按分娩方式对人口水平监测的严重孕产妇发病率指数排名
Lindsay S Womack1, Romeo R Galang1, Lindsay K Admon2,3
1Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Journal of women's health (2002)
|January 27, 2026
概括
严重的孕产妇发病率指标有助于确定分娩期间在医院死亡的情况. 心脏骤停和心律转换是剖腹产和阴道分娩的顶级指标,突出了改善孕产妇护理的领域.
科学领域:
- 产科和妇科 产科和妇科
- 孕产妇健康 孕产妇健康
- 公共卫生监督 公共卫生监督
背景情况:
- 严重的孕产妇发病率 (SMM) 是孕产妇健康质量的关键指标.
- 了解SMM对住院死亡率的影响对于改善产科护理至关重要.
- 分娩方式 (剖腹产与阴道) 可能会影响SMM和死亡率之间的关联.
研究的目的:
- 确定哪些严重的孕产妇发病率指标最能预测住院死亡率.
- 为了比较SMM指标在剖腹产和阴道分娩中识别死亡的有效性.
- 为更好地记录和测量严重的产科并发症提供信息的策略.
主要方法:
- 从国家住院患者样本 (1993-2015) 中分析了87864173例分娩住院病例.
- 根据剖腹产和阴道分娩在医院死亡率的可归因人口比例,单独对22个SMM指标进行排名.
- 确定与孕产妇死亡相关的顶级SMM指标.
主要成果:
- 大多数产妇死亡 (94.2%的剖腹产,73.5%的阴道) 与至少一个SMM指标有关.
- 剖腹产的首要指标包括心脏骤停/心室动和心律转换.
- 阴道分娩的顶级指标包括心律转换和心脏骤停/心室动.
结论:
- 在SMM指标排名中,在剖腹产和阴道分娩之间确定死亡的情况方面,出现了相似之处,但也存在差异.
- 前三项指标占住院死亡率的很大一部分 (78.8%的剖腹产,66.0%的阴道).
- 改善严重产科并发症的记录和人口层面的测量是必不可少的.
相关概念视频
01:14Wilcoxon Signed-Ranks Test for Median of Single Population
454
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
454
01:02Ranks
491
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
491
01:20Spearman's Rank Correlation Test
1.5K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Spearman's test calculates correlation by...
1.5K
01:21Wilcoxon Rank-Sum Test
739
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
739
01:07What is a Mode?
25.9K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
25.9K
01:21Friedman Two-way Analysis of Variance by Ranks
501
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
501