Racial disparities in comorbidity and severe maternal morbidity/mortality in the United States: an analysis of temporal trends
Bibliographic record
Abstract
INTRODUCTION: Severe maternal morbidity and mortality have increased in the USA in recent years. This trend has not been consistent across all racial groups. The reasons behind this, and the relation between preexisting conditions, pregnancy-associated disease and severe maternal morbidity/mortality, have not been fully explored. MATERIAL AND METHODS: Annual data on delivery hospitalizations between 1993 and 2012 were obtained from the Nationwide Inpatient Sample (NIS), representing a 20% sample of hospital discharges from across the USA. Chi-square tests for trend were used to examine temporal patterns in the proportion of pregnancies affected by comorbidities as defined by the Obstetric Comorbidity Score and were stratified by maternal race. Logistic regression was used to determine the impact of temporal increases in comorbidity on severe maternal morbidity/mortality. RESULTS: In 1993, 34.3% of pregnancies had a comorbidity score of ≥1; this significantly increased to 44.1% by 2012 (p < 0.001). Baseline differences were observed between all races (Whites 33.7%, Blacks 34.5%, Hispanics 28.0%, Asian/Pacific Islanders 28.1%). Although significant increases were observed for all races, the relative rate of change was lowest for Whites (26.1% increase) and highest for Asian/Pacific Islanders (49.1% increase). The odds of severe maternal morbidity/mortality have steadily increased over time; however, adjustment for Obstetric Comorbidity Score significantly attenuates this correlation. CONCLUSION: The rate of both preexisting comorbidities and pregnancy-associated disease is increasing in pregnant women in the USA and varies substantially by race. These trends provide valuable insight into the increasing complexity of pregnancy in the USA and explain a proportion of the observed increase in severe maternal morbidity/mortality.
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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 | 0.003 |
| 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) | 0.001 | 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".