Predicting mental health treatment access in high-symptom adolescents: Machine learning approaches

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Description: Objective: A majority of adolescents experiencing depression never access treatment. To increase access to effective mental health care, it is critical to understand factors associated with increased versus decreased odds of adolescent treatment access. This study investigated the relative importance of individual depression symptoms and sociodemographic variables in predicting whether and where adolescents with depression accessed mental health treatments. Method: We performed a pre-registered, secondary analysis of data from the 2017 National Survey of Drug Use and Health (NSDUH), a nationally representative sample of non-institutionalized civilians in the United States. Using four cross-validated random forest models, we predicted whether high-symptom adolescents (N = 1,671; ages 12-17 years) accessed specific mental health treatments in the previous 12 months (“yes/no” for inpatient, outpatient, school, any). Results: 53.38% of adolescents with elevated depressive symptoms accessed treatment of any kind. Even with depressive symptoms and sociodemographic factors included as predictors, pre-registered random forests explained < 0.00% of pseudo out-of-sample deviance in adolescent access to inpatient, outpatient, school, or overall treatments. Exploratory elastic net models explained 0.80 - 2.50% of pseudo out-of-sample deviance in adolescent treatment access across all four treatment types. Conclusions: Neither individual depressive symptoms nor any socioeconomic variables meaningfully predicted specific or overall mental health treatment access in high-symptom adolescents. This study highlights substantial limitations in our capacity to predict whether and where high-symptom adolescents access mental health treatment and underscores the broader need for more accessible, scalable adolescent depression treatments.

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