Examining Trends in Obstetric Quality Measures for Monitoring Health Care Disparities
Bibliographic record
Abstract
BACKGROUND: Elective delivery (ED) before 39 weeks, low-risk cesarean delivery, and episiotomy are routinely reported obstetric quality measures and have been the focus of quality improvement initiatives over the past decade. OBJECTIVE: To estimate trends and differences in obstetric quality measures by race/ethnicity. RESEARCH DESIGN: We used 2008-2014 linked birth certificate-hospital discharge data from New York City to measure ED before 39 gestational weeks (ED <39), low-risk cesarean, and episiotomy by race/ethnicity. Measures were following the Joint Commission and National Quality Forum specifications. Average annual percent change (AAPC) was estimated using Poisson regression for each measure by race/ethnicity. Risk differences (RD) for non-Hispanic black women, Hispanic women, and Asian women compared with non-Hispanic white women were calculated. RESULTS: ED<39 decreased among whites [AAPC=-2.7; 95% confidence interval (CI), -3.7 to -1.7), while it increased among blacks (AAPC=1.3; 95% CI, 0.1-2.6) and Hispanics (AAPC=2.4; 95% CI, 1.4-3.4). Low-risk cesarean decreased among whites (AAPC=-2.8; 95% CI, -4.6 to -1.0), and episiotomy decreased among all groups. In 2008, white women had higher risk of most measures, but by 2014 incidence of ED<39 was increased among Hispanics (RD=2/100 deliveries; 95% CI, 2-4) and low-risk cesarean was increased among blacks (RD=3/100; 95% CI, 0.5-6), compared with whites. Incidence of episiotomy was lower among blacks and Hispanics than whites, and higher among Asian women throughout the study period. CONCLUSIONS: Existing measures do not adequately assess health care disparities due to modest risk differences; nonetheless, continued monitoring of trends is warranted to detect possible emergent 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".