Use of heroin, prescription painkillers, methamphetamines, and fentanyl led to a national health crisis in 2017, resulting in 1852 overdose deaths in Indiana. Governor Eric J. Holcomb made tackling substance use in the state one of his highest priorities, calling on all Hoosiers to collaborate. In October 2017, Indiana University (IU) President Michael A. McRobbie responded, announcing that the University would be initiating the Responding to the Addictions Crisis Grand Challenge (AGC). Partners included Governor Holcomb, IU Health, and Eskenazi Health. Leveraging the university's research strengths and partnering with more than 160 community organizations across the state, the AGC sought to address substance use facing Indiana and beyond. Fifty interdisciplinary research projects were created through the AGC, focusing on IU's greatest strength in five areas: (1) education, training, and certification; (2) data science and analysis; (3) policy analysis, economics, and law; (4) basic, applied, and translational research; (5) community engagement and workforce development. Diversity, equity, and inclusion implications were often considered. This supplement describes the IU approach to address the health of the people of the State, investigator initiated projects and research conducted to inform practice, strategy and policy.
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http://dx.doi.org/10.1177/00469580241254993 | DOI Listing |
Curr Pain Headache Rep
January 2025
Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
Purpose Of Review: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potentially transform opioid prescribing practices and addiction prevention strategies. In this review paper, not only do we discuss some of the current literature around predicting various opioid-related outcomes, but we also briefly point out the next steps to improve trustworthiness of these AI models prior to real-time use in clinical workflow.
Recent Findings: Machine learning-based predictive models for identifying risk for persistent postoperative opioid use have been reported for spine surgery, knee arthroplasty, hip arthroplasty, arthroscopic joint surgery, outpatient surgery, and mixed surgical populations.
Adv Sci (Weinh)
January 2025
Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi'an, 710032, China.
Feeding behavior changes induced by opioid addiction significantly contribute to the worsening opioid crisis. Activation of the reward system has shown to provoke binge eating disorder in individuals with opioid use disorder, whereas prolonged opioid exposure leads to weight loss. Understanding the mechanisms underlying these phenomena is essential for addressing this pressing societal issue.
View Article and Find Full Text PDFPublic Health
January 2025
Department of Public Health Policy and Management, School of Global Public Health, New York University, 726 Broadway, New York, NY, 10012, United States.
Objective: To assess the substance use disorder (SUD) prevention and response activities that county governments in New York advertise.
Study Design: Cross-sectional study.
Methods: We coded websites of county public health, mental health, emergency, and social service departments to identify whether any government agency provided information about ten evidence-based SUD services.
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