Literature DB >> 26922852

Factors related to suicidal ideation in stroke patients in South Korea.

Eun-Young Park1, Jung-Hee Kim2.   

Abstract

BACKGROUND: Suicide rates in Korea have increased dramatically. Stroke is considered one of the most debilitating neurological disorders, resulting in physical impairment, disability, and death. AIM: The present study attempted to examine factors related to suicidal ideation in community-dwelling stroke patients.
METHODS: The Korea Welfare Panel Study was used to investigate the relationship between demographic and psychological variables and suicidal ideation among these individuals. Depression was assessed using the Center for Epidemiological Studies Depression Scale 11 (CES-D-11). Self-esteem was assessed using Rosenberg's Self-Esteem Scale.
RESULTS: The prevalence of suicidal thought among stroke patients was estimated at 13.99%. Multiple logistic regression analysis indicated that both older age and depression were significant independent risk factors for suicidal ideation.
CONCLUSION: High-priority health care plans can prevent suicide in stroke patients suffering from depression. Assessing risk for suicide and monitoring the high-risk group is integral to health care. Stroke patients with depression, particularly older patients, would be prime targets for suicide intervention programs.

Entities:  

Keywords:  Depression; related factors; stroke; suicidal ideation

Mesh:

Year:  2016        PMID: 26922852     DOI: 10.3109/09638237.2015.1101412

Source DB:  PubMed          Journal:  J Ment Health        ISSN: 0963-8237


  5 in total

1.  Meta-analysis of risk factors associated with suicidal ideation after stroke.

Authors:  Shuangmei Zhang; Anrong Wang; Weifeng Zhu; Zhaoyang Qiu; Zhaoxu Zhang
Journal:  Ann Gen Psychiatry       Date:  2022-01-05       Impact factor: 3.455

2.  Fractures as a suicidal behavior risk factor: A nationwide population-based cohort study.

Authors:  Chun-Hao Tsai; Wan-Ju Cheng; Chih-Hsin Muo; Tsung-Li Lin
Journal:  Medicine (Baltimore)       Date:  2019-01       Impact factor: 1.817

3.  Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study.

Authors:  Jeongyoon Lee; Tae-Young Pak
Journal:  SSM Popul Health       Date:  2022-09-14

4.  A machine learning approach for predicting suicidal ideation in post stroke patients.

Authors:  Seung Il Song; Hyeon Taek Hong; Changwoo Lee; Seung Bo Lee
Journal:  Sci Rep       Date:  2022-09-23       Impact factor: 4.996

5.  Gender-specific factors related to suicidal ideation among community-dwelling stroke survivors: The 2013 Korean Community Health Survey.

Authors:  Mina Kim; Young-Hoon Lee
Journal:  PLoS One       Date:  2018-08-02       Impact factor: 3.240

  5 in total

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