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.
Keywords: Heroin; Machine learning; Overdose; Polydrug use; Relapse; Stimulant.
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