Regional Gradients in Institutional Cesarean Delivery Rates: Evidence from Five Countries in Asia
Bibliographic record
Abstract
BACKGROUND: Although the influence of the type of institutional setting on the risk of cesarean birth is well documented, less is known about the regional variations in institution-specific cesarean rates within countries. Our purpose was to examine regional variations in cesarean rates across public and private facilities in five Asian countries with a sizeable private sector: Bangladesh, India, Indonesia, Pakistan, and the Philippines. METHODS: Demographic Health Survey data and a hierarchical model were used to assess regional variations in the mode of delivery while controlling for a wide range of socioeconomic, demographic, and maternal risk factors. RESULTS: The risk of cesarean birth was greater in a private facility than in a government hospital by 36-48 percent in India and Indonesia and by 130 percent in Bangladesh. Regional gradients in cesarean birth were found to be steeper for deliveries in private facilities than in government hospitals in India, Indonesia, and the Philippines. The residents of India's high-use states were 55 percent more likely to undergo a cesarean delivery in a government hospital and 83 percent more likely in a private facility than their counterparts in the medium-use states. Similarly, compared to the residents of the Philippines's medium-use provinces, giving birth in a government facility increased the likelihood of a cesarean delivery by 84 percent and by 173 percent in a private facility. CONCLUSIONS: Large regional variations in cesarean rates suggest the need for more informed clinical decision making with respect to the selection of cases for cesarean delivery and the establishment of well-developed guidelines and standards at the provincial or state levels.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".