The impact of substantial financial incentives on C-section rates: Evidence from Iran
Bibliographic record
Abstract
Delivery by Cesarean section (C-section) is necessary in 10%–20% of births, but unnecessary C-sections result in elevated rates of maternal and infant morbidity and mortality and have high financial costs. For all of these reasons, excessive C-section rates have long been viewed as a serious public health problem. Iran has one of the highest rates of C-sections in the world, so reducing those rates (and the associated maternal and infant morbidity and mortality) has been an obvious public health priority. In 2014, the Iranian Ministry of Health and Medical Education created substantial financial incentives discouraging the use of C-sections in public hospitals, and it subsequently extended a modified version of these incentives to nonpublic hospitals. We examine the impact of these reforms on C-section frequency and health outcomes. C-section rates in Iranian public hospitals declined by almost 5%, with higher reductions for first-time mothers, and smaller reductions for mothers with higher-risk pregnancies (e.g., mothers with hypertension or diabetes). We contribute by using a difference-in-differences (DiD) approach to show that physician-level financial incentives explain roughly two-thirds of the decline and patient-level financial incentives explain most of the rest. We also contribute by showing these reforms resulted in improved outcomes, with fewer maternal deaths and neonatal intensive care unit admissions. Our findings indicate that economic incentives do affect C-section rates, but more aggressive strategies will be necessary to reduce C-section rates to the levels typically recommended by public health authorities (10%–20% of births).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".