特定原因死亡率的性别差异:七个欧洲国家的区域趋势,1996-2019年
Markus Sauerberg1, Sebastian Klüsener1,2,3, Michael Mühlichen1
1Federal Institute for Population Research (BiB), Wiesbaden, Germany.
European journal of public health
|July 28, 2023
概括
由于非生物因素,男性死亡率更高. 减少来自心血管疾病和瘤的男性过度死亡率显著缩小了欧洲各地区的性别死亡率差异 (SMD).
科学领域:
- 人口统计学 人口统计学
- 公共卫生 公共卫生
- 空间流行病学 空间流行病学
背景情况:
- 男人过度死亡率是一个重要的社会和健康问题,通常与非生物因素有关.
- 男人过度死亡率的亚国家级差异受到的关注不如国家模式那么多.
- 本研究研究了七个欧洲国家的性别死亡率差异 (SMDs) 的空间模式.
研究的目的:
- 评估特定死亡原因对SMD自20世纪90年代中期以来的演变的贡献.
- 分析欧洲各地区中小微企业的全国差异.
- 为减少死亡率不平等而制定有针对性的社会和卫生政策提供信息.
主要方法:
- 利用一个新收集的,空间详细的数据资源,涵盖228个地区.
- 应用了成熟的人口统计学方法来分析死亡率数据.
- 自20世纪90年代中期以来,研究了性别死亡率差异 (SMDs) 的趋势.
主要成果:
- 肌肉疾病的减少主要是由于心血管疾病 (50-60%) 和瘤 (20-30%) 的男性死亡率降低.
- 在西欧,瘤死亡率趋势显著减少了SMD;在东欧和中欧,心血管疾病的变化是关键.
- 虽然男性因外部原因的死亡率较高,但这些死亡在整体SMD中扮演的角色很小.
结论:
- 对中小企业的区域分析对于制定有效的社会和卫生政策至关重要.
- 有针对性的干预措施可以帮助减少和预防男女之间的死亡率不平等.
- 了解死亡原因的空间变化是解决性别死亡率差异的关键.
相关概念视频
Comparing the Survival Analysis of Two or More Groups
225
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
225
Assumptions of Survival Analysis
157
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
157
Bias in Epidemiological Studies
356
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
356
Cancer Survival Analysis
386
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
386
Applications of Life Tables
93
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
93
Statistical Methods for Analyzing Epidemiological Data
414
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
414


