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 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".