Maternal Mortality in the United States
Bibliographic record
Abstract
Rigorous studies carried out by the National Center for Health Statistics show that previously reported increases in maternal mortality rates in the United States were an artifact of changes in surveillance. The pregnancy checkbox, introduced in the revised 2003 death certificate and implemented by the states in a staggered manner, resulted in increased identification of maternal deaths and in reported maternal mortality rates. This Commentary summarizes the findings of the National Center for Health Statistics reports, describes temporal trends and the current status of maternal mortality in the United States, and discusses future concerns. Although the National Center for Health Statistics studies, based on recoding of death certificate information (after excluding information from the pregnancy checkbox), showed that crude maternal mortality rates did not change significantly between 2002 and 2018, age-adjusted analyses show a temporal reduction in the maternal mortality rate (21% decline, 95% CI 13-28). Specific causes of maternal death, which were not affected by the pregnancy checkbox, such as preeclampsia, showed substantial temporal declines. However, large racial disparities continue to exist: Non-Hispanic Black women had a 2.5-fold higher maternal mortality rate compared with non-Hispanic White women in 2018. This overview of maternal mortality underscores the need for better surveillance and more accurate identification of maternal deaths, improved clinical care, and expanded public health initiatives to address social determinants of health. Challenges with ascertaining maternal deaths notwithstanding, several causes of maternal death (unaffected by surveillance artifacts) show significant temporal declines, even though there remains substantial scope for preventing avoidable maternal death and reducing disparities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".