医疗补助保险连续性与新诊断的儿童和青少年癌症患者的存活率之间的关联
Xin Hu1,2,3, Sharon M Castellino2,3,4, Anne C Kirchhoff5,6
1Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA.
JCO oncology practice
|September 30, 2024
概括
在癌症诊断之前和之后,持续的医疗补助覆盖改善了儿科和青少年瘤患者的生存率. 仅在诊断时获得医疗补助与更高的死亡风险有关.
科学领域:
- 在瘤学瘤学.
- 医疗保健服务研究 医疗服务研究
- 公共卫生 公共卫生
背景情况:
- 许多儿童癌症患者在诊断后才获得医疗补助.
- 延迟保险覆盖可能会阻碍早期癌症检测和治疗,可能会恶化生存结果.
- 关于持续与延迟的医疗补助入学在儿科瘤学中的生存影响的数据有限.
研究的目的:
- 调查从前诊断到后诊断的连续医疗补助覆盖是否为儿科和青少年癌症患者提供生存益处.
- 为了比较连续医疗补助覆盖的患者和在诊断时或诊断后获得覆盖的患者之间的生存结果.
主要方法:
- 在1,800名儿科/青少年癌症患者 (年龄 ≤21岁) 的回顾性队列研究中,诊断为2007-2016年.
- 将电子病历与医疗补助行政数据联系起来,以评估保险连续性. 诊断前6个月至诊断后6个月.
- 使用多变量回归来分析保险模式与全因/癌症特异性存活率在诊断后5年之间的关联.
主要成果:
- 在1,293名医疗补助计划注册患者中,47.6%的患者获得了持续覆盖,而36.3%的患者在诊断时/诊断后获得了覆盖.
- 与连续覆盖的患者相比,在诊断时/诊断后获得医疗补助的患者具有明显更高的全因死亡 (HR 1.41) 和癌症特定死亡 (HR 1.46) 风险.
- 不与医疗补助数据相关的患者在诊断时拥有私人或其他/没有保险.
结论:
- 在癌症诊断期间持续的医疗补助覆盖与儿科和青少年患者的生存率改善有关.
- 这些发现强调了对接受癌症治疗的弱势群体进行一致保险的重要性.
- 该研究强调了由于政策变化而导致的近期医疗补助覆盖损失的关键影响.
更多相关视频
04:04Endobronchial Ultrasound-guided Intratumoral Injection of Cisplatin for the Treatment of Isolated Mediastinal Recurrence of Lung Cancer
Published on: February 12, 2017
10.4K
08:34Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
Published on: February 6, 2019
20.3K
相关概念视频
01:21Cancer Survival Analysis
329
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...
329
01:20Actuarial Approach
63
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,...
63
01:15Assumptions of Survival Analysis
101
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.
101
01:20Comparing the Survival Analysis of Two or More Groups
156
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...
156
01:24Kaplan-Meier Approach
104
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,...
104
01:18Introduction To Survival Analysis
188
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...
188