School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China; School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong.
School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China.
Asian J Psychiatr. 2024 Dec;102:104279. doi: 10.1016/j.ajp.2024.104279. Epub 2024 Oct 17.
This study aimed to investigate the intrinsic and situational predictors of depression under the health ecological model.
Two waves (2011 and 2013) of survey data were collected from the CHARLS. A total of 5845 older adults (≧60) were included, and depression was defined as CESD-10 score ≧10. Random forest combined with interpretable methods were utilized to select important predictors of depression. Multilevel logit model was used to examine the associations of intrinsic and situational predictors with depression.
After a 2-year follow up, 1822 individuals (31.17 %) developed depression. Interpretable analyses showed that both intrinsic and situational variables were predictive for depression. Multilevel logit model showed that age, gender, number of chronic diseases, number of pain areas, life satisfaction, and toilet distance were significantly associated with depression.
Both intrinsic and situational factors were found to be associated with depression among community older population, highlighting their significance for early prevention from the perspective of public health.
本研究旨在健康生态学模型下探讨抑郁的内在和情境预测因素。
本研究从 CHARLS 中收集了两波（2011 年和 2013 年）调查数据。共纳入 5845 名（≧60 岁）老年人，将 CESD-10 评分≧10 定义为抑郁。采用随机森林结合可解释方法筛选抑郁的重要预测因素。采用多水平逻辑回归模型检验内在和情境预测因素与抑郁的关联。
经过 2 年的随访，1822 人（31.17%）出现抑郁。可解释分析表明，内在和情境变量均对抑郁具有预测作用。多水平逻辑回归模型显示，年龄、性别、慢性病数量、疼痛部位数量、生活满意度和厕所距离与抑郁显著相关。
社区老年人群中，内在和情境因素均与抑郁相关，这凸显了从公共卫生角度对其进行早期预防的重要性。
