The p Factor Consistently Predicts Long-Term Psychiatric and Functional Outcomes in Anxiety-Disordered Youth

Treatment "non-responders": the experience of short-term psychoanalytic psychotherapy among depressed adolescents, their parents and therapists

Introduction

Short-term psychoanalytic psychotherapy (STPP) is an evidence-based treatment for adolescents with depression, but like all treatment approaches, not all patients benefit from it. Previous investigations of the process of STPP have mostly focused on successful cases, and only a few studies have included the perspectives of young people, their parents, and therapists in the understanding of treatment non-response.

Methods

Semi-structured interviews were carried out with young people who were considered "non-responders" to STPP, as well as with their parents and therapists. These cases were analyzed using a descriptive-interpretative approach.

Results

The data analysis revealed three themes: (1) Therapy as a safe space; (2) Can short-term psychotherapy ever be enough?; and (3) Therapists making links and connections that did not make sense to the young people.

Discussion

This study's findings indicate that "poor outcome" psychotherapy does not necessarily equate to a "poor experience" of psychotherapy, with different stakeholders appreciating the treatment setting as a "safe space." However, they also suggest that some felt that a relatively short-term treatment could not lead to substantial change and that young people in STPP might have a more negative view of their outcomes compared to their parents and therapists. Finally, the findings indicate that some interventions made by clinicians in STPP feel wrong or do not make sense to young people, potentially affecting the therapy process.

A proof-of-concept study testing the factor structure of the Stop Signal Task: overlap with substance use and mental health symptoms

Background: Research utilizing experimental tasks usually does not report estimates of internal reliability of measurement. However, modern measurement theories conceptualize reliability as sample dependent indicating that reliability should be empirically demonstrated in the samples used to make inferences.Objectives: Test whether confirmatory factor analytic (CFA) estimates of reliability can be applied to a commonly used task measuring response inhibition (the Stop Signal Task) to predict substance use (alcohol and cannabis) and mental health symptoms.Methods: Thirty-seven participants between the ages of 18-20 (72% female; 16% Asian, 3% Native American, 11% Black or African American, 59% White; 32% Latino/a/x) were recruited via social media advertisement and attended a laboratory visit. The Stop Signal Reaction Time (SSRT) was calculated as the outcome for three experimental blocks and used as indicators in a CFA.Results: CFA suggests the task yields reliable scores; factor loadings were statistically significant (p < .05) and substantial (standardized loadings ranged from .74 to .94). However, reliability increased across experimental blocks and error was non-trivial (ranging from 50% to 12% of the variance). The inhibition factor predicted higher maximum number of drinks consumed (β = .37, p < .05), higher frequency of cannabis use (β = .39, p < .05), and more cannabis use occasions within using days (β = .40, p < .05), as well as facets of mental health (anxious/depression, attention, and anxiety problems; all p's < .05).Conclusion: Results support the utility of CFA to test for reliability of measurement, with the ability to inhibit dominant responses serving as a transdiagnostic correlate of substance use and mental health problems.

A General Factor of Psychopathology Predicts Treatment and Long-Term Outcomes in Children and Adolescents With Obsessive-Compulsive Disorder

OBJECTIVE

Children and adolescents with obsessive-compulsive disorder (OCD) are at risk for long-term adversity, but factors influencing long-term outcomes are unclear. A general factor of psychopathology, often referred to as the p factor, captures variance shared by all mental disorders and has predicted long-term outcomes in youth with anxiety and depressive disorders. The p factor has never been examined in relation to outcomes in pediatric OCD. Here, we examine whether the p factor predicts 4 important outcomes over both short and long durations in youth with OCD.

METHOD

We used data from the Nordic Long-term OCD Treatment Study (NordLOTS), in which youth with OCD (N = 248, mean age = 12.83 years [SD = 2.72], 51.6% girls) received exposure-based cognitive-behavioral therapy. The p factor was estimated using parent-reported Child Behavior Checklist data at baseline and was examined in relation to clinician-rated OCD severity, clinician-rated psychosocial functioning, self-reported depressive symptoms, and self- and parent-reported quality of life directly after treatment and 1, 2, and 3 years after treatment.

RESULTS

The p factor was associated with acute treatment outcomes for OCD severity and psychosocial functioning, but not for depressive symptoms and quality of life. For the long-term outcomes, the p factor was significantly associated with all outcomes except OCD severity. The p factor outperformed traditional psychiatric comorbidity as a predictor of long-term outcomes.

CONCLUSION

Youth with OCD who experience symptoms across multiple psychiatric domains have poorer long-term outcomes. Compared to traditional classification of psychiatric diagnoses, assessing psychopathology using a dimensional p factor approach may be advantageous for informing prognosis in pediatric OCD.

Data-driven, generalizable prediction of adolescent sleep disturbances in the multisite Adolescent Brain Cognitive Development Study

STUDY OBJECTIVES

Sleep disturbances are common in adolescence and associated with a host of negative outcomes. Here, we assess associations between multifaceted sleep disturbances and a broad set of psychological, cognitive, and demographic variables using a data-driven approach, canonical correlation analysis (CCA).

METHODS

Baseline data from 9093 participants from the Adolescent Brain Cognitive Development (ABCD) Study were examined using CCA, a multivariate statistical approach that identifies many-to-many associations between two sets of variables by finding combinations for each set of variables that maximize their correlation. We combined CCA with leave-one-site-out cross-validation across ABCD sites to examine the robustness of results and generalizability to new participants. The statistical significance of canonical correlations was determined by non-parametric permutation tests that accounted for twin, family, and site structure. To assess the stability of the associations identified at baseline, CCA was repeated using 2-year follow-up data from 4247 ABCD Study participants.

RESULTS

Two significant sets of associations were identified: (1) difficulty initiating and maintaining sleep and excessive daytime somnolence were strongly linked to nearly all domains of psychopathology (r2 = 0.36, p < .0001); (2) sleep breathing disorders were linked to BMI and African American/black race (r2 = 0.08, p < .0001). These associations generalized to unseen participants at all 22 ABCD sites and were replicated using 2-year follow-up data.

CONCLUSIONS

These findings underscore interwoven links between sleep disturbances in early adolescence and psychological, social, and demographic factors.

Machine learning-based health environmental-clinical risk scores in European children

BACKGROUND

Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.

METHODS

In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions.

RESULTS

ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health.

CONCLUSIONS

Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

Reactive and control processes in the development of internalizing and externalizing problems across early childhood to adolescence

Reactive and control processes - e.g., negative emotionality and immediacy preference - may predict distinct psychopathology trajectories. However, externalizing and internalizing problems change in behavioral manifestation across development and across contexts, thus necessitating the use of different measures and informants across ages. This is the first study that created developmental scales for both internalizing and externalizing problems by putting scores from different informants and measures onto the same scale to examine temperament facets as risk factors. Multidimensional linking allowed us to examine trajectories of internalizing and externalizing problems from ages 2 to 15 years (N = 1,364) using near-annual ratings by mothers, fathers, teachers, other caregivers, and self report. We examined reactive and control processes in early childhood as predictors of the trajectories and as predictors of general versus specific psychopathology in adolescence. Negative emotionality at age 4 predicted general psychopathology and unique externalizing problems at age 15. Wait times on an immediacy preference task at age 4 were negatively associated with age 15 general psychopathology, and positively associated with unique internalizing problems. Findings demonstrate the value of developmental scaling for examining development of psychopathology across a lengthy developmental span and the importance of considering reactive and control processes in development of psychopathology.

A Psychometric Evaluation of the Expanded Version of the Inventory of Depression and Anxiety Symptoms (IDAS-II) in Children and Adolescents

The expanded version of the Inventory of Depression and Anxiety Symptoms (IDAS-II) is a self-report measure of 18 empirically derived internalizing symptom dimensions. The measure has shown good psychometric properties in adults but has never been evaluated in children and adolescents. A Swedish version of the IDAS-II was administered to 633 children and adolescents (Mage =16.6 [SD = 2.0]) and 203 adults (Mage = 35.4 [SD = 12.1]). The model/data fit of the 18-factor structure was excellent in both samples and measurement invariance across age groups was supported. All scales showed good to excellent internal consistency and psychometric properties replicated in the younger youth sample (< 16 years). Among youth, good convergent validity was established for all scales and divergent validity for most scales. The IDAS-II was better at identifying youth with current mental health problems than an internationally recommended scale of internalizing symptoms. In conclusion, the IDAS-II shows promise as a measure of internalizing symptoms in youth.

Unpacking the p-factor. Associations Between Maladaptive Personality Traits and General Psychopathology in Female and Male Adolescents

Adolescence is a period of rapid physical, psychological, and neural maturation that makes youth vulnerable to emerging psychopathology, highlighting the need for improved identification of psychopathology risk indicators. Recently, a higher-order latent psychopathology factor (p-factor) was identified that explains latent liability for psychopathology beyond internalizing and externalizing difficulties. However, recent proposals suggest reconceptualizing the p-factor model in terms of impairments in personality encompassing difficulties in both self-regulation (borderline features) and self-esteem (narcissistic features), but this remains untested. To address this, this study examined the p-factor structure and the contribution of borderline and narcissistic features using two cross-sectional data collections. In Study 1, 974 cisgender adolescents (63% assigned females at birth; age range: 13-19; Mage = 16.68, SD = 1.40) reported on internalizing and externalizing problems (YSR) to test via structural equation models (SEM) different theoretical models for adolescent psychopathology. In Study 2, 725 cisgender adolescents (64.5% assigned females at birth; age range: 13-19; Mage = 16.22, SD = 1.32) reported internalizing and externalizing problems (YSR), borderline personality features (BPFSC-11), and narcissistic personality traits (PNI), to explore, via SEM, the contribution of borderline and narcissistic traits to the p-factor and accounting for gender differences. Results confirmed the utility of a bi-factor model in adolescence. Furthermore, findings highlighted the contribution of borderline features and narcissistic vulnerability to general psychopathology. The study provides the first evidence supporting a p-factor model reconceptualized in terms of personality impairments encompassing difficulties in self-regulation and self-esteem in adolescents.

The p factor outweighs the specific internalizing factor in predicting recurrences of adolescent depression

BACKGROUND

The early prediction of adolescent depression recurrence poses a significant challenge in the field. This study aims to investigate and compare the abilities of the general psychopathology factor (p) and the specific internalizing factor, in predicting depression recurrence over a 2-year course, as well as identifying remitted depressed adolescents from healthy adolescents. Longitudinal changes of these two factors in different trajectory groups were also tracked to examine their sensitivity to sustained remission and relapse.

METHODS

We included 255 baseline-remitted depressed adolescents and a healthy control group (n = 255) matched in age, sex, and race, sourced from the Adolescent Brain Cognitive Development Study. The linear mixed model was employed for the statistical analysis.

RESULTS

The p factor not only effectively discriminated between remitted depressed adolescents and healthy controls but also robustly predicted the depression recurrence over a subsequent 2-year course. The specific internalizing factor could only differentiate remitted depressed adolescents from healthy controls. Additionally, a noteworthy longitudinal decline of the p factor in the sustained-remission group was observed.

CONCLUSIONS

Psychopathology factors serve as the inherent and enduring measurement of long-term mental health aberrations. Longitudinal results indicate that the p factor is more sensitive to respond to sustained remission than the internalizing factor. The ability of the overall p factor to anticipate depression relapse, unlike the specific internalizing factor, suggests the clinical interventions should monitor and mitigate the coincident symptoms across all dimensions to preempt relapse of adolescent depression, rather than an exclusive focus on internalizing symptoms.

The Role of Maternal Personality Organization and of the p Factor in Predicting Parental Distress, the Quality of Parental Care, and Offspring's Dysregulation Symptoms

Background

It has been posited that parental distress, the quality of maternal care and offspring's dysregulation can be predicted by maternal maladaptive characteristics. However, only a few studies have considered mothers' personality organizations and features of the p factor in mothers as possible predictors of symptoms in their children.

Methods

In a sample of N=524 subjects, this study evaluated the effect of mothers' personality organization and of the p factor on parental distress, parental care and offspring's dysregulation symptoms. Mothers filled out the IPO, the ASR, and the PSI-SF; children's teachers filled out the TRF; children were administered the PBI.

Results

We found that different mother groups (neurotic, borderline, psychotic organization) have distinct impact on parental distress, quality of care, and children's dysregulation, mediated by the p factor.

Conclusion

This study can contribute to the understanding of the key factors underpinning mothers and children's psychopathology.

Associations Between General and Specific Psychopathology Factors and 10-Year Clinically Relevant Outcomes in Adult Swedish Twins and Siblings

Importance

General and specific factors of psychopathology are associated with future adverse outcomes, indicating that they might be useful for identifying individuals at greatest risk. However, it remains unknown if these associations are attributable to confounders that may influence both the psychopathology factors and later outcomes.

Objective

To analyze associations between psychopathology factors and clinically relevant outcomes within family pairs, adjusting for unmeasured confounds by applying co-twin control and sibling comparison designs.

Design, Setting, and Participants

This longitudinal cohort study with a follow-up range of 9 to 13 years included all Swedish twins born from 1959 to 1985 who participated in the Study of Twin Adults: Genes and Environment (60% response rate) and the oldest pair of all Swedish siblings born from 1959 to 1985 per the Multi-Generation Register. Twins were evaluated based on responses to a hierarchical factor model derived using multivariate statistics. Sibling pairs were evaluated based on psychiatric diagnoses per the Swedish National Patient Register. Information on outcome events and prescriptions were derived from the National Patient Register, Prescribed Drug Register, and Crime Register. Baseline assessment was in August 2005, and data were analyzed from January 2022 to February 2023.

Exposures

Hierarchical factor model consisting of 1 general and 4 specific factors fit to 48 psychiatric symptoms on which twin participants self-reported in 2005 and 1 general and 3 specific factors fit to 9 register-based psychiatric diagnoses assigned to sibling participants prior to 2005.

Main Outcomes and Measures

Outcomes consisted of 7 register-based events occurring after 2005, including suicidal behavior, substance overdoses, and criminal suspicion or convictions (data available until the end of 2013), and prescription of antidepressants, antialcohol or antiopioid medication, antipsychotics, and stimulants (data available until the end of 2017).

Results

The study included 32 328 twins (mean [SD] age, 34 [8] years; 16 076 [49.73%] male) and 1 942 106 siblings (mean [SD] age, 34 [7] years; 991 500 [51.05%] male). General psychopathology was significantly associated with all 7 outcomes within sibling pairs (mean within-pair odds ratio [OR], 2.28; 95% CI, 2.19-2.37) and dizygotic twin pairs (within-pairs OR, 1.65; 95% CI, 1.38-1.98) and with 3 outcomes within monozygotic twin pairs (mean within-pairs OR, 1.77; 95% CI, 1.35-2.36). Within sibling pairs, the specific internalizing factor was associated with antidepressant prescriptions (within-pairs OR, 1.65; 95% CI, 1.59-1.71), the specific substance misuse factor was associated prescription of antialcohol and antiopioid medication (within-pairs OR, 2.36; 95% CI, 2.20-2.54), and the specific psychotic factor was associated with antipsychotic medications (within-pairs OR, 1.61; 95% CI, 1.51-1.72). Similar results emerged within twin pairs.

Conclusion and Relevance

In this cohort study, general psychopathology was significantly associated with all 7 outcomes within sibling and dizygotic twin pairs and 3 outcomes within monozygotic twin pairs at 10 years. Within twin and sibling pairs, the specific factors were primarily associated with related outcomes. Several of the associations in this cohort study could not be attributed to unmeasured confounds shared by family members, suggesting that interventions toward broad psychopathology dimensions might help reduce the risk of future clinically relevant events.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Trajectories of change in general psychopathology levels among depressed adolescents in short-term psychotherapies

OBJECTIVE

to identify and describe trajectories of change in general psychopathology (p) levels among depressed adolescents who received one of three types of short-term therapies (namely Cognitive-Behavioural Therapy, Short-Term Psychoanalytic Psychotherapy, and a Brief Psychosocial Intervention).

METHOD

Participants were 465 adolescents with MDD who participated in an RCT comparing three treatments for depression. Narrow-band measures of depression, anxiety, obsessions-compulsions, and conduct problems were assessed at six-time points, and bifactor analysis was performed to extract p factor scores. These scores were submitted to Latent Class Growth Analyses to identify patterns of change over time.

RESULTS

Three different trajectories of change in p were identified. Two trajectories displayed reductions in p across time-points: one a rapid decrease, and the other slower but steady improvement. The third trajectory indicated a limited decrease in p up until the 12th week after baseline but no further improvement at subsequent time-points. Patients' baseline p significantly predicted their outcome trajectories.

CONCLUSION

Exploring change in p seemed to describe more parsimoniously the patients' outcomes than the narrow-band assessment of depressive symptoms. Patients with high baseline p were more likely to have poorer outcomes, potentially indicating a need to develop more intensive and tailored treatments for this population.

The harmonium model and its unified system view of psychopathology: a validation study by means of a convolutional neural network

The harmonium model (HM) is a recent conceptualization of the unifying view of psychopathology, namely the idea of a general mechanism underpinning all mental disorders (the p factor). According to HM, psychopathology consists of a low dimensional Phase Space of Meaning (PSM), where each dimension of meaning maps a component of the environmental variability. Accordingly, the lower thenumber of independent dimensions in the PSM, and hence its intrinsic complexity, the more limited the way of interpreting the environment. The current simulation study, based on a Convolutional Neural Network (CNN) framework, aims at validating the HM low-dimensionality hypothesis. CNN-based classifiers were employed to simulate normotypical and pathological cognitive processes. Results revealed that normotypical and pathological CNNs were different in terms of both classification performance and layer activation patterns. Using Principal Component Analysis to characterize the PSM associated with the two algorithms, we found that the performance of the normotypical CNN relies on a larger and more evenly distributed number of components, compared with the pathological one. This finding might be indicative of the fact that psychopathology can be modelled as a low-dimensional, poorly modulable PSM, which means the environment is detected through few components of meaning, preventing complex information patterns from being taken into account.

Taboo obsessions and their association with suicidality in obsessive-compulsive disorder

Individuals with obsessive-compulsive disorder (OCD) more often think about, attempt, and die by suicide than individuals from the general population. Sexual and religious obsessions (i.e., taboo obsessions) have been linked to increased risk of suicidality, but it is unclear if they explain additional risk over and above other risk factors. We refined the recently proposed multidimensional hierarchical model of OCD and explored how each symptom dimension in the model was associated with suicidality in a random half (n = 500) of a well-characterized cohort of patients with OCD. Symptom dimensions and other risk factors significantly associated with suicidality were included in a confirmatory multivariable model conducted with the other half of the sample (n = 501). The predictive confirmatory model accounted for 19% of the variance in suicidality. Taboo obsessions, the general OCD factor (i.e., having many different OCD symptoms at the same time), lifetime major depression, and lifetime substance use disorders significantly predicted suicidality in this model. Lifetime major depression explained most unique variance in suicidality (5.6%) followed by taboo obsessions and the general OCD factor (1.9% each). Taboo obsessions explain a small but significant proportion of variance in suicidality and should be considered an independent risk factor for suicidality in patients with OCD.

Identifying Youth at Risk for Suicidal Thoughts and Behaviors Using the "p" factor in Primary Care: An Exploratory Study

Suicide is a major, preventable public health problem. The general factor of psychopathology ("p" factor) might help improve detection and prediction of individuals at risk for suicide. This cross-sectional proof-of-concept study tests whether the p-factor score is associated with suicidal thoughts and behaviors (STB) better than a depression scale alone. Youth (N = 841; mean age 18.02, SD = 3.36) in primary care were universally screened using the Behavioral Health Screen (BHS). Factor analysis and ROC results showed the BHS assesses the p-factor, and the p-factor score demonstrates higher classification accuracy of several types of STB than a depression scale. The p-factor could help clinicians in the identification of youths with STB.

The Hierarchical Taxonomy of Psychopathology (HiTOP) in psychiatric practice and research

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Technology-delivered cognitive-behavioral therapy for pediatric anxiety disorders: a meta-analysis of remission, posttreatment anxiety, and functioning

BACKGROUND

The efficacy of technology-delivered cognitive-behavioral therapy (tCBT) for pediatric anxiety disorders (ADs) is uncertain as no meta-analysis has examined outcomes in trials that used structured diagnostic assessments at pre- and posttreatment.

METHODS

We carried out a systematic review and meta-analysis of randomized controlled trials (RCTs) of tCBT for pediatric ADs that included participants <18 years of age with a confirmed primary AD according to a structured diagnostic interview. Nine studies with 711 participants were included.

RESULTS

tCBT outperformed control conditions for remission for primary AD (37.9% vs. 10.2%; k = 9; OR = 4.73; p < .0001; I²  = 0%; moderate certainty), remission for all ADs (19.5% vs. 5.3%; k = 8; OR = 3.32; p < .0001; I²  = 0%; moderate certainty), clinician-rated functioning (k = 7; MD = -4.38; p < .001; I²  = 56.9%; low certainty), and caregiver-reported anxiety (k = 7; SMD = 0.27; p = .02; I²  = 41.4%; low certainty), but not for youth-reported anxiety (k = 9; SMD = 0.13; p = .12; I²  = 0%; low certainty). More severe pretreatment anxiety, a lower proportion of completed sessions, no face-to-face sessions, media recruitment, and a larger proportion of females were associated with lower remission rates for primary AD.

CONCLUSIONS

tCBT has a moderate effect on remission for pediatric ADs and clinician-rated functioning, a small effect on caregiver-reported anxiety, and no statistically significant effect on youth-reported anxiety. The certainty of these estimates is low to moderate. Remission rates vary substantially across trials and several factors that may influence remission were identified. Future research should examine for whom tCBT is most appropriate and what care to offer the large proportion that does not remit. Future RCTs should consider contrasting tCBT with partial tCBT (e.g., including therapist-led exposure) and/or face-to-face CBT.

Modeling environment through a general exposome factor in two independent adolescent cohorts

Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture's the complex network structure of the environment (ie, exposome) using multi-level data (participant's report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, N = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, N = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (B = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (B = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth's mental and general health.