TY - JOUR
T1 - Identifying individual and environmental predictors of opioid and psychostimulant use among adolescents and young adults following outpatient treatment
AU - Davis, Jordan P.
AU - Rao, Prathik
AU - Dilkina, Bistra
AU - Prindle, John
AU - Eddie, David
AU - Christie, Nina C.
AU - DiGuiseppi, Graham
AU - Saba, Shaddy
AU - Ring, Colin
AU - Dennis, Michael
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Background: The United States (US) continues to grapple with a drug overdose crisis. While opioids remain the main driver of overdose deaths, deaths involving psychostimulants such as methamphetamine are increasing with and without opioid involvement. Recent treatment admission data reflect overdose fatality trends suggesting greater psychostimulant use, both alone and in combination with opioids. Adolescents and young adults are particularly vulnerable with generational trends showing that these populations have particularly high relapse rates following treatment. Methods: We assessed demographic, psychosocial, psychological comorbidity, and environmental factors (percent below the poverty line, percent unemployed, neighborhood homicide rate, population density) that confer risk for opioid and/or psychostimulant use following substance use disorder treatment using two complementary machine learning approaches—random forest and least absolute shrinkage and selection operator (LASSO) modelling—with latency to opioid and/or psychostimulant as the outcome variable. Results: Individual level predictors varied by substance use disorder severity, with age, tobacco use, criminal justice involvement, race/ethnicity, and mental health diagnoses emerging at top predictors. Environmental variabels including US region, neighborhood poverty, population, and homicide rate around patients’ treatment facility emerged as either protective or risk factors for latency to opioid and/or psychostimulant use. Conclusions: Environmental variables emerged as one of the top predictors of latency to use across all levels of substance use disorder severity. Results highlight the need for tailored treatments based on severity, and implicate environmental variables as important factors influencing treatment outcomes.
AB - Background: The United States (US) continues to grapple with a drug overdose crisis. While opioids remain the main driver of overdose deaths, deaths involving psychostimulants such as methamphetamine are increasing with and without opioid involvement. Recent treatment admission data reflect overdose fatality trends suggesting greater psychostimulant use, both alone and in combination with opioids. Adolescents and young adults are particularly vulnerable with generational trends showing that these populations have particularly high relapse rates following treatment. Methods: We assessed demographic, psychosocial, psychological comorbidity, and environmental factors (percent below the poverty line, percent unemployed, neighborhood homicide rate, population density) that confer risk for opioid and/or psychostimulant use following substance use disorder treatment using two complementary machine learning approaches—random forest and least absolute shrinkage and selection operator (LASSO) modelling—with latency to opioid and/or psychostimulant as the outcome variable. Results: Individual level predictors varied by substance use disorder severity, with age, tobacco use, criminal justice involvement, race/ethnicity, and mental health diagnoses emerging at top predictors. Environmental variabels including US region, neighborhood poverty, population, and homicide rate around patients’ treatment facility emerged as either protective or risk factors for latency to opioid and/or psychostimulant use. Conclusions: Environmental variables emerged as one of the top predictors of latency to use across all levels of substance use disorder severity. Results highlight the need for tailored treatments based on severity, and implicate environmental variables as important factors influencing treatment outcomes.
KW - Heroin
KW - Machine learning
KW - Overdose
KW - Polydrug use
KW - Relapse
KW - Stimulant
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U2 - 10.1016/j.drugalcdep.2022.109359
DO - 10.1016/j.drugalcdep.2022.109359
M3 - Article
C2 - 35219997
AN - SCOPUS:85125144627
SN - 0376-8716
VL - 233
JO - Drug and alcohol dependence
JF - Drug and alcohol dependence
M1 - 109359
ER -