摘要：Background Physician assessment historically has been the most common method of analyzing verbal autopsy (VA) data. Recently, the World Health Organization endorsed two automated methods, Tariff 2.0 and InterVA–4, which promise greater objectivity and low- er cost. A disadvantage of the Tariff method is that it requires a train- ing data set from a prior validation study, while InterVA relies on clinically specified conditional probabilities. We undertook to vali- date the hierarchical expert algorithm analysis of VA data, an auto- mated, intuitive, deterministic method that does not require a train- ing data set. Methods Using Population Health Metrics Research Consortium study hospital source data, we compared the primary causes of 1629 neonatal and 1456 1–59 month–old child deaths from VA expert al- gorithms arranged in a hierarchy to their reference standard causes. The expert algorithms were held constant, while five prior and one new "compromise" neonatal hierarchy, and three former child hier- archies were tested. For each comparison, the reference standard data were resampled 1000 times within the range of cause–specific mor- tality fractions (CSMF) for one of three approximated community scenarios in the 2013 WHO global causes of death, plus one random mortality cause proportions scenario. We utilized CSMF accuracy to assess overall population–level validity, and the absolute difference between VA and reference standard CSMFs to examine particular causes. Chance–corrected concordance (CCC) and Cohen's kappa were used to evaluate individual–level cause assignment.
