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Psychosocial Correlates of Opioid Use Profiles among Young Adults in a Longitudinal Study across 6 US Metropolitan Areas

Datos Bibliográficos

ID21645751
AutoresCaroline Fuss (0000-0001-9526-7223, George Washington University), Katelyn F Romm (TSET Health Promotion Research Center, Stephenson Cancer Center, Department of Pediatrics, College of Medicine,University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA), Katelyn Romm (0000-0002-9552-0732, University of Oklahoma Health Sciences Center), Natalie D Crawford (0000-0002-4859-6304, Emory University), Kristin R V Harrington (0000-0002-9306-4980, Emory University), Yan Wang (0009-0009-9633-0023, George Washington University), Yan Ma (0000-0001-9735-5868, University of Pittsburgh), Tamara Taggart (0000-0001-9240-1212, George Washington University), Monica S Ruiz (0000-0002-7384-4090, George Washington University), Carla J Berg (0000-0001-8931-1961, George Washington University, autor de correspondencia)
Año2023
Volumen58
Número8
Páginas981-988
Fecha de publicación2023-07-03
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaSubstance Use & Misuse (JOURNAL)
Identificadores de la revistaISSN: 1082-6084 • E-ISSN: 1532-2491
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10826084.2023.2201839
PMID37082785
OpenAlexW4366602421
IdiomaEN
Citas recibidas1
Referencias citadas44

Background: Examining opioid use profiles over time and related factors among young adults is crucial to informing prevention efforts. Objectives: This study analyzed baseline data (Fall 2018) and one-year follow-up data from a cohort of 2,975 US young adults (Mage=24.55, 42.1% male; 71.7% White; 11.4% Hispanic). Multinomial logistic regression was used to examine: 1) psychosocial correlates (i.e. adverse childhood experiences [ACEs], depressive symptoms, parental substance use) of lifetime opioid use (i.e. prescription use vs. nonuse, nonmedical prescription [NMPO] use, and heroin use, respectively); and 2) psychosocial correlates and baseline lifetime use in relation to past 6-month use at one-year follow-up (i.e. prescription use vs. nonuse and NMPO/heroin use, respectively). Results: At baseline, lifetime use prevalence was: 30.2% prescription, 9.7% NMPO, and 3.1% heroin; past 6-month use prevalence was: 7.6% prescription, 2.5% NMPO, and 0.9% heroin. Compared to prescription users, nonusers reported fewer ACEs and having parents more likely to use tobacco, but less likely alcohol; NMPO users did not differ; and heroin users reported more ACEs and having parents more likely to use cannabis but less likely alcohol. At one-year follow-up, past 6-month use prevalence was: 4.3% prescription, 1.3% NMPO, and 1.4% heroin; relative to prescription users, nonusers were less likely to report baseline lifetime opioid use and reported fewer ACEs, and NMPO/heroin users were less likely to report baseline prescription opioid use but more likely heroin use. Conclusions: Psychosocial factors differentially correlate with young adult opioid use profiles, and thus may inform targeted interventions addressing different use patterns and psychosocial risk factors

Drug · Environmental health · Heroin · Injury prevention · Logistic regression · Medical prescription · Multinomial logistic regression · Opioid · Poison control · Psychiatry · Psychosocial · Young adult · Demography · Maternal Mental Health During Pregnancy and Postpartum · Medicine · Opioid Use Disorder Treatment · Prenatal Substance Exposure Effects · Gerontology · Internal Medicine

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Obras citantes distintas1
Citas por año1
Intervalo de citas2025 - 2025 (1)
Velocidad de citaciónrecent
Altamente citadoNo
Tipos de citaNeutras: 1
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