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

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Abstract

A large proportion 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 used individual depression symptoms and sociodemographic variables to predict whether and where 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 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. 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 and underscores the broader need for more accessible, scalable adolescent depression treatments.

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Data Availability

The data reported in this manuscript were obtained from publicly available data, the 2017 National Survey on Drug Use and Health (NSDUH): https://www.datafiles.samhsa.gov/study-dataset/national-survey-drug-use-and-health-2017-nsduh-2017-ds0001-nid17939.

Code Availability

All code used to conduct data analysis in the present study is available for review in Supplements 1 and 2.

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Authors and Affiliations

  1. Department of Psychology, Stony Brook University, Stony Brook, NY, 11794-2500, USA

    Mallory L. Dobias, Michael B. Sugarman, Michael C. Mullarkey & Jessica L. Schleider

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  1. Mallory L. Dobias
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  2. Michael B. Sugarman
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Contributions

Conceptualization: MD and MS; Methodology: MD and MM; Data processing and formal analysis: MD; Writing—original draft preparation: all authors contributed to original draft preparation; Writing—review and editing: all authors contributed to review and editing the final manuscript; All authors read and approved the final manuscript.

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Correspondence to Mallory L. Dobias.

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Conflict of interest

The authors declare that they have no conflict of interest or competing interest related to the present study. Dr. Schleider has received grant and research support unrelated to this study from the American Psychological Foundation, the Klingenstein Third Generation Foundation, the Center on the Developing Child at Harvard University, Limbix Health, Inc., and the National Institutes of Health. Dr. Schleider, Ms. Dobias, and Mr. Mullarkey are under contract with New Harbinger Publications to co-author a therapy workbook for adolescents that is unrelated to this study. Ms. Dobias receives research support unrelated to this study from a Stony Brook University Graduate Council Fellowship and from Psi Chi International Honors Society. Mr. Mullarkey receives grants and research support unrelated to this study from Limbix Health Inc. and University of Texas-Austin CENTRAL/Bridging Barriers.

Ethics Approval

The present study was conducted using publicly-available, de-identified data from the 2017 National Survey of Drug Use and Health (NSDUH): https://www.datafiles.samhsa.gov/study-dataset/national-survey-drug-use-and-health-2017-nsduh-2017-ds0001-nid17939. No ethical approval was required for this study.

Informed Consent

All secondary data analyses were conducted using de-identified data that is publicly available on the Substance Abuse and Mental Health Services Administration website. As such, no additional informed consent was required for this study.

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Dobias, M.L., Sugarman, M.B., Mullarkey, M.C. et al. Predicting Mental Health Treatment Access Among Adolescents With Elevated Depressive Symptoms: Machine Learning Approaches. Adm Policy Ment Health 49, 88–103 (2022). https://doi.org/10.1007/s10488-021-01146-2

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