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Geo-Analytic Decision Support for Federal Behavioral Health Prioritization Among American Indian and Alaska Native
David M Paschane1, Michael Ontiveros2, John E Snyder3
1Office of Clinical and Preventive Services, Indian Health Service, 5600 Fishers Lane, Rockville, MD, 20857, United States, 1 202-256-5763.
Background:
Chronic diseases, including behavioral health topics, impose a significant burden on health systems, especially among rural and tribal communities. In response, federal agencies require reliable data-driven decision-support for investigating emerging needs, prioritizing responses, and conducting surveillance. Federal decision-support models may benefit from geo-analytic methods capable of integrating multicriteria evidence, interagency policy-driven parameters, and disparate jurisdictional interests.
Objective:
This study aims to identify candidate federal priorities for intergovernmental and interagency cooperation using exploratory geo-analytic decision-support frameworks, specifically related to behavioral health risks and services among tribal populations.
Methods:
We tested illustrative federal decision parameters within 3 exploratory geo-analytic frameworks using publicly available US data. These included alcohol-related and drug-related mortality data from 2018 to 2023 from the Centers for Disease Control and Prevention (CDC); facility locations, operating hours, and Euclidean-distance approximations of physical access to behavioral health services from the Indian Health Service (IHS); and federal transportation and rest stop data used to examine hypothesized interpersonal-risk conditions. Analyses were performed from February through August 2025.
Results:
The exploratory analyses identified 78 counties across 13 states as prioritized investigation areas based on elevated and increasing alcohol-related or drug-related death rates. The 6-year pooled crude death rates ranged from 1197.8 to 1848.7 per 100,000 American Indian or Alaska Native (AI/AN) persons. Two regional analyses demonstrated methods for further prioritization. One analysis estimated that 22,029 and 234,852 AI/AN persons resided in low physical access areas for services during typical and nontypical business hours, respectively. Another analysis identified 12 potential interpersonal risk-exposure locations within a region of 28 rest stop facilities among 80 AI/AN communities.
Conclusions:
Three exploratory geo-analytic frameworks demonstrate methodological feasibility for identifying candidate geographic priorities that could inform federal-tribal and interagency investigations, service access barriers, and interpersonal risk events. The recommendation is to validate and expand the methodology with rural and tribal systems, leading to a candidate federal decision-support model for future interagency initiatives.
