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

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Abstract

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 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.

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Funding

This research was supported by funding from Comisión Nacional contra las Adicciones (2013–2015) to the National Institute of Psychiatry Ramón de la Fuente Muñíz (Villatoro Velázquez, PI).

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Authors and Affiliations

  1. Department of Psychology, Utah State University, Logan, USA

    Alejandro L. Vázquez, Melanie M. Domenech Rodríguez, Tyson S. Barrett & Sarah Schwartz

  2. Instituto Nacional de Psiquiatría Ramón de la Fuente Muñíz, Calzada Mexico Xochimilco 101, Huipulco, 14370, Cuidad México, Mexico

    Nancy G. Amador Buenabad, Marycarmen N. Bustos Gamiño, María de Lourdes Gutiérrez López & Jorge A. Villatoro Velázquez

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  1. Alejandro L. Vázquez
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  2. Melanie M. Domenech Rodríguez
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Correspondence to Jorge A. Villatoro Velázquez.

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The authors have declare that they have conflict of interest.

Ethical Approval

Approval to perform analyses on the archived, deidentified data was obtained from the Institutional Review Board at [masked for peer review]. Approval for the parent project was obtained from [masked for peer review]. All research activities were performed in accordance with the ethical standards articulated in the 1964 Declaration of Helsinki, its later amendments, and the 1979 Belmont Report.

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The Secretary of Public Education in Mexico provided the ENCODE team consent to survey students. Active parental consent was not obtained as the Secretary of Public Education provided the consent to survey students. Student accented to participation prior to the administration of the survey and those that did not want to participate could chose to do so.

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Vázquez, A.L., Domenech Rodríguez, M.M., Barrett, T.S. et al. Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children. Prev Sci 21, 171–181 (2020). https://doi.org/10.1007/s11121-020-01089-4

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