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Journal Article

Citation

Wang J, Smail-Crevier R, Nannarone M, Manuel D, MacQueen G, Patten SB, Lashewicz B, Schmitz N. J. Affect. Disord. 2020; 265: 410-415.

Affiliation

Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.jad.2020.01.099

PMID

32090767

Abstract

BACKGROUND: Prevention and early detection of depression is a top public health priority. Accurate perception of depression risk may play an important role in health behavior change and prevention of depression. However, the way in which people in the community perceive their risk of developing depression is currently unknown.

METHODS: We analyzed the baseline data from a randomized controlled trial in 358 men and 356 women who are at high risk of having a major depressive episode (MDE). The predicted risk was assessed by sex-specific multivariable risk predictive algorithms for MDE. We compared participants' perceived risk and their predicted risk. Accurate risk perception was defined as perceived risk is in the range of predicted risk ± 10%.

RESULTS: In men, 29.7% perceived their risk accurately; 47.5% overestimated their risk; 22.8% underestimated their risk. In women, the proportions were 21.7%, 59.6% and 18.7%, respectively. Compared to men, women were more likely to overestimate their risk and less likely to be accurate. Regression modeling revealed that poor self-rated health and higher predicted depression risk were associated with inaccuracy of risk perception in men; a family history of MDE, higher psychological distress and lower predicted risk were associated with inaccuracy of risk perception in women.

CONCLUSIONS: Individuals who are at high risk of developing depression tend to overestimate their risk, especially women. Inaccurate depression risk perception is related to people's health status. Educational interventions are needed to enhance the accuracy of risk perception to encourage positive behavior change and uptake of preventive strategies.

Copyright © 2020 Elsevier B.V. All rights reserved.


Language: en

Keywords

Depression; Multivariable risk predictive algorithms; Personalized risk; Risk perception

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