对美国膀癌特定死亡率趋势的纵向分析
Isabella R Pompa1, David Qi1, Anushka Ghosh1
1Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
Bladder cancer (Amsterdam, Netherlands)
|January 4, 2024
概括
从1999年到2020年,美国的整体膀癌死亡率下降. 然而,在85岁及以上的个体中,膀癌特异性死亡率和转移发病率增加,凸显了针对性干预的需要.
科学领域:
- 在瘤学瘤学.
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 膀癌是美国癌症死亡的重要原因之一.
- 最近诊断和治疗方面的进展改善了膀癌的治疗.
- 了解死亡率趋势对于公共卫生战略至关重要.
研究的目的:
- 分析从1999年到2020年美国膀癌死亡率和发病率的纵向趋势.
- 通过各种人口 (性别,种族,种族,年龄) 和地理 (地区,城市化) 因素来检查这些趋势.
- 为了确定特定的人口经历不断增加的死亡率.
主要方法:
- 利用CDC WONDER和NAACCR数据库进行年龄调整的膀癌死亡率和发病率 (1999-2020).
- 对于膀癌特异性死亡率 (BCSM) 和发病率的估计趋势和年平均百分比变化 (AAPC).
- 在2023年5月至2023年10月期间进行的数据分析.
主要成果:
- 总体而言,BCSM每年下降0.4%,与2015-2020年相比显著下降 (AAPC:-2.0%).
- 在85岁以上的个人中,BCSM和转移性膀癌的发病率增加 (BCSM AAPC:0.8%;转移性发病率AAPC:2.5%).
- 在南方,农村地区以及非西班牙裔白人和亚洲或太平洋岛民人口中,在某些年份观察到BCSM的增加.
结论:
- 膀癌死亡率在过去二十年中在全国范围内有所下降.
- 在老年人 (85岁以上) 中,BCSM和转移发病率的显著增加趋势存在.
- 需要有针对性的战略来解决差异,并改善特定的人口和地理群体的结果.
相关概念视频
01:21Cancer Survival Analysis
355
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...
355
01:18Introduction To Survival Analysis
242
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
242
01:20Actuarial Approach
79
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
79
01:20Comparing the Survival Analysis of Two or More Groups
195
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...
195
01:09Truncation in Survival Analysis
209
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
209
01:25Statistical Methods for Analyzing Epidemiological Data
369
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:
369