Search

Background: Many studies rely on the International Classification of Diseases, 9th or 10th Revision, Clinical Modification codes to define obesity in electronic health records data. While prior studies found misclassification and low sensitivity of codes for pediatric obesity, it remains unclear whether this misclassification is random and what are the implications of combining different code types to define obesity. Methods: We assessed prevalence, sensitivity, and specificity of obesity codes among 7.4 million children aged 2-19 years over 2014-2021. Among those with obesity in 2021, we estimated the probability of receiving any code or a specific code type by patient characteristics. Results: Obesity code utilization increased in prevalence from 3.9% in 2014 to 9.8% in 2021; prevalence of obesity based on BMI increased from 17.4% to 20.5%. Code sensitivity increased from 19.8% to 40.8%. Among children with obesity in 2021, those with severe obesity (reference: no severe obesity) and chronic disease (reference: no chronic disease) were more likely to get a code, and the highest likelihood was associated with obesity diagnosis codes (vs. status codes). Conclusions: Despite increases, obesity code utilization remained low. Obesity code misclassification is not random and certain child characteristics (e.g., severe obesity or chronic disease) are associated with a higher probability of getting a code. There are also significant differences by code type; thus, caution should be taken before combining obesity codes as a proxy for obesity status, especially in longitudinal analyses. More universal documentation of obesity may improve the quality of care and the use of these data for evaluation and research purposes.

Importance: Universal screening to identify unfavorable lipid levels is recommended for US children aged 9 to 11 years and adolescents aged 17 to 21 years (hereafter, young adults); however, screening benefits in these individuals have been questioned. Current use of lipid screening and prevalence of elevated lipid measurements among US youths is not well understood., Objective: To investigate the prevalence of ambulatory pediatric lipid screening and elevated or abnormal lipid measurements among US screened youths by patient characteristic and test type., Design, Setting, and Participants: This cross-sectional study used data from the IQVIA Ambulatory Electronic Medical Record database and included youths aged 9 to 21 years with 1 or more valid measurement of height and weight during the observation period (2018-2021). Body mass index (BMI) was calculated and categorized using standard pediatric BMI percentiles (9-19 years) and adult BMI categories (≥20 years). The data were analyzed from October 6, 2022, to January 18, 2023., Main Outcomes and Measures: Lipid measurements were defined as abnormal if 1 or more of the following test results was identified: total cholesterol (≥200 mg/dL), low-density lipoprotein cholesterol (≥130 mg/dL), very low-density lipoprotein cholesterol (≥31 mg/dL), non-high-density lipoprotein cholesterol (≥145 mg/dL), and triglycerides (≥100 mg/dL for children aged 9 years or ≥130 mg/dL for patients aged 10-21 years). After adjustment for age group, sex, race and ethnicity, and BMI category, adjusted prevalence ratios (aPRs) and 95% CIs were calculated., Results: Among 3 226 002 youths (23.9% aged 9-11 years, 34.8% aged 12-16 years, and 41.3% aged 17-21 years; 1 723 292 females [53.4%]; 60.0% White patients, 9.5% Black patients, and 2.4% Asian patients), 11.3% had 1 or more documented lipid screening tests. The frequency of lipid screening increased by age group (9-11 years, 9.0%; 12-16 years, 11.1%; 17-21 years, 12.9%) and BMI category (range, 9.2% [healthy weight] to 21.9% [severe obesity]). Among those screened, 30.2% had abnormal lipid levels. Compared with youths with a healthy weight, prevalence of an abnormal result was higher among those with overweight (aPR, 1.58; 95% CI, 1.56-1.61), moderate obesity (aPR, 2.16; 95% CI, 2.14-2.19), and severe obesity (aPR, 2.53; 95% CI, 2.50-2.57)., Conclusions and Relevance: In this cross-sectional study of prevalence of lipid screening among US youths aged 9 to 21 years, approximately 1 in 10 were screened. Among them, abnormal lipid levels were identified in 1 in 3 youths overall and 1 in 2 youths with severe obesity. Health care professionals should consider implementing lipid screening among children aged 9 to 11 years, young adults aged 17 to 21 years, and all youths at high cardiovascular risk.

Background: Youth with excess weight are at risk of developing type 2 diabetes (T2DM). Guidelines recommend screening for prediabetes and/or T2DM after 10 years of age or after puberty in youth with excess weight who have ≥1 risk factor(s) for T2DM. Electronic health records (EHRs) offer an opportunity to study the use of tests to detect diabetes in youth. Methods: We examined the frequency of (1) diabetes testing and (2) elevated test results among youth aged 10-19 years with at least one BMI measurement in an EHR from 2019 to 2021. We examined the presence of hemoglobin A1C (A1C), fasting plasma glucose (FPG), or oral glucose tolerance test (2-hour plasma glucose [2-hrPG]) results and, among those tested, the frequency of elevated values (A1C ≥6.5%, FPG ≥126 mg/dL, or 2-hrPG ≥200 mg/dL). Patients with pre-existing diabetes ( n  = 6793) were excluded. Results: Among 1,024,743 patients, 17% had overweight, 21% had obesity, including 8% with severe obesity. Among patients with excess weight, 10% had ≥1 glucose test result. Among those tested, elevated values were more common in patients with severe obesity (27%) and obesity (22%) than in those with healthy weight (8%), and among Black youth (30%) than White youth (13%). Among patients with excess weight, >80% of elevated values fell in the prediabetes range. Conclusions: In youth with excess weight, the use of laboratory tests for prediabetes and T2DM was infrequent. Among youth with test results, elevated FPG, 2hrPG, or A1C levels were most common in those with severe obesity and Black youth.

Background: Data sources for assessing pediatric chronic diseases and associated screening practices are rare. One example is non-alcoholic fatty liver disease (NAFLD), a common chronic liver disease prevalent among children with overweight and obesity. If undetected, NAFLD can cause liver damage. Guidelines recommend screening for NAFLD using alanine aminotransferase (ALT) tests in children ≥9 years with obesity or those with overweight and cardiometabolic risk factors. This study explores how real-world data from electronic health records (EHRs) can be used to study NAFLD screening and ALT elevation. Research Design: Using IQVIA's Ambulatory Electronic Medical Record database, we studied patients 2-19 years of age with body mass index ≥85th percentile. Using a 3-year observation period (January 1, 2019 to December 31, 2021), ALT results were extracted and assessed for elevation (≥1 ALT result ≥22.1 U/L for females and ≥25.8 U/L for males). Patients with liver disease (including NAFLD) or receiving hepatotoxic medications during 2017-2018 were excluded. Results: Among 919,203 patients 9-19 years of age, only 13% had ≥1 ALT result, including 14% of patients with obesity and 17% of patients with severe obesity. ALT results were identified for 5% of patients 2-8 years of age. Of patients with ALT results, 34% of patients 2-8 years of age and 38% of patients 9-19 years of age had ALT elevation. Males 9-19 years of age had a higher prevalence of ALT elevation than females (49% vs. 29%). Conclusions: EHR data offered novel insights into NAFLD screening: despite screening recommendations, ALT results among children with excess weight were infrequent. Among those with ALT results, ALT elevation was common, underscoring the importance of screening for early disease detection.

Background Hypertension and diabetes are associated with increased COVID-19 severity. The association between level of control of these conditions and COVID-19 severity is less well understood. Methods and Results This retrospective cohort study identified adults with COVID-19, March 2020 to February 2022, in 43 US health systems in the National Patient-Centered Clinical Research Network. Hypertension control was categorized as blood pressure (BP) <130/80, 130 to 139/80 to 89, 140 to 159/90 to 99, or ≥160/100 mm Hg, and diabetes control as glycated hemoglobin <7%, 7% to <9%, ≥9%. Adjusted, pooled logistic regression assessed associations between hypertension and diabetes control and severe COVID-19 outcomes. Among 1 494 837 adults with COVID-19, 43% had hypertension and 12% had diabetes. Among patients with hypertension, the highest baseline BP was associated with greater odds of hospitalization (adjusted odds ratio [aOR], 1.30 [95% CI, 1.23-1.37] for BP ≥160/100 versus BP <130/80), critical care (aOR, 1.30 [95% CI, 1.21-1.40]), and mechanical ventilation (aOR, 1.32 [95% CI, 1.17-1.50]) but not mortality (aOR, 1.08 [95% CI, 0.98-1.12]). Among patients with diabetes, the highest glycated hemoglobin was associated with greater odds of hospitalization (aOR, 1.61 [95% CI, 1.47-1.76] for glycated hemoglobin ≥9% versus <7%), critical care (aOR, 1.42 [95% CI, 1.31-1.54]), mechanical ventilation (aOR, 1.12 [95% CI, 1.02-1.23]), and mortality (aOR, 1.18 [95% CI, 1.09-1.27]). Black and Hispanic adults were more likely than White adults to experience severe COVID-19 outcomes, independent of comorbidity score and control of hypertension or diabetes. Conclusions Among 1.5 million patients with COVID-19, higher BP and glycated hemoglobin were associated with more severe COVID-19 outcomes. Findings suggest that adults with poorest control of hypertension or diabetes might benefit from efforts to prevent and initiate early treatment of COVID-19.

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.

Importance: Nearly 40% of US youth aged 2 to 19 years do not have a body mass index (BMI) in the healthy weight category. However, there are no recent estimates for BMI-associated expenditures using clinical or claims data., Objective: To estimate medical expenditures among US youth across all BMI categories along with sex and age groups., Design, Setting, Participants: This cross-sectional study used IQVIA's ambulatory electronic medical records (AEMR) data set linked with IQVIA's PharMetrics Plus Claims database from January 2018 through December 2018. Analysis was performed from March 25, 2022, through June 20, 2022. It included a convenience sample of a geographically diverse patient population from AEMR and PharMetrics Plus. The study sample included privately insured individuals with a BMI measurement in 2018 and excluded patients with pregnancy-related visits., Exposure: BMI categories., Main Outcomes and Measures: Total medical expenditures were estimated using generalized linear model regression with γ distribution and log-link function. For out-of-pocket (OOP) expenditures, a 2-part model was used that included logistic regression to estimate the probability of positive expenditures followed by generalized linear model. Estimates were shown with and without accounting for sex, race and ethnicity, payer type, geographic region, age interacted with sex and BMI categories, and confounding conditions., Results: The sample included 205 876 individuals aged 2 to 19 years; 104 066 were male (50.5%) and the median age was 12 years. Compared with those with healthy weight, total and OOP expenditures were higher for all other BMI categories. Differences in total expenditures were highest for those with severe obesity ($909; 95% CI, $600-$1218) followed by underweight ($671; 95% CI, $286-$1055) compared with healthy weight. Differences in OOP expenditures were highest for those with severe obesity ($121; 95% CI, $86-$155) followed by underweight ($117; 95% CI, $78-$157) compared with healthy weight. Having underweight was associated with higher total expenditures at ages 2 to 5 years and 6 to 11 years by $679 (95% CI, $228-$1129) and $1166 (95% CI, $632-$1700), respectively; having severe obesity was associated with higher total expenditures at ages 2 to 5 years, 6 to 11 years, and 12 to 17 years by $1035 (95% CI, $208-$1863), $821 (95% CI, $414-$1227), and $1088 (95% CI, $594-$1582), respectively., Conclusions and Relevance: The study team found medical expenditures to be higher for all BMI categories when compared with those with healthy weight. These findings may indicate potential economic value of interventions or treatments aimed at reducing BMI-associated health risks.

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., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

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., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

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., (Copyright © 2022 Academic Pediatric Association. All rights reserved.)

Importance: The American Academy of Pediatrics (AAP) recommends laboratory screening for diabetes, dyslipidemia, and liver disease every 2 years among children aged 10 years and older with obesity. Routine screening for hypothyroidism or hyperinsulinemia is not recommended. National data on rates of adherence to AAP-recommended screening and rates of potentially unnecessary endocrine testing are unavailable., Objective: To assess rates of AAP-adherent screening and potentially unnecessary endocrine testing among privately and publicly insured children diagnosed with obesity at well-child visits., Design, Setting, and Participants: This cross-sectional study used data from the 2018-2019 IBM MarketScan Commercial Database (which contains claims from privately insured patients across the US) and the 2018-2019 IBM MarketScan Multi-State Medicaid Database (which contains claims from Medicaid patients in multiple states). A total of 156 773 children aged 10 to 18 years with obesity diagnosis codes at a well-child visit from December 1, 2018, through November 30, 2019, were included. Analysis was performed from May 1 to October 31, 2021., Main Outcomes and Measures: Primary outcomes were the proportion of children who received AAP-adherent screening during the 729 days before to 30 days after a well-child visit and the proportion of children who received 1 or more potentially unnecessary thyroid or insulin tests during the same period. Proportions of children by payer type were compared using logistic regression models adjusted for age and sex. To assess screening that was potentially ordered by primary care clinicians, the proportion of children without AAP-adherent screening before visits who received this screening within 30 days was calculated., Results: Of 156 773 children, the mean (SD) age was 13.5 years (2.5) years; 83 305 (53.1%) were male, and 97 595 (62.3%) were publicly insured. Among publicly insured children, 13 167 (13.5%) were Hispanic or Latino, 34 792 (35.6%) were non-Hispanic Black, 42 029 (43.1%) were non-Hispanic White, 3850 (3.9%) were of other race and/or ethnicity (specific races and ethnicities were not available in the database), and 3757 (3.8%) were of unknown race and ethnicity. Race and ethnicity were not available for privately insured children because these data are not reported in the commercial claims database. During the 729 days before to 30 days after well-child visits, 42 849 patients (27.3%) received AAP-adherent screening, including 13 939 (23.6%) of 59 178 privately insured children and 28 910 (29.6%) of 97 595 publicly insured children (average marginal effect for public insurance: 6.8 [95% CI, 6.3-7.2] percentage points). In addition, 46 592 children (29.7%) received potentially unnecessary thyroid or insulin tests, including 12 834 (21.7%) of 59 178 privately insured children and 23 198 (23.8%) of 97 595 publicly insured children (average marginal effect for public insurance: 2.4 [95% CI, 2.0-2.8] percentage points). Among 129 104 children who did not receive AAP-adherent screening before visits, 15 090 (11.7%) received this screening within 30 days., Conclusions and Relevance: In this cross-sectional study, similar proportions of children diagnosed with obesity at well-child visits received recommended and nonrecommended screening tests for obesity-related co-occurring conditions, suggesting that specific actionable guidelines and interventions are needed to prevent overuse and underuse of screening tests among children with obesity.

Background: In 2019, the United States experienced a nationwide outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI). More than one-half of these patients required admission to an ICU., Research Question: What are the recent literature and expert opinions which inform the diagnosis and management of patients with critical illness with EVALI?, Study Design and Methods: To synthesize information critical to pulmonary/critical care specialists in the care of patients with EVALI, this study examined data available from patients hospitalized with EVALI between August 2019 and January 2020; reviewed the clinical course and critical care experience with those patients admitted to the ICU; and compiled opinion of national experts., Results: Of the 2,708 patients with confirmed or probable EVALI requiring hospitalization as of January 21, 2020, a total of 1,604 (59.2%) had data available on ICU admission; of these, 705 (44.0%) were admitted to the ICU and are included in this analysis. The majority of ICU patients required respiratory support (88.5%) and in severe cases required intubation (36.1%) or extracorporeal membrane oxygenation (6.7%). The majority (93.0%) of these ICU patients survived to discharge. Review of the clinical course and expert opinion provided insight into: imaging; considerations for bronchoscopy; medical treatment, including use of empiric antibiotics, antiviral agents, and corticosteroids; respiratory support, including considerations for intubation, positioning maneuvers, and extracorporeal membrane oxygenation; and patient outcomes., Interpretation: Review of the clinical course of patients with EVALI requiring ICU admission and compilation of expert opinion provided critical insight into pulmonary/critical care-specific considerations for this patient population. Because a large proportion of patients hospitalized with EVALI required ICU admission, it is important to remain prepared to care for patients with EVALI., (Copyright © 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Context: We describe a participatory framework that enhanced and implemented innovative changes to an existing distributed health data network (DHDN) infrastructure to support linkage across sectors and systems. Our processes and lessons learned provide a potential framework for other multidisciplinary infrastructure development projects that engage in a participatory decision-making process., Program: The Childhood Obesity Data Initiative (CODI) provides a potential framework for local and national stakeholders with public health, clinical, health services research, community intervention, and information technology expertise to collaboratively develop a DHDN infrastructure that enhances data capacity for patient-centered outcomes research and public health surveillance. CODI utilizes a participatory approach to guide decision making among clinical and community partners., Implementation: CODI's multidisciplinary group of public health and clinical scientists and information technology experts collectively defined key components of CODI's infrastructure and selected and enhanced existing tools and data models. We conducted a pilot implementation with 3 health care systems and 2 community partners in the greater Denver Metro Area during 2018-2020., Evaluation: We developed an evaluation plan based primarily on the Good Evaluation Practice in Health Informatics guideline. An independent third party implemented the evaluation plan for the CODI development phase by conducting interviews to identify lessons learned from the participatory decision-making processes., Discussion: We demonstrate the feasibility of rapid innovation based upon an iterative and collaborative process and existing infrastructure. Collaborative engagement of stakeholders early and iteratively was critical to ensure a common understanding of the research and project objectives, current state of technological capacity, intended use, and the desired future state of CODI architecture. Integration of community partners' data with clinical data may require the use of a trusted third party's infrastructure. Lessons learned from our process may help others develop or improve similar DHDNs., Competing Interests: The authors have indicated no potential conflicts of interest to disclose.

Context: Integrating longitudinal data from community-based organizations (eg, physical activity programs) with electronic health record information can improve capacity for childhood obesity research., Objective: A governance framework that protects individual privacy, accommodates organizational data stewardship requirements, and complies with laws and regulations was developed and implemented to support the harmonization of data from disparate clinical and community information systems., Participants and Setting: Through the Childhood Obesity Data Initiative (CODI), 5 Colorado-based organizations collaborated to expand an existing distributed health data network (DHDN) to include community-generated data and assemble longitudinal patient records for research., Design: A governance work group expanded an existing DHDN governance infrastructure with CODI-specific data use and exchange policies and procedures that were codified in a governance plan and a delegated-authority, multiparty, reciprocal agreement., Results: A CODI governance work group met from January 2019 to March 2020 to conceive an approach, develop documentation, and coordinate activities. Governance requirements were synthesized from the CODI use case, and a customized governance approach was constructed to address governance gaps in record linkage, a procedure to request data, and harmonizing community and clinical data. A Master Sharing and Use Agreement (MSUA) and Memorandum of Understanding were drafted and executed to support creation of linked longitudinal records of clinical- and community-derived childhood obesity data. Furthermore, a multiparty infrastructure protocol was approved by the local institutional review board (IRB) to expedite future CODI research by simplifying IRB research applications., Conclusion: CODI implemented a clinical-community governance strategy that built trust between organizations and allowed efficient data exchange within a DHDN. A thorough discovery process allowed CODI stakeholders to assess governance capacity and reveal regulatory and organizational obstacles so that the governance infrastructure could effectively leverage existing knowledge and address challenges. The MSUA and complementary governance documents can inform similar efforts., Competing Interests: The authors have indicated no potential conflicts of interest to disclose., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc.)

Importance: New symptoms and conditions can develop following SARS-CoV-2 infection. Whether they occur more frequently among persons with SARS-CoV-2 infection compared with those without is unclear., Objective: To compare the prevalence of new diagnoses of select symptoms and conditions between 31 and 150 days after testing among persons who tested positive vs negative for SARS-CoV-2., Design, Setting, and Participants: This cohort study analyzed aggregated electronic health record data from 40 health care systems, including 338 024 persons younger than 20 years and 1 790 886 persons aged 20 years or older who were tested for SARS-CoV-2 during March to December 2020 and who had medical encounters between 31 and 150 days after testing., Main Outcomes and Measures: International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes were used to capture new symptoms and conditions that were recorded 31 to 150 days after a SARS-CoV-2 test but absent in the 18 months to 7 days prior to testing. The prevalence of new symptoms and conditions was compared between persons with positive and negative SARS-CoV-2 tests stratified by age (20 years or older and young than 20 years) and care setting (nonhospitalized, hospitalized, or hospitalized and ventilated)., Results: A total of 168 701 persons aged 20 years or older and 26 665 younger than 20 years tested positive for SARS-CoV-2, and 1 622 185 persons aged 20 years or older and 311 359 younger than 20 years tested negative. Shortness of breath was more common among persons with a positive vs negative test result among hospitalized patients (≥20 years: prevalence ratio [PR], 1.89 [99% CI, 1.79-2.01]; <20 years: PR, 1.72 [99% CI, 1.17-2.51]). Shortness of breath was also more common among nonhospitalized patients aged 20 years or older with a positive vs negative test result (PR, 1.09 [99% CI, 1.05-1.13]). Among hospitalized persons aged 20 years or older, the prevalence of new fatigue (PR, 1.35 [99% CI, 1.27-1.44]) and type 2 diabetes (PR, 2.03 [99% CI, 1.87-2.19]) was higher among those with a positive vs a negative test result. Among hospitalized persons younger than 20 years, the prevalence of type 2 diabetes (PR, 2.14 [99% CI, 1.13-4.06]) was higher among those with a positive vs a negative test result; however, the prevalence difference was less than 1%., Conclusions and Relevance: In this cohort study, among persons hospitalized after a positive SARS-CoV-2 test result, diagnoses of certain symptoms and conditions were higher than among those with a negative test result. Health care professionals should be aware of symptoms and conditions that may develop after SARS-CoV-2 infection, particularly among those hospitalized after diagnosis.

The COVID-19 pandemic has disproportionately affected people with diabetes, who are at increased risk of severe COVID-19.* Increases in the number of type 1 diabetes diagnoses (1,2) and increased frequency and severity of diabetic ketoacidosis (DKA) at the time of diabetes diagnosis (3) have been reported in European pediatric populations during the COVID-19 pandemic. In adults, diabetes might be a long-term consequence of SARS-CoV-2 infection (4-7). To evaluate the risk for any new diabetes diagnosis (type 1, type 2, or other diabetes) >30 days † after acute infection with SARS-CoV-2 (the virus that causes COVID-19), CDC estimated diabetes incidence among patients aged <18 years (patients) with diagnosed COVID-19 from retrospective cohorts constructed using IQVIA health care claims data from March 1, 2020, through February 26, 2021, and compared it with incidence among patients matched by age and sex 1) who did not receive a COVID-19 diagnosis during the pandemic, or 2) who received a prepandemic non-COVID-19 acute respiratory infection (ARI) diagnosis. Analyses were replicated using a second data source (HealthVerity; March 1, 2020-June 28, 2021) that included patients who had any health care encounter possibly related to COVID-19. Among these patients, diabetes incidence was significantly higher among those with COVID-19 than among those 1) without COVID-19 in both databases (IQVIA: hazard ratio [HR] = 2.66, 95% CI = 1.98-3.56; HealthVerity: HR = 1.31, 95% CI = 1.20-1.44) and 2) with non-COVID-19 ARI in the prepandemic period (IQVIA, HR = 2.16, 95% CI = 1.64-2.86). The observed increased risk for diabetes among persons aged <18 years who had COVID-19 highlights the importance of COVID-19 prevention strategies, including vaccination, for all eligible persons in this age group, § in addition to chronic disease prevention and management. The mechanism of how SARS-CoV-2 might lead to incident diabetes is likely complex and could differ by type 1 and type 2 diabetes. Monitoring for long-term consequences, including signs of new diabetes, following SARS-CoV-2 infection is important in this age group., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

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., (© 2021 The Obesity Society (TOS). This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

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., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Long-term symptoms often associated with COVID-19 (post-COVID conditions or long COVID) are an emerging public health concern that is not well understood. Prevalence of post-COVID conditions has been reported among persons who have had COVID-19 (range = 5%-80%), with differences possibly related to different study populations, case definitions, and data sources (1). Few studies of post-COVID conditions have comparisons with the general population of adults with negative test results for SARS-CoV-2, the virus that causes COVID-19, limiting ability to assess background symptom prevalence (1). CDC used a nonprobability-based Internet panel established by Porter Novelli Public Services* to administer a survey to a nationwide sample of U.S. adults aged ≥18 years to compare the prevalence of long-term symptoms (those lasting >4 weeks since onset) among persons who self-reported ever receiving a positive SARS-CoV-2 test result with the prevalence of similar symptoms among persons who reported always receiving a negative test result. The weighted prevalence of ever testing positive for SARS-CoV-2 was 22.2% (95% confidence interval [CI] = 20.6%-23.8%). Approximately two thirds of respondents who had received a positive test result experienced long-term symptoms often associated with SARS-CoV-2 infection. Compared with respondents who received a negative test result, those who received a positive test result reported a significantly higher prevalence of any long-term symptom (65.9% versus 42.9%), fatigue (22.5% versus 12.0%), change in sense of smell or taste (17.3% versus 1.7%), shortness of breath (15.5% versus 5.2%), cough (14.5% versus 4.9%), headache (13.8% versus 9.9%), and persistence (>4 weeks) of at least one initially occurring symptom (76.2% versus 69.6%). Compared with respondents who received a negative test result, a larger proportion of those who received a positive test result reported believing that receiving a COVID-19 vaccine made their long-term symptoms better (28.7% versus 15.7%). Efforts to address post-COVID conditions should include helping health care professionals recognize the most common post-COVID conditions and optimize care for patients with persisting symptoms, including messaging on potential benefits of COVID-19 vaccination., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

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., (Published by Elsevier Inc.)

Background: Late sequelae of COVID-19 have been reported; however, few studies have investigated the time course or incidence of late new COVID-19-related health conditions (post-COVID conditions) after COVID-19 diagnosis. Studies distinguishing post-COVID conditions from late conditions caused by other etiologies are lacking. Using data from a large administrative all-payer database, we assessed type, association, and timing of post-COVID conditions following COVID-19 diagnosis., Methods: Using the Premier Healthcare Database Special COVID-19 Release (release date, 20 October 2020) data, during March-June 2020, 27 589 inpatients and 46 857 outpatients diagnosed with COVID-19 (case-patients) were 1:1 matched with patients without COVID-19 through the 4-month follow-up period (control-patients) by using propensity score matching. In this matched-cohort study, adjusted ORs were calculated to assess for late conditions that were more common in case-patients than control-patients. Incidence proportion was calculated for conditions that were more common in case-patients than control-patients during 31-120 days following a COVID-19 encounter., Results: During 31-120 days after an initial COVID-19 inpatient hospitalization, 7.0% of adults experienced ≥1 of 5 post-COVID conditions. Among adult outpatients with COVID-19, 7.7% experienced ≥1 of 10 post-COVID conditions. During 31-60 days after an initial outpatient encounter, adults with COVID-19 were 2.8 times as likely to experience acute pulmonary embolism as outpatient control-patients and also more likely to experience a range of conditions affecting multiple body systems (eg, nonspecific chest pain, fatigue, headache, and respiratory, nervous, circulatory, and gastrointestinal symptoms) than outpatient control-patients., Conclusions: These findings add to the evidence of late health conditions possibly related to COVID-19 in adults following COVID-19 diagnosis and can inform healthcare practice and resource planning for follow-up COVID-19 care., (Published by Oxford University Press for the Infectious Diseases Society of America 2021.)

As of June 30, 2021, 33.5 million persons in the United States had received a diagnosis of COVID-19 (1). Although most patients infected with SARS-CoV-2, the virus that causes COVID-19, recover within a few weeks, some experience post-COVID-19 conditions. These range from new or returning to ongoing health problems that can continue beyond 4 weeks. Persons who were asymptomatic at the time of infection can also experience post-COVID-19 conditions. Data on post-COVID-19 conditions are emerging and information on rehabilitation needs among persons recovering from COVID-19 is limited. Using data acquired during January 2020-March 2021 from Select Medical* outpatient rehabilitation clinics, CDC compared patient-reported measures of health, physical endurance, and health care use between patients who had recovered from COVID-19 (post-COVID-19 patients) and patients needing rehabilitation because of a current or previous diagnosis of a neoplasm (cancer) who had not experienced COVID-19 (control patients). All patients had been referred to outpatient rehabilitation. Compared with control patients, post-COVID-19 patients had higher age- and sex-adjusted odds of reporting worse physical health (adjusted odds ratio [aOR] = 1.8), pain (aOR = 2.3), and difficulty with physical activities (aOR = 1.6). Post-COVID-19 patients also had worse physical endurance, measured by the 6-minute walk test † (6MWT) (p<0.001) compared with control patients. Among patients referred to outpatient rehabilitation, those recovering from COVID-19 had poorer physical health and functional status than those who had cancer, or were recovering from cancer but not COVID-19. Patients recovering from COVID-19 might need additional clinical support, including tailored physical and mental health rehabilitation services., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Introduction: Severe COVID-19 illness in adults has been linked to underlying medical conditions. This study identified frequent underlying conditions and their attributable risk of severe COVID-19 illness., Methods: We used data from more than 800 US hospitals in the Premier Healthcare Database Special COVID-19 Release (PHD-SR) to describe hospitalized patients aged 18 years or older with COVID-19 from March 2020 through March 2021. We used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions., Results: Among 4,899,447 hospitalized adults in PHD-SR, 540,667 (11.0%) were patients with COVID-19, of whom 94.9% had at least 1 underlying medical condition. Essential hypertension (50.4%), disorders of lipid metabolism (49.4%), and obesity (33.0%) were the most common. The strongest risk factors for death were obesity (adjusted risk ratio [aRR] = 1.30; 95% CI, 1.27-1.33), anxiety and fear-related disorders (aRR = 1.28; 95% CI, 1.25-1.31), and diabetes with complication (aRR = 1.26; 95% CI, 1.24-1.28), as well as the total number of conditions, with aRRs of death ranging from 1.53 (95% CI, 1.41-1.67) for patients with 1 condition to 3.82 (95% CI, 3.45-4.23) for patients with more than 10 conditions (compared with patients with no conditions)., Conclusion: Certain underlying conditions and the number of conditions were associated with severe COVID-19 illness. Hypertension and disorders of lipid metabolism were the most frequent, whereas obesity, diabetes with complication, and anxiety disorders were the strongest risk factors for severe COVID-19 illness. Careful evaluation and management of underlying conditions among patients with COVID-19 can help stratify risk for severe illness.

Importance: Information on underlying conditions and severe COVID-19 illness among children is limited., Objective: To examine the risk of severe COVID-19 illness among children associated with underlying medical conditions and medical complexity., Design, Setting, and Participants: This cross-sectional study included patients aged 18 years and younger with International Statistical Classification of Diseases, Tenth Revision, Clinical Modification code U07.1 (COVID-19) or B97.29 (other coronavirus) during an emergency department or inpatient encounter from March 2020 through January 2021. Data were collected from the Premier Healthcare Database Special COVID-19 Release, which included data from more than 800 US hospitals. Multivariable generalized linear models, controlling for patient and hospital characteristics, were used to estimate adjusted risk of severe COVID-19 illness associated with underlying medical conditions and medical complexity., Exposures: Underlying medical conditions and medical complexity (ie, presence of complex or noncomplex chronic disease)., Main Outcomes and Measures: Hospitalization and severe illness when hospitalized (ie, combined outcome of intensive care unit admission, invasive mechanical ventilation, or death)., Results: Among 43 465 patients with COVID-19 aged 18 years or younger, the median (interquartile range) age was 12 (4-16) years, 22 943 (52.8%) were female patients, and 12 491 (28.7%) had underlying medical conditions. The most common diagnosed conditions were asthma (4416 [10.2%]), neurodevelopmental disorders (1690 [3.9%]), anxiety and fear-related disorders (1374 [3.2%]), depressive disorders (1209 [2.8%]), and obesity (1071 [2.5%]). The strongest risk factors for hospitalization were type 1 diabetes (adjusted risk ratio [aRR], 4.60; 95% CI, 3.91-5.42) and obesity (aRR, 3.07; 95% CI, 2.66-3.54), and the strongest risk factors for severe COVID-19 illness were type 1 diabetes (aRR, 2.38; 95% CI, 2.06-2.76) and cardiac and circulatory congenital anomalies (aRR, 1.72; 95% CI, 1.48-1.99). Prematurity was a risk factor for severe COVID-19 illness among children younger than 2 years (aRR, 1.83; 95% CI, 1.47-2.29). Chronic and complex chronic disease were risk factors for hospitalization, with aRRs of 2.91 (95% CI, 2.63-3.23) and 7.86 (95% CI, 6.91-8.95), respectively, as well as for severe COVID-19 illness, with aRRs of 1.95 (95% CI, 1.69-2.26) and 2.86 (95% CI, 2.47-3.32), respectively., Conclusions and Relevance: This cross-sectional study found a higher risk of severe COVID-19 illness among children with medical complexity and certain underlying conditions, such as type 1 diabetes, cardiac and circulatory congenital anomalies, and obesity. Health care practitioners could consider the potential need for close observation and cautious clinical management of children with these conditions and COVID-19.

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., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. John House reports employment with Premier, Inc. No other potential conflicts of interest were disclosed.

Objective: 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/m 2 ) 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., Methods: 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., Results: 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., Conclusions: %BMIp95, SBP, and DBP significantly decreased among MEND+ attendees when implemented in community-based clinical practice settings at DH FQHC., (Published by Elsevier Inc.)

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.

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 .

Background: Older adults and people from certain racial and ethnic groups are disproportionately represented in coronavirus disease 2019 (COVID-19) hospitalizations and deaths., Methods: Using data from the Premier Healthcare Database on 181   813 hospitalized adults diagnosed with COVID-19 during March-September 2020, we applied multivariable log-binomial regression to assess the associations between age and race/ethnicity and COVID-19 clinical severity (intensive care unit [ICU] admission, invasive mechanical ventilation [IMV], and death) and to determine whether the impact of age on clinical severity differs by race/ethnicity., Results: Overall, 84   497 (47%) patients were admitted to the ICU, 29   078 (16%) received IMV, and 27   864 (15%) died in the hospital. Increased age was strongly associated with clinical severity when controlling for underlying medical conditions and other covariates; the strength of this association differed by race/ethnicity. Compared with non-Hispanic White patients, risk of death was lower among non-Hispanic Black patients (adjusted risk ratio, 0.96; 95% CI, 0.92-0.99) and higher among Hispanic/Latino patients (risk ratio [RR], 1.15; 95% CI, 1.09-1.20), non-Hispanic Asian patients (RR, 1.16; 95% CI, 1.09-1.23), and patients of other racial and ethnic groups (RR, 1.13; 95% CI, 1.06-1.21). Risk of ICU admission and risk of IMV were elevated among some racial and ethnic groups., Conclusions: These results indicate that age is a driver of poor outcomes among hospitalized persons with COVID-19. Additionally, clinical severity may be elevated among patients of some racial and ethnic minority groups. Public health strategies to reduce severe acute respiratory syndrome coronavirus 2 infection rates among older adults and racial and ethnic minorities are essential to reduce poor outcomes., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2020.)

Coronavirus disease 2019 (COVID-19) is a complex clinical illness with potential complications that might require ongoing clinical care (1-3). Few studies have investigated discharge patterns and hospital readmissions among large groups of patients after an initial COVID-19 hospitalization (4-7). Using electronic health record and administrative data from the Premier Healthcare Database,* CDC assessed patterns of hospital discharge, readmission, and demographic and clinical characteristics associated with hospital readmission after a patient's initial COVID-19 hospitalization (index hospitalization). Among 126,137 unique patients with an index COVID-19 admission during March-July 2020, 15% died during the index hospitalization. Among the 106,543 (85%) surviving patients, 9% (9,504) were readmitted to the same hospital within 2 months of discharge through August 2020. More than a single readmission occurred among 1.6% of patients discharged after the index hospitalization. Readmissions occurred more often among patients discharged to a skilled nursing facility (SNF) (15%) or those needing home health care (12%) than among patients discharged to home or self-care (7%). The odds of hospital readmission increased with age among persons aged ≥65 years, presence of certain chronic conditions, hospitalization within the 3 months preceding the index hospitalization, and if discharge from the index hospitalization was to a SNF or to home with health care assistance. These results support recent analyses that found chronic conditions to be significantly associated with hospital readmission (6,7) and could be explained by the complications of underlying conditions in the presence of COVID-19 (8), COVID-19 sequelae (3), or indirect effects of the COVID-19 pandemic (9). Understanding the frequency of, and risk factors for, readmission can inform clinical practice, discharge disposition decisions, and public health priorities such as health care planning to ensure availability of resources needed for acute and follow-up care of COVID-19 patients. With the recent increases in cases nationwide, hospital planning can account for these increasing numbers along with the potential for at least 9% of patients to be readmitted, requiring additional beds and resources., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

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., (© 2020 The Obesity Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Background: Childhood obesity is linked with adverse health outcomes and associated costs. Current information on the relationship between childhood obesity and inpatient costs is limited., Objective: The objective of this study was to describe trends and quantify the link between childhood obesity diagnosis and hospitalization length of stay (LOS), costs, and charges., Research Design: We use the National Inpatient Sample data from 2006 to 2016., Subjects: The sample includes hospitalizations among children aged 2-19 years. The treatment group of interest includes child hospitalizations with an obesity diagnosis., Measures: Hospital LOS, charges, and costs associated with a diagnosis of obesity., Results: We find increases in obesity-coded hospitalizations and associated charges and costs during 2006-2016. Obesity as a primary diagnosis is associated with a shorter hospital LOS (by 1.8 d), but higher charges and costs (by $20,879 and $6049, respectively); obesity as a secondary diagnosis is associated with a longer LOS (by 0.8 d), and higher charges and costs of hospitalizations (by $3453 and $1359, respectively). The most common primary conditions occurring with a secondary diagnosis of obesity are pregnancy conditions, mood disorders, asthma, and diabetes; the effect of a secondary diagnosis of obesity on LOS, charges, and costs holds across these conditions., Conclusions: Childhood obesity diagnosis-related hospitalizations, charges, and costs increased substantially during 2006-2016, and obesity diagnosis is associated with higher hospitalization charges and costs. Our findings provide clinicians and policymakers with additional evidence of the economic burden of childhood obesity and further justify efforts to prevent and manage the disease.

As of July 5, 2020, approximately 2.8 million coronavirus disease 2019 (COVID-19) cases and 130,000 COVID-19-associated deaths had been reported in the United States (1). Populations historically affected by health disparities, including certain racial and ethnic minority populations, have been disproportionally affected by and hospitalized with COVID-19 (2-4). Data also suggest a higher prevalence of infection with SARS-CoV-2, the virus that causes COVID-19, among persons experiencing homelessness (5). Safety-net hospitals, † such as Boston Medical Center (BMC), which provide health care to persons regardless of their insurance status or ability to pay, treat higher proportions of these populations and might experience challenges during the COVID-19 pandemic. This report describes the characteristics and clinical outcomes of adult patients with laboratory-confirmed COVID-19 treated at BMC during March 1-May 18, 2020. During this time, 2,729 patients with SARS-CoV-2 infection were treated at BMC and categorized into one of the following mutually exclusive clinical severity designations: exclusive outpatient management (1,543; 56.5%), non-intensive care unit (ICU) hospitalization (900; 33.0%), ICU hospitalization without invasive mechanical ventilation (69; 2.5%), ICU hospitalization with mechanical ventilation (119; 4.4%), and death (98; 3.6%). The cohort comprised 44.6% non-Hispanic black (black) patients and 30.1% Hispanic or Latino (Hispanic) patients. Persons experiencing homelessness accounted for 16.4% of patients. Most patients who died were aged ≥60 years (81.6%). Clinical severity differed by age, race/ethnicity, underlying medical conditions, and homelessness. A higher proportion of Hispanic patients were hospitalized (46.5%) than were black (39.5%) or non-Hispanic white (white) (34.4%) patients, a finding most pronounced among those aged <60 years. A higher proportion of non-ICU inpatients were experiencing homelessness (24.3%), compared with homeless patients who were admitted to the ICU without mechanical ventilation (15.9%), with mechanical ventilation (15.1%), or who died (15.3%). Patient characteristics associated with illness and clinical severity, such as age, race/ethnicity, homelessness, and underlying medical conditions can inform tailored strategies that might improve outcomes and mitigate strain on the health care system from COVID-19., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Importance: To date, limited information is available on the characteristics of adolescents with e-cigarette, or vaping, product use-associated lung injury (EVALI)., Objective: To inform public health and clinical practice by describing differences in demographics, substance use behaviors, and clinical characteristics of EVALI among adolescents compared with adults., Design, Setting, and Participants: Surveillance data reported to the Centers for Disease Control and Prevention during the 2019 EVALI outbreak were used to calculate adjusted prevalence ratios (aPRs) with 95% CIs and to test differences between 360 hospitalized or deceased adolescents vs 859 young adults and 936 adults with EVALI (N = 2155)., Main Outcomes and Measures: Demographics, substance use behaviors, and clinical characteristics., Results: Included in this cross-sectional study were 360 hospitalized or deceased adolescents (age range, 13-17 years; 67.9% male) vs 859 young adults (age range, 18-24 years; 72.4% male) and 936 adults (age range, 25-49 years; 65.6% male) with EVALI. Adolescents diagnosed as having EVALI reported using any nicotine-containing (62.4%), any tetrahydrocannabinol (THC)-containing (81.7%), and both (50.8%) types of e-cigarette or vaping products. Informal sources for obtaining nicotine-containing and THC-containing e-cigarette or vaping products were more commonly reported by adolescents (50.5% for nicotine and 96.5% for THC) than young adults (19.8% for nicotine [aPR, 2.49; 95% CI, 1.78-3.46] and 86.9% for THC [aPR, 1.11; 95% CI, 1.05-1.18]) or adults (24.3% for nicotine [aPR, 2.06; 95% CI, 1.49-2.84] and 75.1% for THC [aPR, 1.29; 95% CI, 1.19-1.40]). Mental, emotional, or behavioral disorders were commonly reported; a history of attention-deficit/hyperactivity disorder was almost 4 times more likely among adolescents (18.1%) than adults (4.9%) (aPR, 3.74; 95% CI, 1.92-7.26). A history of asthma was more likely to be reported among adolescents (43.6%) than adults (28.3%) (aPR, 1.53; 95% CI, 1.14-2.05). Gastrointestinal and constitutional symptoms were more common in adolescents (90.9% and 97.3%, respectively) than adults (75.3% and 94.5%, respectively) (aPR, 1.20; 95% CI, 1.13-1.28 and aPR, 1.03; 95% CI, 1.00-1.06, respectively). Because of missing data, percentages may not be able to be calculated from data provided., Conclusions and Relevance: Public health and clinical professionals should continue to provide information to adolescents about the association between EVALI and THC-containing e-cigarette or vaping product use, especially those products obtained through informal sources, and that the use of any e-cigarette or vaping product is unsafe. Compared with adults, it appears that adolescents with EVALI more frequently have a history of asthma and mental, emotional, or behavioral disorders, such as attention-deficit/hyperactivity disorder, and report nonspecific problems, including gastrointestinal and constitutional symptoms; therefore, obtaining a confidential substance use history that includes e-cigarette or vaping product use is recommended.

On March 13, 2020, the United States declared a national emergency in response to the coronavirus disease 2019 (COVID-19) pandemic. Subsequently, states enacted stay-at-home orders to slow the spread of SARS-CoV-2, the virus that causes COVID-19, and reduce the burden on the U.S. health care system. CDC* and the Centers for Medicare & Medicaid Services (CMS) † recommended that health care systems prioritize urgent visits and delay elective care to mitigate the spread of COVID-19 in health care settings. By May 2020, national syndromic surveillance data found that emergency department (ED) visits had declined 42% during the early months of the pandemic (1). This report describes trends in ED visits for three acute life-threatening health conditions (myocardial infarction [MI, also known as heart attack], stroke, and hyperglycemic crisis), immediately before and after declaration of the COVID-19 pandemic as a national emergency. These conditions represent acute events that always necessitate immediate emergency care, even during a public health emergency such as the COVID-19 pandemic. In the 10 weeks following the emergency declaration (March 15-May 23, 2020), ED visits declined 23% for MI, 20% for stroke, and 10% for hyperglycemic crisis, compared with the preceding 10-week period (January 5-March 14, 2020). EDs play a critical role in diagnosing and treating life-threatening conditions that might result in serious disability or death. Persons experiencing signs or symptoms of serious illness, such as severe chest pain, sudden or partial loss of motor function, altered mental state, signs of extreme hyperglycemia, or other life-threatening issues, should seek immediate emergency care, regardless of the pandemic. Clear, frequent, highly visible communication from public health and health care professionals is needed to reinforce the importance of timely care for medical emergencies and to assure the public that EDs are implementing infection prevention and control guidelines that help ensure the safety of their patients and health care personnel., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

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 ≤ 95 th 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 ≥ 95 th 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., Competing Interests: The authors have no conflict of interest to declare., (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.)

Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No conflicts of interest were disclosed. All members of the Lung Injury Response Clinical Working Group have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Carolyn S. Calfee reports a grant from the FDA/NIH (Tobacco Center of Regulatory Science [TCORS]) for a project entitled Impact of Different E-cigarette Characteristics on Acute Lung Injury; a grant from GlaxoSmithKline for an observational study on sepsis and ARDS biomarkers; a grant and personal fees from Bayer for an observational study on pulmonary hypertension in ARDS and for medical consultation; and personal fees from Roche/Genentech for consultation on potential therapies for ARDS, and personal fees from Prometic, CSL Behring, and Quark for serving on medical advisory boards for ARDS. No other potential conflicts of interest were disclosed.

Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Obesity-related clinical decision support tools in electronic health records (EHRs) can improve pediatric care, but the degree of adoption of these tools is unknown. DocStyles 2015 survey data from US pediatric healthcare providers (n = 1,156) were analyzed. Multivariable logistic regression identified provider characteristics associated with three EHR functionalities: automatically calculating body mass index (BMI) percentile (AUTO), displaying BMI trajectory (DISPLAY), and flagging abnormal BMIs (FLAG). Most providers had EHRs (88%). Of those with EHRs, 90% reporting having AUTO, 62% DISPLAY, and 54% FLAG functionalities. Only provider age was associated with all three functionalities. Compared to providers aged > 54 years, providers < 40 years had greater odds for: AUTO (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.58-5.70), DISPLAY (aOR, 2.07; 95% CI, 1.38-3.12), and FLAG (aOR, 1.67; 95% CI, 1.14-2.44). Future investigations can elucidate causes of lower adoption of EHR functions that display growth trajectories and flag abnormal BMIs., Competing Interests: Conflict of Interest: The authors declare that they have no competing interests. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention., (Copyright © 2019. The Korean Society of Clinical Nutrition.)

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., (Published by Elsevier Inc.)

Background: Childhood obesity care management options can be delivered in community-, clinic-, and hospital-settings. The referral practices of clinicians to these various settings have not previously been characterized beyond the local level. This study describes the management strategies and referral practices of clinicians caring for pediatric patients with obesity and associated clinician characteristics in a geographically diverse sample. Methods: This cross-sectional study used data from the DocStyles 2017 panel-based survey of 891 clinicians who see pediatric patients. We used multivariable logistic regression to estimate associations between the demographic and practice characteristics of clinicians and types of referrals for the purposes of pediatric weight management. Results: About half of surveyed clinicians (54%) referred <25% of their pediatric patients with obesity for the purposes of weight management. Only 15% referred most (≥75%) of their pediatric patients with obesity for weight management. Referral types included clinical referrals, behavioral referrals, and weight management program (WMP) referrals. Within these categories, the percentage referrals ranged from 19% for behavioral/mental health professionals to 72% for registered dieticians. Among the significant associations, female clinicians had higher odds of referral to community and clinical WMP; practices in the Northeast had higher odds of referral to subspecialists, dieticians, mental health professionals, and clinical WMP; and clinics having ≥15 well child visits per week were associated with higher odds of referral to subspecialists, mental health professionals, and health educators. Not having an affiliation with teaching hospitals and serving low-income patients were associated with lower odds of referral to mental health professionals, and community and clinical WMP. Compared to pediatricians, family practitioners, internists, and nurse practitioners had higher odds of providing referrals to mental health professionals and to health educators. Conclusion: This study helps characterize the current landscape of referral practices and management strategies of clinicians who care for pediatric patients with obesity. Our data provide insight into the clinician, clinical practice, and reported patient characteristics associated with childhood obesity referral types. Understanding referral patterns and management strategies may help improve care for children with obesity and their families.

Hypertension is an important modifiable risk factor for cardiovascular morbidity and mortality, and hypertension in adolescents and young adults is associated with long-term negative health effects (1,2).* In 2017, the American Academy of Pediatrics (AAP) released a new Clinical Practice Guideline (3), which updated 2004 pediatric hypertension guidance † with new thresholds and percentile references calculated from a healthy-weight population. To examine trends in youth hypertension and the impact of the new guideline on classification of hypertension status, CDC analyzed data from 12,004 participants aged 12-19 years in the 2001-2016 National Health and Nutrition Examination Survey (NHANES). During this time, prevalence of hypertension declined, using both the new (from 7.7% to 4.2%, p<0.001) and former (from 3.2% to 1.5%, p<0.001) guidelines, and declines were observed across all weight status categories. However, because of the new percentile tables and lower threshold for hypertension (4), application of the new guideline compared with the former guideline resulted in a weighted net estimated increase of 795,000 U.S. youths being reclassified as having hypertension using 2013-2016 data. Youths who were older, male, and those with obesity accounted for a disproportionate share of persons reclassified as having hypertension. Clinicians and public health professionals might expect to see a higher prevalence of hypertension with application of the new guideline and can use these data to inform actions to address hypertension among youths. Strategies to improve cardiovascular health include adoption of healthy eating patterns and increased physical activity (3)., Competing Interests: No conflicts of interest were reported.

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., (© 2018 The Obesity Society.)

Importance: Severe obesity in childhood is associated with negative health consequences. A previous study examined trends in severe obesity among preschool-aged children in low-income families during 1998 to 2010. No recent trends have been reported., Objective: To examine trends in severe obesity by age, sex, and race/ethnicity among enrollees in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) aged 2 to 4 years during 2000 to 2014., Design, Setting, and Participants: Serial cross-sectional data from 22.6 million young children enrolled in WIC from 50 states, the District of Columbia, and 5 US territories from 2000 to 2014. Data analysis was conducted from February 16, 2017, to March 9, 2017., Main Outcomes and Measures: Prevalence of severe obesity. Severe obesity was defined as a sex-specific body mass index-for-age 120% or more of the 95th percentile on the 2000 Centers for Disease Control and Prevention growth charts. Children's weights and heights were measured. Children whose sex, weight, height, or body mass index was missing or biologically implausible were excluded., Results: The prevalence of severe obesity was 1.96% in 2014. During 2000 to 2004, the prevalence increased significantly overall from 1.80% to 2.11% (adjusted prevalence difference [APD], 0.26%) and among all the age, sex, and racial/ethnic groups except for Asian/Pacific Islander (APD, 0.05%-0.54% across groups with increases). The largest relative increase occurred in children aged 4 years (adjusted prevalence ratio [APR], 1.21) and non-Hispanic white (APR, 1.22) and American Indian/Alaska Native children (APR, 1.19). During 2004 to 2010, the prevalence decreased significantly overall (APD, -0.05%), among boys, children aged 2 and 3 years, and non-Hispanic black and Asian/Pacific Islander children (APD, -0.05% to -0.18%). During 2010 to 2014, the prevalence decreased significantly overall from 2.12% to 1.96% (APD, -0.14%) and among all demographic groups (APD, -0.04% to -0.30% across groups). The largest relative decrease occurred in children aged 2 years (APR, 0.88) and Hispanic (APR, 0.92), American Indian/Alaska Native (APR, 0.89), and Asian/Pacific Islander (APR, 0.87) children., Conclusions and Relevance: This study provides updated prevalence and trends of severe obesity among young children enrolled in WIC and reports recent modest declines in severe obesity in all subgroups. Ongoing surveillance can assess whether declines continue into the future among low-income children.

Background: BMI z-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 BMIz with BMI expressed as a percentage of the 95th percentile (%BMIp95) and a modified z-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 BMIz levels differed from the other metrics. Among severely obese 2-year-old girls, for example, the mean BMIz decreased by 0.6 SD between examinations, but there were only small changes in BMIp95 and modified BMIz. Some 2-year-old girls had BMIz decreases of >1 SD, even though they had large increases in BMI, %BMIp95 and modified BMIz., Conclusions: Among children with severe obesity, BMIz changes may be due to differences in the transformations used to estimate levels of BMIz rather than to changes in body size. The BMIs of these children could be expressed relative to the 95th percentile or as modified z-scores.