Academic journal article Iranian Journal of Public Health

Spatial Analysis of China Province-Level Perinatal Mortality

Academic journal article Iranian Journal of Public Health

Spatial Analysis of China Province-Level Perinatal Mortality

Article excerpt

(ProQuest: ... denotes formulae omitted.)


As an important parameter of health, perinatal mortality serves as a crucial indicator of maternal care, maternal health and nutrition. Besides, it is also a reflection of socio-economic development. To improve the health status of pregnant women, new mothers and newborns, most of the governments have set the reduction of perinatal mortality as their key development goal. As a key indicator, perinatal mortality provides information necessary for decision-makers to identify problems, track temporal, geographical trends and disparities, assess changes in public health policy and practice (1, 2).

There is an annual perinatal death of over 6.3 million across the world, among which the developing countries account for the largest share, with 27% in the least developed countries alone. "In developing countries, only about 40% of deliveries occur in health facilities and little more than one in two takes place with the assistance of a doctor, midwife or qualified nurse" (3). As the largest developing country, China's perinatal mortality rate dropped from 1247(per 100000) in 2003 to 589 (per 100000) in 2013. On the national level, China has witnessed a significant decrease in its perinatal mortality rate (4). However, it is reported that the mortality rate is unevenly distributed across the nation, with some provinces extremely higher than the national average while others extremely lower. For example, in 2013, the perinatal mortality rate of Tibet is 2404 (per 100000); 5 times higher than the national average.

Since the statistical data show that there are regional differences in perinatal mortality, further researches should be conducted to examine what factors have contributed to the regional differences. Recently, as tools for analyzing spatial data improved, spatial analysis has been wildly used in projects of public health and epidemiology, such as infant mortality rates, associations between birth defects and exposures, socio-economic status and neural tube defects (5, 6). Compared with traditional methods, spatial analysis is more instrumental in decision-making, planning, information management and dissemination in epidemiological research. With spatial analysis, the regional variation in health problems, spatial distribution and transmission route of epidemic and regional difference in disease outbreak can be easily modeled.

In investigating China province-level perinatal mortality rates in 31 regions during the period 2003-2013, this study offers a unique contribution to the relevant literature. Its objectives are: 1) using novel spatial analysis to examine whether regional differences existed in perinatal mortality and clarify the spatial pattern of perinatal mortality. 2) using spatial econometrics models to determine how the relative socio-economic factors and health care resources affected perinatal mortality.

Materials and Methods

Data source

This research is the study of risk-modifying factors on health based on populations defined spatially, risk-modifying factors is averaged for the populations in each spatial unit and then compared using spatial statistical method, therefore, we categorized this research into Ecological study. Data are obtained from Statistical Yearbooks of China and Statistical Yearbooks of China Health. In order to avoid the aggregation bias, there are two approaches can be applied into research. The first uses box plot to confirm the outliers, once all outliers are determined, they will be deleted from raw data. The second is enlarging the sample size, the larger sample size will result more precise estimation. Although the two approaches can be applied to eliminate the aggregation bias, the spatial analysis only supports balanced panel data, if the first approach is adopted, the whole data structure would be unbalanced. Therefore, the decade from 1996 to 2013 is selected as the observation period and the 31 province-level regions as the subject for analysis, which include 22 provinces, 4 municipalities and 5 autonomous regions. …

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