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Policy Points From 2011-2023, US children's health deteriorated across >170 indicators. Chronic conditions rose from 25.8% to 31%, risk of anxiety tripled, and autism and learning disability more than doubled. Policies based on biomedical models have failed to address these trends. Life course models regard health as a developmental process, with mismatches between children's physical, nutritional, and relational environments and the ones they are evolutionarily calibrated to expect as likely drivers of declining health. State policies can help to overcome mismatches and optimize developmental ecosystems. New Mexico is pioneering and testing an approach linking early childhood policy innovation with population-level developmental measurement. The health of US children has deteriorated significantly across multiple indicators with trends accelerating since 2007. From 2011-2023, the prevalence of chronic conditions among 3-19-year-olds rose from 25.8 to 31%, obesity rose from 17 to 20%, and autism and learning disabilities more than doubled. Rates of anxiety, loneliness, depression, early onset of menstruation and trouble sleeping all increased. Biomedically driven policy and practice have improved treatment of certain established medical conditions (e.g., childhood cancers and congenital heart disease), but failed to stem increasing mental health challenges, and neurodevelopmental disorders. Life course models point to fundamental changes in the early developmental ecosystem resulting in profound mismatches between the conditions children are evolutionarily primed and historically calibrated to expect and those they currently experience as primary drivers of these changes. The Industrial Revolution accelerated changes in children's physical, relational, and nutritional environments; temporal rhythms; and cognitive demands. The Digital Revolution further accelerated the speed and degree of change, potentially outpacing children's capacity for adaptive developmental responses. These multiple environmental changes do not act in isolation but interact in multilevel, dynamic, and complex ways to form the developmental ecosystems shaping children's developmental trajectories. In this paper we suggest a reorientation of child health policy and resulting interventions-expanding the focus beyond isolated social risks and behavioral deficits to address ecosystem-level influences and shift population outcomes by altering health development trajectories. We also suggest a suite of actionable state-level policy solutions geared to improve the configuration of these developmental ecosystems. We highlight state policy innovations such as New Mexico's comprehensive portfolio of early childhood policy changes and infrastructure upgrades along with ongoing population-level measurement of children's health development to monitor policy impact.
Neal Halfon, Shirley Russ
Objective: This retrospective study examined the associations between subjective cognitive difficulties, performance scores on standardized cognitive tests, and quality of life (QoL) among pediatric patients with long COVID. Method: This study included 82 pediatric patients with long COVID referred for a neuropsychological evaluation by a specialty pediatric Post-COVID clinic (50 F, Mage = 14.52 years, SD = 3.04, range = 6-19). Patients completed standardized neuropsychological assessments of intellectual functioning, learning/memory, working memory, auditory attention, processing speed, executive function, and performance validity, as well as the Pediatric Quality of Life Inventory (PedsQLTM). Other outcomes of interest included subjective cognitive impairment and psychiatric symptoms. Relationships between subjective cognitive difficulties and cognitive performance on QoL were analyzed using hierarchical multiple linear regressions, with COVID course factors and psychiatric symptoms as covariates. Results: Our pediatric cohort reported very low overall QoL (M = 52.41, SD = 16.95), with the average score surpassing the published clinical cutoff for children with major chronic health conditions. Subjective cognitive difficulties significantly predicted lower QoL, even after controlling for psychiatric symptoms. Cognitive test performance on any domain did not significantly determine QoL. Conclusions: These findings offer initial support that subjective cognitive difficulties, but not cognitive test performance, significantly correlate with lower QoL in pediatric patients with long COVID. The results highlight the importance of screening for perceived subjective cognitive impairment beyond mental health screening in youth with long COVID. Treatment and school planning for children with long COVID should consider self-reported cognitive difficulties and psychological health and how these impact their daily functioning across home, school, and social settings.
Audrey Davis, Gray Vargas, Kathleen Parker et al.
INTRODUCTION: Premature birth is associated with an increased risk of cognitive and motor developmental problems. Early prediction of these outcomes is crucial for timely intervention and adequate follow-up. This study aimed to predict cognitive and motor outcomes in preterm-born infants using brain volumetric features in combination with clinical and demographic perinatal characteristics, and to compare the performance of three different machine learning algorithms. METHODS: We acquired quantitative structural brain MRI at approximately 30 weeks postmenstrual age (PMA) and clinical and demographic birth characteristics including gestational age, sex, birthweight, and maternal education level. Machine learning techniques were applied to predict cognitive (N = 63) and motor (N = 41) outcomes at two years corrected age, assessed using the Bayley Scales of Infant and Toddler Development III, in infants born before 30 weeks gestation. We evaluated the performance of logistic regression, linear support vector classifier (linear SVC), and k-nearest neighbour (KNN). RESULTS: Employing volumetric features only, KNN achieved a validation accuracy of 0.64, showing modest predictive power for cognitive outcomes. Adding clinical characteristics did not significantly improve accuracy. The models did not perform better than chance when predicting motor outcomes. CONCLUSION: Volumetric features demonstrated modest predictive ability for cognitive outcomes, and the addition of clinical features did not significantly improve performance. The low to moderate overall validation accuracy underscores, however, the challenges faced in the clinical application of machine learning in neuroimaging. The reliability and clinical applicability of predictions of outcomes from advanced neuroimaging data will largely depend on the quantity and quality of the data, methodological standardisation, and the generalisability of the prediction models.
Fotios Savvopoulos, Sophie de Munck, Gerbrich E van den Bosch et al.
The neonatal period surrounding term represents a critical window for corpus callosum (CC) maturation, when axonal growth, pre-myelination, and fiber organization rapidly advance to support emerging motor and cognitive functions, yet preterm birth disrupts this process with unclear mechanisms and long-term consequences. To address this gap, we employed a normative modeling framework, based on a large term-born cohort, to characterize segment-specific CC developmental trajectories and then quantify individual deviations in preterm infants at term-equivalent age (TEA). Preterm infants exhibited selective CC microstructural abnormalities-most pronounced in the genu, anterior body, and splenium-while midsagittal area remained within normative limits. Using partial least squares correlation, we examined associations with neurobehavioral outcomes during toddler period and identified a multivariate brain-behavior coupling unique to the preterm cohort. Specifically, higher gross motor and cognitive scores but lower fine motor score were linked to a coordinated CC profile of increased anisotropy, higher splenial neurite density, reduced diffusivity, and lower genu orientation dispersion. This relationship was absent in term-born controls. These findings suggest that microstructure-informed deviation mapping at TEA may be sensitive to subtle CC alterations that are not apparent on macrostructural measures and are associated with later functional outcomes. This sensitivity to subtle and outcome-related variation highlights the value of this approach as a complement to conventional imaging, and lays the groundwork for future efforts to characterize early white matter development and identify preterm infants at elevated neurodevelopmental risk.
Wei Wang, Liyuan Yang, Ke Wu et al.
OBJECTIVES: Autism spectrum disorder (ASD) shows heterogeneous neurodevelopmental patterns across childhood, juvenile development, and adulthood, so binary MRI classification may not fully capture age-and sex-related variation. This study proposes a proof-of-concept framework for multiple classification using ABIDE-II T1-weighted structural MRI (sMRI). METHODS: After image-quality screening, sMRI slices were processed using Canny edge detection, brain-region cropping, 224 × 224 resizing, and augmentation. Images were organized into sex-based four-class, age-based four-class, and combined age-and-sex eight-class datasets, separating child, young, juvenile, adult, male, and female groups. A Batch-Normalized Grid-Optimized CNN (BN-GO-CNN) was developed to learn structural representations directly from preprocessed sMRI, with grid search used to select key CNN hyperparameters. The model was compared with VGG16, DenseNet201, MobileNetV2, and EfficientNet-B0 using identical image partitions. RESULTS: BN-GO-CNN achieved validation accuracies of 67.0 %, 64.0 %, and 60.5 % for sex-based, age-based, and combined age-and-sex classification, respectively. These outcomes remained above chance but showed limited class separability. CONCLUSIONS: The modest performance suggests that label-related sMRI differences are subtle, supporting this framework as a preliminary test of CNN-assisted sMRI classification for future research use rather than a clinically reliable ASD diagnostic system.
Yu Ke, Qingliang He, Wei Jiang et al.
Addressing the mental health needs of children and young people requires integrated and multidisciplinary action to reduce the risk of detrimental outcomes. Schools offer a unique environment for such provision. However, there is a critical need for school-based interventions that are feasible, co-designed, transdiagnostic, sustainable, and cost-effective beyond the conclusion of the trial, while aligning with pupil and teacher preferences. The current study reports the development phase of a new school-based transdiagnostic mental health intervention, embedded into routine classroom lessons, which aims to promote mental health and prevent the onset or escalation of mental health symptoms in young people. The study involved a total of 32 participants aged 15-63 years (Mage = 32.72, SD = 12.73), across three groups: one group of teachers (Mage = 36.80, SD = 11.29) took part in four sequential development workshops, one group of young people (Mage = 15.85, SD = 0.69) took part in one development workshop, and a new group of teachers who were not part of the initial development took part in one refinement workshop (Mage = 37.38, SD = 9.26). The study identified mental health priority areas in the classroom and brought together teachers and young people to co-develop activities that can be integrated directly into routine teaching to support and promote mental health. The intervention was deemed feasible and acceptable by both teachers and young people, subject to contextual considerations. Teachers perceived the activities to have a positive impact on young people's mental health, engagement, learning, energy, and overall enjoyment in class. The development of this intervention, with its co-production approach, has the potential to bridge the gap in intervention translation to the classroom and ultimately benefit young people's mental health.
Tracy M Stewart, Gillean McCluskey, Hamish Johnson et al.
In youth-i.e., adolescents and young adults-mental disorders represent a major burden on well-being and quality of life. This scientometric study aimed to map research trends concerning any mental disorder in youth. We searched the Web of Science Core Collection for studies published between 01/01/1985 and 28/08/2025, using keywords on mental disorders and youth. Co-citation reference networks were extracted. R-bibliometrix/CiteSpace were used. 65,684 studies were included, forming a well-structured co-reference network. A trend in the 1980s concerned the prevalence/outcomes of specific mental disorders such as depressive and anxiety disorders. In the 1990s/2000s, research trends concerned assessment tools, pharmacotherapy (e.g., antidepressants, antipsychotics), bipolar disorders and schizophrenia. In the 2010s, early intervention for psychosis was a dominant focus, influencing prevention in mental health. The 2010s also focused on ADHD, eating disorders, gaming disorder, autism, depression and suicide, fMRI, and transgender health, beyond gender dysphoria. In the 2020s, a recent trend has increasingly focused on global evidence on the age at onset and burden across mental disorders, access to care, network analysis, and COVID-19. Over time, research has broadened the focus from adolescence to youth across mental health problems. Both disorder-specific and transdiagnostic research trends were observed. Novel trends in 2023-2025 included substance use, lifestyle psychiatry, evidence synthesis and non-suicidal self-injury. Most studies were conducted in Western countries. Underrepresented research areas included the involvement of youth with lived/living experience in identifying meaningful outcomes, open science, implementation research, economic evaluations, machine-learning approaches, and social determinants of mental health.
Gabriele Lo Buglio, Srividya N Iyer, Muhammad Omair Husain et al.
ABSTRACT: OBJECTIVES: To map artificial intelligence (AI)-enabled mental health applications for adolescents and distinguish the available evidence on clinical functions, predictive or diagnostic performance, symptom-level effectiveness, safety, governance and implementation readiness. DESIGN: Scoping review conducted using Joanna Briggs Institute guidance and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews. DATA SOURCES: PubMed/MEDLINE, Scopus, the Cochrane Library and ScienceDirect were searched from 1 January 2014 to 17 June 2026. ELIGIBILITY CRITERIA: English-language peer-reviewed sources addressing AI-enabled screening, prediction, assessment, monitoring, therapeutic support, care navigation, safeguarding or implementation for adolescents or school-aged youth were eligible. Adult-only studies, non-AI digital tools, protocols without results and unverifiable or promotional sources were excluded. CHARTING AND SYNTHESIS: Primary studies and evidence syntheses were analysed separately. 95 primary studies were mapped using mutually exclusive principal AI-modality and clinical-function categories; effectiveness, safety and implementation domains were non-mutually exclusive. RESULTS: The database searches identified 11 167 records, and 1 additional evidence synthesis was identified and assessed during revision; 103 sources were included, comprising 95 primary studies and 8 evidence syntheses. Structured-data machine learning was the dominant modality (42/95, 44.2%), and risk prediction or prognosis was the leading clinical function (45/95, 47.4%). Predictive or diagnostic performance was reported in 83 studies (87.4%), but symptom-level effectiveness in only 1 (1.1%). No primary study provided a dedicated prospective AI-safety evaluation. Implementation evidence focused on bias or equity monitoring (28/95, 29.5%), privacy or data governance (26/95, 27.4%), human oversight (19/95, 20.0%), acceptability (18/95, 18.9%) and feasibility (15/95, 15.8%). No study evaluated sustained routine-care scale-up or implementation cost as a principal outcome. CONCLUSIONS: Within the retrieved evidence base, adolescent mental-health AI research was concentrated in technical development, with limited prospective evidence of clinical benefit, safety and routine implementation. The field is dominated by prediction and internal validation, whereas symptom-level benefit, prospective safety, workflow integration, scale-up and sustainability remain largely untested. Future applications should be evaluated as supervised, human-in-the-loop systems with explicit safeguards for consent, privacy, fairness, accountability and crisis escalation.
Meita Dhamayanti, Susaldi Susaldi, Fanni Hanifa et al.