Maternal mortality ratio in China from 1990 to 2019: trends, causes and correlations
Bibliographic record
Abstract
BACKGROUND: Maternal mortality ratio is an important indicator to evaluate the health status in developing countries. Previous studies on maternal mortality ratio in China were limited to certain areas or short periods of time, and there was a lack of research on correlations with public health funding. This study aimed to assess the trends in the maternal mortality ratio, the causes of maternal death, and the correlations between maternal mortality ratio and total health financing composition in China from 1990 to 2019. METHODS: Data in this longitudinal study were collected from the China Health Statistics Yearbooks (1991-2020) and China Statistical Yearbook 2020. Linear regression analysis was used to assess the trends in the maternal mortality ratio in China. Pearson correlation analysis was used to assess the correlations between national maternal mortality ratio and total health financing composition. RESULTS: The yearly trends of the national, rural and urban maternal mortality ratio were - 2.290 (p < 0.01), - 3.167 (p < 0.01), and - 0.901 (p < 0.01), respectively. The gap in maternal mortality ratio between urban and rural areas has narrowed. Obstetric hemorrhage was the leading cause of maternal death. The mortalities ratios for the main causes of maternal death all decreased in China from 1990 to 2019. The hospital delivery rate in China increased, with almost all pregnant women giving birth in hospitals in 2019. Government health expenditure as a proportion of total health expenditure was negatively correlated with the maternal mortality ratio (r = - 0.667, p < 0.01), and out-of-pocket health expenditure as a proportion of total health expenditure was positively correlated with the maternal mortality ratio (r = 0.516, p < 0.01). CONCLUSION: China has made remarkable progress in improving maternal survival, especially in rural areas. The maternal mortality ratio in China showed a downward trend over time. To further reduce the maternal mortality ratio, China should take effective measures to prevent obstetric hemorrhage, increase the quality of obstetric care, improve the efficiency and fairness of the government health funding, reduce income inequality, and strengthen the medical security system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".