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Improving the mental health of children and adolescents is a critical priority in China. However, only minimal information is available concerning the prevalence of mental disorders. In the present study, a two-stage national survey has been completed in five regions of China. In the first stage, data from the Achenbach Child Behavior Checklist (CBCL) were collected for each participant to identify the prevalence of the behavioral and emotional problems in China. A multistage cluster stratified random sampling design was used, and a total of 71,929 individuals aged 6–16 years were collected. In this large cohort, 36,893 (49.86%) were males, and 49,015 (66.24%) were living in urban communities. Overall, 17.6% (90% confidence interval 17.4%, 17.9%) of the children and adolescents were identified as having behavioral and emotional problems. Moreover, age and sex effect was found for the CBCL scores. The boys’ cohort had a higher prevalence of behavioral and emotional problems than girls (18.6% versus 16.6%). In the older cohort aged 12–16, the prevalence was higher than for the younger cohort aged 6–11 (19.0% versus 15.9%). Notably, it seems that the prevalence of the behavioral and emotional problems for children and adolescents had a rise in the past 30 years, but the scarcity of pediatric psychiatrists in China needs to be addressed. To reduce the behavioral and emotional problems, the whole society, including mental health providers, educators, and parents, needs to focus on and pay more attention to it. It will have long-term benefits for China.

The development of objective assessment tools for attention deficit hyperactivity disorder (ADHD) has become a hot research topic in recent years. This study was conducted to explore the feasibility and availability of virtual reality (VR) for evaluating symptoms of ADHD. School-aged children were recruited. The children with ADHD or without ADHD were assigned into the ADHD group or Control group, respectively. They were all evaluated using the Conners' Parent Rating Scale (CPRS), Child Behavior Checklist (CBCL), Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), and a VR test. The correct items, incorrect items, and the accuracy rate of the VR test of the children with ADHD were significantly different with those of the children in the Control group. The correct items, incorrect items, total time, and accuracy of the VR test were significantly correlated with the scores of IVA-CPT (auditory attention and visual attention), CPRS (impulsion/hyperactivity and ADHD index), and CBCL (attention problems and social problems), respectively. The results supported the discriminant validity of the VR test for evaluating ADHD in school-age children suffering from learning problems. The VR test results are associated with the commonly used clinical measurements results. A VR test is interesting for children and therefore it attracts them to complete the test; whilst at the same time, it can also effectively evaluate ADHD symptoms.

Objectives Retrospective harmonisation of data obtained through different instruments creates measurement error, even if the underlying concepts are assumed the same. We tested a novel method for item‐level data harmonisation of two widely used instruments that measure emotional and behavioural problems: the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire (SDQ). Methods Item content of the CBCL and SDQ was mapped onto four dimensions: emotional problems, peer relationship problems, hyperactivity/inattention and conduct problems. A diverse test sample was drawn from four prospective longitudinal birth cohort studies in Australia and Europe who used one or both instruments. The pooled sample included 5188 data points assessing children and adolescents aged 6–13 years (N = 257–704 participants per cohort). Measurement invariance was assessed using latent variable multi‐group confirmatory factor analysis. Results Fifteen items from the CBCL and SDQ were mapped onto four dimensions allowing for measurement invariance testing as part of a stepwise process. Partial strict invariance between CBCL and SDQ assessments was established for all four dimensions. Conclusions The harmonised dimensions of emotional, peer relationship, hyperactivity/inattention and conduct problems are invariant across the CBCL and SDQ suggesting that these dimensions can be reliably compared with limited measurement error.

Assessing stability and change of children’s psychopathology symptoms can help elucidate whether specific behaviors are transient developmental variations or indicate persistent psychopathology. This study included 6930 children across early childhood (T1), late childhood (T2) and early adolescence (T3), from the general population. Latent profile analysis identified psychopathology subgroups and latent transition analysis quantified the probability that children remained within, or transitioned across psychopathology subgroups. We identified four psychopathology subgroups; no problems (T1: 85.9%, T2: 79.0%, T3: 78.0%), internalizing (T1: 5.1%, T2: 9.2%, T3: 9.0%), externalizing (T1: 7.3%, T2: 8.3%, T3: 10.2%) and the dysregulation profile (DP) (T1: 1.7%, T2: 3.5%, T3: 2.8%). From T1 to T2, 44.7% of the children remained in the DP. Between T2 and T3, 33.6% remained in the DP; however, 91.4% were classified in one of the psychopathology subgroups. Our findings suggest that for many children, internalizing or externalizing symptoms encompass a transient phase within development. Contrary, the DP resembles a severe at-risk state in which the predictive value for being in one of the psychopathology subgroups increases over time.

Mental health problems are the major cause of disability among adolescents. Personalized prevention may help to mitigate the development of mental health problems, but no tools are available to identify individuals at risk before they require mental health care. We identified children without mental health problems at baseline but with six different clinically relevant problems at 1- or 2-year follow-up in the Adolescent Brain Cognitive Development (ABCD) study. We used machine learning analysis to predict the development of these mental health problems with the use of demographic, symptom and neuroimaging data in a discovery (N = 3236) and validation (N = 3851) sample. The discovery sample (N = 168-513 per group) consisted of participants with MRI data and were matched with healthy controls on age, sex, IQ, and parental education level. The validation sample (N = 84-231) consisted of participants without MRI data. Subclinical symptoms at 9-10 years of age could accurately predict the development of six different mental health problems before the age of 12 in the discovery and validation sample (AUCs = 0.71-0.90). The additive value of neuroimaging in the discovery sample was limited. Multiclass prediction of the six groups showed considerable misclassification, but subclinical symptoms could accurately differentiate between the development of externalizing and internalizing problems (AUC = 0.79). These results suggest that machine learning models can predict conversion to mental health problems during a critical period in childhood using subclinical symptoms. These models enable the personalization of preventative interventions for children at increased risk, which may reduce the incidence of mental health problems.

IntroductionChildren in a prodromal state manifesting as truancy or social isolation (hikikomori) often complain of problems that are physical in nature and are subject to significant changes. We developed the Child Psychosis-Risk Screening System (CPSS) that incorporates childhood psycho-behavioral characteristics revealed through a retrospective survey of schizophrenia patients into its algorithm.ObjectivesOur research aimed to test the risk identification of pediatric and psychiatric clinic outpatients using the CPSS.MethodsWe conducted an epidemiological study involving 204 outpatients between the ages of 6 and 14 years who had been examined at a pediatric or psychiatric clinic using the CBCL and clinical data from medical charts. Logistic regression analysis and T-tests were performed using each clinical data variable to clarify the risk of the CPSS calculated from the CBCL data and contributing factors.ResultsThe results of the logistic regression analysis demonstrated that the diagnostic category (physical illness or DSM-5 diagnosis) and chief complaint did not contribute to differentiate between the high-risk and low-risk groups. Meanwhile, the environmental factors of “abuse” and “social isolation” did contribute to the discrimination of the two groups.ConclusionsThe fact that the diagnostic category during childhood does not contribute to the discrimination of the high- risk group warrants attention. It is possible that the high-risk group only had a latent endophenotype that had not yet manifested during this period. The factors suggested to have an association with the high-risk group may be reflecting activators and the dynamic state of the critical period for psychosis.DisclosureNo significant relationships.

Background: Exposure to violence perpetrated on a mother by her intimate partner (IPV or intimate partner violence) has an impact on the psychosocial adjustment of her children. In addition, the violence suffered by mothers could affect parental competences. Methods: Through the Child Behavior Checklist (CBCL), this work analyzes the psychosocial adjustment in children between 6 and 17 years old who live with their mothers in shelters after having experienced IPV situations. It also explores the association between mothers’ parenting competences and children’s adjustment in shelters. Results: The evaluation shows a negative correlation between the quality of mothers’ care of their children during their stay in shelters and the rate of children’s behavioral problems, so that the better the parental competences of mothers, the lower the rate of behavioral problems presented by children. Conclusions: As a result of IPV, mother–child relationships can be affected. Children exposed to IPV may exhibit more externalizing behavior problems and their mothers may have difficulty demonstrating competent parenting behaviors while living in a shelter. Work should be aimed at reestablishing parenting competences in mothers and the quality of mother–child interactions while they remain in the shelters, in an effort to mitigate the psychosocial consequences of IPV for their children.

Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments. We used data from six samples from the Reproducible Brain Charts initiative (N = 8,606, ages 5-22 years, 41.0% females). We harmonized items from two psychopathology instruments, Child Behavior Checklist (CBCL) and GOASSESS, based on semantic content. We estimated bifactor models using confirmatory factor analysis, and calculated their model fit, factor reliability, between-instrument invariance, and authenticity (i.e., the correlation and factor score difference between the harmonized and original models). Five out of 12 model configurations presented acceptable fit and were instrument-invariant. Correlations between the harmonized factor scores and the original full-item models were high for the p-factor (>0.89) and small to moderate (0.12-0.81) for the specific factors. 6.3%-50.9% of participants presented factor score differences between harmonized and original models higher than 0.5 z-score. The CBCL-GOASSESS harmonization indicates that few models provide reliable specific factors and are instrument-invariant. Moreover, authenticity was high for the p-factor and moderate for specific factors. Future studies can use this framework to examine the impact of harmonizing instruments in psychiatric research.

This study compared maternal responsiveness to children with two neurodevelopmental disorders sharing different but, in some cases, overlapping social phenotypes—Williams syndrome (WS) and autism spectrum disorder (ASD)—and explored the relations between maternal responsiveness and child emotional/behavioural problems (EBP). The sample included 16 pre-schoolers with WS and 43 with ASD, and their mothers. Responsiveness was assessed during a mother–child interaction task. Mothers completed the CBCL 1½–5, providing a measure of EBP. No significant differences emerged between groups, and most dyads were characterized by less responsive behaviours. Maternal responsiveness proved related to child developmental age, but not with EBP. These results provide further insight into the rearing environment of children with neurodevelopmental disorders, highlighting the need for early relationship-based interventions.

Background There are overlapping effects of screen time and sleep on children’s behavior. The purpose of this study was to explore the relationship of screen time with behavior problems in children with autism spectrum disorder (ASD) and the probable mediating effects of sleep, in order to provide evidence for the need for clinical identification and intervention. Methods A sample of 358 preschoolers with ASD were included. We investigated the children’s basic characteristics of sex and age, ASD symptoms (ABC, CARS, and ADOS-2), neurodevelopment (GDS-C), sleep habits (CSHQ), and behavior (CBCL). Pearson correlation tests were used to determine the direct correlations among children’s screen time, CBCL, and CSHQ. Linear regression analysis was used to explore whether screen time predicted total score of CBCL. Multi-step linear regression analysis was used to investigate the mediating effect of sleep on the relationship between screen time and total score of CBCL. Results Screen time before bedtime was correlated with CBCL and CSHQ, which indicated that screen time before bedtime was correlated with sleep and behavior in children with ASD. Screen time before bedtime was a predictor of CBCL total score (indicating children’s behavior), and CSHQ total score (indicating children’s sleep habits) played a partial mediating role between screen time before bedtime and children’s behavior. Conclusion Clinicians should support and educate parents of children with ASD, which should focus on managing screen time, especially screen time before bedtime.