Assessing Racial/Ethnic Variation and Trends in Vaginal Birth after Cesarean in California: A Retrospective Cohort Study Using Linked Birth Certificate and Hospital Discharge Records
Bibliographic record
Abstract
Abstract Increasing the vaginal birth after cesarean (VBAC) rate to 18% was a Healthy People 2020 goal. Detailed data on racial/ethnic differences in VBAC rates is lacking and can inform efforts to equitably increase VBAC rates. This study aimed to assess racial/ethnic variation in VBAC rates and to describe group trends in VBAC rates in California between 2011 and 2021. This retrospective cohort study used a database of birth certificates linked to hospital discharge records. We analyzed singleton, term live births among people who had a history of at least one prior cesarean birth, no identified contraindications to a vaginal birth, and self-identified their racial/ethnic group as Hispanic or non-Hispanic (American Indian-Alaskan Native (AIAN), Asian, Black, Hawaiian/Pacific Islander, or white). VBAC births were identified from birth certificate records. Differences between VBAC rates were assessed using univariable and multivariable Poisson log-linear regression while adjusting for potential confounders. A total of 607,808 birthing people were included (2,234 AIAN, 84,899 Asian, 34,217 Black, 2,559 Hawaiian/Pacific Islander, 334,116 Hispanic, 149,783 white). Over the study period, Hawaiian/Pacific Islander birthing people had the highest average VBAC rate at 11.5% (AIAN, 6.5%; Asian, 8.8%; Black, 8.0%; Hispanic, 7.4%; white, 9.5%). In adjusted models, Black (aRR = 1.06, 95% CI: 1.01–1.11) and Hawaiian/Pacific Islander (aRR = 1.43, 95% CI: 1.27–1.61) birthing people were more likely to have a VBAC compared with white birthing people, while Hispanic birthing people were less likely (aRR = 0.96, 95% CI: 0.93–0.98). VBAC rates increased significantly (p < 0.001) over time for all groups except AIAN birthing people. VBAC rates increased for most racial/ethnic groups in California. With the exception of the Hawaiian/Pacific Islander group, there were small and likely not clinically significant differences in the chances for a VBAC across groups. No group in California met the Healthy People 2020 goal VBAC rate of 18%.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".