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全球心血管疾病死亡率预测。

Global Cardiovascular Diseases Death Rate Prediction.

作者信息

Gaidai Oleg, Cao Yu, Loginov Stas

机构信息

Shanghai Ocean University, Shanghai, China.

Shanghai Ocean University, Shanghai, China.

出版信息

Curr Probl Cardiol. 2023 May;48(5):101622. doi: 10.1016/j.cpcardiol.2023.101622. Epub 2023 Jan 29.

Abstract

Cardiovascular diseases (CVD) are heart and blood vessels diseases with considerable morbidity and mortality and presenting worldwide public health burden, moreover CVDs are the leading cause of death globally. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of cardiovascular diseases mortality probability. Objective has been to determine extreme cardiovascular diseases death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. Design of this analysis was based on applying novel statistical methods directly to a raw clinical data, with subsequent data analysis using multicenter, population-based, medical survey data based bio-statistical approach. For this study, cardiovascular diseases annual numbers of recorded deaths in all 195 world countries were chosen. The suggested methodology can be used in various public health applications, based on their clinical survey data.

摘要

心血管疾病(CVD)是心脏和血管疾病,具有相当高的发病率和死亡率,给全球公共卫生带来负担,此外,心血管疾病是全球主要的死亡原因。本文描述了一种新颖的生物系统可靠性方法,特别适用于多区域环境和健康系统,经过足够长的时间观察,可对心血管疾病死亡率概率进行可靠的长期预测。目标是确定在任何感兴趣区域的任何时间点极端心血管疾病死亡率概率。处理多区域过程时间观测的传统统计方法不具备有效处理广泛区域维度和不同区域观测之间相互关联的优势。本分析的设计基于直接将新颖的统计方法应用于原始临床数据,随后使用基于多中心、人群的医学调查数据的生物统计方法进行数据分析。对于本研究,选取了世界上所有195个国家记录的心血管疾病年度死亡人数。所建议的方法可基于其临床调查数据用于各种公共卫生应用。

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