Verbal Autopsy Through Integrated Language Analysis (VITAL)

Understanding trends and patterns in cause of death is essential for designing and implementing effective public health policy. In large parts of the world, most deaths happen outside of a healthcare setting and there is no medically certified record of the cause, stymying efforts to improve health in such settings and also creating downstream effects for health systems globally. Verbal Autopsies (VAs) consist of interviews with a person familiar with the circumstances surrounding a recent death and involve both a structured interview and a free-text narrative. The narrative is currently underutilized or wholly ignored in most settings. Our proposal leverages innovative Natural Language Processing (NLP) tools to ascertain cause of death from VA narratives. Our first Aim produces the most extensive and most heterogeneous collection of VAs with gold standard (medically certified) causes, which is essential for training machine learning and Artificial Intelligence (AI) models. Aim 2 critically evaluates both classical and cutting-edge NLP models to ascertain the cause of death. Along with evaluating the accuracy in ascertaining cause of death, we perform a detailed error analysis to understand potential sources of bias across demographic or other contextual factors that arise from the combination of currently available NLP algorithms and available data to train these models. Ascertaining cause of death is incredibly challenging, and even the most advanced algorithm will make mistakes. Ignoring this reality leads to inaccurate and biased estimates of patterns and trends in cause of death (e.g., the fraction of deaths due to malaria from urban areas compared to rural areas or whether the burden of cardiovascular disease has changed from one time to another). Aim 3 leverages cutting-edge statistical tools and the datasets assembled in Aim 1 to correct for these biases. We also perform detailed sample size calculations to understand the number of VAs required to detect changes in patterns and trends. 1 Project Number: 1R21HD119931-01 | Fiscal Year: 2025 | NIH Institute/Center: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | Principal Investigator: Li Liu (+1 co-PI) | Institution: JOHNS HOPKINS UNIVERSITY, BALTIMORE, MD | Award Amount: $434,068 | Activity Code: R21 | Study Section: Social Sciences and Population Studies A Study Section[SSPA] View on NIH RePORTER: https://reporter.nih.gov/project-details/1R21HD11993101