# Preferences and predictors driving opioid-involved polysubstance use profiles and trajectories: Implications for improving care > **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $2,339,396 ## Abstract Project Summary/Abstract Recent changes in overdose mortality are driven by rapid increases in polysubstance-involved deaths, most often including opioids followed by stimulants. Prior studies have found important individual (e.g., Black/African American race, comorbid mental/physical health), social (e.g., family supports), and community factors (e.g., treatment availability) associated with PSU, treatment, and outcomes. However, little is known about the modifiable individual-level motivators (e.g. use to alleviate withdrawal) and preferences driving high- risk polysubstance use (PSU) behaviors (e.g., intravenous use, higher frequency of opioid-involved PSU, intentional use of synthetic opioids, and simultaneous use of substances), which is important to inform and tailor treatment services. Behavioral economic (BE) theory provides a novel framework and objective measures to understand substance use preferences, emphasizing substance use is impacted by drug valuation (i.e., demand) and devaluing of uncertain negative outcomes (e.g., overdose risk) in complex environments and can inform clinical care. This is especially critical given that people with opioid-involved PSU are among the least likely to receive effective overdose prevention or treatments. As we have shown, although >85% of people who died from opioid, stimulant or alcohol-involved overdose are seen in healthcare settings (e.g., primary and mental health care), only a tiny fraction receive effective care, suggesting new strategies are needed that can be implemented in health systems, an important common touchpoint for this population. Data are critically needed to understand these intricate individual-level patterns, motivators and preferences driving PSU, along with known community and social factors, to improve care for people with opioid-involved PSU. We will conduct a prospective cohort study (N=400), recruiting a diverse sample of adults with opioid- involved PSU (over-sampling stimulant co-use and Black/African Americans to enhance representation) from 2 large health systems in Michigan. Following baseline enrollment, we will collect weekly detailed data to reliably capture nuanced and dynamic patterns and motivators of use and co-use (e.g., simultaneous or sequential, overdose risk behaviors, etc.) over four weeks. Additional measures across baseline, 4-,8-, and 12-months (e.g., behavioral economic choice preferences, comorbid mental/physical health, functioning, other social and community factors, and treatment use) will provide comprehensive information on profiles and longitudinal trajectories of PSU behaviors and treatment. Cohort data will be complemented by stakeholder interviews to elucidate patient and provider perspectives on PSU and how to tailor strategies (e.g., naloxone and low-barrier MOUD treatment) to enhance uptake. This project will have high public health impact by providing critical new insights on motives and patterns of PSU, BE choice preferences across... ## Key facts - **NIH application ID:** 10584684 - **Project number:** 1R01DA057591-01 - **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR - **Principal Investigator:** Lara Nicole Coughlin - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $2,339,396 - **Award type:** 1 - **Project period:** 2022-09-30 → 2025-09-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10584684 ## Citation > US National Institutes of Health, RePORTER application 10584684, Preferences and predictors driving opioid-involved polysubstance use profiles and trajectories: Implications for improving care (1R01DA057591-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10584684. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*