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. Children were classified as having underweight, normal weight, overweight, or class 1, 2, or 3 obesity based on Body Mass Index percentile for age and sex. Primary outcomes were need for positive pressure respiratory support and ICU admission. Subgroup analyses were performed for children with a primary diagnosis of asthma. Outcomes were modeled with mixed-effects multivariable logistic regression incorporating age, sex, and payer as fixed effects. 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.

Measuring BMI change among children and adolescents.

BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.

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. The minimum number of examinations per child was six. Each child's rebound age was estimated using locally weighted regression (lowess), a smoothing technique. RESULTS: Children who had a rebound age < 3 years were, on average, 7 kg/m2 heavier after age 14 years than were children with a rebound age ≥ 7 years. However, BMI after age 14 years was more strongly associated with BMI at the rebound than with rebound age (r = 0.57 vs. -0.44). Furthermore, a child's BMI at age 3 years provided more information on BMI after age 14 years than did rebound age. In addition, rebound age provided no information on subsequent BMI if a child's BMI at age 6 years was known. CONCLUSIONS: Although 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.

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. Children with complex chronic conditions were excluded. Mixed-effects multivariable logistic regression was used to assess associations between BMI categories and hospitalization or 7- and 30-day PED revisits, adjusted for covariates (age, sex, race and ethnicity, and payer). RESULTS: Among 107 446 children with 218 180 PED evaluations for LRTD, 4.5% had underweight, 56.4% had healthy normal weight, 16.1% had overweight, 14.6% had class 1 obesity, 5.5% had class 2 obesity, and 3.0% had class 3 obesity. Underweight was associated with increased risk of hospital admission compared with normal weight (odds ratio [OR] 1.76; 95% confidence interval [CI] 1.69-1.84). Overweight (OR 0.87; 95% CI 0.85-0.90), class 1 obesity (OR 0.88; 95% CI 0.85-0.91), and class 2 obesity (OR 0.91; 95% CI 0.87-0.96) had negative associations with hospital admission. Class 1 and class 2, but not class 3, obesity had small positive associations with 7- and 30-day PED revisits. CONCLUSIONS: We found an inverse relationship between patient weight category and risk for hospital admission in children evaluated in the PED for LRTD.

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. We then compared the longitudinal tracking of the BMI metrics in electronic health record data from 1.17 million children in PEDSnet using the intraclass correlation coefficient to determine if 1 metric was superior. RESULTS: Our examples show that using CDC BMIz for high BMIs can result in nonsensical results. All alternative metrics showed higher tracking than CDC BMIz among children with obesity. Of the alternatives, modified BMIz performed poorly among children with severe obesity, and %median performed poorly among children who did not have obesity at their first visit. The highest intraclass correlation coefficients were generally seen for extended BMIz, adjusted %median, and %CDC95th percentile. CONCLUSIONS: Based on the examples of differences in the BMI metrics, the longitudinal tracking results and current familiarity BMI z-scores and percentiles. Both extended BMIz and extended BMI percentiles may be suitable replacements for the current z-scores and percentiles. These metrics are identical to those in the CDC growth charts for BMIs of <95th percentile and are superior for very high BMIs. Researchers' familiarity with the current CDC z-scores and clinicians with the CDC percentiles may ease the transition to the extended BMI scale.

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. Results: 1499 caregivers participated (18% Black, 11% Hispanic, 32% public insurance, 12% rural). 54% of caregivers intended to vaccinate their children, while 34% were unsure and 12% would not. Caregivers of younger children (aOR 3.70, CI 2.36-5.79), Black children (aOR 2.11, CI 1.50-2.96), and from disadvantaged communities (aOR 1.59, CI 1.05-2.42) were more likely to be unsure and not vaccinate their children. Caregivers from rural communities were more likely not to vaccinate their children (aOR 2.51, CI 1.56-4.05). Fewer caregivers of younger children, Black children, and from disadvantaged communities believed in the safety or efficacy of the vaccines (p < 0.001), while fewer caregivers of younger children and from rural communities believed in their children's susceptibility to COVID-19 or risk of getting severe disease from COVID-19 (p < 0.05). While the majority (72%) of caregivers were influenced by health experts, fewer from communities of color and disadvantaged communities were (p<0.001). Conclusions: Caregivers of younger children and from communities of color, rural communities, and disadvantaged communities in Delaware expressed more COVID-19 vaccine hesitancy. Policy implications: This study explores beliefs of different communities in Delaware, which are important to tailoring public health messaging and strategies to increase vaccine uptake in these communities.

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. METHODS: Adolescents were randomized to 1 of 2 groups (adolescent or dyad) at their initial weight management clinic visit. Adolescents received a fitness tracker and counseling around activity data in addition to standard treatment. A caregiver of adolescents in the dyad group also received a fitness tracker. Satisfaction with the fitness tracker, fitness tracker utilization patterns, and physical activity patterns were evaluated over 3 months. RESULTS: A total of 88 adolescents were enrolled, with 69% (61/88) being female, 36% (32/88) black, 23% (20/88) Hispanic, and 63% (55/88) with severe obesity. Most adolescents reported that the fitness tracker was helping them meet their healthy lifestyle goals (69%) and be more motivated to achieve a healthy weight (66%). Despite this, 68% discontinued use of the fitness tracker by the end of the study. There were no significant differences between the adolescent and the dyad group in outcomes, but adolescents in the dyad group were 12.2 times more likely to discontinue using their fitness tracker if their caregiver also discontinued use of their fitness tracker (95% CI 2.4-61.6). Compared with adolescents who discontinued use of the fitness tracker during the study, adolescents who continued to use the fitness tracker recorded a higher number of daily steps in months 2 and 3 of the study (mean 5760 vs 4148 in month 2, P=.005, and mean 5942 vs 3487 in month 3, P=.002). CONCLUSIONS: Despite high levels of satisfaction with the fitness trackers, fitness tracker discontinuation rates were high, especially among adolescents whose caregivers also discontinued use of their fitness tracker. More studies are needed to determine how to sustain the use of fitness trackers among adolescents with obesity and engage caregivers in adolescent weight management interventions.