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Objective: Many US youth experienced accelerated weight gain during the early COVID‐19 pandemic. Using an ambulatory electronic health record data set, the authors compared children's rates of BMI change in three periods: pre‐pandemic (January 2018‐February 2020), early pandemic (March‐December 2020), and later pandemic (January‐November 2021). Methods: This study used mixed‐effects models to examine differences in rates of change in BMI, weight, and obesity prevalence among the three periods. Covariates included time as a continuous variable, a variable indicating in which period each BMI was taken, sex, age, and initial BMI category. Results: In a longitudinal cohort of 241,600 children aged 2 through 19 years with ≥4 BMI measurements, the monthly rates of BMI change (kilograms per meters squared) were 0.056 (95% CI: 0.056‐0.057) in the pre‐pandemic period, 0.104 (95% CI: 0.102‐0.106) in the early pandemic, and 0.035 (95% CI: 0.033‐0.036) in the later pandemic. The estimated prevalence of obesity in this cohort was 22.5% by November 2021. Conclusions: In this large, geographically diverse cohort of US youth, accelerated rates of BMI change observed during 2020 were largely attenuated in 2021. Positive rates indicate continued weight gain rather than loss, albeit at a slower rate. Childhood obesity prevalence remained high, which raises concern about long‐term consequences of excess weight and underscores the importance of healthy lifestyle interventions. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess the association of BMI with inpatient care cost, duration, and acute complications among patients hospitalized for COVID‐19 at 273 US hospitals. Methods: Children (aged 2–17 years) and adults (aged ≥18 years) hospitalized for COVID‐19 during March 2020–July 2021 and with measured BMI in a large electronic administrative health care database were included. Generalized linear models were used to assess the association of BMI categories with the cost and duration of inpatient care. Results: Among 108,986 adults and 409 children hospitalized for COVID‐19, obesity prevalence was 53.4% and 45.0%, respectively. Among adults, overweight and obesity were associated with higher cost of care, and obesity was associated with longer hospital stays. Children with severe obesity had higher cost of care but not significantly longer hospital stays, compared with those with healthy weight. Children with severe obesity were 3.7 times (95% CI: 1.4–9.5) as likely to have invasive mechanical ventilation and 62% more likely to have an acute complication (95% CI: 39%–90%), compared with children with healthy weight. Conclusions: These findings show that patients with a high BMI experience significant health care burden during inpatient COVID‐19 care. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Self-efficacy is a crucial factor in enabling pediatric primary care providers (PCPs) to deliver recommended care to children with overweight and obesity. This study, conducted with a large, national sample of PCPs, aimed to identify key factors, which may contribute to PCP self-efficacy for obesity-related care, from a list of previously reported barriers and facilitators. METHODS: A national random sample of American Academy of Pediatrics members was surveyed in 2017 (analytic n = 704). Factor analysis was used to identify self-efficacy variables from relevant indicators and assess fit. Multivariable linear regression analyses were conducted to identify key predictors of PCP self-efficacy from reported facilitators or barriers to care, including characteristics of the PCP, practice, community, and payment systems. RESULTS: Two PCP self-efficacy variables were identified: health risk assessment and patient-centered counseling. Both were positively predicted by relevant training, the belief that pediatricians play an important role in obesity, and awareness of barriers to payment for dietitians or weight management programs. Both were negatively predicted by a perceived lack of available PCP time for counseling and inadequacy of available referral resources to assist with treatment. Additional predictors of counseling self-efficacy included PCP beliefs that they are paid for treatment (+) and that patients/families lack time for healthy behaviors (-). Electronic health record clinical decision supports or registries and patient social disadvantage were not predictive. CONCLUSIONS: Results suggest multiple potential roles and strategies for local and national organizations seeking to facilitate improvements to PCP self-efficacy in caring for children with overweight and obesity. [ABSTRACT FROM AUTHOR]

The Mind, Exercise, Nutrition, Do It! 7–13 (MEND 7–13) program was adapted in 2016 by 5 Denver Health federally qualified health centers (DH FQHC) into MEND+, integrating clinician medical visits into the curriculum and tracking health measures within an electronic health record (EHR). We examined trajectories of body mass index (BMI, kg/m2) percentile, and systolic and diastolic blood pressures (SBP and DBP) among MEND+ attendees in an expanded age range of 4 to 17 years, and comparable nonattendees. Data from April 2015 to May 2018 were extracted from DH FQHC EHR for children eligible for MEND+ referral (BMI ≥85th percentile). The sample included 347 MEND+ attendees and 21,061 nonattendees. Mixed-effects models examined average rate of change for BMI percent of the 95th percentile (%BMIp95), SBP and DBP (mm Hg), after completion of the study period. Most children were ages 7 to 13 years, half were male, and most were Hispanic. An average of 4.2 MEND+ clinical sessions were attended. Before MEND+, %BMIp95 increased by 0.247 units/month among MEND+ attendees. After attending, %BMIp95 decreased by 0.087 units/month (P <.001). Eligible nonattendees had an increase of 0.084/month in %BMIp95. Before MEND+ attendance, SBP and DBP increased by 0.041 and 0.022/month, respectively. After MEND+ attendance, SBP and DBP decreased by 0.254/month (P <.001) and 0.114/month (P <.01), respectively. SBP and DBP increased by 0.033 and 0.032/month in eligible nonattendees, respectively. %BMIp95, SBP, and DBP significantly decreased among MEND+ attendees when implemented in community-based clinical practice settings at DH FQHC. [ABSTRACT FROM AUTHOR]

Summary: Background and Objective: As obesity among children and adolescents is associated with major health risks, including the persistence of obesity into adulthood, there has been interest in targeting prevention efforts at children and adolescent. The longitudinal tracking of BMI and obesity, as well as the effects of initial age and duration of follow‐up on this tracking, were examined in a large electronic health record (EHR) database. Methods: The data consisted of 2.04 million children who were examined from 2006 through 2018. These children were initially examined between ages 2 and 9 years and had a final examination, on average, 4 years later. Results: Overall, children with obesity at one examination were 7.7 times more likely to have obesity at a subsequent examination than children with a BMI ≤ 95th percentile. Further, 71% of children with obesity at one examination continued to have obesity at re‐examination. Although 2‐year‐olds had a relative risk of 5.5 and a positive predictive value of 54%, then sensitivity of obesity at younger ages was low. Of the children who were re‐examined after age 10 y and found to have obesity, only 22% had a BMI ≥ 95th percentile at age 2 years. Conclusions: Despite the tracking of obesity at all ages, these results agree with previous reports that have found that an elevated BMI at a very young age will identify only a small proportion of older children with obesity. [ABSTRACT FROM AUTHOR]

Introduction: The U.S. Preventive Services Task Force recommends clinicians screen children aged 6 years or older for obesity and offer or refer children with obesity to intensive weight management programs. This study explores clinician awareness of weight management programs meeting the recommendation, adherence to the recommendation of screening and referral, and associations between provider and practice characteristics and weight management program referrals.Methods: This cross-sectional study used data from the DocStyles survey 2017, a web-based panel survey, analyzed in 2017. Among 1,023 clinicians who see pediatric patients, this study examined clinician awareness of weight management programs in their communities that met the recommendation, practice of screening for childhood obesity, and referral to weight management programs. Multivariable logistic regression estimated associations between the demographic and practice characteristics of clinicians and weight management program referrals.Results: Only 24.6% of surveyed clinicians were aware of a weight management program that met the U.S. Preventive Services Task Force recommendation in their community; of those aware, 88.9% referred patients to these weight management programs. Most (83.6%) clinicians screened children for obesity in ≥75% of visits. Overall, 53.5% of clinicians provided referrals to weight management programs. Referral was higher among female clinicians and clinicians serving mostly middle-income patients. Providers without teaching hospital privileges had lower odds of referral.Conclusions: Adherence to clinical recommendations is essential to curbing the childhood obesity epidemic. Only one in four surveyed clinicians were aware of weight management programs in their community meeting U.S. Preventive Services Task Force criteria. Half of clinicians referred pediatric patients with obesity to a weight management program. Results suggest efforts are needed to increase awareness of, and referral to, weight management programs meeting the recommendation. [ABSTRACT FROM AUTHOR]

Objective: Although the tracking of BMI levels from childhood to adulthood has been examined, there is little information on the within-person variability of BMI.Methods: Longitudinal data from 11,591 schoolchildren, 3,096 of whom were reexamined as adults, were used to explore the tracking and variability of BMI levels. This article focuses on changes in age-adjusted levels of BMI.Results: There was strong tracking of BMI levels. The correlation of adjusted BMI levels was r = 0.88, and 78% of children with severe obesity at one examination had severe obesity at the next examination (mean interval, 2.7 years). Further, an increase in adjusted BMI from +5 kg/m2 (above the median) to + 10 increased the risk for adult BMI ≥ 40 by 2.7-fold. However, BMI levels among children and adolescents were variable. Over a 9- to 15-month interval, the SD of adjusted BMI change was 0.9 kg/m2 , and 0.7% of children had an absolute change ≥ 3.5. This variability was associated with the interval between examinations and with the initial BMI.Conclusions: Despite the high degree of tracking of BMI, annual changes of 3.5 kg/m2 or more are plausible. Knowledge of this variability is important when following a child over time. [ABSTRACT FROM AUTHOR]

Background:BMIz-scores (BMIz) based on the Centers for Disease Control and Prevention (CDC) growth charts among children do not accurately characterise BMI levels among children with very high BMIs. These limitations may be particularly relevant in longitudinal and intervention studies, as the large changes in the L (normality) and S (dispersion) parameters with age can influence BMIz. Aim:To compare longitudinal changes in BMIzwith BMI expressed as a percentage of the 95th percentile (%BMIp95) and a modifiedz-score calculated as log(BMI/M)/S. Subjects and methods:A total of 45 414 2–4-year-olds with severe obesity (%BMIp95 ≥ 120). Results:Changes in very high BMIzlevels differed from the other metrics. Among severely obese 2-year-old girls, for example, the mean BMIzdecreased by 0.6 SD between examinations, but there were only small changes in BMIp95 and modified BMIz. Some 2-year-old girls had BMIzdecreases of >1 SD, even though they had large increases in BMI, %BMIp95 and modified BMIz. Conclusions:Among children with severe obesity, BMIzchanges may be due to differences in the transformations used to estimate levels of BMIzrather than to changes in body size. The BMIs of these children could be expressed relative to the 95th percentile or as modifiedz-scores. [ABSTRACT FROM AUTHOR]

Objective: Although the Centers for Disease Control and Prevention (CDC) growth charts are widely used, BMI-for-age z-Scores (BMIz) are known to be uninformative above the 97th percentile. This study compared the relations of BMIz and other BMI metrics (%BMIp95 , percent of 95th percentile, and ΔBMIp95 , BMI minus 95th percentile) to circumferences, skinfolds, and fat mass. We were particularly interested in the differences among children with severe obesity (%BMIp95  ≥ 120).Methods: Data was used from 30,003 2- to 19-year-olds who were examined from 1999-2000 through 2013-2014 in the National Health and Nutrition Examination Survey (NHANES).Results: The theoretical maximum BMIz based on the growth charts varied by more than threefold across ages. The BMI metrics were strongly intercorrelated, but BMIz was less strongly related to the adiposity measures than were ΔBMIp95 and %BMIp95 . Among children with severe obesity, circumferences and triceps skinfold showed almost no association with BMIz (r ≤ 0.10), whereas associations with %BMIp95 and ΔBMIp95 ranged from r = 0.32 to 0.79. Corresponding associations with fat mass ÷ height2 ranged from r = 0.40 (BMIz) to r =0.82 (%BMIp95 ) among 8- to 19-year-olds.Conclusions: Among children with severe obesity, BMIz is only weakly associated with other measures of body fatness. Very high BMIs should be expressed relative to the CDC 95th percentile, particularly in studies that evaluate obesity interventions. [ABSTRACT FROM AUTHOR]

Purpose: Determine prevalence of overweight and obesity as reported in Head Start Program Information Reports. Design: Serial cross-sectional census reports from 2012–2018. Setting: Head Start programs countrywide, aggregated from program level to state and national level. Subjects: Population of children enrolled in Head Start with reported weight status data. Measures: Prevalence of overweight (body mass index [BMI] ≥85th percentile to <95th percentile) and obesity (BMI ≥95th percentile). Analysis: Used descriptive statistics to present the prevalence of overweight and obesity by state. Performed unadjusted regression analysis to examine annual trends or average annual changes in prevalence. Results: In 2018, the prevalence of overweight was 13.7% (range: 8.9% in Alabama to 20.4% in Alaska). The prevalence of obesity was 16.6% (range: 12.5% in South Carolina to 27.1% in Alaska). In the unadjusted regression model, 34 states and the District of Columbia did not have a linear trend significantly different from zero. There was a statistically significant positive trend in obesity prevalence for 13 states and a negative trend for 3 states. Conclusion: The prevalence of obesity and overweight in Head Start children remained stable but continues to be high. Head Start reports may be an additional source of surveillance data to understand obesity prevalence in low-income young children. [ABSTRACT FROM AUTHOR]