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Indications and determinants of caesarean section delivery: Evidence from a population-based study in Matlab, Bangladesh

机译:剖宫产的指征和决定因素:来自孟加拉国Matlab的基于人群的研究的证据

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摘要

Background and methodsCaesarean section (C-section) is a major obstetric intervention for saving lives of women and their newborns from pregnancy and childbirth related complications. Un-necessary C-sections may have adverse impact upon maternal and neonatal outcomes. In Bangladesh there is paucity of data on clinical indication of C-section at population level. We conducted a retrospective study in icddr,b Health and Demographic Surveillance System (HDSS) area of Matlab to look into the indications and determinants of C-sections. All resident women in HDSS service area who gave birth in 2013 with a known birth outcome, were included in the study. Women who underwent C-section were identified from birth and pregnancy files of HDSS and their indication for C-section were collected reviewing health facility records where the procedure took place, supplemented by face-to-face interview of mothers where data were missing. Indications of C-section were presented as frequency distribution and further divided into different groups following 3 distinct classification systems. Socio-demographic predictors were explored following statistical method of binary logistic regression.
机译:背景与方法剖宫产术是一种重要的产科干预措施,旨在挽救妇女及其新生儿的生命,使其免受妊娠和分娩相关的并发症的影响。不必要的剖腹产可能对孕产妇和新生儿结局有不利影响。在孟加拉国,有关人群剖腹产的临床指征的数据很少。我们在Matlab的icddr,b健康与人口监测系统(HDSS)领域进行了一项回顾性研究,以研究剖腹产的适应症和决定因素。该研究纳入了HDSS服务区中所有在2013年分娩并有已知分娩结果的常住妇女。从HDSS的出生和妊娠档案中识别出进行剖腹产的妇女,并收集其剖腹产的指征,回顾进行该手术的医疗机构的记录,并辅以面对面采访母亲的资料,以了解缺少数据的母亲。剖腹产指征以频率分布表示,并根据3个不同的分类系统进一步分为不同的组。按照二元逻辑回归的统计方法探索了社会人口统计学预测因子。

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