An open resource for transdiagnostic research in pediatric mental health and learning disorders.
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ABSTRACT:
SUBMITTER: Alexander LM
PROVIDER: S-EPMC5735921 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectivesInpatient pediatric mental health is a priority topic for national quality measurement and improvement, but nationally representative data on the patients admitted or their diagnoses are lacking. Our objectives were: to describe pediatric mental health hospitalizations at general medical facilities admitting children nationally; to assess which pediatric mental health diagnoses are frequent and costly at these hospitals; and to examine whether the most frequent diagnoses are similar to those at free-standing children's hospitals.MethodsWe examined all discharges in 2009 for patients aged 3 to 20 years in the nationally representative Kids' Inpatient Database (KID) and in the Pediatric Health Information System (free-standing children's hospitals). Main outcomes were frequency of International Classification of Diseases, Ninth Revision, Clinical Modification-defined mental health diagnostic groupings (primary and nonprimary diagnosis) and, using KID, resource utilization (defined by diagnostic grouping aggregate annual charges).ResultsNearly 10% of pediatric hospitalizations nationally were for a primary mental health diagnosis, compared with 3% of hospitalizations at free-standing children's hospitals. Predictors of hospitalizations for a primary mental health problem were older age, male gender, white race, and insurance type. Nationally, the most frequent and costly primary mental health diagnoses were depression (44.1% of all mental health admissions; $1.33 billion), bipolar disorder (18.1%; $702 million), and psychosis (12.1%; $540 million).ConclusionsWe identified the child mental health inpatient diagnoses with the highest frequency and highest costs as depression, bipolar disorder, and psychosis, with substance abuse an important comorbid diagnosis. These diagnoses can be used as priority conditions for pediatric mental health inpatient quality measurement.
| S-EPMC3966505 | biostudies-literature
Project description:This study aimed to build on the relationship of well-established self-report and behavioral assessments to the latent constructs positive (PVS) and negative valence systems (NVS), cognitive systems (CS), and social processes (SP) of the Research Domain Criteria (RDoC) framework in a large transnosological population which cuts across DSM/ICD-10 disorder criteria categories. One thousand four hundred and thirty one participants (42.1% suffering from anxiety/fear-related, 18.2% from depressive, 7.9% from schizophrenia spectrum, 7.5% from bipolar, 3.4% from autism spectrum, 2.2% from other disorders, 18.4% healthy controls, and 0.2% with no diagnosis specified) recruited in studies within the German research network for mental disorders for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) were examined with a Mini-RDoC-Assessment including behavioral and self-report measures. The respective data was analyzed with confirmatory factor analysis (CFA) to delineate the underlying latent RDoC-structure. A revised four-factor model reflecting the core domains positive and negative valence systems as well as cognitive systems and social processes showed a good fit across this sample and showed significantly better fit compared to a one factor solution. The connections between the domains PVS, NVS and SP could be substantiated, indicating a universal latent structure spanning across known nosological entities. This study is the first to give an impression on the latent structure and intercorrelations between four core Research Domain Criteria in a transnosological sample. We emphasize the possibility of using already existing and well validated self-report and behavioral measurements to capture aspects of the latent structure informed by the RDoC matrix.
| S-EPMC10085934 | biostudies-literature
Project description:ObjectivesTransdiagnostic risk factors-disrupted processes common to psychopathology-link adverse childhood experiences to severe mental disorders (i.e. major depressive disorder, bipolar disorder, and schizophrenia spectrum disorders); however, transdiagnostic protective factors are understudied. The present study investigated the association between a positive mental health framework of protective intra- and interpersonal resources and severe mental disorders in individuals with adverse childhood experiences. We hypothesized that (1) individuals with adverse childhood experiences will experience more severe mental disorders and poorer intra- and interpersonal resources than those without adverse childhood experiences; (2) intrapersonal (e.g. general coping) and interpersonal resources (e.g. emotional support) will interact to predict severe mental disorders.MethodsA total of 1929 adults participated in this population-based study. Participants were assessed for adverse childhood experiences, severe mental disorders, and intra- and interpersonal resources (general coping, general affect, emotional support, interpersonal skills, spirituality, and personal growth and autonomy) via structured interviews and self-reports.ResultsAs hypothesized, individuals with adverse childhood experiences (62.6%) experienced more severe mental disorders and poorer intra- and interpersonal resources than those without adverse childhood experiences. Among those with adverse childhood experiences, emotional support interacted with general coping and general affect to predict severe mental disorders; general coping and general affect were negatively associated with severe mental disorders at high (+1 SD) and low (-1 SD) emotional support, respectively.ConclusionsThe present study identified interactions between specific intrapersonal (i.e. general coping and general affect) and interpersonal resources (i.e. emotional support); knowing among whom and when to intervene are essential for optimal treatment of adverse childhood experiences and severe mental disorders.
| S-EPMC9513503 | biostudies-literature
Project description:In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. Yet, the vast majority of neuroimaging studies still focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural correlates through a novel and data-driven meta-analytical method. A data-driven meta-analysis was carried out which grouped similar experiments' topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. One hundred forty-seven experiments were retrieved (3124 cases compared to 3100 controls): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Our results may reflect transdiagnostic neural correlates of pediatric psychiatric disorders, but also underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently to examine differences between disorders.
| S-EPMC8941086 | biostudies-literature
Project description:There is an emergent and intensive dialogue in the United States with regard to the accessibility, reproducibility, and rigor of health research. This discussion is also closely aligned with the need to identify sustainable ways to expand the national research enterprise and to generate actionable results that can be applied to improve the nation's health. The principles and practices of Open Science offer a promising path to address both goals by facilitating (1) increased transparency of data and methods, which promotes research reproducibility and rigor; and (2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge in proximal domains, thereby resulting in greater productivity and a reduction in redundant research investments.AcademyHealth's Electronic Data Methods (EDM) Forum implemented a proof-of-concept open science platform for health research called the Collaborative Informatics Environment for Learning on Health Outcomes (CIELO).The EDM Forum conducted a user-centered design process to elucidate important and high-level requirements for creating and sustaining an open science paradigm.By implementing CIELO and engaging a variety of potential users in its public beta testing, the EDM Forum has been able to elucidate a broad range of stakeholder needs and requirements related to the use of an open science platform focused on health research in a variety of "real world" settings.Our initial design and development experience over the course of the CIELO project has provided the basis for a vigorous dialogue between stakeholder community members regarding the capabilities that will add the greatest value to an open science platform for the health research community. A number of important questions around user incentives, sustainability, and scalability will require further community dialogue and agreement.
| S-EPMC5556258 | biostudies-other
Project description:Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
| S-EPMC7293215 | biostudies-literature
Project description:Mental Health Intervention Research Center (MHIRC) DNA Repository
| PRJNA507063 | ENA
Project description:Mental Health Intervention Research Center (MHIRC) DNA Repository
| PRJNA507064 | ENA
Project description:The research agenda for global mental health and substance-use disorders has been largely driven by the exigencies of high health burdens and associated unmet needs in low- and middle-income countries. Implementation research focused on context-driven adaptation and innovation in service delivery has begun to yield promising results that are improving the quality of, and access to, care in low-resource settings. Importantly, these efforts have also resulted in the development and augmentation of local, in-country research capacities. Given the complex interplay between mental health and substance-use disorders, medical conditions, and biological and social vulnerabilities, a revitalized research agenda must encompass both local variation and global commonalities in the impact of adversities, multi-morbidities and their consequences across the life course. We recommend priorities for research - as well as guiding principles for context-driven, intersectoral, integrative approaches - that will advance knowledge and answer the most pressing local and global mental health questions and needs, while also promoting a health equity agenda and extending the quality, reach and impact of scientific enquiry.
| S-EPMC6368440 | biostudies-literature
Project description:BackgroundEmerging adulthood is often associated with mental health problems. About one in three university students report symptoms of depression and anxiety that can negatively affect their developmental trajectory concerning work, intimate relationships, and health. This can interfere with academic performance, as mood and anxiety disorders are key predictors of dropout from higher education. A treatment gap exists, where a considerable proportion of students do not seek help for mood and anxiety symptoms. Offering internet interventions to students with mental health problems could reduce the treatment gap, increase mental health, and improve academic performance. A meta-analysis on internet interventions for university students showed small effects for depression and none for anxiety. Larger trials are recommended to further explore effects of guidance, transdiagnostic approaches, and individual treatment components.MethodsThis study will offer 1200 university students in Sweden participation in a three-armed randomized controlled trial (RCT) evaluating a guided or unguided transdiagnostic internet intervention for mild to moderate depression and anxiety, where the waitlist control group accesses the intervention at 6-month follow-up. Students reporting suicidal ideation/behaviors will be excluded and referred to treatment within the existing healthcare system. An embedded study within the trial (SWAT) will assess at week 3 of 8 whether participants in the guided and unguided groups are at higher risk of failing to benefit from treatment. Those at risk will be randomized to an adaptive treatment strategy, or to continue the treatment as originally randomized. Primary outcomes are symptoms of depression and anxiety. Follow-ups will occur at post-treatment and at 6-, 12-, and 24-month post-randomization. Between-group outcome analyses will be reported, and qualitative interviews about treatment experiences are planned.DiscussionThis study investigates the effects of a transdiagnostic internet intervention among university students in Sweden, with an adaptive treatment strategy employed during the course of treatment to minimize the risk of treatment failure. The study will contribute knowledge about longitudinal trajectories of mental health and well-being following treatment, taking into account possible gender differences in responsiveness to treatment. With time, effective internet interventions could make treatment for mental health issues more widely accessible to the student group.
| S-EPMC10908025 | biostudies-literature