Causes of death in rural southeast Asia by electronic verbal autopsy: a population-based observational study
Bibliographic record
Abstract
BACKGROUND: In low-income and middle-income countries in southeast Asia, most deaths occur outside of the health-care system without a medically certified cause of death. We did a verbal autopsy study to determine the underlying causes of death in rural areas of the region and to estimate premature mortality using years of life lost (YLLs). METHODS: In this population-based observational study, we conducted electronic verbal autopsy surveys for reported deaths in 510 villages in Bangladesh, Cambodia, Laos, Myanmar, and Thailand from 2021 to 2024. With the WHO 2016 verbal autopsy questionnaire, trained fieldworkers conducted interviews, and local physicians independently assigned causes of death by ICD-10 codes. Causes of death were ranked based on cause-specific mortality fractions (CSMFs), and YLLs calculated using corresponding national life expectancy. FINDINGS: Over 90% of reported deaths had a verbal autopsy administered. Among 3413 deaths, 2052 (60%) were male and 1361 (40%) were female, and 3245 (95%) were older than 12 years. Across sites, 64-86% of deaths occurred at home. Non-communicable diseases were the leading causes of death, ranging from 54% in Laos to 70% in Cambodia, with cardiovascular, cerebrovascular, cancer, and digestive conditions being the most common. Maternal, nutritional, and communicable diseases, mainly respiratory infections, diarrhoea, and tuberculosis, accounted for 11-26% of deaths across the sites. Accidents and injuries comprised 7-13% and ranked among the top five causes. Major gaps in death documentation persist across all countries, whereas disease-specific causes of death varied between sites. Non-communicable diseases accounted for the majority of YLLs, representing between 56% and 65% across the five countries. INTERPRETATION: This multicountry study highlighted the significant burden of non-communicable diseases in rural southeast Asia, alongside persistent communicable diseases, emphasising the need for better mortality data and health-care access to reduce this dual burden. FUNDING: Wellcome Trust. TRANSLATIONS: For the Myanmar, Karen, Bangla, Thai, Lao and Khmer translations of the abstract see Supplementary Materials section.
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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".