Time Trends and Regional Disparities of Maternal Mortality Ratio: Evidences From China, 1990-2018.
Bibliographic record
Abstract
Abstract Introduction China is one of the few countries to achieve Millennium Development Goal 4 and 5. This study aimed to access the levels and trends of maternal mortality ratio (MMR) in China from 1990 to 2018. Methods Using descriptive epidemiology, we collected the data from the China Health Statistics Yearbooks and the China Statistical Yearbooks to describe changes in MMRs, hospital delivery rate, per capita GDP, policies, health expenditure indictors and per capita annual income by region from 1990 to 2018. Spearman correlation analysis was used to assess the relevance between MMR and health expenditure indicators. Results The MMR decreased by 79.4% from 1990 to 2018 in China, and the MMR was remarkably lower in the eastern China than that in the western China. In the context of the widening gap between urban and rural wealth from 1990 to 2018, the urban-rural MMR gap narrowed. The MMR in China has always been higher in rural areas than in urban areas. After the implementation of the Two-Child policy in 2015, the urban MMR continued to decline in 2015-2018, and the rural MMR rebounded in 2017. The hospital delivery rate in China has been on the rise, with almost all pregnant women giving birth in hospital by 2018. The MMR was negatively correlated with the percentage of government health expenditure in the total health expenditure. Obstetric hemorrhage has always been the leading cause of maternal death from 1990 to 2018 in urban and rural areas. Conclusions China has made remarkable progress in maternal survival, despite regional differences still exist. The implementation of the Two-Child policy slowed down the decline of MMR in rural areas. Chinese government should focus on the maternal health in western provinces, rural areas, and the floating population in urban areas.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".