BMC Health Services Research (Apr 2024)

Predictive modeling of initiation and delayed mental health contact for depression

  • Vanessa Panaite,
  • Dezon K. Finch,
  • Paul Pfeiffer,
  • Nathan J. Cohen,
  • Amy Alman,
  • Jolie Haun,
  • Susan K. Schultz,
  • Shannon R. Miles,
  • Heather G. Belanger,
  • F. Andrew F. Kozel,
  • Jonathan Rottenberg,
  • Andrew R. Devendorf,
  • Blake Barrett,
  • Stephen L. Luther

DOI
https://doi.org/10.1186/s12913-024-10870-y
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 10

Abstract

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Abstract Background Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment. Methods We obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis. Results 108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586–0.602; machine learning F-measure = 0.46). Conclusions Having VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

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