巴西脑血管疾病死亡率趋势:深入的结点分析
Billy McBenedict1, Wilhelmina N Hauwanga2, Aisha Elamin3
1General and Specialized Surgery, Universidade Federal Fluminense, Niteroi, BRA.
Cureus
|October 26, 2023
概括
巴西的脑血管疾病死亡率在2000年至2021年期间显著下降,女性和所有年龄组的死亡率显著改善. 区域差异仍然存在,突出了针对性预防策略的需要.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 心血管研究研究心血管研究
背景情况:
- 脑血管疾病是全球和巴西死亡和残疾的主要原因.
- 尽管医疗保健系统取得了进展,中风发病率和死亡率仍然很高.
- 关于巴西不断演变的脑血管疾病死亡率趋势的持续,全面研究存在研究缺口.
研究的目的:
- 分析2000年至2021年巴西脑血管疾病死亡率的趋势.
- 检查按年龄,性别,州和地理区域的死亡率模式.
- 确定导致死亡率趋势的因素,并为公共卫生战略提供信息.
主要方法:
- 描述性,生态,时间序列研究,利用全国性的巴西死亡率数据 (2000-2021).
- 在20岁以上的人群中,计算每10万人年龄调整的死亡率 (AAMR).
- 加入点回归分析以评估人口和地理类别的死亡率趋势 (年均百分比变化 - APC).
主要成果:
- 总体而言,脑血管疾病的死亡率在男性和女性中都显著下降.
- 与男性相比,女性的死亡率下降幅度更大.
- 所有年龄组都显示出明显的下降,尽管50岁及以上的人群的比例更高. 在所有五大宏观地区都观察到显著的下降趋势,南方和中西部显示稳定下降,而北方和东北地区最初在下降之前增加.
结论:
- 在巴西各地观察到脑血管疾病死亡率的持续下降趋势.
- 政策干预,提高意识和更健康的生活方式可能导致了下降.
- 解决区域差异和专注于预防对于进一步降低巴西脑血管疾病死亡率至关重要.
相关概念视频
Cancer Survival Analysis
357
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...
357
Statistical Methods for Analyzing Epidemiological Data
385
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:
385
Comparing the Survival Analysis of Two or More Groups
201
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...
201
Assumptions of Survival Analysis
136
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.
136
Kaplan-Meier Approach
154
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
154
Relative Risk
191
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
191


