Emily Peterson
Pamela Gutman
Tribal Nations often lack accurate, granular, and comprehensive information on the true burden of health indicators across diverse American Indian/Alaska Native (AI/AN) population structures needed for different use cases. Racial misclassification in state health records is 30% higher for the AI/AN population compared to other race groups due to higher proportions of multi-ethnic persons within AI/AN populations. Limitations in data accuracy and coverage result in distorted estimates of the total burden to AI/AN populations. Importantly, official health and mortality statistics used for monitoring and surveillance do not comprehensively or accurately enumerate diverse AI/AN populations defined by community definitions, i.e., (1) self-identification/affiliation with the AI/AN racial classification based on the U.S. Census racial categories, (2) Tribal citizenship with a Tribal, and (3) residence on Tribal lands. Through a collaboration between Cherokee Nation Public Health and Emory University, we address these critical gaps through the development of customizable model-based data integration approaches to produce comprehensive and accurate small-area assessments for Tribal populations fusing information from state and Tribal data sources. We translate model-based approaches to usable and sustainable data science surveillance tools designed to empower local Tribal health departments in self-surveillance and management and to uphold Tribal authority not only over data but also over the surveillance systems themselves. This work serves as a critical step toward the development of a scalable and generalizable framework for Tribal Nations to generate accurate and actionable health estimates. Tribal-led decision-making at every stage of construction ensures adherence to Tribal principles of self-determination and collective benefit.