Temporal trends in peripartum hysterectomy among individuals with a previous cesarean delivery by race/ethnicity in the United States: A population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: Rates of severe maternal morbidity have highlighted persistent and growing racial disparities in the United States (US). We aimed to contrast temporal trends in peripartum hysterectomy by race/ethnicity and quantify the contribution of changes in maternal and obstetric factors to temporal variations in hysterectomy rates. METHODS: We conducted a population-based, retrospective study of 5,739,569 US residents with a previous cesarean delivery, using National Vital Statistics System's Natality Files (2011-2021). Individuals were stratified by self-identified race/ethnicity and classified into four periods based on year of delivery. Temporal changes in hysterectomy rates were estimated using odds ratios (ORs) and 95% confidence intervals (CIs). We used sequential logistic regression models to quantify the contribution of maternal and obstetric factors to temporal variations in hysterectomy rates. RESULTS: Over the study period, the peripartum hysterectomy rate increased from 1.23 (2011-2013) to 1.44 (2019-2021) per 1,000 deliveries (OR 2019-2021 vs. 2011-2013 = 1.17, 95% CI 1.10 to 1.25). Hysterectomy rates varied by race/ethnicity with the highest rates among Native Hawaiian and Other Pacific Islander (NHOPI; 2.73 per 1,000 deliveries) and American Indian or Alaskan Native (AIAN; 2.67 per 1,000 deliveries) populations in 2019-2021. Unadjusted models showed a temporal increase in hysterectomy rates among AIAN (2011-2013 rate = 1.43 per 1,000 deliveries; OR 2019-2021 vs. 2011-2013 = 1.87, 95% CI 1.02 to 3.45) and White (2011-2013 rate = 1.13 per 1,000 deliveries; OR 2019-2021 vs. 2011-2013 = 1.21, 95% CI 1.11 to 1.33) populations. Adjustment ranged from having no effect among NHOPI individuals to explaining 14.0% of the observed 21.0% increase in hysterectomy rates among White individuals. CONCLUSION: Nationally, racial disparities in peripartum hysterectomy are evident. Between 2011-2021, the rate of hysterectomy increased; however, this increase was confined to AIAN and White individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".