Hospital variations in caesarean delivery rates: An analysis of national data in China, 2016-2020
Bibliographic record
Abstract
Background: The impact of China's use of caesarean delivery on global public health has been a long-term concern. The number of private hospitals is increasing in China and likely driving up caesarean delivery rates, yet specifics remain unknown. We aimed to investigate variations in caesarean delivery rates across and within hospital types in China. Methods: We retrieved data on hospital characteristics and national hospital-level annually aggregated data on the number of deliveries and caesarean deliveries from 2016-2020, covering 7085 hospitals in 31 provinces of mainland China, from the National Clinical Improvement System. We categorized hospitals as public-non-referral (n = 4103), public-referral (n = 1805) and private (n = 1177). Among the private hospitals, 89.1% (n = 1049) were non-referral regarding obstetrical services for uncomplicated pregnancies. Results: Among 38 517 196 deliveries, 16 744 405 were caesarean, giving an overall rate of 43.5% with a minor range of 42.9%-43.9% over time. Median rates differed across hospital types, from 47.0% (interquartile range (IQR) = 39.8%-55.9%) in public-referral, 45.8% (36.2%-55.8%) in private, and 40.3% (30.6%-50.6%) in public-non-referral hospitals. The stratified analyses corroborated the results, except for the northeastern region, where the median rates did not differ across the public-non-referral (58.9%), public-referral (59.3%), and private (58.8%) hospitals, while all ranked higher than the other regions, regardless of hospital type and urbanization levels. The rates within hospital types differed as well, especially in the rural areas of the western region of China, where the difference of rates between the 5th and 95th percentiles was 55.6% (IQR = 4.9%-60.5%) in public-non-referral, 51.5% (IQR = 19.6%-71.1%) in public-referral, and 64.6% (IQR = 14.8%-79.4%) in private hospitals. Conclusions: Variation across hospital types in China was pronounced, with the highest rates either in public-referral or private hospitals, except in the northeastern region, where no variation was observed among the high rates of caesarean deliveries. Variation within each hospital type was pronounced, especially in rural areas of the western region.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".