# Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality > **NIH NIH R03** · UNIVERSITY OF CALIFORNIA SANTA CRUZ · 2022 · $77,000 ## Abstract Project Abstract Cause of death data are essential for understanding the burden of disease, emerging health needs, and the effectiveness of public health interventions. Few low- and middle-income countries (LMIC) have adequate vital statistics systems that produce high quality statistics on causes of death. Verbal autopsy (VA) is a widely adopted tool to collect information on causes of death when full autopsy and death certification are not possible. The current analytical methods for VA are significantly limited by the lack of generalizability. Existing VA methods yield inaccurate cause-of-death assignment and biased estimates of the distribution of deaths when they are deployed to populations that are different than the populations based on which the models are developed. In this project, we will develop robust, domain adaptive, and computationally feasible methods to assign causes to individual deaths and estimate cause-specific mortality, by completing the following aims: (i) to develop statistical models to characterize multiple heterogeneous VA datasets; (ii) to develop and evaluate domain adaptive algorithms for cause-of-death assignment in new populations; and (iii) to extend the unified domain adaptation framework to routine VA analysis pipeline. This new framework will improve on existing VA methods by utilizing the full information available in reference deaths from multiple populations to achieve robustness to data shift across populations. The framework will also incorporate the complex dependence relationship in the collected signs and symptoms in an interpretable manner, and allow fast and streamlined implementation compatible with standard VA questionnaires. We will develop the first unified framework for domain adaptive cause-of-death assignment using VA data and offer critical insights into the relationship between the signs and symptoms collected by VA and causes of death. The project will lay the groundwork for future research, such as integrating VAs with additional covariates and biomarker information collected from medical history or tissue samples, and designing systematic cause-of-death monitoring and surveillance using large-scale VA surveys. ## Key facts - **NIH application ID:** 10576014 - **Project number:** 1R03HD110962-01 - **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA CRUZ - **Principal Investigator:** Zehang Li - **Activity code:** R03 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $77,000 - **Award type:** 1 - **Project period:** 2022-09-30 → 2024-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10576014 ## Citation > US National Institutes of Health, RePORTER application 10576014, Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality (1R03HD110962-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10576014. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*