# Leveraging CDC Opioid Overdose Surveillance Funding from the Albuquerque Area Southwest Tribal Epidemiology Center to Create Tribal Data and Culturally Center Medications for Opioid Use Disorder

> **NIH NIH R61** · LOYOLA UNIVERSITY CHICAGO · 2020 · $606,504

## Abstract

Project Summary/Abstract
Fatal opioid overdose rates are higher among American Indian/Alaska Native populations than among Hispanics,
African Americans, and Asian Americans, and are just below non-Hispanic Whites. AI/AN opioid overdose rates
vary significantly by state and county; however, tribe-level differences are difficult to ascertain due to
decentralized data systems that divide state health data and Indian Health Service data. While county-level
health data is often used as a proxy for tribal data, state data often misclassify AN/AN patients, and in counties
containing the lands of multiple tribes, county data may blur significant inter-tribal variation. In addition to limited
tribe-specific data, treatment for opioid use disorders also often fails to account for the diversity of tribal
populations. On average, patients who take medication for opioid use disorder (MOUD), and specifically
methadone or buprenorphine, exhibit improved treatment retention and reduced risk of drug overdose compared
to patients not taking MOUD. Some research also shows improved retention for Naltrexone, another MOUD.
Because MOUD interventions are rarely tailored to the specific cultural contexts of AI/AN patients, social and
cultural barriers to treatment persist in AI/AN communities. To address these problems, we propose to leverage
Center for Disease Control funding awarded to the Albuquerque Area Southwest Tribal Epidemiology Center
(AASTEC) for improving data quality in opioid overdose surveillance in New Mexico in a two-phase research
project. The project will draw upon a community advisory board composed of clinicians and Indian health facility
staff, and use a collaboration of epidemiologists from AASTEC and the New Mexico Department of Health, and
academic researchers at the University of Utah, University of New Mexico, and Columbia University. In the first
phase, we will enhance tribal specificity of AI/AN opioid use disorder and overdose data by linking and geocoding
New Mexico vital statistics and syndromic surveillance data. We will then use these data in predictive models to
determine the role of modifiable risk and protective factors for specific tribal communities. We will disseminate
analysis reports to tribal communities and seek partnerships with tribes and Indian health facilities for the second
phase of our research, which entails a community-based participatory research project that will develop and test
a culturally centered implementation program for MOUD for use in AI/AN communities. We will use a stepped
wedge randomized design in four Indian health facilities to test initiation, retention, relapse, and acceptability of
culturally centered MOUD among patients and clinic staff over time. The proposed research will strengthen
partnerships between tribal communities, AASTEC, and academic researchers, and better align opioid research
with tribal values and priorities. We anticipate our research will not only result in publications in academ...

## Key facts

- **NIH application ID:** 10531498
- **Project number:** 7R61DA049382-03
- **Recipient organization:** LOYOLA UNIVERSITY CHICAGO
- **Principal Investigator:** Erin F Madden
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $606,504
- **Award type:** 7
- **Project period:** 2022-04-22 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10531498

## Citation

> US National Institutes of Health, RePORTER application 10531498, Leveraging CDC Opioid Overdose Surveillance Funding from the Albuquerque Area Southwest Tribal Epidemiology Center to Create Tribal Data and Culturally Center Medications for Opioid Use Disorder (7R61DA049382-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10531498. Licensed CC0.

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