Burden of cardiovascular disease among elderly: based on the Global Burden of Disease Study 2019.

BACKGROUND: The burden of elderly cardiovascular disease (CVD) has received increasing attention with population ageing worldwide. AIMS: We reported on the global CVD burden in elderly individuals over 70, 1990-2019. METHODS AND RESULTS: Based on the Global Burden of Disease Study 2019, elderly CVD burden data were analysed. Temporal burden trends were analysed with the joinpoint model. The slope index and concentration index were used to evaluate health inequality. From 1990 to 2019, the global elderly CVD incidence, prevalence, death, and disability-adjusted life year rates generally decreased. However, the current burden remains high. The rapid growth in burden in parts of sub-Saharan Africa and Asia is a cause for concern. Countries with a higher socio-demographic index (SDI) have generally seen a greater decrease in burden, while countries with a lower SDI have generally experienced increases or smaller declines in burden. Health inequality analysis confirmed that the burden was gradually concentrating towards countries with a low SDI. Among the different CVDs, ischaemic heart disease causes the greatest burden in elderly individuals. Most CVD burdens increase with age, but stroke and peripheral vascular disease show markedly different distributional characteristics. In addition, the burden of hypertensive heart disease shows an unusual shift towards high-SDI countries. High systolic blood pressure was consistently the leading risk factor for CVD among elderly individuals. CONCLUSION: The burden of CVD in older people remains severe and generally tends to shift to lower-SDI countries. Policymakers need to take targeted measures to reduce its harm.

Construction and Application of Psychological Quality Assessment Model for College Students Based on Extensive Data Analysis.

This paper constructs a platform framework for extensive data analysis of college students' psychological quality with the help of the thinking mode of big data and related technologies and proposes the construction principles, data sources, data processing methods, data platform construction, and platform application of big data analysis platform for college students' psychological quality assessment. This paper combines the application methods of big data technology, collects the management data related to the psychological quality assessment of college students, saves them into the system database with certain storage logic, and realizes the function of psychological quality assessment through the design of selected psychological quality assessment data, data management and data resource management and other parts based on the data results of extensive data analysis. This study provides some insights into the psychological quality assessment of college students. The strength of association between the variables of psychological quality assessment of college students changes over time, but the overall psychological structure is more stable. This stable psychological structure characteristic is conducive to constructing the policy of constant psychological education in large universities.