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
| dc.contributor.author | Nikita Desai | |
| dc.contributor.author | Lukasz Aleksandrowicz | |
| dc.contributor.author | Pierre Miasnikof | |
| dc.contributor.author | Ying Lu | |
| dc.contributor.author | Jordana Leitao | |
| dc.contributor.author | Peter Byass | |
| dc.contributor.author | Stephen Tollman | |
| dc.contributor.author | Paul Mee | |
| dc.contributor.author | Dewan Alam | |
| dc.contributor.author | Suresh Kumar Rathi | |
| dc.contributor.author | Abhishek Singh | |
| dc.contributor.author | Rajesh Kumar | |
| dc.contributor.author | Faujdar Ram | |
| dc.contributor.author | Prabhat Jha | |
| dc.date.accessioned | 2024-04-02T10:42:08Z | |
| dc.date.available | 2024-04-02T10:42:08Z | |
| dc.date.issued | 2014 | |
| dc.description.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 | |
| dc.description.librarian | PM2023 | |
| dc.faculty | Faculty of Health Sciences | |
| dc.identifier.uri | https://hdl.handle.net/10539/38288 | |
| dc.language.iso | en | |
| dc.school | Public Health | |
| dc.subject | Causes of death, Computer-coded verbal autopsy (CCVA), InterVA-4, King-Lu, Physician-certified verbal autopsy (PCVA), Random forest, Tariff method, Validation, Verbal autopsy | |
| dc.title | 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 | |
| dc.type | Article |