Open Access Research article

Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries: systematic review

Jordana Leitao1, Nikita Desai1, Lukasz Aleksandrowicz1, Peter Byass2, Pierre Miasnikof1, Stephen Tollman234, Dewan Alam5, Ying Lu6, Suresh Kumar Rathi1, Abhishek Singh7, Wilson Suraweera1, Faujdar Ram7 and Prabhat Jha1*

Author Affiliations

1 Centre for Global Heath Research, St Michael’s Hospital, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

2 Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

3 Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

4 International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) Network, Accra, Ghana

5 International Centre for Diarrhoeal Diseases Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh

6 Department of Humanities and Social Sciences in the Professions, Steinhardt School of Culture, Education and Human Development, New York University, New York, USA

7 International Institute for Population Sciences, Mumbai, India

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BMC Medicine 2014, 12:22  doi:10.1186/1741-7015-12-22

Published: 4 February 2014

Abstract

Background

Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.

Methods

The reviewed studies assessed methods’ performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.

Results

The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.

Conclusions

There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.

Keywords:
Causes of death; Computer-coded verbal autopsy; InterVA; King and Lu; Physician-certified verbal autopsy; Random forest; Simplified symptom pattern; Tariff; Validity; Verbal autopsy