在阿拉巴马州乳腺癌生存率的种族差异的结构性决定因素
Mackenzie E Fowler1, Hayden D Reeves1, Melissa J Smith1
1University of Alabama at Birmingham, Birmingham, AL, United States.
概括
邻里贫困显著影响了阿拉巴马州黑人和白人妇女之间乳腺癌生存率的差异. 解决这些社会经济因素可以消除近一半的黑人-白人死亡率差距.
科学领域:
- 在瘤学瘤学.
- 健康差异 在健康上的差异
- 社会流行病学 社会流行病学
背景情况:
- 在乳腺癌存活率方面,种族差异仍然存在,与白人女性相比,黑人女性面临更高的死亡风险.
- 调查这种差异的根本原因对于开发有效的干预措施和改善结果至关重要.
研究的目的:
- 检查农村-城市居住和区域贫困指数 (ADI) 在阿拉巴马州种族和乳腺癌存活率之间的关联中的调解作用.
- 量化社区贫困如何解释黑人和患有乳腺癌的白人女性之间的生存差距.
主要方法:
- 在阿拉巴马州 (2010-2019) 诊断患有乳腺癌的25,195名成年黑人或白人妇女的基于人口的队列的回顾性分析.
- 暴露:自我报告的种族. 调解者:农村-城市状态 (农村-城市通勤区代码) 和社区贫困 (州级ADI). 结果:整体存活率.
- 在对相关共变量进行调整后,进行了统计中介分析.
主要成果:
- 白人女性在诊断时年龄较大,有较低的ADI得分,与黑人女性相比,农村居民比例更高.
- 在调整农村-城市状态和ADI后,观察到种族生存效应的显著部分调解 (比例调解:45%).
- 区域贫困指数 (ADI) 被确定为间接影响的主要驱动因素,而不是农村-城市地位.
结论:
- 邻居贫困的差异约占阿拉巴马州黑人和白人乳腺癌患者生存差异的45%.
- 改善社区贫困可以大幅减少黑人与白人乳腺癌死亡率差距,强调社会经济因素对癌症结果的重要性.
相关概念视频
01:21Cancer Survival Analysis
457
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...
457
01:20Comparing the Survival Analysis of Two or More Groups
297
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...
297
01:15Assumptions of Survival Analysis
199
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.
199