Citation

Chekroud AM, Foster D, Zheutlin AB, Gerhard DM, Roy B, Koutsouleris N, Chandra A, Esposti MD, Subramanyan G, Gueorguieva R, Paulus M, Krystal JH. Psychiatr. Serv. 2018; 69(8): 927-934.

Affiliation

Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma.

Copyright

(Copyright © 2018, American Psychiatric Association)

DOI

10.1176/appi.ps.201800094

PMID

29962307

Abstract

OBJECTIVE: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.

METHODS: Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.

RESULTS: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all).

CONCLUSIONS: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.

Language: en

Keywords

Depression; access to treatment; adherence; computational psychiatry; healthcare delivery; machine learning