InterVA-5

InterVA-5 processes verbal autopsy (VA) data according to WHO 2016 standards to categorize circumstantial determinants of death (COMCATs) and assign numeric likelihoods to six circumstantial categories for each death.

Key Features:

• Integration of COMCATs: Integrates Circumstances Of Mortality CATegories (COMCATs) to systematically categorize circumstantial determinants such as logistical barriers, service utilization issues, and health system responses.
• Assignment of Numeric Likelihoods: Assigns numeric likelihoods to six circumstantial categories for each death to quantify the probability of each COMCAT per case.
• Scalability and Consistency: Implements a pragmatic approach that is scalable and consistent to support reproducible COMCAT assignment across datasets and contexts.
• Enhanced VA Functionality: Extends verbal autopsy analysis to include circumstantial determinants alongside VA data, enabling a more comprehensive interpretation of mortality determinants.

Scientific Applications:

• Health planning and policy-making: Supports health planning and policy-making by contextualizing mortality data with circumstantial determinants to identify barriers to healthcare access and systemic inadequacies.
• Mortality surveillance in low- and middle-income settings: Applicable in settings with inadequate routine death registration to provide systematic mortality information for populations where many deaths are unrecorded.
• Evaluation of global health objectives: Facilitates evaluation of progress towards the Sustainable Development Goals (SDGs) and Universal Health Coverage (UHC) by providing nuanced information on determinants of death.
• Empirical demonstration: Applied to 4,116 deaths from the Agincourt Health and Socio-Demographic Surveillance System (South Africa, 2012–2016), highlighting factors such as lack of resources to access healthcare, poor disease recognition, and inadequate health system responses.

Methodology:

Collects information on medical signs, symptoms, and circumstances from witnesses of death and processes these inputs to assign numeric likelihoods to six circumstantial COMCAT categories; assigned COMCATs are examined for correlation with demographic variables such as age and sex and with local knowledge about causes of death.