Understanding the factors associated with differences in caesarean section rates at hospital level: the case of Latin America
Bibliographic record
Abstract
As in many other regions of the world, caesarean section (CS) rates in Latin America are increasing. Studies elsewhere have shown that providing feedback to caregivers regarding their own performance relative to their peers can significantly reduce the rates. Our objectives are to calculate risk-adjusted CS rates for hospitals in Latin America and to identify factors associated with differences among risk-adjusted rates. We included 120 randomly selected institutions in eight countries of Latin America, representing 97 095 pregnancies. We used random-effects models to calculate a risk-adjusted rate for each hospital and to identify hospitals significantly higher or lower than a benchmark rate. We conducted a regression analysis to identify characteristics of hospitals associated with differences among risk-adjusted rates. The overall CS rate was 35%, ranging from 0% to 85%. Risk-adjusted CS rates ranged from 11% to 78%. Three-quarters of hospitals had risk-adjusted rates significantly above the previously identified benchmark of 20%. Characteristics of institutions explained 48% of the variability among risk-adjusted rates, including being a private as opposed to a public institution, having some economic incentive for CS as opposed to no incentive, and having > or = 50 maternity beds. Strategies to halt further increases in CS rates and reduce rates to levels that reflect the best quality of care, are urgently needed worldwide. The involvement of local quality control departments is an essential component in achieving success. Our results can be used to identify institutions that can be targets for further interventions to reduce CS rates.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".