Reducing unnecessary caesarean sections: scoping review of financial and regulatory interventions
Bibliographic record
Abstract
BACKGROUND: Caesarean sections (CS) are increasing worldwide. Financial incentives and related regulatory and legislative factors are important determinants of CS rates. This scoping review examines the evidence base of financial, regulatory and legislative interventions intended to reduce CS rates. METHODS: We searched MEDLINE, EMBASE, CINAHL and two trials registers in June 2019. Both experimental and observational intervention studies were eligible for inclusion. Primary outcome measures were: CS, spontaneous vaginal and instrumental birth rates. We assessed quality of evidence using Grading of Recommendations, Assessment, Development and Evaluation (GRADE) method. RESULTS: We identified 9057 articles and assessed 65 full-texts. We included 16 observational studies. Most of the studies were conducted in high-income countries. Three studies assessed payment methods for health workers: equalising physician fees for vaginal and caesarean delivery reduced CS rates in one study; however, little or no difference in CS rates was found in the remaining two studies. Nine studies assessed payment methods for health organisations: There was no difference in CS rates between diagnosis-related group (DRG) payment system compared to fee-for-service system in one study. However, DRG system was associated with lower odds for CS in another study. There was little or no difference in CS rates following implementation of global budget payment (GBP) system in two studies. Vaginal birth after caesarean section (VBAC) increased after implementation of a case-based payment system in one study. Caesarean section increased while VBAC rates decreased following implementation of a cap-based payment system in another study. Financial incentive for providers to promote vaginal delivery combined with free vaginal delivery policy was found to reduce CS rates in one study. Studied regulatory and legislative interventions (comprising legislatively imposed practice guidelines for physicians in one study and multi-faceted strategy which included policies to control CS on maternal request in another study) were found to reduce CS rates. The GRADE quality of evidence varied from very low to low. CONCLUSIONS: Available evidence on the effects of financial and regulatory strategies intended to reduce unnecessary CS is inconclusive given inconsistency in effects and low quality of the available evidence. More rigorous studies are needed.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".