Limitations to current methods to estimate cause of death: a validation study of a verbal autopsy model

Abstract

Background: Accurate information on causes of death (CoD) is essential to estimate burden of disease, track global progress, prioritize cost-effective interventions, and inform policies to reduce mortality. In low-income settings, where a significant proportion of deaths take place at home or in poorly-resourced peripheral health facilities, data on CoD often relies on verbal autopsies (VAs). Validations of VAs have been performed against clinical diagnosis, but never before against an acceptable gold standard: the complete diagnostic autopsy (CDA). Methods: We have validated a computer-coded verbal autopsy method -the InterVA- using individual and population metrics to determine CoD against the CDA, in 316 deceased patients of different age groups who died in a tertiary-level hospital in Maputo, Mozambique between 2013 and 2015. Results: We found a low agreement of the model across all age groups at the individual (kappa statistic ranging from -0.030 to 0.232, lowest in stillbirths and highest in adults) and population levels (chance-corrected cause-specific mortality fraction accuracy ranging from -1.00 to 0.62, lowest in stillbirths, highest in children). The sensitivity in identifying infectious diseases was low (0% for tuberculosis, diarrhea, and disseminated infections, 32% for HIV-related infections, 33% for malaria and 36% for pneumonia). Of maternal deaths, 26 were assigned to eclampsia but only four patients actually died of eclampsia. Conclusions: These findings do not lead to building confidence in current estimates of CoD. They also call to the need to implement autopsy methods where they may be feasible, and to improve the quality and performance of current VA techniques.

Keywords: Mozambique; Validation; cause of death; complete diagnostic autopsy; verbal autopsy.

Figure 1.. Alluvial diagrams of the differences in assignment of individual causes of death established by the Complete Diagnostic Autopsy (CDA) and InterVA (Interpreting Verbal Autopsy) model by study group.

The stacked blocks represent the causes of death (CoDs) determined by the CDA (left) and by the InterVA model (right), and their size as proportional to the cause-specific mortality fractions (CSMFs). The branches between blocks represent differences in the composition of the CoDs between the CDA and the InterVA model, being their thickness proportional to the number of cases contained in both blocks connected by the branch. Each CoD is represented by a different color, which is the same in both diagnostic methods. The color of the branches is determined by the cause of actual death (CDA). The concordant cases between the CDA and the InterVA model are represented by branches connected to blocks of the same color. In contrast, misclassified cases are shown as branches connected to blocks of different color.

Figure 2.. Alluvial diagrams of the differences in the individual cause of death as established by the complete diagnostic autopsy (CDA) and the InterVA (Interpreting Verbal Autopsy) model among patients who died of infectious diseases.

The stacked blocks represent the causes of death (CoDs) determined by the CDA (left) and by the InterVA model (right), and their size as proportional to the cause-specific mortality fractions (CSMFs). The branches between blocks represent differences in the composition of the CoDs between the CDA and the InterVA model, being their thickness proportional to the number of cases contained in both blocks connected by the branch. Each CoD is represented by a different color, which is the same in both diagnostic methods. The color of the branches is determined by the cause of actual death (CDA). The concordant cases between the CDA and the InterVA model are represented by branches connected to blocks of the same color. In contrast, misclassification cases are shown as branches connected to blocks of different color. Diseminated infections: bacterial sepsis of the newborn (n=21), puerperal sepsis (n=6), streptococcal sepsis (n=5) and other sepsis (n=19) HIV/AIDS related infections: candidiasis (n=1), congenital viral diseases (n=1), cryptococcosis (n=11), cytomegaloviral disease (n=7), herpes simplex infection (n=1), miliary tuberculosis (n=20), salmonella infection (n=1), pneumocystosis (n=5), respiratory tuberculosis bacteriologically and histologically confirmed (n=2), toxoplasmosis (n=7) and tuberculous meningitis (n=1) Other infections: acute pericarditis (n=2), pyelonephritis (n=2), congenital viral diseases (n=2), chorioamnionitis (n=2), GBS infection (n=2), tetanus (n=1), peritonitis (n=3), rabies (n=3) and zygomycosis (n=1) Non-infectious diseases (by the InterVA model): congenital malformations (n=1), intrapartum complication (n=2), eclampsia (n=12), obstetric haemorrhage (n=10), non-obstetric diseases (n=5)and other diseases (n=29).