Drug overdose is a common cause of non-AIDS death among people with HIV and the leading cause of death for people who inject drugs. People with HIV are often exposed to opioid medications during their HIV care experience; others may continue to use illicit opioids despite their disease status. In either situation, there may be a heightened risk for nonfatal or fatal overdose. The potential mechanisms for this elevated risk remain controversial. We systematically reviewed the literature on the HIV-overdose association, meta-analyzed results, and investigated sources of heterogeneity, including study characteristics related to hypothesize biological, behavioral, and structural mechanisms of the association. Forty-six studies were reviewed, 24 of which measured HIV status serologically and provided data quantifying an association. Meta-analysis results showed that HIV seropositivity was associated with an increased risk of overdose mortality (pooled risk ratio 1.74, 95% confidence interval 1.45, 2.09), although the effect was heterogeneous (Q = 80.3, P < 0.01, I(2) = 71%). The wide variability in study designs and aims limited our ability to detect potentially important sources of heterogeneity. Causal mechanisms considered in the literature focused primarily on biological and behavioral factors, although evidence suggests structural or environmental factors may help explain the greater risk of overdose among HIV-infected drug users. Gaps in the literature for future research and prevention efforts as well as recommendations that follow from these findings are discussed.
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http://dx.doi.org/10.1097/QAD.0b013e32834f19b6 | DOI Listing |
Stat Med
February 2025
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
There is a growing number of Phase I dose-finding studies that use a model-based approach, such as the CRM or the EWOC method to estimate the dose-toxicity relationship. It is common to assume that all patients will have similar toxicity risk given the dose regardless of patients' individual characteristics. In many trials, however, some patients' covariates (e.
View Article and Find Full Text PDFEur J Clin Pharmacol
January 2025
Department of the Acute Pain Service, St. Luke's University Health Network, 801 Ostrum St, Bethlehem, PA, 18015, USA.
Purpose: Opioid medications remain a common treatment for acute pain in hospitalized patients. This study aims to identify factors contributing to opioid overdose in the inpatient population, addressing the gap in data on which patients are at higher risk for opioid-related adverse events in the hospital setting.
Methods: A retrospective chart review of inpatients receiving at least one opioid medication was performed at a large academic medical center from January 1, 2022, through December 31, 2022.
Int J Drug Policy
January 2025
Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York University, New York City, NY, USA. Electronic address:
Background: Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical.
Methods: Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020).
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.
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