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Open Access Research article

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

Christopher JL Murray1*, Rafael Lozano12, Abraham D Flaxman1, Peter Serina1, David Phillips1, Andrea Stewart1, Spencer L James1, Alireza Vahdatpour1, Charles Atkinson1, Michael K Freeman1, Summer Lockett Ohno1, Robert Black3, Said Mohammed Ali4, Abdullah H Baqui3, Lalit Dandona15, Emily Dantzer6, Gary L Darmstadt7, Vinita Das8, Usha Dhingra109, Arup Dutta11, Wafaie Fawzi12, Sara Gómez2, Bernardo Hernández1, Rohina Joshi13, Henry D Kalter3, Aarti Kumar14, Vishwajeet Kumar14, Marilla Lucero15, Saurabh Mehta16, Bruce Neal13, Devarsetty Praveen17, Zul Premji18, Dolores Ramírez-Villalobos2, Hazel Remolador15, Ian Riley19, Minerva Romero2, Mwanaidi Said18, Diozele Sanvictores15, Sunil Sazawal109, Veronica Tallo15 and Alan D Lopez20

Author Affiliations

1 Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Avenue Suite 600, Seattle, WA 98121, USA

2 National Institute of Public Health, Universidad 655, 62100 Cuernavaca, Morelos, Mexico

3 Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St #5041, Baltimore, MD 21205, USA

4 Public Health Laboratory-IdC, P.O. BOX 122 Wawi Chake Chake Pemba, Zanzibar, Tanzania

5 Public Health Foundation of India, ISID Campus, 4 Institutional Area, Vasant Kunj, New Delhi 110070, India

6 Brigham and Women's Hospital, 75 Francis St, Boston, MA 02215, USA

7 Global Development, Bill and Melinda Gates Foundation, PO Box 23350, Seattle, WA 98012, USA

8 CSM Medical University, Shah Mina Road, Chowk, Lucknow, Uttar Pradesh 226003, India

9 Dept of International Health, Johns Hopkins Bloomberg School of Public Health, E5521, 615 N. Wolfe Street, Baltimore, MD 21205, USA

10 Public Health Laboratory-Ivo de Carneri, Wawi, Chake-Chake, Pemba, Zanzibar, Tanzania

11 Johns Hopkins University, 214A Basement, Vinobapuri Lajpat Nagar-II, New Delhi 110024, India

12 Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115-6018, USA

13 The George Institute for Global Health, The University of Sydney, 83/117 Missenden Rd, Camperdown, NSW 2050, Australia

14 Community Empowerment Lab, Shivgarh, India

15 Research Institute for Tropical Medicine, Corporate Ave, Muntinlupa City 1781, Philippines

16 Division of Nutritional Sciences, Cornell University, 314 Savage Hall, Ithaca, NY 14853, USA

17 The George Institute for Global Health, 839C, Road No. 44A, Jubilee Hills, Hyderabad 500033, India

18 Muhimbili University of Health and Allied Sciences, United Nations Rd, Dar es Salaam, Tanzania

19 School of Population Health, University of Queensland, Level 2 Public Health Building School of Population Health, Herston Road, Herston, QLD 4006, Australia

20 University of Melbourne School of Population and Global Health, Building 379, 207 Bouverie St., Parkville 3010, VIC, Australia

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BMC Medicine 2014, 12:5  doi:10.1186/1741-7015-12-5

Published: 9 January 2014



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.


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.


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.


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.

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