors , including more limited access to care, later stage of diagnosis, more comorbidities, and lower receipt of guideline-concordant care, which are driven by broader social and economic inequities.

COVID-19 DEATHS

AIAN, Hispanic, NHPI, and Black people have higher rates of COVID-19 deaths compared to White people. As of March 2024, provisional age-adjusted data from the Centers for Disease Control and Prevention (CDC) show that between 2020 and 2023, AIAN people are roughly two times as likely as White people to die from COVID-19, and Hispanic, NHPI and Black people are about 1.5 times as likely to die from COVID-19 (Figure 26). Asian people have lower COVID-19 death rates during this period compared to all other race and ethnicity groups.

Obesity rates vary across race and ethnicity groups. As of 2022, Black (43%), AIAN (39%), and Hispanic (37%) adults all have higher obesity rates than White adults (32%), while Asian adults (13%) have a lower obesity rate (Figure 27).

Mental Health and Drug Overdose Deaths

Overall rates of mental illness are lower for people of color compared to White people but could be underdiagnosed among people of color. About one in five Hispanic and Black (21% and 20%, respectively) adults and 17% of Asian adults report having a mental illness compared to 25% of White adults (Figure 28). Among  adolescents , the share with symptoms of a past year major depressive episode were not significantly different across racial and ethnic groups, with roughly one in five White (21%) and Hispanic (20%) adolescents, 17% of Black, and about one in seven Asian (15%), and AIAN (14%) adolescents reporting symptoms. Data are not available for NHPI people. Research suggests that a lack of  culturally sensitive  screening  tools  that detect mental illness, coupled with  structural barriers could contribute to  underdiagnosis  of mental illness among people of color.

AIAN and White people have the highest rates of deaths by suicide as of 2022. People of color have been disproportionately affected by recent increases in deaths by suicide compared with their White counterparts. As of 2022, AIAN (27.1 per 100,000) and White (17.6 per 100,000) people have the highest rates of deaths by suicide compared to all other racial and ethnic groups (Figure 29). Rates of deaths by suicide are also over three times higher among AIAN adolescents (32.9 per 100,000) than White adolescents (10.6 per 100,000). In contrast, Black, Hispanic, and Asian adolescents have lower rates of suicide deaths compared to their White peers.

Drug overdose death rates increased among AIAN, Black, Hispanic, and Asian people between 2021 and 2022. As of 2022, AIAN people continue to have the highest rates of drug overdose deaths (65.2 per 100,000 in 2022) compared with all other racial and ethnic groups. Drug overdose death rates among Black people (47.5 per 100,000) exceed rates for White people (35.6 per 100,000), reflecting larger increases among Black people in recent years (Figure 30). Hispanic (22.7 per 100,000), NHPI (18.8 per 100,000), and Asian (5.3 per 100,000) people have lower rates of drug overdose deaths than White people (35.6 per 100,000). Data on drug overdose deaths among adolescents show that while White adolescents account for the largest share of drug overdose deaths, Black and Hispanic adolescents have experienced the fastest increase in these deaths in recent years.

Social Determinants of Health

Racial disparities in social and economic factors.

Social determinants of health are the conditions in which people are born, grow, live, work, and age. They include factors like socioeconomic status, education, immigration status, language, neighborhood and physical environment, employment, and social support networks, as well as access to health care. There has been extensive research and recognition that addressing social, economic, and environmental factors that influence health is important for advancing health equity. Research also shows how racism and discrimination drive inequities across these factors and impact health and well-being.  

Black, Hispanic, AIAN, and NHPI people fare worse compared to White people across most examined measures of social determinants of health (Figure 31). Experiences for Asian people are more mixed relative to White people across these examined measures. Reliable or disaggregated data for NHPI people are missing for a number of measures.

WORK STATUS, FAMILY INCOME, AND EDUCATION

Across racial and ethnic groups, most nonelderly people live in a family with a full-time worker, but Black, Hispanic, AIAN, and NHPI nonelderly people are more likely than White people to be in a family with income below poverty (Figure 32). While most people across racial and ethnic groups live in a family with a full-time worker, disparities persist. AIAN (68%), Black (73%), NHPI (77%), and Hispanic (81%) people are less likely than White people (83%) to have a full-time worker in the family. In contrast, Asian people (86%) are more likely than their White counterparts (83%) to have a full-time worker in the family. Despite the majority of people living in a family with a full-time worker, over one in five AIAN (25%) and Black (22%) people have family incomes below the federal poverty level, over twice the share as White people (10%), and rates of poverty were also higher among Hispanic (17%) and NHPI (16%) people.

Black, Hispanic, AIAN, and NHPI people have lower levels of educational attainment compared to their White counterparts. Among people ages 25 and older, over two thirds (69%) of White people have completed some post-secondary education, compared to less than half (45%) of Hispanic people, just over half of AIAN and NHPI people (both at 52%), and about six in ten Black people (58%) (Figure 33). Asian people are more likely than White people to have completed at least some post-secondary education, with 74% completing at least some college.

NET WORTH AND HOME OWNERSHIP

Black and Hispanic families have less wealth than White families. Wealth can be defined using net worth, a measure of the difference between a family’s assets and liabilities. The median net worth for White households is $285,000 compared to $44,900 for Black households and $61,600 for Hispanic households (Figure 34). Asian households have the highest median net worth of $536,000. Data are not available for AIAN and NHPI people.

People of color are less likely to own a home than White people (Figure 35). Nearly eight in ten (77%) White people own a home compared to 70% of Asian people, 62% of AIAN people, 55% of Hispanic people, and about half of Black (49%) and NHPI (48%) people.

FOOD SECURITY, HOUSING QUALITY, AND INTERNET ACCESS

Black and Hispanic adults and children are more likely to experience food insecurity compared to their White counterparts. Among adults, AIAN (18%), Black (14%), and Hispanic (12%) adults report low or very low food security compared to White adults (6%) (Figure 36). Among children, AIAN (24%), Black (21%) and Hispanic (15%) children are over twice as likely to be food insecure than White children (6%). Data are not available for NHPI adults and children.

People of color are more likely to live in crowded housing than their White counterparts (Figure 37). Among White people, 3% report living in a crowded housing arrangement, that is having more than one person per room, as defined by the American Community Survey. In contrast, almost three in ten (28%) NHPI people, roughly one in five (18%) Hispanic people, 16% AIAN people, and about one in ten Asian (12%) and Black (8%) people report living in crowded housing.

AIAN, NHPI, and Black people are less likely to have internet access than White people (Figure 38). Higher shares of AIAN (12%), and Black and NHPI people (both at 6%) say they have no internet access compared to their White counterparts (4%). In contrast, Asian people (2%) are less likely to report no internet access than White people (4%).

TRANSPORTATION

People of color are more likely to live in a household without access to a vehicle than White people (Figure 39) . About one in eight Black people (12%) and about one in ten AIAN (9%) and Asian (8%) people live in a household without a vehicle available followed by 7% of Hispanic and NHPI people. White people are the least likely to report not having access to a vehicle in the household (4%).

CITIZENSHIP AND ENGLISH PROFICIENCY

Asian, Hispanic, NHPI, and Black people include higher shares of noncitizen immigrants compared to White people. Asian and Hispanic people have the highest shares of noncitizen immigrants at 25% and 19%, respectively (Figure 40). Asian people are projected to become the largest immigrant group in the United States by 2055. Immigrants are more likely to be uninsured than citizens and face increased barriers to accessing health care.

Hispanic and Asian people are more likely to have LEP compared to White people. Almost one in three Asian (31%) and Hispanic (28%) people report speaking English less than very well compared to White people (1%)(Figure 41). Adults with LEP are more likely to report worse health status and increased barriers in accessing health care compared to English proficient adults.

EXPERIENCES WITH RACISM, DISCRIMINATION, AND UNFAIR TREATMENT

Racism is an underlying driver of health disparities, and repeated and ongoing exposure to perceived experiences of racism and discrimination can increase risks for poor health outcomes. Research has shown that exposure to racism and discrimination can lead to  negative  mental health  outcomes  and certain negative impacts on physical health, including depression, anxiety, and hypertension.

Black, AIAN, Hispanic, and Asian adults are more likely to report certain experiences with discrimination in daily life compared with their White counterparts, with the greatest frequency reported among Black and AIAN adults.  A 2023 KFF survey shows that at least half of AIAN (58%), Black (54%), and Hispanic (50%) adults and about four in ten (42%) Asian adults say they experienced at least one type of discrimination in daily life in the past year (Figure 42). These experiences include receiving poorer service than others at restaurants or stores; people acting as if they are afraid of them or as if they aren’t smart; being threatened or harassed; or being criticized for speaking a language other than English. Data are not available for NHPI adults.

About one in five (18%) Black adults and roughly one in eight AIAN (12%) adults, followed by roughly one in ten Hispanic (11%), and Asian (10%) adults who received health care in the past three years report being treated unfairly or with disrespect by a health care provider because of their racial or ethnic background.  These shares are higher than the 3% of White adults who report this (Figure 43). Overall, roughly three in ten (29%) AIAN adults and one in four (24%) Black adults say they were treated unfairly or with disrespect by a health care provider in the past three years for any reason compared with 14% of White adults.

About the Data

Data sources.

This chart pack is based on the KFF Survey on Racism, Discrimination, and Health and KFF analysis of a wide range of health datasets, including the 2022 American Community Survey, the 2022 Behavioral Risk Factor Surveillance System, the 2022 National Health Interview Survey, the 2022 National Survey on Drug Use and Health, and the 2022 Survey of Consumer Finances as well as from several online reports and databases including the Centers for Disease Control and Prevention (CDC) Morbidity and Mortality Weekly Report (MMWR) on vaccination coverage, the National Center for Health Statistics (NCHS) National Vital Statistics Reports, the CDC Influenza Vaccination Dashboard Flu Vaccination Coverage Webpage Report, the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) Atlas, the United States Cancer Statistics Incidence and Mortality Web-based Report, the 2022 CDC Natality Public Use File, CDC Web-based Injury Statistics Query and Reporting System (WISQARS) database, and the CDC WONDER online database.

Methodology

Unless otherwise noted, race/ethnicity was categorized by non-Hispanic White (White), non-Hispanic Black (Black), Hispanic, non-Hispanic American Indian and Alaska Native (AIAN), non-Hispanic Asian (Asian), and non-Hispanic Native Hawaiian or Pacific Islander (NHPI). Some datasets combine Asian and NHPI race categories limiting the ability to disaggregate data for these groups. Non-Hispanic White persons were the reference group for all significance testing. All noted differences were statistically significant differences at the p<0.05. We include data for smaller population groups wherever available. Instances in which the unweighted sample size for a subgroup is less than 50 or the relative standard error is greater than 30% are noted in the figures, and confidence intervals for those measures are included in the figure.

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What is a mental disorder? An exemplar-focused approach

Dan j. stein.

1 SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa

Andrea C. Palk

2 Department of Philosophy, Stellenbosch University, Stellenbosch, South Africa

Kenneth S. Kendler

3 Virginia Institute of Psychiatric and Behavioral Genetics and Departments of Psychiatry, and Human and Molecular Genetics, School of Medicine/Virginia Commonwealth University, VA, USA

The question of ‘what is a mental disorder?’ is central to the philosophy of psychiatry, and has crucial practical implications for psychiatric nosology. Rather than approaching the problem in terms of abstractions, we review a series of exemplars – real-world examples of problematic cases that emerged during work on and immediately after DSM-5, with the aim of developing practical guidelines for addressing future proposals. We consider cases where (1) there is harm but no clear dysfunction, (2) there is dysfunction but no clear harm, and (3) there is possible dysfunction and/or harm, but this is controversial for various reasons. We found no specific criteria to determine whether future proposals for new entities should be accepted or rejected; any such proposal will need to be assessed on its particular merits, using practical judgment. Nevertheless, several suggestions for the field emerged. First, while harm is useful for defining mental disorder, some proposed entities may require careful consideration of individual v. societal harm, as well as of societal accommodation. Second, while dysfunction is useful for defining mental disorder, the field would benefit from more sharply defined indicators of dysfunction. Third, it would be useful to incorporate evidence of diagnostic validity and clinical utility into the definition of mental disorder, and to further clarify the type and extent of data needed to support such judgments.

Introduction

The question of ‘what is a mental disorder?’ is foundational in philosophy of psychiatry, and also has enormous practical importance for clinicians and patients. This question has therefore been addressed in successive revisions of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM). Given ongoing work on the revision of DSM-5, it is timely to ask this question again.

Many previous attempts have applied a conceptual approach to the definition of mental disorder. These have produced limited progress, particularly in assisting with decisions about specific conditions. Thus, it may be useful to try a different approach to this critical problem. Rather than focusing on abstractions, we review a series of exemplars – real-world examples of problematic cases that emerged during work on and immediately after DSM-5. From these cases, we hoped to extract practical guidelines for considering future proposals for the inclusion of entities in the nosology.

What is a mental disorder?

The question of ‘what is a mental disorder’, is crucial, in part, because the real possibility exists of erroneously classifying various kinds of social deviance or behavioral variation as ‘disorder’, when they are better conceptualized using other categories, such as ‘non-pathological individual differences’, ‘lifestyle choice’, or ‘crime’. A paradigmatic example from DSM is that of homosexuality, which was conceptualized in DSM-I as a disorder, (American Psychiatric Association, 1952 ) but by DSM-5 was no longer mentioned (American Psychiatric Association, 2013 ; Drescher, 2015 ).

Many authors have emphasized that what counts as a disease or disorder changes over time and across place, and have accused medicine and psychiatry of failing to recognize how idioms of distress are shaped by culture (Kirmayer, 2005 ; Kleinman, 1988 ). Others have accused the DSM of over-medicalizing (Frances, 2014 ; Horwitz, 2007 ; Szasz, 2007 ). These criticisms are driven by disagreements about the advantages and disadvantages of the medicalization of putative mental conditions. Central to these debates is the degree to which our disorders can be best understood as independent biological entities (naturalism/objectivism) or value-laden social constructs (normativism/constructivism) (Agich, 1983 ; Boorse, 1975 ; Fulford, 2001 ; Nordenfelt, 2007 ; Sadler, 2005 ; Stein, 2008 ; Zachar & Kendler, 2017 ).

Prior proposals have attempted to move beyond the polarities of naturalism and constructivism. Zachar suggested that mental disorders are ‘practical kinds’ (Zachar, 2002 ), shifting the issue from whether disorder categories reference scientific entities, to how effectively they facilitate particular scientific or clinical goals (Zachar & Kendler, 2017 ). In influential work, Wakefield defined mental disorders as ‘harmful dysfunctions’, and depicted dysfunction in evolutionary terms (Wakefield, 1992 ).

A strong form of realism holds that, just as the periodic table depicts the properties of molecular entities, so a medical or psychiatric nosology can carve nature at its joints – as a series of ‘natural kinds’ (Kendler, 2016 ; Stein, 2008 ). Softer forms of realism, likely more appropriate for conceptualizing mental disorders, regard exemplars like biological species as more appropriate for psychiatric disorders as the boundaries between different species are fuzzy, and not amenable to depiction in tabular format (Kendler, 2016 ; Stein, 2008 ).

We find aspects of both pragmatic approaches and Wakefield's characterization helpful, and use them as a framework for organizing our exemplars. More specifically, in reviewing real-world cases relevant to DSM-5 we will rely on the notions of ‘harm’ and of ‘dysfunction’. Harm may be indexed by the presence of distress and impairment, while dysfunction may be inferred when psychobiological mechanisms produce symptoms and associated harm. Nevertheless, as our exemplars will demonstrate, judgments about harm and dysfunction entail a range of additional complex considerations.

DSM definitions of mental disorder

DSM has responded to these debates in its definitions of mental disorders. Thus DSM-III emphasizes, for example, that clinicians should not misclassify or label a cultural expression of distress or political deviance as a disease (American Psychiatric Association, 1980 ). Subsequent editions of DSM have emphasized that the boundaries of mental disorders are fuzzy ( Table 1 ) (American Psychiatric Association, 2000 , 2013 ).

DSM-IV definition of mental disorder

During the development of DSM-5, along with others, we attempted to further clarify the DSM criteria for a mental disorder ( Table 2 ) (Stein et al., 2010 ). While our proposal differs modestly from the later DSM-5 wording ( Table 3 ), three differences are relevant here. First, while the DSM-5 definition refers to dysfunction in ‘psychological, biological, or developmental processes,’ we prefer ‘psychobiological’, to emphasize that psychology and biology are intertwined constructs that encompass development, as well as other life-course constructs.

DSM-V proposal for the definition of mental/psychiatric disorder

DSM-5 definition of mental disorder

Second, our proposal suggested that the consequences of a mental disorder are clinically significant distress or disability (B). The DSM-5 wording indicates that mental disorders are usually associated with significant distress or impairment. The word ‘usually’ may be technically accurate, in that on rare occasions, a mental disorder is listed in DSM-5, and there is no ‘clinical criterion’ (First & Wakefield, 2013 ). However, given that psychiatric symptoms are often on a continuum with normality, the clinical criterion is one key way of providing a relatively valid and reliable marker of underlying dysfunction, so lessening the risk of false positives and over-medicalization (Cooper, 2013 ). Other ways in which clinical criteria can validly and reliably point to underlying dysfunction include descriptions of symptom severity, excessiveness, frequency, and duration (First & Wakefield, 2013 ).

Third, our proposal made reference to considerations of diagnostic validity and clinical utility. This explicitly emphasizes that decisions about proposals for new entities must address empirical data. Certainly, data on diagnostic validity and clinical utility of proposed entities were carefully assessed during the DSM-5 revision process.

Examining different exemplars

We now turn to a number of test-cases that emerged during DSM-5. While conceptual work is crucial, it is important to examine its conclusions in the context of specific empirical examples, which may then produce greater clarity on the underlying conceptual issues.

We explore, in turn, several different types of cases, categorized along the following lines: (1) entities associated with harm, but for which there is limited evidence of underlying dysfunction, (2) entities involving dysfunction but without strong evidence that they produce harm, and (3) entities involving possible harm and dysfunction, and thus possibly indicative of a disorder, but which are controversial for various reasons. While the third category deals explicitly with controversial cases, controversy is present in all three categories.

Harm but no clear psychobiological dysfunction

A number of conditions are associated with harm to individuals and/or society, but are not considered disorders because they lack evidence of underlying psychobiological dysfunction. Entities that fall under this rubric include unwanted physical, mental, or behavioral changes (e.g. those that accompany aging), more enduring traits that entail suffering or produce negative impacts but are not considered disorders (e.g. laziness), and behavior that is more appropriately classified as culturally or socially deviant rather than as a mental disorder (e.g. racism). The appropriate responses to distress or impairment associated with these entities would generally be regarded as emanating from moral, cultural, or social domains, rather than from the domain of health. Closer examination of specific exemplars suggests, however, that judgments of whether or not an entity should be included in the nosology reflect a number of different considerations ( Table 4 ).

Key considerations regarding the inclusion of putative entities in the nosology

Aging is associated with a range of negative sequelae. Furthermore, there is a growing understanding of the specific psychobiological mechanisms that lead to symptoms associated with aging and these harms, bolstering the claim that aging involves dysfunction (De Grey, 2007 ). That said, a range of causal mechanisms presumably underly the spectrum of aging from premature aging (e.g. progeria) to typical senescence. Indeed, a view that emphasizes the normality of aging may concede that physicians counsel individuals on a range of measures to sustain health and curb aging but call into question the inclusion of mild neurocognitive disorder in DSM-5. The concern is that this risks pathologizing minor forgetfulness associated with the aging process, particularly given the lack of treatment and the potentially harmful effects of receiving such a diagnosis (Rattan, 2014 ). That said, the more future medical interventions for mild neurocognitive disorder target mechanisms relevant to premature aging, and are shown efficacious and cost-effective, the more useful such a diagnosis will, arguably, be. Thus, judgments about the inclusion of entities in the nosology may, in part, reflect the existence, efficacy, and cost-efficiency of health interventions.

Time-limited and non-incapacitating anxiety associated with threat (e.g. a possible job loss), and suffering associated with loss (e.g. death of a parent), may be experienced as unwelcome, and clinicians may play a useful role in helping to alleviate them. Nevertheless, anxiety and sadness in the face of threat and loss are generally considered to be appropriate, rather than dysfunctional, responses. During the development of DSM-5, there was considerable debate about the removal of the bereavement exclusion criteria from the diagnosis of major depression (Zachar, First, & Kendler, 2017 ). The removal of this clause is consistent with the fact that depressions that are precipitated by a range of other common stressors (e.g. romantic rejection; serious medical problems) were not excluded. While critics argued that this decision reflected over-medicalization, a counter-argument is that it is important to ensure that diagnostic criteria allow appropriate diagnosis and treatment of depression in the context of bereavement (Prigerson, Boelen, Xu, Smith, & Maciejewski, 2021 ). Thus, judgments about thresholds for a putative disorder in the nosology may require consideration of epistemic values such as the internal consistency of criteria.

Racism is a phenomenon that has been associated with great harm and suffering (Schmitt, Branscombe, Postmes, & Garcia, 2014 ). While extreme racism may be a symptom of psychopathology, and there is some evidence of an association between, for example, racism and certain personality types (Adorno, 1969 ), there is little evidence that racism, in general, is the result of underlying psychobiological dysfunction. Rather, there is relatively widespread consensus that racist beliefs and behavior are largely a product of socialization and culture. We would therefore argue that racism is not a disorder; it is a phenomenon that, while sanctioned in some cultures in the past, is now a form of social deviance that should be addressed by a range of different social and educational interventions. Thus, judgments about the inclusion of an entity in the nosology may require rigorous reflection on cultural and social values.

Similar logic would hold for a range of other socially deviant or problematic behaviors (Aristotle, 1985 ), including those redolent of the seven deadly sins of laziness, gluttony, acquisitiveness, aggression, lust, jealousy, and pride. Prima facie, these are more appropriately understood and responded to in moral or socio-cultural terms rather than with health interventions. That said, psychotherapy may usefully target such behaviors or traits, and public health may usefully advocate for healthy eating and sexual behaviors. Furthermore, this matter is complicated by the fact that when clearly excessive, such traits can point to underlying psychobiological dysfunction; indeed, symptoms such as apathy, hyperphagia, hoarding, violence, hypersexuality, obsessional jealousy, and grandiosity may be indicative of a psychiatric disorder, and are appropriately listed in the DSM-5 glossary. Thus, judgments about the inclusion of a disorder in the nosology are based, in part, on evidence of clear excessiveness of behaviors/traits, and associated features that point to dysfunction.

Psychobiological dysfunction but no clear harm

In this category, we include various conditions for which there is some evidence of underlying psychobiological dysfunction, even if this is not fully understood. Conditions in this category may have been regarded as harmful, in the sense of disadvantageous, or socially deviant, in the past, but this view has been contested due to social change. While conditions in this category may point to differences rather than disorder, individuals with these conditions may still experience disadvantage and suffering. It may therefore be crucial to ensure support and treatment for those who seek it. Again, a closer examination of specific exemplars suggests that judgments of whether or not an entity should be included in the nosology reflect a number of different considerations ( Table 4 ).

The notion of disability has been extensively challenged by rights-based advocacy groups and organizations that have focused on promoting inclusivity, equality, and respect (Charlton, 1998 ). A paradigmatic example is deafness, which although not a psychiatric entity, is nevertheless useful as a point of departure for further discussion of analogous behavioral conditions where the presence of harm is contested. Deafness is the result of underlying alterations in structures and mechanisms of hearing, consistent with dysfunction. Moreover, given the challenges of participating in a hearing society, deafness has been widely viewed as disadvantageous, and characterized as a medical condition. However, this has been challenged by the view that deafness itself is not intrinsically harmful; rather, it is societal responses, or lack of response in terms of ensuring adequate accommodation, that produces harm. A view of deafness as a disability has been replaced with a view of deafness as a cultural identity (Padden & Humphries, 2005 ). This identity is referred to as Deaf, rather than deaf, which refers simply to hearing loss. While there have been rare, but controversial, cases of Deaf parents wishing to utilize preimplantation genetic diagnosis to select for deafness, many members of the Deaf community, given the choice of having children with or without hearing, opt for the former (Camporesi, 2010 ; Wallis, 2020 ).

Deaf culture has some parallels with groups that are open about their unusual psychological behaviors or traits, but who argue that these are not associated with harm. It turns out, for example, that hearing voice is prevalent in the general population, and that these experiences may not necessarily be indicative of a serious mental disorder (Maijer, Begemann, Palmen, Leucht, & Sommer, 2018 ). In the absence of harm, it is difficult to argue for the medicalization of such experiences, and there are now support groups for those with these experiences (Longden, 2017 ). That said, hearing voices may be a symptom of a range of mental disorders, other than psychotic disorders, and there is evidence from community surveys that such symptoms are associated with significant disability, which is unlikely to be simply a reflection of lack of social accommodation (Navarro-Mateu et al., 2017 ; Pierre, 2010 ). Thus, judgments about whether or not an entity should be included in the nosology require nuanced assessment of the extent of harm, as reflected in distress and impairment.

Autistic spectrum disorder (ASD) which is associated with alterations in structures and mechanisms underlying behavior (Van Rooij et al., 2018 ), has traditionally been viewed as a harmful condition. However, there is a contrary position, which may be particularly relevant to milder cases of ASD. In this view, the positive attributes associated with ASD (e.g. high levels of creativity and mathematical ability) are emphasized and neurodiversity is celebrated, shifting the onus onto neuro-typical society to accommodate neuro-atypical persons (Glannon, 2007 ). However, despite the growing prevalence of persons with ASD who choose to see themselves as situated on a spectrum of normal variation, there are many individuals and families who seek health interventions or advocate for more scientific research to cure or prevent ASD (Walsh, Elsabbagh, Bolton, & Singh, 2011 ). These disagreements are perhaps indicative of the heterogeneous and dimensional nature of both ASD and its impact; in severe cases care rather than accommodation is required. Thus, judgments about whether or not an entity should be included in the nosology require careful assessment of the extent to which social accommodation is possible.

A similar set of issues emerges for gender identity disorder (GID) or transsexualism, which were removed from DSM-5 and ICD-11 and replaced by gender dysphoria (GD) and gender incongruence, respectively. These latter categories address cases in which there is significant distress due to conflicts between assigned and identified gender. In the case of GD, there is some preliminary evidence of neuroanatomical differences between transgender and cisgender persons which may arguably indicate underlying dysfunction (Burke, Manzouri, & Savic, 2017 ). Moreover, there is also some evidence of harmfulness, for example, a high risk of suicide (Garcia-Vega, Camero, Fernandez, & Villaverde, 2018 ). This could be sufficient for inclusion in our third category, however, we mention GD here because, despite the evidence that distress is intrinsic to the condition, it has also been argued that this distress is a product of stigmatization and social rejection. The shift from social rejection to acceptance of homosexuality, has bolstered this argument for some. On the other hand, from a clinical utility perspective, the inclusion of GD in the nosology is precisely important for ensuring medical and psychiatric care for individuals with this condition who request such care. Judgments about whether or not an entity should be included in the nosology may require careful balancing of the advantages and disadvantages of medicalization (Parens, 2013 ).

Possible harm and psychobiological dysfunction, but controversial

In the third category, we include conditions for which there is some evidence of underlying psychobiological dysfunction and actual or potential harm, but which are controversial for various reasons. First, the controversy may be attributed to a lack of certainty about whether or not a condition does, in fact, reflect underlying psychobiological dysfunction, or whether inclusion would represent over-medicalization. Second, the controversy could arise due to the fact that harm, in the sense of clinically significant distress or impairment, may be present only as a risk, which may not be actualized, so that inclusion of the condition may lead to overdiagnosis. Concerns about medicalization and overdiagnosis both reflect a critical stance towards the expansion of disorder constructs (Hofmann, 2016 ). Third, a condition may be indicative of disorder but considered controversial, in the sense of inappropriate for inclusion in the nosology, due to various pragmatic concerns. This could include a risk of misuse in legal contexts or negative implications for public health. These kinds of pragmatic considerations shift the focus from whether or not a condition is a disorder to whether or not a particular disorder belongs in a diagnostic manual ( Table 4 ).

Medicalization concerns

Compulsive sexual behavior disorder was rejected for DSM-5 but is included in ICD-11 as an impulse control disorder (Grant & Chamberlain, 2016 ). There is a growing evidence base on this disorder. Still, hypersexuality is not necessarily pathological, and there is currently little direct evidence that those who present clinically for the treatment of compulsive sexual behavior have underlying psychobiological dysfunction. Thus, such dysfunction needs to be inferred on the basis of clinical criteria such as severity and duration of symptoms (Kafka, 2010 ). As noted earlier, psychiatry should be wary of medicalizing conditions redolent of the seven sins, focusing rather on advocating for healthy sexual behavior. At the same time, psychiatry clearly has a role when hypersexuality reflects an underlying medical or psychiatric disorder, and it may well have a role when symptoms are truly excessive and associated with a great deal of distress and impairment. For example, it is not clear whether a person who compulsively watches pornography, but is able to limit viewing to the privacy of the home, has a disorder. While personal relationships may be negatively impacted, such a person can be described as functioning, as long as there is control over the behavior. We would be more inclined to regard a person who cannot limit viewing of pornography to a particular time of day or place and feels compelled to watch it while at work, with risk of job loss, as having a disorder. Judgments about whether or not an entity should be included in the nosology may require careful assessment of the degree of loss of control, and related impairment, particularly in the case of compulsive or addictive behaviors.

Internet gaming disorder was included in DSM-5 as a condition for further study, and gaming disorder is included as a mental disorder in ICD-11 (Billieux, Flayelle, Rumpf, & Stein, 2019 ). There is some evidence of underlying alterations in psychobiological structures and mechanisms in gambling disorder, which is included in both nosologies, but less evidence that this is the case in gaming disorder. Behavioral addictions are controversial partly because they raise questions as to whether underlying alterations in structures or mechanisms are sufficient to explain the behavior (which may be viewed as a lifestyle choice rather than as a loss of control). Proposals for new behavioral addictions such as gaming disorder also face the difficulty that there is simply less evidence for newly emergent conditions. Similarly, the brain disease model of substance use disorders has been critiqued (Hammer et al., 2013 ). Still, there is a strong argument that substance use disorders are mental disorders, with evidence of alterations in a range of psychobiological processes that are associated with loss of control, and that can be targeted by health interventions.

Overdiagnosis concerns

Attenuated psychosis syndrome (APS), which is associated both with evidence of psychobiological dysfunction and potential harm in the case of conversion, was included in DSM-5 as a condition for further study (Tsuang et al., 2013 ). APS elicits concerns about overdiagnosis, mainly due to the possibility that interventions for individuals who meet the criteria may cause harm (Zachar, First, & Kendler, 2020 ). There are some parallels between APS and other risk-syndromes such as hypercholesterolaemia or hypertension. Once it was clear that high levels of cholesterol were risky, these were defined as pathological. With the introduction of statins, and evidence that these agents lowered risks, thresholds for diagnosis were lowered; with the introduction of generic statins, and great cost-efficiencies, such thresholds were further decreased. It is possible that an analogous perspective may be useful in defining thresholds for anxiety disorders and depression. However, in the case of APS, there are arguably insufficient data demonstrating risk if untreated, as well as insufficient data demonstrating safety, efficacy, and cost-efficiency of interventions. Moreover, medical risk-syndromes may differ from the risk associated with a psychotic disorder due to the high levels of stigmatization associated with the latter. Nevertheless, it is possible that the issue of whether, and when, to intervene in the case of evidence of psychiatric risk will become increasingly pertinent given the potential for identifying predictive biomarkers – for example, from molecular genetics research (Palk, Dalvie, de Vries, Martin, & Stein, 2019 ).

Suicidal behavior disorder is included in DSM-5 as a condition for further study. Clearly, it is important for clinicians to be aware of suicidal behavior, and this is often an important target of treatment. On the other hand, suicidal behavior may be due to a range of different mental disorders, reflecting a range of different kinds of dysfunction. Furthermore, suicidal behavior is not always associated with a mental disorder; there is a compelling argument that in particular medical circumstances, it is understandable and appropriate for patients to make a decision to end their lives. Suicide can also arise as a form of political protest or a culturally sanctioned response to shame. Judgments about diagnostic validity may be complex, including consideration of a range of different empirical data of varying quality. This point is also exemplified by other entities included in DSM-5 as conditions for further study, namely persistent complex bereavement disorder, depressive episodes with short-duration hypomania, caffeine use disorder, non-suicidal self-injury, and neurobehavioral disorder associated with prenatal alcohol exposure (American Psychiatric Association, 2013 ).

Pragmatic concerns

Simple (type) schizophrenia (SS) or simple deteriorative disorder has long been controversial (Serra-Mestres et al., 2000 ). It has not been included in the nosology since DSM-III (although it was included in DSM-IV as a condition for further study), and while it was in ICD-10 it is not in ICD-11. There is indeed some evidence that simple schizophrenia is a rare deteriorative disorder characterized by nonspecific negative symptoms and an absence of psychotic symptoms. However, while previous iterations of DSM contained schizophrenia sub-types, these were appropriately removed due to a lack of diagnostic validity and reliability, and evidence that schizophrenia is a spectrum disorder (Serra-Mestres et al., 2000 ; Whitwell, Bramham, & Moriarty, 2018 ). Nevertheless, the fact that there continue to be patients who present with these kinds of deteriorative symptoms has been used to support claims that the diagnosis remains relevant (Whitwell et al., 2018 ). This exemplar illustrates that there is a distinction between judgments regarding whether a condition is a mental disorder, and judgments regarding whether it should be included in the nosology.

Paraphilic coercive disorder (PCD) was considered, but ultimately rejected, for inclusion in DSM-5 (Stern, 2010 ). PCD illustrates issues at the boundary between the medical and legal systems, and highlights disagreements about the nature of psychopathology and moral responsibility. There is inconclusive evidence of underlying psychobiological dysfunction or of harm to the individual (other than that following legal transgression) (Knight, 2010 ). However, more relevant here is the real risk of the PCD diagnosis being misused in legal contexts to either inappropriately exculpate a rapist, or to detain persons indefinitely, if deemed to be at risk of sexual reoffending (Wakefield, 2011 ). The debates surrounding PCD highlight how pragmatic considerations inform decisions about nosology. Such considerations include maintaining societal trust in the integrity of psychiatric diagnosis and protecting the reputation of the profession, as well as anticipating potentially harmful consequences of including certain constructs as disorders.

Importantly, as social mores change, so too may considerations about the cost-benefit of including particular entities in the nosology. Premenstrual dysphoric disorder (PMDD), formerly known as late luteal phase dysphoric disorder, is well described in the psychiatric literature. There is clear evidence that specific psychobiological mechanisms are altered in those with this condition, and that those with this condition may benefit from medical treatments (Epperson et al., 2012 ). Still, this entity was not included in DSM-IV, as concerns were raised that the diagnosis would impact negatively on women, confirming stereotypes that they had less ability to fulfil professional obligations (Zachar & Kendler, 2015 ). In DSM-5, perhaps partly because of advances in our understanding of and treatment of PMDD, and perhaps partly because of continued advances in gender parity, PMDD was included in the manual. Judgments about the inclusion of entities in the nosology may need to weigh up responsibilities to patients v. responsibilities to society as a whole.

Taken together, these exemplars may help shed light on key conceptual issues involved in including a proposed entity in the classification.

One set of conceptual issues surround the notion of ‘harm’. Harm refers to suffering or disadvantage associated with a particular condition, and is operationalized with the ‘clinical criterion’ of DSM-5 using the phrase ‘significant distress and/or impairment’. It has often been emphasized, including by DSM-5, that this criterion is ‘fuzzy’, and also that not all distress/impairment points to a mental disorder. However, our exemplars indicate a number of additional complexities.

First, decisions about the introduction of new entities into the nosology need to balance the harm to the individual with harm to society. This is seen in the discussion of PCD and PMDD. The introduction of PCD has significant potential for societal harm, and the proposal to introduce this disorder was rejected. While there were concerns about such harm for PMDD, societal changes have significantly mitigated these concerns, and the proposal to introduce this disorder was accepted. Furthermore, putative PCDs are relatively rare and PMDD relatively common, so the possibility of clinical benefit to those affected is greater for the latter (Hartlage, Breaux, & Yonkers, 2014 ; Robinson & Ismail, 2015 ; Thornton, 2010 ; Wollert, 2011 ). Second, there may be significant debate about the extent to which harm is due to the failure of society to accommodate differences. This is seen in debates around the inclusion of homosexual and gender dysphoria in the nosology. In the former case, exclusion was agreed upon, while in the latter case inclusion was advocated.

While the concept of ‘harm’ is a useful one for defining mental disorder, when new entities are proposed in the future, it will be important to consider, for some of them, more sharply, the issue of individual v. societal harm, as well as the issue of societal accommodation. Notably, our exemplars seem to indicate that profiles of harm may change over time as societies change. Although this is seen in only a very small number of exemplars, this means that we cannot provide future decision-makers with algorithmic advice about what proposal to accept or reject across the board. Just as the clinical criterion requires careful clinical judgment, so in the case of these disorders, decisions will require careful practical judgment, that weighs up a range of relevant considerations.

The second set of conceptual issues is those concerning the notion of ‘dysfunction’. In some medical disorders there is persuasive evidence of biological dysfunction (e.g. in progeria and in schizophrenia, neurogenetic mechanisms are causally linked to distressing and impairing symptoms). However, in many mental conditions, causal mechanisms are poorly understood, and psychobiological dysfunction is inferred on the basis of crude markers such as the severity of symptoms and the extent of associated distress and impairment (e.g. in mild cognitive impairment and in social anxiety disorder). Furthermore, our exemplars point to additional considerations.

In particular, in some cases of putative mental disorder, even though there are symptoms, as well as associated distress and impairment, there are still reasons to doubt the presence of underlying psychobiological dysfunction. First, the symptoms may simply reflect apparently normal processes, such as memory loss with age, or bereavement symptoms after a loss. Second, the symptoms may represent an understandable response to particular circumstances, other than those in Table 3 , criterion C. Suicidal ideation, for example, may be reasonable under certain circumstances. Thus, judgments about dysfunction, again, require careful practical judgment, weighing up a range of relevant considerations.

While the concept of ‘dysfunction’ is a useful one for conceptualizing mental disorders, when new entities are proposed in the future, it would be ideal to have more sharply defined indicators of dysfunction. Symptom severity, excessiveness, and duration may be helpful in indexing dysfunction (e.g. pointing to hypersexuality, or obsessional jealousy), but they are rough indicators that run the risk of relying on a statistical definition of dysfunction. At the same time, it is notable how rarely molecular evidence, per se , is able to index dysfunction; crucially, biological difference does not point to dysfunction .

The third set of conceptual issues relates to the type and extent of data required to reach conclusions about harm and dysfunction. In our proposed DSM-5 definition of mental disorder, we emphasized the importance of evidence for diagnostic validity and clinical utility. Diagnostic validity is supported, in part, by data that point to the involvement of specific etiological mechanisms; such data support assertions that psychobiological dysfunction is present and can be addressed by health interventions. Clinical utility is supported, in part, by data indicating that clinical assessment and intervention will be helpful; such data support assertions that harm is present and can be diminished. These issues are not listed in the DSM-5 text defining mental disorders, but our exemplars suggest that they are useful considerations.

Thus, across different proposals for disorders, there have been differences in the type and extent of data that support diagnostic validity and clinical utility. This is apparent in discussions of behavioral addictions, APS, and simple type schizophrenia. In behavioral addictions, some entities (e.g. gambling) have a great deal of data supporting diagnostic validity and clinical utility, while others (e.g. gaming) have fewer supporting data. In the case of simple type schizophrenia there are insufficient data to demonstrate diagnostic validity, and in the case of APS, there are insufficient data to demonstrate clinical utility.

It is notable that most discussions of the definition of mental disorders focus on conceptual issues and are therefore quite different from a data-oriented approach to the validation of entities, once they are considered to be disorders. It may be useful to incorporate explicitly the importance of a validation-oriented approach into conceptual discussions. Some in the field expect that once internet gaming gathers more high-quality validity and utility data, it too will be accepted as a disorder. Our view is that the field should recognize the potential importance of evidence of diagnostic validity and clinical utility in the definition of a mental disorder, and that future revisions further clarify the type and extent of data needed to support such judgments.

In summary, this paper has taken an exemplar-based approach to the question of defining mental disorders. We had hoped to extract a set of practical guidelines that future nosologists could draw on when discussing proposals for new entities. The conceptual issues that emerge from our exemplars are, however, complex, indicating that any future proposal will need to be assessed on its particular merits, using practical judgment. Nevertheless, several proposals for the field emerged. First, while harm is useful for defining mental disorder, some proposed entities may require careful consideration of individual v. societal harm, as well as of societal accommodation. Second, while dysfunction is useful for conceptualizing mental disorders, the field would benefit from developing more sharply defined indicators of dysfunction. Third, it would be useful to incorporate evidence of diagnostic validity and clinical utility into the definition of a mental disorder and to further clarify the type and extent of data needed to support such judgments.

Financial support

This research received no specific grant from any funding agency, commercial or not-for-profit sectors. However, Dan J Stein is funded by the South African Medical Research Council.

Conflict of interest

Dan J Stein has received support from Johnson & Johnson, Lundbeck, Servier, and Takeda for work unrelated to the topic of this manuscript, Andrea C Palk and Kenneth S Kendler have no conflicts of interest.

COVID-19: Long-term effects

Some people continue to experience health problems long after having COVID-19. Understand the possible symptoms and risk factors for post-COVID-19 syndrome.

Most people who get coronavirus disease 2019 (COVID-19) recover within a few weeks. But some people — even those who had mild versions of the disease — might have symptoms that last a long time afterward. These ongoing health problems are sometimes called post- COVID-19 syndrome, post- COVID conditions, long COVID-19 , long-haul COVID-19 , and post acute sequelae of SARS COV-2 infection (PASC).

What is post-COVID-19 syndrome and how common is it?

Post- COVID-19 syndrome involves a variety of new, returning or ongoing symptoms that people experience more than four weeks after getting COVID-19 . In some people, post- COVID-19 syndrome lasts months or years or causes disability.

Research suggests that between one month and one year after having COVID-19 , 1 in 5 people ages 18 to 64 has at least one medical condition that might be due to COVID-19 . Among people age 65 and older, 1 in 4 has at least one medical condition that might be due to COVID-19 .

What are the symptoms of post-COVID-19 syndrome?

The most commonly reported symptoms of post- COVID-19 syndrome include:

• Symptoms that get worse after physical or mental effort
• Lung (respiratory) symptoms, including difficulty breathing or shortness of breath and cough

Other possible symptoms include:

• Neurological symptoms or mental health conditions, including difficulty thinking or concentrating, headache, sleep problems, dizziness when you stand, pins-and-needles feeling, loss of smell or taste, and depression or anxiety
• Joint or muscle pain
• Heart symptoms or conditions, including chest pain and fast or pounding heartbeat
• Digestive symptoms, including diarrhea and stomach pain
• Blood clots and blood vessel (vascular) issues, including a blood clot that travels to the lungs from deep veins in the legs and blocks blood flow to the lungs (pulmonary embolism)
• Other symptoms, such as a rash and changes in the menstrual cycle

Keep in mind that it can be hard to tell if you are having symptoms due to COVID-19 or another cause, such as a preexisting medical condition.

It's also not clear if post- COVID-19 syndrome is new and unique to COVID-19 . Some symptoms are similar to those caused by chronic fatigue syndrome and other chronic illnesses that develop after infections. Chronic fatigue syndrome involves extreme fatigue that worsens with physical or mental activity, but doesn't improve with rest.

Why does COVID-19 cause ongoing health problems?

Organ damage could play a role. People who had severe illness with COVID-19 might experience organ damage affecting the heart, kidneys, skin and brain. Inflammation and problems with the immune system can also happen. It isn't clear how long these effects might last. The effects also could lead to the development of new conditions, such as diabetes or a heart or nervous system condition.

The experience of having severe COVID-19 might be another factor. People with severe symptoms of COVID-19 often need to be treated in a hospital intensive care unit. This can result in extreme weakness and post-traumatic stress disorder, a mental health condition triggered by a terrifying event.

What are the risk factors for post-COVID-19 syndrome?

You might be more likely to have post- COVID-19 syndrome if:

• You had severe illness with COVID-19 , especially if you were hospitalized or needed intensive care.
• You had certain medical conditions before getting the COVID-19 virus.
• You had a condition affecting your organs and tissues (multisystem inflammatory syndrome) while sick with COVID-19 or afterward.

Post- COVID-19 syndrome also appears to be more common in adults than in children and teens. However, anyone who gets COVID-19 can have long-term effects, including people with no symptoms or mild illness with COVID-19 .

What should you do if you have post-COVID-19 syndrome symptoms?

If you're having symptoms of post- COVID-19 syndrome, talk to your health care provider. To prepare for your appointment, write down:

• When your symptoms started
• What makes your symptoms worse
• How often you experience symptoms
• How your symptoms affect your activities

Your health care provider might do lab tests, such as a complete blood count or liver function test. You might have other tests or procedures, such as chest X-rays, based on your symptoms. The information you provide and any test results will help your health care provider come up with a treatment plan.

In addition, you might benefit from connecting with others in a support group and sharing resources.

• Long COVID or post-COVID conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects.html. Accessed May 6, 2022.
• Post-COVID conditions: Overview for healthcare providers. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/post-covid-conditions.html. Accessed May 6, 2022.
• Mikkelsen ME, et al. COVID-19: Evaluation and management of adults following acute viral illness. https://www.uptodate.com/contents/search. Accessed May 6, 2022.
• Saeed S, et al. Coronavirus disease 2019 and cardiovascular complications: Focused clinical review. Journal of Hypertension. 2021; doi:10.1097/HJH.0000000000002819.
• AskMayoExpert. Post-COVID-19 syndrome. Mayo Clinic; 2022.
• Multisystem inflammatory syndrome (MIS). Centers for Disease Control and Prevention. https://www.cdc.gov/mis/index.html. Accessed May 24, 2022.
• Patient tips: Healthcare provider appointments for post-COVID conditions. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/post-covid-appointment/index.html. Accessed May 24, 2022.
• Bull-Otterson L, et al. Post-COVID conditions among adult COVID-19 survivors aged 18-64 and ≥ 65 years — United States, March 2020 — November 2021. MMWR Morbidity and Mortality Weekly Report. 2022; doi:10.15585/mmwr.mm7121e1.

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