Qatar's National Mental Health Study-The World Mental Health Qatar.

BACKGROUND: We provide an overview of Qatar's first epidemiological study on prevalence, predictors, and treatment contact for mood and anxiety disorders. AIMS: We highlight the importance of the three-pronged study, its aims, and its key components. MATERIALS & METHODS: The first component comprised a probability-based representative survey of Qatari and non-Qatari (Arab) adult males and females recruited from the general population and interviewed using the International Diagnostic Interview (CIDI version 3.3). The second component, a clinical reappraisal study, assessed concordance between diagnoses based on the CIDI and independent clinical assessments conducted by trained clinical interviewers. The third component comprised a resting-state functional magnetic resonance imaging study of healthy survey respondents who were matched to patients with psychosis. RESULTS: 5000 survey interviews provided data on prevalence and treatment of common mental disorders. Clinical re-interviews (N = 485) provided important diagnostic validity data. Finally, state-of-the art structural and functional brain markers for psychosis were also collected (N = 100). DISCUSSION: Descriptive epidemiological data were collected to inform future mental health priorities in Qatar and situates these within a global context. CONCLUSION: The study fills important gaps in regional and global estimates and establish necessary baseline to develop comprehensive risk estimates for mental health in Qatar's young population.

Twelve-month prevalence, persistence, severity, and treatment of mood and anxiety disorders in Qatar's national mental health study.

OBJECTIVES: To estimate 12-month prevalence, persistence, severity, and treatment of mental disorders and socio-demographic correlates in Qatar. METHODS: We conducted the first national population-based telephone survey of Arab adults between 2019 and 2022 using the Composite International Diagnostic Interview and estimated 12-month DSM-5 mood and anxiety disorders and their persistence (the proportion of lifetime cases who continue to meet 12-month criteria). RESULTS: The 12-month prevalence of any disorder was 21.1% (10.4% mild, 38.7% moderate, and 50.9% severe) and was associated with: younger age, female, previously married, and with persistence of any disorder. Persistence was 74.7% (64.0% mood and 75.6% anxiety) and was significantly associated with secondary education or lower. Minimally adequate treatment received among those with any 12-month mental disorder was 10.6% (74.6% in healthcare and 64.6% non-healthcare sectors). Severity and the number of disorders significantly associated with each other and with treatment received (χ2 = 7.24, p = 0.027) including adequate treatment within the mental health specialty sector (χ2 = 21.42, p < 0.001). CONCLUSIONS: Multimorbidity and sociodemographics were associated with 12-month mental disorder. Treatment adequacy in Qatar are comparable to high-income countries. Low treatment contact indicate need for population-wide mental health literacy programes in addition to more accessible and effective mental health services.

[Features of the clinical picture of affective disorders in women during the menopausal transition and early postmenopause]. / Osobennosti klinicheskoi kartiny affektivnykh rasstroistv u zhenshchin v period menopauzal'nogo perekhoda i rannei postmenopauzy.

OBJECTIVE: To determine the clinical and psychopathological features of affective disorders in women in the perimenopausal and early postmenopausal periods. MATERIAL AND METHODS: The study included 90 female patients receiving inpatient psychiatric care for affective disorders, among them 41 patients were perimenopausal (group 1) and 49 were early postmenopausal (group 2). Clinical and psychopathological, psychometric (the Hospital Anxiety and Depression Scale - HADS, the Hamilton Depression and Anxiety Scales - HAM-D and HAM-A, the Hypomania Checklist-32 - HCL-32, the Bipolarity Index (BI), the Insomnia Severity Index - ISI, the Pittsburgh Sleep Quality Index - PSQI) and statistical methods were used. RESULTS: Symptoms of atypical (63.4%) and anxious (87.8%) depression predominated among perimenopausal patients, and melancholic depression (59.2%) prevailed in early postmenopause. Patients in group 1 had higher anxiety scores on HADS and HAM-A compared to group 2 (p=0.003 and p=0.01). At the same time, early postmenopausal women had higher depression scores on the HADS and HAM-D (p=0.001). ISI and PSQI scores in postmenopause were significantly higher than in perimenopause (p=0.001 and p=0.009). CONCLUSION: The clinical features of affective disorders as well as severity and nature of the accompanying sleep disturbances vary depending on the stage of menopause, which must be considered when prescribing additional methods for examination and treatment of these disorders.

Mood disturbances in newly diagnosed Parkinson's Disease patients reflect intrathecal inflammation.

In Parkinson's disease (PD), neuroinflammation may be involved in the pathogenesis of mood disorders, contributing to the clinical heterogeneity of the disease. The cerebrospinal fluid (CSF) levels of interleukin (IL)-1ß, IL-2, IL-6, IL-7, IL-8, IL-9, IL-12, IL-17, interferon (IFN)γ, macrophage inflammatory protein 1-alpha (MIP-1a), MIP-1b, granulocyte colony stimulating factor (GCSF), eotaxin, tumor necrosis factor (TNF), and monocyte chemoattractant protein 1 (MCP-1), were assessed in 45 newly diagnosed and untreated PD patients and in 44 control patients. Spearman's correlations were used to explore possible associations between CSF cytokines and clinical variables including mood. Benjamini-Hochberg (B-H) correction for multiple comparisons was applied. Linear regression was used to test significant associations correcting for other clinical variables. In PD patients, higher CSF concentrations of the inflammatory molecules IL-6, IL-9, IFNγ, and GCSF were found (all B-H corrected p < 0.02). Significant associations were found between BDI-II and the levels of IL-6 (Beta = 0.438; 95%CI 1.313-5.889; p = 0.003) and IL-8 (Beta = 0.471; 95%CI 0.185-0.743; p = 0.002). Positive associations were also observed between STAI-Y state and both IL-6 (Beta = 0.452; 95%CI 1.649-7.366; p = 0.003), and IL-12 (Beta = 0.417; 95%CI 2.238-13.379; p = 0.007), and between STAI-Y trait and IL-2 (Beta = 0.354; 95%CI 1.923-14.796; p = 0.012), IL-6 (Beta = 0.362; 95%CI 0.990-6.734; p = 0.01), IL-8 (Beta = 0.341; 95%CI 0.076-0.796; p = 0.019), IL-12 (Beta = 0.328; 95%CI 0.975-12.135; p = 0.023), and IL-17 (Beta = 0.334; 95CI 0.315-4.455; p = 0.025). An inflammatory CSF milieu may be associated with depression and anxiety in the early phases of PD, supporting a role of neuroinflammation in the pathogenesis of mood disturbances.

Identifying predictive factors for mood recurrence in early-onset major mood disorders: A 4-year, multicenter, prospective cohort study.

We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR. The discrepancies in wake-up times between weekdays and weekends, along with disrupted circadian rhythms, are identified as a notable predictor of ODR. Our findings confirm that we need to be aware of different predictors for each form of mood recurrences in patients with early-onset mood disorders. In clinical practice, we expect that information obtained from the initial assessment of patients with mood disorders, such as mood disorder type, family history of BD, regularity of wake-up time, and disruption of circadian rhythms, can help predict the risk of recurrence for each patient, allowing for early detection and timely intervention.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Developing and Implementing a Web-Based Branching Logic Survey to Support Psychiatric Crisis Evaluations of Individuals With Developmental Disabilities: Qualitative Study and Evaluation of Validity.

BACKGROUND: Individuals with developmental disabilities (DD) experience increased rates of emotional and behavioral crises that necessitate assessment and intervention. Psychiatric disorders can contribute to crises; however, screening measures developed for the general population are inadequate for those with DD. Medical conditions can exacerbate crises and merit evaluation. Screening tools using checklist formats, even when designed for DD, are too limited in depth and scope for crisis assessments. The Sources of Distress survey implements a web-based branching logic format to screen for common psychiatric and medical conditions experienced by individuals with DD by querying caregiver knowledge and observations. OBJECTIVE: This paper aims to (1) describe the initial survey development, (2) report on focus group and expert review processes and findings, and (3) present results from the survey's clinical implementation and evaluation of validity. METHODS: Sources of Distress was reviewed by focus groups and clinical experts; this feedback informed survey revisions. The survey was subsequently implemented in clinical settings to augment providers' psychiatric and medical history taking. Informal and formal consults followed the completion of Sources of Distress for a subset of individuals. A records review was performed to identify working diagnoses established during these consults. RESULTS: Focus group members (n=17) expressed positive feedback overall about the survey's content and provided specific recommendations to add categories and items. The survey was completed for 231 individuals with DD in the clinical setting (n=161, 69.7% men and boys; mean age 17.7, SD 10.3; range 2-65 years). Consults were performed for 149 individuals (n=102, 68.5% men and boys; mean age 18.9, SD 10.9 years), generating working diagnoses to compare survey screening results. Sources of Distress accuracy rates were 91% (95% CI 85%-95%) for posttraumatic stress disorder, 87% (95% CI 81%-92%) for anxiety, 87% (95% CI 81%-92%) for episodic expansive mood and bipolar disorder, 82% (95% CI 75%-87%) for psychotic disorder, 79% (95% CI 71%-85%) for unipolar depression, and 76% (95% CI 69%-82%) for attention-deficit/hyperactivity disorder. While no specific survey items or screening algorithm existed for unspecified mood disorder and disruptive mood dysregulation disorder, these conditions were caregiver-reported and working diagnoses for 11.7% (27/231) and 16.8% (25/149) of individuals, respectively. CONCLUSIONS: Caregivers described Sources of Distress as an acceptable tool for sharing their knowledge and insights about individuals with DD who present in crisis. As a screening tool, this survey demonstrates good accuracy. However, better differentiation among mood disorders is needed, including the addition of items and screening algorithm for unspecified mood disorder and disruptive mood dysregulation disorder. Additional validation efforts are necessary to include a more geographically diverse population and reevaluate mood disorder differentiation. Future study is merited to investigate the survey's impact on the psychiatric and medical management of distress in individuals with DD.

Validation of the structured interview section of the penn computerized adaptive test for neurocognitive and clinical psychopathology assessment (CAT GOASSESS).

Large-scale studies and burdened clinical settings require precise, efficient measures that assess multiple domains of psychopathology. Computerized adaptive tests (CATs) can reduce administration time without compromising data quality. We examined feasibility and validity of an adaptive psychopathology measure, GOASSESS, in a clinical community-based sample (N = 315; ages 18-35) comprising three groups: healthy controls, psychosis, mood/anxiety disorders. Assessment duration was compared between the Full and CAT GOASSESS. External validity was tested by comparing how the CAT and Full versions related to demographic variables, study group, and socioeconomic status. The relationships between scale scores and criteria were statistically compared within a mixed-model framework to account for dependency between relationships. Convergent validity was assessed by comparing scores of the CAT and the Full GOASSESS using Pearson correlations. The CAT GOASSESS reduced interview duration by more than 90 % across study groups and preserved relationships to external criteria and demographic variables as the Full GOASSESS. All CAT GOASSESS scales could replace those of the Full instrument. Overall, the CAT GOASSESS showed acceptable psychometric properties and demonstrated feasibility by markedly reducing assessment time compared to the Full GOASSESS. The adaptive version could be used in large-scale studies or clinical settings for intake screening.

The Child Behavior Check List Usefulness in Screening for Severe Psychopathology in Youth: A Narrative Literature Review.

OBJECTIVE: This article will review the use of the CBCL to diagnose youth with psychopathological disorders focusing on: ADHD, Mood Disorders, Autism Spectrum disorders, and Disruptive Disorders. METHOD: Using a narrative review approach, we investigate the usefulness of the CBCL as a screening tool to detect childhood onset psychopathology across different diagnostic syndromes. RESULTS: The available literature supports the use of the CBCL for ADHD screening and as a measure of ADHD severity. While some studies support a specific profile linked with childhood bipolar disorder, replication studies for this profile found mixed results. The CBCL was also found to be useful in screening for patients presenting with Autism Spectrum Disorders, Conduct Disorder, and Childhood Bipolar Disorder all of which presents with more severely impaired scores. CONCLUSION: The CBCL holds promise as a screening tool for childhood psychopathology.

Neural Responses to Intranasal Oxytocin in Youths With Severe Irritability.

OBJECTIVE: The authors investigated the neural impact of intranasal oxytocin on emotion processing areas in youths with severe irritability in the context of disruptive mood and behavior disorders. METHODS: Fifty-two participants with severe irritability, as measured by a score ≥4 on the Affective Reactivity Index (ARI), with diagnoses of disruptive behavior disorders (DBDs) and/or disruptive mood dysregulation disorder (DMDD) were randomly assigned to treatment with intranasal oxytocin or placebo daily for 3 weeks. Assessments were conducted at baseline and at the end of the trial; the primary outcomes were measures of irritability on the ARI and ratings on the Clinical Global Impressions severity scale (CGI-S) focusing on DBD and DMDD symptoms, and secondary outcomes included the CGI improvement scale (CGI-I) and ratings of proactive and reactive aggressive behavior on the Reactive-Proactive Aggression Questionnaire. Forty-three participants (22 in the oxytocin group and 21 in the placebo group) completed pre- and posttreatment functional MRI (fMRI) scans with the affective Stroop task. RESULTS: Youths who received oxytocin showed significant improvement in CGI-S and CGI-I ratings compared with those who received placebo. In the fMRI data, blood-oxygen-level-dependent (BOLD) responses to emotional stimuli in the dorsomedial prefrontal cortex and posterior cingulate cortex were significantly reduced after oxytocin compared with placebo. These BOLD response changes were correlated with improvement in clinical severity. CONCLUSIONS: This study provides initial and preliminary evidence that intranasal oxytocin may induce neural-level changes in emotion processing in youths with irritability in the context of DBDs and DMDD. This may lead to symptom and severity changes in irritability.

Study protocol for a randomized controlled trial testing the efficacy of Emotion Detectives In-Out: a blended version of the unified protocol for transdiagnostic treatment of emotional disorders in Portuguese children.

BACKGROUND: Childhood emotional disorders (EDs; i.e., anxiety and depressive disorders) are currently a public health concern. Their high prevalence, long-term effects, and profound influence on the lives of children and families highlight the need to identify and treat these disorders as early and effectively as possible. This clinical trial will examine the efficacy of a blended version (i.e., combining face-to-face and online sessions into one treatment protocol) of the Unified Protocol for Children (the "Emotion Detectives In-Out" program). This program is a manualized cognitive-behavioral therapy for the transdiagnostic treatment of EDs in children aged 7 to 12 years that aims to reduce the intensity and frequency of strong and aversive emotional experiences by helping children learn how to confront those emotions and respond to them in more adaptive ways. METHODS: This study is designed as a multicenter equivalence randomized controlled parallel-group two-arm trial comparing the Emotion Detectives In-Out program with an evidenced-based group intervention for children with anxiety disorders (the Coping Cat program). Participants will be children aged between 7 and 12 years with an anxiety disorder or with clinically significant anxiety symptoms as well as one of their parents or a legal representative. A minimum sample size of 138 children (69 per group) is needed to test whether the efficacy of the proposed intervention is equivalent to that of the well-established Coping Cat intervention. DISCUSSION: We expect Emotion Detectives In-Out to be a feasible and efficacious alternative intervention for treating children's EDs by allowing for a greater increase in children's access to care. A blended format is expected to overcome common barriers to treatment (e.g., parents´ lack of time to attend regular sessions) and make the intervention more accessible to families. TRIAL REGISTRATION: The clinical trial is registered at ClinicalTrials.gov (Identifier: NCT05747131, date assigned February 28, 2023).

Knowledge and Behavior of Primary Care Physicians Regarding Utilization of Standardized Tools in Screening and Assessment of Anxiety, Depression, and Mood Disorders at a Large Integrated Health System.

INTRODUCTION: Standardized screening, objective evaluation, and management of behavioral health conditions are major challenges in primary care. The Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and Mood Disorder Questionnaire (MDQ) provide standardized screening and symptom management tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), and Mood Disorders (MD), respectively. This study explores family physicians' knowledge, attitudes, and practices regarding the utilization of GAD-7, PHQ-9, and MDQ in outpatient primary care offices. METHODS: The study method was a cross-sectional electronic and paper survey utilizing a self-administered questionnaire that assessed primary care physicians' demographics, knowledge, attitudes, and practices in rural and urban outpatient clinical settings regarding GAD-7, PHQ-9, and MDQ. Statistical software SAS 9.4 was used for descriptive and Chi-Square statistics. RESULTS: Out of 320 total participants,145 responded (45.3%). Responding family physicians demonstrated a high level of familiarity with the GAD-7 (97.9%), PHQ-9 (97.9%), and MDQ (81.3%) assessment tools. However, the reported utilization rates were relatively lower than knowledge, with 62.7%, 73.1%, and 31.9% extremely likely or likely to utilize the GAD-7, PHQ-9, and MDQ as screening and monitoring tools, respectively. Less than a quarter of the total respondents use the objective score for the future management of GAD, with significantly more residents utilizing the score for GAD-7 compared to attendings (P < .05). There was no statistical significance difference between residents and attendings for the objective evaluation of Major Depressive Disorder (P = .26) and Mood Disorders (P = .05). CONCLUSIONS: Despite being knowledgeable of the utility of GAD-7, PHQ-9, and MDQ, the primary care physicians in a large integrated health system in Central Pennsylvania and Northern Maryland report inconsistent utilization in their practice. Further studies are needed to determine the underlying factors contributing to the suboptimal usage of these screening tools and ways to increase it.

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models.

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

An expert opinion on the pharmacological interventions for Disruptive Mood Dysregulation Disorder (DMDD).

INTRODUCTION: Disruptive Mood Dysregulation Disorder (DMDD) was officially introduced as a new diagnostic entity in the Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition (DSM-5), under the category of depressive disorders. AREAS COVERED: A comprehensive overview and a critical commentary on the currently investigated psychopharmacological approaches for the treatment of DMDD have been here provided. EXPERT OPINION: Behavioral and psychosocial interventions should be considered as first-line treatment strategies. When ineffective or partially effective, psychopharmacological strategy is recommended. Overall, pharmacological strategy should be preferred in those individuals with psychiatric comorbidities (e.g. ADHD). Indeed, so far published studies on pharmacological strategies in DMDD are scant and heterogeneous (i.e. age, assessment tools, symptomatology profile, comorbidity, and so forth). Therefore, DMDD psychopharmacological guidelines are needed, particularly to guide clinicians toward the patient's typical symptom profile who could benefit from psychopharmacological strategy.

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach.

BACKGROUND: Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders. OBJECTIVE: In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking. METHODS: Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals. RESULTS: The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages. CONCLUSIONS: This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.

Electroencephalography findings in menstrually-related mood disorders: A critical review.

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being luteal-phase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.

Screening for affective dysregulation in school-aged children: relationship with comprehensive measures of affective dysregulation and related mental disorders.

Affective dysregulation (AD) is characterized by irritability, severe temper outbursts, anger, and unpredictable mood swings, and is typically classified as a transdiagnostic entity. A reliable and valid measure is needed to adequately identify children at risk of AD. This study sought to validate a parent-rated screening questionnaire, which is part of the comprehensive Diagnostic Tool for Affective Dysregulation in Children (DADYS-Screen), by analyzing relationships with comprehensive measures of AD and related mental disorders in a community sample of children with and without AD. The sample comprised 1114 children aged 8-12 years and their parents. We used clinical, parent, and child ratings for our analyses. Across all raters, the DADYS-Screen showed large correlations with comprehensive measures of AD. As expected, correlations were stronger for measures of externalizing symptoms than for measures of internalizing symptoms. Moreover, we found negative associations with emotion regulation strategies and health-related quality of life. In receiver operating characteristic (ROC) analyses, the DADYS-Screen adequately identified children with AD and provided an optimal cut-off. We conclude that the DADYS-Screen appears to be a reliable and valid measure to identify school-aged children at risk of AD.

Executive function in children with disruptive mood dysregulation disorder compared to attention-deficit/hyperactivity disorder and oppositional defiant disorder, and in children with different irritability levels.

Addressing current challenges in research on disruptive mood dysregulation disorder (DMDD), this study aims to compare executive function in children with DMDD, children with attention-deficit/hyperactivity disorder (ADHD), and children with oppositional defiant disorder (ODD). We also explore associations between irritability, a key DMDD characteristic, and executive function in a clinical sample regardless of diagnosis. Our sample include children (6-12 years) referred to child psychiatric clinics. Measures of daily-life (parent-reported questionnaire) and performance-based (neuropsychological tasks) executive function were applied. Identifying diagnoses, clinicians administered a standardized semi-structured diagnostic interview with parents. Irritability was assessed by parent-report. First, we compared executive function in DMDD (without ADHD/ODD), ADHD (without DMDD/ODD), ODD (without DMDD/ADHD) and DMDD + ADHD (without ODD). Second, we analyzed associations between executive function and irritability using the total sample. In daily life, children with DMDD showed clinically elevated and significantly worse emotion control scores compared to children with ADHD, and clinically elevated scores on cognitive flexibility compared to norm scores. Children with DMDD had significantly less working memory problems than those with ADHD. No differences were found between DMDD and ODD. Increased irritability was positively associated with emotional dyscontrol and cognitive inflexibility. For performance-based executive function, no diagnostic differences or associations with irritability were observed. We discuss how, in daily life, children with high irritability-levels get overwhelmed by feelings without accompanying regulatory capacities.