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: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: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: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:Mental Health Intervention Research Center (MHIRC) DNA Repository
| PRJNA507063 | ENA
Project description:Mental Health Intervention Research Center (MHIRC) DNA Repository
| PRJNA507064 | ENA
Project description:Despite a longstanding and widespread influence of the diagnostic approach to mental ill health, there is an emerging and growing consensus that such psychiatric nosologies may no longer be fit for purpose in research and clinical practice. In their place, there is gathering support for a "transdiagnostic" approach that cuts across traditional diagnostic boundaries or, more radically, sets them aside altogether, to provide novel insights into how we might understand mental health difficulties. Removing the distinctions between proposed psychiatric taxa at the level of classification opens up new ways of classifying mental health problems, suggests alternative conceptualizations of the processes implicated in mental health, and provides a platform for novel ways of thinking about onset, maintenance, and clinical treatment and recovery from experiences of disabling mental distress. In this Introduction to a Special Section on Transdiagnostic Approaches to Psychopathology, we provide a narrative review of the transdiagnostic literature in order to situate the Special Section articles in context. We begin with a brief history of the diagnostic approach and outline several challenges it currently faces that arguably limit its applicability in current mental health science and practice. We then review several recent transdiagnostic approaches to classification, biopsychosocial processes, and clinical interventions, highlighting promising novel developments. Finally, we present some key challenges facing transdiagnostic science and make suggestions for a way forward. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
| S-EPMC7027356 | biostudies-literature
Project description:BackgroundA large proportion of university students show symptoms of common mental disorders, such as depression, anxiety, substance use disorders and eating disorders. Novel interventions are required that target underlying factors of multiple disorders.AimsTo evaluate the efficacy of a transdiagnostic trait-focused web-based intervention aimed at reducing symptoms of common mental disorders in university students.MethodStudents were recruited online (n=1047, age: M=21.8, SD=4.2) and categorised into being at high or low risk for mental disorders based on their personality traits. Participants were allocated to a cognitive-behavioural trait-focused (n=519) or a control intervention (n=528) using computerised simple randomisation. Both interventions were fully automated and delivered online (trial registration: ISRCTN14342225). Participants were blinded and outcomes were self-assessed at baseline, at 6 weeks and at 12 weeks after registration. Primary outcomes were current depression and anxiety, assessed on the Patient Health Questionnaire (PHQ9) and Generalised Anxiety Disorder Scale (GAD7). Secondary outcome measures focused on alcohol use, disordered eating, and other outcomes.ResultsStudents at high risk were successfully identified using personality indicators and reported poorer mental health. A total of 520 students completed the 6-week follow-up and 401 students completed the 12-week follow-up. Attrition was high across intervention groups, but comparable to other web-based interventions. Mixed effects analyses revealed that at 12-week follow up the trait-focused intervention reduced depression scores by 3.58 (p<.001, 95%CI [5.19, 1.98]) and anxiety scores by 2.87 (p=.018, 95%CI [1.31, 4.43]) in students at high risk. In high-risk students, between group effect sizes were 0.58 (depression) and 0.42 (anxiety). In addition, self-esteem was improved. No changes were observed regarding the use of alcohol or disordered eating.ConclusionsThis study suggests that a transdiagnostic web-based intervention for university students targeting underlying personality risk factors may be a promising way of preventing common mental disorders with a low-intensity intervention.Trial registrationControlledTrials.com ISRCTN14342225.
| S-EPMC3988039 | biostudies-literature
Project description:BackgroundCognitive impairment is common in people with mental disorders, leading to transdiagnostic classification based on cognitive characteristics. However, few studies have used this approach for intellectual abilities and functional outcomes.AimsThe present study aimed to classify people with mental disorders based on intellectual abilities and functional outcomes in a data-driven manner.MethodSeven hundred and forty-nine patients diagnosed with schizophrenia, bipolar disorder, major depression disorder or autism spectrum disorder and 1030 healthy control subjects were recruited from facilities in various regions of Japan. Two independent k-means cluster analyses were performed. First, intelligence variables (current estimated IQ, premorbid IQ, and IQ discrepancy) were included. Second, number of work hours per week was included instead of premorbid IQ.ResultsFour clusters were identified in the two analyses. These clusters were specifically characterised in terms of IQ discrepancy in the first cluster analysis, whereas the work variable was the most salient feature in the second cluster analysis. Distributions of clinical diagnoses in the two cluster analyses showed that all diagnoses were unevenly represented across the clusters.ConclusionsIntellectual abilities and work outcomes are effective classifiers in transdiagnostic approaches. The results of our study also suggest the importance of diagnosis-specific strategies to support functional recovery in people with mental disorders.
| S-EPMC9230699 | biostudies-literature
Project description:BackgroundThe access to empirically-supported treatments for common mental disorders in children and adolescents is often limited. Mental health apps might extend service supplies, as they are deemed to be cost-efficient, scalable and appealing for youth. However, little is known about the quality of available apps. Therefore, we aimed to systematically evaluate current mobile-based interventions for pediatric anxiety, depression and posttraumatic stress disorder (PTSD).MethodsSystematic searches were conducted in Google Play Store and Apple App Store to identify relevant apps. To be eligible for inclusion, apps needed to be: (1) designed to target either anxiety, depression or PTSD in youth (0-18 years); (2) developed for children, adolescents or caregivers; (3) provided in English or German; (4) operative after download. The quality of eligible apps was assessed with two standardized rating systems (i.e., Mobile App Rating Scale (MARS) and ENLIGHT) independently by two reviewers.ResultsOverall, the searches revealed 3806 apps, with 15 mental health apps (0.39%) fulfilling our inclusion criteria. The mean overall scores suggested a moderate app quality (MARS: M = 3.59, SD = 0.50; ENLIGHT: M = 3.22, SD = 0.73). Moreover, only one app was evaluated in an RCT. The correlation of both rating scales was high (r = .936; p < .001), whereas no significant correlations were found between rating scales and user ratings (p > .05).ConclusionsOur results point to a rather poor overall app quality, and indicate an absence of scientific-driven development and lack of methodologically sound evaluation of apps. Thus, future high-quality research is required, both in terms of theoretically informed intervention development and assessment of mental health apps in RCTs. Furthermore, institutionalized best-practices that provide central information on different aspects of apps (e.g., effectiveness, safety, and data security) for patients, caregivers, stakeholders and mental health professionals are urgently needed.
| S-EPMC8438844 | biostudies-literature