The Trends in Atrial Fibrillation-Related Mortality before, during, and after the COVID-19 Pandemic Peak in the United States.
Inon DimriAriel RoguinNashed HamudaRami Abu FanneMaguli BarelEran LeshemOfer KoboGilad MargolisPublished in: Journal of clinical medicine (2024)
Background : During the first months of the COVID-19 outbreak, an increase was observed in atrial fibrillation (AF)-related mortality in the United States (U.S). We aimed to investigate AF-related mortality trends in the U.S. before, during, and after the COVID-19 pandemic peak, stratified by sociodemographic factors. Methods : using the Wide-Ranging Online Data for Epidemiologic Research database of the Centers for Disease Control and Prevention, we compared the AF-related age-adjusted mortality rate (AAMR) among different subgroups in the two years preceding, during, and following the pandemic peak (2018-2019, 2020-2021, 2022-2023). Result : By analyzing a total of 1,267,758 AF-related death cases, a significant increase of 24.8% was observed in AF-related mortality during the pandemic outbreak, followed by a modest significant decrease of 1.4% during the decline phase of the pandemic. The most prominent increase in AF-related mortality was observed among males, among individuals younger than 65 years, and among individuals of African American and Hispanic descent, while males, African American individuals, and multiracial individuals experienced a non-statistically significant decrease in AF-related mortality during the pandemic decline period. Conclusions : Our findings suggest that in future healthcare crises, targeted healthcare policies and interventions to identify AF, given its impact on patients' outcomes, should be developed while addressing disparities among different patient populations.
Keyphrases
- atrial fibrillation
- african american
- healthcare
- cardiovascular events
- sars cov
- coronavirus disease
- risk factors
- heart failure
- left atrial
- public health
- physical activity
- oral anticoagulants
- type diabetes
- left ventricular
- adipose tissue
- machine learning
- insulin resistance
- drug delivery
- acute coronary syndrome
- ejection fraction
- weight loss
- mitral valve
- big data
- patient reported outcomes
- deep learning