Abstract

Background In Ethiopia, most deaths take place at home and routine certification of cause of death by physicians is lacking. As a result, reliable cause of death (CoD) data are often not available. Recently, a computerized method for interpretation of verbal autopsy (VA) data, called InterVA, has been developed and used. It calculates the probability of a set of CoD given the presence of circumstances, signs, and symptoms reported during VA interviews. We applied the InterVA model to describe CoD in a rural population of Ethiopia. Objective VA data for 436/599 (72.7%) deaths that occurred during 2010–2011 were included. InterVA-4 was used to interpret the VA data into probable cause of death. Cause-specific mortality fraction was used to describe frequency of occurrence of death from specific causes. Results InterVA-4 was able to give likely cause(s) of death for 401/436 of the cases (92.0%). Overall, 35.0% of the total deaths were attributed to communicable diseases, and 30.7% to chronic non-communicable diseases. Tuberculosis (12.5%) and acute respiratory tract infections (10.4%) were the most frequent causes followed by neoplasms (9.6%) and diseases of circulatory system (7.2%). Conclusion InterVA-4 can produce plausible estimates of the major public health problems that can guide public health interventions. We encourage further validation studies, in local settings, so that InterVA can be integrated into national health surveys.

Highlights

  • In Ethiopia, most deaths take place at home and routine certification of cause of death by physicians is lacking

  • verbal autopsy (VA) data were collected for a total of 436/599 (72.7%) deaths that occurred in Kilite Awlaelo Health and Demographic Surveillance System (KA-HDSS) during 2010Á2011

  • These were processed by the interpretation of VA data (InterVA)-4 model which assigned cause(s) of death to all except 35 cases (8.0%) which were completely indeterminate

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Summary

Introduction

In Ethiopia, most deaths take place at home and routine certification of cause of death by physicians is lacking. Reliable cause of death (CoD) data are often not available. A computerized method for interpretation of verbal autopsy (VA) data, called InterVA, has been developed and used. It calculates the probability of a set of CoD given the presence of circumstances, signs, and symptoms reported during VA interviews. We applied the InterVA model to describe CoD in a rural population of Ethiopia. InterVA-4 was used to interpret the VA data into probable cause of death. Results: InterVA-4 was able to give likely cause(s) of death for 401/436 of the cases (92.0%). We encourage further validation studies, in local settings, so that InterVA can be integrated into national health surveys

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