Differences in caesarean rates across women's socio‐economic status by diverse obstetric indications: Cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: The existing inconsistent association between the caesarean rate and maternal socio-economic status (SES) may be the result of a failure to examine the association across indications for caesarean. This study examined the variation in caesarean rates by maternal SES across diverse obstetric-indications. METHODS: Data on demographics, education, insurance status, medical-conditions, and obstetric characteristics needed to classify deliveries according to Robson's 10 obstetric-groups were extracted from the 2015 US birth certificate data (n = 3 988 733). Multivariable log-binomial regression was used to analyse the data adjusting for confounders. RESULTS: The caesarean rate was 34.1% for women with high SES and 26.8% for those with low SES. After adjustment for confounders, the rate was similar between women with graduate degrees and those who did not complete high school (relative risk (RR) 1.0, 95% confidence interval (CI) 0.9, 1.1). However, different rates of caesareans across SES were observed for particular obstetric-indications. Notably, women with graduate education compared to those who did not complete high school were more likely to have a caesarean (RR 3.0, 95% CI 2.9, 3.1) for a low-risk condition (group 1: nulliparous women with single, cephalic, ≥37 gestational weeks, and spontaneous labour). Women with private insurance were more likely to have a caesarean in almost all obstetric groups, compared to those without private insurance or Medicaid. CONCLUSION: Examining the overall caesarean rate obscures the relationship between SES and the use of caesarean for particular obstetric-indications. The unequal utilisation of caesareans across SES highlights overuse and potential underuse of the caesareans among American women.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".