Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing. OBJECTIVE: To differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis. METHODS: We developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network. RESULTS: The classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted). CONCLUSIONS: Prevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history.

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. Mean national adherence rates for the two measures related to timeliness of care were below 50%. For each measure, scientific evidence to support the association between adherence and improved clinical outcomes was scarce. Investment in academic-agency partnered research to standardize methods for publicly reporting adherence to national child mental health quality measures and validation of these measures should be a national priority for child healthcare research.

High-Risk Phenotypes of Early Psychiatric Readmission in Bipolar Disorder With Comorbid Medical Illness.

BACKGROUND: Individuals with co-existing serious mental illness and non-psychiatric medical illness are at high risk of acute care utilization. Mining of electronic health record data can help identify and categorize predictors of psychiatric hospital readmission in this population. OBJECTIVE: This study aimed to identify modifiable predictors of psychiatric readmission among individuals with comorbid bipolar disorder and medical illness. This goal was accomplished by applying objective variable selection via machine learning techniques. METHOD: This was a retrospective analysis of electronic health record data derived from 77,296 episodes of care from 2006 to 2016 within the University of California Health Care System. Data included 1,250 episodes of care involving patients with bipolar disorder and serious comorbid medical illnesses (defined by transfer between medicine and psychiatry services or concomitant primary medical and psychiatric admission diagnoses). Machine learning (classification trees) was used to identify potential predictors of 30-day psychiatric readmission across hospital encounters. Predictors included demographics, medical and psychiatric diagnoses, medication regimen, and disposition. The algorithm was internally validated using 10-fold cross-validation. RESULTS: The model predicted 30-day readmission with high accuracy (98% unbalanced model, 88% balanced model). Modifiable predictors of readmission were length of stay, transfers between medical and psychiatric services, discharge disposition to home, and all-cause acute health service utilization in the year before the index hospitalization. CONCLUSION: Among bipolar disorder patients with comorbid medical conditions, characteristics of the index hospitalization (e.g., duration, transfer, and disposition) emerged as more predictive than static properties of the patient (e.g., sociodemographic factors and psychiatric comorbidity burden). Findings identified phenotypes of patients at high risk for rehospitalization and suggest potential ways of modifying the risk of early readmission.

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.

Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits.

Importance: Suicide is a leading cause of death among young people. Accurate detection of self-injurious thoughts and behaviors (SITB) underpins equity in youth suicide prevention. Objectives: To compare methods of detecting SITB using structured electronic health information and measure algorithmic performance across demographics. Design, Setting, and Participants: This cross-sectional study used medical records among youths aged 6 to 17 years with at least 1 mental health-related emergency department (ED) visit in 2017 to 2019 to an academic health system in Southern California serving 787 000 unique individuals each year. Analyses were conducted between January and September 2023. Exposures: Multiexpert electronic health record review ascertained the presence of SITB using the Columbia Classification Algorithm of Suicide Assessment. Random forest classifiers with nested cross-validation were developed using (1) International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for nonfatal suicide attempt and self-harm and chief concern and (2) all available structured data, including diagnoses, medications, and laboratory tests. Main Outcome and Measures: Detection performance was assessed overall and stratified by age group, sex, and race and ethnicity. Results: The sample comprised 2702 unique youths with an MH-related ED visit (1384 youths who identified as female [51.2%]; 131 Asian [4.8%], 266 Black [9.8%], 719 Hispanic [26.6%], 1319 White [48.8%], and 233 other race [8.6%]; median [IQR] age, 14 [12-16] years), including 898 children and 1804 adolescents. Approximately half of visits were related to SITB (1286 visits [47.6%]). Sensitivity of SITB detection using only codes and chief concern varied by age group and increased until age 15 years (6-9 years: 59.3% [95% CI, 48.5%-69.5%]; 10-12 years: 69.0% [95% CI, 63.8%-73.9%]; 13-15 years: 88.4% [95% CI, 85.1%-91.2%]; 16-17 years: 83.1% [95% CI, 79.1%-86.6%]), while specificity remained constant. The area under the receiver operating characteristic curve (AUROC) was lower among preadolescents (0.841 [95% CI, 0.815-0.867]) and male (0.869 [95% CI, 0.848-0.890]), Black (0.859 [95% CI, 0.813-0.905]), and Hispanic (0.861 [95% CI, 0.831-0.891]) youths compared with adolescents (0.925 [95% CI, 0.912-0.938]), female youths (0.923 [95% CI, 0.909-0.937]), and youths of other races and ethnicities (eg, White: 0.901 [95% CI, 0.884-0.918]). Augmented classification (ie, using all available structured data) outperformed classification with codes and chief concern alone (AUROC, 0.975 [95% CI, 0.968-0.980] vs 0.894 [95% CI, 0.882-0.905]; P < .001). Conclusions and Relevance: In this study, diagnostic codes and chief concern underestimated SITB prevalence, particularly among minoritized youths. These results suggest that priority on algorithmic fairness in suicide prevention strategies must extend to accurate detection of youths with suicide-related emergencies.

Risk of suicide attempts and self-harm after 1.4 million general medical hospitalizations of men with mental illness.

BACKGROUND: The short-term risk of suicide after medical hospital discharge is four times higher among men compared with women. As previous work has identified female-specific antecedents of suicide-related behavior after medical hospitalization of women with serious mental illness, we examined predictors among a similar population of men with multimorbidity. METHODS: Classification and regression tree (CART) models were developed and validated using electronic health records (EHRs) from 1,423,161 medical (non-psychiatric) hospitalizations of men ≥ 18-years-old with an existing diagnosis of a depressive disorder, bipolar disorder, or chronic psychosis. Hospitalizations occurred between 2009 and 2017. Risk groups were evaluated using an independent testing set. The primary outcome was readmission within one year associated with ICD-9 or -10 code for self-harm or attempt. RESULTS: The 1-year readmission rate for intentional self-harm and suicide attempt was 3.9% (55,337/1,423,161 hospitalizations). The classification model discriminated risk with area under the curve (AUC) 0.73 (Confidence Interval [95%CI] 0.68-0.74), accuracy 0.82 (95%CI 0.71-0.83), sensitivity 82.6% (95%CI 81.2-84), and specificity 83.1% (95%CI 81.7-84.5). Strongest predictors were medical comorbidity, prior self-harm, age, and prior hospitalization. Men with greater medical comorbidity burden and prior self-harm were at highest risk (Odds Ratio [OR] 3.10, 95%CI 3.02-3.18), as were men < 62-years-old with few medical comorbidities (OR 1.11 95%CI 1.08-1.13). LIMITATIONS: The study focused on medical hospitalizations for suicide attempt and thus captured only severe attempts resulting in hospitalization. CONCLUSIONS: After medical hospitalization, men with serious mental illness experienced a high risk of self-harm (1:25 hospitalizations). Risk was particularly elevated among younger patients without prior medical conditions and older patients with medical comorbidity and prior self-harm.

Detection of Suicidal Behavior and Self-harm Among Children Presenting to Emergency Departments: A Tree-based Classification Approach.

Suicide is the second leading cause of death of U.S. children over 10 years old. Application of statistical learning to structured EHR data may improve detection of children with suicidal behavior and self-harm. Classification trees (CART) were developed and cross-validated using mental health-related emergency department (MH-ED) visits (2015-2019) of children 10-17 years (N=600) across two sites. Performance was compared with the CDC Surveillance Case Definition ICD-10-CM code list. Gold-standard was child psychiatrist chart review. Visits were suicide-related among 284/600 (47.3%) children. ICD-10-CM detected cases with sensitivity 70.7 (95%CI 67.0-74.3), specificity 99.0 (98.8-100), and 85/284 (29.9%) false negatives. CART detected cases with sensitivity 85.1 (64.7-100) and specificity 94.9 (89.2-100). Strongest predictors were suicide-related code, MH- and suicide-related chief complaints, site, area deprivation index, and depression. Diagnostic codes miss nearly one-third of children with suicidal behavior and self-harm. Advances in EHR-based phenotyping have the potential to improve detection of childhood-onset suicidality.

Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.

BACKGROUND: Although suicide is a leading cause of death among children, the optimal approach for using health care data sets to detect suicide-related emergencies among children is not known. OBJECTIVE: This study aimed to assess the performance of suicide-related International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and suicide-related chief complaint in detecting self-injurious thoughts and behaviors (SITB) among children compared with clinician chart review. The study also aimed to examine variations in performance by child sociodemographics and type of self-injury, as well as develop machine learning models trained on codified health record data (features) and clinician chart review (gold standard) and test model detection performance. METHODS: A gold standard classification of suicide-related emergencies was determined through clinician manual review of clinical notes from 600 emergency department visits between 2015 and 2019 by children aged 10 to 17 years. Visits classified with nonfatal suicide attempt or intentional self-harm using the Centers for Disease Control and Prevention surveillance case definition list of ICD-10-CM codes and suicide-related chief complaint were compared with the gold standard classification. Machine learning classifiers (least absolute shrinkage and selection operator-penalized logistic regression and random forest) were then trained and tested using codified health record data (eg, child sociodemographics, medications, disposition, and laboratory testing) and the gold standard classification. The accuracy, sensitivity, and specificity of each detection approach and relative importance of features were examined. RESULTS: SITB accounted for 47.3% (284/600) of the visits. Suicide-related diagnostic codes missed nearly one-third (82/284, 28.9%) and suicide-related chief complaints missed more than half (153/284, 53.9%) of the children presenting to emergency departments with SITB. Sensitivity was significantly lower for male children than for female children (0.69, 95% CI 0.61-0.77 vs 0.84, 95% CI 0.78-0.90, respectively) and for preteens compared with adolescents (0.66, 95% CI 0.54-0.78 vs 0.86, 95% CI 0.80-0.92, respectively). Specificity was significantly lower for detecting preparatory acts (0.68, 95% CI 0.64-0.72) and attempts (0.67, 95% CI 0.63-0.71) than for detecting ideation (0.79, 95% CI 0.75-0.82). Machine learning-based models significantly improved the sensitivity of detection compared with suicide-related codes and chief complaint alone. Models considering all 84 features performed similarly to models considering only mental health-related ICD-10-CM codes and chief complaints (34 features) and models considering non-ICD-10-CM code indicators and mental health-related chief complaints (53 features). CONCLUSIONS: The capacity to detect children with SITB may be strengthened by applying a machine learning-based approach to codified health record data. To improve integration between clinical research informatics and child mental health care, future research is needed to evaluate the potential benefits of implementing detection approaches at the point of care and identifying precise targets for suicide prevention interventions in children.

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. Method: This is a retrospective, cross-sectional cohort study using electronic health records from four academic health systems, comparing percent volume change and adjusted risk of child MH-ED visits among children aged 3-17 years, matched on 36-week (3/18/19-11/25/19 vs. 3/16/20-11/22/20) and 12-week seasonal time intervals. Adjusted incidence rate ratios (IRR) were calculated using multivariate Poisson regression. Results: Visits declined during spring-fall 2020 (n = 3892 vs. n = 5228, -25.5%) and during spring (n = 1051 vs. n = 1839, -42.8%), summer (n = 1430 vs. n = 1469, -2.6%), and fall (n = 1411 vs. n = 1920, -26.5%), compared with 2019. There were greater declines among males (28.2% vs. females -22.9%), children 6-12-year (-28.6% vs. -25.9% for 3-5 years and -22.9% for 13-17 years), and Black children (-34.8% vs. -17.7% to -24.9%). Visits also declined for developmental disorders (-17.0%) and childhood-onset disorders (e.g., attention deficit and hyperactivity disorders; -18.0%). During summer-fall 2020, suicide-related visits rose (summer +29.8%, fall +20.4%), but were not significantly elevated from 2019 when controlling for demographic shifts. In contrast, MH-ED use during spring-fall 2020 was significantly reduced for intellectual disabilities (IRR 0.62 [95% CI 0.47-0.86]), developmental disorders (IRR 0.71 [0.54-0.92]), and childhood-onset disorders (IRR 0.74 [0.56-0.97]). Conclusions: The early pandemic brought overall declines in child MH-ED use alongside co-occurring demographic and diagnostic shifts. Children vulnerable to missed detection during instructional disruptions experienced disproportionate declines, suggesting need for future longitudinal research in this population.

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-19­related 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 3­17 years in 44 U.S. children's hospitals in 2020 compared with 2019, by using matched data for 36- and 12-calendar-week intervals. Results: Decline in MH ED discharges accounted for about half of the decline in ED discharges and hospitalizations for primary GM disorders (−24.8% vs. −49.1%), and MH hospitalizations declined 3.4 times less (−8.0% vs. −26.8%) in 2020. Suicide attempt or self-injury and depressive disorders accounted for >50% of acute MH care encounters before and after the statewide school closures. The increase in both ED discharges and hospitalizations for suicide attempt or self-injury was 5.1 percentage points (p<0.001). By fall 2020, MH hospitalizations for suicide attempt or self-injury rose by 41.7%, with a 43.8% and 49.2% rise among adolescents and girls, respectively. Conclusions: Suicide or self-injury and depressive disorders drove acute MH care encounters in 44 U.S. children's hospitals after COVID-19­related school closures. Research is needed to identify continuing risk indicators (e.g., sociodemographic characteristics, psychiatric disorder types, and social determinants of health) of acute child MH care.

Predicting suicidal behavior and self-harm after general hospitalization of adults with serious mental illness.

Individuals with psychiatric disorders are vulnerable to adverse mental health outcomes following physical illness. This longitudinal cohort study defined risk profiles for readmission for suicidal behavior and self-harm after general hospitalization of adults with serious mental illness. Structured electronic health record data were analyzed from 15,644 general non-psychiatric index hospitalizations of individuals with depression, bipolar, and psychotic disorders admitted to an urban health system in the southwestern United States between 2006 and 2017. Using data from one-year prior to and including index hospitalization, supervised machine learning was implemented to predict risk of readmission for suicide attempt and self-harm in the following year. The Classification and Regression Tree algorithm produced a classification prediction with an area under the receiver operating curve (AUC) of 0.86 (95% confidence interval (CI) 0.74-0.97). Incidence of suicide-related behavior was highest after general non-psychiatric hospitalizations of individuals with prior suicide attempt or self-harm (18%; 69 cases/389 hospitalizations) and lowest after hospitalizations associated with very high medical morbidity burden (0 cases/3090 hospitalizations). Predictor combinations, rather than single risk factors, explained the majority of risk, including concomitant alcohol use disorder with moderate medical morbidity, and age ≤55-years-old with low medical morbidity. Findings suggest that applying an efficient and highly interpretable machine learning algorithm to electronic health record data may inform general hospital clinical decision support, resource allocation, and preventative interventions for medically ill adults with serious mental illness.

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. Covariates were investigated with meta-regression and subgroup analyses. RESULTS: Thirty-three studies met inclusion criteria, containing information on 83,361 children and adolescents, of which raw counts of readmitted vs. non-readmitted youths were available for 76,219. Of these youths, 13.2% (N=10,076) were readmitted. The mean±SD study follow-up was 15.9±15.0 months, and time to readmission was 13.1±12.8 months. Readmission was associated with, but not limited to, suicidal ideation at index hospitalization (pooled odds ratio [ORpooled]=2.35, 95% confidence interval [CI]=1.64-3.37), psychotic disorders (ORpooled=1.87, 95% CI=1.53-2.28), prior hospitalization (ORpooled=2.51, 95% CI=1.76-3.57), and discharge to residential treatment (ORpooled=1.84, 95% CI=1.07-3.16). There was evidence of moderate study bias. Prior investigations were methodologically and substantively heterogeneous, particularly for measurement of family-level factors. CONCLUSIONS: Interventions to reduce child psychiatric readmissions should place priority on youths with indicators of high clinical severity, particularly with a history of suicidality, psychiatric comorbidity, prior hospitalization, and discharge to residential treatment. Standardization of methods to determine prevalence rates of readmissions and their predictors is needed to mitigate potential biases and inform a national strategy to reduce repeated child psychiatric hospital readmissions.

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. For each of these computational approaches, case examples are provided to illustrate their role in mental health services research. Together, adaptation of these methods underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Medication Adherence Among Children and Adolescents with Severe Mental Illness: A Systematic Review and Meta-Analysis.

OBJECTIVE: To perform a systematic review and meta-analysis of studies investigating predictors of medication adherence in children and adolescents with severe mental illness (SMI). METHOD: A systematic literature search was conducted in PubMed/MEDLINE, Web of Science, and PsycINFO from 1980 through October 1st, 2017, for original peer-reviewed articles that investigated predictors of adherence to psychopharmacologic treatment among children (≤18-years-old) with a primary psychotic disorder, bipolar disorder, depression, recent suicide attempt, or psychiatric hospitalization. Effect sizes (ESs) for individual predictors were extracted and combined using DerSimonian-Laird random-effects meta-analysis. Meta-regression and moderator analyses were conducted to investigate subgroups. This review complied with Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guidelines. RESULTS: A total of 28 studies (n = 180,870) met inclusion criteria; 65.9% (±20.9%) of children and adolescents with SMI were medication adherent. Adherence was associated with patient and family attitudes toward care, adherence to psychotherapy, and insight. Nonadherence was associated with illness severity, substance use, and attention-deficit/hyperactivity disorder. Heterogeneity was moderate-to-large for most ES estimates (I2 > 50%). Age, sex, underlying diagnosis, and study methodology emerged as significant moderators. CONCLUSION: Medication nonadherence among youth with SMI is highly prevalent. Children and adolescents with more severe illness and higher comorbidity burden are at greater risk for nonadherence. Positive interpersonal care processes and adherence to nonpharmacological treatment may be protective. These findings inform development of a risk profile for nonadherence among youth with SMI. Future prospective research is needed to address the shortcomings in the existing literature and inform interventions to improve adherence.

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. METHOD: We investigated 527 patients with bipolar I disorder clustered within 102 families using a latent class approach. Predictors were added stepwise into the model. Anxiety and commonly associated medical illnesses were added as covariates. RESULTS: The rate of somatic symptoms in this sample was 73% (mean 1.7 symptoms), and 27.3% had a comorbid anxiety disorder. A two-class model, with a subgroup at high-risk for somatization, gave the best fit to the data. Multilevel mixture modeling accounted for family clusters. Somatic symptoms were independently associated with disease severity, defined as earlier age of first seeking psychiatric help (x = 21.7 vs x = 24.7, p = 0.005) and first psychiatric hospitalization (x = 25.7 vs x = 28.2, p = 0.03), greater probability of attempting suicide (x = 0.41 vs x = 0.32, p = 0.047), and rapid-cycling disease course (x = 0.57 vs x = 0.36, p < 0.001). Persons with few or no somatic symptoms were more likely to be hospitalized for severe mania (x = 0.63 vs x = 0.51; p = 0.013), but did not significantly differ in hospitalization for severe depression. LIMITATIONS: The study is correlational. Information on pharmacologic interventions and comorbid diseases was limited. CONCLUSIONS: Somatic symptoms in bipolar disorder could be an independent indicator for disease severity, suicidality, and rapid-cycling disease course. In severe mental illness, somatic and psychological symptoms must be jointly addressed.

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. The odds for somatic symptoms in BSD were compared with unipolar depression (UPD) and general population or mixed psychiatric controls. Studies were retrieved from four electronic databases utilizing Boolean operations and reference list searches. Pooled data estimates were derived using random-effects methods. RESULTS: Out of 2634 studies, 23 were eligible for inclusion, yielding an N of 106,785 patients. The estimated prevalence of somatic symptoms in BSD was 47.8%. The estimated prevalence of BSD in persons with somatic symptoms was 1.4%. Persons with BSD had a higher prevalence of somatic symptoms compared with population or mixed psychiatric controls (OR 1.82, 95% CI 1.14-2.92). Persons with BSD had a similar prevalence of somatic symptoms compared with UPD controls (OR 0.99, 95% CI 0.68-1.44). LIMITATIONS: This study is correlational; thus causal inferences cannot be made. Reporting of somatic symptoms likely varies with BSD severity and subtype. Some studies reported insufficient information regarding comorbid medical conditions and medications. CONCLUSIONS: Persons with BSD suffer from somatic symptoms at a rate nearly double that of the general population, a rate similar to persons with UPD. Our results suggest the utility of an integrated care model in which primary care and specialist physicians collaborate with mental health professionals to jointly address psychological and bodily symptoms.

The impact of exercise in improving executive function impairments among children and adolescents with ADHD, autism spectrum disorder, and fetal alcohol spectrum disorder: a systematic review and meta-analysis

Abstract Objective: he goal of this work was to perform a systematic review and meta-analysis evaluating and comparing exercise related improvements in various executive function (EF) domains among children and adolescents with attention-deficit hyperactivity disorder (ADHD), Autism Spectrum Disorders (ASD), and Fetal Alcohol Spectrum Disorders (FASD). Methods: A systematic literature research was conducted in PubMed, CENTRAL, and PsycInfo from October 1st, 2018 through January 30th, 2019 for original peer-reviewed articles investigating the relationship between exercise interventions and improvements in three domains of executive function (working memory, attention/set shifting, and response inhibition) among children and adolescents with ADHD, ASD, and FASD. Effect sizes (ES) were extracted and combined with random-effects meta-analytic methods. Covariates and moderators were then analyzed using meta-regression and subgroup analyses. Results: A total of 28 studies met inclusion criteria, containing information on 1,281 youth (N=1197 ADHD, N= 54 ASD, N=30 FASD). For ADHD, exercise interventions were associated with moderate improvements in attention/set-shifting (ES 0.38, 95% CI 0.01-0.75, k=14) and approached significance for working memory (ES 0.35, 95%CI −0.17-0.88, k=5) and response inhibition (ES 0.39, 95%CI −0.02-0.80, k=12). For ASD and FASD, exercise interventions were associated with large improvements in working memory (ES 1.36, 95%CI 1.08-1.64) and response inhibition (ES 0.78, 95%CI 0.21-1.35) and approached significance for attention/set-shifting (ES 0.69, 95% −0.28-1.66). There was evidence of substantial methodologic and substantive heterogeneity among studies. Sample size, mean age, study design, and the number or duration of intervention sessions did not significantly moderate the relationship between exercise and executive function. Conclusion: Exercise interventions among children and adolescents with neurodevelopmental disorders were associated with moderate improvements in executive function domains. Of note, studies of youth with ASD and FASD tended to report higher effect sizes compared to studies of youth with ADHD, albeit few existing studies. Exercise may be a potentially cost-effective and readily implementable intervention to improve executive function in these populations.