安大略省黑人分娩后死亡率:呼吁提供更好的基于种族和社会人口统计数据
Carolina Lavin Venegas1, Nicole F Roberts2, Alicia St Hill2
1Better Outcomes Registry & Network (BORN) Ontario, Ottawa, ON; Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON.
Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et gynecologie du Canada : JOGC
|September 12, 2025
概括
在加拿大安大略省的黑人,在产科结果中面临更高的风险. 改善关于种族和社会决定因素的数据收集对于推进围产期健康公平至关重要.
科学领域:
- 产科和妇科 产科和妇科
- 公共卫生 公共卫生
- 健康 公平 研究 健康 公平 研究
背景情况:
- 产科结果的种族差异是一个全球性的问题,黑人面临着高的孕产妇死亡风险.
- 现有的加拿大关于这些差异的数据,特别是对于黑人分娩的人来说,是有限的.
- 解决健康不平等问题需要对包括种族在内的社会人口统计学因素提供可靠的数据.
研究的目的:
- 在安大略省10年的时间内调查黑人分娩个体死亡的原因.
- 突出需要改进数据收集,使用和治理健康数据的基于种族和社会决定因素.
- 强调这些数据对于推动围产期健康公平的有影响力的变化的重要性.
主要方法:
- 对安大略省20名黑人分娩人员的死亡率数据的回顾性分析.
- 在这个群体中十年来对死亡原因的检查.
- 审查当前的数据治理实践与社会人口统计信息相关的围产健康.
主要成果:
- 在安大略省的黑人分娩人员中确定了特定的死亡原因.
- 突出了围产期健康监测中收集和利用基于种族的数据的重大差距.
- 强调数据在监测和推进健康公平方面的关键作用.
结论:
- 改善健康数据的种族和社会决定因素的收集,使用和治理对于解决黑人孕产妇死亡率至关重要.
- 增强数据对于做出明智决策至关重要,以推动系统变革并实现围产期健康公平.
- 卫生系统,社区和生活经验倡导者之间的合作是推动健康公平倡议的关键.
相关概念视频
01:22Applications of Life Tables
328
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
328
01:52Regression Toward the Mean
6.9K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K
01:20Comparing the Survival Analysis of Two or More Groups
565
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...
565
01:21Cancer Survival Analysis
650
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...
650
01:18Introduction To Survival Analysis
756
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...
756
01:22Life Tables
506
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
506