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  • 0050 Descriptive network analysis of co-occurring drug use disorders and associated predictors among adolescents and emerging adults presenting to an urban emergency department

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Alcohol and substance abuse
0050 Descriptive network analysis of co-occurring drug use disorders and associated predictors among adolescents and emerging adults presenting to an urban emergency department
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  1. M Myers1,
  2. V Lyons2,3,
  3. M Walton1,4,
  4. J Heinze1,5,6,
  5. R Cunningham5,6,7,8,9,
  6. J Goldstick5,7,8
  1. 1University of Michigan Injury Prevention Center, Ann Arbor, USA
  2. 2Department of Epidemiology, School of Public Health, University of Washington, Seattle, USA
  3. 3Harborview Injury Prevention and Research Center, University of Washington, Seattle, USA
  4. 4Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, USA
  5. 5Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, USA
  6. 6Youth Violence Prevention Center, University of Michigan School of Public Health, Ann Arbor, USA
  7. 7Injury Prevention Center, University of Michigan, Ann Arbor, USA
  8. 8Department of Emergency Medicine, University of Michigan, Ann Arbor, USA
  9. 9Hurley Medical Center, Department of Emergency Medicine, Flint, USA

Abstract

Statement of purpose Estimate frequency of drug use disorder (DUD), multiple substance co-diagnosis network characteristics, and predictors of DUD among youth entering an urban emergency department (ED).

Methods/Approach Drug-using youth age 14–24 (n=599; 349 assault-injured) presenting to a Level-1 ED were recruited. Participants were contacted at baseline and at 6-, 12-, 18-, and 24- months post-baseline and administered validated measures of peer/parental behaviors, violence/crime exposure, drug use self-efficacy, and alcohol use. Participants were administered the MINI neuropsychiatric interview to diagnose use disorder (abuse/dependence) with nine substances. Dependencies between co-DUD diagnosis were estimated using Ising network models. Repeated measures logistic regression models were used to determine predictors of DUD.

Results Among 2,630 assessments, 1,128 (42.9%) were DUD diagnoses; 21.7% were co-diagnoses with multiple drugs. Cannabis use disorder was the most frequent diagnosis (n=1,050), with the cannabis/prescription sedative combination the most common co-diagnosis (n=112). The cocaine/prescription opioid combination showed the strongest partial correlation and was the most central element in the network. Regression models show positive peer behaviors and parental support to be protective factors for DUD diagnosis, while interpersonal violence exposure, community violence/crime exposure, alcohol use quantity, other mental health diagnoses, and drug use self-efficacy were DUD risk factors.

Conclusions DUD is prevalent in this population and associated with personal, social, and community exposures. Among those with DUD, diagnosis with multiple drug use disorders was common; network analyses showed several large partial correlations between substances, with cocaine and prescription opioid use disorder co-diagnosis being the most strongly associated.

Significance DUD is linked to a variety of injuries including suicide, partner violence, firearm violence, death by homicide, and violence/injury in general. Understanding the relationship between co-occurring DUDs and associated risk factors allow for targeted intervention strategies.

http://dx.doi.org/10.1136/injuryprev-2021-SAVIR.31

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