Nonobstetric Maternal Mortality Trends by Race in the United States From 2000 to 2019 [ID: 1376551]
Bibliographic record
Abstract
INTRODUCTION: Public health interventions to reduce maternal mortality have focused on direct and indirect obstetric causes, while neglecting nonobstetric causes of death. The study objective was to examine recent trends in maternal deaths from nonobstetric causes in the United States. METHODS: A population-based cross-sectional study was conducted in the United States using data from the “Birth Data” and “Mortality Multiple Cause” files compiled by the Centers for Disease Control and Prevention from 2000 to 2019. The annual incidence of maternal deaths attributed to nonobstetric causes per 100,00 live births were calculated across racial groups using official death certificates, while the effects of race on the risk of nonobstetric maternal mortality and temporal changes over the study period were examined using logistic regression models. RESULTS: From 2000 to 2019, a total of 7,334 women died during pregnancy and childbirth from nonobstetric causes, where 31.3% of these deaths were caused by transport accidents and 27.3% by accidental poisoning. American Indian women were found to be at the highest risk of nonobstetric maternal mortality (odds ratio 2.20, 95% CI 1.90–2.56), and 46.1% of all deaths among pregnant American Indian women were caused by nonobstetric complications. The risk of nonobstetric maternal mortality increased overall during the study period, with a greater increase among Black (1.15, 1.13–1.17) and American Indian women (1.17, 1.13–1.21). CONCLUSION: Nonobstetric causes of death have become increasingly prevalent in the United States, overall and especially in American Indian women. Novel interventions to address these nonobstetric factors should especially target American Indian women to improve maternal outcomes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".