Racial Disparities in Maternal Blood Transfusion in the United States by Mode of Delivery: A Population-Based Retrospective Cohort Study [ID 2683649]
Bibliographic record
Abstract
INTRODUCTION: Despite well-documented racial disparities in maternal health in the United States, gaps remain in characterizing the distribution of these disparities within specific interventions and outcomes during childbirth, such as blood transfusion. We aimed to assess racial disparities in maternal blood transfusion in the United States overall and stratified by mode of delivery. METHODS: We performed a population-based retrospective cohort study of term, live births (2016–2021) using U.S. Natality Files. Regression models were constructed to estimate adjusted odds ratios (aORs) and 95% CIs of maternal blood transfusion (during labor or delivery) by self-identified maternal race in the total population, and among subgroups stratified by mode of delivery. Models were adjusted for maternal and obstetric practice factors. RESULTS: The study included 15,936,920 deliveries; maternal blood transfusion occurred in 3.7 per 1,000 deliveries. Compared with White individuals (3.5 per 1,000 transfusion rate), higher odds of transfusion were seen among American Indian and Alaskan Native (AIAN) (aOR 2.31; 95% CI, 2.19–2.44) and Pacific Islander individuals (aOR 1.65; 95% CI, 1.46–1.86). The frequency of transfusion and racial disparities in transfusion rates varied substantially by mode of delivery. For example, among forceps deliveries, compared with White individuals (9.1 per 1,000 transfusion rate), Chinese individuals had a nearly twofold higher rate of transfusion (aOR 1.93; 95% CI, 1.30–2.87), whereas Black individuals had a nearly 30% lower rate (aOR 0.71; 95% CI, 0.56–0.91). CONCLUSION: Racial disparities in maternal blood transfusion persist after adjustment for several confounders, particularly within AIAN and Pacific Islander individuals, and vary by mode of delivery.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".