Agreement between physicians and the InterVA-4 model in assigning causes of death: the role of recall period and characteristics specific to the deceased and the respondent.

BACKGROUND

In the absence of routine death registration, the InterVA model is a new methodology being used as a physician alternative method to interpret verbal autopsy (VA) data in resource-poor settings. However, various studies indicate that there are significant discrepancies between the two approaches in assigning causes of deaths. This study evaluated the role of recall period and characteristics that were specific to the deceased and the respondent in affecting the level of agreement between the approaches.

METHODS

A population-based cross-sectional study was conducted from March to April, 2012. All adults aged ≥14 years and died between 01 January, 2010, and 15 February, 2012, were included in the study. Data were collected by using a pre-tested and modified WHO designed verbal autopsy questionnaire. The verbal autopsy interviews were reviewed by the InterVA-4 model and the physicians. Cohen's kappa statistic with 95% CI was applied to compare the strength of the agreement between the model and the physician review.

RESULTS

A total of 408 VA interviews were successfully completed and reviewed by the InterVA model and the physicians. Both approaches showed an overall agreement in 294 (72.1%) of the cases [kappa = 0.48, 95% CI: 0.42 - 0.60]. The level of agreement between the approaches was low [kappa ≤0.40] when the deceased was female, 50 and above years old, single, illiterate, rural dweller, belonged to a family of 1-4 people living together, and died at home. This was also true when the recall period was ≤1 year, and the respondent was a relative other than parent/marital partner, lived with the deceased, and had medical information.

CONCLUSION

This study identified important variables affecting the strength of agreement between the InterVA-4 model and the physician in assigning causes of death. The results are believed to significantly contribute to the process of identifying the actual underlying causes of deaths in the population, and may thus serve to promote informed health policy decisions in resource-poor settings.