The elderly population in our country is large and growing rapidly, and depression is a common emotional disorder and mental health problem among the elderly population. The mental health of the female elderly population is increasingly becoming a focus of social concern.

To explore the influencing factors of depression symptoms in the female elderly population from a multi-level and comprehensive perspective of health ecology, and provide theoretical basis for identifying and intervening in depression symptoms in the elderly female population in China.

In January 2024, we extracted for the 2020 survey data from the China Health and Retirement Longitudinal Survey (CHARLS), and a group of female elderly adults aged 60 years and above were selected for the study (n=4 594). Based on the health ecology model, the influencing factors were divided into five levels: personal characteristics layer, behavioural characteristics layer, interpersonal network layer, living and working conditions layer, and policy environment layer. The χ² test and binary Logistic regression model were used to explore the influencing factors of depression symptoms and to establish a health ecology model of depression symptoms in the Chinese female elderly population.

The detection rate of depression symptoms in the Chinese female elderly population was 48.06% (2 208/4 595). Logistic regression analysis showed that age of ≥80 years (OR=0.601, 95%CI=0.449-0.804), sleep duration of≥6 h (OR=0.561, 95%CI=0.493-0.639), satisfaction with life (OR=0.256, 95%CI=0.199-0.330), better self-rated physical health (OR=0.459, 95%CI=0.395-0.533), urban household registration (OR=0.717, 95%CI=0.603-0.853), satisfaction with children (OR=0.666, 95%CI=0.472-0.940), education level of junior high school and above (OR=0.712, 95%CI=0.582-0.871), family income >50 000 yuan (OR=0.822, 95%CI=0.704-0.959) and the per capita GDP of the city is 50 000 to 100 000 yuan (OR=0.841, 95%CI=0.730-0.970) were the protective factors for the development of depression symptoms in the Chinese female elderly population (P<0.05). Having become disabled (OR=1.786, 95%CI=1.556-2.050), suffering from chronic diseases (OR=1.159, 95%CI=1.014-1.324), central region (OR=1.298, 95%CI=1.107-1.522) and western region (OR=1.407, 95%CI=1.183-1.675) were the risk factors for depression symptoms in the Chinese female elderly population (P<0.05) .

The detection rate of depression symptoms in the Chinese female elderly population is relatively high, and there are many influencing factors, including: age in the personal characteristics layer; sleep time, satisfaction with life, self-related of physical health, disability, and chronic disease in the behavioral characteristics layer; household registration type, satisfaction with children, and geographical distribution in the interpersonal network layer; education and family income in the living and working conditions layer; the per capita GDP of the city in the policy environment layer. Effective intervention measures should be taken at all layers, targeting key populations, in order to reduce the incidence of depression symptoms among the Chinese elderly women.

我国老年人口基数庞大且增长迅速，抑郁是老年人群常见的情绪障碍和心理健康问题，而女性老年人群的心理健康问题日益成为社会关注的焦点。

基于健康生态学模型多层次、全方位探讨女性老年人群抑郁症状的影响因素，为识别及干预中国女性老年人群的抑郁症状提供理论依据。

于2024年1月提取中国健康与养老追踪调查（CHARLS）2020年调查数据，选取≥60岁女性老年人群为研究对象（n=4 594）。基于健康生态学模型将影响因素分为个人特征层、行为特征层、人际网络层、生活和工作条件层、政策环境层5个层面，采用χ²检验和二分类Logistic回归模型探讨抑郁症状的影响因素，并建立中国女性老年人群抑郁症状健康生态学模型。

中国女性老年人群抑郁症状检出率为48.06%（2 208/4 594）。Logistic回归分析结果显示，年龄≥80岁（OR=0.601，95%CI=0.449~0.804）、睡眠时间≥6 h（OR=0.561，95%CI=0.493~0.639）、对生活满意（OR=0.256，95%CI=0.199~0.330）、自评身体健康状况好（OR=0.459，95%CI=0.395~0.533）、城市户口（OR=0.717，95%CI=0.603~0.853）、对子女满意（OR=0.666，95%CI=0.472~0.940）、受教育程度为初中及以上（OR=0.712，95%CI=0.582~0.871）、家庭收入>5万元（OR=0.822，95%CI=0.704~0.959）、所在城市人均国内生产总值（GDP）为5万~10万元（OR=0.841，95%CI=0.730~0.970）是中国女性老年人群发生抑郁症状的保护因素（P<0.05）；已失能（OR=1.786，95%CI=1.556~2.050）、患慢性病（OR=1.159，95%CI=1.014~1.324）、中部地区（OR=1.298，95%CI=1.107~1.522）和西部地区（OR=1.407，95%CI=1.183~1.675）是中国女性老年人群发生抑郁症状的危险因素（P<0.05）。

中国女性老年人群抑郁症状检出率较高，影响因素较多，包括个人特征层的年龄，行为特征层的睡眠时间、对生活满意度、自评身体健康情况、失能情况、慢性病情况，人际网络层的户口类型、对子女满意度、地理分布，生活和工作条件层的受教育情况、家庭收入，政策环境层的所在城市人均GDP。应从各个层面，针对重点人群，联合采取有效干预措施，减少中国女性老年人群抑郁症状的发生。