Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children
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Date
2020
Journal Title
Journal ISSN
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Publisher
Kluwer Academic/Plenum Publishers
Abstract
Description
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; Mage = 10.40, SDage =
0.82). Participants reported information on individual (e.g., gender, grade, religiosity, sensation
seeking, self-esteem, perceived risk of substance use) and socio-ecological (e.g., neighborhood
quality, community type, peer influences, parenting) factors, 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 discriminator 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 sex appear to be high impact screening questions associated with substance initiation
during Mexican childhood. Further research is needed to assess the feasibility of automated risk
screening and intervention delivery utilizing machine learning