Trends and Disparities in Severe Maternal Morbidity Indicator Categories during Childbirth Hospitalization in California from 1997 to 2017
Bibliographic record
Abstract
OBJECTIVE: Severe maternal morbidity (SMM) is increasing and characterized by substantial racial and ethnic disparities. Analyzing trends and disparities across time by etiologic or organ system groups instead of an aggregated index may inform specific, actionable pathways to equitable care. We explored trends and racial and ethnic disparities in seven SMM categories at childbirth hospitalization. STUDY DESIGN: = 10,580,096) using the Centers for Disease Control and Prevention's SMM index. Cases were categorized into seven nonmutually exclusive indicator categories (cardiac, renal, respiratory, hemorrhage, sepsis, other obstetric, and other medical SMM). We compared prevalence and trends in SMM indicator categories overall and by racial and ethnic group using logistic and linear regression. RESULTS: SMM occurred in 1.16% of births and nontransfusion SMM in 0.54%. Hemorrhage SMM occurred most frequently (27 per 10,000 births), followed by other obstetric (11), respiratory (7), and sepsis, cardiac, and renal SMM (5). Hemorrhage, renal, respiratory, and sepsis SMM increased over time for all racial and ethnic groups. The largest disparities were for Black individuals, including over 3-fold increased odds of other medical SMM. Renal and sepsis morbidity had the largest relative increases over time (717 and 544%). Sepsis and hemorrhage SMM had the largest absolute changes over time (17 per 10,000 increase). Disparities increased over time for respiratory SMM among Black, U.S.-born Hispanic, and non-U.S.-born Hispanic individuals and for sepsis SMM among Asian or Pacific Islander individuals. Disparities decreased over time for sepsis SMM among Black individuals yet remained substantial. CONCLUSION: Our research further supports the critical need to address SMM and disparities as a significant public health priority in the United States and suggests that examining SMM subgroups may reveal helpful nuance for understanding trends, disparities, and potential needs for intervention. KEY POINTS: · By SMM subgroup, trends and racial and ethnic disparities varied yet Black individuals consistently had highest rates.. · Hemorrhage, renal, respiratory, and sepsis SMM significantly increased over time.. · Disparities increased for respiratory SMM among Black, U.S.-born Hispanic and non-U.S.-born Hispanic individuals and for sepsis SMM among Asian or Pacific Islander individuals..
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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 | 0.000 | 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".