Background: The BMI z-score is a standardized measure of weight status and weight change in children and adolescents. BMI z-scores from various growth references are often considered comparable, and differences among them are underappreciated. Methods: This study reanalyzed data from a weight management clinical study of liraglutide in pubertal adolescents with obesity using growth references from CDC 2000, CDC Extended, World Health Organization (WHO), and International Obesity Task Force. Results: BMI z-score treatment differences varied 2-fold from -0.13 (CDC 2000) to -0.26 (WHO) overall and varied almost 4-fold from -0.05 (CDC 2000) to -0.19 (WHO) among adolescents with high baseline BMI z-score. Conclusions: Depending upon the growth reference used, BMI z-score endpoints can produce highly variable treatment estimates and alter interpretations of clinical meaningfulness. BMI z-scores cited without the associated growth reference cannot be accurately interpreted.

OBJECTIVES: To examine the prevalence and trends in severe obesity among 16.6 million children aged 2 to 4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) from 2010 to 2020. METHODS: Severe obesity was defined as a sex-specific BMI for age ≥120% of the 95th percentile on the Centers for Disease Control and Prevention growth charts or BMI ≥35 kg/m2. Joinpoint regression was used to identify when changes occurred in the overall trend. Logistic regression was used to compute the adjusted prevalence differences between years controlling for sex, age, and race and ethnicity. RESULTS: The prevalence of severe obesity significantly decreased from 2.1% in 2010 to 1.8% in 2016 and then increased to 2.0% in 2020. From 2010 to 2016, the prevalence decreased significantly among all sociodemographic subgroups except for American Indian/Alaska Native (AI/AN) children. The largest decreases were among 4-year-olds, Asian/Pacific Islander and Hispanic children, and children from higher-income households. However, from 2016 to 2020, the prevalence increased significantly overall and among sociodemographic subgroups, except for AI/AN and non-Hispanic white children. The largest increases occurred in 4-year-olds and Hispanic children. Among 56 WIC agencies, the prevalence significantly declined in 17 agencies, and 1 agency (Mississippi) showed a significant increase from 2010 to 2016. In contrast, 21 agencies had significant increases, and only Alaska had a significant decrease from 2016 to 2020. CONCLUSIONS: Although severe obesity prevalence in toddlers declined from 2010 to 2016, recent trends are upward. Early identification and access to evidence-based family healthy weight programs for at-risk children can support families and child health.

In December 2022, the Centers for Disease Control and Prevention (CDC) released Extended BMI-for-age growth charts1,2 for children and adolescents with high BMI values. These charts extend to a BMI of 60 and add 4 growth curves (98th, 99th, 99.9th, and 99.99th percentiles). Obesity among children and adolescents is defined as BMI ≥95th percentile of BMI-for-age and severe obesity as BMI ≥120% of the 95th percentile or ≥35.3 The recent American Academy of Pediatrics guideline for the treatment of obesity recommends using percentages of the 95th percentile of BMI-for-age to indicate different levels of severe obesity.4 This analysis compares CDC extended BMI-for-age percentiles with 120% and 140% of the 95th percentile and illustrates the differences between the prevalence of US children and adolescents 2 to 19 years of age with a BMI ≥ extended 98th percentile using the newly defined curve and those ≥120% of the 95th percentile using 2017 to March 2020 National Health and Nutritional Examination (NHANES) data.

IMPORTANCE: Information on the probability of weight loss among US adults with overweight or obesity is limited. OBJECTIVE: To assess the probability of 5% or greater weight loss, 10% or greater weight loss, body mass index (BMI) reduction to a lower BMI category, and BMI reduction to the healthy weight category among US adults with initial overweight or obesity overall and by sex and race. DESIGN, SETTING, AND PARTICIPANTS: This cohort study obtained data from the IQVIA ambulatory electronic medical records database. The sample consists of US ambulatory patients 17 years or older with at least 3 years of BMI information from January 1, 2009, to February 28, 2022. Minimum age was set at 17 years to allow for the change in BMI or weight starting at 18 years. Maximum age was censored at 70 years. EXPOSURES: Initial BMI (calculated as weight in kilograms divided by height in meters squared) category was the independent variable of interest, and the categories were as follows: lower than 18.5 (underweight), 18.5 to 24.9 (healthy weight), 25.0 to 29.9 (overweight), 30.0 to 34.9 (class 1 obesity), 35.0 to 39.9 (class 2 obesity), and 40.0 to 44.9 and 45.0 or higher (class 3 or severe obesity). MAIN OUTCOMES AND MEASURES: The 2 main outcomes were 5% or greater weight loss (ie, a ≥5% reduction in initial weight) and BMI reduction to the healthy weight category (ie, BMI of 18.5-24.9). RESULTS: The 18 461 623 individuals in the sample had a median (IQR) age of 54 (40-66) years and included 10 464 598 females (56.7%) as well as 7.7% Black and 72.3% White patients. Overall, 72.5% of patients had overweight or obesity at the initial visit. Among adults with overweight and obesity, the annual probability of 5% or greater weight loss was low (1 in 10) but increased with higher initial BMI (from 1 in 12 individuals with initial overweight to 1 in 6 individuals with initial BMI of 45 or higher). Annual probability of BMI reduction to the healthy weight category ranged from 1 in 19 individuals with initial overweight to 1 in 1667 individuals with initial BMI of 45 or higher. Both outcomes were generally more likely among females than males and were highest among White females. Over the 3 to 14 years of follow-up, 33.4% of persons with overweight and 41.8% of persons with obesity lost 5% or greater of their initial weight. At the same time, 23.2% of persons with overweight and 2.0% of persons with obesity reduced BMI to the healthy weight category. CONCLUSIONS AND RELEVANCE: Results of this cohort study indicate that the annual probability of 5% or greater weight loss was low (1 in 10) despite the known benefits of clinically meaningful weight loss, but 5% or greater weight loss was more likely than BMI reduction to the healthy weight category, especially for patients with the highest initial BMIs. Clinicians and public health efforts can focus on messaging and referrals to interventions that are aimed at clinically meaningful weight loss (ie, ≥5%) for adults at any level of excess weight.

We thank Rolland-Cachera1 for their letter concerning our article.2 We found that children with an early body mass index (BMI) rebound had a higher BMI after age 10 years, but the BMI value at the age of rebound provided more information. As stated in the text accompanying Table 2, a regression model that included age at rebound accounted for 31% of the subsequent BMI variability. In contrast, including a child’s BMI (rather than age) at rebound accounted for 45% of the variability. As illustrated in Figure 1 and Figure 2 of our article, both the age and BMI at rebound were important predictors of the probability of obesity after age 10 years. |

BACKGROUND AND OBJECTIVES: Changes in BMI z score (BMIz) are widely used in weight control programs and interventions to monitor changes in body fatness, but this metric may not be optimal. We examined the ability of 3 BMI metrics to assess adiposity change among children with a wide range of BMIs. METHODS: The sample comprised 343 3-year-old children with serial measurements of BMI and body fatness every 4 months over 4 years. We compared correlations between changes in body fatness, calculated with dual-energy-x-ray absorptiometry, and changes in 3 BMI metrics: BMIz and percentage of the 50th (%50th) and 95th (%95th) percentiles in the CDC growth charts. RESULTS: About 21% of the participants were Black and 79% were white. Changes in body fatness over 4 years were more strongly associated with changes in %50th and %95th than with changes in BMIz. Correlations with %body fat among all children were r = 0.64 for BMIz versus r = 0.77 to 0.78 for %50th and %95th (P < .001 for differences between the correlations). Stratified analyses showed the difference between the correlations were similar among boys and girls, among white children and Black children, and among children without obesity and those with obesity. CONCLUSIONS: Changes in adiposity among young children are better captured by expressing changes in BMI as a percentage of the 50th or 95th percentiles instead of BMIz change. Using the best BMI metric will allow pediatricians to better assess a child's change in body fatness over time.

OBJECTIVE: To demonstrate the utility of growthcleanr, an anthropometric data cleaning method designed for electronic health records (EHR). MATERIALS AND METHODS: We used all available pediatric and adult height and weight data from an ongoing observational study that includes EHR data from 15 healthcare systems and applied growthcleanr to identify outliers and errors and compared its performance in pediatric data with 2 other pediatric data cleaning methods: (1) conditional percentile (cp) and (2) PaEdiatric ANthropometric measurement Outlier Flagging pipeline (peanof). RESULTS: 687226 children (<20 years) and 3267293 adults contributed 71246369 weight and 51525487 height measurements. growthcleanr flagged 18% of pediatric and 12% of adult measurements for exclusion, mostly as carried-forward measures for pediatric data and duplicates for adult and pediatric data. After removing the flagged measurements, 0.5% and 0.6% of the pediatric heights and weights and 0.3% and 1.4% of the adult heights and weights, respectively, were biologically implausible according to the CDC and other established cut points. Compared with other pediatric cleaning methods, growthcleanr flagged the most measurements for exclusion; however, it did not flag some more extreme measurements. The prevalence of severe pediatric obesity was 9.0%, 9.2%, and 8.0% after cleaning by growthcleanr, cp, and peanof, respectively. CONCLUSION: growthcleanr is useful for cleaning pediatric and adult height and weight data. It is the only method with the ability to clean adult data and identify carried-forward and duplicates, which are prevalent in EHR. Findings of this study can be used to improve the growthcleanr algorithm.

OBJECTIVE: Many U.S. youth experienced accelerated weight gain during the early COVID-19 pandemic. Using an ambulatory electronic health record dataset, we compared children's rates of BMI change in three periods: prepandemic (January 2018-February 2020), early pandemic (March-December 2020), and later pandemic (January-November 2021). METHODS: We 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 2-19years with 4 BMIs, the monthly rates of BMI change (kg/m(2) ) were 0.056 (95%CI: 0.056, 0.057) prepandemic, 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 U.S. 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. This article is protected by copyright. All rights reserved.

OBJECTIVE: There have been conflicting reports concerning weight gain among adults during the COVID-19 epidemic. Although early studies reported large weight increases, several of these analyses were based on convenience samples or self-reported information. The objective of the current study is to examine the pandemic-related weight increase associated with the pandemic through May 2021. METHODS: We selected 4.25 million adults (18 to 84 y) in an electronic health record database who had at least two weight measurements between January 2019 and February 2020 and one after June 2020. We contrasted weight changes before and after March 2020 using mixed-effects regression models. RESULTS: Compared with pre-pandemic weight trend, there was a small increase (0.1 kg) in weight in the first year of the pandemic (March 2020 through March 2021). Weight changes during the pandemic varied by sex, age, and initial BMI, but the largest mean increase across these characteristics was < 1.3 kg. Weight increases were generally greatest among women, adults with a BMI of 30 or 35 kg/m(2) , and younger adults. CONCLUSION: Our results indicate that the mean weight gain among adults during the COVID-19 pandemic may be small. This article is protected by copyright. All rights reserved.

In this issue of Pediatrics, Cunningham et al1 explore obesity incidence trends in school-aged children from kindergarten through fifth grade in 2 cohorts of the Early Childhood Longitudinal Study (ECLS). The earlier cohort was followed from 1998 to 2004 and the later cohort from 2010 to 2016. The ECLS results show an increase in incidence of obesity in the 2010 cohort compared with the 1998 cohort. Moreover, among children who entered kindergarten without obesity, 29% more non-Hispanic Black children developed obesity by fifth grade in the later cohort compared with the earlier one, whereas obesity incidence remained unchanged or decreased in other race and ethnicity groups.

Hendrickson and Pitt1 expressed concern that a child whose height and weight are both at the US Centers for Disease Control and Prevention (CDC)–defined 97th percentile would have a body mass index (BMI) above the 85th percentile of the CDC growth charts2 rather than in the normal weight range. Although the authors suggested that this seemingly counterintuitive observation may be the result of different data sets in the growth charts or a weakness of the BMI formula, the association of BMI z score with to z scores for weight and height was addressed by Cole3 in 2002. I further examined the associations between these sex- and age-standardized z scores for weight, height, and BMI and compared the association of body fat with both BMI z score and a metric based on the weight percentile minus height percentile difference (WHD).

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.

Too often, pediatric obesity clinical trials and interventions having an otherwise solid scientific premise and addressing highly relevant questions use the Centers for Disease Control and Prevention (CDC) BMI z score as the primary efficacy end point. The use of the CDC z scores has the potential to affect and lead to incorrect conclusions drawn by the authors of studies, particularly if many participants have BMI values above the 97th percentile, which reduces the rigor of these studies. Reporting on outcomes and predictors of treatment response within pediatric obesity clinical trials and interventions sheds light on vital clinical questions; therefore, the inclusion of multiple BMI metrics in clinical trials could advance the understanding of which metric is optimal for assessing change.

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/m(2) 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.

Obesity is a serious health concern in the United States, affecting more than one in six children (1) and putting their long-term health and quality of life at risk.* During the COVID-19 pandemic, children and adolescents spent more time than usual away from structured school settings, and families who were already disproportionally affected by obesity risk factors might have had additional disruptions in income, food, and other social determinants of health.(†) As a result, children and adolescents might have experienced circumstances that accelerated weight gain, including increased stress, irregular mealtimes, less access to nutritious foods, increased screen time, and fewer opportunities for physical activity (e.g., no recreational sports) (2,3). CDC used data from IQVIA's Ambulatory Electronic Medical Records database to compare longitudinal trends in body mass index (BMI, kg/m(2)) among a cohort of 432,302 persons aged 2-19 years before and during the COVID-19 pandemic (January 1, 2018-February 29, 2020 and March 1, 2020-November 30, 2020, respectively). Between the prepandemic and pandemic periods, the rate of BMI increase approximately doubled, from 0.052 (95% confidence interval [CI] = 0.051-0.052 to 0.100 (95% CI = 0.098-0.101) kg/m(2)/month (ratio = 1.93 [95% CI = 1.90-1.96]). Persons aged 2-19 years with overweight or obesity during the prepandemic period experienced significantly higher rates of BMI increase during the pandemic period than did those with healthy weight. These findings underscore the importance of efforts to prevent excess weight gain during and following the COVID-19 pandemic, as well as during future public health emergencies, including increased access to efforts that promote healthy behaviors. These efforts could include screening by health care providers for BMI, food security, and social determinants of health, increased access to evidence-based pediatric weight management programs and food assistance resources, and state, community, and school resources to facilitate healthy eating, physical activity, and chronic disease prevention.

Background: Infants and young children with high weight-for-length are at increased risk for obesity in later life. This study describes prevalence of high weight-for-length and examines changes during 2010-2018 among 11,366,755 infants and young children 3-23 months of age in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Methods: Children's weights and lengths were measured. High weight-for-length was defined as ≥2 standard deviations above sex and age-specific median on World Health Organization growth charts. Adjusted prevalence differences (APDs) between years were calculated as 100 times marginal effects from logistic regression models. APD was statistically significant if 95% confidence interval did not include 0. Results: Adjusted prevalence of high weight-for-length decreased from 2010 to 2014, and leveled off through 2018 overall, in boys and girls, those 6-11 and 18-23 months of age, and non-Hispanic whites, non-Hispanic blacks, Hispanics, and Asians/Pacific Islanders. For 12-17 months old and American Indian/Alaska Native infants and young children, adjusted prevalence decreased from 2010 to 2014, and then increased slightly through 2018. Among 56 WIC state or territorial agencies, 33 had significant decreases between 2010 and 2018, whereas 8 had significant increases. Between 2014 and 2018, prevalence decreased significantly in 12 agencies and increased significantly in 23. Conclusions: The results indicate overall declines in prevalence of high weight-for-length from 2010 to 2018, with a prevalence stabilization since 2014. Continued surveillance is needed. Obesity prevention strategies in WIC and multiple settings are important for ensuring healthy child growth.

Obesity* is a recognized risk factor for severe COVID-19 (1,2), possibly related to chronic inflammation that disrupts immune and thrombogenic responses to pathogens (3) as well as to impaired lung function from excess weight (4). Obesity is a common metabolic disease, affecting 42.4% of U.S. adults (5), and is a risk factor for other chronic diseases, including type 2 diabetes, heart disease, and some cancers.(†) The Advisory Committee on Immunization Practices considers obesity to be a high-risk medical condition for COVID-19 vaccine prioritization (6). Using data from the Premier Healthcare Database Special COVID-19 Release (PHD-SR),(§) CDC assessed the association between body mass index (BMI) and risk for severe COVID-19 outcomes (i.e., hospitalization, intensive care unit [ICU] or stepdown unit admission, invasive mechanical ventilation, and death). Among 148,494 adults who received a COVID-19 diagnosis during an emergency department (ED) or inpatient visit at 238 U.S. hospitals during March-December 2020, 28.3% had overweight and 50.8% had obesity. Overweight and obesity were risk factors for invasive mechanical ventilation, and obesity was a risk factor for hospitalization and death, particularly among adults aged <65 years. Risks for hospitalization, ICU admission, and death were lowest among patients with BMIs of 24.2 kg/m(2), 25.9 kg/m(2), and 23.7 kg/m(2), respectively, and then increased sharply with higher BMIs. Risk for invasive mechanical ventilation increased over the full range of BMIs, from 15 kg/m(2) to 60 kg/m(2). As clinicians develop care plans for COVID-19 patients, they should consider the risk for severe outcomes in patients with higher BMIs, especially for those with severe obesity. These findings highlight the clinical and public health implications of higher BMIs, including the need for intensive COVID-19 illness management as obesity severity increases, promotion of COVID-19 prevention strategies including continued vaccine prioritization (6) and masking, and policies to ensure community access to nutrition and physical activities that promote and support a healthy BMI.

OBJECTIVE: The current CDC BMI z-scores are inaccurate for BMIs ≥ 97(th) percentile. We, therefore, considered 5 alternatives that can be used across the entire BMI distribution: modified BMIz, %CDC95th percentile, extended BMIz, %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 (EHR) data from 1.17 million children in PEDSnet using the intraclass correlation coefficient (ICC) to determine if one 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 ICCs were generally seen for extended BMIz, adjusted %median, and %CDC95(th) percentile. CONCLUSIONS: Based on the examples of differences in the BMI metrics, the longitudinal tracking results, and current familiarity BMI z-scores and percentiles, extended BMIz and extended BMI percentile may be suitable replacements for the current z-scores and percentiles. These metrics are identical to those in the CDC growth charts for BMIs < 95(th) 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.

Objective: The beginning of postinfancy increase in BMI has been termed the adiposity rebound, and an early rebound increases the risk for obesity in adolescence and adulthood. We examined whether the relation of the age at BMI rebound (age(rebound)) to subsequent BMI is independent of childhood BMI. Design: From the electronic health records of 2.8 million children, we selected 17,077 children examined at least once each year between ages 2 and <8 years, and who were reexamined between age 10 and <16 years. The mean age at the last visit was 12 years (SD = 1). We identified age(rebound) for each child using lowess, a smoothing technique. Results: Children who had an age(rebound) <3 years were, on average, 6.8 kg/m(2) heavier after age 10 years than were children with an age(rebound) >7 years. However, BMI after age 10 years was more strongly associated with BMI at the rebound (BMI(rebound)) than with age(rebound) (r = 0.63 vs. -0.49). Although the relation of age(rebound) to BMI at the last visit was mostly independent of the BMI(rebound), adjustment for age-5 BMI reduced the association's magnitude by about 55%. Conclusions: Both age(rebound) and the BMI(rebound) are independently related to BMI and obesity after age 10 years. However, a child's BMI(rebound) and at ages 5 and 7 years accounts for more of the variability in BMI levels after age 10 years than does age(rebound).

OBJECTIVE: The Mind, Exercise, Nutrition, Do It! 7-13 (MEND 7-13) program was adapted in 2016 by five 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/m(2)) percentile, and systolic and diastolic blood pressures (SBP & DBP) among MEND+ attendees in an expanded age range of 4-17 years, and comparable non-attendees. METHODS: Data from April 2015 to May 2018 were extracted from DH FQHC EHR for children eligible for MEND+ referral (BMI ≥85(th) percentile). The sample included 347 MEND+ attendees and 21,061 non-attendees. Mixed-effects models examined average rate of change for BMI percent of the 95(th) percentile (%BMIp95), SBP, and DBP, after completion of the study period. RESULTS: Most children were ages 7-13 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<0.001). Eligible non-attendees 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<0.001) and 0.114/month (p<0.01), respectively. SBP and DBP increased by 0.032 and 0.033/month in eligible non-attendees, respectively. CONCLUSIONS: %BMIp95, SBP, and DBP significantly decreased among MEND+ attendees when implemented in community-based clinical practice settings at DH FQHC.

BACKGROUND: The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3(rd) and 97(th) and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95(th) percentile. Extrapolating beyond the 97(th) percentile is not recommended and leads to compressed z-score values. AIM: This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95(th) percentile using an extended reference population. SUBJECTS AND METHODS: Data from youth at or above the 95(th) percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. RESULTS: The modelled distributions above the 95(th) percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. CONCLUSION: This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

This study uses NHANES data to assess trends in obesity and severe obesity stratified by race and Hispanic origin among US residents from 1999 to 2018.

OBJECTIVE: Several cross-sectional studies have shown that height in childhood is correlated with BMI and with body fatness, and two longitudinal studies have reported that childhood height is associated with adult BMI. This study explored this longitudinal association in an electronic health record database of 2.8 million children. METHODS: Children were initially examined between the ages of 2 and 13.9 years and, on average, were reexamined 4 years later. RESULTS: As expected, there was a cross-sectional correlation between height-for-age z score and BMI that increased from r = -0.06 (age of 2 years) to r = 0.37 (age of 9-10 years). In addition, height-for-age at the first visit was related to subsequent BMI and obesity, with the prevalence of subsequent obesity increasing about fourfold over six categories of height-for-age at the first visit. About 40% of this longitudinal association was independent of initial BMI, but its magnitude decreased with initial age. For example, the initial height-for-age of children who were 12 years of age or older was only weakly associated with subsequent BMI. CONCLUSIONS: Health professionals should recognize that greater childhood height-for-age before 12 years of age may be a marker for increased risk of subsequent obesity.

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. Method(s): 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. Result(s): 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. Conclusion(s): 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. Copyright Published 2020. This article is a U.S. Government work and is in the public domain in the USA. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd

Obesity negatively affects children's health because of its associations with cardiovascular disease risk factors, type 2 diabetes, asthma, fatty liver disease, victimization stemming from social stigma and bullying, and poor mental health (e.g., anxiety and depression) (1). Children who have overweight or obesity in early childhood are approximately four times as likely to have overweight or obesity in young adulthood as their normal weight peers (2). Obesity prevalence is especially high among children from low-income families (3). In 2010, the overall upward trend in obesity prevalence turned downward among children aged 2-4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), a program of the U.S. Department of Agriculture (USDA); prevalence decreased significantly in all racial/ethnic groups and in 34 of the 56 WIC state or territory agencies during 2010-2014 (4). A more recent study among young children enrolled in WIC reported that the overall obesity prevalence decreased from 15.9% in 2010 to 13.9% in 2016 and statistically significant decreases were observed in all age, sex, and racial/ethnic subgroups (3). However, this study did not provide obesity trends at the state level. In collaboration with USDA, CDC used data from the WIC Participant and Program Characteristics (WIC PC) to update state-specific trends through 2016. During 2010-2016, modest but statistically significant decreases in obesity prevalence among children aged 2-4 years enrolled in WIC occurred in 41 (73%) of 56 WIC state or territory agencies. Comprehensive approaches that create positive changes to promote healthy eating and physical activity for young children from all income levels,* strengthen nutrition education and breastfeeding support among young children enrolled in WIC, and encourage redemptions of healthy foods in WIC food packages could help maintain or accelerate these declining trends.

Body mass index z-score (BMIz) based on the CDC growth charts is widely used, but it is inaccurate above the 97th percentile. We explored the performance of alternative metrics based on the absolute distance or % distance of a child's BMI from the median BMI for sex and age. We used longitudinal data from 5628 children who were first examined < 12 y to compare the tracking of three BMI metrics: distance from median, % distance from median, and % distance from median on a log scale. We also explored the effects of adjusting these metrics for age differences in the distribution of BMI. The intra-class correlation coefficient (ICC) was used to compare tracking of the metrics.

Prevalence of childhood obesity is high in the United States, especially among children from lower-income families.1 Among children aged 2 through 4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), obesity prevalence increased between 2000 and 2010 but declined through 2014.2 The decline was statistically significant among all racial/ethnic groups and in 34 of 56 state WIC agencies. The present study examines trends in overweight and obesity by age, sex, and race/ethnicity using WIC data from 2010 to 2016.

Importance: Differences in obesity by sex, age group, race and Hispanic origin among US adults have been reported, but differences by urbanization level have been less studied. Objectives: To provide estimates of obesity by demographic characteristics and urbanization level and to examine trends in obesity prevalence by urbanization level. Design, Setting, and Participants: Serial cross-sectional analysis of measured height and weight among adults aged 20 years or older in the 2001-2016 National Health and Nutrition Examination Survey, a nationally representative survey of the civilian, noninstitutionalized US population. Exposures: Sex, age group, race and Hispanic origin, education level, smoking status, and urbanization level as assessed by metropolitan statistical areas (MSAs; large: >/=1 million population). Main Outcomes and Measures: Prevalence of obesity (body mass index [BMI] >/=30) and severe obesity (BMI >/=40) by subgroups in 2013-2016 and trends by urbanization level between 2001-2004 and 2013-2016. Results: Complete data on weight, height, and urbanization level were available for 10792 adults (mean age, 48 years; 51% female [weighted]). During 2013-2016, 38.9% (95% CI, 37.0% to 40.7%) of US adults had obesity and 7.6% (95% CI, 6.8% to 8.6%) had severe obesity. Men living in medium or small MSAs had a higher age-adjusted prevalence of obesity compared with men living in large MSAs (42.4% vs 31.8%, respectively; adjusted difference, 9.8 percentage points [95% CI, 5.1 to 14.5 percentage points]); however, the age-adjusted prevalence among men living in non-MSAs was not significantly different compared with men living in large MSAs (38.9% vs 31.8%, respectively; adjusted difference, 4.8 percentage points [95% CI, -2.9 to 12.6 percentage points]). The age-adjusted prevalence of obesity was higher among women living in medium or small MSAs compared with women living in large MSAs (42.5% vs 38.1%, respectively; adjusted difference, 4.3 percentage points [95% CI, 0.2 to 8.5 percentage points]) and among women living in non-MSAs compared with women living in large MSAs (47.2% vs 38.1%, respectively; adjusted difference, 4.7 percentage points [95% CI, 0.2 to 9.3 percentage points]). Similar patterns were seen for severe obesity except that the difference between men living in large MSAs compared with non-MSAs was significant. The age-adjusted prevalence of obesity and severe obesity also varied significantly by age group, race and Hispanic origin, and education level, and these patterns of variation were often different by sex. Between 2001-2004 and 2013-2016, the age-adjusted prevalence of obesity and severe obesity significantly increased among all adults at all urbanization levels. Conclusions and Relevance: In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity and severe obesity in 2013-2016 varied by level of urbanization, with significantly greater prevalence of obesity and severe obesity among adults living in nonmetropolitan statistical areas compared with adults living in large metropolitan statistical areas.

Importance: Differences in childhood obesity by demographics and urbanization have been reported. Objective: To present data on obesity and severe obesity among US youth by demographics and urbanization and to investigate trends by urbanization. Design, Setting, and Participants: Measured weight and height among youth aged 2 to 19 years in the 2001-2016 National Health and Nutrition Examination Surveys, which are serial, cross-sectional, nationally representative surveys of the civilian, noninstitutionalized population. Exposures: Sex, age, race and Hispanic origin, education of household head, and urbanization, as assessed by metropolitan statistical areas (MSAs; large: >/= 1 million population). Main Outcomes and Measures: Prevalence of obesity (body mass index [BMI] >/=95th percentile of US Centers for Disease Control and Prevention [CDC] growth charts) and severe obesity (BMI >/=120% of 95th percentile) by subgroups in 2013-2016 and trends by urbanization between 2001-2004 and 2013-2016. Results: Complete data on weight, height, and urbanization were available for 6863 children and adolescents (mean age, 11 years; female, 49%). In 2013-2016, the prevalence among youth aged 2 to 19 years was 17.8% (95% CI, 16.1%-19.6%) for obesity and 5.8% (95% CI, 4.8%-6.9%) for severe obesity. Prevalence of obesity in large MSAs (17.1% [95% CI, 14.9%-19.5%]), medium or small MSAs (17.2% [95% CI, 14.5%-20.2%]) and non-MSAs (21.7% [95% CI, 16.1%-28.1%]) were not significantly different from each other (range of pairwise comparisons P = .09-.96). Severe obesity was significantly higher in non-MSAs (9.4% [95% CI, 5.7%-14.4%]) compared with large MSAs (5.1% [95% CI, 4.1%-6.2%]; P = .02). In adjusted analyses, obesity and severe obesity significantly increased with greater age and lower education of household head, and severe obesity increased with lower level of urbanization. Compared with non-Hispanic white youth, obesity and severe obesity prevalence were significantly higher among non-Hispanic black and Hispanic youth. Severe obesity, but not obesity, was significantly lower among non-Hispanic Asian youth than among non-Hispanic white youth. There were no significant linear or quadratic trends in obesity or severe obesity prevalence from 2001-2004 to 2013-2016 for any urbanization category (P range = .07-.83). Conclusions and Relevance: In 2013-2016, there were differences in the prevalence of obesity and severe obesity by age, race and Hispanic origin, and household education, and severe obesity was inversely associated with urbanization. Demographics were not related to the urbanization findings.

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/m(2) (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/m(2) , 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/m(2) or more are plausible. Knowledge of this variability is important when following a child over time.