Explore the vast range of titles available.

The United States has poorer child health outcomes than other wealthy nations despite greater per capita spending on health care for children. To better understand this phenomenon, we examined mortality trends for the US and nineteen comparator nations in the Organization for Economic Cooperation and Development for children ages 0-19 from 1961 to 2010 using publicly available data. While child mortality progressively declined across all countries, mortality in the US has been higher than in peer nations since the 1980s. From 2001 to 2010 the risk of death in the US was 76 percent greater for infants and 57 percent greater for children ages 1-19. During this decade, children ages 15-19 were eighty-two times more likely to die from gun homicide in the US. Over the fifty-year study period, the lagging US performance amounted to over 600,000 excess deaths. Policy interventions should focus on infants and on children ages 15-19, the two age groups with the greatest disparities, by addressing perinatal causes of death, automobile accidents, and assaults by firearm.

The risk of cardiovascular outcomes following SARS-CoV-2 infection has been reported in adults, but evidence in children and adolescents is limited. This paper assessed the risk of a multitude of cardiac signs, symptoms, and conditions 28-179 days after infection, with outcomes stratified by the presence of congenital heart defects (CHDs), using electronic health records (EHR) data from 19 children’s hospitals and health institutions from the United States within the RECOVER consortium between March 2020 and September 2023. The cohort included 297,920 SARS-CoV-2-positive individuals and 915,402 SARS-CoV-2-negative controls. Every individual had at least a six-month follow-up after cohort entry. Here we show that children and adolescents with prior SARS-CoV-2 infection are at a statistically significant increased risk of various cardiovascular outcomes, including hypertension, ventricular arrhythmias, myocarditis, heart failure, cardiomyopathy, cardiac arrest, thromboembolism, chest pain, and palpitations, compared to uninfected controls. These findings were consistent among patients with and without CHDs. Awareness of the heightened risk of cardiovascular disorders after SARS-CoV-2 infection can lead to timely referrals, diagnostic evaluations, and management to mitigate long-term cardiovascular complications in children and adolescents. Post-acute sequelae of SARS-CoV-2 infection affecting the cardiovascular system have been reported, but evidence in young people is limited. Here, the authors quantify the incidence of a range of outcomes in children and adolescents using electronic health records from the United States.

Mike English and colleagues argue that as efforts are made towards achieving universal health coverage it is also important to build capacity to develop regionally relevant evidence to improve healthcare.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

The Johns Hopkins ACG System is widely used to predict patient healthcare service use and costs. Most applications have focused on adult populations. In this study, we evaluated the use of the ACG software to predict pediatric unplanned hospital admission in a given month, based on the past year's clinical information captured by electronic health records (EHRs). EHR data from a multi-state pediatric integrated delivery system were obtained for 920,051 patients with at least one physician visit during January 2009 to December 2016. Over this interval an average of 0.36% of patients each month had an unplanned hospitalization. In a 70% training sample, we used the generalized linear mixed model (GLMM) to generate regression coefficients for demographic, clinical predictors derived from the ACG system, and prior year hospitalizations. Applying these coefficients to a 30% test sample to generate risk scores, we found that the area under the receiver operator characteristic curve (AUC) was 0.82. Omitting prior hospitalizations decreased the AUC from 0.82 to 0.80, and increased under-estimation of hospitalizations at the greater risk levels. Patients in the top 5% of risk scores accounted for 43% and the top 1% of risk scores accounted for 20% of all unplanned hospitalizations. A predictive model based on 12-months of demographic and clinical data using the ACG system has excellent predictive performance for 30-day pediatric unplanned hospitalization. This model may be useful in population health and care management applications targeting patients likely to be hospitalized. External validation at other institutions should be done to confirm our results.

Post-acute sequelae of SARS-CoV-2 infection (PASC) remain a major public health challenge. Although previous studies have focused on characterising PASC in children and adolescents after an initial infection, the risks of PASC after reinfection with the omicron variant remain unclear. We aimed to assess the risk of PASC diagnosis (U09.9) and symptoms and conditions potentially related to PASC in children and adolescents after a SARS-CoV-2 reinfection during the omicron period. This retrospective cohort study used data from 40 children's hospitals and health institutions in the USA participating in the Researching COVID to Enhance Recovery (RECOVER) Initiative. We included patients younger than 21 years at the time of cohort entry; with documented SARS-CoV-2 infection after Jan 1, 2022; and who had at least one health-care visit within 24 months to 7 days before the first infection. The second SARS-CoV-2 infection was confirmed by positive PCR, antigen tests, or a diagnosis of COVID-19 that occurred at least 60 days after the first infection. The primary endpoint was a clinician-documented diagnosis of PASC (U09.9). Secondary endpoints were 24 symptoms and conditions previously identified as being potentially related to PASC. We used the modified Poisson regression model to estimate the relative risk (RR) between the second and first infection episodes, adjusted for demographic, clinical, and health-care utilisation factors using exact and propensity-score matching. We identified 407 300 (87·5%) of 465 717 eligible children and adolescents with a first infection episode and 58 417 (12·5%) with a second infection episode from Jan 1, 2022, to Oct 13, 2023, in the RECOVER database. 233 842 (50·2%) patients were male and 231 875 (49·8%) were female. The mean age was 8·17 years (SD 6·58). The incident rate of PASC diagnosis (U09.9) per million people per 6 months was 903·7 (95% CI 780·9–1026·5) in the first infection group and 1883·7 (1565·1–2202·3) in the second infection group. Reinfection was associated with a significantly increased risk of an overall PASC diagnosis (U09.9) (RR 2·08 [1·68–2·59]) and a range of symptoms and conditions potentially related to PASC (RR range 1·15–3·60), including myocarditis, changes in taste and smell, thrombophlebitis and thromboembolism, heart disease, acute kidney injury, fluid and electrolyte disturbance, generalised pain, arrhythmias, abnormal liver enzymes, chest pain, fatigue and malaise, headache, musculoskeletal pain, abdominal pain, mental ill health, POTS or dysautonomia, cognitive impairment, skin conditions, fever and chills, respiratory signs and symptoms, and cardiovascular signs and symptoms. Children and adolescents face a significantly higher risk of various PASC outcomes after reinfection with SARS-CoV-2. These findings add to previous evidence linking paediatric long COVID to multisystem effects and highlight the need to promote vaccination in younger populations and support ongoing research to better understand PASC, identify high-risk subgroups, and improve prevention and care strategies. National Institutes of Health.

Racial/ethnic differences are associated with the symptoms and conditions of post-acute sequelae SARS-CoV-2 infection (PASC) in adults. These differences may exist among children and warrant further exploration. We conducted a retrospective cohort study with difference-in-differences analyzes to assess these differences in children and adolescents under the age of 21. The study utilized data from the RECOVER Initiative in the United States, which aims to learn about the long-term effects of COVID-19. The cohort included 225,723 patients with SARS-CoV-2 infection or COVID-19 diagnosis between March 2020 and October 2022. The study compared minority racial/ethnic groups to Non-Hispanic White (NHW) individuals, stratified by severity during the acute phase of COVID-19. Within the severe group, Asian American/Pacific Islanders (AAPI) had a higher prevalence of fever/chills and respiratory signs and symptoms, Hispanic patients showed greater hair loss prevalence in severe COVID-19 cases, while Non-Hispanic Black (NHB) patients had fewer skin symptoms in comparison to NHW patients. Within the non-severe group, AAPI patients had increased POTS/dysautonomia and respiratory symptoms, and NHB patients showed more cognitive symptoms than NHW patients. In conclusion, racial/ethnic differences related to COVID-19 exist among PASC symptoms and conditions in pediatrics, and these differences are associated with the severity of illness during acute COVID-19. Post-acute sequelae of SARS-CoV-2 infection (PASC) have been shown to vary by race/ethnicity, but evidence is limited for children and adolescents. Here, the authors investigate variations in PASC by race/ethnicity in those aged under 21 years using electronic health record data from the United States.

Assessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship. This study aims to test the validity of natural language processing (NLP) and machine learning (ML) algorithms in identifying different attributes of pain interference and fatigue symptoms experienced by child and adolescent survivors of cancer versus the judgment by PRO content experts as the gold standard to validate NLP/ML algorithms. This cross-sectional study focused on child and adolescent survivors of cancer, aged 8 to 17 years, and caregivers, from whom 391 meaning units in the pain interference domain and 423 in the fatigue domain were generated for analyses. Data were collected from the After Completion of Therapy Clinic at St. Jude Children's Research Hospital. Experienced pain interference and fatigue symptoms were reported through in-depth interviews. After verbatim transcription, analyzable sentences (ie, meaning units) were semantically labeled by 2 content experts for each attribute (physical, cognitive, social, or unclassified). Two NLP/ML methods were used to extract and validate the semantic features: bidirectional encoder representations from transformers (BERT) and Word2vec plus one of the ML methods, the support vector machine or extreme gradient boosting. Receiver operating characteristic and precision-recall curves were used to evaluate the accuracy and validity of the NLP/ML methods. Compared with Word2vec/support vector machine and Word2vec/extreme gradient boosting, BERT demonstrated higher accuracy in both symptom domains, with 0.931 (95% CI 0.905-0.957) and 0.916 (95% CI 0.887-0.941) for problems with cognitive and social attributes on pain interference, respectively, and 0.929 (95% CI 0.903-0.953) and 0.917 (95% CI 0.891-0.943) for problems with cognitive and social attributes on fatigue, respectively. In addition, BERT yielded superior areas under the receiver operating characteristic curve for cognitive attributes on pain interference and fatigue domains (0.923, 95% CI 0.879-0.997; 0.948, 95% CI 0.922-0.979) and superior areas under the precision-recall curve for cognitive attributes on pain interference and fatigue domains (0.818, 95% CI 0.735-0.917; 0.855, 95% CI 0.791-0.930). The BERT method performed better than the other methods. As an alternative to using standard PRO surveys, collecting unstructured PROs via interviews or conversations during clinical encounters and applying NLP/ML methods can facilitate PRO assessment in child and adolescent cancer survivors.

Many factors influence the health and well-being of children and the adults they will become. Yet there are significant gaps in how trajectories of healthy development are measured, how the potential for leading a healthy life is evaluated, and how that information can guide upstream policies and investments. The Gross Developmental Potential (GDP2) is proposed as a new capabilities-based framework for assessing threats to thriving and understanding progress in achieving lifelong health and wellbeing. Moving beyond the Gross Domestic Product’s (GDP) focus on economic productivity as a measure of progress, the GDP2 focuses on seven essential developmental capabilities for lifelong health and wellbeing. The GDP2 capability domains include Health -living a healthy life; Needs-satisfying basic human requirements; Communication-expressing and understanding thoughts and feelings; Learning-lifelong learning; Adaption -adapting to change; Connections -connecting with others; and Community -engaging in the community. The project team utilized literature reviews and meetings with the subject and technical experts to develop the framework. The framework was then vetted in focus groups of community leaders from three diverse settings. The community leaders' input refined the domains and their applications. This prototype GDP2 framework will next be used to develop specific measures and indices and guide the development of community-level GDP2 dashboards for local sense-making, learning, and application.

ObjectivesHigh ambient temperatures may contribute to acute asthma exacerbation, a leading cause of morbidity in children. We quantified associations between hot-season ambient temperatures and asthma exacerbation in children ages 0–18 years in Philadelphia, PA.MethodsWe created a time series of daily counts of clinical encounters for asthma exacerbation at the Children’s Hospital of Philadelphia linked with daily meteorological data, June–August of 2011–2016. We estimated associations between mean daily temperature (up to a 5-day lag) and asthma exacerbation using generalised quasi-Poisson distributed models, adjusted for seasonal and long-term trends, day of the week, mean relative humidity,and US holiday. In secondary analyses, we ran models with adjustment for aeroallergens, air pollutants and respiratory virus counts. We quantified overall associations, and estimates stratified by encounter location (outpatient, emergency department, inpatient), sociodemographics and comorbidities.ResultsThe analysis included 7637 asthma exacerbation events. High mean daily temperatures that occurred 5 days before the index date were associated with higher rates of exacerbation (rate ratio (RR) comparing 33°C–13.1°C days: 1.37, 95% CI 1.04 to 1.82). Associations were most substantial for children ages 2 to <5 years and for Hispanic and non-Hispanic black children. Adjustment for air pollutants, aeroallergens and respiratory virus counts did not substantially change RR estimates.ConclusionsThis research contributes to evidence that ambient heat is associated with higher rates of asthma exacerbation in children. Further work is needed to explore the mechanisms underlying these associations.