Community and big-data system approaches to identifying and understanding the health impact of xylazine

PROJECT SUMMARY The US opioid crisis has been worsened by the emergence of fentanyl adulterated or associated with the veterinary sedative xylazine (FAAX). Designated by the White House as an “emerging threat to the US” in 2023, FAAX exacerbates overdose risk, contributes to severe skin wounds, and is associated with withdrawal.

Naloxone, an opioid antagonist, does not directly reverse xylazine’s sedative effect, exacerbating overdose risk. Our understanding of how FAAX-related skin wounds appear and are treated is limited, but the effects of these wounds are profound. Moreover, withdrawal from FAAX and its effect on medication for opioid use disorder, the

treatment of choice for opioid use disorder, is unknown. Further, there is no widely available point-of-care test for xylazine to inform real-time clinical practice, limiting our ability to link those exposed to FAAX to treatment. Recognizing the absence of a widely available point-of-care test, the long-term objective of this proposal is to

develop a rule-based natural language processing (NLP) algorithm to identify FAAX-exposed patients. To achieve this, the applicant proposes an exploratory, sequential, mixed methods study that builds upon his formative research. Approximately 20-24 in-depth interviews with people who use drugs (PWUD) exposed to

FAAX in Rhode Island (RI) will be conducted to explore FAAX-related overdose, skin wounds, withdrawal experiences, and self-treatment (AIM 1). Then, 8-10 key informant interviews with medical providers in RI to PWUD exposed to FAAX will be conducted to understand emergent FAAX treatment practices and iteratively

refine a vocabulary list of FAAX symptom descriptions (i.e., NLP dictionary) following a modified Delphi approach (AIM 2A). Then, we will apply the NLP dictionary via an NLP algorithm to the free-text electronic health records of ~24,000 patients (≥18-years old & opioid or injection drug use diagnostic code) who received emergency

department care between 2015-2023 from a RI health system to identify patients exposed to FAAX (AIM 2B). This fellowship will advance the applicant’s expertise beyond what would developed in his doctoral program and enable the application of this skill set to an urgent public health priority aligned with NIDA’s Notice of Special

Interest NOT-DA-24-012 (Xylazine: Understanding its use and consequences). Through the sponsorship of an interdisciplinary team with a collective 40+ years of substance use research and expertise in behavioral sciences, epidemiology, addiction medicine, and natural language processing, the applicant will complete the proposed

research and the following training goals: (1) receive training in the design, conduct, and analysis of mixed methods research; (2) grow content knowledge and expertise in substance use and socio-epidemiologic research methods to end drug-related harms; (3) develop skills in the use of NLP techniques applied to large

datasets; and (4) further the applicants professional development, academic leadership, and scholarly productivity. Completion of this F31 will position the applicant as a mixed methods behavioral scientist who applies computational methods to large datasets via NIH-funded research to improve the health of PWUD.