通过使用链接的行政数据来定义堕胎队列的更准确的方法:申请加拿大安大略省
Laura Schummers1,2, Kimberlyn McGrail3, Elizabeth K Darling2,4
1Department of Family Practice, University of British Columbia.
International journal of population data science
|August 31, 2023
概括
新的方法通过使用多个数据源和定义护理情节来准确识别堕胎队列. 这种方法改善了堕胎监测和研究,特别是因为护理超出了医院的范围.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 堕胎护理正在从仅限医院服务转向包括初级保健在内的分布式模式.
- 这种转变需要改进的方法来识别堕胎队伍的研究和监测.
- 目前的方法可能是不完整的和偏见的,由于护理交付的不断变化的景观.
研究的目的:
- 开发和评估一种改进的方法,以使用相关的行政数据来定义堕胎队列.
- 与标准方法相比,评估这种新的堕胎监测方法的性能.
主要方法:
- 应用了四个原则:排除早期怀孕损失/产后手术,使用多个数据源,定义护理情节,并使用堕胎日期的等级算法.
- 利用了加拿大安大略省 (2018年1月1日至2020年3月15日) 所有堕胎事件的基于人口的队列.
- 计算了风险差异,以比较药物与手术,第一与第二季度,以及新方法和标准方法之间的并发症发生率.
主要成果:
- 仅在医院的数据低估了药物堕胎 (16.1%与31.4%) 和第一季度堕胎 (82.1%与94.5%).
- 使用多个数据源和链接数据的新方法提高了识别堕胎类型的准确性,并减少了对并发症的低估.
- 排除早期怀孕失误或分娩事件增加了估计的并发症发生率,突出了全面队列定义的重要性.
结论:
- 准确识别堕胎队伍需要新的方法,超越传统的医院监测.
- 随着药物流产扩展到初级和办公室环境中,监测方法必须适应完整性和有效性.
- 调整的方法对于在所有护理环境中完全捕捉程序至关重要,以确保可靠的堕胎监测和研究.
相关概念视频
Statistical Methods for Analyzing Epidemiological Data
408
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:
408
Data Collection by Observations
12.1K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
12.1K
Comparing the Survival Analysis of Two or More Groups
222
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...
222
Study Designs in Epidemiology
264
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
264
Biostatistics: Overview
272
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
272
Cancer Survival Analysis
381
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...
381


