A Bayesian Learning Model for Joint Risk Prediction of Alcohol and Cannabis Use Disorders

TL;DR

This paper introduces a Bayesian joint risk prediction model for alcohol and cannabis use disorders in adolescents, utilizing longitudinal data to improve early identification and intervention strategies.

Contribution

It presents a novel joint Bayesian learning framework that predicts risks for both disorders simultaneously, outperforming separate models in predictive accuracy.

Findings

Model achieves AUCs of 0.719-0.750 across datasets.

Joint modeling outperforms separate univariate models.

Model identifies high-risk adolescents for targeted interventions.

Abstract

Substance use disorders (SUDs) are a serious public health concern in the United States. Alcohol and cannabis are two of the most widely used substances. For adolescent/youth users of alcohol or cannabis, we propose a joint Bayesian learning model to predict their risks of developing alcohol use disorder (AUD) and cannabis use disorder (CUD) in adulthood based on their personal risk factors. The model is trained on nationally representative longitudinal data from Add Health (n = 12503). It consists of sub-models that predict the two SUDs for three groups of users-those who use alcohol only, cannabis only, and both substances - based on shared as well as unique risk factors. The model comprises of ten predictors. We externally validate the model on two independent datasets. The areas under the receiver operating characteristic curves for AUD and CUD, respectively, are: (a) 0.719 and 0.690 based on 5-fold cross-validation, (b) 0.748 and 0.710 based on validation dataset 1, and (c) 0.650 and 0.750 based on validation dataset 2. A simulation study shows that the proposed joint modeling approach generally performs better than separate univariate modeling of the corresponding dependent outcomes in terms of predictive accuracy. Our model may help in identifying adolescent substance users at high risk of developing SUD in adulthood, who can then be helped with appropriate intervention.