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Purpose: This study examined the use of behavioral design strategies to improve healthier food sales., Design: A quasi-experimental, one-group, repeated measures design examined changes in food sales following behavioral design adjustments., Setting: United States military base hospital dining facility., Subjects: U.S. military service members, retirees, and civilian employees., Intervention: Behavioral design changes included placement, layout, messaging, default healthy bundling, a stoplight rating system, strategic positioning of healthy items on menu boards, and an increase in healthier snacks., Measures: Food sales were assessed by point-of-sales data., Analysis: T-tests examined total sales of each food adjusted weekly between baseline and intervention and intervention and post-intervention. 16 food items targeted by the intervention were examined. Weekly food sales were calculated for the 18-week baseline, 18-week intervention, and 9-week post-intervention. Further, analysis estimated negative binomial models for food item sales., Results: The hospital dining facility served 600 to 900 meals per day. Weekly foods sales decreased during the intervention for desserts, cooked starches, hummus, and yogurt ( P < 0.01). Sales increased during the intervention for fruit cups, cooked vegetables, vegetable and turkey burgers, grilled chicken, packaged salads, French fries, hamburgers, and hot dogs ( P < 0.02)., Conclusion: This study demonstrates that a mixture of behavioral design strategies can be operationalized with reasonable fidelity and can lead to increases in the sales of some healthy foods in military worksites., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: Coronavirus disease 2019 (COVID-19) may be associated with gestational diabetes mellitus (GDM); however, evidence is limited by sample sizes and lack of control groups., Methods: To assess the GDM risk after COVID-19 in pregnancy, we constructed a retrospective cohort of pregnancies ending March 2020-October 2022 using medical claims. People with COVID-19 diagnosis claims from conception to 21 gestational weeks (n = 57 675) were matched 1:2 to those without COVID-19 during pregnancy (n = 115 350) by age range, pregnancy start month, and encounter year-month. GDM (claim ≥23 gestational weeks) relative risk and risk difference overall, by race and ethnicity, and variant period were estimated using log-binomial models., Results: GDM risk was higher among those with COVID-19 during pregnancy compared to those without (adjusted risk ratio [aRR] = 1.12; 95% confidence interval [CI], 1.08-1.15). GDM risk was significantly associated with COVID-19 in non-Hispanic White (aRR = 1.08; 95% CI, 1.04-1.14), non-Hispanic Black (aRR = 1.15; 95% CI, 1.07-1.24), and Hispanic (aRR = 1.17; 95% CI, 1.10-1.24) groups. GDM risk was significantly higher during pre-Delta (aRR = 1.17; 95% CI, 1.11-1.24) compared to Omicron (aRR = 1.07; 95% CI, 1.02-1.13) periods, but neither differed from the Delta period (aRR = 1.10; 95% CI, 1.04-1.17). The adjusted risk difference was 0%-2% for all models., Conclusions: COVID-19 during pregnancy was modestly associated with GDM in claims-based data, especially during earlier SARS-CoV-2 variant periods. Because these associations are based on COVID-19 in claims data, studies employing systematic testing are warranted., Competing Interests: Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2024.)

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.

Background: There are no recent estimates for hypertension-associated medical expenditures. This study aims to estimate hypertension-associated incremental medical expenditures among privately insured US adults., Methods: We conducted a retrospective cohort study using IQVIA's Ambulatory Electronic Medical Records-US data set linked with PharMetrics Plus claims data. Among privately insured adults aged 18 to 64 years, hypertension was identified as having ≥1 diagnosis code or ≥2 blood pressure measurements of ≥140/90 mm Hg, or ≥1 antihypertensive medication in 2021. Annual total expenditures (in 2021 $US) were estimated using a generalized linear model with gamma distribution and log-link function adjusting for demographic characteristics and cooccurring conditions. Out-of-pocket expenditures were estimated using a 2-part model that included logistic and generalized linear model regression. Overlap propensity score weights from logistic regression were used to obtain a balanced sample on hypertension status., Results: Among the 393 018 adults, 156 556 (40%) were identified with hypertension. Compared with individuals without hypertension, those with hypertension had $2926 (95% CI, $2681-$3170) higher total expenditures and $328 (95% CI, $300-$355) higher out-of-pocket expenditures. Adults with hypertension had higher total inpatient ($3272 [95% CI, $1458-$5086]) and outpatient ($2189 [95% CI, $2009-$2369]) expenditures when compared with those without hypertension. Hypertension-associated incremental total expenditures were higher for women ($3242 [95% CI, $2915-$3569]) than for men ($2521 [95% CI, $2139-$2904])., Conclusions: Among privately insured US adults, hypertension was associated with higher medical expenditures, including higher inpatient and out-of-pocket expenditures. These findings may help assess the economic value of interventions effective in preventing hypertension., Competing Interests: None.

Given the high and growing prevalence of obesity among adults in the United States, obesity treatment and prevention are important topics in biomedical and public health research. Although researchers recognize the significance of this problem, much remains unknown about safe and effective prevention and treatment of obesity in adults. In response to the worsening obesity epidemic and the many unknowns regarding the disease, a group of key scientific and program staff members of the National Institutes of Health (NIH) and other federal and non-government agencies gathered virtually in September 2021 to discuss the current state of obesity research, research gaps, and opportunities for future research in adult obesity prevention and treatment. The current article synthesizes presentations given by attendees and shares their organizations' current initiatives and identified gaps and opportunities. By integrating the information discussed in the meeting and current initiatives, we identify potential targets and overlapping priorities for future research, including health equity and disparities in obesity, the heterogeneity of obesity, and the use of technological and innovative approaches in interventions., (© 2024 Patient‐Centered Outcomes Research Institute and The Author(s). Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: People with HIV (human immunodeficiency virus; PWH) have higher rates of cardiovascular disease than people without HIV. However, limited information exists about hypertension prevalence and associated risk factors in PWH., Methods: This cross-sectional study included adult patients in the 2022 IQVIATM Ambulatory Electronic Medical Record-US data. HIV was identified based on ≥2 HIV diagnosis codes or a positive HIV test. Hypertension was identified by diagnosis codes, ≥2 blood pressure (BP) readings ≥130/80 mm Hg, or an antihypertensive medication prescription. Among those with hypertension, control was defined as the most recent BP < 130/80 mm Hg. Logistic models using the marginal standardization method were used to estimate adjusted prevalence ratios (aPR) of hypertension and hypertension control among all patients and PWH specifically, controlling for covariates., Results: Of 7,533,379 patients, 19,102 (0.3%) had HIV. PWH had higher hypertension prevalence (66% vs. 54%, aPR:1.14, 95% CI: 1.13-1.15) compared with people without HIV. Among persons with hypertension, PWH were more likely to have controlled hypertension (aPR: 1.10, 95% CI: 1.07-1.13) compared with people without HIV. Among PWH, those from the South were more likely to have hypertension (aPR: 1.07, 95% CI: 1.02-1.12) than PWH from the Northeast, while Black PWH were less likely to have controlled hypertension (aPR: 0.72, 95% CI: 0.67-0.77) than White PWH., Conclusions: PWH were more likely to have hypertension than people without HIV. Geographic and racial disparities in hypertension prevalence and control were observed among PWH. Optimal care for PWH includes comprehensive strategies to screen for, prevent, and manage hypertension., (© Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2024.)

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.

Introduction: Screening for prediabetes and type 2 diabetes may allow earlier detection, diagnosis, and treatment. The US Preventive Services Task Force recommends screening every 3 years for abnormal blood glucose among adults aged 40 to 70 years with overweight or obesity. Using IQVIA Ambulatory Electronic Medical Records, we estimated the proportion of adults aged 40 to 70 years with overweight or obesity who received blood glucose testing within 3 years from baseline in 2016., Methods: We identified 1,338,509 adults aged 40 to 70 years with overweight or obesity in 2016 and without pre-existing diabetes. We included adults whose records were present in the data set for at least 2 years before their index body mass index (BMI) in 2016 and 3 years after the index BMI (2017-2019), during which we examined the occurrence of blood glucose testing. We calculated the unadjusted and adjusted prevalence of receiving blood glucose testing., Results: The unadjusted prevalence of receiving blood glucose testing was 33.4% when it was defined as having a hemoglobin A 1c or fasting plasma glucose measure. The unadjusted prevalence was 74.3% when we expanded the definition of testing to include random plasma glucose and unspecified glucose measures. Adults with obesity were more likely to receive the test than those with overweight. Men (vs women) and adults aged 50 to 59 years (vs other age groups) had higher testing rates., Conclusion: Our findings could inform clinical and public health promotion efforts to improve screening for blood glucose levels among adults with overweight or obesity.

Background: Hypertension is an important risk factor for cardiovascular diseases. Electronic health records (EHRs) may augment chronic disease surveillance. We aimed to develop an electronic phenotype (e-phenotype) for hypertension surveillance., Methods: We included 11,031,368 eligible adults from the 2019 IQVIA Ambulatory Electronic Medical Records-US (AEMR-US) dataset. We identified hypertension using three criteria, alone or in combination: diagnosis codes, blood pressure (BP) measurements, and antihypertensive medications. We compared AEMR-US estimates of hypertension prevalence and control against those from the National Health and Nutrition Examination Survey (NHANES) 2017-18, which defined hypertension as BP ≥130/80 mm Hg or ≥1 antihypertensive medication., Results: The study population had a mean (SD) age of 52.3 (6.7) years, and 56.7% were women. The selected three-criteria e-phenotype (≥1 diagnosis code, ≥2 BP measurements of ≥130/80 mm Hg, or ≥1 antihypertensive medication) yielded similar trends in hypertension prevalence as NHANES: 42.2% (AEMR-US) vs. 44.9% (NHANES) overall, 39.0% vs. 38.7% among women, and 46.5% vs. 50.9% among men. The pattern of age-related increase in hypertension prevalence was similar between AEMR-US and NHANES. The prevalence of hypertension control in AEMR-US was 31.5% using the three-criteria e-phenotype, which was higher than NHANES (14.5%)., Conclusions: Using an EHR dataset of 11 million adults, we constructed a hypertension e-phenotype using three criteria, which can be used for surveillance of hypertension prevalence and control., (© Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2023.)

Recent advances in technology can be leveraged to enhance public health research and practice. This study aimed to assess the effects of mobility and policy changes on COVID-19 case growth and the effects of policy changes on mobility using data from Google Mobility Reports, information on public health policy, and COVID-19 testing results. Multiple bivariate regression analyses were conducted to address the study objectives. Policies designed to limit mobility led to decreases in mobility in public areas. These policies also decreased COVID-19 case growth. Conversely, policies that did not restrict mobility led to increases in mobility in public areas and led to increases in COVID-19 case growth. Mobility increases in public areas corresponded to increases in COVID-19 case growth, while concentration of mobility in residential areas corresponded to decreases in COVID-19 case growth. Overall, restrictive policies were effective in decreasing COVID-19 incidence in the Dominican Republic, while permissive policies led to increases in COVID-19 incidence., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Inc.)

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.

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.

Background: Small sample sizes have limited prior studies' ability to capture severe COVID-19 outcomes, especially among Ad26.COV2.S vaccine recipients. This study of 18.9 million adults aged ≥18 years assessed relative vaccine effectiveness (rVE) in three recipient cohorts: (1) primary Ad26.COV2.S vaccine and Ad26.COV2.S booster (2 Ad26.COV2.S), (2) primary Ad26.COV2.S vaccine and mRNA booster (Ad26.COV2.S+mRNA), (3) two doses of primary mRNA vaccine and mRNA booster (3 mRNA)., Methods: We analyzed two de-identified datasets linked using privacy-preserving record linkage (PPRL): insurance claims and retail pharmacy COVID-19 vaccination data. We assessed the presence of COVID-19 diagnosis during January 1-March 31, 2022 in: (1) any claim, (2) outpatient claim, (3) emergency department (ED) claim, (4) inpatient claim, and (5) inpatient claim with intensive care unit (ICU) admission. rVE for each outcome comparing three recipient cohorts (reference: two Ad26.COV2.S doses) was estimated from adjusted Cox proportional hazards models., Results: Compared with two Ad26.COV2.S doses, Ad26.COV2.S+mRNA and three mRNA doses were more effective against all COVID-19 outcomes, including 57% (95% CI: 52-62) and 62% (95% CI: 58-65) rVE against an ED visit; 44% (95% CI: 34-52) and 54% (95% CI: 48-59) rVE against hospitalization; and 48% (95% CI: 22-66) and 66% (95% CI: 53-75) rVE against ICU admission, respectively., Conclusions: This study demonstrated that Ad26.COV2.S + mRNA doses were as good as three doses of mRNA, and better than two doses of Ad26.COV2.S. Vaccination continues to be an important preventive measure for reducing the public health impact of COVID-19., Competing Interests: Potential conflicts of interest . The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2023.)

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.)

Introduction: SARS-CoV-2, the virus that causes COVID-19, has caused more than 100.2 million infections and more than 1 million deaths in the US as of November 2022, yet information on the economic burden associated with post-COVID-19 conditions is lacking. We estimated the possible economic burden associated with post-COVID-19 conditions by comparing direct medical costs among patients younger than 65 years with and without COVID-19 in the postacute period., Methods: Commercially insured children and adults with a COVID-19 diagnosis (cases) during April-August 2020 were matched to those without COVID-19 (controls) on a 1:4 ratio. Direct medical costs represented 1-, 3-, and 6-month total expenditures per person starting 31 days after the diagnosis date. We used a 2-part model to evaluate cost differences among individuals with and without COVID-19, adjusted for patient characteristics., Results: Costs were higher among cases compared with controls. Direct medical costs among child cases were 1.82, 1.72, and 1.70 times higher than controls over 1, 3, and 6 months, respectively. Direct medical costs among adult cases were 1.69, 1.54, and 1.46 times higher than costs among controls over 1, 3, and 6 months, respectively. Relative differences in costs were highest among adults aged 50 to 64 years. In a subset of people with COVID-19, costs were higher among hospitalized cases compared with nonhospitalized cases., Conclusion: Our findings suggest a considerable economic burden of COVID-19 even after the resolution of acute illness, highlighting the importance of prevention and mitigation measures to reduce the economic impact of COVID-19 on the US health care system.

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: A total of 4.25 million adults (18 to 84 years) in an electronic health record database who had at least two weight measurements between January 2019 and February 2020 and one after June 2020 were selected. Weight changes before and after March 2020 were contrasted using mixed-effects regression models., Results: Compared with the 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 BMI of 30 or 35 kg/m 2 , and younger adults., Conclusions: The results indicate that the mean weight gain among adults during the COVID-19 pandemic may be small., (© 2022 The Obesity Society (TOS). This article has been contributed to by U.S. Government employees and their work 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.)

Post-COVID-19 (post-COVID) symptoms and conditions* are new, recurring, or ongoing health problems that occur 4 or more weeks after infection with SARS-CoV-2 (the virus that causes COVID-19). Previous studies have characterized and estimated the incidence of post-COVID conditions among adults (1,2), but data among children and adolescents are limited (3-8). Using a large medical claims database, CDC assessed nine potential post-COVID signs and symptoms (symptoms) and 15 potential post-COVID conditions among 781,419 U.S. children and adolescents aged 0-17 years with laboratory-confirmed COVID-19 (patients with COVID-19) compared with 2,344,257 U.S. children and adolescents without recognized COVID-19 (patients without COVID-19) during March 1, 2020-January 31, 2022. The analysis identified several symptoms and conditions with elevated adjusted hazard ratios among patients with COVID-19 (compared with those without). The highest hazard ratios were recorded for acute pulmonary embolism (adjusted hazard ratio [aHR] = 2.01), myocarditis and cardiomyopathy (1.99), venous thromboembolic event (1.87), acute and unspecified renal failure (1.32), and type 1 diabetes (1.23), all of which were rare or uncommon in this study population. Conversely, symptoms and conditions that were most common in this study population had lower aHRs (near or below 1.0). Patients with COVID-19 were less likely than were patients without to experience respiratory signs and symptoms, symptoms of mental conditions, muscle disorders, neurological conditions, anxiety and fear-related disorders, mood disorders, and sleeping disorders. COVID-19 prevention strategies, including vaccination for all eligible children and adolescents, are critical to prevent SARS-CoV-2 infection and subsequent illness, including post-COVID symptoms and conditions (9)., 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: 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., (© 2022 World Obesity Federation. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

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.

Importance: People experiencing incarceration (PEI) and people experiencing homelessness (PEH) have an increased risk of COVID-19 exposure from congregate living, but data on their hospitalization course compared with that of the general population are limited., Objective: To compare COVID-19 hospitalizations for PEI and PEH with hospitalizations among the general population., Design, Setting, and Participants: This cross-sectional analysis used data from the Premier Healthcare Database on 3415 PEI and 9434 PEH who were evaluated in the emergency department or were hospitalized in more than 800 US hospitals for COVID-19 from April 1, 2020, to June 30, 2021., Exposures: Incarceration or homelessness., Main Outcomes and Measures: Hospitalization proportions were calculated. and outcomes (intensive care unit admission, invasive mechanical ventilation [IMV], mortality, length of stay, and readmissions) among PEI and PEH were compared with outcomes for all patients with COVID-19 (not PEI or PEH). Multivariable regression was used to adjust for potential confounders., Results: In total, 3415 PEI (2952 men [86.4%]; mean [SD] age, 50.8 [15.7] years) and 9434 PEH (6776 men [71.8%]; mean [SD] age, 50.1 [14.5] years) were evaluated in the emergency department for COVID-19 and were hospitalized more often (2170 of 3415 [63.5%] PEI; 6088 of 9434 [64.5%] PEH) than the general population (624 470 of 1 257 250 [49.7%]) (P < .001). Both PEI and PEH hospitalized for COVID-19 were more likely to be younger, male, and non-Hispanic Black than the general population. Hospitalized PEI had a higher frequency of IMV (410 [18.9%]; adjusted risk ratio [aRR], 1.16; 95% CI, 1.04-1.30) and mortality (308 [14.2%]; aRR, 1.28; 95% CI, 1.11-1.47) than the general population (IMV, 88 897 [14.2%]; mortality, 84 725 [13.6%]). Hospitalized PEH had a lower frequency of IMV (606 [10.0%]; aRR, 0.64; 95% CI, 0.58-0.70) and mortality (330 [5.4%]; aRR, 0.53; 95% CI, 0.47-0.59) than the general population. Both PEI and PEH had longer mean (SD) lengths of stay (PEI, 9 [10] days; PEH, 11 [26] days) and a higher frequency of readmission (PEI, 128 [5.9%]; PEH, 519 [8.5%]) than the general population (mean [SD] length of stay, 8 [10] days; readmission, 28 493 [4.6%])., Conclusions and Relevance: In this cross-sectional study, a higher frequency of COVID-19 hospitalizations for PEI and PEH underscored the importance of adhering to recommended prevention measures. Expanding medical respite may reduce hospitalizations in these disproportionately affected populations.

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.)

Background: Information on the costs of inpatient care for patients with coronavirus disease 2019 (COVID-19) is very limited. This study estimates the per-patient cost of inpatient care for adult COVID-19 patients seen at >800 US hospitals., Methods: Patients aged ≥18 years with ≥1 hospitalization during March 2020-July 2021 with a COVID-19 diagnosis code in a large electronic administrative discharge database were included. We used validated costs when reported; otherwise, costs were calculated using charges multiplied by cost-to-charge ratios. We estimated costs of inpatient care per patient overall and by severity indicator, age, sex, underlying medical conditions, and acute complications of COVID-19 using a generalized linear model with log link function and gamma distribution., Results: The overall cost among 654673 patients hospitalized with COVID-19 was $16.2 billion. Estimated per-patient hospitalization cost was $24 826. Among surviving patients, estimated per-patient cost was $13 090 without intensive care unit (ICU) admission or invasive mechanical ventilation (IMV), $21 222 with ICU admission alone, and $59 742 with IMV. Estimated per-patient cost among patients who died was $27 017. Adjusted cost differential was higher among patients with certain underlying conditions (eg, chronic kidney disease [$12 391], liver disease [$8878], cerebrovascular disease [$7267], and obesity [$5933]) and acute complications (eg, acute respiratory distress syndrome [$43 912], pneumothorax [$25 240], and intracranial hemorrhage [$22 280])., Conclusions: The cost of inpatient care for COVID-19 patients was substantial through the first 17 months of the pandemic. These estimates can be used to inform policy makers and planners and cost-effectiveness analysis of public health interventions to alleviate the burden of COVID-19., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2021.)

Background: Clinical severity of coronavirus disease 2019 (COVID-19) may vary over time; trends in clinical severity at admission during the pandemic among hospitalized patients in the United States have been incompletely described, so a historical record of severity over time is lacking., Methods: We classified 466677 hospital admissions for COVID-19 from April 2020 to April 2021 into 4 mutually exclusive severity grades based on indicators present on admission (from most to least severe): Grade 4 included intensive care unit (ICU) admission and invasive mechanical ventilation (IMV); grade 3 included non-IMV ICU and/or noninvasive positive pressure ventilation; grade 2 included diagnosis of acute respiratory failure; and grade 1 included none of the above indicators. Trends were stratified by sex, age, race/ethnicity, and comorbid conditions. We also examined severity in states with high vs low Alpha (B.1.1.7) variant burden., Results: Severity tended to be lower among women, younger adults, and those with fewer comorbidities compared to their counterparts. The proportion of admissions classified as grade 1 or 2 fluctuated over time, but these less-severe grades comprised a majority (75%-85%) of admissions every month. Grades 3 and 4 consistently made up a minority of admissions (15%-25%), and grade 4 showed consistent decreases in all subgroups, including states with high Alpha variant burden., Conclusions: Clinical severity among hospitalized patients with COVID-19 has varied over time but has not consistently or markedly worsened over time. The proportion of admissions classified as grade 4 decreased in all subgroups. There was no consistent evidence of worsening severity in states with higher vs lower Alpha prevalence., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2021.)

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.

Viral infections are a common cause of myocarditis, an inflammation of the heart muscle (myocardium) that can result in hospitalization, heart failure, and sudden death (1). Emerging data suggest an association between COVID-19 and myocarditis (2-5). CDC assessed this association using a large, U.S. hospital-based administrative database of health care encounters from >900 hospitals. Myocarditis inpatient encounters were 42.3% higher in 2020 than in 2019. During March 2020-January 2021, the period that coincided with the COVID-19 pandemic, the risk for myocarditis was 0.146% among patients diagnosed with COVID-19 during an inpatient or hospital-based outpatient encounter and 0.009% among patients who were not diagnosed with COVID-19. After adjusting for patient and hospital characteristics, patients with COVID-19 during March 2020-January 2021 had, on average, 15.7 times the risk for myocarditis compared with those without COVID-19 (95% confidence interval [CI] = 14.1-17.2); by age, risk ratios ranged from approximately 7.0 for patients aged 16-39 years to >30.0 for patients aged <16 years or ≥75 years. Overall, myocarditis was uncommon among persons with and without COVID-19; however, COVID-19 was significantly associated with an increased risk for myocarditis, with risk varying by age group. These findings underscore the importance of implementing evidence-based COVID-19 prevention strategies, including vaccination, to reduce the public health impact of COVID-19 and its associated complications., 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.)

Objective: To assess whether risk of severe outcomes among patients with type 1 diabetes mellitus (T1DM) hospitalized for coronavirus disease 2019 (COVID-19) differs from that of patients without diabetes or with type 2 diabetes mellitus (T2DM)., Research Design and Methods: Using the Premier Healthcare Database Special COVID-19 Release records of patients discharged after COVID-19 hospitalization from U.S. hospitals from March to November 2020 ( N = 269,674 after exclusion), we estimated risk differences (RD) and risk ratios (RR) of intensive care unit admission or invasive mechanical ventilation (ICU/MV) and of death among patients with T1DM compared with patients without diabetes or with T2DM. Logistic models were adjusted for age, sex, and race or ethnicity. Models adjusted for additional demographic and clinical characteristics were used to examine whether other factors account for the associations between T1DM and severe COVID-19 outcomes., Results: Compared with patients without diabetes, T1DM was associated with a 21% higher absolute risk of ICU/MV (RD 0.21, 95% CI 0.19-0.24; RR 1.49, 95% CI 1.43-1.56) and a 5% higher absolute risk of mortality (RD 0.05, 95% CI 0.03-0.07; RR 1.40, 95% CI 1.24-1.57), with adjustment for age, sex, and race or ethnicity. Compared with T2DM, T1DM was associated with a 9% higher absolute risk of ICU/MV (RD 0.09, 95% CI 0.07-0.12; RR 1.17, 95% CI 1.12-1.22), but no difference in mortality (RD 0.00, 95% CI -0.02 to 0.02; RR 1.00, 95% CI 0.89-1.13). After adjustment for diabetic ketoacidosis (DKA) occurring before or at COVID-19 diagnosis, patients with T1DM no longer had increased risk of ICU/MV (RD 0.01, 95% CI -0.01 to 0.03) and had lower mortality (RD -0.03, 95% CI -0.05 to -0.01) in comparisons with patients with T2DM., Conclusions: Patients with T1DM hospitalized for COVID-19 are at higher risk for severe outcomes than those without diabetes. Higher risk of ICU/MV in patients with T1DM than in patients with T2DM was largely accounted for by the presence of DKA. These findings might further guide recommendations related to diabetes management and the prevention of COVID-19., (© 2021 by the American Diabetes Association.)

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.)

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.

We described antibiotic use among inpatients with coronavirus disease 2019 (COVID-19). Most COVID-19 inpatients received antibiotic therapy. We also described hospital-wide antibiotic use during 2020 compared with 2019, stratified by hospital COVID-19 burden. Although total antibiotic use decreased between years, certain antibiotic use increased with higher COVID-19 burden., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2021.)

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.

CDC recommends a combination of evidence-based strategies to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 (1). Because the virus is transmitted predominantly by inhaling respiratory droplets from infected persons, universal mask use can help reduce transmission (1). Starting in April, 39 states and the District of Columbia (DC) issued mask mandates in 2020. Reducing person-to-person interactions by avoiding nonessential shared spaces, such as restaurants, where interactions are typically unmasked and physical distancing (≥6 ft) is difficult to maintain, can also decrease transmission (2). In March and April 2020, 49 states and DC prohibited any on-premises dining at restaurants, but by mid-June, all states and DC had lifted these restrictions. To examine the association of state-issued mask mandates and allowing on-premises restaurant dining with COVID-19 cases and deaths during March 1-December 31, 2020, county-level data on mask mandates and restaurant reopenings were compared with county-level changes in COVID-19 case and death growth rates relative to the mandate implementation and reopening dates. Mask mandates were associated with decreases in daily COVID-19 case and death growth rates 1-20, 21-40, 41-60, 61-80, and 81-100 days after implementation. Allowing any on-premises dining at restaurants was associated with increases in daily COVID-19 case growth rates 41-60, 61-80, and 81-100 days after reopening, and increases in daily COVID-19 death growth rates 61-80 and 81-100 days after reopening. Implementing mask mandates was associated with reduced SARS-CoV-2 transmission, whereas reopening restaurants for on-premises dining was associated with increased transmission. Policies that require universal mask use and restrict any on-premises restaurant dining are important components of a comprehensive strategy to reduce exposure to and transmission of SARS-CoV-2 (1). Such efforts are increasingly important given the emergence of highly transmissible SARS-CoV-2 variants in the United States (3,4)., 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.

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 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.)

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.

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.

Wolf control to reduce cattle depredation is an important issue to ecology and agriculture in the United States. Two recent papers use the same dataset having wolf population characteristics and cattle depredation, but come to opposing conclusions concerning the link between wolf control and cattle depredation. Our paper aims to resolve this issue by using the same dataset and developing a model based on a causal association that would explain the nature of the relationship between wolf control and cattle depredation. We use the data on wolf population, number of cattle, number of wolves killed and number of cattle killed, from the U.S. Fish and Wildlife Services Interagency Annual Wolf Reports over the period of 1987-2012. We find a positive link between wolf control and cattle depredation. However, it would be incorrect to infer that wolf control has a positive effect on the number of cattle depredated. We maintain that this link comes from a growing wolf population, which increases cattle depredation, and in turn, causes an increase in the number of wolves killed. While the wolf population is growing, we see both wolf removal and cattle depredation simultaneously grow. It is not until the wolf population growth nears the steady state, that removal of wolves has a sufficient negative effect to reduce or stabilize the number of cattle depredated.