Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged <18 Years - United States, March 1, 2020-June 28, 2021.

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.

A Governance Framework to Integrate Longitudinal Clinical and Community Data in a Distributed Data Network: The Childhood Obesity Data Initiative.

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.

The Childhood Obesity Data Initiative: A Case Study in Implementing Clinical-Community Infrastructure Enhancements to Support Health Services Research and Public Health.

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.

Late Conditions Diagnosed 1-4 Months Following an Initial Coronavirus Disease 2019 (COVID-19) Encounter: A Matched-Cohort Study Using Inpatient and Outpatient Administrative Data-United States, 1 March-30 June 2020.

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.

A Longitudinal Comparison of Alternatives to Body Mass Index Z-Scores for Children with Very High Body Mass Indexes.

OBJECTIVE: The current Centers for Disease Control and Prevention (CDC) body mass index (BMI) z-scores are inaccurate for BMIs of ≥97th percentile. We, therefore, considered 5 alternatives that can be used across the entire BMI distribution: modified BMI-for-age z-score (BMIz), BMI expressed as a percentage of the 95th percentile (%CDC95th percentile), extended BMIz, BMI expressed as a percentage of the median (%median), and %median adjusted for the dispersion of BMIs. STUDY DESIGN: We illustrate the behavior of the metrics among children of different ages and BMIs. We then compared the longitudinal tracking of the BMI metrics in electronic health record data from 1.17 million children in PEDSnet using the intraclass correlation coefficient to determine if 1 metric was superior. RESULTS: Our examples show that using CDC BMIz for high BMIs can result in nonsensical results. All alternative metrics showed higher tracking than CDC BMIz among children with obesity. Of the alternatives, modified BMIz performed poorly among children with severe obesity, and %median performed poorly among children who did not have obesity at their first visit. The highest intraclass correlation coefficients were generally seen for extended BMIz, adjusted %median, and %CDC95th percentile. CONCLUSIONS: Based on the examples of differences in the BMI metrics, the longitudinal tracking results and current familiarity BMI z-scores and percentiles. Both extended BMIz and extended BMI percentiles may be suitable replacements for the current z-scores and percentiles. These metrics are identical to those in the CDC growth charts for BMIs of <95th percentile and are superior for very high BMIs. Researchers' familiarity with the current CDC z-scores and clinicians with the CDC percentiles may ease the transition to the extended BMI scale.

Longitudinal Trends in Body Mass Index Before and During the COVID-19 Pandemic Among Persons Aged 2-19 Years - United States, 2018-2020.

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/m2) 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/m2/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.

Body Mass Index and Risk for COVID-19-Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death - United States, March-December 2020.

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/m2, 25.9 kg/m2, and 23.7 kg/m2, respectively, and then increased sharply with higher BMIs. Risk for invasive mechanical ventilation increased over the full range of BMIs, from 15 kg/m2 to 60 kg/m2. 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.

Outcomes Among Patients Referred to Outpatient Rehabilitation Clinics After COVID-19 diagnosis - United States, January 2020-March 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.

Long-Term Symptoms Among Adults Tested for SARS-CoV-2 - United States, January 2020-April 2021.

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.

Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 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.

Hospital Length of Stay, Charges, and Costs Associated With a Diagnosis of Obesity in US Children and Youth, 2006-2016.

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.

Characteristics of Hospitalized COVID-19 Patients Discharged and Experiencing Same-Hospital Readmission - United States, March-August 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.

Potential Indirect Effects of the COVID-19 Pandemic on Use of Emergency Departments for Acute Life-Threatening Conditions - United States, January-May 2020.

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.

Race/Ethnicity, Underlying Medical Conditions, Homelessness, and Hospitalization Status of Adult Patients with COVID-19 at an Urban Safety-Net Medical Center - Boston, Massachusetts, 2020.

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.

Hypertension Among Youths - United States, 2001-2016.

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).

The Limitations of Transforming Very High Body Mass Indexes into z-Scores among 8.7 Million 2- to 4-Year-Old Children.

OBJECTIVE: To examine the associations among several body mass index (BMI) metrics (z-scores, percent of the 95th percentile (%BMIp95) and BMI minus 95th percentile (ΔBMIp95) as calculated in the growth charts from the Centers for Disease Control and Prevention (CDC). It is known that the widely used BMI z-scores (BMIz) and percentiles calculated from the growth charts can differ substantially from those that directly observed in the data for BMIs above the 97th percentile (z = 1.88). STUDY DESIGN: Cross-sectional analyses of 8.7 million 2- to 4-year-old children who were examined from 2008 through 2011 in the CDC's Pediatric Nutrition Surveillance System. RESULTS: Because of the transformation used to calculate z-scores, the theoretical maximum BMIz varied by >3-fold across ages. This results in the conversion of very high BMIs into a narrow range of z-scores that varied by sex and age. Among children with severe obesity, levels of BMIz were only moderately correlated (r ~ 0.5) with %BMIp95 and ΔBMIp95. Among these children with severe obesity, BMIz levels could differ by more than 1 SD among children who had very similar levels of BMI, %BMIp95 and ΔBMIp95 due to differences in age or sex. CONCLUSIONS: The effective upper limit of BMIz values calculated from the CDC growth charts, which varies by sex and age, strongly influences the calculation of z-scores for children with severe obesity. Expressing these very high BMIs relative to the CDC 95th percentile, either as a difference or percentage, would be preferable to using BMI-for-age, particularly when assessing the effectiveness of interventions.

Longitudinal changes in BMI z-scores among 45 414 2-4-year olds with severe obesity.

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.

Characteristics of Children Aged <18 Years with Zika Virus Disease Acquired Postnatally - U.S. States, January 2015-July 2016.

Zika virus is an emerging mosquito-borne flavivirus that typically causes an asymptomatic infection or mild illness, although infection during pregnancy is a cause of microcephaly and other serious brain abnormalities. Guillain-Barré syndrome and other neurologic complications can occur in adults after Zika virus infection. However, there are few published reports describing postnatally acquired Zika virus disease among children. During January 2015-July 2016, a total of 158 cases of confirmed or probable postnatally acquired Zika virus disease among children aged <18 years were reported to CDC from U.S. states. The median age was 14 years (range = 1 month-17 years), and 88 (56%) were female. Two (1%) patients were hospitalized; none developed Guillain-Barré syndrome, and none died. All reported cases were travel-associated. Overall, 129 (82%) children had rash, 87 (55%) had fever, 45 (29%) had conjunctivitis, and 44 (28%) had arthralgia. Health care providers should consider a diagnosis of Zika virus disease in children who have an epidemiologic risk factor and clinically compatible illness, and should report cases to their state or local health department.

Clinical Inquiries Received by CDC Regarding Suspected Ebola Virus Disease in Children--United States, July 9, 2014-January 4, 2015.

The 2014­2015 Ebola virus disease (Ebola) epidemic is the largest in history and represents the first time Ebola has been diagnosed in the United States. On July 9, 2014, CDC activated its Emergency Operations Center and established an Ebola clinical consultation service to assist U.S. state and local public health officials and health care providers with the evaluation of suspected cases. CDC reviewed all 89 inquiries received by the consultation service during July 9, 2014­ January 4, 2015, about children (persons aged ≤18 years). Most (56 [63%]) children had no identifiable epidemiologic risk factors for Ebola; among the 33 (37%) who did have an epidemiologic risk factor, in every case this was travel from an Ebola-affected country. Thirty-two of these children met criteria for a person under investigation (PUI) because of clinical signs or symptoms. Fifteen PUIs had blood samples tested for Ebola virus RNA by reverse transcription­polymerase chain reaction; all tested negative. Febrile children who have recently traveled from an Ebola-affected country can be expected to have other common diagnoses, such as malaria and influenza, and in the absence of epidemiologic risk factors for Ebola, the likelihood of Ebola is extremely low. Delaying evaluation and treatment for these other more common illnesses might lead to poorer clinical outcomes. Additionally, many health care providers expressed concerns about whether and how parents should be allowed in the isolation room. While maintaining an appropriate level of vigilance for Ebola, public health officials and health care providers should ensure that pediatric PUIs receive timely triage, diagnosis, and treatment of other more common illnesses, and care reflecting best practices in supporting children's psychosocial needs.