The diagnosis of mental disorder initially appears relatively straightforward: Patients present with symptoms or visible signs of illness; health professionals make diagnoses based primarily on these ...symptoms and signs; and they prescribe medication, psychotherapy, or both, accordingly. However, despite a dramatic expansion of knowledge about mental disorders during the past half century, understanding of their components and processes remains rudimentary. We provide histories and descriptions of three systems with different purposes relevant to understanding and classifying mental disorder. Two major diagnostic manuals—the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders—provide classification systems relevant to public health, clinical diagnosis, service provision, and specific research applications, the former internationally and the latter primarily for the United States. In contrast, the National Institute of Mental Health's Research Domain Criteria provides a framework that emphasizes integration of basic behavioral and neuroscience research to deepen the understanding of mental disorder. We identify four key issues that present challenges to understanding and classifying mental disorder: etiology, including the multiple causality of mental disorder; whether the relevant phenomena are discrete categories or dimensions; thresholds, which set the boundaries between disorder and nondisorder; and comorbidity, the fact that individuals with mental illness often meet diagnostic requirements for multiple conditions. We discuss how the three systems' approaches to these key issues correspond or diverge as a result of their different histories, purposes, and constituencies. Although the systems have varying degrees of overlap and distinguishing features, they share the goal of reducing the burden of suffering due to mental disorder.
The distinction between normality and psychopathology has long been subject to debate. DSM-III and DSM-IV provided a definition of mental disorder to help clinicians address this distinction. As part ...of the process of developing DSM-V, researchers have reviewed the concept of mental disorder and emphasized the need for additional work in this area. Here we review the DSM-IV definition of mental disorder and propose some changes. The approach taken here arguably takes a middle course through some of the relevant conceptual debates. We agree with the view that no definition perfectly specifies precise boundaries for the concept of mental/psychiatric disorder, but in line with a view that the nomenclature can improve over time, we aim here for a more scientifically valid and more clinically useful definition.
In both child and adult psychiatry, empirical evidence has now accrued to suggest that a single dimension is able to measure a person's liability to mental disorder, comorbidity among disorders, ...persistence of disorders over time, and severity of symptoms. This single dimension of general psychopathology has been termed "p," because it conceptually parallels a dimension already familiar to behavioral scientists and clinicians: the "g" factor of general intelligence. As the g dimension reflects low to high mental ability, the p dimension represents low to high psychopathology severity, with thought disorder at the extreme. The dimension of p unites all disorders. It influences present/absent status on hundreds of psychiatric symptoms, which modern nosological systems typically aggregate into dozens of distinct diagnoses, which in turn aggregate into three overarching domains, namely, the externalizing, internalizing, and psychotic experience domains, which finally aggregate into one dimension of psychopathology from low to high: p. Studies show that the higher a person scores on p, the worse that person fares on measures of family history of psychiatric illness, brain function, childhood developmental history, and adult life impairment. A dimension of p may help account for ubiquitous nonspecificity in psychiatry: multiple disorders share the same risk factors and biomarkers and often respond to the same therapies. Here, the authors summarize the history of the unidimensional idea, review modern research into p, demystify statistical models, articulate some implications of p for prevention and clinical practice, and outline a transdiagnostic research agenda. AJP AT 175: Remembering Our Past As We Envision Our Future October 1910: A Study of Association in Insanity Grace Helen Kent and A.J. Rosanoff: "No sharp distinction can be drawn between mental health and mental disease; a large collection of material shows a gradual and not an abrupt transition from the normal state to pathological states."(Am J Psychiatry 1910; 67(2):317-390 ).
Objective: Diagnosis is a cornerstone of clinical practice for mental health care providers, yet traditional diagnostic systems have well-known shortcomings, including inadequate reliability, high ...comorbidity, and marked within-diagnosis heterogeneity. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a data-driven, hierarchically based alternative to traditional classifications that conceptualizes psychopathology as a set of dimensions organized into increasingly broad, transdiagnostic spectra. Prior work has shown that using a dimensional approach improves reliability and validity, but translating a model like HiTOP into a workable system that is useful for health care providers remains a major challenge. Method: The present work outlines the HiTOP model and describes the core principles to guide its integration into clinical practice. Results: Potential advantages and limitations of the HiTOP model for clinical utility are reviewed, including with respect to case conceptualization and treatment planning. A HiTOP approach to practice is illustrated and contrasted with an approach based on traditional nosology. Common barriers to using HiTOP in real-world health care settings and solutions to these barriers are discussed. Conclusions: HiTOP represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility.
What is the public health significance of this article?
Redefining a taxonomy of psychopathology according to data results in dimensions, not categories, that can be organized hierarchically-with at least six higher level spectra near the top of the model and more specific lower level components and traits at the bottom. This approach may improve case conceptualizations and align more closely with transdiagnostic treatments, while also specifying more narrow targets for intervention. A case illustration shows how the HiTOP model can be used in clinical practice today, although additional research is needed to fully assess the utility of this approach for providers and patients.
Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network ...approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, potentially making it possible to predict treatment change, relapse, and recovery. In this article, we discuss the complex network approach as an alternative to the latent disease model and its implications for classification, therapy, relapse, and recovery.
Children and adolescents with low socioeconomic status (SES) suffer from mental health problems more often than their peers with high SES. The aim of the current study was to investigate the direct ...and interactive association between commonly used indicators of SES and the exposure to stressful life situations in relation to children's mental health problems.
The prospective BELLA cohort study is the mental health module of the representative, population-based German National Health Interview and Examination Survey for children and adolescents (KiGGS). Sample data include 2,111 participants (aged 7-17 years at baseline) from the first three measurement points (2003-2006, 2004-2007 and 2005-2008). Hierarchical multiple linear regression models were conducted to analyze associations among the SES indicators household income, parental education and parental unemployment (assessed at baseline), number of stressful life situations (e.g., parental accident, mental illness or severe financial crises; 1- and 2-year follow-ups) and parent-reported mental health problems (Strength and Difficulties Questionnaire; 2-year follow-up).
All indicators of SES separately predicted mental health problems in children and adolescents at the 2-year follow-up. Stressful life situations (between baseline and 2-year follow-up) and the interaction of parental education and the number of stressful life situations remained significant in predicting children's mental health problems after adjustment for control variables. Thereby, children with higher educated parents showed fewer mental health problems in a stressful life situation. No moderating effect was found for household income and parental employment. Overall, the detected effect sizes were small. Mental health problems at baseline were the best predictor for mental health problems two years later.
Children and adolescents with a low SES suffer from multiple stressful life situations and are exposed to a higher risk of developing mental health problems. The findings suggest that the reduction of socioeconomic inequalities and interventions for families with low parental education might help to reduce children's mental health problems.
Adverse childhood experiences (ACEs) are well-established risk factors for health problems in a population. However, it is not known whether screening for ACEs can accurately identify individuals who ...develop later health problems.
To test the predictive accuracy of ACE screening for later health problems.
This study comprised 2 birth cohorts: the Environmental Risk (E-Risk) Longitudinal Twin Study observed 2232 participants born during the period from 1994 to 1995 until they were aged 18 years (2012-2014); the Dunedin Multidisciplinary Health and Development Study observed 1037 participants born during the period from 1972 to 1973 until they were aged 45 years (2017-2019). Statistical analysis was performed from May 28, 2018, to July 29, 2020.
ACEs were measured prospectively in childhood through repeated interviews and observations in both cohorts. ACEs were also measured retrospectively in the Dunedin cohort through interviews at 38 years.
Health outcomes were assessed at 18 years in E-Risk and at 45 years in the Dunedin cohort. Mental health problems were assessed through clinical interviews using the Diagnostic Interview Schedule. Physical health problems were assessed through interviews, anthropometric measurements, and blood collection.
Of 2232 E-Risk participants, 2009 (1051 girls 52%) were included in the analysis. Of 1037 Dunedin cohort participants, 918 (460 boys 50%) were included in the analysis. In E-Risk, children with higher ACE scores had greater risk of later health problems (any mental health problem: relative risk, 1.14 95% CI, 1.10-1.18 per each additional ACE; any physical health problem: relative risk, 1.09 95% CI, 1.07-1.12 per each additional ACE). ACE scores were associated with health problems independent of other information typically available to clinicians (ie, sex, socioeconomic disadvantage, and history of health problems). However, ACE scores had poor accuracy in predicting an individual's risk of later health problems (any mental health problem: area under the receiver operating characteristic curve, 0.58 95% CI, 0.56-0.61; any physical health problem: area under the receiver operating characteristic curve, 0.60 95% CI, 0.58-0.63; chance prediction: area under the receiver operating characteristic curve, 0.50). Findings were consistent in the Dunedin cohort using both prospective and retrospective ACE measures.
This study suggests that, although ACE scores can forecast mean group differences in health, they have poor accuracy in predicting an individual's risk of later health problems. Therefore, targeting interventions based on ACE screening is likely to be ineffective in preventing poor health outcomes.
Great interest exists in identifying methods to predict neuropsychiatric disease states and treatment outcomes from high-dimensional data, including neuroimaging and genomics data. The goal of this ...review is to highlight several potential problems that can arise in studies that aim to establish prediction.
A number of neuroimaging studies have claimed to establish prediction while establishing only correlation, which is an inappropriate use of the statistical meaning of prediction. Statistical associations do not necessarily imply the ability to make predictions in a generalized manner; establishing evidence for prediction thus requires testing of the model on data separate from those used to estimate the model's parameters. This article discusses various measures of predictive performance and the limitations of some commonly used measures, with a focus on the importance of using multiple measures when assessing performance. For classification, the area under the receiver operating characteristic curve is an appropriate measure; for regression analysis, correlation should be avoided, and median absolute error is preferred.
To ensure accurate estimates of predictive validity, the recommended best practices for predictive modeling include the following: (1) in-sample model fit indices should not be reported as evidence for predictive accuracy, (2) the cross-validation procedure should encompass all operations applied to the data, (3) prediction analyses should not be performed with samples smaller than several hundred observations, (4) multiple measures of prediction accuracy should be examined and reported, (5) the coefficient of determination should be computed using the sums of squares formulation and not the correlation coefficient, and (6) k-fold cross-validation rather than leave-one-out cross-validation should be used.
Background
To date, no national‐scale psychiatric epidemiological survey for children and adolescents has been conducted in China. In order to inform government officials and policymakers and to ...develop a comprehensive plan for service providers, there was a clear need to conduct an up‐to‐date systematic nationwide psychiatric epidemiological survey.
Methods
We conducted a two‐stage large‐scale psychiatric point prevalence survey. Multistage cluster stratified random sampling was used as the sampling strategy. Five provinces were selected by comprehensively considering geographical partition, economic development, and rural/urban factors. In Stage 1, the Child Behavior Checklist was used as the screening tool. In Stage 2, Mini‐International Neuropsychiatric Interview for Children and Adolescents and a diagnostic process based on the Diagnostic and Statistical Manual were used to make the diagnoses. Sampling weights and poststratification weights were employed to match the population distributions. Exploratory analyses were also performed using socio‐demographic factors. Prevalence in socio‐demographic factor subgroups and overall were estimated. Rao‐Scott adjusted chi‐square tests were utilized to determine if between‐group differences were present. Factor interactions were checked by logistic regression analyses.
Results
A total of 73,992 participants aged 6–16 years of age were selected in Stage 1. In Stage 2, 17,524 individuals were screened and diagnosed. The weighted prevalence of any disorder was 17.5% (95% CI: 17.2–18.0). Statistically significant differences in prevalence of any psychiatric disorder were observed between sexes χ2(1, N = 71,929) = 223.0, p < .001, age groups χ2(1, N = 71,929) = 18.6, p < .001 and developed vs. developing areas χ2(1, N = 71,929) = 2,129.6, p < .001, while no difference was found between rural and urban areas χ2(1, N = 71,929) = 1.4, p = .239. Male, younger individuals, children, and adolescents from developed areas had higher prevalence of any psychiatric disorder. The prevalence of any psychiatric disorder was found to decrease with the age in the male group, while the female group increased with the age. Individuals diagnosed with attention‐deficit hyperactivity disorder, oppositional defiant disorder, a tic disorder, conduct disorder, and major depression disorder had the highest rates of comorbidity.
Conclusions
The prevalence of any psychiatric disorder we found is the highest ever reported in China. These results urgently need to be addressed by public mental health service providers and policymakers in order to provide access to the necessary treatments and to reduce the long‐term negative impact of these conditions on families and the society as a whole.
...mental health services should be scaled up as an essential component of universal health coverage and should be fully integrated into the global response to other health priorities, including ...non-communicable diseases, maternal and child health, and HIV/AIDS. ...barriers and threats to mental health need to be addressed; these include the lack of awareness of the value of mental health in social and economic development, the lack of attention to mental health promotion and protection across sectors, the severe demand-side constraints for mental health care caused by stigma and discrimination, and the increasing threats to mental health due to global challenges such as climate change and growing inequality. ...mental health needs to be protected by public policies and developmental efforts; these intersectoral actions should be undertaken by each country's leaders to engage a wide range of stakeholders within and beyond health, including sectors in education, workplaces, social welfare, gender empowerment, child and youth services, criminal justice and development, and humanitarian assistance. ...investments in research and innovation should grow and harness novel approaches from diverse disciplines such as genomics, neuroscience, health services research, clinical sciences, and social sciences, both for implementation research on scaling up mental health interventions and for discovery research to advance understanding of causes and mechanisms of mental disorders and develop effective interventions to prevent and treat them.
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