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Objectives: To compare the cause of death distribution using the Physician Coded Verbal Autopsy approach versus the Interpreting Verbal Autopsy model, based on information from a French verbal autopsy questionnaire, in rural north-western Burkina Faso., Methods: Data from 5649 verbal autopsy questionnaires reviewed by local physicians at the Nouna Health and Demographic Surveillance Site between 1998 and 2007 were considered for analyses. Information from VA interviews was extracted to create a set of standard indicators needed to run the Interpreting Verbal Autopsy model. Cause-specific mortality fractions were used to compare Physician Coded Verbal Autopsy and Interpreting Verbal Autopsy results., Results: At the population level, 62.5% of causes of death using the Interpreting Verbal Autopsy model corresponded with those determined by two or three physicians. Although seven of the 10 main causes of death were present in both approaches, the comparison of percentages of single causes of death shows discrepancies, dominated by higher malaria rates found in the Physician Coded Verbal Autopsy approach., Conclusion: Our results confirm that national mortality statistics, which are partly based on verbal autopsies, must be carefully interpreted. Difficulties in determining malaria as cause of death in holoendemic malaria regions might result in higher discrepancies than those in non-endemic areas. As neither Physician Coded Verbal Autopsy nor Interpreting Verbal Autopsy results represent a gold standard, uncertainty levels with either procedure are high., (© 2012 Blackwell Publishing Ltd.)

Background: In countries where tracking mortality and clinical cause of death are not routinely undertaken, gathering verbal autopsies (VA) is the principal method of estimating cause of death. The most common method for determining probable cause of death from the VA interview is Physician-Certified Verbal Autopsy (PCVA). A recent alternative method to interpret Verbal Autopsy (InterVA) is a computer model using a Bayesian approach to derive posterior probabilities for causes of death, given an a priori distribution at population level and a set of interview-based indicators. The model uses the same input information as PCVA, with the exception of narrative text information, which physicians can consult but which were not inputted into the model. Comparing the results of physician coding with the model, large differences could be due to difficulties in diagnosing malaria, especially in holo-endemic regions. Thus, the aim of the study was to explore whether physicians' access to electronically unavailable narrative text helps to explain the large discrepancy in malaria cause-specific mortality fractions (CSMFs) in physician coding versus the model., Methods: Free-texts of electronically available records (N = 5,649) were summarised and incorporated into the InterVA version 3 (InterVA-3) for three sub-groups: (i) a 10%-representative subsample (N = 493) (ii) records diagnosed as malaria by physicians and not by the model (N = 1035), and (iii) records diagnosed by the model as malaria, but not by physicians (N = 332). CSMF results before and after free-text incorporation were compared., Results: There were changes of between 5.5-10.2% between models before and after free-text incorporation. No impact on malaria CSMFs was seen in the representative sub-sample, but the proportion of malaria as cause of death increased in the physician sub-sample (2.7%) and saw a large decrease in the InterVA subsample (9.9%). Information on 13/106 indicators appeared at least once in the free-texts that had not been matched to any item in the structured, electronically available portion of the Nouna questionnaire., Discussion: Free-texts are helpful in gathering information not adequately captured in VA questionnaires, though access to free-text does not explain differences in physician and model determination of malaria as cause of death.