Data integration methods to develop culturally-relevant small area American Indian and Alaska Native health monitoring systems correcting for data inequities

Music City Center 

Tribal Nations often lack accurate, granular, and comprehensive information on the true burden of mortality indicators across diverse American Indian/Alaska Native (AI/AN) population structures needed for different use cases. Racial misclassification in State death certificates 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. Additionally, US Census reported AI/AN population statistics suffer from severe under-representation at every geographic resolution due to small sample sizes, challenges in data collection, cost, and privacy algorithms. Limitations in data accuracy and coverage for both numerator and denominator data result in distorted estimates of the total burden to AI/AN populations. Importantly, official mortality and population statistics do not comprehensively or accurately enumerate diverse AI/AN populations defined by (1) Self-identification/affiliation with the AI/AN racial classification based on the U.S. Census racial categories (racial category), (2) Tribal citizenship with a Tribal Nation (legal category), and (3) Residence on Tribal lands (geographic category). To address these critical gaps, we propose a Bayesian hierarchical model integrating small-area disease mapping and measurement error methods allowing for customizable model components and integration of multiple data sources. Our results point the way toward more comprehensive assessment and modeling of the multiple sources of structured and unstructured uncertainty involved in fusing information from these heterogeneous sources. This proof-of-concept study will serve as a critical step toward the development of a scalable and generalizable framework for Tribal Nations to generate accurate and actionable health estimates. This work aims to empower Tribal Nations with the data sovereignty necessary to monitor and address health disparities within AI/AN communities.

Sampling challenges