Global health priorities are set and progress assessed by measuring how many lives are ended or affected by various diseases. Most low and middle income countries do not have adequate vital statistics, with the result that two-thirds of global deaths are not registered and have no cause. Verbal autopsy (VA) has the potential to become a feasible, affordable method for assessing cause of death when full autopsy and death certification are not possible. Existing VA methods do not yield reproducible, comparable cause of death assignments and can be unacceptably slow. This project aims to develop affordable, robust, calibrated methods to assign causes to individual deaths and characterize the distribution of deaths by cause by completing the following four activities: (1) develop a new standard, automated, statistical method to assign causes to deaths from VA data - InSilicoVA, (2) use additional facility-based data and debiased physician-assigned causes to create new, improved symptom-cause information modules used by InSilicoVA to inform the assignment of causes, (3) conduct an exhaustive, fair comparison of existing automated methods for assigning causes from VA data, including InSilicoVA, and (4) reduce measurement error in VA data by conducting fieldwork at multiple long-term field sites in Africa to understand and standardize the VA interview and then test the effectiveness of a new interview design and language processing procedures informed by that understanding. InSilicoVA will improve on existing methods by (1) being fully probabilistic, (2) keeping individual and population levels consistent (3) reporting consistent uncertainty at both levels, (4) utilizing information from the narrative section of the A interview, (5) accounting for measurement error, and (6) incorporating a hierarchical structure to allow aggregation and comparison across broad regions. At the end of this project, the community of VA users will have (1) full, open-source access to InSilicoVA, (2) valuable, new symptom-cause information modules for use in InSilicoVA and other methods, (3) urgently needed, objective information describing the performance of existing automated VA cause-assignment methods, and (4) an innovative new standardized VA interview design and standard operating procedures for handling language and VA data during and after the VA interview. The overall effect will be to increase the number of deaths with comparable causes determined using a reproducible method with quantifiable accuracy. This, in turn, will provide an opportunity to improve national, regional and global population health indicators in parts of the world without adequate vital statistics systems. With better and more timely information on population health, resources can be allocated and interventions targeted more efficiently and with greater effect. More lives will be improved and extended.
Two-thirds of global deaths are not recorded or given a cause. Not knowing exactly what kills people makes it hard to design, test and implement effective public health interventions, and this results in more people suffering and dying. This project develops new methods and improves existing methods for understanding which causes of death account for the majority of deaths in populations that do not have traditional vital statistics systems. The results of this project will provide the potential to better understand the important causes of death and assess the impact of interventions designed to prevent them.