Applying the InterVA-4 model to determine causes of death in rural Ethiopia

Glob Health Action. 2014 Oct 29:7:25550. doi: 10.3402/gha.v7.25550. eCollection 2014.

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.

Keywords: Ethiopia; Health and Demographic Surveillance System; InterVA; cause of death; chronic non-communicable.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Autopsy
  • Cause of Death*
  • Child
  • Child, Preschool
  • Data Collection / methods*
  • Ethiopia / epidemiology
  • Female
  • Humans
  • Infant
  • Infant, Newborn
  • Male
  • Middle Aged
  • Mortality / trends*
  • Population Surveillance
  • Rural Population
  • Software