Establishing the Ethiopian Obstetric Surveillance System for Monitoring Maternal Outcomes in Eastern Ethiopia: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Although the majority of maternal deaths and complications occur in low-resource settings, almost all existing strong registration and confidential enquiry systems are found in high-resource settings. We developed and piloted the Ethiopian Obstetric Surveillance System (EthOSS), based on the successful United Kingdom Obstetric Surveillance System (UKOSS) methodology, in 3 regions in Ethiopia to improve ongoing surveillance and tracking of maternal morbidities and deaths, as well as confidential enquiry, compared to the currently used maternal death surveillance and response program in Ethiopia. METHODS: We launched the EthOSS monthly case notification system in 13 hospitals in eastern Ethiopia in April 2021. Study participants included women admitted to the hospitals from April to September 2021 with major adverse obstetric conditions during pregnancy, childbirth, or within 42 days of termination of pregnancy. Designated clinicians at the hospitals used a simple online system to report the number of cases and maternal deaths monthly to the EthOSS team. We present findings on the incidence and case fatality rates for adverse conditions included in the EthOSS. RESULTS: Over the 6-month pilot period, 904 women with at least 1 EthOSS condition were included in the study, of whom 10 died (case fatality rate, 1.1%). Almost half (46.6%, 421/904) sustained major obstetric hemorrhage, 38.7% (350/904) severe anemia, 29.5% (267/904) eclampsia, 8.8% (80/904) sepsis, and 2.2% (20/904) uterine rupture. To enable care improvement alongside surveillance, the local committee received training on confidential enquiry into maternal deaths from internal and external experts. CONCLUSIONS: In this facility-based project, data on severe adverse obstetric conditions were captured through voluntary reporting by clinicians. Further analysis is essential to assess the robustness of these data, and confidential enquiry into maternal deaths for specific cases is planned to investigate the appropriateness of care.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".