A network analysis of suicidal ideation, depressive symptoms, and subjective well-being in a community population

• Published in: Journal of psychiatric research 2021-10, Vol.142, p.263-271
• Main Authors: Suh, Won Young, Lee, Jongjun, Yun, Je-Yeon, Sim, Jin-ah, Yun, Young Ho
• Format: Article
• Language: English
• Subjects: Depressive symptoms; Directed acyclic graph; Gaussian graphical model; Subjective well-being; Suicidal ideation
• ISSN: 0022-3956; 1879-1379
• DOI: 10.1016/j.jpsychires.2021.08.008

Although the relationship between subjective well-being (SWB) and suicidal ideation (SI) has been illustrated in previous research, few studies have conceptualized SWB as a comprehensive measure of life satisfaction in multiple domains, nor have they considered possible mediators such as depressive symptoms. Therefore, the present study aimed to identify dimensions of SWB correlated with SI, and to analyze associations among SWB sub-domains, depressive symptoms, and SI in a community population. A total of 1200 community adults in South Korea, aged 20–86 years, completed self-report questionnaires on demographics, depressive mood (Patient Health Questionnaire-9 [PHQ-9]), SI (item 9 of the PHQ-9), and 14 SWB sub-domains (Subjective Well-Being Inventory). Factors associated with SI, and interactions among SI, depressive mood, and SWB, were identified by logistic regression and phenotype network analyses, respectively. The five main factors influencing the regularized partial correlation network were life satisfaction, self-blame, job, hopelessness, and fatigue. Pathways were observed from work-life balance and life satisfaction to hopelessness; from self-blame and fatigue to safety and health; and from sleep disturbance, concentration difficulties, self-blame, and hopelessness to SI. Making job activities more emotionally rewarding, the potential for career progression and regular work hours could address anhedonia, hopelessness and sleep disturbance, respectively, thus enhancing SWB and reducing SI in the community population. •Phenotype network analysis modeled interactions among subjective well-being, depressive symptoms, and suicidal ideation.•Work-life balance and life satisfaction influenced anhedonia and hopelessness.•Self-blame and fatigue influenced safety and health.•Sleep disturbances, concentration difficulties, self-blame, and hopelessness promoted suicidal ideation.