Predicting Mental Health Treatment Access Among Adolescents With Elevated Depressive Symptoms: Machine Learning Approaches

This study used individual depression symptoms and sociodemographic variables to predictwhether andwhere adolescents with depression accessed mental health treatments. 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 adolescents with elevated past-year depressive symptoms (N  = 1,671; ages 12–17 years) accessed specific mental health treatments in the previous 12 months (“yes/no” for inpatient, outpatient, school, any). 53.38% of adolescents with elevated depressive symptoms accessed treatment of any kind. Even with depressive symptoms and sociodemographic f actors 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. Neither individual depressive symptoms nor any socioeconomic variables meaningfully predicted specific or overall mental health treatment access in adolescents with elevated past-year symptoms. This study highlights substantial limitations in our capacity to predict whether and where adolescents access mental health treatment a nd undersco...
Source: Administration and Policy in Mental Health and Mental Health Services Research - Category: Psychiatry Source Type: research