Simplified Symptom Pattern Method for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards

We investigated specific parameters in SP's Bayesian framework that allow for its optimal performance in both assigning individual cause of death and in determining cause-specific mortality fractions. We evaluated these outcomes of the method separately for adult, child, and neonatal verbal autopsies in 500 different population constructs of verbal autopsy data to analyze its ability in various settings.We determined that a modified, simpler version of Symptom Pattern (termed Simplified Symptom Pattern, or SSP) performs better than the previously-developed approach. Across 500 samples of verbal autopsy testing data, SSP achieves a median cause-specific mortality fraction accuracy of 0.710 for adults, 0.739 for children, and 0.751 for neonates. In individual cause of death assignment in the same testing environment, SSP achieves 45.8% chance-corrected concordance for adults, 51.5% for children, and 32.5% for neonates.The Simplified Symptom Pattern Method for verbal autopsy can yield reliable and reasonably accurate results for both individual cause of death assignment and for determining cause-specific mortality fractions. The method demonstrates that verbal autopsies coupled with SSP can be a useful tool for analyzing mortality patterns and determining individual cause of death from verbal autopsy data.Methods for analyzing verbal autopsies (VAs) seek to predict causes of death and/or cause-specific mortality fractions (CSMFs) based solely on a decedent's signs and symptoms leading up to death. The signs and symptoms for a given death are recorded in an interview with a member of the decedent's family. The family member's responses can then be analyzed to deduce the true cause of death through either physician-certified verbal autopsy (PCVA) or computer-coded verbal autopsy (CCVA). One CCVA approach proposed in 2007 by Murray et al. [1] was the Symptom Pattern (SP) Method. SP is a Bayesian approach that implements statistical machinery similar to the InterVA program