Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents

This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. Data were from a Time 4 (Mage = 18.22)...

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Veröffentlicht in:PloS one 2022-09, Vol.17 (9), p.e0274998
Hauptverfasser: Negriff, Sonya, Dilkina, Bistra, Matai, Laksh, Rice, Eric
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Dilkina, Bistra
Matai, Laksh
Rice, Eric
description This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. Data were from a Time 4 (Mage = 18.22) of longitudinal study of the effects of maltreatment on adolescent development (n = 350; CW = 222; non-CW = 128; 56%male). Marijuana use in the past 12 months (none versus any) was obtained from a single item self-report. Risk factors entered into the model included mental health, parent/family social support, peer risk behavior, self-reported risk behavior, self-esteem, and self-reported adversities (e.g., abuse, neglect, witnessing family violence or community violence). The ML approaches indicated 80% accuracy in predicting marijuana use in the CW group and 85% accuracy in the non-CW group. In addition, the top features differed for the CW and non-CW groups with peer marijuana use emerging as the most important risk factor for CW youth, whereas externalizing behavior was the most important for the non-CW group. The most important common risk factor between group was gender, with males having higher risk. This is the first study to examine the shared and unique risk factors for marijuana use for CW and non-CW youth using a machine learning approach. The results support our assertion that there may be similar risk factors for both groups, but there are also risks unique to each population. Therefore, risk factors derived from normative populations may not have the same importance when used for CW youth. These differences should be considered in clinical practice when assessing risk for substance use among adolescents.
doi_str_mv 10.1371/journal.pone.0274998
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Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. Data were from a Time 4 (Mage = 18.22) of longitudinal study of the effects of maltreatment on adolescent development (n = 350; CW = 222; non-CW = 128; 56%male). Marijuana use in the past 12 months (none versus any) was obtained from a single item self-report. Risk factors entered into the model included mental health, parent/family social support, peer risk behavior, self-reported risk behavior, self-esteem, and self-reported adversities (e.g., abuse, neglect, witnessing family violence or community violence). The ML approaches indicated 80% accuracy in predicting marijuana use in the CW group and 85% accuracy in the non-CW group. 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Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. Data were from a Time 4 (Mage = 18.22) of longitudinal study of the effects of maltreatment on adolescent development (n = 350; CW = 222; non-CW = 128; 56%male). Marijuana use in the past 12 months (none versus any) was obtained from a single item self-report. Risk factors entered into the model included mental health, parent/family social support, peer risk behavior, self-reported risk behavior, self-esteem, and self-reported adversities (e.g., abuse, neglect, witnessing family violence or community violence). The ML approaches indicated 80% accuracy in predicting marijuana use in the CW group and 85% accuracy in the non-CW group. In addition, the top features differed for the CW and non-CW groups with peer marijuana use emerging as the most important risk factor for CW youth, whereas externalizing behavior was the most important for the non-CW group. The most important common risk factor between group was gender, with males having higher risk. This is the first study to examine the shared and unique risk factors for marijuana use for CW and non-CW youth using a machine learning approach. The results support our assertion that there may be similar risk factors for both groups, but there are also risks unique to each population. Therefore, risk factors derived from normative populations may not have the same importance when used for CW youth. These differences should be considered in clinical practice when assessing risk for substance use among adolescents.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36129944</pmid><doi>10.1371/journal.pone.0274998</doi><tpages>e0274998</tpages><orcidid>https://orcid.org/0000-0002-1660-6301</orcidid><oa>free_for_read</oa></addata></record>
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subjects Accuracy
Addictions
Adolescent
Adolescent development
Adolescents
Adults
Aggression
Alcohol use
Biology and Life Sciences
Cannabis
Caregivers
Child
Child abuse & neglect
Child development
Child welfare
Child Welfare - psychology
Children
Comparative analysis
Computer and Information Sciences
Drug use
Drugs and youth
Families & family life
Health aspects
Health behavior
Health risks
Humans
Learning algorithms
Longitudinal Studies
Machine Learning
Male
Marijuana
Marijuana Use - epidemiology
Medicine and Health Sciences
Mental health
Orphans
People and Places
Personality
Physical Sciences
Postal codes
Prevention
Psychopathology
Research and Analysis Methods
Risk analysis
Risk assessment
Risk behavior
Risk Factors
Risk factors (Health)
Risk taking
Self esteem
Social aspects
Social behavior
Social interactions
Social Sciences
Social support
Substance abuse
Substance use
Substance-Related Disorders - epidemiology
Teenagers
Violence
Youth
title Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents
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