Literature DB >> 32090767

The accuracy of depression risk perception in high risk Canadians.

JianLi Wang1, Rachel Smail-Crevier2, Molly Nannarone2, Douglas Manuel3, Glenda MacQueen4, Scott B Patten5, Bonnie Lashewicz6, Norbert Schmitz7.   

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.

Entities:  

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

Mesh:

Year:  2020        PMID: 32090767     DOI: 10.1016/j.jad.2020.01.099

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  3 in total

1.  Integrative PheWAS analysis in risk categorization of major depressive disorder and identifying their associations with genetic variants using a latent topic model approach.

Authors:  Xiangfei Meng; Yue Li; Michelle Wang; Kieran J O'Donnell; Jean Caron; Michael J Meaney
Journal:  Transl Psychiatry       Date:  2022-06-08       Impact factor: 7.989

2.  Users' perceptions about receiving personalized depression risk information: findings from a qualitative study.

Authors:  Heidi Eccles; Doaa Nadouri; Molly Nannarone; Bonnie Lashewicz; Norbert Schmitz; Scott B Patten; Douglas G Manuel; JianLi Wang
Journal:  BMC Psychiatry       Date:  2021-11-18       Impact factor: 3.630

3.  A comparative analysis on risk communication between international and Chinese literature from the perspective of knowledge domain visualization.

Authors:  Huiling Dong; Qunhong Wu; Yue Pang; Bingyi Wu
Journal:  Environ Health Prev Med       Date:  2021-05-28       Impact factor: 3.674

  3 in total

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