Understanding the surge in elective caesarean sections: Role of older women's childbirth choices on younger women in India
Bibliographic record
Abstract
The global rise in Caesarean sections (CS), including India’s increase from 8.5% in 2005-06 to 21.5% in 2019-21, poses a significant public health challenge. This study investigates the factors driving elective CS decisions, focusing on how older women’s childbirth experiences influence younger women’s choices within the same household, using data from the National Family Health Survey-5. Multivariable logistic regression and propensity score matching (PSM) were applied to see the influence of older women’s Elective CS decisions on their younger peers within the same household. Results show that younger women were more likely to choose elective CS if older women previously had one (29.0% vs. 15.1%, AOR = 1.72). Other significant predictors include mass media exposure (AOR = 1.13), private healthcare (AOR = 2.84), and older maternal age (AOR = 2.54 for ages 35-40 years). Regional differences were evident, with South India showing the highest CS rates among younger women (40.4%), while older women had CS rates. Wealth and education also played a role, with the richest women having higher odds (AOR = 2.00) and secondary education showing the greatest effect (AOR = 1.43). PSM analysis found an eight percent higher likelihood of elective CS among younger women if older women had one (ATT = 0.086; p < 0.001). In conclusion, the study shows that the childbirth experiences of older women strongly affect younger women's decisions to opt for elective CS, highlighting the important role of influence within households in shaping these choices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".