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
Date
2014
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Abstract
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
Description
Keywords
Causes of death, Computer-coded verbal autopsy (CCVA), InterVA-4, King-Lu, Physician-certified verbal autopsy (PCVA), Random forest, Tariff method, Validation, Verbal autopsy