Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department

English version

Autoři: Jason E. Goldstick ^aff001; Maureen A. Walton ^aff002; Amy S. B. Bohnert ^aff002; Justin E. Heinze ^aff002; Rebecca M. Cunningham ^aff001
Působiště autorů: Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, United States of America ^aff001; Injury Prevention Center, University of Michigan, Ann Arbor, MI, United States of America ^aff002; Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, MI, United States of America ^aff003; Youth Violence Prevention Center, University of Michigan School of Public Health, Ann Arbor, MI, United States of America ^aff004; Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI, United States of America ^aff005; Hurley Medical Center, Department of Emergency Medicine, Flint, MI, United States of America ^aff006
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227140

Souhrn

Background

Precipitants of alcohol use transitions can differ from generalized risk factors. We extend prior research by predicting transitions in alcohol use disorder (AUD) during adolescence and emerging adulthood.

Methods

From 12/2009-9/2011, research assistants recruited 599 drug-using youth age 14–24 from Level-1 Emergency Department in Flint, Michigan. Youth were assessed at baseline and four biannual follow-ups, including a MINI Neuropsychiatric interview to diagnose AUD (abuse/dependence). We modeled AUD transitions using continuous time Markov Chains with transition probabilities modulated by validated measures of demographics, anxiety/depression symptoms, cannabis use, peer drinking, parental drinking, and violence exposure. Separate models were fit for underage (<21) and those of legal drinking age.

Results

We observed 2,024 pairs of consecutive AUD states, including 264 transitions (119 No-AUD→AUD; 145 AUD→No-AUD); 194 (32.4%) individuals were diagnosed with AUD at ≥1 assessment. Among age 14–20, peer drinking increased AUD onset (No-AUD→AUD transition) rates (Hazard ratio—HR = 1.70; 95%CI: [1.13,2.54]), parental drinking lowered AUD remission (AUD→No-AUD transition) rates (HR = 0.53; 95%CI: [0.29,0.97]), and cannabis use severity both hastened AUD onset (HR = 1.18; 95%CI: [1.06,1.32]) and slowed AUD remission (HR = 0.85; 95%CI: [0.76,0.95]). Among age 21–24, anxiety/depression symptoms both increased AUD onset rates (HR = 1.35; 95%CI: [1.13,1.60]) and decreased AUD remission rates (HR = 0.74; 95%CI: [0.63,0.88]). Friend drinking hastened AUD onset (HR = 1.18, 95%CI: [1.05,1.33]), and slowed AUD remission (HR = 0.84; 95%CI: [0.75,0.95]). Community violence exposure slowed AUD remission (HR = 0.69, 95%CI: [0.48,0.99]). In both age groups, males had >2x the AUD onset rate of females, but there were no sex differences in AUD remission rates. Limitations, most notably that this study occurred at a single site, are discussed.

Conclusions

Social influences broadly predicted AUD transitions in both age groups. Transitions among younger youth were predicted by cannabis use, while those among older youth were predicted more by internalizing symptoms and stress exposure (e.g., community violence). Our results suggest age-specific AUD etiology, and contrasts between prevention and treatment strategies.

Klíčová slova:

Markov models – Critical care and emergency medicine – Alcohol consumption – Age groups – Mental health and psychiatry – Cannabis – Medical risk factors – Alcoholism

Zdroje

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