Adverse Events in Maternal Care: Investigating Racial/Ethnic Disparities at the System Level
Bibliographic record
Abstract
Pregnancy related deaths are elevated among women of color, and Black women are 3 to 4 times more likely to die from pregnancy-related causes than white women. Women of color also experience higher rates of severe maternal morbidity (SMM). Half of all maternal deaths and SMM cases are considered preventable with timely and appropriate care. Poor maternal health outcomes and racial/ethnic disparities are the result of multilevel variables including poor quality of care. Few studies have investigated the underlying mechanisms within clinical systems that undermine safety for women of color. This research investigates systems issues contributing to adverse outcomes in maternal care and disparities based on the examination of patient safety incidents (PSIs) reported in the obstetric care units in a large, academic health system in 2019 and 2020. Trends in event type and harm score were examined and the data was disaggregated by race/ethnicity and cross tabulated with unit, event type, and harm score to examine disparities in adverse events. Of the 693 reported incidents, non-Hispanic White (NHW) and non-Hispanic Black (NHB) patients accounted for 43.8% each. Hispanic patients accounted for 7.9% of reported incidents and patients categorized as “Other” accounted for 4.3% of the reported incidents. In both 2019 and 2020, the odds ratio demonstrated a higher likelihood of a reported event for non-Hispanic Black patients (1.99, 95%CI, 1.56 -2.52 and 1.70, 95% CI 1.28-2.25, respectively) and patients categorized as “Other” (15.34, 95% CI 7.25-32.44 and 4.43, 95%CI 1.85-10.58). These findings can facilitate the identification of mechanisms within the clinical system contributing to variation in adverse outcomes for women of color and support the design of more precise interventions and sustained, effective delivery.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".