Geographical Variations in Maternal Mortality Due to Obstetric Haemorrhage and Its Association with Potential Risk Factors in Central and Western China: A Bayesian Spatiotemporal Analysis

Abstract

Background: China planned to achieve its fifth Millennium Development Goals by 2015, including reducing maternal mortality. The leading cause of maternal mortality in the country is obstetric haemorrhage. However, there is limited evidence on the burden of maternal mortality due to obstetric haemorrhage. It is also unclear which ecological-level risk factor is associated with maternal mortality due to obstetric haemorrhage. Methods: The data on maternal mortality due to obstetric haemorrhage were collected from the China Maternal Mortality Surveys conducted in 1832 counties of mainland China over the 2010-2013 period. The Bayesian spatiotemporal regression and the Geodetector model were used to examine the spatiotemporal distribution of the maternal mortality ratio (MMR) due to obstetric haemorrhage, as well as its association with per capita income, the proportion of pregnant women who received antenatal care at least five times, the hospital delivery rate, and the distance from the nearest blood station (DBS). Findings: The overall MMR due to obstetric haemorrhage decreased in China, but the trends largely varied by region. There were 568 hotspot counties, located mostly in China's western regions. Western China had the highest MMR due to obstetric haemorrhage. The proportion of pregnant women who had five or more maternal checkups and the DBS were the major risk factors for maternal mortality due to obstetric haemorrhage. The MMR increased from 1.92 to 2.51 to 7.06, with an increase in the DBS from 100 km, respectively. Interpretation: The findings provide information for accurate spatial targets and public health policies to reduce maternal deaths at both country and regional levels. Funding Statement: The case study was supported by the MOST (2012CB955503), the NSFC (41271404 and 41421001), the China Medical Board (grant number 11-065), and the United Nations International Children's Emergency Fund (2016EJH011). The data were collected by Sichuan University and processed by the Institute of Geographic Sciences and Natural Resources Research. Declaration of Interests: The authors declare they have no actual or potential competing financial interests.