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A verbal autopsy (VA) consists of a survey with a relative or close contactof a person who has recently died. VA surveys are commonly used to infer likelycauses of death for individuals when deaths happen outside of hospitals orhealthcare facilities. Several statistical and algorithmic methods areavailable to assign cause of death using VA surveys. Each of these methodsrequire as inputs some information about the joint distribution of symptoms andcauses. In this note, we examine the generalizability of this symptom-causeinformation by comparing different automated coding methods using variouscombinations of inputs and evaluation data. VA algorithm performance isaffected by both the specific SCI themselves and the logic of a givenalgorithm. Using a variety of performance metrics for all existing VAalgorithms, we demonstrate that in general the adequacy of the informationabout the joint distribution between symptoms and cause affects performance atleast as much or more than algorithm logic.

Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-assignment Algorithms for Verbal Autopsy