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Background Malaria, a preventable parasitic disease, causes most child deaths in sub-Saharan Africa (SSA). Reliable cause-of-death data are essential to evaluate progress toward the national and global malaria control goals. However, civil registration and vital statistics are often weak and incomplete in many low- and middle-income countries. In such circumstances, verbal autopsy (VA) provides an alternative means of mortality surveillance. In some settings, VA has been paired with Minimally Invasive Tissue Sampling (MITS) to obtain detailed biological confirmation of the causes of death. Here, 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. Method We examined 3129 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. 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.

Over the past 70 years, significant advances have been made in determining the causes of death in populations not served by official medical certification of cause at the time of death using a technique known as Verbal Autopsy (VA). VA involves an interview of the family or caregivers of the deceased after a suitable bereavement interval about the circumstances, signs and symptoms of the deceased in the period leading to death. The VA interview data are then interpreted by physicians or, more recently, computer algorithms, to assign a probable cause of death. VA was originally developed and applied in field research settings. This paper traces the evolution of VA methods with special emphasis on the World Health Organization's (WHO)'s efforts to standardize VA instruments and methods for expanded use in routine health information and vital statistics systems in low- and middle-income countries (LMICs). These advances in VA methods are culminating this year with the release of the 2022 WHO Standard Verbal Autopsy (VA) Toolkit. This paper highlights the many contributions the late Professor Peter Byass made to the current VA standards and methods, most notably, the development of InterVA, the most commonly used automated computer algorithm for interpreting data collected in the WHO standard instruments, and the capacity building in low- and middle-income countries (LMICs) that he promoted. This paper also provides an overview of the methods used to improve the current WHO VA standards, a catalogue of the changes and improvements in the instruments, and a mapping of current applications of the WHO VA standard approach in LMICs. It also provides access to tools and guidance needed for VA implementation in Civil Registration and Vital Statistics Systems at scale.

We recently published in BMC Medicine an evaluation of the comparative diagnostic performance of InSilicoVA, a software to map the underlying causes of death from verbal autopsy interviews. The developers of this software claim to have failed to replicate our results and appear to have also failed to locate our replication archive for this work. In this Correspondence, we provide feedback on how this might have been done more usefully and offer some suggestions to improve future attempts at reproducible research. We also offer an alternative interpretation of the results presented by Li et al., namely that, out of 100 verbal autopsy interviews, InSilicoVA will, at best, correctly identify the underlying cause of death in 40 cases and incorrectly in 60 – a markedly inferior performance to alternative existing approaches.

Background A verbal autopsy (VA) is an interview conducted with the caregivers of someone who has recently died to describe the circumstances of the death. In recent years, several algorithmic methods have been developed to classify cause of death using VA data. The performance of one method—InSilicoVA—was evaluated in a study by Flaxman et al., published in BMC Medicine in 2018. The results of that study are different from those previously published by our group. Methods Based on the description of methods in the Flaxman et al. study, we attempt to replicate the analysis to understand why the published results differ from those of our previous work. Results We failed to reproduce the results published in Flaxman et al. Most of the discrepancies we find likely result from undocumented differences in data pre-processing, and/or values assigned to key parameters governing the behavior of the algorithm. Conclusion This finding highlights the importance of making replication code available along with published results. All code necessary to replicate the work described here is freely available on GitHub.