Using verbal autopsy to measure causes of death: the comparative performance of existing methods

Christopher Murray, Rafael Lozano, Abraham Flaxman, Peter Serina, David Phillips, Andrea Stewart, Spencer James, Alireza Vahdatpour, Charles Atkinson, Michael Freeman, Summer Ohno, Robert Black, Said Ali, Abdullah Baqui, Lalit Dandona, Emily Dantzer, Gary Darmstadt, Vinita Das, Usha Dhingra, Arup DuttaWafaie Fawzi, Sara Gomez, Bernardo Hernandez, Rohina Joshi, Henry Kalter, Aarti Kumar, Vishwajeet Kumar, M Lucero, Saurabh Mehta, Bruce Neal, D Praveen, Zul Premji, Dolores Ramirez-Villalobos, Hazel Remolador, Ian Riley, Minerva Romero, Mwanaidi Said, Diozele Sanvictores, Sunil Sazawal, Veronica Tallo, Alan Lopez

    Research output: Contribution to journalArticlepeer-review

    108 Citations (Scopus)


    Background: Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability.Methods: We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution.Results: Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause.Conclusions: Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.

    Original languageEnglish
    Article number5
    Number of pages19
    JournalBMC Medicine
    Issue number1
    Publication statusPublished - 2014


    • Cause of death
    • InterVA
    • King-Lu
    • Random forests
    • Symptom pattern
    • Tariff
    • VA
    • Validation
    • Verbal autopsy


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