Suicide risk assessment involves integrating patient disclosure of suicidal ideation and non-specific risk factors such as family history, past suicidal behaviour, and psychiatric symptoms. A death version of the implicit association test (D-IAT) has been developed to provide an objective measure of the degree to which the self is affiliated with life or death. However, this has inconsistently been associated with past and future suicidal behaviour. Here, we systematically review and quantitatively synthesize the literature examining the D-IAT and suicide attempts. We searched psychINFO, Medline, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) from inception until 9 February 2021 to identify publications reporting D-IAT scores and suicide attempts (PROSPERO; CRD42020194394). Using random-effects models, we calculated standardized mean differences (SMD) and odds ratios (ORs) for retrospective suicide attempts. We then calculated ORs for future suicide attempts. ORs were dichotomized using a cutoff of zero representing equipoise between self-association with life and death. Eighteen studies met our inclusion criteria (n = 9551). The pooled SMD revealed higher D-IAT scores in individuals with a history of suicide attempt (SMD = 0.25, 95% CI 0.15 to 0.35); however, subgroup analyses demonstrated heterogeneity with acute care settings having lower effect sizes than community settings. Dichotomized D-IAT scores discriminated those with a history of suicide attempt from those without (OR 1.38 95% CI 1.01 to 1.89) and predicted suicide attempt over a six-month follow-up period (OR 2.99 95% CI 1.45 to 6.18; six studies, n = 781). The D-IAT may have a supplementary role in suicide risk assessment; however, determination of acute suicide risk and related clinical decisions should not be based solely on D-IAT performance.

A history of suicide attempt (SA) is a strong predictor of future suicide re-attempts or suicide. The aim of this systematic review is to evaluate the efficacy of psychotherapeutic interventions specifically designed for the prevention of suicide re-attempts. A systematic search from 1980 to June 2020 was performed via the databases PubMed and Google Scholar. Only randomized controlled trials were included which clearly differentiated suicidal self-harm from non-suicidal self-injury in terms of intent to die. Moreover, psychotherapeutic interventions had to be focused on suicidal behaviour and the numbers of suicide re-attempts had to be used as outcome variables. By this procedure, 18 studies were identified. Statistical comparison of all studies revealed that psychotherapeutic interventions in general were significantly more efficacious than control conditions in reducing the risk of future suicidal behaviour nearly by a third. Separate analyses revealed that cognitive-behavioural therapy as well as two different psychodynamic approaches were significantly more efficacious than control conditions. Dialectical behaviour therapy and elementary problem-solving therapy were not superior to control conditions in reducing the number of SAs. However, methodological reasons may explain to some extent these negative results. Considering the great significance of suicidal behaviour, there is unquestionably an urgent need for further development of psychotherapeutic techniques for the prevention of suicide re-attempts. Based on the encouraging results of this systematic review, it can be assumed that laying the focus on suicidal episodes might be the key intervention for preventing suicide re-attempts and suicides.

Approval and prescription of psychotropic drugs should be informed by the strength of evidence for efficacy. Using a Bayesian framework, we examined (1) whether psychotropic drugs are supported by substantial evidence (at the time of approval by the Food and Drug Administration), and (2) whether there are systematic differences across drug groups. Data from short-term, placebo-controlled phase II/III clinical trials for 15 antipsychotics, 16 antidepressants for depression, nine antidepressants for anxiety, and 20 drugs for attention deficit hyperactivity disorder (ADHD) were extracted from FDA reviews. Bayesian model-averaged meta-analysis was performed and strength of evidence was quantified (i.e. BFBMA). Strength of evidence and trialling varied between drugs. Median evidential strength was extreme for ADHD medication (BFBMA = 1820.4), moderate for antipsychotics (BFBMA = 365.4), and considerably lower and more frequently classified as weak or moderate for antidepressants for depression (BFBMA = 94.2) and anxiety (BFBMA = 49.8). Varying median effect sizes (ESschizophrenia = 0.45, ESdepression = 0.30, ESanxiety = 0.37, ESADHD = 0.72), sample sizes (Nschizophrenia = 324, Ndepression = 218, Nanxiety = 254, NADHD = 189.5), and numbers of trials (kschizophrenia = 3, kdepression = 5.5, kanxiety = 3, kADHD = 2) might account for differences. Although most drugs were supported by strong evidence at the time of approval, some only had moderate or ambiguous evidence. These results show the need for more systematic quantification and classification of statistical evidence for psychotropic drugs. Evidential strength should be communicated transparently and clearly towards clinical decision makers.

Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

China accounts for 17% of the global disease burden attributable to mental, neurological and substance use disorders. As a country undergoing profound societal change, China faces growing challenges to reduce the disease burden caused by psychiatric disorders. In this review, we aim to present an overview of progress in neuroscience research and clinical services for psychiatric disorders in China during the past three decades, analysing contributing factors and potential challenges to the field development. We first review studies in the epidemiological, genetic and neuroimaging fields as examples to illustrate a growing contribution of studies from China to the neuroscience research. Next, we introduce large-scale, open-access imaging genetic cohorts and recently initiated brain banks in China as platforms to study healthy brain functions and brain disorders. Then, we show progress in clinical services, including an integration of hospital and community-based healthcare systems and early intervention schemes. We finally discuss opportunities and existing challenges: achievements in research and clinical services are indispensable to the growing funding investment and continued engagement in international collaborations. The unique aspect of traditional Chinese medicine may provide insights to develop a novel treatment for psychiatric disorders. Yet obstacles still remain to promote research quality and to provide ubiquitous clinical services to vulnerable populations. Taken together, we expect to see a sustained advancement in psychiatric research and healthcare system in China. These achievements will contribute to the global efforts to realize good physical, mental and social well-being for all individuals.

This article casts a critical eye over the development of American psychiatry from 1980 to the present. It notes the rapid decline of psychoanalysis that followed the publication of DSM III; the rising influence of genetics and neuroscience; the re-emphasis on the biology of mental illness; and the collapse of public psychiatry that accompanied deinstitutionalization. It argues that while genetics and neuroscience have made scientific progress, the clinical utility of their findings to date has been very limited. The fifth edition of the DSM was supposed to base itself on this new science but that proved impossible. Diagnosis remains purely phenomenological and controversial. One of the ironies of research on psychiatric genetics is that has failed to find either a Mendelian origin of schizophrenia and depression or to validate the importance of hypothesized candidate genes. Genome-wide association studies have instead uncovered risk factors for major mental illnesses, but these overlap considerably, and the genetic associations are not dispositive. Most of those who carry these genetic variants do not develop mental illness. The status of psychopharmacology since the mid-1950s is scrutinized, as is the influence of the pharmaceutical industry on contemporary psychiatry, and the implications of its recent decision to abandon work in this arena. The paper concludes with an assessment of the crisis that it contends confronts contemporary American psychiatry: its overemphasis on biology; the urgent questions that persist about diagnosis and therapeutics; concerns about the directions of future research; and its inability to reduce the excess mortality that plagues the mentally ill.

Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Twenty-first century urbanization poses increasing challenges for mental health. Epidemiological studies have shown that mental health problems often accumulate in urban areas, compared to rural areas, and suggested possible underlying causes associated with the social and physical urban environments. Emerging work indicates complex urban effects that depend on many individual and contextual factors at the neighbourhood and country level and novel experimental work is starting to dissect potential underlying mechanisms. This review summarizes findings from epidemiology and population-based studies, neuroscience, experimental and experience-based research and illustrates how a combined approach can move the field towards an increased understanding of the urbanicity-mental health nexus.

Delay discounting paradigms have gained widespread popularity across clinical research. Given the prevalence in the field, researchers have set lofty expectations for the importance of delay discounting as a key transdiagnostic process and a ‘core’ process underlying specific domains of dysfunction (e.g. addiction). We believe delay discounting has been prematurely reified as, in and of itself, a core process underlying psychological dysfunction, despite significant concerns with the construct validity of discounting rates. Specifically, high delay discounting rates are only modestly related to measures of psychological dysfunction and therefore are not ‘core’ to these more complex behavioral problems. Furthermore, discounting rates do not appear to be specifically related to any disorder(s) or dimension(s) of psychopathology. This raises fundamental concerns about the utility of discounting, if the measure is only loosely associated with most forms of psychopathology. This stands in striking contrast to claims that discounting can serve as a ‘marker’ for specific disorders, despite never demonstrating adequate sensitivity or specificity for any disorder that we are aware of. Finally, empirical evidence does not support the generalizability of discounting rates to other decisions made either in the lab or in the real-world, and therefore discounting rates cannot and should not serve as a summary measure of an individual's decision-making patterns. We provide recommendations for improving future delay discounting research, but also strongly encourage researchers to consider whether the empirical evidence supports the field's hyper-focus on discounting.

The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias – either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation – the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.