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J Glob Health. 2015 Jun;5(1):010402. doi: 10.7189/jogh.05.010402.

Comparing verbal autopsy cause of death findings as determined by physician coding and probabilistic modelling: a public health analysis of 54 000 deaths in Africa and Asia.

Author information

1
WHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Umeå University, Sweden ; 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 ; IMMPACT, Institute of Applied Health Sciences, School of Medicine and Dentistry, University of Aberdeen, Aberdeen, UK.
2
Africa Centre for Health and Population Studies, University of KwaZulu-Natal, KwaZulu-Natal, South Africa ; INDEPTH Network, Accra, Ghana.
3
WHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Umeå University, Sweden ; UCL Institute for Global Health, University College London, London, UK.
4
Eastern Mediterranean Regional Office, World Health Organization, Cairo, Egypt.
5
KEMRI/CDC Research and Public Health Collaboration, Kisumu, Kenya.
6
KEMRI/CDC Research and Public Health Collaboration, Kisumu, Kenya ; CDC Malaria Branch, Atlanta, GA, USA.
7
KEMRI/CDC Research and Public Health Collaboration, Kisumu, Kenya ; CDC Center for Global Health, Atlanta, GA, USA.
8
WHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Umeå University, Sweden ; 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.
9
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.
10
School of Informatics, City University London, London, UK.
11
World Health Organization, Geneva, Switzerland.
12
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 ; INDEPTH Network, Accra, Ghana ; Hanoi Medical University, Hanoi, Vietnam.
13
WHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Umeå University, Sweden ; 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 ; INDEPTH Network, Accra, Ghana.

Abstract

BACKGROUND:

Coverage of civil registration and vital statistics varies globally, with most deaths in Africa and Asia remaining either unregistered or registered without cause of death. One important constraint has been a lack of fit-for-purpose tools for registering deaths and assigning causes in situations where no doctor is involved. Verbal autopsy (interviewing care-givers and witnesses to deaths and interpreting their information into causes of death) is the only available solution. Automated interpretation of verbal autopsy data into cause of death information is essential for rapid, consistent and affordable processing.

METHODS:

Verbal autopsy archives covering 54 182 deaths from five African and Asian countries were sourced on the basis of their geographical, epidemiological and methodological diversity, with existing physician-coded causes of death attributed. These data were unified into the WHO 2012 verbal autopsy standard format, and processed using the InterVA-4 model. Cause-specific mortality fractions from InterVA-4 and physician codes were calculated for each of 60 WHO 2012 cause categories, by age group, sex and source. Results from the two approaches were assessed for concordance and ratios of fractions by cause category. As an alternative metric, the Wilcoxon matched-pairs signed ranks test with two one-sided tests for stochastic equivalence was used.

FINDINGS:

The overall concordance correlation coefficient between InterVA-4 and physician codes was 0.83 (95% CI 0.75 to 0.91) and this increased to 0.97 (95% CI 0.96 to 0.99) when HIV/AIDS and pulmonary TB deaths were combined into a single category. Over half (53%) of the cause category ratios between InterVA-4 and physician codes by source were not significantly different from unity at the 99% level, increasing to 62% by age group. Wilcoxon tests for stochastic equivalence also demonstrated equivalence.

CONCLUSIONS:

These findings show strong concordance between InterVA-4 and physician-coded findings over this large and diverse data set. Although these analyses cannot prove that either approach constitutes absolute truth, there was high public health equivalence between the findings. Given the urgent need for adequate cause of death data from settings where deaths currently pass unregistered, and since the WHO 2012 verbal autopsy standard and InterVA-4 tools represent relatively simple, cheap and available methods for determining cause of death on a large scale, they should be used as current tools of choice to fill gaps in cause of death data.

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