Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association.

BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

The Impact of Expanding Telehealth-Delivered Dietary Interventions on Long-Term Cardiometabolic Health.

A healthy diet is an important protective factor to prevent cardiometabolic disease. Traditional face-to-face dietary interventions are often episodic, expensive, and may have limited effectiveness, particularly among older adults and people living in rural areas. Telehealth-delivered dietary interventions have proven to be a low-cost and effective alternative approach to improve dietary behaviors among adults with chronic health conditions. In this study, we developed a validated agent-based model of cardiometabolic health conditions to project the impact of expanding telehealth-delivered dietary interventions among older adults in the state of Georgia, a state with a large rural population. We projected the incidence of major cardiometabolic health conditions (type 2 diabetes, hypertension, and high cholesterol) with the implementation of telehealth-delivered dietary interventions versus no intervention among all older adults and 3 subpopulations (older adults with diabetes, hypertension, and high cholesterol, separately). The results showed that expanding telehealth-delivered dietary interventions could avert 22,774 (95% confidence interval [CI]: 22,091-23,457) cases of type 2 diabetes, 19,732 (19,145-20,329) cases of hypertension, and 18,219 (17,672-18,766) cases of high cholesterol for 5 years among older adults in Georgia. The intervention would have a similar effect in preventing cardiometabolic health conditions among the 3 selected subpopulations. Therefore, expanding telehealth-delivered dietary interventions could substantially reduce the burden of cardiometabolic health conditions in the long term among older adults and those with chronic health conditions.

Trends in Food Sources and Diet Quality Among US Children and Adults, 2003-2018.

Importance: Time trends and population disparities in nutritional quality of foods from major US sources, including grocery stores, restaurants, schools, worksites, and other sources, are not well established. Objective: To investigate patterns and trends in diet quality by food sources among US children and adults overall and in sociodemographic subgroups. Design, Setting, and Participants: This serial, cross-sectional survey study included respondents from 8 National Health and Nutrition Examination Survey cycles (2003-2018) with valid dietary recalls. Data were analyzed from April 16, 2020, to July 20, 2020. Exposures: Survey cycle, food source, and key sociodemographic subgroups. Main Outcomes and Measures: Mean diet quality of foods (meals, snacks, and beverages) consumed per person, characterized by the American Heart Association diet score (range, 0-80, with higher scores indicating healthier diets), the Healthy Eating Index 2015 (range, 0-100, with higher scores indicating healthier diets), and their components. For the American Heart Association diet score, poor diet was defined as less than 40.0% adherence (score, <32.0), intermediate diet as 40.0% to 79.9% adherence (score, 32.0-63.9), and ideal as 80.0% or greater adherence (score, ≥64.0). Results: The study included 20 905 children 5 to 19 years of age (mean [SD] age, 12.1 [5.24] years; 51.0% male) and 39 757 adults 20 years or older (mean [SD] age, 47.3 [15.1] years; 51.9% female). Diet quality of foods consumed from grocery stores increased modestly in children (53.2% to 45.1% with poor diet quality; P = .006 for trend) and adults (40.1% to 32.9% with poor diet quality; P = .001 for trend), with smaller changes for restaurants among children (84.8% to 79.6% with poor diet quality; P = .003 for trend). Changes for restaurants among adults were not statistically significant (65.4% to 65.2% with poor diet quality; P = .07 with poor diet quality); the same was true of worksites (adults: 55.6% to 50.7% with poor diet quality; P = .25 for trend). Food quality from other sources worsened (children: 40.0% to 51.7% with poor diet quality; adults: 33.8% to 43.8% with poor diet quality; P < .001 for trend each). The largest improvement in diet quality was in schools, with the percentage with poor diet quality decreasing from 55.6% to 24.4% (P < .001 for trend), mostly after 2010, and with equitable improvements across population subgroups. Findings were similar for Healthy Eating Index 2015. Significant disparities in diet quality trends were seen by sex, race/ethnicity, educational level, and household income for food consumed from grocery stores. For example, the proportion of foods consumed from grocery stores that were of poor diet quality decreased among high-income adults (from 36.9% to 26.5%; P = .001 for trend) but not among low-income adults (from 45.8% to 41.3%; P = .09 for trend). Conclusions and Relevance: By 2017-2018, foods consumed at schools improved significantly and provided the best mean diet quality of major US food sources, without population disparities. Additional improvements are needed from all major US food sources, with particular attention on equity.

Coronavirus Disease 2019 Hospitalizations Attributable to Cardiometabolic Conditions in the United States: A Comparative Risk Assessment Analysis.

BACKGROUND Risk of coronavirus disease 2019 (COVID-19) hospitalization is robustly linked to cardiometabolic health. We estimated the absolute and proportional COVID-19 hospitalizations in US adults attributable to 4 major US cardiometabolic conditions, separately and jointly, and by race/ethnicity, age, and sex. METHODS AND RESULTS We used the best available estimates of independent associations of cardiometabolic conditions with a risk of COVID-19 hospitalization; nationally representative data on cardiometabolic conditions from the National Health and Nutrition Examination Survey 2015 to 2018; and US COVID-19 hospitalizations stratified by age, sex, and race/ethnicity from the Centers for Disease Control and Prevention's Coronavirus Disease 2019-Associated Hospitalization Surveillance Network database and from the COVID Tracking Project to estimate the numbers and proportions of COVID-19 hospitalizations attributable to diabetes mellitus, obesity, hypertension, and heart failure. Inputs were combined in a comparative risk assessment framework, with probabilistic sensitivity analyses and 1000 Monte Carlo simulations to jointly incorporate stratum-specific uncertainties in data inputs. As of November 18, 2020, an estimated 906 849 COVID-19 hospitalizations occurred in US adults. Of these, an estimated 20.5% (95% uncertainty interval [UIs], 18.9-22.1) of COVID-19 hospitalizations were attributable to diabetes mellitus, 30.2% (UI, 28.2-32.3) to total obesity (body mass index ≥30 kg/m2), 26.2% (UI, 24.3-28.3) to hypertension, and 11.7% (UI, 9.5-14.1) to heart failure. Considered jointly, 63.5% (UI, 61.6-65.4) or 575 419 (UI, 559 072-593 412) of COVID-19 hospitalizations were attributable to these 4 conditions. Large differences were seen in proportions of cardiometabolic risk-attributable COVID-19 hospitalizations by age and race/ethnicity, with smaller differences by sex. CONCLUSIONS A substantial proportion of US COVID-19 hospitalizations appear attributable to major cardiometabolic conditions. These results can help inform public health prevention strategies to reduce COVID-19 healthcare burdens.

Sentiment Classification for Chinese Text Based on Interactive Multitask Learning

In this paper, an interactive multitask learning method for Chinese text sentiment classification is proposed. Here, the classic BiLSTM + attention + CRF model is used to obtain full use of the interaction relationship between tasks, and it simultaneously solves the two tasks of emotional dictionary expansion and sentiment classification. The proposed method divides text sentiment classification and emotional dictionary expansion into primary task and subtask, and it adopts the Enhanced Language Representation with Informative Entities (ERNIE) model as the text representation learning model for the primary task. Then, through the maximum pooling layer and the fully connected layer, the text sentiment classification task is completed. Meanwhile, the classical BiLSTM + attention + CRF model is used to extract emotional words from the text in the subtask. In addition, the multitask information interaction mechanism is used, and the prediction information on the autonomous subtask is fed back into the potential representation of the two tasks. After iterative training, the performance of the two tasks is further optimized. Micro-blogs with COVID-19 are used here as the subject to form the experimental data set. The results demonstrate the superiority of the proposed method over other approaches, and they further verify the superiority of ERNIE over BERT, RoBERTa and XLNet for the sentiment classification of Chinese text.