Introduction: To inform overdose prevention, this study assessed both recent trends in opioid overdose mortality across opioid categories and receipt of prescription opioid analgesics among Veterans who died from overdose in the Veterans Health Administration.
Methods: Using Veterans Health Administration records linked to National Death Index data, annual cohorts (2010-2016) of Veterans who received Veterans Health Administration care were obtained and were examined by opioid overdose categories (natural/semisynthetic opioids, heroin, methadone, and other synthetic opioids) on (1) overdose rates and changes in rates adjusted for age, sex, and race/ethnicity; and (2) Veterans Health Administration prescription opioid receipt. Analyses were conducted in 2018.
Results: The overall rate of opioid overdose among Veterans increased from 14.47 per 100,000 person-years in 2010 to 21.08 per 100,000 person-years in 2016 (adjusted rate ratio=1.65, 95% CI=1.51, 1.81). There was a decline in methadone overdose (adjusted rate ratio=0.66, 95% CI=0.51, 0.84) and no significant change in natural/semisynthetic opioid overdose (adjusted rate ratio=1.08, 95% CI=0.94, 1.24). However, the synthetic opioid overdose rate (adjusted rate ratio=5.46, 95% CI=4.41, 6.75) and heroin overdose rate (adjusted rate ratio=4.91, 95% CI=3.92, 6.15) increased substantially. Among all opioid overdose decedents, prescription opioid receipt within 3 months before death declined from 54% in 2010 to 26% in 2016.
Conclusions: Opioid overdose rates among Veterans Health Administration Veterans increased because of increases in heroin and synthetic opioid overdose rates. Prescriptions of opioids declined among patients who died from all categories of opioid overdose; by 2016, only a minority received an opioid analgesic from Veterans Health Administration within 3 months of overdose. Future prevention efforts should extend beyond patients actively receiving opioid prescriptions.
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http://dx.doi.org/10.1016/j.amepre.2019.01.016 | DOI Listing |
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 PDFJ 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 PDFJ 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 PDFNPJ 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 PDFBMJ 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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