Expanding midwifery care in the United States: Implications for clinical outcomes and cost
Bibliographic record
Abstract
BACKGROUND: This study compared clinical and financial outcomes for low-risk birthing people between those attended by midwives and those attended by obstetricians during hospital births. METHODS: We conducted a retrospective cohort analysis of births from January 1, 2016 to December 31, 2020 at hospitals participating in a perinatal quality improvement collaborative, Obstetrical Care Outcomes Assessment Program (OB COAP), in the Northwest region of the United States and estimated risk ratios using a multivariate regression approach with a modified Poisson binomial for mode of delivery, labor interventions, and newborn outcomes comparing midwife-led to obstetrician-led care. Using publicly available data on average costs of vaginal and cesarean births, we then extrapolated the cost differences in care between midwives and obstetricians. RESULTS: Births in the midwife group were less likely to be associated with induction (17.6% vs. 20.3% RR 0.74; 95% CI 0.70-0.78), epidural use (58.9% vs. 76.3% RR 0.78; 95% CI 0.77-0.80), and episiotomy (2.2% vs. 3.4% RR 0.68; 95% CI 0.58-0.81). Cesarean birth was also lower in the midwifery group (7.8% vs. 12.3% RR 0.68, 95% CI 0.62-0.73), without a corresponding increase in risk in adverse neonatal outcomes. We estimated that expanding midwifery care to 100% of low-risk births across the United States could save as much as $340 million per year. CONCLUSIONS: Midwifery care is associated with a lower risk of cesarean birth and other interventions versus care provided by obstetricians and is therefore likely lower-cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".