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Analgesic Overdose in Patients With Dental Pain. A Cross-Sectional Study in Two Dental Emergency Clinics.
Basic Clin Pharmacol Toxicol
February 2025
Department of Odontology, Section of Oral Biology and Immunopathology, University of Copenhagen, Copenhagen, Denmark.
Dental pain is common, and many patients use analgesics to alleviate the pain. Analgesics are readily accessible, and overdosing may lead to severe complications. This study explores the extent of analgesic overdosing in patients with dental pain.
View Article and Find Full Text PDFSpatial-Structural Mechanisms of Racialized Disparities in Overdose Mortality: A Spatiotemporal Analysis.
J Racial Ethn Health Disparities
January 2025
Department of Pharmacology & Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA.
Efforts to understand and respond to the opioid crisis have focused on overdose fatalities. Overdose mortality rates (ratios of overdoses resulting in death) are rarely examined though they are important indicators of harm reduction effectiveness. Factors that vary across urban communities likely determine which community members are receiving the resources needed to reduce fatal overdose risk.
View Article and Find Full Text PDFExpanded Naloxone Distribution by Opioid Overdose Prevention Programs to High-Need Populations and Neighborhoods in New York City.
J Urban Health
January 2025
Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA.
From 2014 to 2017, the drug overdose death rate per 100,000 in New York City (NYC) increased by 81%, with 57% of overdoses in 2017 involving the opioid fentanyl. In response, overdose education and naloxone dispensing (OEND) efforts were expanded in NYC, informed by neighborhood-level and population-level opioid overdose fatality rates. We describe the demographic and geographical distribution of naloxone by NYC opioid overdose prevention programs (OOPPs; the primary distributor of naloxone to laypersons in NYC) as OEND was expanded in NYC.
View Article and Find Full Text PDFA systematic review of machine learning applications in predicting opioid associated adverse events.
NPJ Digit Med
January 2025
Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, United Kingdom.
Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions.
View Article and Find Full Text PDFOpioid agonist therapy discontinuation in British Columbia: a cross-sectional study of people who access harm reduction services.
BMJ Open
January 2025
The University of British Columbia, Vancouver, British Columbia, Canada
Objectives: This study evaluates the prevalence and correlates of opioid agonist therapy (OAT) discontinuation across British Columbia (BC), using a sample of individuals who used substances and accessed harm reduction sites.
Design: This study uses data from the 2019 cross-sectional Harm Reduction Client Survey (HRCS).
Setting: The 2019 survey was administered from October to December at 22 harm reduction supply distribution sites across the 5 Regional Health Authorities of BC.
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