Researchers have launched the Cause of Death Determination Ascertainment (CODA) project, a three-year, Gates Foundation-funded effort to improve mortality data in low-income countries. Vital Strategies leads the project and says many deaths in Sub-Saharan Africa and large parts of Asia occur outside health facilities and therefore lack reliable causes. According to the World Health Organization, only about one in ten deaths in Africa are registered.
CODA draws on historical and validated datasets to train its AI. It can operate offline and sync later. In communities, community health workers conduct post-mortem interviews with relatives and enter details. The system adjusts suggested questions as new information appears and converts spoken testimony into structured data for the cause-of-death algorithm.
In health facilities, physicians can enter patient history, observations and test results. The project will run limited trials in South Africa and Bangladesh and will form a scientific advisory committee to address ethical, legal and cultural concerns. Project leaders note that autopsies give the highest-quality data but are costly and limited in scale.
Difficult words
- mortality — number of deaths in a population
- register — officially record an event or personregistered
- post-mortem — interview or examination after a person dies
- algorithm — a set of rules for solving problems
- autopsy — medical examination of a body after deathautopsies
- community health worker — local person trained to provide basic health carecommunity health workers
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- Do you think asking relatives in post-mortem interviews gives accurate information? Why or why not?
- What are the advantages and problems of using AI to find causes of death in areas without many health facilities?
- How could local communities help improve the registration of deaths in their area?
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