Byeon Haewon
Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae-si 50834, Gyeonsangnam-do, Korea.
J Pers Med. 2021 Jul 7;11(7):645. doi: 10.3390/jpm11070645.
This cross-sectional study developed a nomogram that could allow medical professionals in the primary care setting to easily and visually confirm high-risk groups of depression. This study analyzed 4011 elderly people (≥60 years old) who completed a health survey, blood pressure, physical measurement, blood test, and a standardized depression screening test. A major depressive disorder was measured using the Korean version of the Patient Health Questionnaire (PHQ-9). This study built a model for predicting major depressive disorders using logistic regression analysis to understand the relationship of each variable with major depressive disorders. In the result, the prevalence of depression measured by PHQ-9 was 6.8%. The results of multiple logistic regression analysis revealed that the major depressive disorder of the elderly living alone was significantly ( < 0.05) related to monthly mean household income, the mean frequency of having breakfast per week for the past year, moderate-intensity physical activity, subjective level of stress awareness, and subjective health status. The results of this study implied that it would be necessary to continuously monitor these complex risk factors such as household income, skipping breakfast, moderate-intensity physical activity, subjective stress, and subjective health status to prevent depression among older adults living in the community.
这项横断面研究开发了一种列线图,可使基层医疗环境中的医学专业人员轻松直观地确认抑郁症高危人群。本研究分析了4011名完成健康调查、血压测量、体格检查、血液检测及标准化抑郁症筛查测试的老年人(≥60岁)。使用韩国版患者健康问卷(PHQ-9)测量重度抑郁症。本研究采用逻辑回归分析建立了预测重度抑郁症的模型,以了解各变量与重度抑郁症之间的关系。结果显示,用PHQ-9测量的抑郁症患病率为6.8%。多元逻辑回归分析结果表明,独居老年人的重度抑郁症与月平均家庭收入、过去一年每周吃早餐的平均频率、中等强度体育活动、主观压力感知水平及主观健康状况显著相关(<0.05)。本研究结果表明,有必要持续监测家庭收入、不吃早餐、中等强度体育活动、主观压力和主观健康状况等这些复杂的风险因素,以预防社区老年人患抑郁症。