Data integration methods to improve small area mortality estimation for tribal and rural populations - tribal nations often lack accurate, granular, and comprehensive information on the true impact of…

Emory University received a $1.19 million Project Grant from the National Institute on Minority Health and Health Disparities (NIMHD) under the Minority Health and Health Disparities Research program (CFDA 93.307), effective September 1, 2025, through August 31, 2028. The award supports the development and application of advanced statistical methodologies to improve small area mortality estimation for American Indian and Alaska Native (AI/AN) populations. The project addresses critical data quality gaps, including a 30% higher misclassification error rate in death certificates for AI/AN populations compared to other demographic groups and significant underreporting in U.S. Census statistics. The research will employ Bayesian hierarchical small area disease mapping combined with measurement error methodologies to produce accurate, granular mortality estimates across distinct AI/AN population categories defined by self-reported status, tribal citizenship, and residence on tribal lands.

The project will be applied specifically to opioid mortality rates across Oklahoma's 77 counties for the period 2015–2024, with Phase 1 focusing on county-year opioid mortality rate estimates for tribal citizens by linking Oklahoma State death certificate data with Cherokee Nation tribal registry information. Phase 2 will extend mortality rate estimation across additional AI/AN population groups. Sub-awards have been issued to Cherokee Nation (December 18, 2025) and Mercer University (December 4, 2025) to support data integration and analysis components, enabling a comprehensive examination of mortality trends across rural and non-rural AI/AN communities to inform public health interventions and policy decisions.

Data integration methods to improve small area mortality estimation for tribal and rural populations - tribal nations often lack accurate, granular, and comprehensive information on the true impact of mortality indicators across american indian/alaska native (AI/an) population groups needed for different use cases. Misclassification error in state death certificates is disproportionately 30% higher for the AI/an population compared to other demographic groups due to higher proportions of persons with a range of backgrounds within AI/an populations. Additionally, US census reported AI/an population statistics suffer from severe under-reporting at every geographic resolution due to small sample sizes, challenges in data collection, cost, and privacy algorithms. Importantly, official mortality and population statistics do not comprehensively or accurately enumerate distinct AI/an populations defined by (1) self- reported AI/an based on U.S. Census defined classification (census category), (2) tribal citizenship with a tribal nation (legal category), and (3) residence on tribal lands (geographic category). To address this data GAP, our approach combines bayesian hierarchical small area disease mapping with measurement error methodologies to produce small area mortality estimates across AI/an groups for both rural and non-rural areas with improved accuracy. Within the comprehensive methodological framework, model components are customized to produce population-specific mortality estimates with increased accuracy across multiple populations. We apply our methodological approach to obtain county-year trends in AI/an opioid mortality rates (omrs) for the 77 counties in oklahoma between years 2015-2024 across the distinct AI/an population groups and county-specific rural status. In phase 1, we estimate county-year omrs for tribal citizens by linking data from oklahoma state death certificates with cherokee nation tribal registry data. In phase 2, we estimate county-year omrs for self-affiliated AI/an populations using misclassification rates from phase 1 and assess trends for total populations-at-risk in counties inside and outside tribal boundaries. Of utmost importance to the study is the independent application and use of the proposed methodology by local health departments. We will develop a data science pipeline consisting of both educational and computational resources for local health analysts to adopt and implement the proposed methodology as a mortality self-monitoring resource.