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Predicting fatal opioid-involved overdoses

A social-ecological framework matched to a linked-data warehouse

Datos Bibliográficos

ID14905643
AutoresRic Bayly (0009-0008-3295-1704, Tufts University), Jack Cordes (0000-0003-2706-615X, Tufts University), Dana Bernson (0000-0003-1510-4489, Massachusetts Department of Public Health), Leland K Ackerson (0000-0002-0984-0757, University of Massachusetts Lowell), Marc R Larochelle (0000-0001-7344-1200, Boston Medical Center), Ghada H Hassan (The University of Texas Health Science Center at Houston), Cici Bauer (0000-0002-2337-7965, The University of Texas Health Science Center at Houston, autor de correspondencia), Thomas J Stopka (0000-0003-2314-8924, Tufts University, autor de correspondencia)
Año2025
Volumen137
Páginas104730
Fecha de publicación2025-03-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaInternational Journal of Drug Policy (JOURNAL)
Identificadores de la revistaISSN: 0955-3959 • E-ISSN: 1873-4758
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.drugpo.2025.104730
PMID39929056
OpenAlexW4407286994
IdiomaEN
Referencias citadas40

Business · Computer security · Data mining · Data warehouse · Medical emergency · Opioid · Opioid epidemic · Opioid overdose · Computer Science · HIV, Drug Use, Sexual Risk · Medicine · Opioid Use Disorder Treatment · Psychology · Substance Abuse Treatment and Outcomes · Internal Medicine

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  • Proximal, Distal, and the Politics of Causation

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  • Individual, interpersonal, and neighborhood measures associated with opioid use stigma

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Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae