Bayesian Model Projecting Cardiovascular Disease Related Mortality Trends in the United States

Abstract

Background As future cardiovascular disease mortality trends have public health implications, we aimed to project ischemic heart disease (IHD), cerebrovascular disease (CeVD), and heart failure (HF) mortality rates for adults (40–79 years). Methods and Results In this population‐level study, we linked the yearly mortality rates (per 100 000 US residents) (2000–2019) with IHD, CeVD, or HF as the primary cause of death from the Centers for Disease Control and Prevention Wide‐Ranging Online Data for Epidemiologic Research with the midyear US population estimates (2000–2035) for adults (40–79 years). We calculated the observed age‐standardized mortality rates (2000–2019) (per 100 000 residents) (aSMR) and fitted Bayesian age‐period‐cohort models to project aSMR for IHD, CeVD, and HF up to 2035 in the United States. Between 2019 (last year of observed data) and 2035 (last year of projected results), the US population (40–79 years) will increase by 16% and age. The IHD aSMR will reduce from 111.9 (in 2019) to 81.8 (66.7–96.9) in 2035, an effect observed for all age groups. The CeVD aSMR will remain comparable between 2019 (37.4) and 2035 (38.6 [30.7–46.5]). The HF aSMR will increase from 16.5 in 2019 to reach 30.9 (13–48.8) in 2035; such increases were observed in all age groups. Conclusions In the United States, between 2020 and 2035, the aSMR for IHD is expected to decrease, for CeVD will remain stable, and for HF will increase substantially. These data can inform resource allocation for future public health initiatives.

Keywords

• age period cohort models
• atherosclerotic cardiovascular disease
• Bayesian modeling
• cardiovascular health
• cerebrovascular disease
• heart failure