Age-Related Disparities in National Maternal Mortality Trends in the United States From 2000 to 2019 [ID: 1376567]
Bibliographic record
Abstract
INTRODUCTION: Over the past two decades, there has been a trend toward increasing maternal age in the United States. The study objective was to examine the association of maternal age with maternal mortality in the United States and examine temporal trends in mortality by maternal age. METHODS: A nationwide population-based cross-sectional study in the United States between 2000 and 2019 was conducted using data from the Centers for Disease Control and Prevention’s “Birth Data” and “Mortality Multiple Cause” data files. Annual incidence and period trends in maternal deaths were calculated using the annual maternal deaths over annual live births across age groups. Multivariate logistic regression models were used to estimate the association between maternal age and risk of maternal mortality and calculate temporal changes in risk of mortality over the study period. RESULTS: Between 2000 and 2019, 21,241 deaths were observed in women during pregnancy and childbirth for an average incidence of 26.3 maternal deaths/100,000 births (95% CI 21.8–31.2). Of all deaths, 6,870 (32.3%) were in women 35 years or older, while only 15.1% of live births were attributed to women 35 years or older. Compared with women 25–29 years of age, there was a significantly greater risk of maternal mortality among women 35–39 (odds ratio 1.60, 95% CI 1.53–1.67), 40–44 (3.78, 3.60–3.99), 45–49 (28.49, 26.49–30.65), and 50–54 (343.50, 319.44–369.37). Risk of mortality increased over time, with the greatest rise in women 35 years or older. CONCLUSION: In the United States, maternal mortality increased during the past two decades, especially in women 35 years or older. Given these findings, targeted strategies to reduce the increasing maternal mortality should become a priority.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".