Search

Background Most children with atopic dermatitis(AD) suffer from sleep disturbance, but reliable and valid assessment tools are lacking. Objectives To test PROMIS (Patient Reported Outcomes Measurement Information System) sleep measures in pediatric AD and to develop an algorithm to screen, assess and intervene to reduce sleep disturbance. Methods A cross-sectional study was conducted with AD children ages 5-17 years and one parent(n=61), who completed sleep, itch, and AD-specific questionnaires; clinicians assessed disease severity. All children wore actigraphy watches for 1-week-objective sleep assessment. Results PROMIS sleep disturbance parent-proxy-reliability was high (Cronbach's α=0.90) and differentiated among Patient Oriented Eczema Measure (POEM)-determined disease severity groups (mean±SD in mild vs. moderate vs. severe was 55.7±7.5 vs. 59.8±10.8 vs. 67.1±9.5, p Limitations This was a local sample. Conclusions Sleep disturbance in pediatric AD should be screened using the POEM sleep question, with further assessment using the PROMIS sleep disturbance measure or objective sleep monitoring if needed.

Children with glomerular disease have unique risk factors for compromised bone health. Studies addressing skeletal complications in this population are lacking.This retrospective cohort study utilized data from PEDSnet, a national network of pediatric health systems with standardized electronic health record data for more than 6.5 million patients from 2009 to 2021. Incidence rates (per 10,000 person-years) of fracture, slipped capital femoral epiphysis (SCFE), and avascular necrosis/osteonecrosis (AVN) in 4598 children and young adults with glomerular disease were compared with those among 553,624 general pediatric patients using Poisson regression analysis. The glomerular disease cohort was identified using a published computable phenotype. Inclusion criteria for the general pediatric cohort were two or more primary care visits 1 year or more apart between 1 and 21 years of age, one visit or more every 18 months if followed3 years, and no chronic progressive conditions defined by the Pediatric Medical Complexity Algorithm. Fracture, SCFE, and AVN were identified using SNOMED-CT diagnosis codes; fracture required an associated x-ray or splinting/casting procedure within 48 hours.We found a higher risk of fracture for the glomerular disease cohort compared with the general pediatric cohort in girls only (incidence rate ratio [IRR], 1.6; 95% CI, 1.3 to 1.9). Hip/femur and vertebral fracture risk were increased in the glomerular disease cohort: adjusted IRR was 2.2 (95% CI, 1.3 to 3.7) and 5 (95% CI, 3.2 to 7.6), respectively. For SCFE, the adjusted IRR was 3.4 (95% CI, 1.9 to 5.9). For AVN, the adjusted IRR was 56.2 (95% CI, 40.7 to 77.5).Children and young adults with glomerular disease have significantly higher burden of skeletal complications than the general pediatric population.

The Biologic Abatement and Capturing Kids' Outcomes and Flare Frequency in Juvenile Spondyloarthritis (BACK-OFF JSpA) study is a randomized, pragmatic trial investigating different tumour necrosis factor inhibitor de-escalation strategies for children with sustained inactive disease. In this project, we elicited concept rankings that aided in the selection of the patient-reported outcome (PRO) measures that should be examined as part of the BACK-OFF JSpA trial.We conducted a discrete choice experiment to evaluate individuals' preferences regarding PROs. Stakeholders assessed a discrete list of 21 outcome concepts, each of which had a Patient-Reported Outcome Measurement Information System (PROMIS) measure associated with it. PROMIS measures are self- or proxy-reported instruments that are universally applicable to the general population and all chronic conditions. Stakeholders were required to make choices instead of expressing the strength of a preference.Fourteen caregivers, 12 patients (9-22 years old), 16 rheumatologists and three executives from health insurance companies completed the exercise, which took approximately 10 min. The discrete choice experiment resulted in an estimate of the relative importance of each outcome and rank. All stakeholder groups agreed that the primary PRO should be 'Pain Interference', a measure that evaluates the effect of pain on a child's everyday activities, including its impact on social, emotional, mental and physical functioning. Patients and caregivers were mostly aligned in their top priorities, with patients valuing physical health (50% of the top 10) whereas caregivers were more interested in mental health (60% of the top 10). Rheumatologists and health insurance executives were most interested in physical health outcomes, which were ranked 80% and 60% of their top 10 PROs, respectively. Overall, the patients had the most diverse set of prioritized outcomes, including at least one of each category in their top 10 rank order of importance. Patients were also the only stakeholders to prioritize 'social' health.Patients and caregivers were mostly aligned in their outcome priority rankings. The rank-order list directly informed the creation of a profile of PRO measures for our upcoming trial.Stakeholder partners helped with acquisition of data and lead parent partners helped interpret data.

Pediatric positive health refers to children's assessments of their well-being. The purpose of this study was to contrast positive health for children aged 8 to 17 years with and without chronic physical and mental health conditions.Data were drawn from the National Institutes of Health Environmental influences on Child Health Outcomes (ECHO) research program. Participants included 1764 children ages 8 to 17 years from 13 ECHO cohorts. We measured positive health using the Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Global Health and Life Satisfaction patient-reported outcome (PRO) measures. We used multiple regression to examine cross-sectional associations between the PROs and parent-reported health conditions and sociodemographic variables. We defined a meaningful difference in average scores as a PROMIS T-score difference of3.The sample included 45% 13 to 17-year-olds, 50% females, 8% Latinx, and 23% Black/African-American. Fifty-four percent had a chronic health condition. Of the 16 chronic conditions included in the study, only chronic pain (β = -3.5; 95% CI: -5.2 to -1.9) and depression (β = -6.6; 95% CI: -8.5 to -4.6) were associated with scoring3 points lower on global health. Only depression was associated with3 points lower on life satisfaction (β = -6.2; 95% CI: -8.1 to -4.3). Among those with depression, 95% also had another chronic condition.Many children with chronic conditions have similar levels of positive health as counterparts without chronic conditions. The study results suggest that negative associations between chronic conditions and positive health may be primarily attributable to presence or co-occurrence of depression.

Nursing professional organizations and media sources indicated early in the pandemic that the physical and psychological effects of COVID-19 might be distinct and possibly greater in nurses than in other types of healthcare workers (HCWs).Based on survey data collected in Healthcare Worker Exposure Response and Outcomes (HERO), a national registry of U.S. HCWs, this study compared the self-reported experiences of nurses with other HCWs during the first 13 months of the pandemic.Nurse responses were compared to responses of nonnurse HCWs in terms of viral exposure, testing and infection, access to personal protective equipment (PPE), burnout, and well-being. Logistic regression models were used to examine associations between nurse and nonnurse roles for the binary end points of viral testing and test positivity for COVID-19. We also examined differences by race/ethnicity and high-risk versus low-risk practice settings.Of 24,343 HCWs in the registry, one third self-identified as nurses. Nurses were more likely than other HCWs to report exposure to SARS-CoV-2, problems accessing PPE, and decreased personal well-being, including burnout, feeling tired, stress, trouble sleeping, and worry. In adjusted models, nurses were more likely than nonnurse HCWs to report viral testing and test positivity for COVID-19 infection. Nurses in high-risk settings were more likely to report viral exposure and symptoms related to well-being; nurses in low-risk settings were more likely to report viral testing and test positivity. Black or Hispanic nurses were most likely to report test positivity.Differences were identified between nurses and nonnurse HCWs in access to PPE, physical and mental well-being measures, and likelihood of reporting exposure and infection. Among nurses, testing and infection differed based on race and ethnicity, and type of work setting. Our findings suggest further research and policy are needed to elucidate and address social and occupational disparities.

OBJECTIVE To identify associations between weight status and clinical outcomes in children with lower respiratory tract infection (LRTI) or asthma requiring hospitalization. METHODS We performed a retrospective cohort study of 2 to 17 year old children hospitalized for LRTI and/or asthma from 2009 to 2019 using electronic health record data from the PEDSnet clinical research network. Children RESULTS We identified 65 132 hospitalizations; 6.7% with underweight, 57.8% normal weight, 14.6% overweight, 13.2% class 1 obesity, 5.0% class 2 obesity, and 2.8% class 3 obesity. Overweight and obesity were associated with positive pressure respiratory support (class 3 obesity versus normal weight odds ratio [OR] 1.62 [1.38–1.89]) and ICU admission (class 3 obesity versus normal weight OR 1.26 [1.12–1.42]), with significant associations for all categories of overweight and obesity. Underweight was also associated with positive pressure respiratory support (OR 1.39 [1.24–1.56]) and ICU admission (1.40 [1.30–1.52]). CONCLUSIONS Both underweight and overweight or obesity are associated with increased severity of LRTI or asthma in hospitalized children.

Objectives The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods. Materials and Methods We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data. Results When missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression. Discussion and Conclusion Missing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.

Vegetation may influence asthma exacerbation through effects on aeroallergens, localized climates, air pollution, or children’s behaviors and stress levels. We investigated the association between residential vegetation and asthma exacerbation by conducting a matched case–control study based on electronic health records of asthma patients, from the Children’s Hospital of Philadelphia (CHOP). Our study included 17,639 exacerbation case events and 34,681 controls selected from non-exacerbation clinical visits for asthma, matched to cases by age, sex, race/ethnicity, public payment source, and residential proximity to the CHOP main campus ED and hospital. Overall greenness, tree canopy, grass/shrub cover, and impervious surface were assessed near children’s homes (250 m) using satellite imagery and high-resolution landcover data. We used generalized estimating equations to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between each vegetation/landcover measure and asthma exacerbation, with adjustment for seasonal and sociodemographic factors—for all cases, and for cases defined by diagnosis setting and exacerbation frequency. Lower odds of asthma exacerbation were observed in association with greater levels of tree canopy near the home, but only for children who experienced multiple exacerbations in a year (OR = 0.94 per 10.2% greater tree canopy coverage, 95% CI = 0.90–0.99). Our findings suggest possible protection for asthma patients from tree canopy, but differing results by case frequency suggest that potential benefits may be specific to certain subpopulations of asthmatic children. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11524-022-00633-7.

BACKGROUND AND OBJECTIVES: Performing adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Electronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of >6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (n=350) and noncases (n=350). RESULTS: Final algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and ≥60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by ≥30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by ≥30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months. CONCLUSIONS: Electronic health record–based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.

STRUCTURED ABSTRACTObjectivesPost-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.Materials and MethodsWe used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N=1309) to children with (N=6545) and without (N=6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls.ResultsWe found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise.DiscussionOur study addresses methodological limitations of prior studies that rely on pre-specified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes.ConclusionWe identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.LAY SUMMARYPediatric long COVID in children does not currently have a precise clinical definition, in part due to its widely varying presentation in kids. By comparing children diagnosed with long COVID to children who had COVID-19 but were not diagnosed with long COVID, this study identified several groups of symptoms and conditions that are associated with pediatric long COVID. These findings can be used towards developing a precise definition of long COVID in children for use in future studies.

This study compared the importance of age at adiposity rebound versus childhood BMI to subsequent BMI levels in a longitudinal analysis.From the electronic health records of 4.35 million children, a total of 12,228 children were selected who were examined at least once each year between ages 2 and 7 years and reexamined after age 14 years. The minimum number of examinations per child was six. Each child's rebound age was estimated using locally weighted regression (lowess), a smoothing technique.Children who had a rebound age3 years were, on average, 7 kg/mAlthough rebound age is related to BMI after age 14 years, a child's BMI at age 3 years provides more information and is easier to obtain.

BackgroundAs 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.Methods and FindingsIn 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.ConclusionsThe 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.Funding SourceThis research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.DisclaimerThe content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.

BackgroundResearch demonstrates that SARS-CoV-2 infection (COVID-19) among adults disproportionately impacts racial and ethnic minorities and those living in lower-income communities. Similar research in children is limited due, in part, to the relatively low COVID-19 incidence in children compared to adults. This analysis, conducted as part of the RECOVER Initiative, explores this question.MethodsElectronic health record (EHR) data from PEDSnet, a multi-institutional research network of pediatric healthcare organizations, were geocoded and linked to two indices of contextual social deprivation: the Area Deprivation Index and the Child Opportunity Index. Univariate statistics were employed to test the association between each index and COVID19 positivity among children ages 0-20 tested at one of six Children’s hospitals. Multivariate logistic regression was used to explore the relationship between these social context indices and racial disparities in positivity, controlling co-variates.ResultsBoth ADI and COI were significantly associated with COVID-19 positivity in univariate and adjusted models, particularly in the pre-delta and delta variant waves. ADI showed a stronger association. Higher rates of positivity were found for non-Hispanic Black, Hispanic, and multi-racial children compared to non-Hispanic White children. These racial disparities remained significant after control for either index and for other variables.ConclusionADI and COI are significantly associated with COVID-19 test positivity in a population of children and adolescents tested in children’s hospital settings. These social contextual variables do not fully explain racial disparities, arguing that racial disparities are not solely a reflection of socioeconomic status. Future disparities research should consider both race and social context.

BackgroundPost-acute sequelae of SARS-Co-V-2 infection (PASC) is associated with worsening diabetes trajectory. It is unknown whether PASC in children with type 1 diabetes (T1D) manifests as worsening diabetes trajectory.ObjectiveTo explore the association between SARS-CoV-2 infection (COVID-19) and T1D-related healthcare utilization (for diabetic ketoacidosis [DKA] or severe hypoglycemia [SH]) or Hemoglobin (Hb) A1c trajectory.MethodsWe included children ResultsFrom 03/01/2020-06/22/2022, 2,404 and 1,221 youth met entry criteria for the Broad and Narrow cohorts, respectively. The hazard ratio for utilization was (HR 1.45 [95%CI,0.97,2.16]). In the Narrow Cohort, the rate of change (slope) of HbA1c increased 91-180 days after cohort entry for those with COVID-19 (0.138 vs. -0.002, p=0.172). Beyond 180 days, greater declines in HbA1c were observed in the positive cohort (-0.104 vs. 0.008 per month, p=0.024).ConclusionWhile a trend towards worse outcomes following COVID-19 in T1D patients was observed, these findings were not statistically significant. Continued clinical monitoring of youth with T1D following COVID-19 is warranted.Authorship StatementAuthorship has been determined according to ICMJE recommendationsDisclaimerThe content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.Funding Source⍰This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.

BackgroundMulti-system inflammatory syndrome in children (MIS-C) represents one of the most severe post-acute sequelae of SARS-CoV-2 infection in children, and there is a critical need to characterize its disease patterns for improved recognition and management. Our objective was to characterize subphenotypes of MIS-C based on presentation, demographics and laboratory parameters.MethodsWe conducted a retrospective cohort study of children with MIS-C from March 1, 2020 - April 30, 2022 and cared for in 8 pediatric medical centers that participate in PEDSnet. We included demographics, symptoms, conditions, laboratory values, medications and outcomes (ICU admission, death), and grouped variables into eight categories according to organ system involvement. We used a heterogeneity-adaptive latent class analysis model to identify three clinically-relevant subphenotypes. We further characterized the sociodemographic and clinical characteristics of each subphenotype, and evaluated their temporal patterns.FindingsWe identified 1186 children hospitalized with MIS-C. The highest proportion of children (44·4%) were aged between 5-11 years, with a male predominance (61.0%), and non- Hispanic white ethnicity (40·2%). Most (67·8%) children did not have a chronic condition. Class 1 represented children with a severe clinical phenotype, with 72·5% admitted to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 represented a mild presentation, with fewer organ systems involved, lower CRP, troponin values and less cardiac involvement. Class 1 initially represented 51·1% of children early in the pandemic, which decreased to 33·9% from the pre-delta period to the omicron period.InterpretationMIS-C has a spectrum of clinical severity, with degree of laboratory abnormalities rather than the number of organ systems involved providing more useful indicators of severity. The proportion of severe/critical MIS-C decreased over time.Research in contextEvidence before this studyWe searched PubMed and preprint articles from December 2019, to July 2022, for studies published in English that investigated the clinical subphenotypes of MIS-C using the terms “multi-system inflammatory syndrome in children” or “pediatric inflammatory multisystem syndrome” and “phenotypes”. Most previous research described the symptoms, clinical characteristics and risk factors associated with MIS-C and how these differ from acute COVID-19, Kawasaki Disease and Toxic Shock Syndrome. One single-center study of 63 patients conducted in 2020 divided patients into Kawasaki and non-Kawasaki disease subphenotypes. Another CDC study evaluated 3 subclasses of MIS-C in 570 children, with one class representing the highest number of organ systems, a second class with predominant respiratory system involvement, and a third class with features overlapping with Kawasaki Disease. However, this study evaluated cases from March to July 2020, during the early phase of the pandemic when misclassification of cases as Kawasaki disease or acute COVID-19 may have occurred. Therefore, it is not known from the existing literature whether the presentation of MIS-C has changed with newer variants such as delta and omicron.Added value of this studyPEDSnet provides one of the largest MIS-C cohorts described so far, providing sufficient power for detailed analyses on MIS-C subphenotypes. Our analyses span the entire length of the pandemic, including the more recent omicron wave, and provide an update on the presentations of MIS-C and its temporal dynamics. We found that children have a spectrum of illness that can be characterized as mild (lower inflammatory markers, fewer organ systems involved), moderate (4-6 organ involvement with clinical overlap with acute COVID-19) and severe (higher inflammatory markers, critically ill, more likely to have cardiac involvement, with hypotension/shock and need for vasopressors).Implications of all the available evidenceThese results provide an update to the subphenotypes of MIS-C including the more recent delta and omicron periods and aid in the understanding of the various presentations of MIS-C. These and other findings provide a useful framework for clinicians in the recognition of MIS-C, identify factors associated with children at risk for increased severity, including the importance of laboratory parameters, for risk stratification, and to facilitate early evaluation, diagnosis and treatment.

Using electronic health record data combined with primary chart review, we identified 7 children across 8 pediatric medical centers with a diagnosis of Multisystem Inflammatory Syndrome in Children (MIS-C) who were managed as outpatients. These findings should prompt a discussion about modifying the case definition to allow for such a possibility.

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.

Background and Objectives: Neighborhood environments may play a role in the development of child obesity by providing or limiting opportunities for children to be physically active and access healt...

OBJECTIVE Sleep hygiene recommendations are commonly given to address patient-reported concerns about sleep, yet few studies have examined the relationship between sleep hygiene and sleep disturbances in the context of pediatric oncology. Because poor sleep may affect the patient's experience of cancer-related symptoms, understanding whether sleep hygiene practices influence sleep disturbances and symptoms may be important to improving symptom burden. METHODS One hundred and two caregivers of children ages 5-17 and 59 patients ages 8-17 receiving treatment for cancer completed parallel measures of child sleep, sleep hygiene, pain, fatigue, and nausea. Sleep hygiene practices were described, correlates between measures were examined, and the indirect relationship of sleep hygiene on symptom burden through sleep disturbances was tested using PROCESS. RESULTS Patients received adequate sleep for age but sleep timing was later than recommended for more than half of the sample and consistency in sleep times was poor. Sleep disturbances were moderately related to all symptoms, with the exception of patient-reported fatigue. Consistent sleep habits were indirectly related to fewer cancer-related symptoms of pain, fatigue, and nausea through sleep disturbances by caregiver report but not patient report. CONCLUSION Sleep disturbances are closely related to pain, fatigue, and nausea in pediatric cancer. Consistency in sleep/wake routines and schedules may be important to experiencing fewer sleep disturbances and lower symptom burden. Providing recommendations supporting consistent sleep habits broadly across pediatric oncology may be more effective than only presenting sleep hygiene recommendations to patients experiencing poor sleep.

We previously found that antibiotic use at24 months of age was associated with slightly higher body weight at 5 years of age. In this study, we examine associations of early life antibiotic prescriptions with weight outcomes at 108 to 132 months of age ("10 years").We used electronic health record data from 2009 through 2016 from 10 health systems in PCORnet, a national distributed clinical research network. We examined associations of any (vs no) antibiotics at24 months of age with body mass index z-score (BMI-z) at 10 years adjusted for confounders selected a priori. We further examined dose response (number of antibiotic episodes) and antibiotic spectrum (narrow and broad).Among 56,727 included children, 57% received any antibiotics at24 months; at 10 years, mean (standard deviation) BMI-z was 0.54 (1.14), and 36% had overweight or obesity. Any versus no antibiotic use at24 months was associated with a slightly higher BMI-z at 10 years among children without a complex chronic condition (β 0.03; 95% confidence interval [CI] 0.01, 0.05) or with a complex chronic condition (β 0.09; 95% CI 0.03, 0.15). Any versus no antibiotic use was not associated with odds of overweight or obesity at 10 years among children without (odds ratio 1.02; 95% CI 0.97, 1.07) or with a complex chronic condition (odds ratio 1.07; 95% CI 0.96, 1.19).The small and likely clinically insignificant associations in this study are consistent with our previous 5-year follow-up results, suggesting that, if this relationship is indeed causal, early increases in weight are small but maintained over time.

BACKGROUND Children belonging to the same birth cohort (i.e., born in the same year) experience shared exposure to a common obesity-related milieu during the critical early years of development-e.g., secular beliefs and feeding practices, adverse chemical exposures, food access and nutrition assistance policies-that set the stage for a shared trajectory of obesity as they mature. Fundamental cause theory suggests that inequitable distribution of recent efforts to stem the rise in child obesity may exacerbate cohort-based disparities over time. METHODS Data were from electronic health records spanning 2007-2016 linked to birth records for children ages 2-19 years. We used hierarchical age-period-cohort models to investigate cohort effects on disparities in obesity related to maternal education. We hypothesized that maternal education-based disparities in prevalence of obesity would be larger among more recent birth cohorts. RESULTS Sex-stratified models adjusted for race/ethnicity showed substantial obesity disparities by maternal education that were evident even at young ages: prevalence among children with maternal education

ImportanceThe post-acute sequelae of SARS-CoV-2 (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited.ObjectiveTo identify diagnosed symptoms, diagnosed health conditions and medications associated with PASC in children.Design, Setting and ParticipantsRetrospective cohort study using electronic health records from 9 US children’s hospitals for individuals ExposureSARS-CoV-2 PCR positivity.Main Outcomes and MeasuresWe identified syndromic (symptoms), systemic (conditions), and medication PASC features in the 28-179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting PCR-positive with PCR-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race/ethnicity, and time-period of cohort entrance. We estimated the incidence proportion for any syndromic, systemic or medication PASC feature in the two groups to obtain a burden of PASC estimate.ResultsAmong 659,286 children in the study sample, 59,893 (9.1%) tested positive by PCR for SARS-CoV-2. Most were tested in outpatient testing facility (50.3%) or office (24.6%) settings. The most common syndromic, systemic, and medication features were loss of taste or smell (aHR 1.96 [95% CI 1.16-3.32), myocarditis (aHR 3.10 [95% CI 1.94-4.96]), and cough and cold preparations (aHR 1.52 [95% CI 1.18-1.96]). The incidence of at least one systemic/syndromic/medication feature of PASC was 41.9% among PCR-positive children versus 38.2% among PCR-negative children, with an incidence proportion difference of 3.7% (95% CI 3.2-4.2%). A higher strength of association for PASC was identified in those cared for in the ICU during the acute illness phase, children less than 5 years-old, and individuals with complex chronic conditions.Conclusions and RelevanceIn this large-scale, exploratory study, the burden of pediatric PASC that presented to health systems was low. Myocarditis was the most commonly diagnosed PASC-associated condition. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC.Key PointsQuestionWhat are the incidence and clinical features of post-acute sequelae of SARS-CoV-2 infection (PASC) in children?FindingsIn this retrospective cohort study of 659,286 children tested for SARS-CoV-2 by polymerase chain reaction (PCR), the symptom, condition and medication with the strongest associations with SARS-CoV-2 infection were loss of taste/smell, myocarditis, and cough and cold preparations. The incidence proportion of non-MIS-C related PASC in the PCR-positive group exceeded the PCR-negative group by 3.7% (95% CI 3.2-4.2), with increased rates associated with acute illness severity, young age, and medical complexity.MeaningPASC in children appears to be uncommon, with features that differ from adults.

The objectives of this study were to (1) demonstrate the application of percentiles to advance the interpretation of patient-reported outcomes and (2) establish autism-specific percentiles for four Patient-Reported Outcomes Measurement Information System (PROMIS) measures. PROMIS measures were completed by parents of autistic children and adolescents ages 5-17 years as part of two studies (n = 939 parents in the first study and n = 406 parents in the second study). Data from the first study were used to develop autism-specific percentiles for PROMIS parent-proxy sleep disturbance, sleep-related impairment, fatigue, and anxiety. Previously established United States general population percentiles were applied to interpret PROMIS scores in both studies. Results of logistic regression models showed that parent-reported material hardship was associated with scoring in the moderate-severe range (defined as ≥75th percentile in the general population) on all four PROMIS measures (odds ratios 1.7-2.2). In the second study, the percentage of children with severe scores (defined as ≥95th percentile in the general population) was 30% for anxiety, 25% for sleep disturbance, and 17% for sleep-related impairment, indicating a high burden of these problems among autistic children. Few children had scores at or above the autism-specific 95th percentile on these measures (3%-4%), indicating that their scores were similar to other autistic children. The general population and condition-specific percentiles provide two complementary reference points to aid interpretation of PROMIS scores, including corresponding severity categories that are comparable across different PROMIS measures.