目的 对我国老年人抑郁症状变化趋势进行建模,并纳入新冠肺炎疫情相关变量,多维度研究其影响因素,为老年人抑郁的防治提供思路。方法 基于2013—2020年中国健康与养老追踪调查数据库,采用组基轨迹模型拟合老年人抑郁症状发展趋势,将模型确定后的轨迹分类作为因变量,采用无序多分类Logistic回归分析影响因素。结果 拟合得到老年人抑郁症状发展轨迹为4条,即抑郁低风险组、抑郁中风险组、抑郁高风险组、抑郁组,分别占比11.77%、38.43%、37.92%、11.88%。无序多分类Logistic回归分析显示：（1）与男性、有配偶、教育程度大专以下、居住城镇、无躯体疼痛、ADL无受损、IADL无受损相比,农村（OR=1.64,95%CI：1.22~2.21）、有躯体疼痛（OR=1.84,95%CI：1.42~2.39）、IADL有受损（OR=2.01,95%CI：1.44~2.79）及疫情对老年人精神影响（OR=1.19,95%CI：1.09~1.31）是老年人抑郁中风险的影响因素;（2）受教育程度大专及以上（OR=0.55,95%CI：0.35~0.86）、城乡或镇乡结合区（OR=1.79,95%CI：1.07~3.00）、农村（OR=2.15,95%CI：1.55~2.98）、有躯体疼痛（OR=3.97,95%CI：2.97~5.31）、ADL有受损（OR=2.62,95%CI：1.78~3.84）、IADL有受损（OR=3.07,95%CI：2.19~4.29）、疫情对老年人精神影响（OR=1.36,95%CI：1.24~1.49）是老年人抑郁高风险的影响因素;（3）女性（OR=2.07,95%CI：1.43~3.01）、有配偶（OR=1.80,95%CI：1.19~2.72）、城乡或镇乡结合区（OR=2.26,95%CI：1.11~4.57、农村（OR=2.96,95%CI：1.85~4.75）、有躯体疼痛（OR=9.67,95%CI：5.37~17.43）、ADL有受损（OR=4.63,95%CI：2.96~7.23）、IADL有受损（OR=8.03,95%CI：5.20~12.40）、疫情对老年人精神影响（OR=1.46,95%CI：1.32~1.62）是老年人抑郁的影响因素。结论 受新冠肺炎疫情影响,老年人抑郁发展轨迹占比特征发生变化,抑郁高风险及抑郁的占比增加。同时,性别、婚姻状况、受教育程度、居住类型、近2年躯体疼痛史、ADL、IADL、疫情对老年人精神影响为抑郁发展轨迹的影响因素。

Objective To model the trend of depressive symptoms in the elderly population in China and explore the multidimensional influencing factors, including variables related to the COVID-19 pandemic, to provide insights for the prevention and treatment of depression in the elderly. Methods Based on The China Health and Retirement Longitudinal Study database (2013-2020), group-based trajectory modeling was used to fit the development trajectory of depressive symptoms in the elderly. The trajectory classification determined by the model was used as the dependent variable, and unordered multinomial logistic regression was conducted to analyze the influencing factors. Results Four trajectories of depressive symptom development were identified: the low-risk group, the moderate-risk group, the high-risk group, and the depressive group, accounting for 11.77%, 38.43%, 37.92%, and 11.88% of the sample, respectively. Unordered multinomial logistic regression analysis showed that (1) compared to males, those having a spouse, or with an educational background of college degree or below, living in urban areas, without physical pain, without impaired activities of daily living (ADL), and without impaired instrumental activities of daily living (IADL), individuals living in rural areas (OR=1.64, 95%CI: 1.22~2.21), experiencing physical pain (OR=1.84, 95%CI: 1.42~2.39), having IADL (OR=2.01, 95%CI: 1.44~2.79), and being affected by the pandemic (OR=1.19, 95%CI: 1.09~1.31) were associated with a moderate risk of depression in the elderly; (2) having an education background of college degree or above (OR=0.55, 95%CI: 0.35~0.86), living in urban or semi-urban areas (OR=1.79, 95%CI: 1.07~3.00), living in rural areas (OR=2.15, 95%CI: 1.55~2.98), experiencing physical pain (OR=3.97, 95%CI: 2.97~5.31), having impaired ADL (OR=2.62, 95%CI: 1.78~3.84), having impaired IADL (OR=3.07, 95%CI: 2.19~4.29), and being affected by the pandemic (OR=1.36, 95%CI: 1.24~1.49) were factors associated with a high risk of depression in the elderly; (3) being female (OR=2.07, 95%CI: 1.43~3.01), having a spouse (OR=1.80, 95%CI: 1.19~2.72), living in urban or semi-urban areas (OR=2.26, 95%CI: 1.11~4.57), living in rural areas (OR=2.96, 95%CI: 1.85~4.75), experiencing physical pain(OR=9.67, 95%CI: 5.37~17.43), having impaired ADL (OR=4.63, 95%CI: 2.96~7.23), having impaired IADL (OR=8.03, 95%CI: 5.20~12.40), and being affected by the pandemic (OR=1.46, 95%CI:1.32~1.62) were factors associated with depression in the elderly. Conclusion The COVID-19 pandemic has impacted the proportion of different trajectories of depressive symptoms in the elderly, with an increase in the proportion of high-risk and depressive trajectories. Sex, marital status, education background, living arrangement, recent history (last 2 years) of physical pain, ADL, IADL, and the pandemic's impact on the mental health of the elderly are identified as influencing factors for the trajectory of depressive symptoms.