Factors Underlying the Temporal Increase in Maternal Mortality in the United States
Bibliographic record
Abstract
OBJECTIVE: To identify the factors underlying the recent increase in maternal mortality ratios (maternal deaths per 100,000 live births) in the United States. METHODS: We carried out a retrospective study with data on maternal deaths and live births in the United States from 1993 to 2014 obtained from the birth and death files of the Centers for Disease Control and Prevention. Underlying causes of death were examined between 1999 and 2014 using International Classification of Diseases, 10th Revision (ICD-10) codes. Poisson regression was used to estimate maternal mortality rate ratios (RRs) and 95% confidence intervals (CIs) after adjusting for the introduction of a separate pregnancy question and the standard pregnancy checkbox on death certificates and adoption of ICD-10. RESULTS: Maternal mortality ratios increased from 7.55 in 1993, to 9.88 in 1999, and to 21.5 per 100,000 live births in 2014 (RR 2014 compared with 1993 2.84, 95% CI 2.49-3.24; RR 2014 compared with 1999 2.17, 95% CI 1.93-2.45). The increase in maternal deaths from 1999 to 2014 was mainly the result of increases in maternal deaths associated with two new ICD-10 codes (O26.8, ie, primarily renal disease; and O99, ie, other maternal diseases classifiable elsewhere); exclusion of such deaths abolished the increase in mortality (RR 1.09, 95% CI 0.94-1.27). Regression adjustment for improvements in surveillance also abolished the temporal increase in maternal mortality ratios (adjusted maternal mortality ratios 7.55 in 1993, 8.00 per 100,000 live births in 2013; adjusted RR 2013 compared with 1993 1.06, 95% CI 0.90-1.25). CONCLUSION: Recent increases in maternal mortality ratios in the United States are likely an artifact of improvements in surveillance and highlight past underestimation of maternal death. Complete ascertainment of maternal death in populations remains a challenge even in countries with good systems for civil registration and vital statistics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".