Abstract
BackgroundVerbal autopsy can be a useful tool for generating cause of death data in data-sparse regions around the world. The Symptom Pattern (SP) Method is one promising approach to analyzing verbal autopsy data, but it has not been tested rigorously with gold standard diagnostic criteria. We propose a simplified version of SP and evaluate its performance using verbal autopsy data with accompanying true cause of death.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.
Highlights
Verbal autopsy can be a useful tool for generating cause of death data in data-sparse regions around the world
InterVA relies on expert judgment to determine the probability of a particular cause of death given a reported symptom, while Symptom Pattern (SP) is a data-driven approach which invokes 1) King-Lu direct cause-specific mortality fraction (CSMF) estimation [4] as the prior probability distribution, and 2) the actual probability of responses to combinations of items conditional on true cause in verbal autopsy data, which includes the true cause of death
First developed in 2007, the Symptom Pattern Method for verbal autopsy has been subject to in-depth investigation and experimentation
Summary
Verbal autopsy can be a useful tool for generating cause of death data in data-sparse regions around the world. 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 validated verbal autopsy data essentially trains the model, and the resulting model can be applied to verbal autopsy questionnaires for which the true cause of death is unknown. These unknown deaths are assigned a predicted cause of death based on the posterior distribution of the probability of death being due to each cause. Each cause’s predicted deaths can be aggregated to produce estimates of cause-specific mortality fractions in the population of verbal autopsy data being analyzed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
