INTRODUCTION: PCORnet, the National Patient-Centered Clinical Research Network, is a large research network of health systems that map clinical data to a standardized data model. In 2018, we expanded existing infrastructure to facilitate use for public health surveillance. We describe benefits and challenges of using PCORnet for surveillance and describe case studies. METHODS: In 2018, infrastructure enhancements included addition of a table to store patients' residential zip codes and expansion of a modular program to generate population health statistics across conditions. Chronic disease surveillance case studies conducted in 2019 assessed atrial fibrillation (AF) and cirrhosis. In April 2020, PCORnet established an infrastructure to support COVID-19 surveillance with institutions frequently updating their electronic health record data. RESULTS: By August 2023, 53 PCORnet sites (84%) had a 5-digit zip code available on at least 95% of their patient populations. Among 148,223 newly diagnosed AF patients eligible for oral anticoagulant (OAC) therapy, 43.3% were on any OAC (17.8% warfarin, 28.5% any novel oral anticoagulant) within a year of the AF diagnosis. Among 60,268 patients with cirrhosis (2015-2019), common documented etiologies included unknown (48%), hepatitis C infection (23%), and alcohol use (22%). During October 2022 through December 2023, across 34 institutions, the proportion of COVID-19 patients who were cared for in the inpatient setting was 9.1% among 887,051 adults aged 20 years or older and 6.0% among 139,148 children younger than 20 years. CONCLUSIONS: PCORnet provides important data that may augment traditional public health surveillance programs across diverse conditions. PCORnet affords longitudinal population health assessments among large catchments of the population with clinical, treatment, and geographic information, with capabilities to deliver rapid information needed during public health emergencies.

BACKGROUND: Data modernization efforts to strengthen surveillance capacity could help assess trends in use of preventive services and diagnoses of new chronic disease during the COVID-19 pandemic, which broadly disrupted health care access. METHODS: This cross-sectional study examined electronic health record data from US adults aged 21 to 79 years in a large national research network (PCORnet), to describe use of 8 preventive health services (N = 30,783,825 patients) and new diagnoses of 9 chronic diseases (N = 31,588,222 patients) during 2018 through 2022. Joinpoint regression assessed significant trends, and health debt was calculated comparing 2020 through 2022 volume to prepandemic (2018 and 2019) levels. RESULTS: From 2018 to 2022, use of some preventive services increased (hemoglobin A(1c) and lung computed tomography, both P < .05), others remained consistent (lipid testing, wellness visits, mammograms, Papanicolaou tests or human papillomavirus tests, stool-based screening), and colonoscopies or sigmoidoscopies declined (P < .01). Annual new chronic disease diagnoses were mostly stable (6% hypertension; 4% to 5% cholesterol; 4% diabetes; 1% colonic adenoma; 0.1% colorectal cancer; among women, 0.5% breast cancer), although some declined (lung cancer, cervical intraepithelial neoplasia or carcinoma in situ, cervical cancer, all P < .05). The pandemic resulted in health debt, because use of most preventive services and new diagnoses of chronic disease were less than expected during 2020; these partially rebounded in subsequent years. Colorectal screening and colonic adenoma detection by age group aligned with screening recommendation age changes during this period. CONCLUSION: Among over 30 million patients receiving care during 2018 through 2022, use of preventive services and new diagnoses of chronic disease declined in 2020 and then rebounded, with some remaining health debt. These data highlight opportunities to augment traditional surveillance with EHR-based data.

Chronic disease affects 6 in 10 adults in the US, while 4 in 10 adults live with multiple chronic diseases (1). Chronic diseases represent one of the nation’s leading causes of disability and drivers of the nation’s $4.1 trillion in annual health care spending (1). Chronic conditions including heart disease, cancer, stroke, diabetes, and chronic kidney disease dominate the leading causes of death. Furthermore, leading lifestyle risk factors in the US include tobacco use, poor nutrition, physical inactivity, and excessive alcohol use (1). | | Chronic disease prevention and control necessitates a comprehensive strategy to prevent disease (2–4), which is needed now more than ever (5). Information systems innovations are needed to advance health activities and outcomes and to allow decision makers and practitioners to act (4,6–8). Chronic disease data are a foundation that can inform interventions to promote healthy communities, support healthy behaviors and lifestyles, and facilitate effective and coordinated chronic disease prevention and health promotion (5,9). The benefits of an improved chronic disease data landscape include improved management of chronic disease programs, enhanced communication, data exchange, and coordination between federal, state, tribal, local, and territorial health departments and their partners. Additionally, efforts aimed at enhancing chronic disease surveillance practices will better enable a learning health system, precision public health, and improved situational awareness that will ultimately allow people across the US to live longer, healthier lives (10–12).

Thank you for your letter. We recognize that stroke-related laws, policies, and funding vary by state, which impacts the provision of stroke care. Centers for disease control and prevention (CDC’s) Division for Heart Disease and Stroke Prevention has posted resources on stroke-related state policy and capacity at https://www.cdc.gov/dhdsp/pubs/stroke_resources.htm. We also recently published a report1 on evidence-supported state laws to advance stroke care. State-level data from nine funded states drive the Paul Coverdell National Acute Stroke Program’s (PCNASP’s) activities. For example, each PCNASP state receives quarterly reports on its quality improvement activities, and state summaries for each funding cycle and state success stories are shared publicly.2, 3 | | We appreciate Steven Thomas’s comment about evaluating stroke care during the current pandemic. PCNASP’s support for data collection to improve the quality of stroke care in its nine funded states is ongoing through the pandemic and into the future.4, 5 Additionally, national dissemination of the funded states’ successes helps extend these lessons beyond the Coverdell experience.

Cardiorespiratory fitness (CRF) refers to the capacity of the circulatory and respiratory systems to supply oxygen to skeletal muscle mitochondria for energy production needed during physical activity. CRF is an important marker of physical and mental health and academic achievement in youth. However, only 40% of US youth are currently believed to have healthy CRF. In this statement, we review the physiological principles that determine CRF, the tools that are available to assess CRF, the modifiable and nonmodifiable factors influencing CRF, the association of CRF with markers of health in otherwise healthy youth, and the temporal trends in CRF both in the United States and internationally. Development of a cost-effective CRF measurement process that could readily be incorporated into office visits and in field settings to screen all youth periodically could help identify those at increased risk.

Ideal cardiovascular health (CVH) is associated with a lower risk of heart disease and stroke while adverse childhood events (ACEs) are related to health behaviors (e.g., smoking, unhealthy diet) and conditions (e.g., hypertension, diabetes) associated with CVH. Data from the 2019 Behavioral Risk Factor Surveillance System was used to explore ACEs and CVH among 86,584 adults ≥18 years from 20 states. CVH was defined as poor (0-2), intermediate (3-5), and ideal (6-7) from summation of survey indicators (normal weight, healthy diet, adequate physical activity, not smoking, no hypertension, no high cholesterol, and no diabetes). ACEs was summed by number (0,1, 2, 3, and ≥4). A generalized logit model estimated associations between poor and intermediate CVH (ideal as referent) and ACEs accounting for age, race/ethnicity, sex, education, and health care coverage. Overall, 16.7% (95% Confidence Interval[CI]:16.3-17.1) had poor, 72.4% (95%CI:71.9-72.9) had intermediate, and 10.9% (95%CI:10.5-11.3) had ideal CVH. Zero ACEs were reported for 37.0% (95%CI:36.4-37.6), 22.5% (95%CI:22.0-23.0) reported 1, 12.7% (95%CI:12.3-13.1) reported 2, 8.5% (95%CI:8.2-8.9) reported 3, and 19.3% (95%CI:18.8-19.8) reported ≥4 ACEs. Those with 1 (Adjusted Odds Ratio [AOR] = 1.27;95%CI = 1.11-1.46), 2 (AOR = 1.63;95%CI:1.36-1.96), 3 (AOR = 2.01;95%CI:1.66-2.44), and ≥ 4 (AOR = 2.47;95%CI:2.11-2.89) ACEs were more likely to report poor (vs. ideal) CVH compared to those with 0 ACEs. Those who reported 2 (AOR = 1.28;95%CI = 1.08-1.51), 3 (AOR = 1.48;95%CI:1.25-1.75), and ≥ 4 (AOR = 1.59;95%CI:1.38-1.83) ACEs were more likely to report intermediate (vs. ideal) CVH compared to those with 0 ACEs. Preventing and mitigating the harms of ACEs and addressing barriers to ideal CVH, particularly social and structural determinants, may improve health.

CONTEXT: Electronic health record (EHR) data can potentially make chronic disease surveillance more timely, actionable, and sustainable. Although use of EHR data can address numerous limitations of traditional surveillance methods, timely surveillance data with broad population coverage require scalable systems. This report describes implementation, challenges, and lessons learned from the Multi-State EHR-Based Network for Disease Surveillance (MENDS) to help inform how others work with EHR data to develop distributed networks for surveillance. PROGRAM: Funded by the Centers for Disease Control and Prevention (CDC), MENDS is a data modernization demonstration project that aims to develop a timely national chronic disease sentinel surveillance system using EHR data. It facilitates partnerships between data contributors (health information exchanges, other data aggregators) and data users (state and local health departments). MENDS uses query and visualization software to track local emerging trends. The program also uses statistical and geospatial methods to generate prevalence estimates of chronic disease risk measures at the national and local levels. Resulting data products are designed to inform public health practice and improve the health of the population. IMPLEMENTATION: MENDS includes 5 partner sites that leverage EHR data from 91 health system and clinic partners and represents approximately 10 million patients across the United States. Key areas of implementation include governance, partnerships, technical infrastructure and support, chronic disease algorithms and validation, weighting and modeling, and workforce education for public health data users. DISCUSSION: MENDS presents a scalable distributed network model for implementing national chronic disease surveillance that leverages EHR data. Priorities as MENDS matures include producing prevalence estimates at various geographic and subpopulation levels, developing enhanced data sharing and interoperability capacity using international data standards, scaling the network to improve coverage nationally and among underrepresented geographic areas and subpopulations, and expanding surveillance of additional chronic disease measures and social determinants of health.

The coronavirus disease 2019 (COVID-19) pandemic has underscored the need to prevent chronic disease and promote health.1 , 2 More than a million American lives have been lost to COVID-19, and life expectancy decreased between 2018 and 2020.3 , 4 Chronic diseases are major risk factors for COVID-19 morbidity and mortality.5 In addition, COVID-19 morbidity and mortality have been higher among persons from racial and ethnic groups such as those who are African American, Hispanic or Latino, and American Indian or Alaska Native as well as those living at lower SES.6 This has magnified pre-existing health inequities in chronic disease.1 , 2 , 7

OBJECTIVE: To assess the association of body mass index (BMI) with inpatient care cost, duration, and acute complications among patients hospitalized for COVID-19 at 273 U.S. hospitals. METHODS: Children (2-17 years) and adults (≥18 years) hospitalized for COVID-19 during March 2020-July 2021 and measured BMI in a large electronic administrative healthcare database were included. We used generalized linear models 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 costs of care, and obesity was associated with longer hospital stays. Children with severe obesity had a higher cost of care, but not significantly longer hospital stays, compared to those with healthy weights. Children with severe obesity were 3.7 times (95% CI: 1.5 to 9.5) as likely to have invasive mechanical ventilation and 62% more likely to have an acute complication (95% CI, 39-90), compared to children with healthy weight. CONCLUSIONS: These findings show that patients with high BMIs experience significant healthcare burden during inpatient COVID-19 care. This article is protected by copyright. All rights reserved.

The COVID-19 pandemic has magnified longstanding health care and social inequities, resulting in disproportionately high COVID-19-associated illness and death among members of racial and ethnic minority groups (1). Equitable use of effective medications (2) could reduce disparities in these severe outcomes (3). Monoclonal antibody (mAb) therapies against SARS-CoV-2, the virus that causes COVID-19, initially received Emergency Use Authorization (EUA) from the Food and Drug Administration (FDA) in November 2020. mAbs are typically administered in an outpatient setting via intravenous infusion or subcutaneous injection and can prevent progression of COVID-19 if given after a positive SARS-CoV-2 test result or for postexposure prophylaxis in patients at high risk for severe illness.(†) Dexamethasone, a commonly used steroid, and remdesivir, an antiviral drug that received EUA from FDA in May 2020, are used in inpatient settings and help prevent COVID-19 progression(§) (2). No large-scale studies have yet examined the use of mAb by race and ethnicity. Using COVID-19 patient electronic health record data from 41 U.S. health care systems that participated in the PCORnet, the National Patient-Centered Clinical Research Network,(¶) this study assessed receipt of medications for COVID-19 treatment by race (White, Black, Asian, and Other races [including American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and multiple or Other races]) and ethnicity (Hispanic or non-Hispanic). Relative disparities in mAb** treatment among all patients(††) (805,276) with a positive SARS-CoV-2 test result and in dexamethasone and remdesivir treatment among inpatients(§§) (120,204) with a positive SARS-CoV-2 test result were calculated. Among all patients with positive SARS-CoV-2 test results, the overall use of mAb was infrequent, with mean monthly use at 4% or less for all racial and ethnic groups. Hispanic patients received mAb 58% less often than did non-Hispanic patients, and Black, Asian, or Other race patients received mAb 22%, 48%, and 47% less often, respectively, than did White patients during November 2020-August 2021. Among inpatients, disparities were different and of lesser magnitude: Hispanic inpatients received dexamethasone 6% less often than did non-Hispanic inpatients, and Black inpatients received remdesivir 9% more often than did White inpatients. Vaccines and preventive measures are the best defense against infection; use of COVID-19 medications postexposure or postinfection can reduce morbidity and mortality and relieve strain on hospitals but are not a substitute for COVID-19 vaccination. Public health policies and programs centered around the specific needs of communities can promote health equity (4). Equitable receipt of outpatient treatments, such as mAb and antiviral medications, and implementation of prevention practices are essential to reducing existing racial and ethnic inequities in severe COVID-19-associated illness and death.

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.

Vaccination against SARS-CoV-2, the virus that causes COVID-19, is highly effective at preventing COVID-19-associated hospitalization and death; however, some vaccinated persons might develop COVID-19 with severe outcomes(†) (1,2). Using data from 465 facilities in a large U.S. health care database, this study assessed the frequency of and risk factors for developing a severe COVID-19 outcome after completing a primary COVID-19 vaccination series (primary vaccination), defined as receipt of 2 doses of an mRNA vaccine (BNT162b2 [Pfizer-BioNTech] or mRNA-1273 [Moderna]) or a single dose of JNJ-78436735 [Janssen (Johnson & Johnson)] ≥14 days before illness onset. Severe COVID-19 outcomes were defined as hospitalization with a diagnosis of acute respiratory failure, need for noninvasive ventilation (NIV), admission to an intensive care unit (ICU) including all persons requiring invasive mechanical ventilation, or death (including discharge to hospice). Among 1,228,664 persons who completed primary vaccination during December 2020-October 2021, a total of 2,246 (18.0 per 10,000 vaccinated persons) developed COVID-19 and 189 (1.5 per 10,000) had a severe outcome, including 36 who died (0.3 deaths per 10,000). Risk for severe outcomes was higher among persons who were aged ≥65 years, were immunosuppressed, or had at least one of six other underlying conditions. All persons with severe outcomes had at least one of these risk factors, and 77.8% of those who died had four or more risk factors. Severe COVID-19 outcomes after primary vaccination are rare; however, vaccinated persons who are aged ≥65 years, are immunosuppressed, or have other underlying conditions might be at increased risk. These persons should receive targeted interventions including chronic disease management, precautions to reduce exposure, additional primary and booster vaccine doses, and effective pharmaceutical therapy as indicated to reduce risk for severe COVID-19 outcomes. Increasing COVID-19 vaccination coverage is a public health priority.

OBJECTIVE: The purpose of this study was to investigate the association of sodium intake with obesity in US children and adolescents. METHODS: Cross-sectional data were analyzed for 9,026 children and adolescents in the National Health and Nutrition Examination Survey (NHANES) 2009-2016. Usual sodium intake was estimated from 24-hour dietary recalls using a measurement error model. Logistic regression was used to assess the association of sodium intake with overweight/obesity, obesity, and central obesity (waist to height ratio [WtHR] ≥ 0.5; waist circumferences (WC) ≥ age- and sex-specific 90th percentile). RESULTS: Mean (SE) sodium intake was 3,010  (9) and 3,404  (20) mg/d for children and adolescents, respectively. The adjusted odds ratio (AOR) comparing Q4 versus Q1 (87.5th vs. 12.5th percentile of sodium intake) among children was 1.98 (95% CI: 1.19-3.28) for overweight/obesity, 2.20 (1.30-3.73) for obesity, 2.10 (1.12-3.95) for WC ≥ 90th percentile, and 1.68 (0.95-2.97) for WtHR ≥ 0.5, adjusting for demographics, energy, and sugar-sweetened beverage intake. Among adolescents, AOR was 1.81 (0.98-3.37) for overweight/obesity, 1.71 (0.82-3.56) for obesity, 1.62 (0.71-3.66) for WC ≥ 90th percentile, and 1.73 (0.85-3.50) for WtHR ≥ 0.5. CONCLUSIONS: Sodium intake was positively associated with overweight/obesity, obesity, and central obesity among US children independent of energy and SSB intake, but the association did not reach significance among adolescents.

BACKGROUND: In an effort to improve stroke quality of care and patient outcomes, quality of care metrics are monitored to assess utilization of evidence-based stroke care processes as part of the Paul Coverdell National Acute Stroke Program (PCNASP). We aimed to assess temporal trends in defect free care (DFC) received by stroke patients in the PCNASP between 2008 and 2018. METHODS: Quality of care data for ten performance measures were available for 849,793 patients aged ≥18 years who were admitted to a participating hospital with a clinical diagnosis of stroke between 2008 and 2018. A patient who receives care according to all performance measures for which they are eligible, receives "defect-free care" (DFC) (e.g. appropriate medications, assessments and education). Generalized estimating equations were used to examine the factors associated with receipt of DFC. RESULTS: DFC among ischemic stroke patients increased from 38.0% in 2008 to 80.8% in 2018 (p<0.0001), with the largest improvement seen in receipt of stroke education (relative percent change, RPC=64%). Similarly, DFC for hemorrhagic stroke and transient ischemic attack patients increased from 46.7% to 82.6% (RPC=76.9%) and 39.9% to 85.0% (RPC=113.0%) (p<0.001), respectively. Among ischemic stroke patients, the adjusted odds for receiving DFC were lower for women, patients aged 18-54 years, Medicaid or Medicare participants, and patients with atrial fibrillation (p<0.05). CONCLUSIONS: From 2008-2018, receipt of DFC by ischemic stroke patients significantly increased in the PCNASP; however certain subgroups were less likely to receive this level of care. Targeted quality improvement initiatives could result in even further improvements among all stroke patients and help reduce disparities in care.

BACKGROUND: Concern has been expressed by some that sodium reduction could lead to increased prevalence of hyponatremia and hyperkalemia for specific population subgroups. Current concentrations of serum sodium and potassium in the US population can help address this concern. METHODS: We used data from the National Health and Nutrition Examination Survey 2009-2016 to examine mean and selected percentiles of serum sodium and potassium by sex and age group among 25 520 US participants aged 12 years or older. Logistic regression models with predicted residuals were used to examine the age-adjusted prevalence of low serum sodium and high serum potassium among adults aged 20 or older by selected sociodemographic characteristics and by health conditions or medication use. RESULTS: The distributions of serum sodium and potassium concentrations were within normal reference intervals overall and across Dietary Reference Intake life-stage groups, with a few exceptions. Overall, 2% of US adults had low serum sodium (<135 mmol/L) and 0.6% had high serum potassium (>5 mmol/L). Prevalence of low serum sodium and high serum potassium was higher among adults aged 71 or older (4.7 and 2.0%, respectively) and among adults with chronic kidney disease (3.4 and 1.9%), diabetes (5.0 and 1.1%), or using certain medications (which varied by condition), adjusted for age; whereas, prevalence was <1% among adults without these conditions or medications. CONCLUSIONS: Most of the US population has normal serum sodium and potassium concentrations; these data describe population subgroups at higher risk of low serum sodium and high serum potassium and can inform clinical care.

The Centers for Disease Control and Prevention (CDC), the US Food and Drug Administration (FDA), state and local health departments, and public health and clinical stakeholders have investigated a nationwide outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI). 1 As of February 25, 2020, a total of 2,807 hospitalized cases of EVALI have been reported to the CDC from all 50 states, the District of Columbia, and two US territories (Puerto Rico and US Virgin Islands). Sixty-eight deaths have been confirmed in 29 states and the District of Columbia (as of February 18, 2020).2, 3, 4, 5, 6 Mechanisms for lung injury in this syndrome are still being investigated. Vitamin E acetate (VEA) is strongly linked to the EVALI outbreak. VEA has been found in product samples tested by FDA and state laboratories and patient BAL fluid samples tested by the CDC from geographically diverse states. VEA has not been found in the BAL fluid of people who do not have EVALI. However, evidence is not sufficient to rule out the contribution of other chemicals of concern, including chemicals in either tetrahydrocannabinol (THC) or non-THC products, in some of the reported EVALI cases. The current article summarizes evidence as of February 25, 2020, for potential toxicants and mechanisms of toxicity for EVALI.

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

What is already known on this topic? In a recent examination of rehospitalization and death among previously hospitalized patients with e-cigarette or vaping, product use–associated lung injury (EVALI), at least one quarter of rehospitalizations and deaths occurred within 2 days of discharge; comorbidities were common among patients who were rehospitalized or who died after discharge. What is added by this report? Updated guidance recommends posthospitalization outpatient follow-up, optimally within 48 hours of discharge, and emphasizes the importance of preparation for hospital discharge and postdischarge care coordination to reduce risk of rehospitalization and death among hospitalized EVALI patients. What are the implications for public health practice? Incorporating this updated guidance into the management of hospitalized EVALI patients might reduce EVALI-associated morbidity and mortality. © 2020 Department of Health and Human Services. All rights reserved.

Summary What is already known about this topic? Some patients hospitalized for e-cigarette, or vaping, product use–associated lung injury (EVALI) have been rehospitalized or have died after hospital discharge. What is added by this report? Compared with other EVALI patients, rehospitalized patients and patients who died after hospital discharge were more likely to have one or more chronic conditions, including cardiac disease, chronic pulmonary disease, and diabetes, and to be older. At least one quarter of rehospitalizations and deaths occurred within 2 days after discharge. What are the implications for public health practice? Intensive discharge planning, ensuring clinical stability before discharge, optimized case management, and follow-up optimally within 48 hours after hospital discharge might minimize EVALI patients’ risk for rehospitalization and death, especially among patients with chronic conditions. © 2020 Department of Health and Human Services. All rights reserved.

CDC, the Food and Drug Administration (FDA), state and local health departments, and public health and clinical stakeholders are investigating a nationwide outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) (1). CDC has published recommendations for health care providers regarding EVALI (2-4). Recently, researchers from Utah and New York published proposed diagnosis and treatment algorithms for EVALI (5,6). EVALI remains a diagnosis of exclusion because, at present, no specific test or marker exists for its diagnosis, and evaluation should be guided by clinical judgment. Because patients with EVALI can experience symptoms similar to those associated with influenza or other respiratory infections (e.g., fever, cough, headache, myalgias, or fatigue), it might be difficult to differentiate EVALI from influenza or community-acquired pneumonia on initial assessment; EVALI might also co-occur with respiratory infections. This report summarizes recommendations for health care providers managing patients with suspected or known EVALI when respiratory infections such as influenza are more prevalent in the community than they have been in recent months (7). Recommendations include 1) asking patients with respiratory, gastrointestinal, or constitutional symptoms about the use of e-cigarette, or vaping, products; 2) evaluating those suspected to have EVALI with pulse oximetry and obtaining chest imaging, as clinically indicated; 3) considering outpatient management for clinically stable EVALI patients who meet certain criteria; 4) testing patients for influenza, particularly during influenza season, and administering antimicrobials, including antivirals, in accordance with established guidelines; 5) using caution when considering prescribing corticosteroids for outpatients, because this treatment modality has not been well studied among outpatients, and corticosteroids could worsen respiratory infections; 6) recommending evidence-based treatment strategies, including behavioral counseling, to help patients discontinue using e-cigarette, or vaping, products; and 7) emphasizing the importance of annual influenza vaccination for all persons aged >/=6 months, including patients who use e-cigarette, or vaping products.

Over the past decade, blood pressure and sodium intake declined among children and adolescents (ie, youths) in the United States. We updated temporal trends and determined if secular changes in blood pressure might be partly associated with usual sodium intake. We included 12 249 youths aged 8 to 17 years who participated in the National Health and Nutrition Examination Survey from 2003 to 2016 and had blood pressure and dietary data. Logistic regression was used to describe secular trends and the association between usual sodium intake and blood pressure categorized according to 2017 Hypertension Guidelines. The prevalence of youths with combined elevated blood pressure/hypertension (ie, either elevated blood pressure or hypertension) significantly declined from 16.2% in 2003-2004 to 13.3% in 2015-2016 ( P<0.001 for trend), as did hypertension from 6.6% to 4.9% ( P=0.005 for trend). Across the same time period, mean usual sodium intake decreased from 3381 to 3208 mg/day ( P<0.001 for trend). Holding constant survey cycle, sex, age, race and Hispanic origin, and weight status, the adjusted odds ratio per 1000 mg/day of usual sodium intake for elevated blood pressure/hypertension was 1.18 (95% CI, 1.03-1.35) and for hypertension was 1.20 (95% CI, 0.96-1.50). From 2003 to 2016, blood pressure and usual sodium intake declined among youths. Although 1000 mg/day higher usual sodium intake was associated with approximately 20% higher odds of elevated blood pressure/hypertension and hypertension, the association with hypertension was not statistically significant.

Background and Purpose- International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification ( ICD-9-CM and ICD-10-CM) codes are often used for disease surveillance. We examined changes in concordance between ICD-CM codes and clinical diagnoses before and after the transition to ICD-10-CM in the United States (October 1, 2015), and determined if there were systematic variations in concordance by patient and hospital characteristics. Methods- We included Paul Coverdell National Acute Stroke Program patient discharges from 2014 to 2017. Concordance between ICD-CM codes and the clinical diagnosis documented by the physician (assumed as accurate) was calculated for each diagnosis category: ischemic stroke, transient ischemic attack, subarachnoid hemorrhage, and intracerebral hemorrhage. Results- In total, 314 857 patient records were included in the analysis (n=280 hospitals), 55.9% of which were obtained after the transition to ICD-10-CM. While concordance was generally high, a small, and temporary decline occurred from the last calendar quarter of ICD-9-CM (average unadjusted concordance =92.8%) to the first quarter of ICD-10-CM use (91.0%). Concordance differed by diagnosis category and was generally highest for ischemic stroke. In the analysis of ICD-10-CM records, disagreements often occurred between ischemic stroke and transient ischemic attack records and between subarachnoid and intracerebral hemorrhage records. Compared with the smallest hospitals (</=200 beds), larger hospitals had significantly higher odds of concordance (ischemic stroke adjusted odds ratio for >/=400 beds, 1.7; 95% CI, 1.5-1.9). Conclusions- This study identified a small and transient decline in concordance between ICD-CM codes and stroke clinical diagnoses during the coding transition, indicating no substantial impact on the overall identification of stroke patients. Researchers and policymakers should remain aware of potential changes in ICD-CM code accuracy over time, which may affect disease surveillance. Systematic variations in the accuracy of codes by hospital and patient characteristics have implications for quality-of-care studies and hospital comparative assessments.

BACKGROUND: The clinical benefit of intravenous (IV) alteplase in acute ischemic stroke is time dependent. We assessed the overall temporal changes in door-to-needle (DTN) time and examine the factors associated with DTN time </=60 and </=45 minutes. METHODS AND RESULTS: A total of 496 336 acute ischemic stroke admissions were identified in the Paul Coverdell National Acute Stroke Program from 2008 to 2017. We used generalized estimating equations models to examine the factors associated with DTN time </=60 and </=45 minutes, and calculated adjusted odds ratios and 95% CI. Between 2008 and 2017, the percentage of acute ischemic stroke patients who received IV alteplase including those transferred, increased from 6.4% to 15.3%. After excluding those who received IV alteplase at an outside hospital, a total of 39 737 (8%) acute ischemic stroke patients received IV alteplase within 4.5 hours of the time the patient last known to be well. Significant increases were seen in DTN time </=60 minutes (26.4% in 2008 to 66.2% in 2017, P<0.001), as well as DTN time </=45 minutes (10.7% in 2008 to 40.5% in 2017, P<0.001). Patients aged 55 to 84 years were more likely to receive IV alteplase within 60 minutes, while those aged 55 to 74 years were more likely to receive IV alteplase within 45 minutes, as compared with those aged 18 to 54 years. Arrival by emergency medical service, and patients with severe stroke were more likely to receive IV alteplase within 60 and 45 minutes. Conversely, women, black patients as compared with white, and patients with a medical history of diseases associated with stroke were less likely to receive DTN time </=60 or 45 minutes. CONCLUSIONS: Rapid improvements in DTN time were observed in the Paul Coverdell National Acute Stroke Program; however, opportunities to reduce disparities remain.

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(dagger) 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).

CONTEXT: Obesity is a significant health issue in the United States that both clinical and public health systems struggle to address. Electronic health record data could help support multi-sectoral interventions to address obesity. Standards have been identified and created to support the electronic exchange of weight-related data across many stakeholder groups. CASE DESCRIPTION: The Centers for Disease Control and Prevention initiated a public-private partnership including government, industry, and academic technology partners to develop workflow scenarios and supporting systems to exchange weight-related data through standard transactions. This partnership tested the transmission of data using this newly-defined Healthy Weight (HW) profile at multiple health data interoperability demonstration events. FINDINGS: Five transaction types were tested by 12 partners who demonstrated how the standards and related systems support end-to-end workflows around managing weight-related issues in the community. The standard transactions were successfully tested at two Integrating the Healthcare Enterprise (IHE) Connectathon events through 86 validated tests encompassing 38 multi-partner transactions. DISCUSSION: We have successfully demonstrated the transactions defined in the HW profile with a public-private partnership. These tested IT products and HW standards could be used to support a continuum of care around health related issues encompassing both health care and public health functions. CONCLUSION: The use of the HW profile, including a set of transactions and identified standards to implement those transactions, in IT products is a helpful first step in leveraging health information technology to address weight-related issues in the United States. Future work is needed to expand the use of these standards and to assess their use in real world settings.

Electronic information technology standards facilitate high-quality, uniform collection of data for improved delivery and measurement of health care services. Electronic information standards also aid information exchange between secure systems that link health care and public health for better coordination of patient care and better-informed population health improvement activities. We developed international data standards for healthy weight that provide common definitions for electronic information technology. The standards capture healthy weight data on the "ABCDs" of a visit to a health care provider that addresses initial obesity prevention and care: assessment, behaviors, continuity, identify resources, and set goals. The process of creating healthy weight standards consisted of identifying needs and priorities, developing and harmonizing standards, testing the exchange of data messages, and demonstrating use-cases. Healthy weight products include 2 message standards, 5 use-cases, 31 LOINC (Logical Observation Identifiers Names and Codes) question codes, 7 healthy weight value sets, 15 public-private engagements with health information technology implementers, and 2 technical guides. A logic model and action steps outline activities toward better data capture, interoperable systems, and information use. Sharing experiences and leveraging this work in the context of broader priorities can inform the development of electronic information standards for similar core conditions and guide strategic activities in electronic systems.

INTRODUCTION: The prominent decline in U.S. stroke death rates observed for more than 4 decades has slowed in recent years. CDC examined trends and patterns in recent stroke death rates among U.S. adults aged ≥35 years by age, sex, race/ethnicity, state, and census region. METHODS: Trends in the rates of stroke as the underlying cause of death during 2000-2015 were analyzed using data from the National Vital Statistics System. Joinpoint software was used to identify trends in stroke death rates, and the excess number of stroke deaths resulting from unfavorable changes in trends was estimated. RESULTS: Among adults aged ≥35 years, age-standardized stroke death rates declined 38%, from 118.4 per 100,000 persons in 2000 to 73.3 per 100,000 persons in 2015. The annual percent change (APC) in stroke death rates changed from 2000 to 2015, from a 3.4% decrease per year during 2000-2003, to a 6.6% decrease per year during 2003-2006, a 3.1% decrease per year during 2006-2013, and a 2.5% (nonsignificant) increase per year during 2013-2015. The last trend segment indicated a reversal from a decrease to a statistically significant increase among Hispanics (APC = 5.8%) and among persons in the South Census Region (APC = 4.2%). Declines in stroke death rates failed to continue in 38 states, and during 2013-2015, an estimated 32,593 excess stroke deaths might not have occurred if the previous rate of decline could have been sustained. CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Prior declines in stroke death rates have not continued in recent years, and substantial variations exist in timing and magnitude of change by demographic and geographic characteristics. These findings suggest the importance of strategically identifying opportunities for prevention and intervening in vulnerable populations, especially because effective and underused interventions to prevent stroke incidence and death are known to exist.