What the percentage of births in facilities does not measure: readiness for emergency obstetric care and referral in Senegal
Bibliographic record
Abstract
Introduction: Increases in facility deliveries in sub-Saharan Africa have not yielded expected declines in maternal mortality, raising concerns about the quality of care provided in facilities. The readiness of facilities at different health system levels to provide both emergency obstetric and newborn care (EmONC) as well as referral is unknown. We describe this combined readiness by facility level and region in Senegal. Methods: For this cross-sectional study, we used data from nine Demographic and Health Surveys between 1992 and 2017 in Senegal to describe trends in location of births over time. We used data from the 2017 Service Provision Assessment to describe EmONC and emergency referral readiness across facility levels in the public system, where 94% of facility births occur. A national global positioning system facility census was used to map access from lower-level facilities to the nearest facility performing caesareans. Results: Births in facilities increased from 47% in 1992 to 80% in 2016, driven by births in lower-level health posts, where half of facility births now occur. Caesarean rates in rural areas more than doubled but only to 3.7%, indicating minor improvements in EmONC access. Only 9% of health posts had full readiness for basic EmONC, and 62% had adequate referral readiness (vehicle on-site or telephone and vehicle access elsewhere). Although public facilities accounted for three-quarters of all births in 2016, only 16% of such births occurred in facilities able to provide adequate combined readiness for EmONC and referral. Conclusions: Our findings imply that many lower-level public facilities-the most common place of birth in Senegal-are unable to treat or refer women with obstetric complications, especially in rural areas. In light of rising lower-level facility births in Senegal and elsewhere, improvements in EmONC and referral readiness are urgently needed to accelerate reductions in maternal and perinatal mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".