JAMA Pediatrics

Absence of autism risk-stratification tools under 18 months hampers early intervention. In a multinational sample of 1697 participants, aged one month and older, we provide proof-of-concept that temporal molecular dynamics can stratify autism likelihood. Using laser-ablation-inductively-coupled-plasma-mass-spectrometry, we measured elemental intensities along growth increments of single hair strands at [~]800 timepoints. We developed a first-stage model to stratify individuals into a lower autism probability group and applied a second-stage model to the remaining participants, stratifying them into intermediate- and high-probability groups. Models were trained, ensembled, and tuned on participants from California and Sweden, then tested on 580 participants (within- and external-population replication in New York, Mexico, and Japan). Likelihood ratios (95%CI) for autism in low-, intermediate-, and high-probability groups were 0.18(0.15-0.23), 1.09(0.99-1.20), and 2.62(1.55-4.00), respectively. Low-probability classification (first-stage) had sensitivity of 96%(0.91-0.98), and high-probability classification (second-stage) had specificity of 90%(0.86-0.92). Our data support that elemental biodynamics can objectively stratify autism likelihood.

ImportanceThe time to accurately diagnose rare pediatric diseases often spans years. Assessing the diagnostic accuracy of an LLM-based tool on real pediatric cases can help reduce this time, providing quicker diagnoses for patients and their families. ObjectiveTo evaluate the clinical utility of DxGPT as a support tool for differential diagnosis of both common and rare diseases. DesignUnicentric descriptive cross-sectional exploratory study. Anonymized data from 50 pediatric patients medical histories, covering common and rare pathologies, were used to generate clinical case notes. Each clinical case included essential data, with some expanded by complementary tests. SettingThis study was conducted at a reference pediatric hospital, Sant Joan de Deu Barcelona Childrens Hospital. ParticipantsA total of 50 clinical cases were diagnosed by 78 volunteer doctors (medical diagnostic team) with varying experience, each reviewing 3 clinical cases. InterventionsEach clinician listed up to five diagnoses per clinical case note. The same was done on the DxGPT web platform, obtaining the Top-5 diagnostic proposals. To evaluate DxGPTs variability, each note was queried three times. Main Outcome(s) and Measure(s)The study mainly focused on comparing diagnostic accuracy, defined as the percentage of cases with the correct diagnosis, between the medical diagnostic team and DxGPT. Other evaluation criteria included qualitative assessments. The medical diagnostic team also completed a survey on their user experience with DxGPT. ResultsTop-5 diagnostic accuracy was 65% for clinicians and 60% for DxGPT, with no significant differences. Accuracies for common diseases were higher (Clinicians: 79%, DxGPT: 71%) than for rare diseases (Clinicians: 50%, DxGPT: 49%). Accuracy increased similarly in both groups with expanded information, but this increase was only stastitically significant in clinicians (simple 52% vs. expanded 69%; p=0.03). DxGPTs response variability affected less than 5% of clinical case notes. A survey of 48 clinicians rated the DxGPT platform 3.9/5 overall, 4.1/5 for usefulness, and 4.5/5 for usability. Conclusions and RelevanceDxGPT showed diagnostic accuracies similar to medical staff from a pediatric hospital, indicating its potential for supporting differential diagnosis in other settings. Clinicians praised its usability and simplicity. These tools could provide new insights for challenging diagnostic cases. Key Points QuestionIs DxGPT, a large language model-based (LLM-based) tool, effective for differential diagnosis support, specifically in the context of a clinical pediatric setting? FindingsIn this unicentric cross-sectional study, diagnostic accuracy, measured as the proportion of clinical cases where any of the five diagnostic options included the correct diagnosis, showed comparable results between clinicians and DxGPT. Top-5 accuracy was 65% for clinicians and 60% for DxGPT. MeaningThese findings highlight the potential of LLM-based tools like DxGPT to support clinicians in making accurate and timely diagnoses, ultimately improving patient care.

ImportanceEarly identification and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms in preschool-age children is important for mitigating social-emotional and academic problems. Clinical practice guidelines recommend first-line behavior intervention before considering medication treatment for children 4-5-years-old. ObjectiveTo assess variation in rates of ADHD identification and rates and timing of medication treatment in children 3-5-years-old in primary care settings across eight US pediatric health systems and to identify patient factors associated with the time from diagnosis to prescription. DesignRetrospective cohort study of electronic health records. SettingPrimary care clinics affiliated with eight academic institutions participating in the PEDSnet Clinical Research Network. ParticipantsChildren 3-5-years-old seen in primary care between 2016-2023. ExposureADHD diagnosis at age 4-5 years. Main Outcomes and MeasuresOutcomes: (1) rate of ADHD diagnosis; (2) rate of stimulant and non-stimulant prescription after diagnosis before age 7, (3) time from first ADHD-related diagnosis (including symptom-level diagnoses) to medication prescription. Independent variables: institution, year of diagnosis, patient age, sex, race/ethnicity, medical insurance, and presence of comorbidities. ResultsOf 712,478 children seen in primary care at ages 3-5 years, 9,708 (1.4%) received an ADHD diagnosis at age 4-5 years (range 0.5-3.1% across institutions). Of those with ADHD, 76.4% (n=7414) were male, 39.0% (n=3782) were White. Of 9,708 preschool-age children with ADHD, 68.2% (6624) were prescribed ADHD medications before age 7, 42.2% (n=4092) were prescribed medications within 30 days of the first documentation of an ADHD-related diagnosis (range 26.0-49.0% across institution). Asian (aHR 0.50, CI 0.38-0.65), Hispanic (aHR 0.75, CI 0.70-0.81), and Black (aHR 0.90, CI 0.85-0.96) children with ADHD were less likely to be prescribed medication early compared to White children. Older (aHR 1.64, CI 1.57-1.72), male (aHR 1.74, CI 1.11-1.24) and publicly insured (aHR 1.10, CI 1.04-1.17) patients were more likely to be prescribed medication early compared to younger, female and privately insured patients, respectively. Conclusion and RelevanceMany preschool-age children with ADHD seen in primary care in 8 large pediatric health systems were prescribed medications at or shortly after the first documented diagnosis. Future analysis of clinical documentation is needed to understand the reasoning behind early prescription patterns.

The impact of post-acute sequelae of SARS-CoV-2 infection (PASC) in children is underrecognized. We developed an EHR-based algorithm across eight pediatric institutions to identify children with COVID-19 based on serology testing from 3/2020 through 4/2022 who had not been identified by PCR. Overall, serology tests were used 100-fold less than PCR. Seroprevalence of IgG anti-nucleocapsid antibodies remained stable, while rates of positive IgG anti-spike antibodies increased in teenagers after COVID-19 vaccine approval. Through data harmonization and after excluding 1,410 serology test results that may have been influenced by vaccines, we identified 2,714 children that were COVID-19 positive exclusively by serology. These patients were frequently tested as inpatients (24% vs. 2%), had chronic conditions more frequently (37% vs 24%), and a MIS-C diagnosis (23% vs. <1%) compared with PCR-positive children. Identification of children that could have been paucisymptomatic, not tested, or missed is critical to define the burden of PASC in children.

Objective: To evaluate whether a locally hosted open-weight large language model (LLM) can extract documented psychosocial factors from pediatric psychiatric intake notes and apply validated extraction to a large emergency psychiatry cohort. Materials and Methods: We identified emergency department presentations at Cincinnati Children's Hospital Medical Center from January 1, 2016, through December 31, 2024, among patients younger than 18 years with psychiatric billing diagnoses. Using full-text intake notes, gpt-oss:120b classified peer conflict, sleep disruption, and school-related academic, attendance, and disciplinary issues as detected, negated, or indeterminate. Four human raters independently reviewed 50 notes. We compared Fleiss' kappa among humans alone versus humans plus the LLM, assessed repeated-query stability across 50 independent calls per note, and applied the workflow to all eligible notes. Results: Among 37,315 eligible admissions, 22,284 had eligible intake notes; 22,270 produced parseable JSON. In detected-versus-not-detected coding, human-plus-LLM reliability did not differ significantly from human-only reliability across measures (human {kappa} 0.71-0.94; human-plus-LLM {kappa} 0.70-0.93). Stability was associated with human agreement: mean LLM-human agreement increased from 42.6% for classifications with less than 80% stability to 82.7% for classifications with 100% stability (Pearson r = 0.36). Full-cohort extraction showed frequent and overlapping documented factors: sleep disruption was most frequently detected (57.7%), followed by peer conflict (47.2%), academic issues (43.4%), disciplinary issues (43.3%), and attendance issues (16.9%). Discussion: Agreement varied by construct and was strongest when repeated model outputs were stable. Conclusion: Locally hosted open-weight LLMs can support scalable structured extraction of documented psychosocial factors from pediatric psychiatric intake notes after local validation.

Background and ObjectivesWhereas adverse early life experiences (ACEs) correlate with cognitive, emotional and physical health at the population level, existing ACEs screens are only weakly predictive of outcomes for an individual child. This raises the possibility that important elements of the early-life experiences that drive vulnerability and resilience are not being captured. We previously demonstrated that unpredictable parental and household signals constitute an ACE with cross-cultural relevance. We created the 5-item Questionnaire of Unpredictability in Childhood (QUIC-5) that can be readily administered in pediatric clinics. Here, we tested if combined screening with the QUIC-5 and an ACEs measure in this real-world setting significantly improved prediction of child health outcomes. MethodsLeveraging existing screening with the Pediatric ACEs and Related Life Events Screener (PEARLS) at annual well-child visits, we implemented QUIC-5 screening in 19 pediatric clinics spanning the diverse sociodemographic constituency of Orange County, CA. Children (12yr+) and caregivers (for children 0-17years) completed both screens. Health diagnoses were abstracted from electronic health records (N=29,305 children). ResultsFor both screeners, increasing exposures were associated with a higher probability of a mental (ADHD, anxiety, depression, externalizing problems, sleep disorder) or physical (obesity abdominal pain, asthma, headache) health diagnosis. Across most diagnoses, PEARLS and QUIC provided unique predictive contributions. Importantly, for three outcomes (depression, obesity, sleep disorders) QUIC-5 identified vulnerable individuals that were missed by PEARLS alone. ConclusionsScreening for unpredictability as an additional ACE in primary care is feasible, acceptable and provides unique, actionable information about child psychopathology and physical health. Whats Known on This SubjectWhereas ACEs correlate with neurodevelopmental and physical health of children at the population level, ACEs scales (e.g., PEARLS) are only weakly predictive at the level of the individual child. Are important elements of early-life adversity missed by these scales? What This Study AddsBecause unpredictable signals constitute a unique ACE, we developed the Questionnaire of Unpredictability in Childhood (QUIC-5). Administering QUIC-5 and PEARLS to 30,000 families identified youth at risk for depression, obesity and other health problems, who would be missed by PEARLS alone.

ImportanceHeterogeneous mental health outcomes during the COVID-19 pandemic are recognized in the general population, but it has not been systematically assessed in youth with neurodevelopmental disorders (NDD), including autism spectrum (ASD). ObjectiveIdentify subgroups of youth with ASD/NDD based on the pandemic impact on symptoms and service changes, as well as predictors of outcomes. Design, Setting, and ParticipantsThis is a naturalistic observational study conducted across 14 North American and European clinical and/or research sites. Parent responses on the Coronavirus Health and Impact Survey Initiative (CRISIS) adapted for Autism and Related Neurodevelopmental Conditions (AFAR) were cross-sectionally collected from April to October 2020. The sample included 1275, 5-21 year-old youth with ASD and/or NDD who were clinically well-characterized prior to the pandemic. Main Outcomes and MeasuresTo identify impact subgroups, hierarchical clustering analyzed eleven AFAR factors measuring pre- to pandemic changes in clinically relevant symptoms and service access. Random forest classification assessed the relative contribution in predicting subgroup membership of 20 features including socio-demographics, pre-pandemic service, and clinical severity along with indices of COVID-19 related experiences and environments empirically-derived from AFAR parent responses and global open sources. ResultsClustering analyses revealed four ASD/NDD impact subgroups. One subgroup - broad symptom worsening only (20% of the aggregate sample) - included youth with worsening symptoms that were above and beyond that of their ASD/NDD peers and with similar service disruptions as those in the aggregate average. The three other subgroups showed symptom changes similar to the aggregate average but differed in service access: primarily modified services (23%), primarily lost services (6%), and average services/symptom changes (53%). Pre-pandemic factors (e.g., number of services), pandemic environments and experiences (e.g., COVID-19 cases, related restrictions, COVID-19 Worries), and age emerged in unique combinations as distinct protective or risk factors for each subgroup. Together they highlighted the role of universal risk factors, such as risk perception, and the protective role of services before and during the pandemic, in middle childhood. Conclusions and RelevanceConcomitant assessment of changes in both symptoms and services access is critical to understand heterogeneous impact of the pandemic on ASD/NDD youth. It enabled the delineation of pathways to risk and resilience that include universal and ASD/NDD specific contributors.

ObjectiveTo assess the accuracy of a large language model (LLM) in measuring clinician adherence to practice guidelines for monitoring side effects after prescribing medications for children with attention-deficit/hyperactivity disorder (ADHD). MethodsRetrospective population-based cohort study of electronic health records. Cohort included children aged 6-11 years with ADHD diagnosis and >2 ADHD medication encounters (stimulants or non-stimulants prescribed) between 2015-2022 in a community-based primary healthcare network (n=1247). To identify documentation of side effects inquiry, we trained, tested, and deployed an open-source LLM (LLaMA) on all clinical notes from ADHD-related encounters (ADHD diagnosis or ADHD medication prescription), including in-clinic/telehealth and telephone encounters (n=15,593 notes). Model performance was assessed using holdout and deployment test sets, compared to manual chart review. ResultsThe LLaMA model achieved excellent performance in classifying notes that contain side effects inquiry (sensitivity= 87.2%, specificity=86.3/90.3%, area under curve (AUC)=0.93/0.92 on holdout/deployment test sets). Analyses revealed no model bias in relation to patient age, sex, or insurance. Mean age (SD) at first prescription was 8.8 (1.6) years; patient characteristics were similar across patients with and without documented side effects inquiry. Rates of documented side effects inquiry were lower in telephone encounters than in-clinic/telehealth encounters (51.9% vs. 73.0%, p<0.01). Side effects inquiry was documented in 61% of encounters following stimulant prescriptions and 48% of encounters following non-stimulant prescriptions (p<0.01). ConclusionsDeploying an LLM on a variable set of clinical notes, including telephone notes, offered scalable measurement of quality-of-care and uncovered opportunities to improve psychopharmacological medication management in primary care.

As part of a multi-year project that monitored illness-related school closures, we conducted systematic daily online searches from July 27, 2020-June 30, 2022, to identify public announcements of COVID-19-related school closures (COVID-SCs) in the US lasting [&ge;]1 day. We explored the temporospatial patterns of COVID-SCs and analyzed associations between COVID-SCs and national COVID-19 surveillance data. COVID-SCs reflected national surveillance data: correlation was highest between COVID-SCs and both new PCR test positivity (correlation coefficient, r = 0{middle dot}73, CI: 0{middle dot}56, 0{middle dot}84) and new cases (r = 0{middle dot}72, CI: 0{middle dot}54, 0{middle dot}83) in school year (SY) 2020-21, and with hospitalization rates among all ages (rs = 0{middle dot}81, CI: [0{middle dot}67, 0{middle dot}89]) in SY 2021-22. The number of reactive COVID-SCs during SYs 2020-21 and 2021-22 greatly exceeded previously observed numbers of illness-related reactive school closures in the US, notably being nearly 5-fold greater than reactive closures observed during the 2009 H1N1 Pandemic (H1N1pdm09 virus). Article summary lineCOVID-19-related school closures occurred annually in the US and their temporal patterns mirror the general patterns of COVID-19 activity at the national level as observed through routine COVID-19 epidemiological surveillance.

Typically-developing children progress through three distinct language-comprehension phenotypes. 1) The Command Phenotype, emerging by age 2, is characterized by understanding single words and simple commands. 2) The Modifier Phenotype, observed around age 3, is characterized by understanding adjective-noun combinations. 3) The Syntactic Phenotype, reached by age 4, is characterized by understanding stories and complex syntactic structures. This study examined language-comprehension trajectories in autistic children using parent-submitted longitudinal assessments from 6,736 participants, with a mean observation period of 2.2 {+/-} 1.3 years, spanning ages 1.5-22 years. Autistic children advanced through the same three phenotypes as neurotypical children but showed systematic differences. Increasing autism severity both reduced the likelihood of attaining higher-level phenotypes and lengthened the time required to reach them. The Command Phenotype was retained by 11%, 19%, and 39% of individuals with mild, moderate, and severe autism. Among individuals who advanced, median ages for acquiring the Modifier Phenotype were 3.7, 4.6, and 5.7 years for those with mild, moderate, and severe autism. For the Syntactic Phenotype, median ages were 4.8, 5.9, and 6.5 years across the same groups. These findings provide the first large-scale quantification of language-comprehension trajectories in autism and underscore the importance of early intervention.

ImportanceEvaluation of New York State (NYS) Senate Bill 2994A repealing school-entry nonmedical vaccine exemption options suggests that the law was effective to increase school vaccine coverage; however, the laws impact on homeschooling has not been examined. ObjectiveTo evaluate the impact of NYS Senate Bill 2994A on homeschooling prevalence. Design, Setting, and ParticipantsIn this population-based cohort study with interrupted time-series analyses, we estimated changes in homeschooling prevalence following NYS Senate Bill 2994A implementation. The study cohort comprised school districts that submitted annual student enrollment and homeschooling reports for all school years during the study period, 2014-15 through 2019-20. Analyses were conducted in January 2025. ExposureNYS Senate Bill 2994A went into effect in June 2019. Because legislative compliance was not evaluated for most students until the following school year, we considered the 2019-20 school year as the laws effective date. Main Outcomes and MeasuresWe calculated homeschooling prevalence as the number of homeschooling students divided by the total number of students, multiplied by 100. We estimated homeschooling prevalence differences (PD) comparing the time periods before (referent) and after NYS Senate Bill 2994A. Crude and adjusted PDs accounting for longitudinal homeschooling trends were estimated at the population-level, district-level, and county-level. Due to data limitations, PDs for New York City (NYC) were estimated only at the population-level. ResultsAmong 685 (99.3%) NYS school districts, the repeal of nonmedical vaccine exemptions was associated with an overall increase in homeschooling prevalence of 0.1% (95% CI: 0.1%-0.1%) among NYC students and 0.3% (95% CI: 0.2%-0.3%) among students outside of NYC, after adjustment for longitudinal trends. At the district-level, the law was associated with an average 0.4% (95% CI: 0.3%-0.5%) increase in homeschooling prevalence among non-NYC schools. Spatial variation in crude homeschooling PDs was observed in county-level estimates (range: -0.3% to 1.5%; interquartile range: 0.2% to 0.5%). Conclusion and RelevanceWe found evidence that NYS Senate Bill 2994A was associated with small, but significant increases in homeschooling prevalence. These results suggest that a small number of un(der)vaccinated students may have disenrolled from traditional "brick-and-mortar" schools to avoid compliance with the law.

Symptom-based SARS-CoV-2 screening and testing decisions in children have important implications on daycare and school exclusion policies. Single symptoms account for a substantial volume of testing and disruption to in-person learning and childcare, yet their predictive value is unclear, given the clinical overlap with other circulating respiratory viruses and non-infectious etiologies. We aimed to determine the relative frequency and predictive value of single symptoms for paediatric SARS-CoV-2 infections from an Ottawa COVID-19 assessment centre from October 2020 through April 2021. Overall, 46.3% (n=10,688) of pediatric encounters were for single symptoms, and 2.7% of these tested positive. The most common presenting single symptoms were rhinorrhea (31.8%), cough (17.4%) and fever (14.0%). Among children with high-risk exposures children in each age group, the following single symptoms had a higher proportion of positive SARS-CoV-2 cases compared to no symptoms; fever and fatigue (0-4 years); fever, cough, headache, and rhinorrhea (5-12 years); fever, loss of taste or smell, headache, rhinorrhea, sore throat, and cough (13-17 years). There was no evidence that the single symptom of either rhinorrhea or cough predicted SARS-CoV-2 infections among 0-4 year olds, despite accounting for a large volume (61.1%) of single symptom presentations in the absence of high-risk exposures. Symptom-based screening needs to be responsive to changes in evidence and local factors, including the expected resurgence of other respiratory viruses following relaxation of social distancing/masking, to reduce infection-related risks in schools and daycare settings.

Despite the many health risks of physical inactivity, studies have demonstrated individual, family, and environmental determinants of inactivity for autistic children. However, these studies never examined these correlates at the same time. Therefore, the purpose of this study was to explore these ecological domains concurrently when examining physical inactivity correlates for autistic children. This study used data from the 2016-2020 National Survey of Childrens Health. The authors predicted physical inactivity while controlling for child, parental/household, and neighborhood correlates with autism status as the comparison group. When controlling for covariates, children with co-occurring autism and intellectual and developmental disability (IDD) (adjusted odds ratio (aOR)= 1.91, 95% confidence interval (CI): 1.36-2.68) or ASD only (aOR = 1.91, CI: 1.48-2.48) were significantly more likely to be inactive when compared to children without autism or IDD. However, autism medicine and autism severity were not predictors for obese autistic children. These findings indicate that it is important to take a holistic, ecological approach when exploring the correlates of inactivity for autistic children.

ImportancePediatric SARS-CoV-2 infections and hospitalizations are rising in the US and other countries after the emergence of Omicron variant. However data on disease severity from Omicron compared with Delta in children under 5 in the US is lacking. ObjectivesTo compare severity of clinic outcomes in children under 5 who contracted COVID infection for the first time before and after the emergence of Omicron in the US. Design, Setting, and ParticipantsThis is a retrospective cohort study of electronic health record (EHR) data of 79,592 children under 5 who contracted SARS-CoV-2 infection for the first time, including 7,201 infected between 12/26/2021-1/6/2022 when the Omicron predominated (Omicron cohort), 63,203 infected between 9/1/2021-11/15/2021 when the Delta predominated (Delta cohort), and another 9,188 infected between 11/16/2021-11/30/2021 when the Delta predominated but immediately before the Omicron variant was detected in the US (Delta-2 cohort). ExposuresFirst time infection of SARS-CoV-2. Main Outcomes and MeasuresAfter propensity-score matching, severity of COVID infections including emergency department (ED) visits, hospitalizations, intensive care unit (ICU) admissions, and mechanical ventilation use in the 3-day time-window following SARS-CoV-2 infection were compared between Omicron and Delta cohorts, and between Delta-2 and Delta cohorts. Risk ratios, and 95% confidence intervals (CI) were calculated. ResultsAmong 7,201 infected children in the Omicron cohort (average age, 1.49 {+/-} 1.42 years), 47.4% were female, 2.4% Asian, 26.1% Black, 13.7% Hispanic, and 44.0% White. Before propensity score matching, the Omicron cohort were younger than the Delta cohort (average age 1.49 vs 1.73 years), comprised of more Black children, and had fewer comorbidities. After propensity-score matching for demographics, socio-economic determinants of health, comorbidities and medications, risks for severe clinical outcomes in the Omicron cohort were significantly lower than those in the Delta cohort: ED visits: 18.83% vs. 26.67% (risk ratio or RR: 0.71 [0.66-0.75]); hospitalizations: 1.04% vs. 3.14% (RR: 0.33 [0.26-0.43]); ICU admissions: 0.14% vs. 0.43% (RR: 0.32 [0.16-0.66]); mechanical ventilation: 0.33% vs. 1.15% (RR: 0.29 [0.18-0.46]). Control studies comparing Delta-2 to Delta cohorts show no difference. Conclusions and RelevanceFor children under age 5, first time SARS-CoV-2 infections occurring when the Omicron predominated (prevalence >92%) was associated with significantly less severe outcomes than first-time infections in similar children when the Delta variant predominated.

Background and ObjectivesAs survival after pediatric critical illness improves, attention has shifted to post-intensive care syndrome (PICS-p) and specifically the long-term mental health of PICU survivors, who face elevated risks including posttraumatic stress, anxiety, and depression. However, little is known about actual patterns of post-discharge mental health care. The objective of this study is to examine the rates of mental health follow-up and psychopharmacology use among publicly insured children following PICU hospitalization, compared with those hospitalized on acute care wards, using a multi-state administrative dataset. MethodsWe performed a retrospective cohort study using 2016-2021 Medicaid claims across 10-12 states. The cohort comprised children aged 3-18 years discharged home after an index hospitalization and excluded perinatal admissions and hospitalizations primarily for mental health or traumatic brain injury. The primary exposure was pediatric intensive care unit (PICU) admission. The primary outcome was new mental health visits within one-year post-discharge. Secondary outcomes included visit provider type, visit diagnoses category, and new psychiatric prescriptions. We report descriptive statistics and measure associations with covariates using logistic regression. ResultsAmong 144,763 Medicaid-insured pediatric hospitalizations (20.7% with PICU stays), only 8.8% initiated new mental health care. When compared to hospitalizations without PICU exposure, those with PICU exposure were more likely to complete new mental health visits (n=1,697 [6.1%] of PICU hospitalizations vs 5,252 [4.9%] of non-PICU hospitalizations). However, PICU exposure was not independently associated with a new mental health visit after adjustment (OR 1.06, 95% CI 1 - 1.13; p=0.067). Older age, complex chronic conditions, and longer length of stay were associated with new mental health visits. Hospitalizations with a PICU stay were significantly associated with increased rate of visits to psychologists or supportive therapists compared to those without a PICU stay (p<0.001). ConclusionsMental health follow-up after pediatric hospitalization is rare. Future studies should investigate barriers to care and identify effective methods for systematic screening and proactive referral.

Maternal stress and viral illness during pregnancy are associated with neurodevelopmental conditions in offspring. Children born during the COVID-19 pandemic, including those exposed prenatally to maternal SARS-CoV-2 infections, are reaching the developmental age for the assessment of risk for neurodevelopmental conditions. We examined associations between birth during the COVID-19 pandemic, prenatal exposure to maternal SARS-CoV-2 infection, and rates of positive screenings on the Modified Checklist for Autism in Toddlers-Revised (M-CHAT-R). Data were drawn from the COVID-19 Mother Baby Outcomes (COMBO) Initiative. Participants completed the M-CHAT-R as part of routine clinical care (COMBO-EHR cohort) or for research purposes (COMBO-RSCH cohort). Maternal SARS-CoV-2 status during pregnancy was determined through electronic health records. The COMBO-EHR cohort includes n=1664 children (n=442 historical cohort, n=1222 pandemic cohort; n=997 SARS-CoV-2 unexposed prenatally, n=130 SARS-CoV-2 exposed prenatally) who were born at affiliated hospitals between 2018-2023 and who had a valid M-CHAT-R score in their health record. The COMBO-RSCH cohort consists of n=359 children (n=268 SARS-CoV-2 unexposed prenatally, n=91 SARS-CoV-2 exposed prenatally) born at the same hospitals who enrolled into a prospective cohort study that included administration of the M-CHAT-R at 18-months. Birth during the pandemic was not associated with greater likelihood of a positive M-CHAT-R screen in the COMBO-EHR cohort. Maternal SARS-CoV-2 was associated with lower likelihood of a positive M-CHAT-R screening in adjusted models in the COMBO-EHR cohort (OR=0.40, 95% CI=0.22 - 0.68, p=0.001), while analyses in the COMBO-RSCH cohort yielded similar but non-significant results (OR=0.67, 95% CI=0.31-1.37, p=0.29).These results suggest that children born during the first 18 months of the COVID-19 pandemic and those exposed prenatally to a maternal SARS-CoV-2 infection are not at greater risk for screening positive on the M-CHAT-R.

ObjectiveWe sought to characterize clinical presentation and healthcare utilization for pediatric COVID-19 in Western Pennsylvania (PA). MethodsWe established and analyzed a registry of pediatric COVID-19 in Western PA that includes cases in patients <22 years of age cared for by the pediatric quaternary medical center in the area and its associated pediatric primary care network from March 11 through August 20, 2020. ResultsOur cohort included 424 pediatric COVID-19 cases (mean age 12.5 years, 47.4% female); 65% reported exposure and 79% presented with symptoms. The most common initial healthcare contact was through telehealth (45%). Most cases were followed as outpatients, but twenty-two patients (4.5%) were hospitalized: 19 with acute COVID-19 disease, and three for multisystem inflammatory syndrome of children (MIS-C). Admitted patients were younger (p<0.001) and more likely to have pre-existing conditions (p<0.001). Black/Hispanic patients were 5.8 times more likely to be hospitalized than white patients (p=0.012). Five patients (1.2%) were admitted to the PICU, including all three MIS-C cases; two required BiPAP and one mechanical ventilation. All patients survived. ConclusionsWe provide a comprehensive snapshot of pediatric COVID-19 disease in an area with low to moderate incidence. In this cohort, COVID-19 was generally a mild disease; however, [~]5% of children were hospitalized. Pediatric patients can be critically ill with this infection, including those presenting with MIS-C.

Question Are adverse childhood experiences (ACEs) associated with altered growth trajectories in childhood? Findings In this cohort study of 412,549 children and adolescents, ACEs were associated with lower height throughout childhood, earlier pubertal timing, and shorter final stature. Height differences emerged approximately 2 years before ACE documentation and were greatest among those with earlier documentation. Meaning These findings suggest that early adversity affects physical growth in children and may serve as a measurable indicator of the biological consequences of early-life stress, especially in those with documentation of ACEs prior to the onset of typical pubertal growth. Importance Adverse childhood experiences (ACEs) are among the strongest risk factors for long-term mental and physical health complications, yet their impact on physical growth in childhood remains incompletely understood. Objective To determine the association of ACEs on childhood growth trajectories and growth dynamics. Design, Setting and Participants Retrospective cohort study using longitudinal electronic health record data. Data was collected from participants between February 1999 and August 2025. A large academic medical center biobank linked to deidentified electronic health records in the southeastern United States. A total of 412,549 individuals with at least 2 recorded height measurements between the ages of 2 and 20 were included in the primary analysis. Growth curve analyses were performed in a subset of 199,844 individuals with at least 3 height measurements spanning at least 2 years. Genetic analyses were performed in a subset of 10,114 individuals of primarily European ancestry. Exposure(s) Documented exposure to adverse childhood experiences before age 18 years identified through a natural language processing algorithm. Main Outcome(s) and Measure(s) Height-for-age z-scores across childhood, final attained height, and growth curve parameters estimated using SuperImposition by Translation and Rotation (SITAR) modeling. Results Among 412,549 participants, 18,502 (4.5%) had clinically documented ACEs during childhood. ACE documentation was associated with lower height-for-age z-scores throughout childhood and adolescence. Final attained height was significantly lower among ACE-documented individuals, with mean differences of -3.0 cm among males (174.0 cm vs 177.0 cm, p < 0.001) and -1.3 cm among females (161.8 cm vs 163.1 cm, p < 0.001). Height differences emerged approximately 2 years before clinical ACE documentation. Earlier age at first ACE documentation was associated with progressively shorter final attained height, with each year decrease in age at ACE documentation associated with a decrease in final height of -0.20 cm in females and -0.35 cm in males. Those with first ACE documented prior to pubertal age also showed the most pronounced growth dynamic differences, with males demonstrating a mean reduction in size of 5.25 cm (95% CI, -6.79 cm to -3.70 cm) and 1.26-year earlier pubertal timing (95% CI, -1.50 to -1.03 years), and females demonstrating a reduction in growth curve size of 3.62 cm (95% CI, -4.83 to -2.41 cm) and 1.14-year earlier pubertal timing (95% CI, -1.29 to -0.99 years). Conclusions and Relevance In this large clinical cohort, clinically documented ACEs were associated with time-dependent reductions in stature, earlier pubertal timing, and short final attained height. These findings suggest that early childhood adversity may have lasting effects on physical development and highlight growth trajectories as a potential marker of the biological consequences of early-life stress.

IMPORTANCEObesity increases the severe COVID-19 risk. Whether obesity is associated with an increased risk of post-acute sequelae of SARS-Cov-2 infection (PASC) among pediatrics, independent of its impacts on acute infection severity, is unclear. OBJECTIVETo quantify the association between body mass index (BMI) status before SARS-CoV-2 infection and pediatric PASC risk, controlling for acute infection severity. DESIGNRetrospective cohort study occurred from March 2020 to May 2023, with a minimal follow-up of 179 days. SETTINGTwenty-six US childrens hospitals. PARTICIPANTSIndividuals aged 5-20 years with SARS-CoV-2 infection. EXPOSURESElevated BMI status assessed before infection. MAIN OUTCOMES AND MEASURESTo identify PASC, we first used the ICD-10-CM code specific for post-COVID-19 conditions, and a second approach used clusters of symptoms and conditions that constitute the PASC phenotype. BMI was assessed within 18 months before infection; the measure closest to the index date was selected. Relative risk (RR) for BMI-PASC association was quantified by Poisson regression models, adjusting for sociodemographic, acute COVID severity, and other clinical factors. RESULTSAmong the 172136 participants included, the median age of BMI assessment and cohort entry were 12.8 and 13.2 years, 1402 (0.8%) were identified as having PASC with the ICD-10-CM code, and 74317 (43.2%) had [&ge;]1 incident occurrence of PASC symptoms and conditions. Compared with participants with a healthy weight, those who had overweight, obesity, and severe obesity had 4.7% (RR, 1.047; 95% CI, 0.868-1.263), 25.4% (RR, 1.254; 95% CI, 1.064-1.478) and 42.1% (RR, 1.421; 95% CI, 1.253-1.611) higher risk of PASC when identified using the diagnosis code, respectively. The risk for any occurrences of PASC symptoms and conditions also increased in overweight (RR, 1.030; 95% CI, 0.982-1.080), obesity (RR, 1.108; 95% CI, 1.064-1.154), and severe obesity (RR, 1.174; 95% CI, 1.138-1.213), and that for total incident occurrences increased, too, in overweight (RR, 1.053; 95% CI, 1.000-1.109), obesity (RR, 1.137; 95% CI, 1.088-1.188), and severe obesity (RR, 1.182; 95% CI, 1.142-1.223). CONCLUSIONS AND RELEVANCEElevated BMI was associated with a significantly increased PASC risk in a dose-dependent manner. The biological mechanisms for this association should be investigated in future research. Key PointsO_ST_ABSQuestionC_ST_ABSDo children, adolescents, and young adults with overweight and obesity have increased risk of developing post-acute sequelae of SARS-Cov-2 infection (PASC)? FindingsOverweight, obesity, and severe obesity were associated with significantly increased risk of pediatric PASC. Compared with pediatrics with body mass index in the healthy range, those who were overweight, obesity, or severely obesity had an increased incidence of 4.7%, 25.4%, and 42.1% of PASC, respectively. MeaningOverweight and obesity are important risk factors for pediatric PASC. The biological mechanisms for this association should be investigated in the future research.

The net effect of the pandemic mitigation strategies on childhood mortality is not known. During the first year of the COVID-19 pandemic, mitigation policies and behaviors were widespread, and although vaccinations and effective treatments were not yet widely available, the risk of death from SARS-CoV-2 infection was low. In that first year, there was a 7% decrease in medical ("natural causes") mortality among children ages 0-9 during the first pandemic year (5% among infants <1 year and 15% among children ages 1-9) in the United States, resulting in an estimated 1,488 deaths due to medical causes averted among children ages 0-9, and 1,938 deaths averted over 24 months. The usual expected surge in winter medical deaths, particularly among children ages >1 year was absent. However, smaller increases in external ("non-natural causes") mortality were also observed during the study period, which decreased the overall number of pediatric deaths averted during both years and the pandemic period. In total, 1,468 fewer all-cause pediatric deaths than expected occurred in the United States during the first 24 months of the COVID-19 pandemic.