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

Murray, Christopher J. L., Lozano, Rafael, Flaxman, Abraham D., Serina, Peter, Phillips, David, Stewart, Andrea, James, Spencer L., Vahdatpour, Alireza, Atkinson, Charles, Freeman, Michael K., Ohno, Summer Lockett, Black, Robert, Ali, Said Mohammed, Baqui, Abdullah H., Dandona, Lalit, Dantzer, Emily, Darmstadt, Gary L., Das, Vinita, Dhingra, Usha, Dutta, Arup, Fawzi, Wafaie, Gomez, Sara, Hernandez, Bernardo, Joshi, Rohina, Kalter, Henry D., Kumar, Aarti, Kumar, Vishwajeet, Lucero, Marilla, Mehta, Saurabh, Neal, Bruce, Praveen, Devarsetty, Premji, Zul, Ramirez-Villalobos, Dolores, Remolador, Hazel, Riley, Ian, Romero, Minerva, Said, Mwanaidi, Sanvictores, Diozele, Sazawal, Sunil, Tallo, Veronica and Lopez, Alane D. (2014) Using verbal autopsy to measure causes of death: The comparative performance of existing methods. BMC Medicine, 12 1: 5.1-5.19. doi:10.1186/1741-7015-12-5


Author Murray, Christopher J. L.
Lozano, Rafael
Flaxman, Abraham D.
Serina, Peter
Phillips, David
Stewart, Andrea
James, Spencer L.
Vahdatpour, Alireza
Atkinson, Charles
Freeman, Michael K.
Ohno, Summer Lockett
Black, Robert
Ali, Said Mohammed
Baqui, Abdullah H.
Dandona, Lalit
Dantzer, Emily
Darmstadt, Gary L.
Das, Vinita
Dhingra, Usha
Dutta, Arup
Fawzi, Wafaie
Gomez, Sara
Hernandez, Bernardo
Joshi, Rohina
Kalter, Henry D.
Kumar, Aarti
Kumar, Vishwajeet
Lucero, Marilla
Mehta, Saurabh
Neal, Bruce
Praveen, Devarsetty
Premji, Zul
Ramirez-Villalobos, Dolores
Remolador, Hazel
Riley, Ian
Romero, Minerva
Said, Mwanaidi
Sanvictores, Diozele
Sazawal, Sunil
Tallo, Veronica
Lopez, Alane D.
Title Using verbal autopsy to measure causes of death: The comparative performance of existing methods
Journal name BMC Medicine   Check publisher's open access policy
ISSN 1741-7015
Publication date 2014-01-09
Year available 2014
Sub-type Article (original research)
DOI 10.1186/1741-7015-12-5
Open Access Status DOI
Volume 12
Issue 1
Start page 5.1
End page 5.19
Total pages 19
Place of publication London, United Kingdom
Publisher BioMed Central Ltd.
Language eng
Subject 2700 Medicine
Abstract 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.
Keyword Cause of death
InterVA
King Lu
Random forests
Symptom pattern
Verbal autopsy
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Public Health Publications
 
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