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Dynamic risk for first onset of depressive disorders in adolescence: does change matter?

Published online by Cambridge University Press:  22 November 2021

Wenting Mu*
Affiliation:
Department of Psychology, Tsinghua University, Beijing, China
Kaiqiao Li
Affiliation:
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
Yuan Tian
Affiliation:
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
Greg Perlman
Affiliation:
Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
Giorgia Michelini
Affiliation:
Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA Semel Institute for Neuroscience & Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
David Watson
Affiliation:
Department of Psychology, University of Notre Dame, Notre Dame, Indiana, USA
Hans Ormel
Affiliation:
Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
Daniel N. Klein
Affiliation:
Department of Psychology, Stony Brook University, Stony Brook, NY, USA
Roman Kotov*
Affiliation:
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA Semel Institute for Neuroscience & Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
*
Author for correspondence: Wenting Mu, E-mail: mwttwm@gmail.com; Roman Kotov, E-mail: Roman.Kotov@stonybrook.edu
Author for correspondence: Wenting Mu, E-mail: mwttwm@gmail.com; Roman Kotov, E-mail: Roman.Kotov@stonybrook.edu

Abstract

Background

Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The ‘Risk Escalation hypothesis’ posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the ‘Chronic Risk hypothesis’ posits that the average level rather than change predicts first-onset DD.

Methods

We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5–15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset.

Results

Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors.

Conclusions

Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.

Type
Original Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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