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Validation and validity of verbal autopsy proceduresAbstract: The gold standard diagnosis of cause of death (COD) for assessing the validity of VA has been the COD derived from hospital medical records. The main limitations of using hospital-based CODs as the gold standard are: (1) The accuracy of medical records-based COD is debatable, even though some studies have refined the diagnosis with expert review of hospital records; and (2) the composition and distribution of hospital CODs may not be representative of deaths occurring in the community. In addition, if diagnostic algorithms for CCVA are developed from subsets of validation study datasets, their external validity may be compromised. Nevertheless, hospital diagnosis of COD based on defined clinical and laboratory criteria are the only useful gold standard available at present for validating VAs.The validity of InterVA has not previously been tested against a gold standard diagnosis. The reliability of InterVA has been determined by examining the concordance of CSMFs estimated by InterVA and PCVA. Given that the accuracy of PCVA is questionable, estimating concordance between causes of death derived by PCVA and InterVA as a measure of validity needs to be interpreted with caution.Measures used to assess the validity of VA include sensitivity, specificity, positive predictive value, and absolute (absolute error) or relative (relative error) difference between CSMF estimated by VA and true CSMF in the validation data. Sensitivity and specificity that measure accuracy at the individual level vary substantially between causes of death across different VA interpretation methods. The absolute and relative errors of CSMF measure the accuracy of VA at the population level. The variability of the absolute error in CSMF appears to be reasonable for most CODs because often the number of false positive and false negative diagnoses balance out. However, the relative error in CSMF tends to be exaggerated, especially if the CSMF is low.Murray and colleagues in this series recommend de
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