分析和优化公平的美国癌症临床试验中心 通过旅行时间访问
Hassal Lee1, Alexander Shakeel Bates2, Shawneequa Callier3,4
1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York.
JAMA oncology
|March 21, 2024
概括
美国的临床试验场所位于富裕的白人占主导地位的地区. 本研究确定了在癌症临床试验中改善少数群体和低收入人群代表性的机会.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 临床试验管理 临床试验管理
- 健康 公平 卫生 公平
背景情况:
- 临床试验不足以代表种族和少数民族以及社会经济弱势群体.
- 解决这种不平等需要数据驱动的策略来改善访问和代表性.
研究的目的:
- 对美国癌症临床试验中心的种族和社会经济人口统计数据的地理分布进行分析.
- 确定潜在的地点,以改善公平获得临床试验的机会.
主要方法:
- 使用美国人口普查,OpenStreetMap和临床试验地点数据 (2006-2020) 的纵向定量研究.
- 从癌症试验地点到30分钟,60分钟和120分钟的通勤时间内的人口统计学分析.
- 与医院和癌症中心位置相关的社会经济贫困和种族多样性的映射.
主要成果:
- 美国主要的癌症试验中心位于人口较富裕的地区,与全国平均水平相比,白人比例较高.
- 分析显示,美国顶级医院的流域人口在种族和民族多样性方面存在差异.
- 数据允许识别医院通勤距离内的各种人口普查区域,并为美国主要城市生成地图.
结论:
- 在美国癌症试验场所附近的人口中存在显著的社会人口统计偏差.
- 该研究为确定公平的卫星医院地点提供了一个框架,以提高临床试验的多样性.
- 虽然改善了地理接入,但必须解决其他招聘障碍,以实现真正的公平.
相关概念视频
Cancer Survival Analysis
345
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...
345
Hazard Ratio
118
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
118
Comparing the Survival Analysis of Two or More Groups
181
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...
181


