Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad "spectrum level" dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the "problem of comorbidity" by explicitly modeling patterns of co-occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach.

For over a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of psychological symptom co-occurrence. We highlight key ways in which this framework can advance mental health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co‐occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis‐related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

IMPORTANCEThere is widespread interest in associations between maternal perinatal depression and anxiety and offspring development; however, to date, there has been no systematic, meta-analytic review on the long-term developmental outcomes spanning infancy through adolescence. OBJECTIVETo provide a comprehensive systematic review and meta-analysis of the extant literature on associations between maternal perinatal depression and anxiety and social-emotional, cognitive, language, motor, and adaptability outcomes in offspring during the first 18 years of life.DATA SOURCES Six databases were searched (CINAHL Complete, Cochrane Library, Embase, Informit, MEDLINE Complete, and PsycInfo) for all extant studies reporting associations between perinatal maternal mental health problems and offspring development to March 1, 2020.STUDY SELECTION Studies were included if they were published in English; had a human sample, quantitative data, a longitudinal design, and measures of perinatal depression and/or anxiety and social-emotional, cognitive, language, motor, and/or adaptability development in offspring; and investigated an association between perinatal depression or anxiety and childhood development.DATA EXTRACTION AND SYNTHESIS Of 27 212 articles identified, 191 were eligible for meta-analysis. Data were extracted by multiple independent observers and pooled using a fixed-or a random-effects model. A series of meta-regressions were also conducted. Data were analyzed from January 1, 2019, to March 15, 2020. MAIN OUTCOMES AND MEASURESPrimary outcomes included social-emotional, cognitive, language, motor, and adaptability development in offspring during the first 18 years of life.RESULTS After screening, 191 unique studies were eligible for meta-analysis, with a combined sample of 195 751 unique mother-child dyads. Maternal perinatal depression and anxiety were associated with poorer offspring social-emotional (antenatal period, r = 0.21 [95% CI, 0.16-0.27]; postnatal period, r = 0.24 [95% CI, 0.19-0.28]), cognitive (antenatal period, r = −0.12 [95% CI, -0.19 to -0.05]; postnatal period, r = −0.25 [95% CI, -0.39 to -0.09]), language (antenatal period, r = −0.11 [95% CI, −0.20 to 0.02]; postnatal period, r = −0.22 [95% CI, −0.40 to 0.03]), motor (antenatal period, r = −0.07 [95% CI, −0.18 to 0.03]; postnatal period, r = −0.07 [95% CI, −0.16 to 0.03]), and adaptive behavior (antenatal period, r = −0.26 [95% CI, −0.39 to −0.12]) development. Findings extended beyond infancy, into childhood and adolescence. Meta-regressions confirmed the robustness of the results. CONCLUSIONS AND RELEVANCEEvidence suggests that perinatal depression and anxiety in mothers are adversely associated with offspring development and therefore are important targets for prevention and early intervention to support mothers transitioning into parenthood and the health and well-being of next-generation offspring.

Background Lifestyle risk behaviours typically emerge during adolescence, track into adulthood, and commonly co-occur. Interventions targeting multiple risk behaviours in adolescents have the potential to efficiently improve health outcomes, yet further evidence is required to determine their effect. We reviewed the effectiveness of eHealth school-based interventions targeting multiple lifestyle risk behaviours. Methods In this systematic review and meta-analysis, we searched Ovid MEDLINE, Embase, PsycINFO, and the Cochrane Library databases between Jan 1, 2000, and March 14, 2019, with no language restrictions, for publications on school-based eHealth multiple health behaviour interventions in humans. We also screened the grey literature for unpublished data. Eligible studies were randomised controlled trials of eHealth (internet, computers, tablets, mobile technology, or tele-health) interventions targeting two or more of six behaviours of interest: alcohol use, smoking, diet, physical activity, sedentary behaviour, and sleep. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours, or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. This systematic review is registered with PROSPERO, identifier CRD42017072163. Findings Of 10 571 identified records, 22 publications assessing 16 interventions were included, comprising 18 873 students, of whom on average 56•2% were female, with a mean age of 13•41 years (SD 1•52). eHealth schoolbased multiple health behaviour change interventions significantly increased fruit and vegetable intake (standard mean difference 0•11, 95% CI 0•03 to 0•19; p=0•007) and both accelerometer-measured (0•33, 0•05 to 0•61; p=0•02) and self-reported (0•14, 0•05 to 0•23; p=0•003) physical activity, and reduced screen time (-0•09,-0•17 to-0•01; p=0•03) immediately after the intervention; however, these effects were not sustained at follow-up when data were available. No effect was seen for alcohol or smoking, fat or sugar-sweetened beverage or snack consumption. No studies examined sleep or used mobile health interventions. The risk of bias in masking of final outcome assessors and selective outcome reporting was high or unclear across studies and overall we deemd the quality of evidence to be low to very low. Interpretation eHealth school-based interventions addressing multiple lifestyle risk behaviours can be effective in improving physical activity, screen time, and fruit and vegetable intake. However, effects were small and only evident immediately after the intervention. Further high quality, adolescent-informed research is needed to develop eHealth interventions that can modify multiple behaviours and sustain long-term effects.