The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures.

Confirmatory factor analysis was used to compare 6 models of posttraumatic stress disorder (PTSD) symptoms, ranging from 1 to 4 factors, in a sample of 3,695 deployed Gulf War veterans (N = 1,896) and nondeployed controls (N = 1,799). The 4 correlated factors-intrusions, avoidance, hyperarousal, and dysphoria-provided the best fit. The dysphoria factor combined traditional markers of numbing and hyperarousal. Model superiority was cross-validated in multiple subsamples, including a subset of deployed participants who were exposed to traumatic combat stressors. Moreover, convergent and discriminant validity correlations suggested that intrusions may be relatively specific to PTSD, whereas dysphoria may represent a nonspecific component of many disorders. Results are discussed in the context of hierarchical models of anxiety and depression.

The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures.

We describe a new self-report instrument, the Inventory of Depression and Anxiety Symptoms (IDAS), which was designed to assess specific symptom dimensions related to major depression and related anxiety disorders. We created the IDAS by conducting principal factor analyses in three large samples (college students, psychiatric patients, community adults); we also examined the robustness of its psychometric properties in five additional samples (high school students, college students, young adults, postpartum women, psychiatric patients) that were not involved in the scale development process. The IDAS contains 10 specific symptom scales: Suicidality, Lassitude, Insomnia, Appetite Loss, Appetite Gain, Ill Temper, Well-Being, Panic, Social Anxiety, and Traumatic Intrusions. It also includes two broader scales: General Depression (which contains items overlapping with several other IDAS scales) and Dysphoria (which does not). The scales (a) are internally consistent, (b) capture the target dimensions well, and (c) define a single underlying factor. They show strong short-term stability, and display excellent convergent validity and good discriminant validity in relation to other self-report and interviewbased measures of depression and anxiety.

2000) was designed to assess cognitive and somatic symptoms of anxiety as they pertain to one's mood in the moment (state) and in general (trait). This study extended the previous psychometric findings to a clinical sample and validated the STICSA against a well-published measure of anxiety, the State-Trait Anxiety Inventory (STAI; C. D. Spielberger, 1983). Patients (N ϭ 567) at an anxiety disorders clinic were administered a battery of questionnaires. The results of confirmatory factor analyses (Bentler-Bonnett nonnormed fit index, comparative fit index, and Bollen fit index Ͼ .90; root-mean-square error of approximation Ͻ .05); convergent and discriminant validity analyses; and group comparisons supported the reliability and validity of the STICSA as a measure of state and trait cognitive and somatic anxiety. In addition, compared with the STAI (anxiety: rs Յ .52; depression: rs Ն .64), the STICSA was more strongly correlated with another measure of anxiety (rs Ն .67) and was less strongly correlated with a measure of depression (rs Յ .61). These findings suggest that the STICSA may be a purer measure of anxiety symptomatology than is the STAI.