Transitions in childbirth care provision: Understanding the rapid rise in institutional delivery in 21 countries of sub-Saharan Africa and the implications for future strategies
Bibliographic record
Abstract
Abstract Background In sub-Saharan Africa (SSA), institutional births have risen rapidly but mortality has remained high. We examined whether there have been increasing births at hospitals, with skilled attendance, and emergency capacity as indicators of comprehensive, higher-quality childbirth services in 21 SSA countries over the last two decades. Methods We analysed national household surveys between 2001 and 2022 to examine population trends in birth place (hospital or lower-level), attendant, and Caesarean Section (CS) rates by wealth quintile, and routine health facility data for 2022 on volumes of live births and CS by facility level. Countries were classified based on recent institutional delivery coverage (<65%, 65-85%, >85%), to assess patterns of change and future directions in line with a maternal and neonatal mortality transition model. Results Institutional delivery increases were primarily driven by lower-level facilities, which had low birth volumes and limited CS capacity. Yet countries that reached high delivery coverage saw greater gains in hospital births, attendance by doctors, and CS rates among the poorest. As national coverage rose, more deliveries were conducted at higher-volume CS-capable hospitals. Low population CS rates among the poorest persisted everywhere. Conclusion Major increases in institutional deliveries have not sufficiently translated into equitable access to comprehensive, life-saving childbirth care in 21 countries of SSA. Shifts towards hospital deliveries in countries that reached high coverage, consistent with the transition model, can provide guidance to those with lower coverage (<85%). Contextualizing strategies to equitably provide high-quality childbirth care will be transformative for women’s and newborn’s health in SSA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".