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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
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
BMC Medicine, Volume 12, No. 1, Article 22, Year 2014
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Description
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. © 2014 Leitao et al.; licensee BioMed Central Ltd.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC3912516/bin/1741-7015-12-22-S1.doc
https://efashare.b-cdn.net/share/pmc/articles/PMC3912516/bin/1741-7015-12-22-S2.pdf
https://efashare.b-cdn.net/share/pmc/articles/PMC3912516/bin/1741-7015-12-22-S3.doc
Authors & Co-Authors
Leitao, Jordana
Canada, Toronto
Saint Michael's Hospital University of Toronto
Desai, Nikita
Canada, Toronto
Saint Michael's Hospital University of Toronto
Aleksandrowicz, Lukasz
Canada, Toronto
Saint Michael's Hospital University of Toronto
Byass, P.
Sweden, Umea
Umeå Universitet
Miasnikof, Pierre
Canada, Toronto
Saint Michael's Hospital University of Toronto
Tollman, Stephen Meir
Sweden, Umea
Umeå Universitet
South Africa, Tygerberg
South African Medical Research Council
Ghana, Accra
Indepth Network
Alam, Dewan Shamsul
Bangladesh, Dhaka
International Centre for Diarrhoeal Disease Research Bangladesh
Lu, Ying
United States, New York
Nyu Steinhardt
Rathi, Suresh K.
Canada, Toronto
Saint Michael's Hospital University of Toronto
Singh, Abhishek
India, Mumbai
International Institute for Population Sciences
Suraweera, Wilson
Canada, Toronto
Saint Michael's Hospital University of Toronto
Ram, Faujdar
India, Mumbai
International Institute for Population Sciences
Jha, Prabhat K.S.
Canada, Toronto
Saint Michael's Hospital University of Toronto
Statistics
Citations: 94
Authors: 13
Affiliations: 7
Identifiers
Doi:
10.1186/1741-7015-12-22
e-ISSN:
17417015
Research Areas
Health System And Policy
Study Design
Cross Sectional Study
Study Approach
Systematic review