Rural–urban disparities in caesarean deliveries in sub-Saharan Africa: a multivariate non-linear decomposition modelling of Demographic and Health Survey data
Bibliographic record
Abstract
INTRODUCTION: Globally, the rate of caesarean deliveries increased from approximately 16.0 million in 2000 to 29.7 million in 2015. In this study, we decomposed the rural-urban disparities in caesarean deliveries in sub-Saharan Africa. METHODS: Data for the study were extracted from the most recent Demographic and Health Surveys of twenty-eight countries in sub-Saharan Africa. We included 160,502 women who had delivered in health facilities within the five years preceding the survey. A multivariate non-linear decomposition model was employed to decompose the rural-urban disparities in caesarean deliveries. The results were presented using coefficients and percentages. RESULTS: The pooled prevalence of caesarean deliveries in the 28 countries considered in the study was 6.04% (95% CI = 5.21-6.88). Caesarean deliveries' prevalence was highest in Namibia (16.05%; 95% CI = 14.06-18.04) and lowest in Chad (1.32%; 95% CI = 0.91-1.73). For rural-urban disparities in caesarean delivery, the pooled prevalence of caesarean delivery was higher in urban areas (10.37%; 95% CI = 8.99-11.75) than rural areas (3.78%; 95% CI = 3.17-4.39) across the 28 countries. Approximately 81% of the rural-urban disparities in caesarean deliveries were attributable to the differences in child and maternal characteristics. Hence, if the child and maternal characteristics were levelled, more than half of the rural-urban inequality in caesarean deliveries would be reduced. Wealth index (39.2%), antenatal care attendance (13.4%), parity (12.8%), mother's educational level (3.5%), and health insurance subscription (3.1%) explained approximately 72% of the rural-urban disparities in caesarean deliveries. CONCLUSION: This study shows significant rural-urban disparities in caesarean deliveries, with the disparities being attributable to the differences in child and maternal characteristics: wealth index, parity, antenatal care attendance, mother's educational level, and health insurance subscription. Policymakers in the included countries could focus and work on improving the socioeconomic status of rural-dwelling women as well as encouraging antenatal care attendance, women's education, health insurance subscription, and family planning, particularly in rural areas.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".