Open AccessFactors associated with quality of life in patients with depression: A nationwide population-based study
Author(s) -
Yunji Cho,
Joo Kyung Lee,
Do Hoon Kim,
Joo Hyun Park,
MoonYoung Choi,
Hyun Jin Kim,
Myung Ji Nam,
Kang Uk Lee,
Kyungdo Han,
Yong Gyu Park
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0219455
Subject(s) - depression (economics) , national health and nutrition examination survey , medicine , quality of life (healthcare) , mental health , logistic regression , gerontology , cross sectional study , population , obesity , psychiatry , demography , environmental health , nursing , pathology , sociology , economics , macroeconomics
Background Depression, one of the most costly and common mental disorders, is reported to be associated with lower quality of life (QoL) in several studies. Improved understanding of the associated factors with QoL is necessary to optimize long-term outcomes and reduce disability in patients with depression. Therefore, the aim of this study was to identify factors that are associated with lower QoL among patients with depression. Methods The study was based on the Korea National Health and Nutrition Examination Survey, a cross-sectional health examination, years 2008 to 2014. The final analyzed sample consisted of a total of 1,502 study subjects who had been diagnosed by clinicians as having depression. A multivariate logistic regression model was performed to exam the association between the clinical characteristics (age, sex, demographic and health-related characteristics) and QoL. Analysis of covariance was also used to analyze EQ-5D according to mental health. Results Older age, lower level of education, lower income, worse subjective perception of health, unemployment, obesity and mental health struggles were found to be significantly associated with low QoL in depressive individuals after adjustment for multiple covariates. Conclusions This study has outlined grounding data in identifying patients who are at risk of QoL impairment. Policy makers should direct their interests to these individuals and provide appropriate management.