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Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries
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2014 (English)In: BMC Medicine, ISSN 1741-7015, E-ISSN 1741-7015, Vol. 12, no 1, 20- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.

METHODS: We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.

RESULTS: The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%).

CONCLUSIONS: On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.

Place, publisher, year, edition, pages
BioMed Central, 2014. Vol. 12, no 1, 20- p.
Keyword [en]
Causes of death, Computer-coded verbal autopsy (CCVA), InterVA-4, King-Lu, Physician-certified verbal autopsy (PCVA), Random forest, Tariff method, Validation, Verbal autopsy
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:umu:diva-85996DOI: 10.1186/1741-7015-12-20ISI: 000334697300003PubMedID: 24495855OAI: oai:DiVA.org:umu-85996DiVA: diva2:696495
Available from: 2014-02-14 Created: 2014-02-14 Last updated: 2017-12-06Bibliographically approved

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Byass, PeterTollman, StephenMee, Paul

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CiteExportLink to record
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