Understanding maternal mortality from top–down and bottom–up perspectives: Case of Tigray Region, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Unacceptably high levels of preventable maternal mortality persist as a problem across sub-Saharan Africa and much of south Asia. Currently, local assessments of the magnitude of maternal mortality are not often made, so the best available information for health planning may come from global estimates and not reflect local circumstances. METHODS: A community-based cross-sectional survey was designed to identify all live births together with all deaths among women aged 15-49 years retrospectively over a one-year period in six randomly selected districts of Tigray Region, northern Ethiopia. After birth and death identification, Health Extension Workers trained to use the WHO 2012 verbal autopsy (VA) tool visited households to carry out VAs on all deaths among women aged 15-49 years. All pregnancy-related deaths were identified after processing the VA material using the InterVA-4 model, which corresponds to the WHO 2012 VA. A maternal mortality ratio (MMR) was calculated for each District and expressed with a 95% confidence interval (CI). RESULTS: The MMRs across the six sampled Districts ranged from 37 deaths per 100 000 live births (95% CI 1 to 207) to 482 deaths per 100 000 live births (95% CI 309 to 718). The overall MMR for Tigray Region was calculated at 266 deaths per 100 000 live births (95% CI 198 to 350). Direct obstetric causes accounted for 61% of all pregnancy-related deaths. Haemorrhage was the major cause of pregnancy-related death (34%). District-level MMRs were strongly inversely correlated with population density (r(2) = 0.86). CONCLUSION: This simple but well-designed survey approach enabled estimation of maternal mortality in Tigray Region on a local, contemporary basis. It also provided insights into possible local variations in MMR and their determinants. Consequently, this approach could be implemented at regional level in other large sub-Saharan African countries, or at national level in smaller ones to monitor and evaluate maternal health service interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".