C.J.L. Mahende, University of Washington
R. Lozano, University of Washington
A.D. Flaxman, University of Washington
P. Serina, University of Washington
D. Phillips, University of Washington
A. Stewart, University of Washington
S.L. James, University of Washington
A. Vahdatpour, University of Washington
C. Atkinson, University of Washington
M.K. Freeman, University of Washington
S.L. Ohno, University of Washington
R. Black, Johns Hopkins University
S.M. Ali, Public Health Laboratory-IdC
A.H. Baqui, Johns Hopkins University
L. Dandona, University of Washington
E. Dantzer, Brigham and Women's Hospital
G.L. Darmstadt, Bill and Melinda Gates Foundation
V. Das, CSM Medical University
U. Dhingra, Public Health Laboratory-Ivo de Carneri
A. Dutta, Johns Hopkins University
W. Fawzi, Harvard School of Public Health
S. Gomez, National Institute of Public Health
B. Hernandez, University of Washington
R. Joshi, The University of Sydney
H.D. Kalter, Johns Hopkins University
A. Kumar, Community Empowerment Lab
V. Kumar, Community Empowerment Lab
M. Lucero, Research Institute for Tropical Medicine
S. Mehta, Cornell University
Zul Premji, Aga Khan UniversityFollow
D. Ramirez-Villalobos, National Institute of Public Health
H. Remolador, Research Institute for Tropical Medicine
I. Riley, University of Queensland
M. Romero, National Institute of Public Health
M. Said, Muhimbili University of Health and Allied Sciences
D. Sanvictores, Research Institute for Tropical Medicine
S. Sazawal, Public Health Laboratory-Ivo de Carneri
V. Tallo, Research Institute for Tropical Medicine
A. D. Lopez, University of Melbourne

Document Type



Pathology (East Africa)


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. © 2014 Murray et al.; licensee BioMed Central Ltd.

Publication (Name of Journal)

BMC Medicine

Included in

Pathology Commons