Rising trends and persisting inequalities in cesarean section rates in Nepal: evidence from demographic and health surveys 2006-2016
Bibliographic record
Abstract
Abstract BackgroundSustainable development goals require member countries to reduce maternal mortality below 70 per 100,000 live births by 2030. Addressing inequalities in access to emergency obstetric care is crucial for reducing the maternal mortality ratio. This study was done to examine the time trends and socio-demographic inequalities in the utilization of cesarean section (CS) in Nepal during 2006-2016.MethodsWe used data from the Nepal Demographic and Health Surveys (NDHS) conducted during 2006-2016. Women who had a live birth in the last five years of the survey (most recent birth if there were two or more child birth) were the unit of analysis for this study. Absolute and relative inequalities in CS rates by different characteristics were measured in-terms of rate difference and rate ratios, respectively. Bivariate analyses and multivariate logistic regression models were used to assess the rate of cesarean sections by background socio-demographic characteristics of women. ResultsAge and parity adjusted CS rates were found to have increased almost three-fold (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%;95% CI:1.1-4.9), those from poorest wealth quintile (2.4%,95% CI:(1.2-3.7) and those living in province 6(2.4%,95% CI:1.3-3.5) had CS rate below 5%. Whereas, women from the richest income quintile (25.1%,95% CI :20.2-30.1), with higher education (21.2%,95% CI:14.7-27.8) and those delivering in private facilities (37.1%,95% CI:30.5-43.7) had CS rate above 15%. The absolute inequality in CS rate increased for maternal educational status, income quintiles, ecological region, province and place of delivery over the period. Relative inequality increased for provinces and place of delivery. Women from the richest income quintile (OR-3.3,95% CI: 1.6-7.0) and those delivered in private/NGO-run facilities (OR-3.6;95% CI:2.7-4.9) were more than three times more likely to deliver by CS compared to women from the poorest income quintile and those delivering in public facilities, respectively. ConclusionTo improve maternal and newborn health, strategies need to be revised to address the underuse of C-section in poor, mountain region and province 6. Simultaneously, policies and guidelines are needed to reduce overuse in rich women, women with higher education and those delivered 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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".