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

BMC Medicine

Research Article

Emily Dantzer¹  Vinita Das²  Vishwajeet Kumar³  Aarti Kumar³  Usha Dhingra⁴  Sunil Sazawal⁴  Saurabh Mehta⁵  Gary L Darmstadt⁶  Wafaie Fawzi⁷  Charles Atkinson⁸  Bernardo Hernández⁸  Peter Serina⁸  Andrea Stewart⁸  Christopher JL Murray⁸  Summer Lockett Ohno⁸  David Phillips⁸  Alireza Vahdatpour⁸  Spencer L James⁸  Abraham D Flaxman⁸  Michael K Freeman⁸  Rafael Lozano⁹  Lalit Dandona^1,10  Arup Dutta^1,11  Abdullah H Baqui^1,12  Henry D Kalter^1,12  Robert Black^1,12  Zul Premji^1,13  Mwanaidi Said^1,13  Minerva Romero^1,14  Dolores Ramírez-Villalobos^1,14  Sara Gómez^1,14  Said Mohammed Ali^1,15  Diozele Sanvictores^1,16  Marilla Lucero^1,16  Hazel Remolador^1,16  Veronica Tallo^1,16  Ian Riley^1,17  Devarsetty Praveen^1,18  Bruce Neal^1,19  Rohina Joshi^1,19  Alan D Lopez^2,20 

[1] Brigham and Women's Hospital, 75 Francis St, 02215, Boston, MA, USA;CSM Medical University, Shah Mina Road, Chowk, 226003, Lucknow, Uttar Pradesh, India;Community Empowerment Lab, Shivgarh, India;Dept of International Health, Johns Hopkins Bloomberg School of Public Health, E5521, 615 N. Wolfe Street, 21205, Baltimore, MD, USA;Public Health Laboratory-Ivo de Carneri, Wawi, Chake-Chake, Pemba, Zanzibar, Tanzania;Division of Nutritional Sciences, Cornell University, 314 Savage Hall, 14853, Ithaca, NY, USA;Global Development, Bill and Melinda Gates Foundation, PO Box 23350, 98012, Seattle, WA, USA;Harvard School of Public Health, 677 Huntington Avenue, 02115-6018, Boston, MA, USA;Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Avenue Suite 600, 98121, Seattle, WA, USA;Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Avenue Suite 600, 98121, Seattle, WA, USA;National Institute of Public Health, Universidad 655, 62100, Cuernavaca, Morelos, Mexico;Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Avenue Suite 600, 98121, Seattle, WA, USA;Public Health Foundation of India, ISID Campus, 4 Institutional Area, 110070, Vasant Kunj, New Delhi, India;Johns Hopkins University, 214A Basement, Vinobapuri Lajpat Nagar-II, 110024, New Delhi, India;Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St #5041, 21205, Baltimore, MD, USA;Muhimbili University of Health and Allied Sciences, United Nations Rd, Dar es Salaam, Tanzania;National Institute of Public Health, Universidad 655, 62100, Cuernavaca, Morelos, Mexico;Public Health Laboratory-IdC, P.O. BOX 122, Wawi Chake Chake Pemba, Zanzibar, Tanzania;Research Institute for Tropical Medicine, Corporate Ave, 1781, Muntinlupa City, Philippines;School of Population Health, University of Queensland, Level 2 Public Health Building School of Population Health, Herston Road, 4006, Herston, QLD, Australia;The George Institute for Global Health, 839C, Road No. 44A, 500033, Jubilee Hills, Hyderabad, India;The George Institute for Global Health, The University of Sydney, 83/117 Missenden Rd, 2050, Camperdown, NSW, Australia;University of Melbourne School of Population and Global Health, Building 379, 207 Bouverie St., 3010, Parkville, VIC, Australia;

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

DOI  :  10.1186/1741-7015-12-5

 received in 2013-09-28, accepted in 2013-12-10,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundMonitoring 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.MethodsWe 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.ResultsThree 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.ConclusionsPhysician 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.

【 授权许可】

CC BY   
© Murray et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.