Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children

Machine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to utilize machine learning to identify high impact factors that best discriminate between substance users and non-users among a national sample (N = 52,171) of Mexican children (i.e., 5th, 6th grade; M_age = 10.40, SD_age = 0.82). Participants reported information on individual factors (e.g., gender, grade, religiosity, sensation seeking, self-esteem, perceived risk of substance use), socioecological factors (e.g., neighborhood quality, community type, peer influences, parenting), and lifetime substance use (i.e., alcohol, tobacco, marijuana, inhalant). Findings suggest that best friend and father illicit substance use (i.e., drugs other than tobacco or alcohol) and respondent sex (i.e., boys) were consistent and important discriminators between children who tried substances and those that did not. Friend cigarette use was a strong predictor of lifetime use of alcohol, tobacco, and marijuana. Friend alcohol use was specifically predictive of lifetime alcohol and tobacco use. Perceived danger of engaging in frequent alcohol and inhalant use predicted lifetime alcohol and inhalant use. Overall, findings suggest that best friend and father illicit substance use and respondent's sex appear to be high impact screening questions associated with substance initiation during childhood for Mexican youths. These data help practitioners narrow prevention efforts by helping identify youth at highest risk.