Open Access Research article

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

Nikita Desai1, Lukasz Aleksandrowicz1, Pierre Miasnikof1, Ying Lu2, Jordana Leitao1, Peter Byass34, Stephen Tollman456, Paul Mee45, Dewan Alam7, Suresh Kumar Rathi1, Abhishek Singh8, Rajesh Kumar9, Faujdar Ram8 and Prabhat Jha1*

Author Affiliations

1 Centre for Global Heath Research, St. Michael’s Hospital, Dalla Lana School of Public Health, University of Toronto, Toronto Ontario, Canada

2 Center for the Promotion of Research Involving Innovative Statistical Methodology, Steinhardt School of Culture, Education and Human Development, New York University, New York NY, USA

3 WHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Umeå University, Umeå, Sweden

4 Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

5 Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

6 International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) Network, Accra, Ghana

7 International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh

8 International Institute for Population Sciences, Mumbai, Maharashtra, India

9 School of Public Health, Post Graduate Institute of Medical Research and Education, Chandigarh, India

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BMC Medicine 2014, 12:20  doi:10.1186/1741-7015-12-20

Published: 4 February 2014

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.

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