Factors associated with high Cesarean deliveries in China and Brazil - A Call for reducing elective surgeries in moving towards Universal Health Coverage
Bibliographic record
Abstract
Background: Annually 6.2 million unnecessary Cesarean Deliveries (CD) are performed globally. Such high unnecessary CD rate is a concern not only due to the additional financial burden it places on the health system but also because the quality of care could be compromised. There is an even more imperative need to reduce elective surgeries in moving towards the Universal Health Coverage (UHC), to ensure the ever stretched resources are effectively channeled for the best health outcomes. Methods: The paper uses a case study method. China and Brazil were selected primarily due to their alarmingly high number and percentage of the CD rate. Systematic literature review was conducted and the analyses were structured based on the framework “Determinants of High Cesarean Delivery Rates”, developed by the authors. Results: In China and Brazil, cultural belief, fear of labor pain and patient’s perception regarding quality of care may play a role in the patient’s preference for CD but these perceptions are shaped by their health care providers as well. The principal-agent relation comes into play as the health professionals can modify perceived needs of their patients. Availability of health insurance to mothers, physicians’ preference for convenient working hours, and payment incentives for performing higher yielding Cesarean procedures are driving the high CD rate phenomena in China and Brazil. Understand the social determinants of CD and using various instruments to change women’s perceptions of birthing options and physicians’ behaviors are critical in managing the CD rate. Conclusion: In the context of the rapid movement towards UHC, the evidence presented in this paper supports the call for implementing complementary policy interventions and regulation to ensure minimal efficiency loss due to resources being diverted to unnecessary procedures or hospital stays.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".