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

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          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.

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          Ethiopia Demographic and Health Survey 2011

          (2012)
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            Sample registration of vital events with verbal autopsy: a renewed commitment to measuring and monitoring vital statistics.

            Philip W Setel, Osman Sankoh, Chalapati Rao … (2005)
            Registration of births, recording deaths by age, sex and cause, and calculating mortality levels and differentials are fundamental to evidence-based health policy, monitoring and evaluation. Yet few of the countries with the greatest need for these data have functioning systems to produce them despite legislation providing for the establishment and maintenance of vital registration. Sample vital registration (SVR), when applied in conjunction with validated verbal autopsy procedures and implemented in a nationally representative sample of population clusters represents an affordable, cost-effective, and sustainable short- and medium-term solution to this problem. SVR complements other information sources by producing age-, sex-, and cause-specific mortality data that are more complete and continuous than those currently available. The tools and methods employed in an SVR system, however, are imperfect and require rigorous validation and continuous quality assurance; sampling strategies for SVR are also still evolving. Nonetheless, interest in establishing SVR is rapidly growing in Africa and Asia. Better systems for reporting and recording data on vital events will be sustainable only if developed hand-in-hand with existing health information strategies at the national and district levels; governance structures; and agendas for social research and development monitoring. If the global community wishes to have mortality measurements 5 or 10 years hence, the foundation stones of SVR must be laid today.
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              Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS

              Samuel Oti, Catherine Kyobutungi (2010)
              Background Developing countries generally lack complete vital registration systems that can produce cause of death information for health planning in their populations. As an alternative, verbal autopsy (VA) - the process of interviewing family members or caregivers on the circumstances leading to death - is often used by Demographic Surveillance Systems to generate cause of death data. Physician review (PR) is the most common method of interpreting VA, but this method is a time- and resource-intensive process and is liable to produce inconsistent results. The aim of this paper is to explore how a computer-based probabilistic model, InterVA, performs in comparison with PR in interpreting VA data in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). Methods 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. Results 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. Conclusions 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.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Glob Health Action
                Journal ID (iso-abbrev): Glob Health Action
                Journal ID (publisher-id): GHA
                Title: Global Health Action
                Publisher: Co-Action Publishing
                ISSN (Print): 1654-9716
                ISSN (Electronic): 1654-9880
                Publication date (Electronic): 29 October 2014
                Publication date Collection: 2014
                Volume: 7
                Electronic Location Identifier: 10.3402/gha.v7.25550
                Affiliations
                [1 ]Department of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia
                [2 ]INDEPTH Network, Accra, Ghana
                [3 ]CAPHRI, School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
                Author notes
                [* ]Correspondence to: Berhe Weldearegawi, Department of Public Health, College of Health Sciences, Mekelle University, P.O. Box 1871, Mekelle, Ethiopia, Email: berheph@ 123456gmail.com

                Responsible Editors: Heiko Becher, University of Hamburg, Germany; Nawi Ng, Umeå University, Sweden.

                Article
                Publisher ID: 25550
                DOI: 10.3402/gha.v7.25550
                PMC ID: 4220136
                PubMed ID: 25377338
                SO-VID: 3985b0e4-2901-458c-bc41-66083c767bdf
                Copyright © © 2014 Berhe Weldearegawi et al.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 24 July 2014
                Date revision received : 12 August 2014
                Date accepted : 18 August 2014
                Categories
                Subject: Indepth Network Cause-Specific Mortality

                ScienceOpen disciplines: Health & Social care
                Keywords: interva,cause of death,health and demographic surveillance system,chronic non-communicable,ethiopia
                Data availability:
                ScienceOpen disciplines: Health & Social care
                Keywords: interva, cause of death, health and demographic surveillance system, chronic non-communicable, ethiopia

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