Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS

Between August 2002 and December 2008, a total of 1,823 VA interviews were reviewed by physicians in the NUHDSS. Data on these interviews were entered into the InterVA model for interpretation. Cause-specific mortality fractions were then derived from the cause of death data generated by the physicians and by the model. We then estimated the level of agreement between both methods using Kappa statistics.The level of agreement between individual causes of death assigned by both methods was only 35% (κ = 0.27, 95% CI: 0.25 - 0.30). However, the patterns of mortality as determined by both methods showed a high burden of infectious diseases, including HIV/AIDS, tuberculosis, and pneumonia, in the study population. These mortality patterns are consistent with existing knowledge on the burden of disease in underdeveloped communities in Africa.The InterVA model showed promising results as a community-level tool for generating cause of death data from VAs. We recommend further refinement to the model, its adaptation to suit local contexts, and its continued validation with more extensive data from different settings.Developing countries generally lack consistent, timely, and reliable information on the levels and cause of death patterns in their populations [1]. Information about causes of death is needed by health managers and policymakers at every level of governance -from local to national - to plan for, prioritize, and address the health needs of their people [2]. In developed countries, this information is usually made available through well-established vital registration systems [3]. For most of the developing world, where already scarce resources need to be carefully and optimally allocated, vital registration systems are weak, and information on causes of death is often incomplete or nonexistent [1,2].Alternative sources of cause of death (COD) information come from Demographic Surveillance Systems (DSS), which in the last 10 years have been receiving increased atte