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

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 a...

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Autores principales: Goldstick, Jason E., Walton, Maureen A., Bohnert, Amy S. B., Heinze, Justin E., Cunningham, Rebecca M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938309/
https://www.ncbi.nlm.nih.gov/pubmed/31891632
http://dx.doi.org/10.1371/journal.pone.0227140
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author Goldstick, Jason E.
Walton, Maureen A.
Bohnert, Amy S. B.
Heinze, Justin E.
Cunningham, Rebecca M.
author_facet Goldstick, Jason E.
Walton, Maureen A.
Bohnert, Amy S. B.
Heinze, Justin E.
Cunningham, Rebecca M.
author_sort Goldstick, Jason E.
collection PubMed
description 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.
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spelling pubmed-69383092020-01-07 Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department Goldstick, Jason E. Walton, Maureen A. Bohnert, Amy S. B. Heinze, Justin E. Cunningham, Rebecca M. PLoS One Research Article 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. Public Library of Science 2019-12-31 /pmc/articles/PMC6938309/ /pubmed/31891632 http://dx.doi.org/10.1371/journal.pone.0227140 Text en © 2019 Goldstick et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Goldstick, Jason E.
Walton, Maureen A.
Bohnert, Amy S. B.
Heinze, Justin E.
Cunningham, Rebecca M.
Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
title Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
title_full Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
title_fullStr Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
title_full_unstemmed Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
title_short Predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
title_sort predictors of alcohol use transitions among drug-using youth presenting to an urban emergency department
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938309/
https://www.ncbi.nlm.nih.gov/pubmed/31891632
http://dx.doi.org/10.1371/journal.pone.0227140
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