中低收入国家孕产妇死亡前瞻性病因分类系统：全球母婴健康网络注册研究结果。
A prospective cause of death classification system for maternal deaths in low and middle-income countries: results from the Global Network Maternal Newborn Health Registry.
机构信息
Aga Khan University, Karachi, Pakistan.
Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
出版信息
BJOG. 2018 Aug;125(9):1137-1143. doi: 10.1111/1471-0528.15011. Epub 2018 May 21.
OBJECTIVE
To describe the causes of maternal death in a population-based cohort in six low- and middle-income countries using a standardised, hierarchical, algorithmic cause of death (COD) methodology.
DESIGN
A population-based, prospective observational study.
SETTING
Seven sites in six low- to middle-income countries including the Democratic Republic of the Congo (DRC), Guatemala, India (two sites), Kenya, Pakistan and Zambia.
POPULATION
All deaths among pregnant women resident in the study sites from 2014 to December 2016.
METHODS
For women who died, we used a standardised questionnaire to collect clinical data regarding maternal conditions present during pregnancy and delivery. These data were analysed using a computer-based algorithm to assign cause of maternal death based on the International Classification of Disease-Maternal Mortality system (trauma, termination of pregnancy-related, eclampsia, haemorrhage, pregnancy-related infection and medical conditions). We also compared the COD results to healthcare-provider-assigned maternal COD.
MAIN OUTCOME MEASURES
Assigned causes of maternal mortality.
RESULTS
Among 158 205 women, there were 221 maternal deaths. The most common algorithm-assigned maternal COD were obstetric haemorrhage (38.6%), pregnancy-related infection (26.4%) and pre-eclampsia/eclampsia (18.2%). Agreement between algorithm-assigned COD and COD assigned by healthcare providers ranged from 75% for haemorrhage to 25% for medical causes coincident to pregnancy.
CONCLUSIONS
The major maternal COD in the Global Network sites were haemorrhage, pregnancy-related infection and pre-eclampsia/eclampsia. This system could allow public health programmes in low- and middle-income countries to generate transparent and comparable data for maternal COD across time or regions.
TWEETABLE ABSTRACT
An algorithmic system for determining maternal cause of death in low-resource settings is described.
目的
使用标准化、分层、算法性死因（COD）方法描述六个中低收入国家基于人群队列的孕产妇死亡原因。
设计
基于人群的前瞻性观察性研究。
地点
六个中低收入国家的七个地点，包括刚果民主共和国（DRC）、危地马拉、印度（两个地点）、肯尼亚、巴基斯坦和赞比亚。
人群
2014 年至 2016 年 12 月期间居住在研究地点的所有孕产妇死亡。
方法
对于死亡的妇女，我们使用标准化问卷收集有关妊娠和分娩期间存在的孕产妇情况的临床数据。这些数据使用基于计算机的算法进行分析，根据国际疾病分类-孕产妇死亡率系统（创伤、终止妊娠相关、子痫、出血、妊娠相关感染和医疗状况）分配孕产妇死亡原因。我们还将 COD 结果与医疗保健提供者分配的孕产妇 COD 进行了比较。
主要结果测量
分配的孕产妇死亡率原因。
结果
在 158205 名妇女中，有 221 例孕产妇死亡。最常见的算法分配的孕产妇 COD 是产科出血（38.6%）、妊娠相关感染（26.4%）和子痫前期/子痫（18.2%）。算法分配的 COD 与医疗保健提供者分配的 COD 之间的一致性范围从出血的 75%到与妊娠同时发生的医疗原因的 25%。
结论
全球网络站点的主要孕产妇 COD 是出血、妊娠相关感染和子痫前期/子痫。该系统可使中低收入国家的公共卫生计划能够生成跨时间或地区的孕产妇 COD 透明且可比的数据。
推特摘要
描述了一种用于确定资源匮乏环境下孕产妇死因的算法系统。
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