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.
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.
Nikita
N
Desai
Lukasz
L
Aleksandrowicz
Pierre
P
Miasnikof
Ying
Y
Lu
Jordana
J
Leitao
Peter
P
Byass
Stephen
S
Tollman
Paul
P
Mee
Dewan
D
Alam
Suresh Kumar
SK
Rathi
Abhishek
A
Singh
Rajesh
R
Kumar
Faujdar
F
Ram
Centre for Global Heath Research, St, Michael's Hospital, Dalla Lana School of Public Health, University of Toronto, Toronto Ontario, Canada. Prabhat.jha@utoronto.ca.
Prabhat
P
Jha
D001344:Autopsy / Q000379:methods / Q000592:standards*; D002423:Cause of Death; D016208:Databases, Factual / Q000592:standards; D001330:Electronic Data Processing / Q000379:methods / Q000592:standards*; D006801:Humans; D010819:Physician's Role; D011203:Poverty