Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Nov 6, 2025
Open Peer Review Period: Nov 7, 2025 - Jan 2, 2026
Date Accepted: Sep 8, 2026
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Federal Decision Modeling to Strengthen Behavioral Health and Safety in Rural and Tribal Populations
ABSTRACT
Background:
Chronic diseases, notably behavioral health topics, impose a significant burden on health systems, especially among rural and Tribal communities, requiring reliable data-driven federal algorithmic decision modeling for investigating and prioritizing federal investments.
Objective:
To determine if initial algorithmic models can support high-priority federal decision requirements in behavioral health.
Methods:
Design: This original investigation developed and tested three geo-analytic decision models, applied to publicly-available U.S. data, including mortality data from 2018 to 2023 (Centers for Disease Control and Prevention), federal and Tribal behavioral health service operational data (Indian Health Service), and federal transportation data (U.S. Department of Transportation). Data analysis occurred from January 2025 to August 2025. Setting: Federal and Tribal health and safety systems. Participants: The analyses comprised of geographical areas that correspond to behavioral health needs and services among American Indians and Alaska Natives. Main Outcomes and Measures: Alcohol and drug related mortality, travel time and distance barriers to behavioral health services, and collocated factors in risk of human trafficking.
Results:
Nationally, 78 counties among 13 states were identified as prioritized investigation areas based on persistent and increasing alcohol or drug related deaths rates, where such crude death rates ranged from 1,197.8–1,848.7 per 100,000 AI/AN persons. An estimated 22,029 and 234,852 AI/AN persons were identified as residing in low physical access areas for behavioral health services within the American Southwest region during typical and non-typical business hours, respectively. Among the 80 AI/AN communities in the 300-mile study territory, risk indexes indicate 16 interpersonal risk exposure areas, representing an estimated 11,378 AI/AN persons, that require interagency studies to confirm the possibility of home separations and cases of interpersonal injuries.
Conclusions:
Algorithmic modeling of health and safety data can provide the geo-analytic evidence to support federal prioritization and investment decisions regarding complex needs and systems of health services.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.