Submission ID 130223
| Issue/Objective | Malaria, a preventable parasitic disease, causes substantial child mortality in sub-Saharan Africa (SSA). Reliable cause-of-death data are essential but often unavailable where civil registration is weak. Verbal autopsy (VA) provides an alternative and is sometimes paired with Minimally Invasive Tissue Sampling (MITS). We compare malaria-attributed and all-cause mortality among children younger than five years in six SSA countries, using three computer models (GPT-4o, InSilicoVA, and InterVA-5) to assign causes of death, against MITS as the reference standard. |
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| Methodology/Approach | We examined 3,129 under-five deaths enrolled in six Child Health and Mortality Prevention Surveillance (CHAMPS) country sites in SSA between December 2016 and December 2022. Contrived free-text narrative summaries were generated for each record and coded into International Classification of Diseases (ICD-10) codes by GPT-4o. InSilicoVA and InterVA-5 outputs, provided in the World Health Organization 2016 VA codes, were harmonized to ICD-10 for comparison. The primary comparison was the underlying cause of death in VA models and MITS. |
| Results | Sierra Leone had the highest proportion of post‐neonatal deaths attributed to malaria at 30.3% (67/221), followed by Kenya at 17.3% (42/243), then Mozambique at 13% (18/138) and Mali at 5.5% (3/55) as defined by MITS. No malaria‐attributable deaths were observed in neonates and stillbirths. GPT‐4o correctly classified 60 (46.2%) of 130 malaria deaths, compared with 39 (30.0%) for InSilicoVA and 30 (23.1%) for InterVA‐5. At the population level, the GPT‐4o model achieved a higher cause‐specific mortality fraction accuracy (0.36) compared to InSilicoVA (0.07) and InterVA‐5 (0.08). GPT‐4o performed comparatively better in attributing malaria, HIV/AIDS, and diarrhoeal diseases compared to other communicable diseases. |
| Discussion/Conclusion | GPT‐4o demonstrated superior performance over probabilistic VA models in identifying malaria‐attributed deaths. National vital registration authorities and health ministries should consider integrating large language model‐driven tools into their VA systems to enhance diagnostic precision. While less practicable at scale, focal and periodic MITS comparisons are useful for improving verbal autopsy systems. National mortality data are essential to track progress in reducing childhood deaths from malaria and other conditions. |
| Presenters and Affiliations | Ronald Carshon - Marsh Dalla Lana School of Public Health, University of Toronto Ronald Carshon - Marsh Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada. Centre for Global Health Research, Unity Health Toronto, Toronto, ON M5B 1W8, Canada Richard Wen Centre for Global Health Research, Unity Health Toronto, Toronto, ON M5B 1W8, Canada. Thomas Kai Sze Ng Centre for Global Health Research, Unity Health Toronto, Toronto, ON M5B 1W8, Canada Rajeev Kamadod Centre for Global Health Research, Unity Health Toronto, Toronto, ON M5B 1W8, Canada Isaac Bogoch Divisions of General Internal Medicine and Infectious Diseases, Department of Medicine, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5S 3H2, Canada. Susan J Bondy Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada Theodore J Witek Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada. Centre for Global Health Research, Unity Health Toronto, Toronto, ON M5B 1W8, Canada Prabhat Jha Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada. Centre for Global Health Research, Unity Health Toronto, Toronto, ON M5B 1W8, Canada |
