Impact of COVID-19 Diagnosis on Weight Trajectories of Children in the US National COVID Cohort Collaborative.

The COVID-19 pandemic has exacerbated the obesity epidemic, with both adults and children demonstrating rapid weight gain during the pandemic. However, the impact of having a COVID-19 diagnosis on this trend is not known. Using longitudinal data from January 2019 to June 2023 collected by the US National Institute for Health's National COVID Cohort Collaborative (N3C), children (age 2-18 years) with positive COVID-19 test results { = 11,474, 53% male, mean [standard deviation (SD)] age 5.

Reliable prediction of childhood obesity using only routinely collected EHRs may be possible.

Background: Early identification of children at high risk of obesity can provide clinicians with the information needed to provide targeted lifestyle counseling to high-risk children at a critical time to change the disease course.

Objectives: This study aimed to develop predictive models of childhood obesity, applying advanced machine learning methods to a large unaugmented electronic health record (EHR) dataset. This work improves on other studies that have (i) relied on data not routinely available in EHRs (like prenatal data), (ii) focused on single-age predictions, or (iii) not been rigorously validated.

Associations of Longitudinal BMI-Percentile Classification Patterns in Early Childhood with Neighborhood-Level Social Determinants of Health.

Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable. We extracted electronic health record data from 2012 to 2019 for a children's health system that includes two hospitals and wide network of outpatient clinics spanning five East Coast states in the United States.

Community Partnership to Co-Develop an Intervention to Promote Equitable Uptake of the COVID-19 Vaccine Among Pediatric Populations.

Objective: To describe the process of engaging community, caregiver, and youth partners in codeveloping an intervention to promote equitable uptake of the COVID-19 vaccine in non-Hispanic Black (Black) and Hispanic youth who experience higher rates of COVID-19 transmission, morbidity, and mortality but were less likely to receive the COVID-19 vaccine.

Methods: A team of 11 Black and Hispanic community partners was assembled to codevelop intervention strategies with our interdisciplinary research team. We used a mixed-methods crowdsourcing approach with Black and Hispanic youth (n=15) and caregivers of Black and Hispanic youth (n=20) who had not yet been vaccinated against COVID-19, recruited from primary care clinics, to elicit perspectives on the acceptability of these intervention strategies.

The Psychosocial Impact of the COVID-19 Pandemic on Families of Youth of Color: A Prospective Cohort Study.

Objective: Prospectively examine racial and ethnic disparities in exposure to COVID-19-related stressors and their impact on families.

Methods: A racially, ethnically, and socioeconomically diverse cohort of caregivers of youth (n = 1,581) representative of the population served by a pediatric healthcare system completed the COVID-19 Exposure and Family Impact Scales in Oct/Nov 2020 and March/April 2021. Linear mixed-effects models were used to examine exposure to COVID-19-related events (Exposure), impact of the pandemic on family functioning and well-being (Impact), and child and parent distress (Distress) across time and as a function of race and ethnicity, adjusting for other sociodemographic variables.

Associations of longitudinal BMI percentile classification patterns in early childhood with neighborhood-level social determinants of health.

Background: Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable.

Methods: We extracted EHR data from 2012-2019 for a children's health system that includes 2 hospitals and wide network of outpatient clinics spanning 5 East Coast states in the US.

Persistent Disparities in Pediatric Health Care Engagement During the COVID-19 Pandemic.

Objective: The COVID-19 pandemic has disrupted traditional health care, including pediatric health care. We described the impact of the pandemic on disparities in pediatric health care engagement.

Methods: Using a population-based cross-sectional time-series design, we compared monthly ambulatory care visit volume and completion rates (completed vs no-show and cancelled visits) among pediatric patients aged 0-21 years in 4 states in the mid-Atlantic United States during the first year of the COVID-19 pandemic (March 2020-February 2021) with the same period before the pandemic (March 2019-February 2020).

Patient-Reported Outcomes to Describe Global Health and Family Relationships in Pediatric Weight Management.

Patient-reported outcomes (PROs) can assess chronic health. The study aims were to pilot a survey through the PEDSnet Healthy Weight Network (HWN), collecting PROs in tertiary care pediatric weight management programs (PWMP) in the United States, and demonstrate that a 50% enrollment rate was feasible; describe PROs in this population; and explore the relationship between child/family characteristics and PROs. Participants included 12- to 18-year-old patients and parents of 5- to 18-year-olds receiving care at PWMP in eight HWN sites.

Predicting Attrition Patterns from Pediatric Weight Management Programs.

Obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity who are not able to be successfully managed in the primary care setting. Despite their great potential, high dropout rates (referred to as attrition) are a major hurdle in delivering successful interventions.

Active vs Traditional Methods of Recruiting Children for a Clinical Trial in Rural Primary Care Clinics: A Cluster-Randomized Clinical Trial.

Importance: To our knowledge, there are no published randomized clinical trials of recruitment strategies. Rigorously evaluated successful recruitment strategies for children are needed.

Objective: To evaluate the feasibility of 2 recruitment methods for enrolling rural children through primary care clinics to assess whether either or both methods are sufficiently effective for enrolling participants into a clinical trial of a behavioral telehealth intervention for children with overweight or obesity.

Flexible-Window Predictions on Electronic Health Records.

Various types of machine learning techniques are available for analyzing electronic health records (EHRs). For predictive tasks, most existing methods either explicitly or implicitly divide these time-series datasets into predetermined observation and prediction windows. Patients have different lengths of medical history and the desired predictions (for purposes such as diagnosis or treatment) are required at different times in the future.

Conducting a pediatric randomized clinical trial during a pandemic: A shift to virtual procedures.

Background/objective: Prior to the COVID-19 pandemic, our research group initiated a pediatric practice-based randomized trial for the treatment of childhood obesity in rural communities. Approximately 6 weeks into the originally planned 10-week enrollment period, the trial was forced to pause all study activity due to the COVID-19 pandemic. This pause necessitated a substantial revision in recruitment, enrollment, and other study methods in order to complete the trial using virtual procedures.

Associations Between Weight and Lower Respiratory Tract Disease Outcomes in Hospitalized Children.

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 <2 years, those with medical complexity, and those without a calculable BMI were excluded.

Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data.

Disparities in Delaware Caregiver Beliefs about the COVID-19 Vaccine for their Children.

Objective: To describe sociodemographic disparities in caregiver beliefs about the COVID-19 vaccine for their children.

Methods: This was a cross-sectional study, linking caregiver-reported data to geocoded sociodemographic data from child EHRs. Caregivers of children receiving care in a Delaware pediatric healthcare system were invited to complete a survey about COVID-19 vaccine beliefs from March 19 to April 16, 2021.

Medical factors associated with caregiver intention to vaccinate their children against COVID-19.

Objective: To describe medical factors that are associated with caregiver intention to vaccinate their children against COVID-19.

Methods: We conducted a cross-sectional study of families receiving primary care in a mid-Atlantic pediatric healthcare system, linking caregiver-reported data from a survey completed March 19 to April 16, 2021 to comprehensive data from the child's EHR.

Results: 513 families were included (28% Black, 16% Hispanic, 44% public insurance, 21% rural, child age range 0-21 years).

Validation of the COVID-19 Exposure and Family Impact Scales.

Objective: The COVID-19 Exposure and Family Impact Scales (CEFIS) were developed in Spring 2020 to assess effects of the COVID-19 pandemic on families and caregivers. Initial psychometric properties were promising. The current study examined the factor structure and evaluated convergent and criterion validity of the CEFIS in a new sample.

Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database.

Objective: This study compared the importance of age at adiposity rebound versus childhood BMI to subsequent BMI levels in a longitudinal analysis.

Methods: 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.

Weight Status and Risk of Inpatient Admission for Children With Lower Respiratory Tract Disease.

Objectives: To identify associations between weight category and hospital admission for lower respiratory tract disease (LRTD), defined as asthma, community-acquired pneumonia, viral pneumonia, or bronchiolitis, among children evaluated in pediatric emergency departments (PEDs).

Methods: We performed a retrospective cohort study of children 2 to <18 years of age evaluated in the PED at 6 children's hospitals within the PEDSnet clinical research network from 2009 to 2019. BMI percentile of children was classified as underweight, healthy weight, overweight, and class 1, 2, or 3 obesity.

Concurrent Imputation and Prediction on EHR data using Bi-Directional GANs: Bi-GANs for EHR imputation and prediction.

Working with electronic health records (EHRs) is known to be challenging due to several reasons. These reasons include not having: 1) similar lengths (per visit), 2) the same number of observations (per patient), and 3) complete entries in the available records. These issues hinder the performance of the predictive models created using EHRs.

A Longitudinal Comparison of Alternatives to Body Mass Index Z-Scores for Children with Very High Body Mass Indexes.

Objective: The current Centers for Disease Control and Prevention (CDC) body mass index (BMI) z-scores are inaccurate for BMIs of ≥97th percentile. We, therefore, considered 5 alternatives that can be used across the entire BMI distribution: modified BMI-for-age z-score (BMIz), BMI expressed as a percentage of the 95th percentile (%CDC95th percentile), extended BMIz, BMI expressed as a percentage of the median (%median), and %median adjusted for the dispersion of BMIs.

Study Design: We illustrate the behavior of the metrics among children of different ages and BMIs.

Feasibility of Using a Commercial Fitness Tracker as an Adjunct to Family-Based Weight Management Treatment: Pilot Randomized Trial.

Background: Fitness trackers can engage users through automated self-monitoring of physical activity. Studies evaluating the utility of fitness trackers are limited among adolescents, who are often difficult to engage in weight management treatment and are heavy technology users.

Objective: We conducted a pilot randomized trial to describe the impact of providing adolescents and caregivers with fitness trackers as an adjunct to treatment in a tertiary care weight management clinic on adolescent fitness tracker satisfaction, fitness tracker utilization patterns, and physical activity levels.

Electronic Gaming Characteristics Associated with Class 3 Severe Obesity in Youth Who Attend the Pediatric Weight Management Programs of the COMPASS Network.

Background: The prevalence of severe obesity and electronic game use among youth has increased over time.

Methods: We administered a survey assessing gaming and psycho-demographic characteristics to youth aged 11-17 attending five weight management programs. We conducted chi-square and logistic regression analyses to describe the association between class 3 severe obesity and gaming characteristics.