Child, maternal, and adult mortality in rural Ethiopia in 2019: a cross-sectional mortality survey using electronic verbal autopsies
Bibliographic record
Abstract
Background: Ethiopia, with about 10% of Africa's population, has little direct information on causes of death, particularly in rural areas where 80% of Ethiopians live. In 2019-2020, we conducted electronic verbal autopsies (e-VA) to examine causes of death and quantify cause-specific mortality rates in rural Ethiopia. Methods: We examined deaths under 70 years in the three years prior to the survey dates (November 25, 2019-February 29, 2020) among 2% of East Gojjam Zone (Amhara Region) using registered deaths and adding random sampling in this cross-sectional study. Trained surveyors interviewed relatives of the deceased with central dual-physician assignment of causes as the main outcome. We documented details on age, sex and location of death, and derived overall rural death rates using 2007 Census data and the United Nations national estimates for 2019. To these, we applied our sample-weighted causes to derive cause-specific mortality rates. We calculated death risks for the leading causes for major age groups. Findings: We studied 3516 deaths: 55% male, 97% rural, and 68% occurring at home. At ages 5 and older, injuries were notable, accounting for over a third of deaths at 5-14 years, half of the deaths at ages 15-29 years, and a quarter of deaths at ages 30-69 years. Neonatal mortality was high, mostly from prematurity/low birthweight and infections. Among children under 5 (excluding neonates), infections caused nearly two-thirds of deaths. Most maternal deaths (84%) arose from direct causes. After injuries, especially suicide, assaults, and road traffic accidents, vascular disease (15%) and cancer (13%) were the leading causes among adults at 30-69 years. HIV/AIDS and tuberculosis deaths were also important causes among adults. Interpretation: Rural Ethiopia has a high burden of avoidable mortality, particularly injury, including suicide, assaults, and road traffic accidents. Funding: International Development Research Centre, and the Canadian Institutes of Health Research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".