Thursday, March 13, 2025 3/13/2025

Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality

Award Number: R03HD110962
ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = -$1,749 )
2025	2023	UNIVERSITY OF CALIFORNIA SANTA CRUZ	1156 HIGH ST	SANTA CRUZ	CA	95064	SANTA CRUZ	USA	Child Health and Human Development Extramural Research	000	2	1/17/2025	NON-COMPETING CONTINUATION	-$1,749
														Subtotal = -$1,749

Issue Date FY: 2023 ( Subtotal = $72,979 )
2023	2023	UNIVERSITY OF CALIFORNIA SANTA CRUZ	1156 HIGH ST	SANTA CRUZ	CA	95064	SANTA CRUZ	USA	Child Health and Human Development Extramural Research	000	2	8/18/2023	NON-COMPETING CONTINUATION	$72,979
														Subtotal = $72,979

Issue Date FY: 2022 ( Subtotal = $77,000 )
2022	2022	UNIVERSITY OF CALIFORNIA SANTA CRUZ	1156 HIGH ST	SANTA CRUZ	CA	95064	SANTA CRUZ	USA	Child Health and Human Development Extramural Research	000	1	9/27/2022	NEW	$77,000
														Subtotal = $77,000

Grand Total All Awards = $148,230

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Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality

Award Number: R03HD110962

ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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