【摘要】 多发出生缺陷，又称多发畸形(MCAs),是指同一个体发生两种或以上不同系统、器官或组织的缺陷，占所有出生缺陷儿的20%～30%,已成为出生缺陷防控领域重要的公共卫生问题。MCAs的发生可能是随机事件，也可能是病因相关的异常模式。识别MCAs模式可为揭示潜在病因、阐明机制、预测发展趋势以及制定防治策略提供重要线索。目前评估MCAs模式的方法较多，每种计算方法各具优劣。本文综述了五种常用的统计分析策略，包括比例法、多元回归分析、聚类分析、对数线性分析和O/E(Observed/Expected)比，并深入探讨了它们在出生缺陷监测系统中的具体应用、优势和局限。鉴于我国拥有大型出生缺陷监测系统和丰富的数据资源，为MCAs模式研究提供了良好的条件，当前综述MCAs模式的分析方法对充分利用这些资源展开相关研究具有重要意义。更多还原

【Abstract】 Multiple birth defects, also known as multiple congenital anomalies(MCAs), are defined as the simultaneous presence of defects in two or more different systems, organs or tissues in the same individual. MCAs account for 20% to 30% of all children with birth defects and have become an important public health issue in the prevention and control of birth defects. MCAs can occur as random events or as etiologically related patterns of abnormal. Identifying MCAs patterns can provide important clues for revealing the underlying aetiology, elucidating the mechanism, predicting the development trend, and formulating strategiesforprevention and treatment. Currently, there are many methods for assessing the pattern of MCAs, and each calculation method has its own advantages and disadvantages. In this study, we reviewed five commonly used statistical analysis methods for evaluating the pattern of MCAs, including the proportion method, multiple regression analysis, cluster analysis, log-linear analysis, and O/E(Observed/Expected) ratio. Moreover, we elaborated on their applications, strengths, and limitations in the birth defects surveillance system. Given the large birth defects surveillance system and rich data resources in China, which provides good conditions for MCAs research, the current review of the MCAs analysis methods is of great significance for making full use of these resources to conduct related research.更多还原