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

Caroline FussKatelyn F RommNatalie D CrawfordKristin R V HarringtonYan WangYan MaTamara TaggartMonica S RuizCarla J Berg
Published in: Substance use & misuse (2023)
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 ( M age =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.
Keyphrases
  • young adults
  • risk factors
  • mental health
  • depressive symptoms
  • childhood cancer
  • machine learning
  • emergency department
  • cancer therapy
  • deep learning
  • electronic health record
  • big data
  • drug delivery
  • social support