Identifying Precise Targets to Improve Child Mental Health Care Equity: Leveraging Advances in Clinical Research Informatics and Lived Experience.

To reduce child mental health disparities, it is imperative to improve the precision of targets and to expand our vision of social determinants of health as modifiable. Advancements in clinical research informatics and please state accurate measurement of child mental health service use and quality. Participatory action research promotes representation of underserved groups in informatics research and practice and may improve the effectiveness of interventions by informing research across all stages, including the identification of key variables, risk and protective factors, and data interpretation.

Use of Acute Mental Health Care in U.S. Children's Hospitals Before and After Statewide COVID-19 School Closure Orders.

Objective: This study aimed to examine changes in child emergency department (ED) discharges and hospitalizations for primary general medical (GM) and primary psychiatric disorders; prevalence of psychiatric disorders among acute care encounters; and change in acute mental health (MH) care encounters by disorder type and, within these categories, by child sociodemographic characteristics before and after statewide COVID-19–related school closure orders.

Methods: This retrospective, cross-sectional cohort study used the Pediatric Health Information System database to assess percent changes in ED discharges and hospitalizations (N=2,658,474 total encounters) among children ages 3–17 years in 44 U.S.

Mental Health-Related Emergency Department Visits Among Children During The Early COVID-19 Pandemic.

Objective: To measure univariate and covariate-adjusted trends in children's mental health-related emergency department (MH-ED) use across geographically diverse areas of the U.S. during the first wave of the Coronavirus-2019 (COVID-19) pandemic.

Psychiatric Readmission of Children and Adolescents: A Systematic Review and Meta-Analysis.

Objective: To investigate predictors of psychiatric hospital readmission of children and adolescents, a systematic review and meta-analysis was conducted.

Methods: Following PRISMA statement guidelines, a systematic literature search of articles published between 1997 and 2018 was conducted in PubMed/MEDLINE, Google Scholar, and PsycINFO for original peer-reviewed articles investigating predictors of psychiatric hospital readmission among youths (<18 years old). Effect sizes were extracted and combined by using random-effects meta-analysis.

Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research.

An unprecedented amount of clinical information is now available via electronic health records (EHRs). These massive data sets have stimulated opportunities to adapt computational approaches to track and identify target areas for quality improvement in mental health care. In this column, three key areas of EHR data science are described: EHR phenotyping, natural language processing, and predictive modeling.

National Child Mental Health Quality Measures: Adherence Rates and Extent of Evidence for Clinical Validity.

Purpose Of Review: To provide an overview of the selection process and annual updates of the child mental health measures within the Child Core Set, describe national and statewide adherence rates, and summarize findings from a systematic literature review examining measure adherence rates and whether adherence is associated with improved clinical outcomes.

Recent Findings: Five national quality measures target child mental health care processes. On average, national adherence varied widely by state, and performance did not substantially improve during the past 5 years.

Predictors and outcomes of somatization in bipolar I disorder: A latent class mixture modeling approach.

Background: Mood disorders are often associated with somatic symptoms. The role of somatic symptoms on disease progression in unipolar depression is substantially better characterized than that role in bipolar disorder. Moreover, the contribution of comorbid anxiety disorders and medical illness is not well understood.

Medically unexplained somatic symptoms and bipolar spectrum disorders: A systematic review and meta-analysis.

Background: Patients with bipolar spectrum disorders (BSD) frequently report medically unexplained somatic symptoms. However, the prevalence and the consequences for treatment and outcome are currently unknown.

Methods: To estimate the prevalence of somatic symptoms in BSD, we conducted a systematic review and meta-analysis of empirical studies published between 1980 and 2015.