Abstract

BackgroundVerbal autopsy analyses are widely used for estimating cause-specific mortality rates (CSMR) in the vast majority of the world without high-quality medical death registration. Verbal autopsies -- survey interviews with the caretakers of imminent decedents -- stand in for medical examinations or physical autopsies, which are infeasible or culturally prohibited.Methods and FindingsWe introduce methods, simulations, and interpretations that can improve the design of automated, data-derived estimates of CSMRs, building on a new approach by King and Lu (2008). Our results generate advice for choosing symptom questions and sample sizes that is easier to satisfy than existing practices. For example, most prior effort has been devoted to searching for symptoms with high sensitivity and specificity, which has rarely if ever succeeded with multiple causes of death. In contrast, our approach makes this search irrelevant because it can produce unbiased estimates even with symptoms that have very low sensitivity and specificity. In addition, the new method is optimized for survey questions caretakers can easily answer rather than questions physicians would ask themselves. We also offer an automated method of weeding out biased symptom questions and advice on how to choose the number of causes of death, symptom questions to ask, and observations to collect, among others.ConclusionsWith the advice offered here, researchers should be able to design verbal autopsy surveys and conduct analyses with greatly reduced statistical biases and research costs.

Highlights

  • Estimates of cause-specific morality rates (CSMRs) are urgently needed for many research and public policy goals, but high quality death registration data exists in only 23 of 192 countries [1]

  • Verbal autopsy studies are widely used in the developing world to estimate CSMRs, disease surveillance, and sample registration [4,5,6], as well as risk factors, infectious disease outbreaks, and the effects of public health interventions [7,8,9]

  • Most studies choose questions intended to have the highest possible sensitivity and specificity. This emphasis does not help much, because they are not required for accurate CSMR inferences, a point we demonstrate

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Summary

Methods and Findings

Simulations, and interpretations that can improve the design of automated, data-derived estimates of CSMRs, building on a new approach by King and Lu (2008). Most prior effort has been devoted to searching for symptoms with high sensitivity and specificity, which has rarely if ever succeeded with multiple causes of death. Our approach makes this search irrelevant because it can produce unbiased estimates even with symptoms that have very low sensitivity and specificity. The new method is optimized for survey questions caretakers can answer rather than questions physicians would ask themselves. We offer an automated method of weeding out biased symptom questions and advice on how to choose the number of causes of death, symptom questions to ask, and observations to collect, among others

Introduction
Discussion
A Test for Detecting Biased Symptoms
Test Procedure
17. Kalter H
20. World Health Organization: Verbal Autopsy Standards
22. INDEPTH Network
Full Text
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