Predictors of illicit substance abuse/dependence during young adulthood: A machine learning approach

McMaster University. Neuroscience Graduate Program. Hamilton, ON, Canada.
McMaster University. Neuroscience Graduate Program. Hamilton, ON, Canada.
Pontifícia Universidade Católica de Pelotas. Departamento de Saúde e Comportamento. Pelotas, RS, Brasil / Hospital de Clínicas de Porto Alegre. Programa de Transtorno Bipolar. Instituto Nacional de Ciência e Tecnologia Translacional em Medicina. Laboratório de Psiquiatria Molecular. Porto Alegre, RS, Brasil / Universidade Federal do Rio Grande do Sul. Departamento de Psiquiatria. Porto Alegre, RS, Brasil.
Pontifícia Universidade Católica de Pelotas. Departamento de Saúde e Comportamento. Pelotas, RS, Brasil.
Pontifícia Universidade Católica de Pelotas. Departamento de Saúde e Comportamento. Pelotas, RS, Brasil.
Pontifícia Universidade Católica de Pelotas. Departamento de Saúde e Comportamento. Pelotas, RS, Brasil.
Universidade Federal de Pelotas. Departamento de Assuntos Estudantis. Pelotas, RS, Brasil.
Pontifícia Universidade Católica de Pelotas. Departamento de Saúde e Comportamento. Pelotas, RS, Brasil.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
McMaster University. Department of Psychiatry and Behavioural Neurosciences. Mood Disorders Program. Hamilton, ON, Canada / Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton. Hamilton, Ontario, Canada.
Hospital de Clínicas de Porto Alegre. Programa de Transtorno Bipolar. Instituto Nacional de Ciência e Tecnologia Translacional em Medicina. Laboratório de Psiquiatria Molecular. Porto Alegre, RS, Brasil / Universidade Federal do Rio Grande do Sul. Departamento de Psiquiatria. Porto Alegre, RS, Brasil / McMaster University. Department of Psychiatry and Behavioural Neurosciences. Mood Disorders Program. Hamilton, ON, Canada / Instituto Nacional de Ciência e Tecnologia Translacional em Medicina. Porto Alegre, RS, Brasil.
Pontifícia Universidade Católica de Pelotas. Departamento de Saúde e Comportamento. Pelotas, RS, Brasil / McMaster University. Department of Psychiatry and Behavioural Neurosciences. Mood Disorders Program. Hamilton, ON, Canada.

Abstract

Prior studies have found an especially high prevalence of illicit substance use among adolescents and young adults in Brazil. The current study aimed to employ machine learning techniques to identify predictors of illicit substance abuse/dependence among a large community sample of young adults followed for 5 years. This prospective, population-based cohort study included a sample of young adults between the ages of 18–24 years from Pelotas, Brazil at baseline (T1). The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) was used to assess illicit substance abuse/dependence. A clinical interview was conducted to collect data on sociodemographic characteristics and psychopathology. Elastic net was used to generate a regularized linear model for the machine learning component of this study, which followed standard machine learning protocols. A total of 1560 young adults were assessed at T1, while 1244 were reassessed at the 5-year follow-up period (T2). The strongest predictors of illicit substance abuse/dependence at baseline (AUC of 0.83) were alcohol abuse/ dependence, tobacco abuse/dependence, being in a current major depressive episode, history of a lifetime manic episode, current suicide risk, and male sex. The strongest predictors for illicit substance abuse/dependence at the 5-year follow-up (AUC: 0.79) were tobacco abuse/dependence at T1, history of a lifetime manic episode at T1, male sex, alcohol abuse/dependence at T1, and current suicide risk at T1. Our findings indicate that machine learning techniques hold the potential to predict illicit substance abuse/dependence among young adults using sociodemographic/clinical characteristics, with relatively high accuracy.

Keywords

Illicit substance use
Young adults
Prospective cohort study
Mental health

Publisher

Elsevier

Citation

RAKOVSKI, Coral et al. Predictors of illicit substance abuse/dependence during young adulthood: A machine learning approach. Journal of Psychiatric Research, v. 157, p. 168-173, Jan. 2023.

DOI

10.1016/j.jpsychires.2022.11.030

ISSN

0022-3956

Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/58748

Type

Copyright

Restricted access

Embargo date

2030-12-31

Sustainable Development Goals

Collections

Share

Statistics

Metadata

Author

Affilliation