A cluster-randomized trial to reduce caesarean delivery rates in Quebec: cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Widespread increases in caesarean section (CS) rates have sparked concerns about risks to mothers and infants and rising healthcare costs. A multicentre, two-arm, cluster-randomized trial in Quebec, Canada assessed whether an audit and feedback intervention targeting health professionals would reduce CS rates for pregnant women compared to usual care, and concluded that it reduced CS rates without adverse effects on maternal or neonatal health. The effect was statistically significant but clinically small. We assessed cost-effectiveness to inform scale-up decisions. METHODS: A prospective economic evaluation was undertaken using individual patient data from the Quality of Care, Obstetrics Risk Management, and Mode of Delivery (QUARISMA) trial (April 2008 to October 2011). Analyses took a healthcare payer perspective. The time horizon captured hospital-based costs and clinical events for mothers and neonates from labour onset to 3 months postpartum. Resource use was identified and measured from patient charts and valued using standardized government sources. We estimated the changes in CS rates and costs for the intervention group (versus controls) between the baseline and post-intervention periods. We examined heterogeneity between clinical subgroups of high-risk versus low-risk pregnancies and estimated the joint uncertainty in cost-effectiveness over 20,000 trial simulations. We decomposed costs to identify drivers of change. RESULTS: The intervention group experienced per-patient reductions of 0.005 CS (95% confidence interval (CI): -0.015 to 0.004, P = 0.09) and $180 (95% CI: -$277 to - $83, P < 0.001). Women with low-risk pregnancies experienced statistically significant reductions in CS rates and costs; changes for the high-risk subgroup were not significant. The intervention was "dominant" (effective in reducing CS and less costly than usual care) in 86.08% of simulations. It reduced costs in 99.99% of simulations. Cost reductions were driven by lower rates of neonatal complications in the intervention group (-$190, 95% CI: -$255 to - $125, P < 0.001). Given 88,000 annual provincial births, a similar intervention could save $15.8 million (range: $7.3 to $24.4 million) in Quebec annually. CONCLUSIONS: From a healthcare payer perspective, a multifaceted intervention involving audits and feedback resulted in a small reduction in caesarean deliveries and important cost savings. Cost reductions are consistent with improved quality of care in intervention group hospitals. TRIAL REGISTRATION: International Clinical Trials Registry Platform, ISRCTN95086407 . Registered on 23 October 2007.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".