Trend and Sociodemographic Correlates of Cesarean Section Utilization in Nepal: Evidence from Demographic and Health Surveys 2006‐2016
Bibliographic record
Abstract
BACKGROUND: Data from the Nepal Demographic and Health Surveys (NDHS) 2006, 2011, and 2016 were sourced for this study. Women who had a live birth in the last five years of the survey were the unit of analysis for this study. Absolute and relative inequalities in CS utilization were expressed in terms of rate difference and rate ratios, respectively. We used multivariable regression models to assess the CS rate by background sociodemographic characteristics of women. RESULTS: Age and parity-adjusted CS rates were found to have increased almost threefold (from 3.2%, 95% CI: 2.1-4.3 in 2006 to 10.5%; 95% CI: 8.9-11.9 in 2016) over the decade. In 2016, women from mountain region (3.0%), those from the lowest wealth quintile (2.4%), and those living in Karnali province (2.4%) had CS rate below 5%. Whereas women from the highest wealth quintile (25.1%), with higher education (21.2%), and those delivering in private facilities (37.1%) had CS rate above 15%. Women from the highest wealth quintile (OR-3.3; 95% CI: 1.6-7.0) compared to women from the lowest wealth quintile and those delivered in private/NGO-run facilities (OR-3.6; 95% CI: 2.7-4.9) compared to women delivering in public facilities were more than three times more likely to deliver by CS. CONCLUSION: To improve maternal and newborn health, strategies need to be revised to address the underuse of CS among poor, those living in mountain region and Province 2, Lumbini province, Karnali province, and Sudhurpaschim province. Simultaneously, there is a pressing need for policies, guidelines, and continuous monitoring of CS rates to reduce overuse among rich women, women with higher education, and those giving childbirth in private facilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".