Historical chart and dataset showing China infant mortality rate by year from 1950 to 2025.

The infant mortality rate in China declined to 4.5 deaths per 1,000 live births in 2023. As a result, the infant mortality rate in China saw its lowest number in 2023 with 4.5 deaths per 1,000 live births. The infant mortality rate refers to the number of newborns not expected to survive past the first year of life. This is generally expressed as a value per 1,000 live births, and infant mortality also includes neonatal mortality (deaths within the first 28 days of life).

The infant mortality rate in China, for children under the age of one year old, was approximately 195 deaths per thousand births in 1950. This means that for all babies born in 1950, almost one in five did not survive past their first birthday. This rate fell to just under 130 deaths in 1955, before increasing slightly in the next decade, as Chairman Mao Zedong's 'Great Leap Forward' failed to industrialize the country and created a famine that killed millions of people. Over the past half century, China's infant mortality rate has decreased gradually to just ten deaths per thousand births today.

UNICEF's country profile for China, including under-five mortality rates, child health, education and sanitation data.

The infant mortality rate in China decreased by 0.4 deaths per 1,000 live births (-6.78 percent) since the previous year. As a result, the infant mortality rate in China saw its lowest number in 2020 with 5.5 deaths per 1,000 live births.The infant mortality rate refers to the number of newborns not expected to survive past the first year of life. This is generally expressed as a value per 1,000 live births, and infant mortality also includes neonatal mortality (deaths within the first 28 days of life).Find more statistics on other topics about China with key insights such as death rate, crude birth rate, and total fertility rate.

IntroductionUnder-five mortality rate (U5MR) and maternal mortality rate (MMR) are important indicators for evaluating the quality of perinatal health and child health services in a country or region, and are research priorities for promoting maternal and infant safety and maternal and child health. This paper aimed to analysis and predict the trends of U5MR and MMR in China, to explore the impact of social health services and economic factors on U5MR and MMR, and to provide a basis for relevant departments to formulate relevant policies and measures.MethodsThe JoinPoint regression model was established to conduct time trend analysis and describe the trend of neonatal mortality rate (NMR), infant mortality rate (IMR), U5MR and MMR in China from 1991 to 2020. The linear mixed effect model was used to assess the fixed effects of maternal health care services and socioeconomic factors on U5MR and MMR were explored, with year as a random effect to minimize the effect of collinearity. Auto regressive integrated moving average models (ARIMA) were built to predict U5MR and MMR from 2021 to 2025.ResultsThe NMR, IMR, U5MR and MMR from 1991 to 2020 in China among national, urban and rural areas showed continuous downward trends. The NMR, IMR, U5MR and MMR were significantly negatively correlated with gross domestic product (GDP), the proportion of the total health expenditure (THE) to GDP, system management rate, prenatal care rate, post-natal visit rate and hospital delivery rate. The predicted values of national U5MR from 2021 to 2025 were 7.3 ‰, 7.2 ‰, 7.1 ‰, 7.1 ‰ and 7.2 ‰ and the predicted values of national MMR were 13.8/100000, 12.1/100000, 10.6/100000, 9.6/100000 and 8.3/100000.ConclusionChina has made great achievements in reducing the U5MR and MMR. It is necessary for achieving the goals of Healthy China 2030 by promoting the equalization of basic public health services and further optimizing the allocation of government health resources. China’s experience in reducing U5MR and MMR can be used as a reference for developing countries to realize the SDGs.

The Poverty Mapping Project: Global Subnational Infant Mortality Rates data set consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), and a tabular data set of the same and associated data. Over 10,000 national and subnational Units are represented in the tabular and grid data sets, while the shapefile uses approximately 1,000 Units in order to protect the intellectual property of source data sets for Brazil, China, and Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The emergence of environmental nongovernmental organizations (ENGOs) has proved beneficial in improving environmental quality and related health issues. Therefore, this study attempts to investigate the impact of ENGO on human health in China from 1995 to 2020. To investigate the relationship between the variables, we have employed the ARDL model. The ARDL model results demonstrate that the long-run impact of ENGO is negative on infant mortality and death rate, meaning that an increase in the proportion of ENGOs in China considerably decreases infant mortality and death rate. On the other hand, ENGOs have a favorable influence on life expectancy in China, demonstrating ENGOs’ supporting role in raising birth life expectancy. In the short run, estimates of ENGOs have no substantial influence on newborn mortality and death rates in China, whereas ENGOs have a positive and significant impact on life expectancy. These results imply that ENGOs help improves people’s health status in China, which is also supported by the rise in GDP, technology, and health expenditures. The causal analysis confirms the bi-directional causal link between ENGO and IMR and ENGO and LE, while the unidirectional causal link runs from ENGO to DR. The results of the study provide insights into the impact of environmental NGOs on human health in China and may help guide policies aimed at improving public health outcomes through environmental protection efforts.